From 73df4f27f79d6843b74c57bd4867475cd1e64712 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Mon, 11 Sep 2023 13:56:26 +0200 Subject: [PATCH 001/112] enable run with extra columns in driver data --- R/run_pmodel_f_bysite.R | 3 +- vignettes/rsofun_phydro.Rmd | 545 ++++++++++++++++++++++++++++++++++++ 2 files changed, 547 insertions(+), 1 deletion(-) create mode 100644 vignettes/rsofun_phydro.Rmd diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 27d4aac5..612f6887 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -164,7 +164,8 @@ run_pmodel_f_bysite <- function( forcing, params_modl, makecheck = TRUE, - verbose = TRUE + verbose = TRUE, + ... ){ # predefine variables for CRAN check compliance diff --git a/vignettes/rsofun_phydro.Rmd b/vignettes/rsofun_phydro.Rmd new file mode 100644 index 00000000..579a6657 --- /dev/null +++ b/vignettes/rsofun_phydro.Rmd @@ -0,0 +1,545 @@ +--- +title: "Rsofun with Phydro" +author: "Jaideep Joshi" +date: "2023-09-07" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +```{r} +library(tidyverse) +library(reshape2) +``` + + +## Generate data if not already available + +```{r} +generate_data = F +``` + +```{r} +if (generate_data){ + +library(FluxDataKit) + +# read in sites to process +site <- "FR-Pue" + +# get half-hourly data -------------------------------------------------------- +message("- convert to FLUXNET standard CSV file") +hhdf <- suppressWarnings( + try( + fdk_convert_lsm( + site = site, + fluxnet_format = TRUE, + path = "~/Downloads/flux_data_kit_beta/fluxes/" + ) + ) + ) + +if(inherits(hhdf, "try-error")){ + message("!!! conversion to FLUXNET failed !!!") + return(NULL) +} + + +# Add date and time columns to hhdf for easier further processing. +# --------------------------------------------------------- +hhdf = + hhdf |> + mutate(time = lubridate::as_datetime(TIMESTAMP_START, tz = "GMT", format="%Y%m%d%H%M")) |> + mutate(date = lubridate::as_date(time)) + +# Aggregate to daily 24-hr means ---------------------------------------------------------- +message("- downsampling FLUXNET format - 24 hr means") +ddf_24hr_mean <- + try( + hhdf |> + group_by(date) |> + select(-TIMESTAMP_START, -TIMESTAMP_END) |> + summarize_all(.funs = mean) + ) + +# Check aggregation +ddf_24hr_mean %>% + select(SW_IN_F_MDS, SW_OUT, LW_IN_F_MDS, LAI, FPAR, GPP_DT_VUT_REF, LE_F_MDS, TA_F_MDS, date) %>% + mutate(albedo = SW_OUT/SW_IN_F_MDS) %>% + melt("date") %>% + ggplot(aes(y=value, x=date)) + + geom_line(col="aquamarine4") + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") + + +# Aggregate around daily maximum ppfd for acclimating model +# --------------------------------------------------------- +test.3day = hhdf %>% filter(date >= as_date("2011-06-01") & + date <= as_date("2011-06-03") ) + +aggregate_daily_3hr_maxima = function(df){ + # Get the time at which SW_IN is maximum + maxppfd <- df %>% filter(SW_IN_F_MDS == max(SW_IN_F_MDS)) + max_t <- maxppfd$time[1] + + # Select times that lie in 3-hr interval around max_t + df_aroundmax <- df %>% filter(time < (max_t + 1.5*3600) & + time > (max_t - 1.5*3600) ) + + # take mean of selected entries + df_mean <- df_aroundmax |> + select(-TIMESTAMP_START, -TIMESTAMP_END) |> + summarize_all(.funs = mean) + + df_mean +} + +# Test aggregation +# ---------------- +test.3day = hhdf %>% filter(date >= as.Date("2011-06-01") & + date <= as.Date("2011-06-03") ) + +test.3day.3hr = test.3day %>% group_by(date) %>% do(aggregate_daily_3hr_maxima(.)) %>% ungroup() + +test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% + melt("time") %>% + mutate(type="hourly") %>% + rbind(test.3day.3hr %>% + select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% + melt("time") %>% + mutate(type="daily") + ) %>% + ggplot(aes(y=value, x=as.POSIXct(time))) + + geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + + geom_point(data = . %>% filter(type == "daily")) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") + + + +# Apply 3hr maxima aggregation to all data +# ---------------------------------------- +ddf_3hr_maxima <- hhdf |> + group_by(date) |> + do(aggregate_daily_3hr_maxima(.)) |> + ungroup() + + +aggregate_daily_daylength = function(df){ + # Get the time at which SW_IN > 0 + pos_ppfd <- df %>% filter(SW_IN_F_MDS > 0) + tmax <- max(pos_ppfd$time) + tmin <- min(pos_ppfd$time) + + # Select times that lie in 3-hr interval around max_t + df_aroundmax <- df %>% filter(time <= tmax & + time >= tmin ) + + # take mean of selected entries + df_mean <- df_aroundmax |> + select(-TIMESTAMP_START, -TIMESTAMP_END) |> + summarize_all(.funs = mean) |> + mutate(daylength = difftime(tmax, tmin, units="hours") |> as.numeric()) + + df_mean +} + +# Test aggregation +# ---------------- +test.3day.daylen = test.3day %>% group_by(date) %>% do(aggregate_daily_daylength(.)) %>% ungroup() + +test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI) %>% + mutate(daylength = NA) %>% + melt("time") %>% + mutate(type="hourly") %>% + rbind(test.3day.daylen %>% + select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, daylength) %>% + melt("time") %>% + mutate(type="daily") + ) %>% + ggplot(aes(y=value, x=as.POSIXct(time))) + + geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + + geom_point(data = . %>% filter(type == "daily")) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") + + +# Apply daytime mean aggregation to all data +# ------------------------------------------ +ddf_daytime_mean <- hhdf |> + group_by(date) |> + do(aggregate_daily_daylength(.)) |> + ungroup() + +# Check daylength seasonality +ddf_daytime_mean %>% + ggplot(aes(y=daylength, x=date)) + + geom_line()+ + theme_classic() + + +# Calculate daily tmax and tmin from hh data +# ------------------------------------------ +tmaxmin <- + hhdf |> + group_by(date) |> + summarize( + tmax = max(TA_F_MDS), + tmin = min(TA_F_MDS) + ) + +# Creating driver object ------------------------------------------------------ +message("- compiling drivers") +load("../data/p_model_drivers.rda") + +p_hydro_drivers <- p_model_drivers +kfFEC = 2.04 + +# for demo, use just a subset of years +p_hydro_drivers$forcing <- + ddf_24hr_mean |> + dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + left_join(tmaxmin) |> + group_by(date) |> + summarize( + date = date, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = P_F * 48 /(60 * 60 * 24), # P_F [mm timestep-1] * 48 [timesteps day-1] / 86400 [secs day-1 ] + tmin = tmin, # TMIN_F_MDS, + tmax = tmax, # TMAX_F_MDS, + fapar = FPAR, + co2 = CO2_F_MDS, + ccov = 0 + ) |> + list() + +p_hydro_drivers$forcing_acclim <- + ddf_3hr_maxima |> + dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + left_join(tmaxmin) |> + group_by(date) |> + summarize( + date = date, + time = time, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = NA, # P_F * 48 / (60 * 60 * 24), + tmin = tmin, # TMIN_F_MDS, + tmax = tmax, # TMAX_F_MDS, + fapar = FPAR, + co2 = CO2_F_MDS, + ccov = 0 + ) |> + list() + +p_hydro_drivers$forcing_daytime_mean <- + ddf_daytime_mean |> + dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + left_join(tmaxmin) |> + group_by(date) |> + summarize( + date = date, + time = time, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = NA, # P_F * 48 / (60 * 60 * 24), + tmin = tmin, # TMIN_F_MDS, + tmax = tmax, # TMAX_F_MDS, + fapar = FPAR, + co2 = CO2_F_MDS, + ccov = 0, + daylength = daylength + ) |> + list() + + +p_hydro_drivers$forcing_halfhourly <- + hhdf |> + dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(time) |> + summarize( + date = date, + time = time, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = P_F * 48 / (60 * 60 * 24), + fapar = FPAR, + co2 = CO2_F_MDS, + ccov = 0 + ) |> + list() + + +# write all drivers to file +# apply compression to minimize space +filn <- "../data/p_hydro_drivers.rda" +message(paste0("- writing to file: ", filn)) +save(p_hydro_drivers, + file = filn + ) + + +# Write validation data +load("../data/p_model_validation.rda") + +p_hydro_validation <- p_model_validation + +p_hydro_validation$data <- + ddf_24hr_mean |> + dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(date) |> + summarise( + date = date, + time = time, + gpp = GPP_DT_VUT_REF, + le = LE_F_MDS + ) |> + list() + +p_hydro_validation$data_hh <- + hhdf |> + dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(time) |> + summarise( + date = date, + time = time, + gpp = GPP_DT_VUT_REF, + le = LE_F_MDS + ) |> + list() + +p_hydro_validation$data_3hr_mean <- + ddf_3hr_maxima |> + dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(time) |> + summarise( + date = date, + time = time, + gpp = GPP_DT_VUT_REF, + le = LE_F_MDS + ) |> + list() + +p_hydro_validation$data_daytime_mean <- + ddf_daytime_mean |> + dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(time) |> + summarise( + date = date, + time = time, + gpp = GPP_DT_VUT_REF, + le = LE_F_MDS + ) |> + list() + + +filn <- "../data/p_hydro_validation.rda" +message(paste0("- writing to file: ", filn)) +save(p_hydro_validation, + file = filn + ) + + +} + +``` + + +## Read Phydro drivers and check + +```{r} +# rm(list=ls()) + +load("../data/p_hydro_drivers.rda") +load("../data/p_hydro_validation.rda") + +start_date = "2011-06-01" +end_date = "2011-06-06" + +# Check acclimation data against half-hourly +# --- +p_hydro_drivers$forcing_halfhourly[[1]] %>% +filter(date >= start_date & date <= end_date ) %>% + select(time, ppfd, netrad, temp, vpd, fapar) %>% + melt("time") %>% + mutate(type="hourly") %>% + rbind(p_hydro_drivers$forcing_acclim[[1]] %>% + filter(date >= start_date & date <= end_date ) %>% + select(time, ppfd, netrad, temp, vpd, fapar) %>% + melt("time") %>% + mutate(type="daily") + ) %>% + ggplot(aes(y=value, x=as.POSIXct(time))) + + geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + + geom_point(data = . %>% filter(type == "daily")) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") + + +# Check daily-3hr-maxima against daytime_means against 24 hr means +# --- +start_date = "2011-06-01" +end_date = "2015-06-03" + +p_hydro_drivers$forcing[[1]] %>% + filter(date >= start_date & date <= end_date ) %>% + select(date, ppfd, netrad, temp, vpd, fapar) %>% + melt("date") %>% + mutate(type="24-hr mean") %>% + rbind(p_hydro_drivers$forcing_acclim[[1]] %>% + filter(date >= start_date & date <= end_date ) %>% + select(date, ppfd, netrad, temp, vpd, fapar) %>% + melt("date") %>% + mutate(type="3-hr maxima") + ) %>% + rbind(p_hydro_drivers$forcing_daytime_mean[[1]] %>% + filter(date >= start_date & date <= end_date ) %>% + select(date, ppfd, netrad, temp, vpd, fapar) %>% + melt("date") %>% + mutate(type="daytime means") + ) %>% + ggplot(aes(y=value, x=date)) + + geom_line(aes(group=type, col=type), alpha=0.5) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") + + +start_date = "2011-06-01" +end_date = "2015-06-03" + +p_hydro_validation$data[[1]] %>% + filter(date >= start_date & date <= end_date ) %>% + select(date, gpp, le) %>% + melt("date") %>% + mutate(type="24-hr mean") %>% + rbind(p_hydro_validation$data_3hr_mean[[1]] %>% + filter(date >= start_date & date <= end_date ) %>% + select(date, gpp, le) %>% + melt("date") %>% + mutate(type="3-hr maxima") + ) %>% + rbind(p_hydro_validation$data_daytime_mean[[1]] %>% + filter(date >= start_date & date <= end_date ) %>% + select(date, gpp, le) %>% + melt("date") %>% + mutate(type="daytime means") + ) %>% + ggplot(aes(y=value, x=date)) + + geom_line(aes(group=type, col=type), alpha=0.5) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") + + +``` + + +## Model parameters and driver data + +```{r} +params_modl <- list( + kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + soilm_betao = 0.0, + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41 +) + +p_hydro_drivers$forcing[[1]] %>% + dplyr::filter(lubridate::year(date) %in% 2007:2013) %>% + melt("date") %>% + ggplot(aes(y=value, x=as.Date(date))) + + geom_line(col="aquamarine4") + + geom_vline(xintercept = 25, col="pink") + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") + + +load("../data/p_model_drivers.rda") +p_model_drivers$forcing[[1]] %>% + melt("date") %>% + ggplot(aes(y=value, x=as.Date(date))) + + geom_line(col="aquamarine4") + + geom_vline(xintercept = 25, col="pink") + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") +``` + +## Model run + +```{r} +output <- rsofun::runread_pmodel_f( + p_hydro_drivers, + par = params_modl +) +``` + +## Validate + +```{r} +output$data[[1]] %>% select(date, gpp, latenth) %>% + rename(le = latenth) %>% + melt("date") %>% + mutate(group="model") %>% + rbind(p_hydro_validation$data[[1]] %>% + select(date, gpp, le) %>% + mutate(le = le) %>% + melt("date") %>% + mutate(group="obs")) %>% + ggplot(aes(y=value, x=as.Date(date))) + + geom_line(aes(group=group, col=group), alpha=0.7) + + geom_vline(xintercept = 25, col="pink") + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 2) +``` + +```{r} +output$data[[1]] %>% select(-year_dec) %>% + melt("date") %>% + ggplot(aes(y=value, x=as.Date(date))) + + geom_line(col="aquamarine4") + + geom_vline(xintercept = 25, col="pink") + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") +``` From 193079b363d552c60e09b98fba7a273e299f3b72 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Mon, 11 Sep 2023 16:24:27 +0200 Subject: [PATCH 002/112] code compiles with phydro module PROVIDED mod file is first created --- .gitignore | 1 + src/Makevars | 27 +- src/gpp_pmodel.mod.f90 | 2 +- src/photosynth_phydro.f90 | 1458 +++++++++++++++++++++++++++++++++ src/photosynth_pmodel.mod.f90 | 9 +- src/sofunutils.mod.f90 | 696 ++++++++++++++++ 6 files changed, 2186 insertions(+), 7 deletions(-) create mode 100644 src/photosynth_phydro.f90 diff --git a/.gitignore b/.gitignore index ed04b0ea..a054d9b7 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,7 @@ src/*.o src/*.so src/*.dll +src/*.mod # vignette builds vignettes/*.html diff --git a/src/Makevars b/src/Makevars index 7a2f04bd..b84e9cab 100755 --- a/src/Makevars +++ b/src/Makevars @@ -4,7 +4,10 @@ C_OBJS = wrappersc.o # Fortran objects: refer to file names , order reflects dependency structure -FT_OBJS = params_core.mod.o sofunutils.mod.o grid_siterun.mod.o params_siml_pmodel.mod.o params_siml_biomee.mod.o forcing_siterun_pmodel.mod.o forcing_siterun_biomee.mod.o params_soil_biomee.mod.o interface_biosphere_pmodel.mod.o interface_biosphere_biomee.mod.o tile_pmodel.mod.o plant_pmodel.mod.o soiltemp_sitch.mod.o waterbal_splash.mod.o vegdynamics_pmodel.mod.o gpp_pmodel.mod.o gpp_biomee.mod.o photosynth_pmodel.mod.o biosphere_pmodel.mod.o biosphere_biomee.mod.o vegetation_biomee.mod.o soil_biomee.mod.o sofun_r.o +FT_OBJS = params_core.mod.o sofunutils.mod.o grid_siterun.mod.o params_siml_pmodel.mod.o params_siml_biomee.mod.o forcing_siterun_pmodel.mod.o forcing_siterun_biomee.mod.o params_soil_biomee.mod.o \ + interface_biosphere_pmodel.mod.o interface_biosphere_biomee.mod.o tile_pmodel.mod.o plant_pmodel.mod.o soiltemp_sitch.mod.o waterbal_splash.mod.o vegdynamics_pmodel.mod.o \ + photosynth_pmodel.mod.o photosynth_phydro.mod.o \ + gpp_pmodel.mod.o gpp_biomee.mod.o biosphere_pmodel.mod.o biosphere_biomee.mod.o vegetation_biomee.mod.o soil_biomee.mod.o sofun_r.o all: $(SHLIB) clean @@ -23,7 +26,7 @@ forcing_siterun_biomee.mod.o: params_core.mod.o params_siml_biomee.mod.o grid_si params_soil_biomee.mod.o: params_core.mod.o tile_pmodel.mod.o: params_core.mod.o interface_biosphere_pmodel.mod.o waterbal_splash.mod.o: params_core.mod.o tile_pmodel.mod.o plant_pmodel.mod.o sofunutils.mod.o -gpp_pmodel.mod.o: params_core.mod.o sofunutils.mod.o plant_pmodel.mod.o tile_pmodel.mod.o interface_biosphere_pmodel.mod.o photosynth_pmodel.mod.o +gpp_pmodel.mod.o: params_core.mod.o sofunutils.mod.o plant_pmodel.mod.o tile_pmodel.mod.o interface_biosphere_pmodel.mod.o photosynth_pmodel.mod.o photosynth_phydro.mod.o gpp_biomee.mod.o: datatypes.mod.o soil_biomee.mod.o forcing_siterun_biomee.mod.o photosynth_pmodel.mod.o params_core.mod.o sofunutils.mod.o photosynth_pmodel.mod.o: params_core.mod.o sofunutils.mod.o soiltemp_sitch.mod.o: params_core.mod.o sofunutils.mod.o tile_pmodel.mod.o interface_biosphere_pmodel.mod.o @@ -34,6 +37,17 @@ biosphere_biomee.mod.o: params_core.mod.o interface_biosphere_biomee.mod.o datat soil_biomee.mod.o: datatypes.mod.o vegetation_biomee.mod.o: datatypes.mod.o soil_biomee.mod.o gpp_biomee.mod.o datatypes.mod.o: interface_biosphere_biomee.mod.o params_core.mod.o classdefs.mod.o +photosynth_phydro.mod.o: sofunutils.mod.o photosynth_pmodel.mod.o +# precision.mod.o: +# sofunutils_plus.mod.o: precision.mod.o +# phydro_physical.mod.o: precision.mod.o +# phydro_env.mod.o: precision.mod.o phydro_physical.mod.o photosynth_pmodel.mod.o +# phydro_pm.mod.o: precision.mod.o phydro_env.mod.o +# phydro_transpiration.mod.o: precision.mod.o sofunutils_plus.mod.o phydro_pm.mod.o phydro_env.mod.o +# phydro_photosynthesis.mod.o: precision.mod.o photosynth_pmodel.mod.o +# phydro_solvermod.o: precision.mod.o phydro_photosynthesis.mod.o phydro_transpiration.mod.o +# phydro_main.mod.o: precision.mod.o phydro_transpiration.mod.o phydro_solvermod.o + # Source (object) of Fortran modules # : @@ -62,6 +76,15 @@ md_vegdynamics_pmodel.mod: vegdynamics_pmodel.mod.o datatypes.mod: datatypes.o md_soil_biomee.mod: soil_biomee.o md_vegetation_biomee.mod: vegetation_biomee.o +md_precision.mod: precision.o +md_sofunutils_plus.mod: sofunutils_plus.o +md_phydro_physical.mod: phydro_physical.o +md_phydro_env.mod: phydro_env.o +md_phydro_pm.mod: phydro_pm.o +md_phydro_transpiration.mod: phydro_transpiration.o +md_phydro_photosynthesis.mod: phydro_photosynthesis.o +md_phydro_solver.mod: phydro_solver.o +md_phydro_main.mod: phydro_main.o # Dependency of the C wrapper wrappersc.o: sofun_r_mod.mod diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index a2e279ed..e23e6cd2 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -36,7 +36,7 @@ module md_gpp_pmodel use md_grid, only: gridtype use md_photosynth, only: pmodel, zero_pmodel, outtype_pmodel, calc_ftemp_inst_vcmax, calc_ftemp_inst_jmax, & calc_ftemp_inst_rd, calc_kphio_temp, calc_soilmstress - + use md_photosynth_phydro, only: phydro_analytical, phydro_instantaneous_analytical implicit none private diff --git a/src/photosynth_phydro.f90 b/src/photosynth_phydro.f90 new file mode 100644 index 00000000..57b2e138 --- /dev/null +++ b/src/photosynth_phydro.f90 @@ -0,0 +1,1458 @@ +module md_photosynth_phydro + use md_photosynth, only: calc_viscosity_h2o, calc_density_h2o, calc_kmm, calc_gammastar + use md_sofunutils, only: calc_patm, zero, gammad + + + ! ------------------------------------------------------------- + ! Definitions: Precision + !-------------------------------------------------------------- + implicit none + + integer, parameter :: int4=SELECTED_INT_KIND(4) + integer, parameter :: flt4=SELECTED_REAL_KIND(6,37) + integer, parameter :: dbl8=SELECTED_REAL_KIND(15,307) + + ! ------------------------------------------------------------- + ! Definitions: Environment + !-------------------------------------------------------------- + ! list of methods to calculate gs + integer (kind = int4), parameter :: GS_IGF = 0, GS_QNG = 1, GS_APX = 2, GS_APX2 = 3 + + integer (kind = int4), parameter :: ET_DIFFUSION = 0, ET_PM = 1 + + ! Define the data type for ParEnv + type par_env_type + real(kind = dbl8) :: tc ! Temperature [degC] + real(kind = dbl8) :: patm ! Atmospheric pressure [Pa] + real(kind = dbl8) :: vpd ! VPD [Pa] + real(kind = dbl8) :: Rn ! Net radiation [W m-2] + real(kind = dbl8) :: v_wind ! Wind speed [m s-1] + real(kind = dbl8) :: viscosity_water ! [Pa s] + real(kind = dbl8) :: density_water ! [kg m-3] + real(kind = dbl8) :: rho ! Density of air [kg m-3] + real(kind = dbl8) :: cp ! Specific heat capacity of moist air [J kg-1 K-1] + real(kind = dbl8) :: gamma ! Psychrometric constant [Pa K-1] + real(kind = dbl8) :: epsilon ! Slope of saturation-pressure - temp curve [Pa K-1] + real(kind = dbl8) :: lv ! Latent heat of vaporization of water [J kg-1] + integer(kind = int4) :: gs_method = GS_IGF ! GsMethod + integer(kind = int4) :: et_method = ET_DIFFUSION ! ETMethod + end type par_env_type + + ! ! Interface for member subroutines + ! interface par_env_type_interface + ! module procedure :: create_par_env + ! ! module procedure :: calc_temp_dependencies + ! ! module procedure :: print_par_env + ! end interface + + ! ------------------------------------------------------------- + ! Definitions: Phydro transpiration + !-------------------------------------------------------------- + type par_plant_type + real (kind = dbl8) :: conductivity ! = ci/ca, leaf-internal to ambient CO2 partial pressure, ci/ca (unitless) + real (kind = dbl8) :: psi50 ! leaf-internal CO2 partial pressure (Pa) + real (kind = dbl8) :: b ! ci-limitation factor of light-limited assimilation (unitless) + + real (kind = dbl8) :: h_canopy = 20 + real (kind = dbl8) :: h_wind_measurement = 22 + real (kind = dbl8) :: tchome = 25 + + integer (kind = int4) :: gs_method = GS_IGF + end type par_plant_type + + + ! ------------------------------------------------------------- + ! Definitions: Phydro photosynthesis + !-------------------------------------------------------------- + + ! list of methods to model temperature dependencies of Vcmax and Jmax + integer (kind = int4), parameter :: FV_kattge07 = 0, FV_kumarathunge19 = 1, FV_leuning02 = 2 + + ! list of methods to model temperature dependencies of Rd + integer (kind = int4), parameter :: FR_heskel16 = 0, FR_arrhenius = 1, FR_q10 = 2 + + ! list of methods to model temperature dependencies of br + integer (kind = int4), parameter :: FB_atkin15 = 0, FB_kumarathunge19 = 1 + + type par_photosynth_type + real (kind = dbl8) :: kmm + real (kind = dbl8) :: gammastar + real (kind = dbl8) :: phi0 + real (kind = dbl8) :: ca + real (kind = dbl8) :: delta + + integer(kind = int4) :: ftemp_vj_method; + integer(kind = int4) :: ftemp_rd_method; + integer(kind = int4) :: ftemp_br_method; + + real (kind = dbl8) :: Iabs + real (kind = dbl8) :: patm + + real (kind = dbl8) :: fT_vcmax; + real (kind = dbl8) :: fT_jmax; + real (kind = dbl8) :: fT_rd; + + end type par_photosynth_type + + type ACi_type + real(kind=dbl8) :: a + real(kind=dbl8) :: ci + logical :: isVcmaxLimited + end type ACi_type + + + ! ------------------------------------------------------------- + ! Definitions: Phydro solver + !-------------------------------------------------------------- + + type par_cost_type + real (kind = dbl8) :: alpha + real (kind = dbl8) :: gamma + end type par_cost_type + + type dpsi_bounds_type + real (kind = dbl8) :: exact + real (kind = dbl8) :: approx_O2 + real (kind = dbl8) :: Iabs_bound + end type dpsi_bounds_type + + type dfdx_type + real (kind = dbl8) :: dPdx + real (kind = dbl8) :: J + real (kind = dbl8) :: djmax_dJ + real (kind = dbl8) :: dJ_dchi + end type dfdx_type + + ! ------------------------------------------------------------- + ! Definitions: Phydro main + !-------------------------------------------------------------- + + type phydro_result_type + real(kind = dbl8) :: a + real(kind = dbl8) :: e + real(kind = dbl8) :: gs + real(kind = dbl8) :: ci + real(kind = dbl8) :: chi + real(kind = dbl8) :: vcmax + real(kind = dbl8) :: jmax + real(kind = dbl8) :: dpsi + real(kind = dbl8) :: psi_l + real(kind = dbl8) :: nfnct + real(kind = dbl8) :: niter + real(kind = dbl8) :: mc + real(kind = dbl8) :: mj + real(kind = dbl8) :: gammastar + real(kind = dbl8) :: kmm + real(kind = dbl8) :: vcmax25 + real(kind = dbl8) :: jmax25 + real(kind = dbl8) :: rd + real(kind = dbl8) :: isVcmaxLimited + real(kind = dbl8) :: ac + real(kind = dbl8) :: aj + real(kind = dbl8) :: le + real(kind = dbl8) :: le_s_wet + end type phydro_result_type + + type par_control_type + integer(kind = int4) :: gs_method = GS_IGF + integer(kind = int4) :: et_method = ET_DIFFUSION + integer(kind = int4) :: ftemp_vj_method = FV_kumarathunge19 + integer(kind = int4) :: ftemp_rd_method = FR_heskel16 + integer(kind = int4) :: ftemp_br_method = FB_atkin15 + integer(kind = int4) :: scale_alpha = 0 + end type par_control_type + + + + contains + + ! ------------------------------------------------------------- + ! Functions: Physical relationships + !-------------------------------------------------------------- + function calc_esat(TdegC, patm) result(esatval) + real(kind = dbl8), intent(in) :: TdegC, patm + real(kind = dbl8) :: esatval + real(kind = dbl8) :: a, b, c, f + + a = 611.21 + b = 17.502 + c = 240.97 + f = 1.0007 + 3.46e-8 * patm + + esatval = f * a * exp(b * TdegC / (c + TdegC)) + end function calc_esat + + function calc_density_air(tc_air, patm, vpd, moist) result(rho) + real(kind = dbl8), intent(in) :: tc_air, patm, vpd + logical, intent(in) :: moist + real(kind = dbl8) :: rho, tk, R + real(kind = dbl8) :: vp, rv, tv + + tk = tc_air + 273.16 + R = 287.052874 + + if (.not. moist) then + rho = patm / R / tk + else + vp = calc_esat(tc_air, patm) - vpd + rv = 0.622 * vp / (patm - vp) + tv = tk * (1.0 + rv / 0.622) / (1.0 + rv) + + rho = patm / R / tv + end if + end function calc_density_air + + function calc_enthalpy_vap(tc) result(enthalpy) + real(kind = dbl8), intent(in) :: tc + real(kind = dbl8) :: enthalpy, tk, a + + tk = tc + 273.15 + a = tk / (tk - 33.91) + + enthalpy = 1.91846e6 * a**2 + end function calc_enthalpy_vap + + function calc_cp_moist_air(tc) result(cp) + real(kind = dbl8), intent(in) :: tc + real(kind = dbl8) :: cp, my_tc + + my_tc = max(min(tc, 100.0), 0.0) + + cp = (1.0045714270 + & + my_tc * (2.050632750e-3 + & + my_tc * (-1.631537093e-4 + & + my_tc * (6.212300300e-6 - & + my_tc * (8.830478888e-8 - & + my_tc * 5.071307038e-10))))) * 1e3 + + end function calc_cp_moist_air + + function calc_psychro(tc, patm) result(psychro) + real(kind = dbl8), intent(in) :: tc, patm + real(kind = dbl8) :: psychro, Ma, Mv, cp, lv + + Ma = 0.02896 + Mv = 0.018016 + + cp = calc_cp_moist_air(tc) + lv = calc_enthalpy_vap(tc) + + psychro = cp * patm / ((Mv / Ma) * lv) + end function calc_psychro + + function calc_sat_slope(tc) result(slope) + real(kind = dbl8), intent(in) :: tc + real(kind = dbl8) :: slope + + slope = 17.269 * 237.3 * 610.78 * exp(tc * 17.269 / (tc + 237.3)) / ((tc + 237.3)**2) + end function calc_sat_slope + + ! ------------------------------------------------------------- + ! Functions: Environment + !-------------------------------------------------------------- + ! Constructor for ParEnv + subroutine create_par_env(this, tc, patm, vpd, Rn, v_wind) + type(par_env_type), intent(inout) :: this + real(kind = dbl8), intent(in) :: tc, patm, vpd, Rn, v_wind + this%tc = tc + this%vpd = vpd + this%patm = patm + this%Rn = Rn + this%v_wind = v_wind + this%gs_method = GS_IGF + this%et_method = ET_DIFFUSION + call calc_temp_dependencies(this) + end subroutine create_par_env + + ! Separate constructor without v_wind as a parameter + subroutine create_par_env_no_wind(this, tc, patm, vpd, Rn) + type(par_env_type), intent(out) :: this + real(kind = dbl8), intent(in) :: tc, patm, vpd, Rn + call create_par_env(this, tc, patm, vpd, Rn, 3.0d0) ! Default v_wind + end subroutine create_par_env_no_wind + + ! Calculate temperature dependencies + subroutine calc_temp_dependencies(this) + type(par_env_type), intent(inout) :: this + this%viscosity_water = calc_viscosity_h2o(real(this%tc), real(this%patm)) + this%density_water = calc_density_h2o(real(this%tc), real(this%patm)) + this%rho = calc_density_air(this%tc, this%patm, this%vpd, .true.) + this%cp = calc_cp_moist_air(this%tc) + this%gamma = calc_psychro(this%tc, this%patm) + this%epsilon = calc_sat_slope(this%tc) / this%gamma + this%lv = calc_enthalpy_vap(this%tc) + end subroutine calc_temp_dependencies + + ! Print ParEnv information + subroutine print_par_env(this) + type(par_env_type), intent(in) :: this + write(*, *) "Env:" + write(*, *) " tc = ", this%tc, " [degC]" + write(*, *) " patm = ", this%patm, " [Pa]" + write(*, *) " vpd = ", this%vpd, " [Pa]" + write(*, *) " Rn = ", this%Rn, " [W m-2]" + write(*, *) " v_wind = ", this%v_wind, " [m s-1]" + write(*, *) " viscosity_water = ", this%viscosity_water, " [Pa s]" + write(*, *) " density_water = ", this%density_water, " [kg m-3]" + write(*, *) " rho = ", this%rho, " [kg m-3]" + write(*, *) " cp = ", this%cp, " [J kg-1 K-1]" + write(*, *) " gamma = ", this%gamma, " [Pa K-1]" + write(*, *) " epsilon = ", this%epsilon, " [Pa K-1]" + write(*, *) " lv = ", this%lv, " [J kg-1]" + end subroutine print_par_env + + ! ------------------------------------------------------------- + ! Functions: PM + !-------------------------------------------------------------- + + function calc_g_aero(h_canopy, v_wind, z_measurement) result(g_aero) + ! Aerodynamic conductance [m s-1] + ! To convert to mol m-2 s-1, see this: https://rdrr.io/cran/bigleaf/man/ms.to.mol.html (but not convincing) + ! Refs: + ! Eq 13 in Leuning et al (2008). https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2007WR006562 + ! Eq 7 in Zhang et al (2008): https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2017JD027025 + ! Box 4 in https://www.fao.org/3/x0490e/x0490e06.htm + real(kind = dbl8), intent(in) :: h_canopy, v_wind, z_measurement + real(kind = dbl8) :: g_aero, k_karman, d, z_om, z_ov + + k_karman = 0.41 ! von Karman's constant [-] + d = h_canopy * 2.0 / 3.0 ! zero-plane displacement height [m] + z_om = 0.123 * h_canopy ! roughness lengths governing transfer of water and momentum [m] + z_ov = 0.1 * z_om + + g_aero = (k_karman * k_karman * v_wind) / (log((z_measurement - d) / z_om) * log((z_measurement - d) / z_ov)) + end function calc_g_aero + + + function gs_conv(tc, patm) result(gs_conv_value) + ! multiplier to convert: + ! stomatal conductance to CO2 [mol m-2 s-1] ----> stomatal conductance to water [m s-1] + real(kind = dbl8), intent(in) :: tc, patm + real(kind = dbl8) :: gs_conv_value, R + + R = 8.31446261815324 ! Universal gas constant [J mol-1 K-1] + + gs_conv_value = 1.6 * R * (tc + 273.16) / patm + end function gs_conv + + + function calc_transpiration_pm(gs, ga, par_env) result(trans) + ! Calculate PML transpiration [mol m-2 s-1] + ! gs Stomatal conductance to CO2 [mol m-2 s-1] + ! ga Aerodynamic conductance [m s-1] + ! Rn Absorbed net radiation [W m-2] + real(kind = dbl8), intent(in) :: gs, ga + type(par_env_type), intent(in) :: par_env + real(kind = dbl8) :: trans, gw, latent_energy + + gw = gs * gs_conv(par_env%tc, par_env%patm) ! gw in [m s-1] + + latent_energy = (par_env%epsilon * par_env%Rn + (par_env%rho * par_env%cp / par_env%gamma) & + * ga * par_env%vpd) / (par_env%epsilon + 1 + ga / gw) ! latent energy W m-2 + trans = latent_energy * (55.5 / par_env%lv) ! W m-2 ---> mol m-2 s-1 + end function calc_transpiration_pm + + + function calc_max_transpiration_pm(ga, par_env) result(trans_max) + ! Calculate maximum possible PML transpiration for a given ga, calculated by setting gs = inf, [mol m-2 s-1] + ! ga Aerodynamic conductance [m s-1] + ! Rn Absorbed net radiation [W m-2] + real(kind = dbl8), intent(in) :: ga + type(par_env_type), intent(in) :: par_env + real(kind = dbl8) :: trans_max, latent_energy + + latent_energy = (par_env%epsilon * par_env%Rn + (par_env%rho * par_env%cp / par_env%gamma) & + * ga * par_env%vpd) / (par_env%epsilon + 1) ! latent energy W m-2 + trans_max = latent_energy * (55.5 / par_env%lv) ! W m-2 ---> mol m-2 s-1 + end function calc_max_transpiration_pm + + + function calc_gs_pm(Q, ga, par_env) result(gs) + ! Calculate PML stomatal conductance to CO2 [mol m-2 s-1] + ! Q Sap flux [mol m-2 s-1] + ! ga Aerodynamic conductance [m s-1] + ! Rn Absorbed net radiation [W m-2] + real(kind = dbl8), intent(in) :: Q, ga + type(par_env_type), intent(in) :: par_env + real(kind = dbl8) :: gs, Q_energy, den, gw + + Q_energy = Q * (par_env%lv / 55.5) + + den = par_env%epsilon * par_env%Rn + (par_env%rho * par_env%cp / par_env%gamma) & + * ga * par_env%vpd - (1 + par_env%epsilon) * Q_energy + !den = fmax(den, 0) + + gw = ga * Q_energy / den ! stomatal conductance to water [m s-1] + + gs = gw / gs_conv(par_env%tc, par_env%patm) ! stomatal conductance to CO2 [mol m-2 s-1] + end function calc_gs_pm + + + function calc_dE_dgs_pm(gs, ga, par_env) result(dE_dgs) + ! Calculate derivative of transpiration wrt stomatal conductance to CO2 [unitless] - analytical version + real(kind = dbl8), intent(in) :: gs, ga + type(par_env_type), intent(in) :: par_env + real(kind = dbl8) :: dE_dgs, gw, num, den, d_le_dgw + + gw = gs * gs_conv(par_env%tc, par_env%patm) ! [m s-1] + + num = ga * (par_env%epsilon * par_env%Rn + (par_env%rho * par_env%cp / par_env%gamma) * ga * par_env%vpd) + den = par_env%epsilon * gw + gw + ga + + d_le_dgw = (num / den / den) ! derivative of latent energy wrt stomatal conductance for water in m s-1 + + dE_dgs = d_le_dgw * (55.5 / par_env%lv) * gs_conv(par_env%tc, par_env%patm) + end function calc_dE_dgs_pm + + + function calc_dE_dgs_pm_num(gs, ga, par_env) result(dE_dgs) + ! Calculate derivative of transpiration wrt stomatal conductance to CO2 [unitless] - numerical version + real(kind = dbl8), intent(in) :: gs, ga + type(par_env_type), intent(in) :: par_env + real(kind = dbl8) :: dE_dgs, E, E_plus + + E = calc_transpiration_pm(gs, ga, par_env) + E_plus = calc_transpiration_pm(gs + 1.0e-6, ga, par_env) + + dE_dgs = (E_plus - E) / 1.0e-6 + end function calc_dE_dgs_pm_num + + ! ------------------------------------------------------------- + ! Functions: Phydro transpiration + !-------------------------------------------------------------- + ! Constructors for par plant + subroutine init_par_plant(this, cond, psi, b) + class(par_plant_type), intent(out) :: this + real(kind=dbl8), intent(in) :: cond, psi, b + this%conductivity = cond + this%psi50 = psi + this%b = b + end subroutine init_par_plant + + subroutine init_par_plant_6args(this, cond, psi, b, hcanopy, hwind, tchome) + class(par_plant_type), intent(out) :: this + real(kind=dbl8), intent(in) :: cond, psi, b, hcanopy, hwind, tchome + this%conductivity = cond + this%psi50 = psi + this%b = b + this%h_canopy = hcanopy + this%h_wind_measurement = hwind + this%tchome = tchome + end subroutine init_par_plant_6args + + + + !!! Vulnerability curve + !!! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + function P(psi, psi50, b) + real (kind = dbl8), intent(in) :: psi + real (kind = dbl8), intent(in) :: psi50 + real (kind = dbl8), intent(in) :: b + real (kind = dbl8) :: P + P = 0.5 ** ((psi/psi50) ** b) + end + + function Pprime(psi, psi50, b) + real (kind = dbl8), intent(in) :: psi + real (kind = dbl8), intent(in) :: psi50 + real (kind = dbl8), intent(in) :: b + real (kind = dbl8) :: Pprime + Pprime = log(0.5) * P(psi,psi50,b) * b * ((psi/psi50)**(b-1)) / psi50 + end + + function Pprimeprime(psi, psi50, b) + real (kind = dbl8), intent(in) :: psi + real (kind = dbl8), intent(in) :: psi50 + real (kind = dbl8), intent(in) :: b + real (kind = dbl8) :: Pprimeprime + Pprimeprime = log(0.5)*b*((psi/psi50)**(b-1))/psi50 * Pprime(psi, psi50, b) & + + log(0.5)*P(psi, psi50, b)/(psi50*psi50)*b*(b-1)* ((psi/psi50)**(b-2)) + end + + !!! Convert conductivity from m (m3/m2) to mol/m2/s/Mpa + function scale_conductivity(K, par_env) result(K4) + real (kind = dbl8), intent(in) :: K + type(par_env_type), intent(in) :: par_env + real (kind = dbl8) :: K2, K3, K4 + real (kind = dbl8) :: mol_h20_per_kg_h20 = 55.5 + + ! Flow rate in m3/m2/s/Pa + K2 = K/par_env%viscosity_water + + ! Flow rate in mol/m2/s/Pa + K3 = K2 * par_env%density_water * mol_h20_per_kg_h20; + + ! Flow rate in mol/m2/s/Mpa + K4 = K3 * 1e6; + end function scale_conductivity + + + !!! integrate vulnerability curve + function integral_P_analytical(dpsi, psi_soil, psi50, b) result(I) + ! int P(p, p50, b) = -(p/b) * (log2)^(-1/b) * G(1/b, (x/p)^b*log2) <--- G is unnormalized upper incomplete gamma function + ! = -(p/b) * (log2)^(-1/b) * G(1/b) * (1 - I((x/p)^b*log2) <--- I is lower incomplete gamma integral + ! = -(p/b) * (log2)^(-1/b) * G(1/b) * (- I((pl/p)^b*log2 + I((ps/p)^b*log2) <--- I is lower incomplete gamma integral + ! = +(p/b) * (log2)^(-1/b) * G(1/b) * ( I((pl/p)^b*log2 - I((ps/p)^b*log2) <--- I is lower incomplete gamma integral + real (kind = dbl8), intent(in) :: dpsi, psi_soil, psi50, b + real (kind = dbl8) :: I, ps, pl, l2 + integer (kind = int4) :: ifault + ps = psi_soil/psi50; + pl = (psi_soil-dpsi)/psi50; + l2 = log(2.0); + I = (psi50/b) * (l2**(-1/b)) * gamma(1/b) * (gammad(l2*(pl**b), 1/b, ifault) - gammad(l2*(ps**b), 1/b, ifault)) + end + + + function integral_P_approx(dpsi, psi_soil, psi50, b) result(I) + real (kind = dbl8), intent(in) :: dpsi, psi_soil, psi50, b + real (kind = dbl8) :: I + I = -P(psi_soil-dpsi/2.0, psi50, b)*dpsi + end + + + function integral_P_approx2(dpsi, psi_soil, psi50, b) result(I) + real (kind = dbl8), intent(in) :: dpsi, psi_soil, psi50, b + real (kind = dbl8) :: I + I = -(P(psi_soil, psi50, b)+P(psi_soil-dpsi, psi50, b))/2 * dpsi + end + + + function integral_P(dpsi, psi_soil, par_plant) result(I) + real (kind = dbl8), intent(in) :: dpsi, psi_soil + type(par_plant_type), intent(in) :: par_plant + real (kind = dbl8) :: I + + ! if (par_plant%gs_method == GS_QNG) then; I = integral_P_numerical( dpsi, psi_soil, par_plant%psi50, par_plant%b); + if (par_plant%gs_method == GS_IGF) then; I = integral_P_analytical(dpsi, psi_soil, par_plant%psi50, par_plant%b); + else if (par_plant%gs_method == GS_APX) then; I = integral_P_approx( dpsi, psi_soil, par_plant%psi50, par_plant%b); + else if (par_plant%gs_method == GS_APX2) then; I = integral_P_approx2( dpsi, psi_soil, par_plant%psi50, par_plant%b); + else; error stop "Unsupported gs_method specified" + end if + end + + !!! Transpiration and stomatal conductance + !!! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + function calc_sapflux(dpsi, psi_soil, par_plant, par_env) result(E) + real (kind = dbl8), intent(in) :: dpsi, psi_soil + type(par_plant_type), intent(in) :: par_plant + type(par_env_type), intent(in) :: par_env + real (kind = dbl8) :: E, K + + K = scale_conductivity(par_plant%conductivity, par_env) + E = K * (-integral_P(dpsi, psi_soil, par_plant)) + end + + function calc_max_sapflux(psi_soil, par_plant, par_env) result(E) + real (kind = dbl8), intent(in) :: psi_soil + type(par_plant_type), intent(in) :: par_plant + type(par_env_type), intent(in) :: par_env + real (kind = dbl8) :: E, K + + K = scale_conductivity(par_plant%conductivity, par_env) + E = K * (-integral_P(1e20_dbl8, psi_soil, par_plant)) + end + + + ! _ps-dpsi + ! Calculate dpsi that solves _/ K(psi') dpsi' = Q + ! ps + function calc_dpsi_from_sapflux(Q, psi_soil, par_plant, par_env) result(dpsi) + type(par_plant_type) :: par_plant + type(par_env_type) :: par_env + real(kind=dbl8) :: Q, psi_soil, dpsi, Qmax + + Qmax = calc_max_sapflux(psi_soil, par_plant, par_env); + if (Q > Qmax) then + dpsi = 999999999.0_dbl8 + else + dpsi = zero(0.0_dbl8, 100.0_dbl8, f, 1e-6_dbl8) + endif + + contains + + function f(dpsi) + real(kind=dbl8), intent(in) :: dpsi + real(kind=dbl8) :: f + f = calc_sapflux(dpsi, psi_soil, par_plant, par_env) - Q; + end function f + + end function calc_dpsi_from_sapflux + + + ! Calculates regulated stomatal conductance given transpiration/sapflux + ! water balance is assumed + ! plant hydraulic traits, and the environment. + function calc_gs_from_Q(Q, psi_soil, par_plant, par_env) result(gs) + real(dbl8), intent(in) :: Q, psi_soil + type(par_plant_type), intent(in) :: par_plant + type(par_env_type), intent(in) :: par_env + real(dbl8) :: D, gs, ga + + D = (par_env%vpd / par_env%patm) + + if (par_env%et_method == ET_DIFFUSION) then + gs = Q / (1.6d0 * D) + else if (par_env%et_method == ET_PM) then + ga = calc_g_aero(par_plant%h_canopy, dble(par_env%v_wind), par_plant%h_wind_measurement) + gs = calc_gs_pm(Q, ga, par_env) + else + write(*,*) 'Unknown et_method:', par_env%et_method + stop + end if + end function calc_gs_from_Q + + ! Derivative of sapflux wrt dpsi, dQ/ddpsi + function calc_Qprime_analytical(dpsi, psi_soil, par_plant, par_env) result(Qprime) + real(dbl8), intent(in) :: dpsi, psi_soil + type(par_plant_type), intent(in) :: par_plant + type(par_env_type), intent(in) :: par_env + real(dbl8) :: K + real(dbl8) :: Qprime + + K = scale_conductivity(par_plant%conductivity, par_env) + Qprime = K * P(psi_soil - dpsi, par_plant%psi50, par_plant%b) + end function calc_Qprime_analytical + + + function calc_Qprime_approx(dpsi, psi_soil, par_plant, par_env) result(Qprime) + type(par_plant_type) :: par_plant + type(par_env_type) :: par_env + real(kind=dbl8) :: dpsi, psi_soil, Qprime, K + + K = scale_conductivity(par_plant%conductivity, par_env) + Qprime = K * (P(psi_soil - dpsi / 2, par_plant%psi50, par_plant%b) - & + Pprime(psi_soil - dpsi / 2, par_plant%psi50, par_plant%b) * dpsi / 2) + end function calc_Qprime_approx + + function calc_Qprime_approx2(dpsi, psi_soil, par_plant, par_env) result(Qprime) + type(par_plant_type) :: par_plant + type(par_env_type) :: par_env + real(kind=dbl8) :: dpsi, psi_soil, Qprime, K + + K = scale_conductivity(par_plant%conductivity, par_env) + Qprime = K * ((P(psi_soil, par_plant%psi50, par_plant%b) & + + P(psi_soil - dpsi, par_plant%psi50, par_plant%b)) / 2 & + - Pprime(psi_soil - dpsi, par_plant%psi50, par_plant%b) * dpsi / 2) + end function calc_Qprime_approx2 + + ! Derivative of sapflux wrt dpsi, dQ/ddpsi + function calc_Qprime(dpsi, psi_soil, par_plant, par_env) result(Qprime) + type(par_plant_type) :: par_plant + type(par_env_type) :: par_env + real(kind=dbl8) :: dpsi, psi_soil, Qprime + + if (par_env%gs_method == GS_APX) then + Qprime = calc_Qprime_approx(dpsi, psi_soil, par_plant, par_env) + else if (par_env%gs_method == GS_APX2) then + Qprime = calc_Qprime_approx2(dpsi, psi_soil, par_plant, par_env) + else if (par_env%gs_method == GS_IGF) then + Qprime = calc_Qprime_analytical(dpsi, psi_soil, par_plant, par_env) + ! else if (par_env%gs_method == GS_QNG) then + ! Qprime = calc_Qprime_analytical(dpsi, psi_soil, par_plant, par_env) + else + write(*,*) "Unsupported gs_method specified" + stop + end if + end function calc_Qprime + + function calc_dE_dgs_dif(par_env) result(dE_dgs) + type(par_env_type) :: par_env + real(kind=dbl8) :: dE_dgs, D + + D = dble(par_env%vpd) / dble(par_env%patm) + dE_dgs = 1.6 * D + end function calc_dE_dgs_dif + + function calc_dE_dgs_pm_from_gs(gs, par_plant, par_env) result(dE_dgs) + type(par_plant_type) :: par_plant + type(par_env_type) :: par_env + real(kind=dbl8) :: gs, dE_dgs, ga + + ga = calc_g_aero(par_plant%h_canopy, dble(par_env%v_wind), par_plant%h_wind_measurement) + dE_dgs = calc_dE_dgs_pm(gs, ga, par_env) + end function calc_dE_dgs_pm_from_gs + + function calc_dE_dgs_pm_from_dpsi(dpsi, psi_soil, par_plant, par_env) result(dE_dgs) + type(par_plant_type) :: par_plant + type(par_env_type) :: par_env + real(kind=dbl8) :: dpsi, psi_soil, dE_dgs, ga, Q, gs + + ga = calc_g_aero(par_plant%h_canopy, dble(par_env%v_wind), par_plant%h_wind_measurement) + Q = calc_sapflux(dpsi, psi_soil, par_plant, par_env) + gs = calc_gs_pm(Q, ga, par_env) + dE_dgs = calc_dE_dgs_pm(gs, ga, par_env) + end function calc_dE_dgs_pm_from_dpsi + + ! Derivative of E wrt gs + function calc_dE_dgs_from_gs(gs, par_plant, par_env) result(dE_dgs) + type(par_plant_type) :: par_plant + type(par_env_type) :: par_env + real(kind=dbl8) :: gs, dE_dgs + + if (par_env%et_method == ET_DIFFUSION) then + dE_dgs = calc_dE_dgs_dif(par_env) + else if (par_env%et_method == ET_PM) then + dE_dgs = calc_dE_dgs_pm_from_gs(gs, par_plant, par_env) + else + write(*,*) "Unknown et_method:", par_env%et_method + stop + end if + end function calc_dE_dgs_from_gs + + ! Derivative of E wrt gs + function calc_dE_dgs_from_dpsi(dpsi, psi_soil, par_plant, par_env) result(dE_dgs) + type(par_plant_type) :: par_plant + type(par_env_type) :: par_env + real(kind=dbl8) :: dpsi, psi_soil, dE_dgs + + if (par_env%et_method == ET_DIFFUSION) then + dE_dgs = calc_dE_dgs_dif(par_env) + else if (par_env%et_method == ET_PM) then + dE_dgs = calc_dE_dgs_pm_from_dpsi(dpsi, psi_soil, par_plant, par_env) + else + write(*,*) "Unknown et_method:", par_env%et_method + stop + end if + end function calc_dE_dgs_from_dpsi + + ! Derivative of gs wrt dpsi, dgs/ddpsi + ! This version of the function avoids recomputation of gs when it is already known + function calc_gsprime(dpsi, gs, psi_soil, par_plant, par_env) result(gsprime) + type(par_plant_type) :: par_plant + type(par_env_type) :: par_env + real(kind=dbl8) :: dpsi, gs, psi_soil, gsprime, Qprime, Eprime + + Qprime = calc_Qprime(dpsi, psi_soil, par_plant, par_env) + Eprime = calc_dE_dgs_from_gs(gs, par_plant, par_env) + gsprime = Qprime / Eprime + end function calc_gsprime + + ! Derivative of gs wrt dpsi, dgs/ddpsi + ! This version is for use when gs is not known, and needs to be computed anyway + function calc_gsprime_from_dpsi(dpsi, psi_soil, par_plant, par_env) result(gsprime) + type(par_plant_type) :: par_plant + type(par_env_type) :: par_env + real(kind=dbl8) :: dpsi, psi_soil, gsprime, Qprime, Eprime + + Qprime = calc_Qprime(dpsi, psi_soil, par_plant, par_env) + Eprime = calc_dE_dgs_from_dpsi(dpsi, psi_soil, par_plant, par_env) + gsprime = Qprime / Eprime + end function calc_gsprime_from_dpsi + + ! ------------------------------------------------------------- + ! Functions: Phydro photosynthesis + !-------------------------------------------------------------- + subroutine create_par_photosynth(this, tc, patm, kphio, co2, ppfd, fapar, rdark25, tcgrowth, tchome, & + ftemp_vj_method, ftemp_rd_method, ftemp_br_method) + + type(par_photosynth_type), intent(out) :: this + real(kind = dbl8), intent(in) :: tc, patm, kphio, co2, ppfd, fapar, rdark25, tcgrowth, tchome + integer(kind = int4), intent(in) :: ftemp_vj_method, ftemp_rd_method, ftemp_br_method + + ! Calculate temperature scaling factors + this%fT_vcmax = calc_ftemp_inst_vcmax(real(tc), real(tcgrowth), 25.0, ftemp_vj_method) + this%fT_jmax = calc_ftemp_inst_jmax(real(tc), real(tcgrowth), real(tchome), 25.0, ftemp_vj_method) + this%fT_rd = calc_ftemp_inst_rd(real(tc), ftemp_rd_method) + + ! Calculate other parameters + this%kmm = calc_kmm(real(tc), real(patm)) + this%gammastar = calc_gammastar(real(tc), real(patm)) + this%phi0 = kphio !* calc_kphio_temp(real(tc), .false.) + this%Iabs = ppfd * fapar + this%ca = co2 * patm * 1.0d-6 + this%patm = patm + this%delta = rdark25 * this%fT_rd / this%fT_vcmax + + ! Set the temperature scaling methods + this%ftemp_vj_method = ftemp_vj_method + this%ftemp_rd_method = ftemp_rd_method + this%ftemp_br_method = ftemp_br_method + + end subroutine create_par_photosynth + + subroutine print_par_photosynth(this) + type(par_photosynth_type), intent(in) :: this + + print *, "ParPhotosynth: " + print *, " fT_vcmax", this%fT_vcmax + print *, " fT_jmax", this%fT_jmax + print *, " fT_rd", this%fT_rd + print *, " kmm", this%kmm + print *, " gammastar", this%gammastar + print *, " phi0", this%phi0 + print *, " Iabs", this%Iabs + print *, " ca", this%ca + print *, " patm", this%patm + print *, " delta", this%delta + print *, " ftemp_vj_method", this%ftemp_vj_method + print *, " ftemp_rd_method", this%ftemp_rd_method + print *, " ftemp_br_method", this%ftemp_br_method + end subroutine print_par_photosynth + + function calc_ftemp_arrhenius(tk, dha, tkref) result(ftemp) + ! Output: Factor fv to correct for instantaneous temperature response + ! of Vcmax for: + ! + ! Vcmax(temp) = fv * Vcmax(25 deg C) + ! + ! Input: + ! tk - Leaf temperature in Kelvin + ! dha - Activation energy (J/mol) + ! tkref - Reference temperature in Kelvin (default: 298.15 K) + + real(kind = dbl8), intent(in) :: tk, dha, tkref + real(kind = dbl8), parameter :: kR = 8.3145 ! Universal gas constant, J/mol/K + real(kind = dbl8) :: ftemp + + ! Calculate temperature scaling factor using Arrhenius equation + ftemp = exp(dha * (tk - tkref) / (tkref * kR * tk)) + + end function calc_ftemp_arrhenius + + + function calc_ftemp_inst_vcmax(tcleaf, tcgrowth, tcref, method_ftemp) result(fv) + real(kind = flt4), intent(in) :: tcleaf, tcgrowth, tcref + real(kind = dbl8) :: fv + integer(kind = int4), intent(in) :: method_ftemp + real(kind = dbl8), parameter :: Rgas = 8.3145 ! Universal gas constant (J/mol/K) + real(kind = dbl8) :: tkref + real(kind = dbl8) :: tkleaf + real(kind = dbl8) :: Hd, Ha, a_ent, b_ent, dent, fva, fvb + real(kind = dbl8) :: Sv, term_1, term_2, term_3 + + tkref = tcref + 273.15 ! Convert reference temperature to Kelvin + tkleaf = tcleaf + 273.15 ! Convert leaf temperature to Kelvin + + if (method_ftemp == FV_kattge07 .or. method_ftemp == FV_kumarathunge19) then + ! Kattge2007 Parametrization + Hd = 200000.0 ! Deactivation energy (J/mol) + Ha = 71513.0 ! Activation energy (J/mol) + a_ent = 668.39 ! Offset of entropy vs. temperature relationship from Kattge & Knorr (2007) (J/mol/K) + b_ent = 1.07 ! Slope of entropy vs. temperature relationship from Kattge & Knorr (2007) (J/mol/K^2) + + if (method_ftemp == FV_kumarathunge19) then + ! Kumarathunge2019 Implementation: + ! local parameters + a_ent = 645.13 ! Offset of entropy vs. temperature relationship (J/mol/K) + b_ent = 0.38 ! Slope of entropy vs. temperature relationship (J/mol/K^2) + + ! local variables + Ha = 42600.0 + (1140.0 * tcgrowth) ! Acclimation for vcmax + end if + + ! Calculate entropy following Kattge & Knorr (2007), negative slope and y-axis intersect is when expressed as a function of temperature in degrees Celsius, not Kelvin! + dent = a_ent - (b_ent * tcgrowth) ! 'tcgrowth' corresponds to 'tmean' in Nicks, 'tc25' is 'to' in Nick's + + fva = calc_ftemp_arrhenius(tkleaf, Ha, tkref) + fvb = (1.0 + exp((tkref * dent - Hd) / (Rgas * tkref))) / (1.0 + exp((tkleaf * dent - Hd) / (Rgas * tkleaf))) + fv = fva * fvb + elseif (method_ftemp == FV_leuning02) then + ! Ref: Leuning, R. (2002). Temperature dependence of two parameters in a photosynthesis model. Plant, Cell & Environment, 25(9), 1205–1210. https://doi.org/10.1046/j.1365-3040.2002.00898.x + ! Table 2: + Ha = 73637.0 + Hd = 149252.0 + Sv = 486.0 + + term_1 = 1.0 + exp((Sv * tkref - Hd) / (Rgas * tkref)) + term_3 = 1.0 + exp((Sv * tkleaf - Hd) / (Rgas * tkleaf)) + term_2 = exp((Ha / (Rgas * tkref)) * (1.0 - tkref / tkleaf)) ! Careful: In Eq. (1) in Leuning et al. (1992), there is a bracket missing in this term! + + fv = term_1 * term_2 / term_3 + else + write(*,*) "Invalid method_ftemp:", method_ftemp + stop + end if + end function calc_ftemp_inst_vcmax + + + function calc_ftemp_inst_jmax(tcleaf, tcgrowth, tchome, tcref, method_ftemp) result(fv) + real(kind = flt4), intent(in) :: tcleaf, tcgrowth, tchome, tcref + integer(kind = int4), intent(in) :: method_ftemp + + real(kind = dbl8), parameter :: Rgas = 8.3145 ! Universal gas constant (J/mol/K) + real(kind = dbl8) :: tkref ! Convert reference temperature to Kelvin + real(kind = dbl8) :: tkleaf ! Convert leaf temperature to Kelvin + real(kind = dbl8) :: fv + + real(kind = dbl8) :: Hd ! Deactivation energy (J/mol) + real(kind = dbl8) :: Ha ! Activation energy (J/mol) + real(kind = dbl8) :: a_ent ! Offset of entropy vs. temperature relationship from Kattge & Knorr (2007) (J/mol/K) + real(kind = dbl8) :: b_ent ! Slope of entropy vs. temperature relationship from Kattge & Knorr (2007) (J/mol/K^2) + real(kind = dbl8) :: c_ent + real(kind = dbl8) :: dent ! Entropy calculation, equations given in Celsius, not in Kelvin + real(kind = dbl8) :: fva + real(kind = dbl8) :: fvb + + real(kind = dbl8) :: Sv, term_1, term_2, term_3 + + tkref = tcref + 273.15 + tkleaf = tcleaf + 273.15 + + if (method_ftemp == FV_kattge07 .or. method_ftemp == FV_kumarathunge19) then + Hd = 200000.0 + Ha = 49884.0 + a_ent = 659.70 + b_ent = 0.75 + + dent = a_ent - b_ent * tcgrowth + + if (method_ftemp == FV_kumarathunge19) then + Ha = 40710.0 + a_ent = 658.77 + b_ent = 0.84 + c_ent = 0.52 + + dent = a_ent - (b_ent * tchome) - c_ent * (tcgrowth - tchome) + end if + + fva = calc_ftemp_arrhenius(tkleaf, Ha, tkref) + fvb = (1.0 + exp((tkref * dent - Hd) / (Rgas * tkref))) / (1.0 + exp((tkleaf * dent - Hd) / (Rgas * tkleaf))) + fv = fva * fvb + + elseif (method_ftemp == FV_leuning02) then + Ha = 50300.0 + Hd = 152044.0 + Sv = 495.0 + + term_1 = 1.0 + exp((Sv * tkref - Hd) / (Rgas * tkref)) + term_3 = 1.0 + exp((Sv * tkleaf - Hd) / (Rgas * tkleaf)) + term_2 = exp((Ha / (Rgas * tkref)) * (1.0 - tkref / tkleaf)) + + fv = term_1 * term_2 / term_3 + + else + write(*,*) "Invalid method_ftemp:", method_ftemp + stop + end if + + end function calc_ftemp_inst_jmax + + + function calc_ftemp_inst_rd(tc_leaf, method_rd_scale) result(f) + real(kind = dbl8) :: f + real(kind = flt4), intent(in) :: tc_leaf + integer(kind=int4), intent(in) :: method_rd_scale + real(kind = dbl8) :: apar, bpar, dha + + if (method_rd_scale == FR_heskel16) then + ! Heskel et al. (2016) temperature scaling + apar = 0.1012 + bpar = 0.0005 + f = exp(apar * (tc_leaf - 25.0) - bpar * (tc_leaf*tc_leaf - 25.0*25.0)) + elseif (method_rd_scale == FR_arrhenius) then + ! Arrhenius temperature scaling + dha = 20700.0 ! Activation energy taken from Kumarathunge et al. (2019), Table 1, Mature Natural Environment + f = calc_ftemp_arrhenius(dble(tc_leaf) + 273.15, dha, 298.15_dbl8) ! Convert temperature to Kelvin and call calc_ftemp_arrh function + elseif (method_rd_scale == FR_q10) then + ! Q10 temperature scaling according to Tjoelker et al. (2001) + f = (3.22 - 0.046 * tc_leaf)**(tc_leaf - 25.0) / 10.0 + else + write(*,*) "Invalid method_rd_scale:", method_rd_scale + stop + end if + + end function calc_ftemp_inst_rd + + + function calc_brd25(method_rd25, tc_growth) result(rd_to_vcmax) + real(kind = dbl8) :: rd_to_vcmax + real(kind = dbl8), intent(in) :: tc_growth + integer(kind = int4), intent(in) :: method_rd25 + + if (method_rd25 == FB_atkin15) then + rd_to_vcmax = 0.015 ! Ratio of Rdark to Vcmax25, Atkin et al., 2015 for C3 herbaceous + elseif (method_rd25 == FB_kumarathunge19) then + rd_to_vcmax = 0.0360 - 0.0010 * tc_growth ! Acclimated rd_to_vcmax taken from Kumarathunge et al. (2019), Table 1, Mature Natural Environment + else + write(*,*) "Invalid method_rd25:", method_rd25 + stop + end if + + end function calc_brd25 + + + !------------------------------------------------------- + ! Ac / Aj calculations + !------------------------------------------------------- + function QUADM(A, B, C) + real(kind=dbl8) :: QUADM + real(kind=dbl8), intent(in) :: A, B, C + QUADM = (-B - sqrt(B*B - 4.0d0*A*C)) / (2.0d0*A) + end function QUADM + + function QUADP(A, B, C) + real(kind=dbl8) :: QUADP + real(kind=dbl8), intent(in) :: A, B, C + QUADP = (-B + sqrt(B*B - 4.0d0*A*C)) / (2.0d0*A) + end function QUADP + + + function calc_assim_rubisco_limited(gs_in, vcmax, par_photosynth) result(res) + real(kind=dbl8), intent(in) :: gs_in + real(kind=dbl8), intent(in) :: vcmax + type(ACi_type) :: res + type(par_photosynth_type) :: par_photosynth + real(kind=dbl8) :: ca, d, A, B, C, gs + + gs = gs_in + + ca = par_photosynth%ca + gs = gs * 1.0d6 / par_photosynth%patm + d = par_photosynth%delta + + A = -gs + B = gs * ca - gs * par_photosynth%kmm - vcmax*(1.0d0-d) + C = gs * ca * par_photosynth%kmm + vcmax * (par_photosynth%gammastar + par_photosynth%kmm*d) + + res%ci = QUADM(A, B, C) + res%a = gs * (ca - res%ci) + res%isVcmaxLimited = .true. + + end function calc_assim_rubisco_limited + + + function calc_assim_light_limited(gs_in, jmax, par_photosynth) result(res) + real(kind=dbl8), intent(in) :: gs_in + real(kind=dbl8), intent(in) :: jmax + type(ACi_type) :: res + type(par_photosynth_type) :: par_photosynth + real(kind=dbl8) :: ca, d, phi0iabs, jj, jlim, A, B, C, gs + + gs = gs_in + + ca = par_photosynth%ca + gs = gs * 1.0d6 / par_photosynth%patm + gs = gs + 1.0d-12 + d = par_photosynth%delta + + phi0iabs = par_photosynth%phi0 * par_photosynth%Iabs + jj = 4.0d0 * phi0iabs / jmax + jlim = phi0iabs / sqrt(1.0d0 + jj*jj) + + A = -gs + B = gs * ca - gs * 2.0d0 * par_photosynth%gammastar - jlim * (1.0d0-d) + C = gs * ca * 2.0d0 * par_photosynth%gammastar + jlim * (par_photosynth%gammastar + d*par_photosynth%kmm) + + res%ci = QUADM(A, B, C) + res%a = gs * (ca - res%ci) + res%isVcmaxLimited = .false. + + end function calc_assim_light_limited + + + function calc_assimilation_limiting(vcmax, jmax, gs, par_photosynth) result(Aout) + real(kind=dbl8), intent(in) :: vcmax, jmax + real(kind=dbl8), intent(in) :: gs + type(ACi_type) :: Ac, Aj, Aout + type(par_photosynth_type) :: par_photosynth + + Ac = calc_assim_rubisco_limited(gs, vcmax, par_photosynth) + Aj = calc_assim_light_limited(gs, jmax, par_photosynth) + + if (Ac%ci > Aj%ci) then + Aout = Ac + else + Aout = Aj + end if + end function calc_assimilation_limiting + + ! ------------------------------------------------------------- + ! Functions: Phydro solver + !-------------------------------------------------------------- + + !!! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + !!! Phydro analytical solver (acclimating) + !!! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + function calc_J(gs, x, par_photosynth) result(J) + real (kind = dbl8), intent(in) :: gs, x + type(par_photosynth_type), intent(in) :: par_photosynth + real (kind = dbl8) :: g, K, ca, d, J + g = par_photosynth%gammastar / par_photosynth%ca + k = par_photosynth%kmm / par_photosynth%ca + ca = par_photosynth%ca / par_photosynth%patm*1e6 + d = par_photosynth%delta + J = 4*gs*ca*(1-x)*(x+ 2*g)/(x*(1-d)-(g+d*k)) + end + + + function calc_jmax_from_J(J, par_photosynth) result(jmax) + real (kind = dbl8), intent(in) :: J + type(par_photosynth_type), intent(in) :: par_photosynth + real (kind = dbl8) :: pp, pj, jmax + pp = 4*par_photosynth%phi0 * par_photosynth%Iabs; + pj = pp/J; + jmax = pp/sqrt(pj*pj-1); + end + + + function calc_djmax_dJ(J, par_photosynth) result(djdj) + real (kind = dbl8), intent(in) :: J + type(par_photosynth_type), intent(in) :: par_photosynth + real (kind = dbl8) :: pp, sq, psq, djdj + pp = 4*par_photosynth%phi0 * par_photosynth%Iabs + sq = sqrt(pp*pp-J*J) + psq = pp/sq + djdj = psq*psq*psq + end + + + function calc_dJ_dchi(gs, x, par_photosynth) result(djdx) + real (kind = dbl8), intent(in) :: gs, x + type(par_photosynth_type), intent(in) :: par_photosynth + real (kind = dbl8) :: g, K, ca, d, djdx, d1 + g = par_photosynth%gammastar / par_photosynth%ca + k = par_photosynth%kmm / par_photosynth%ca + ca = par_photosynth%ca / par_photosynth%patm*1e6 + d = par_photosynth%delta + ! gs*ca * ((d*(2*g*(k + 1) + k*(2*x - 1) + x^2) + 2*g^2 + g*(2*x - 3) - x^2)/(d*(k + x) + g - x)^2) + d1 = d*(k + x) + g - x; + djdx = 4*gs*ca * ((d*(2*g*(k + 1) + k*(2*x - 1) + x*x) - ((x-g)*(x-g)+3*g*(1-g)))/(d1*d1)); + ! gs*ca*(3*(g-1)*g/(g-x)^2 - 1) + end + + + function calc_dJ_ddpsi(gsprime, x, par_photosynth) result(djdp) + real (kind = dbl8), intent(in) :: gsprime, x + type(par_photosynth_type), intent(in) :: par_photosynth + real (kind = dbl8) :: g, K, ca, d, djdp + g = par_photosynth%gammastar / par_photosynth%ca + k = par_photosynth%kmm / par_photosynth%ca + ca = par_photosynth%ca / par_photosynth%patm*1e6 + d = par_photosynth%delta + djdp = 4*gsprime*ca*(1-x)*(x+2*g)/(x*(1-d)-(g+d*k)) + end + + + function calc_x_from_dpsi(dpsi, gsprime, par_photosynth, par_cost) result(x) + real (kind = dbl8), intent(in) :: dpsi, gsprime + type(par_photosynth_type), intent(in) :: par_photosynth + type(par_cost_type), intent(in) :: par_cost + + real (kind = dbl8) gstar, Km, ca, br, y, ca2, x + + gstar = par_photosynth%gammastar/par_photosynth%patm*1e6 + Km = par_photosynth%kmm/par_photosynth%patm*1e6 + ca = par_photosynth%ca/par_photosynth%patm*1e6 + br = par_photosynth%delta + y = par_cost%gamma + + ca2 = ca*ca; + x = (-2*ca*dpsi*(gstar + br*Km)*y + & + ca2*((3 - 2*br)*gstar + br*Km)*gsprime + & + -sqrt(2.0D+00)*sqrt( & + ca2*dpsi*((-3 + 2*br)*gstar - br*Km)*((-1 + br)*ca + gstar + & + br*Km)*y* & + (-2*dpsi*y + (ca + 2*gstar)* & + gsprime)))/ & + (ca2*(2*(-1 + br)*dpsi*y + ((3 - 2*br)*gstar + br*Km)* & + gsprime)) + + if (x < (gstar + br*Km)/(ca - br*ca)) x = (gstar + br*Km)/(ca - br*ca)+1e-12 + end + + + function dFdx(dpsi, psi_soil, par_plant, par_env, par_photosynth, par_cost) result(res) + real (kind = dbl8), intent(in) :: dpsi, psi_soil + type(par_plant_type), intent(in) :: par_plant + type(par_env_type), intent(in) :: par_env + type(par_photosynth_type), intent(in) :: par_photosynth + type(par_cost_type), intent(in) :: par_cost + + real (kind = dbl8) :: Q, gs, gsprime, X, J, ca, g, djmax_dJ, dJ_dchi, dP_dx + type(dfdx_type) :: res + + Q = calc_sapflux(dpsi, psi_soil, par_plant, par_env) + gs = calc_gs_from_Q(Q, psi_soil, par_plant, par_env) + gsprime = calc_gsprime(dpsi, gs, psi_soil, par_plant, par_env) + + X = calc_x_from_dpsi(dpsi, gsprime, par_photosynth, par_cost) + + J = calc_J(gs, X, par_photosynth) + + ca = par_photosynth%ca / par_photosynth%patm*1e6 + g = par_photosynth%gammastar / par_photosynth%ca + + djmax_dJ = calc_djmax_dJ(J, par_photosynth) + dJ_dchi = calc_dJ_dchi(gs, X, par_photosynth) + + dP_dx = -gs*ca - par_cost%alpha * djmax_dJ * dJ_dchi + + res = dfdx_type(dP_dx, J, djmax_dJ, dJ_dchi) + end + + + function calc_dpsi_bound(psi_soil, par_plant, par_env, par_photosynth, par_cost) result(bounds) + real (kind = dbl8), intent(in) :: psi_soil + type(par_plant_type), intent(in) :: par_plant + type(par_env_type), intent(in) :: par_env + type(par_photosynth_type), intent(in) :: par_photosynth + type(par_cost_type), intent(in) :: par_cost + + type(dpsi_bounds_type) :: bounds + + real (kind = dbl8) :: gstar, ca, y, K, Pox, Ppox, Pppox + real (kind = dbl8) :: a,b,c,del + real (kind = dbl8) :: ex, appo2, iabsb, use_bound + real (kind = dbl8) :: ga, Qmax, max_dpsi + + gstar = par_photosynth%gammastar/par_photosynth%patm*1e6 + ca = par_photosynth%ca/par_photosynth%patm*1e6 + y = par_cost%gamma + + K = scale_conductivity(par_plant%conductivity, par_env)/(1.6*par_env%vpd/par_env%patm); + + Pox = P(psi_soil, par_plant%psi50, par_plant%b); + Ppox = Pprime(psi_soil, par_plant%psi50, par_plant%b); + Pppox = Pprimeprime(psi_soil, par_plant%psi50, par_plant%b); + + a = (ca + 2*gstar)*K*Pppox*4.0d0/8.0d0; + b = -(2*y + (ca + 2*gstar)*K*Ppox); + c = (ca + 2*gstar)*K*Pox; + del = b*b-4*a*c; + + appo2 = (-b-sqrt(del))/(2*a) + ex = zero(0.0d0, 10.0d0, f2, 1d-6) + + use_bound = ex + + iabsb = zero(use_bound*0.001, use_bound*0.99, f1, 1D-6); + + ! If using PM, find max dpsi from max possible transpiration + if (par_env%et_method == ET_PM) then + ga = calc_g_aero(par_plant%h_canopy, dble(par_env%v_wind), par_plant%h_wind_measurement); + Qmax = calc_max_transpiration_pm(ga, par_env); + max_dpsi = calc_dpsi_from_sapflux(Qmax, psi_soil, par_plant, par_env); + iabsb = min(max_dpsi, iabsb); + endif + + + bounds = dpsi_bounds_type(ex, appo2, iabsb) + + contains + + function f2(dpsi) result(gg) + real(kind = dbl8), intent(in) :: dpsi + real(kind = dbl8) :: gg, gsprime + gsprime = calc_gsprime_from_dpsi(dpsi, psi_soil, par_plant, par_env) + gg = (-2*dpsi*y + (ca + 2*gstar)*gsprime) + end + + function f1(dpsi) result(J) + real(kind = dbl8), intent(in) :: dpsi + real(kind = dbl8) :: J, gs, x, Q, gsprime + Q = calc_sapflux(dpsi, psi_soil, par_plant, par_env); + gs = calc_gs_from_Q(Q, psi_soil, par_plant, par_env); + gsprime = calc_gsprime(dpsi, gs, psi_soil, par_plant, par_env); + x = calc_x_from_dpsi(dpsi,gsprime, par_photosynth, par_cost); + J = calc_J(gs, x, par_photosynth)-4.0d0*par_photosynth%phi0*par_photosynth%Iabs; + end + + end + + + + !!! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + !!! Phydro analytical solver (instantaneous) + !!! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + function calc_dP_ddpsi(dpsi, vcmax, jmax, psi_soil, par_plant, par_env, par_photosynth, par_cost) result(dP_ddpsi) + real(kind=dbl8), intent(in) :: dpsi, vcmax, jmax, psi_soil + type(par_plant_type), intent(in) :: par_plant + type(par_env_type), intent(in) :: par_env + type(par_photosynth_type), intent(in) :: par_photosynth + type(par_cost_type), intent(in) :: par_cost + real(kind=dbl8) :: gstar, Km, ca, br, y, Q, gs, P, dpsi1, Q1, gs1, P1 + type(ACi_type) :: Assim, Assim1 + real(kind=dbl8) :: dP_ddpsi + + gstar = par_photosynth%gammastar / par_photosynth%patm * 1.0d6 + Km = par_photosynth%kmm / par_photosynth%patm * 1.0d6 + ca = par_photosynth%ca / par_photosynth%patm * 1.0d6 + br = par_photosynth%delta + y = par_cost%gamma + + Q = calc_sapflux(dpsi, psi_soil, par_plant, par_env) + gs = calc_gs_from_Q(Q, psi_soil, par_plant, par_env) + Assim = calc_assimilation_limiting(vcmax, jmax, gs, par_photosynth) + P = Assim%a - y * dpsi * dpsi + + dpsi1 = dpsi + 1.0d-6 + Q1 = calc_sapflux(dpsi1, psi_soil, par_plant, par_env) + gs1 = calc_gs_from_Q(Q1, psi_soil, par_plant, par_env) + Assim1 = calc_assimilation_limiting(vcmax, jmax, gs1, par_photosynth) + P1 = Assim1%a - y * (dpsi1) * (dpsi1) + + ! print *, "dpsi = ", dpsi + ! print *, "P ", dpsi, Q, gs, Assim%a + ! print *, "P1", dpsi, Q1, gs1, Assim1%a + + dP_ddpsi = (P1 - P) / 1.0d-6 + + end function calc_dP_ddpsi + + + function calc_dpsi_bound_inst(psi_soil, par_plant, par_env, par_photosynth, par_cost) result(bound) + real(kind=dbl8), intent(in) :: psi_soil + type(par_plant_type), intent(in) :: par_plant + type(par_env_type), intent(in) :: par_env + type(par_photosynth_type), intent(in) :: par_photosynth + type(par_cost_type), intent(in) :: par_cost + real(kind=dbl8) :: bound, ga, Qmax, max_dpsi + + bound = 100.0d0 + + ! If using PM, find max dpsi from max possible transpiration + if (par_env%et_method == ET_PM) then + ga = calc_g_aero(par_plant%h_canopy, dble(par_env%v_wind), par_plant%h_wind_measurement) + Qmax = calc_max_transpiration_pm(ga, par_env) + max_dpsi = calc_dpsi_from_sapflux(Qmax, psi_soil, par_plant, par_env) + bound = min(max_dpsi, bound) + end if + + end function calc_dpsi_bound_inst + + + ! ------------------------------------------------------------- + ! Functions: Phydro main + !-------------------------------------------------------------- + function phydro_analytical(tc, tg, ppfd, netrad, vpd, co2, elv, fapar, kphio, psi_soil, rdark, vwind, & + par_plant, par_cost, par_control) result(res) + real(kind=dbl8), intent(in) :: tc, tg, ppfd, netrad, vpd, co2, elv, fapar, kphio, psi_soil, rdark, vwind + type(par_plant_type), intent(in) :: par_plant + type(par_cost_type), intent(inout) :: par_cost + type(par_control_type), intent(in) :: par_control + type(par_env_type) :: par_env + type(par_photosynth_type) :: par_photosynth + type(phydro_result_type) :: res + + real(kind=dbl8) :: pa, e, gs, gsprime, x, J, jmax, vcmax, a, dpsi_opt + type(dpsi_bounds_type) :: bounds + + pa = calc_patm(real(elv)) + call create_par_photosynth(par_photosynth, tc, pa, kphio, co2, ppfd, fapar, rdark, tg, par_plant%tchome, & + par_control%ftemp_vj_method, par_control%ftemp_rd_method, par_control%ftemp_br_method) + call create_par_env(par_env, tc, pa, vpd, netrad, vwind) + + if (par_control%scale_alpha > 0) par_cost%alpha = par_cost%alpha / par_photosynth%fT_jmax ! Convert alpha from cost of jmax to cost of jmax25 + par_env%gs_method = par_control%gs_method + par_env%et_method = par_control%et_method + + bounds = calc_dpsi_bound(dble(psi_soil), par_plant, par_env, par_photosynth, par_cost) + dpsi_opt = zero(bounds%Iabs_bound * 0.001, bounds%Iabs_bound * 0.999, profit_fun, 1.0d-6) + + e = calc_sapflux(dpsi_opt, dble(psi_soil), par_plant, par_env) + gs = calc_gs_from_Q(e, dble(psi_soil), par_plant, par_env) + gsprime = calc_gsprime(dpsi_opt, gs, dble(psi_soil), par_plant, par_env) + x = calc_x_from_dpsi(dpsi_opt, gsprime, par_photosynth, par_cost) + J = calc_J(gs, x, par_photosynth) + jmax = calc_jmax_from_J(J, par_photosynth) + vcmax = (J / 4.0d0) * (x * par_photosynth%ca + par_photosynth%kmm) / (x * par_photosynth%ca + 2.0d0 * par_photosynth%gammastar) + a = gs * (par_photosynth%ca / par_photosynth%patm * 1.0d6) * (1.0d0 - x) + + res%a = a + res%e = e + res%ci = x * par_photosynth%ca + res%gs = gs + res%chi = x + res%vcmax = vcmax + res%jmax = jmax + res%dpsi = dpsi_opt + res%psi_l = psi_soil - dpsi_opt + res%nfnct = -999 + res%mc = (x * par_photosynth%ca - par_photosynth%gammastar) / (x * par_photosynth%ca + par_photosynth%kmm) + res%mj = (x * par_photosynth%ca - par_photosynth%gammastar) / (x * par_photosynth%ca + 2.0d0 * par_photosynth%gammastar) + res%gammastar = par_photosynth%gammastar + res%kmm = par_photosynth%kmm + res%vcmax25 = vcmax / par_photosynth%fT_vcmax + res%jmax25 = jmax / par_photosynth%fT_jmax + res%rd = vcmax * par_photosynth%delta + res%isVcmaxLimited = 0.5d0 + res%ac = a + res%aj = a + res%le = e * 0.018015d0 * par_env%lv + res%le_s_wet = (1.0d0 - fapar) * netrad * (par_env%epsilon / (1.0d0 + par_env%epsilon)) + + contains + + function profit_fun(dpsi) + real(kind = dbl8), intent(in) :: dpsi + real(kind = dbl8) :: profit_fun + type(dfdx_type) :: dfdx_res + dfdx_res = dFdx(dpsi, dble(psi_soil), par_plant, par_env, par_photosynth, par_cost) + profit_fun = dfdx_res%dPdx + end + + end function phydro_analytical + + function phydro_instantaneous_analytical(vcmax25, jmax25, tc, tg, ppfd, netrad, vpd, co2, elv, & + fapar, kphio, psi_soil, rdark, vwind, par_plant, par_cost, par_control) result(res) + real(kind=dbl8), intent(in) :: vcmax25, jmax25, tc, tg, ppfd, netrad, vpd, co2, elv, fapar, kphio, psi_soil, rdark, vwind + type(par_plant_type), intent(in) :: par_plant + type(par_cost_type), intent(inout) :: par_cost + type(par_control_type), intent(in) :: par_control + type(par_env_type) :: par_env + type(par_photosynth_type) :: par_photosynth + type(phydro_result_type) :: res + real(kind=dbl8) :: pa, e, gs + real(kind=dbl8) :: bound, jmax, vcmax, dpsi_opt + type(ACi_type) :: Aa, Ac, Aj + + pa = calc_patm(real(elv)) + call create_par_photosynth(par_photosynth, tc, pa, kphio, co2, ppfd, fapar, rdark, tg, par_plant%tchome, & + par_control%ftemp_vj_method, par_control%ftemp_rd_method, par_control%ftemp_br_method) + call create_par_env(par_env, tc, pa, vpd, netrad, vwind) + + ! call print_par_photosynth(par_photosynth) + + ! optionally convert alpha from cost of jmax to cost of jmax25 + if (par_control%scale_alpha > 0) par_cost%alpha = par_cost%alpha / par_photosynth%fT_jmax ! + par_env%gs_method = par_control%gs_method + par_env%et_method = par_control%et_method + + vcmax = vcmax25 * par_photosynth%fT_vcmax + jmax = jmax25 * par_photosynth%fT_jmax + + bound = calc_dpsi_bound_inst(psi_soil, par_plant, par_env, par_photosynth, par_cost) + dpsi_opt = zero(0.0d0, 0.99d0 * bound, profit_fun_inst, 1.0d-6) + + e = calc_sapflux(dpsi_opt, psi_soil, par_plant, par_env) + gs = calc_gs_from_Q(e, psi_soil, par_plant, par_env) + Aa = calc_assimilation_limiting(vcmax, jmax, gs, par_photosynth) + Ac = calc_assim_rubisco_limited(gs, vcmax, par_photosynth) + Aj = calc_assim_light_limited(gs, jmax, par_photosynth) + + res%a = Aa%a + res%e = e + res%ci = Aa%ci + res%gs = gs + res%chi = Aa%ci / par_photosynth%ca + res%vcmax = vcmax + res%jmax = jmax + res%dpsi = dpsi_opt + res%psi_l = psi_soil - dpsi_opt + res%mc = (Aa%ci - par_photosynth%gammastar) / (Aa%ci + par_photosynth%kmm) + res%mj = (Aa%ci - par_photosynth%gammastar) / (Aa%ci + 2.0d0 * par_photosynth%gammastar) + res%gammastar = par_photosynth%gammastar + res%kmm = par_photosynth%kmm + res%vcmax25 = vcmax25 + res%jmax25 = jmax25 + res%rd = vcmax * par_photosynth%delta + res%isVcmaxLimited = merge(1.d0, 0.d0, Aa%isVcmaxLimited) + res%ac = Ac%a + res%aj = Aj%a + res%le = e * 0.018015d0 * par_env%lv + res%le_s_wet = (1.0d0 - fapar) * netrad * (par_env%epsilon / (1.0d0 + par_env%epsilon)) + + contains + + function profit_fun_inst(dpsi) + real(kind = dbl8), intent(in) :: dpsi + real(kind = dbl8) :: profit_fun_inst + profit_fun_inst = calc_dP_ddpsi(dpsi, vcmax, jmax, psi_soil, par_plant, par_env, par_photosynth, par_cost) + end + + + end function phydro_instantaneous_analytical + + +end module md_photosynth_phydro diff --git a/src/photosynth_pmodel.mod.f90 b/src/photosynth_pmodel.mod.f90 index 5f851934..85df5f3c 100644 --- a/src/photosynth_pmodel.mod.f90 +++ b/src/photosynth_pmodel.mod.f90 @@ -11,7 +11,8 @@ module md_photosynth private public pmodel, zero_pmodel, outtype_pmodel, calc_ftemp_inst_jmax, calc_ftemp_inst_vcmax, & - calc_ftemp_inst_rd, calc_kphio_temp, calc_soilmstress + calc_ftemp_inst_rd, calc_kphio_temp, calc_soilmstress, & + calc_viscosity_h2o, calc_density_h2o, calc_kmm, calc_gammastar !---------------------------------------------------------------- ! MODULE-SPECIFIC, PRIVATE VARIABLES @@ -864,11 +865,11 @@ function calc_kphio_temp( dtemp, c4, kphio, kphio_par_a, kphio_par_b ) result( k end if else - - kphio_temp = kphio * max(0.0, min(1.0, (1.0 + kphio_par_a * (dtemp - kphio_par_b)**2))) + ! ! new + ! kphio_temp = kphio * max(0.0, min(1.0, (1.0 + kphio_par_a * (dtemp - kphio_par_b)**2))) ! old: - ! kphio_temp = kphio * (0.352 + 0.022 * dtemp - 3.4e-4 * dtemp**2) ! Based on Bernacchi et al., 2003 + kphio_temp = kphio * (0.352 + 0.022 * dtemp - 3.4e-4 * dtemp**2) ! Based on Bernacchi et al., 2003 end if end function calc_kphio_temp diff --git a/src/sofunutils.mod.f90 b/src/sofunutils.mod.f90 index 20159a1c..cb02fd87 100644 --- a/src/sofunutils.mod.f90 +++ b/src/sofunutils.mod.f90 @@ -6,6 +6,10 @@ module md_sofunutils !------------------------------------------------------------------------- implicit none + integer, parameter :: int4=SELECTED_INT_KIND(4) + integer, parameter :: flt4=SELECTED_REAL_KIND(6,37) + integer, parameter :: dbl8=SELECTED_REAL_KIND(15,307) + contains function dampen_variability( var, tau, var_memory ) result( out_memory ) @@ -1159,4 +1163,696 @@ function radians( x ) result( radians_out ) end function radians + function zero ( a, b, f, t ) result(val) + + !*****************************************************************************80 + ! + !! ZERO seeks the root of a function F(X) in an interval [A,B]. + ! + ! Discussion: + ! + ! The interval [A,B] must be a change of sign interval for F. + ! That is, F(A) and F(B) must be of opposite signs. Then + ! assuming that F is continuous implies the existence of at least + ! one value C between A and B for which F(C) = 0. + ! + ! The location of the zero is determined to within an accuracy + ! of 6 * MACHEPS * abs ( C ) + 2 * T. + ! + ! Thanks to Thomas Secretin for pointing out a transcription error in the + ! setting of the value of P, 11 February 2013. + ! + ! Licensing: + ! + ! This code is distributed under the GNU LGPL license. + ! + ! Modified: + ! + ! 11 February 2013 + ! + ! Author: + ! + ! Original FORTRAN77 version by Richard Brent. + ! FORTRAN90 version by John Burkardt. + ! + ! Reference: + ! + ! Richard Brent, + ! Algorithms for Minimization Without Derivatives, + ! Dover, 2002, + ! ISBN: 0-486-41998-3, + ! LC: QA402.5.B74. + ! + ! Parameters: + ! + ! Input, real (kind = dbl8) A, B, the endpoints of the change of + ! sign interval. + ! + ! Input, real (kind = dbl8) MACHEP, an estimate for the relative machine + ! precision. + ! + ! Input, real (kind = dbl8) T, a positive error tolerance. + ! + ! Input, external real (kind = dbl8) F, the name of a user-supplied + ! function, of the form "FUNCTION F ( X )", which evaluates the + ! function whose zero is being sought. + ! + ! Output, real (kind = dbl8) ZERO, the estimated value of a zero of + ! the function F. + ! + implicit none + + real (kind = dbl8) :: a, b, c, d, e + real (kind = dbl8) :: f + real (kind = dbl8) :: fa, fb, fc + real (kind = dbl8) :: m + real (kind = dbl8) :: machep + real (kind = dbl8) :: p, q, r, s, sa, sb + real (kind = dbl8) :: t + real (kind = dbl8) :: tol + real (kind = dbl8) :: val + + machep = epsilon ( 1D+00 ) + ! + ! Make local copies of A and B. + ! + sa = a + sb = b + fa = f ( sa ) + fb = f ( sb ) + + c = sa + fc = fa + e = sb - sa + d = e + + do + + if ( abs ( fc ) < abs ( fb ) ) then + + sa = sb + sb = c + c = sa + fa = fb + fb = fc + fc = fa + + end if + + tol = 2.0D+00 * machep * abs ( sb ) + t + m = 0.5D+00 * ( c - sb ) + + if ( abs ( m ) <= tol .or. fb == 0.0D+00 ) then + exit + end if + + if ( abs ( e ) < tol .or. abs ( fa ) <= abs ( fb ) ) then + + e = m + d = e + + else + + s = fb / fa + + if ( sa == c ) then + + p = 2.0D+00 * m * s + q = 1.0D+00 - s + + else + + q = fa / fc + r = fb / fc + p = s * ( 2.0D+00 * m * q * ( q - r ) - ( sb - sa ) * ( r - 1.0D+00 ) ) + q = ( q - 1.0D+00 ) * ( r - 1.0D+00 ) * ( s - 1.0D+00 ) + + end if + + if ( 0.0D+00 < p ) then + q = - q + else + p = - p + end if + + s = e + e = d + + if ( 2.0D+00 * p < 3.0D+00 * m * q - abs ( tol * q ) .and. & + p < abs ( 0.5D+00 * s * q ) ) then + d = p / q + else + e = m + d = e + end if + + end if + + sa = sb + fa = fb + + if ( tol < abs ( d ) ) then + sb = sb + d + else if ( 0.0D+00 < m ) then + sb = sb + tol + else + sb = sb - tol + end if + + fb = f ( sb ) + + if ( ( 0.0D+00 < fb .and. 0.0D+00 < fc ) .or. & + ( fb <= 0.0D+00 .and. fc <= 0.0D+00 ) ) then + c = sa + fc = fa + e = sb - sa + d = e + end if + + end do + + val = sb + + end function zero + + function alngam ( xvalue, ifault ) + + !*****************************************************************************80 + ! + !! ALNGAM computes the logarithm of the gamma function. + ! + ! Modified: + ! + ! 13 January 2008 + ! + ! Author: + ! + ! Original FORTRAN77 version by Allan Macleod. + ! FORTRAN90 version by John Burkardt. + ! + ! Reference: + ! + ! Allan Macleod, + ! Algorithm AS 245, + ! A Robust and Reliable Algorithm for the Logarithm of the Gamma Function, + ! Applied Statistics, + ! Volume 38, Number 2, 1989, pages 397-402. + ! + ! Parameters: + ! + ! Input, real (kind = dbl8) XVALUE, the argument of the Gamma function. + ! + ! Output, integer (kind = int4) IFAULT, error flag. + ! 0, no error occurred. + ! 1, XVALUE is less than or equal to 0. + ! 2, XVALUE is too big. + ! + ! Output, real (kind = dbl8) ALNGAM, the logarithm of the gamma function of X. + ! + implicit none + + real (kind = dbl8) alngam + real (kind = dbl8), parameter :: alr2pi = 0.918938533204673D+00 + integer (kind = int4) ifault + real (kind = dbl8), dimension ( 9 ) :: r1 = (/ & + -2.66685511495D+00, & + -24.4387534237D+00, & + -21.9698958928D+00, & + 11.1667541262D+00, & + 3.13060547623D+00, & + 0.607771387771D+00, & + 11.9400905721D+00, & + 31.4690115749D+00, & + 15.2346874070D+00 /) + real (kind = dbl8), dimension ( 9 ) :: r2 = (/ & + -78.3359299449D+00, & + -142.046296688D+00, & + 137.519416416D+00, & + 78.6994924154D+00, & + 4.16438922228D+00, & + 47.0668766060D+00, & + 313.399215894D+00, & + 263.505074721D+00, & + 43.3400022514D+00 /) + real (kind = dbl8), dimension ( 9 ) :: r3 = (/ & + -2.12159572323D+05, & + 2.30661510616D+05, & + 2.74647644705D+04, & + -4.02621119975D+04, & + -2.29660729780D+03, & + -1.16328495004D+05, & + -1.46025937511D+05, & + -2.42357409629D+04, & + -5.70691009324D+02 /) + real (kind = dbl8), dimension ( 5 ) :: r4 = (/ & + 0.279195317918525D+00, & + 0.4917317610505968D+00, & + 0.0692910599291889D+00, & + 3.350343815022304D+00, & + 6.012459259764103D+00 /) + real (kind = dbl8) :: x + real (kind = dbl8) :: x1 + real (kind = dbl8) :: x2 + real (kind = dbl8), parameter :: xlge = 5.10D+05 + real (kind = dbl8), parameter :: xlgst = 1.0D+30 + real (kind = dbl8) xvalue + real (kind = dbl8) y + + x = xvalue + alngam = 0.0D+00 + ! + ! Check the input. + ! + if ( xlgst <= x ) then + ifault = 2 + return + end if + + if ( x <= 0.0D+00 ) then + ifault = 1 + return + end if + + ifault = 0 + ! + ! Calculation for 0 < X < 0.5 and 0.5 <= X < 1.5 combined. + ! + if ( x < 1.5D+00 ) then + + if ( x < 0.5D+00 ) then + + alngam = - log ( x ) + y = x + 1.0D+00 + ! + ! Test whether X < machine epsilon. + ! + if ( y == 1.0D+00 ) then + return + end if + + else + + alngam = 0.0D+00 + y = x + x = ( x - 0.5D+00 ) - 0.5D+00 + + end if + + alngam = alngam + x * (((( & + r1(5) * y & + + r1(4) ) * y & + + r1(3) ) * y & + + r1(2) ) * y & + + r1(1) ) / (((( & + y & + + r1(9) ) * y & + + r1(8) ) * y & + + r1(7) ) * y & + + r1(6) ) + + return + + end if + ! + ! Calculation for 1.5 <= X < 4.0. + ! + if ( x < 4.0D+00 ) then + + y = ( x - 1.0D+00 ) - 1.0D+00 + + alngam = y * (((( & + r2(5) * x & + + r2(4) ) * x & + + r2(3) ) * x & + + r2(2) ) * x & + + r2(1) ) / (((( & + x & + + r2(9) ) * x & + + r2(8) ) * x & + + r2(7) ) * x & + + r2(6) ) + ! + ! Calculation for 4.0 <= X < 12.0. + ! + else if ( x < 12.0D+00 ) then + + alngam = (((( & + r3(5) * x & + + r3(4) ) * x & + + r3(3) ) * x & + + r3(2) ) * x & + + r3(1) ) / (((( & + x & + + r3(9) ) * x & + + r3(8) ) * x & + + r3(7) ) * x & + + r3(6) ) + ! + ! Calculation for 12.0 <= X. + ! + else + + y = log ( x ) + alngam = x * ( y - 1.0D+00 ) - 0.5D+00 * y + alr2pi + + if ( x <= xlge ) then + + x1 = 1.0D+00 / x + x2 = x1 * x1 + + alngam = alngam + x1 * ( ( & + r4(3) * & + x2 + r4(2) ) * & + x2 + r4(1) ) / ( ( & + x2 + r4(5) ) * & + x2 + r4(4) ) + + end if + + end if + + return + end + + + function alnorm ( x, upper ) + + !*****************************************************************************80 + ! + !! ALNORM computes the cumulative density of the standard normal distribution. + ! + ! Modified: + ! + ! 13 January 2008 + ! + ! Author: + ! + ! Original FORTRAN77 version by David Hill. + ! FORTRAN90 version by John Burkardt. + ! + ! Reference: + ! + ! David Hill, + ! Algorithm AS 66: + ! The Normal Integral, + ! Applied Statistics, + ! Volume 22, Number 3, 1973, pages 424-427. + ! + ! Parameters: + ! + ! Input, real (kind = dbl8) X, is one endpoint of the semi-infinite interval + ! over which the integration takes place. + ! + ! Input, logical UPPER, determines whether the upper or lower + ! interval is to be integrated: + ! .TRUE. => integrate from X to + Infinity; + ! .FALSE. => integrate from - Infinity to X. + ! + ! Output, real (kind = dbl8) ALNORM, the integral of the standard normal + ! distribution over the desired interval. + ! + implicit none + + real (kind = dbl8), parameter :: a1 = 5.75885480458D+00 + real (kind = dbl8), parameter :: a2 = 2.62433121679D+00 + real (kind = dbl8), parameter :: a3 = 5.92885724438D+00 + real (kind = dbl8) alnorm + real (kind = dbl8), parameter :: b1 = -29.8213557807D+00 + real (kind = dbl8), parameter :: b2 = 48.6959930692D+00 + real (kind = dbl8), parameter :: c1 = -0.000000038052D+00 + real (kind = dbl8), parameter :: c2 = 0.000398064794D+00 + real (kind = dbl8), parameter :: c3 = -0.151679116635D+00 + real (kind = dbl8), parameter :: c4 = 4.8385912808D+00 + real (kind = dbl8), parameter :: c5 = 0.742380924027D+00 + real (kind = dbl8), parameter :: c6 = 3.99019417011D+00 + real (kind = dbl8), parameter :: con = 1.28D+00 + real (kind = dbl8), parameter :: d1 = 1.00000615302D+00 + real (kind = dbl8), parameter :: d2 = 1.98615381364D+00 + real (kind = dbl8), parameter :: d3 = 5.29330324926D+00 + real (kind = dbl8), parameter :: d4 = -15.1508972451D+00 + real (kind = dbl8), parameter :: d5 = 30.789933034D+00 + real (kind = dbl8), parameter :: ltone = 7.0D+00 + real (kind = dbl8), parameter :: p = 0.398942280444D+00 + real (kind = dbl8), parameter :: q = 0.39990348504D+00 + real (kind = dbl8), parameter :: r = 0.398942280385D+00 + logical up + logical upper + real (kind = dbl8), parameter :: utzero = 18.66D+00 + real (kind = dbl8) x + real (kind = dbl8) y + real (kind = dbl8) z + + up = upper + z = x + + if ( z < 0.0D+00 ) then + up = .not. up + z = - z + end if + + if ( ltone < z .and. ( ( .not. up ) .or. utzero < z ) ) then + + if ( up ) then + alnorm = 0.0D+00 + else + alnorm = 1.0D+00 + end if + + return + + end if + + y = 0.5D+00 * z * z + + if ( z <= con ) then + + alnorm = 0.5D+00 - z * ( p - q * y & + / ( y + a1 + b1 & + / ( y + a2 + b2 & + / ( y + a3 )))) + + else + + alnorm = r * exp ( - y ) & + / ( z + c1 + d1 & + / ( z + c2 + d2 & + / ( z + c3 + d3 & + / ( z + c4 + d4 & + / ( z + c5 + d5 & + / ( z + c6 )))))) + + end if + + if ( .not. up ) then + alnorm = 1.0D+00 - alnorm + end if + + return + end + + + function gammad ( x, p, ifault ) + + !*****************************************************************************80 + ! + !! GAMMAD computes the Lower Incomplete Gamma Integral y(a,x)/G(a) + ! + ! Auxiliary functions: + ! + ! ALOGAM = logarithm of the gamma function, + ! ALNORM = algorithm AS66 + ! + ! Modified: + ! + ! 20 January 2008 + ! + ! Author: + ! + ! Original FORTRAN77 version by B Shea. + ! FORTRAN90 version by John Burkardt. + ! + ! Reference: + ! + ! B Shea, + ! Algorithm AS 239: + ! Chi-squared and Incomplete Gamma Integral, + ! Applied Statistics, + ! Volume 37, Number 3, 1988, pages 466-473. + ! + ! Parameters: + ! + ! Input, real (kind = dbl8) X, P, the parameters of the incomplete + ! gamma ratio. 0 <= X, and 0 < P. + ! + ! Output, integer (kind = int4) IFAULT, error flag. + ! 0, no error. + ! 1, X < 0 or P <= 0. + ! + ! Output, real (kind = dbl8) GAMMAD, the value of the incomplete + ! Gamma integral. + ! + implicit none + + real (kind = dbl8) a + ! real (kind = dbl8) alnorm + ! real (kind = dbl8) alngam + real (kind = dbl8) an + real (kind = dbl8) arg + real (kind = dbl8) b + real (kind = dbl8) c + real (kind = dbl8), parameter :: elimit = - 88.0D+00 + real (kind = dbl8) gammad + integer (kind = int4) ifault + real (kind = dbl8), parameter :: oflo = 1.0D+37 + real (kind = dbl8) p + real (kind = dbl8), parameter :: plimit = 1000.0D+00 + real (kind = dbl8) pn1 + real (kind = dbl8) pn2 + real (kind = dbl8) pn3 + real (kind = dbl8) pn4 + real (kind = dbl8) pn5 + real (kind = dbl8) pn6 + real (kind = dbl8) rn + real (kind = dbl8), parameter :: tol = 1.0D-14 + logical upper + real (kind = dbl8) x + real (kind = dbl8), parameter :: xbig = 1.0D+08 + + gammad = 0.0D+00 + ! + ! Check the input. + ! + if ( x < 0.0D+00 ) then + ifault = 1 + return + end if + + if ( p <= 0.0D+00 ) then + ifault = 1 + return + end if + + ifault = 0 + + if ( x == 0.0D+00 ) then + gammad = 0.0D+00 + return + end if + ! + ! If P is large, use a normal approximation. + ! + if ( plimit < p ) then + + pn1 = 3.0D+00 * sqrt ( p ) * ( ( x / p )**( 1.0D+00 / 3.0D+00 ) & + + 1.0D+00 / ( 9.0D+00 * p ) - 1.0D+00 ) + + upper = .false. + gammad = alnorm ( pn1, upper ) + return + + end if + ! + ! If X is large set GAMMAD = 1. + ! + if ( xbig < x ) then + gammad = 1.0D+00 + return + end if + ! + ! Use Pearson's series expansion. + ! (Note that P is not large enough to force overflow in ALOGAM). + ! No need to test IFAULT on exit since P > 0. + ! + if ( x <= 1.0D+00 .or. x < p ) then + + arg = p * log ( x ) - x - alngam ( p + 1.0D+00, ifault ) + c = 1.0D+00 + gammad = 1.0D+00 + a = p + + do + + a = a + 1.0D+00 + c = c * x / a + gammad = gammad + c + + if ( c <= tol ) then + exit + end if + + end do + + arg = arg + log ( gammad ) + + if ( elimit <= arg ) then + gammad = exp ( arg ) + else + gammad = 0.0D+00 + end if + ! + ! Use a continued fraction expansion. + ! + else + + arg = p * log ( x ) - x - alngam ( p, ifault ) + a = 1.0D+00 - p + b = a + x + 1.0D+00 + c = 0.0D+00 + pn1 = 1.0D+00 + pn2 = x + pn3 = x + 1.0D+00 + pn4 = x * b + gammad = pn3 / pn4 + + do + + a = a + 1.0D+00 + b = b + 2.0D+00 + c = c + 1.0D+00 + an = a * c + pn5 = b * pn3 - an * pn1 + pn6 = b * pn4 - an * pn2 + + if ( pn6 /= 0.0D+00 ) then + + rn = pn5 / pn6 + + if ( abs ( gammad - rn ) <= min ( tol, tol * rn ) ) then + exit + end if + + gammad = rn + + end if + + pn1 = pn3 + pn2 = pn4 + pn3 = pn5 + pn4 = pn6 + ! + ! Re-scale terms in continued fraction if terms are large. + ! + if ( oflo <= abs ( pn5 ) ) then + pn1 = pn1 / oflo + pn2 = pn2 / oflo + pn3 = pn3 / oflo + pn4 = pn4 / oflo + end if + + end do + + arg = arg + log ( gammad ) + + if ( elimit <= arg ) then + gammad = 1.0D+00 - exp ( arg ) + else + gammad = 1.0D+00 + end if + + end if + + return + end + + end module md_sofunutils From b1d9b27f96b208b954f9e986af3ea24e3045c19b Mon Sep 17 00:00:00 2001 From: Jaideep Date: Mon, 11 Sep 2023 20:43:01 +0200 Subject: [PATCH 003/112] basic working gpp calc; compiles correctly; use_phydro option --- R/run_pmodel_f_bysite.R | 5 +- src/Makevars | 11 +-- src/biosphere_pmodel.mod.f90 | 3 +- src/gpp_pmodel.mod.f90 | 98 ++++++++++++++++--- src/params_siml_pmodel.mod.f90 | 1 + ...h_phydro.f90 => photosynth_phydro.mod.f90} | 0 src/sofun_r.f90 | 5 +- src/wrappersc.c | 5 +- vignettes/rsofun_phydro.Rmd | 3 + 9 files changed, 105 insertions(+), 26 deletions(-) rename src/{photosynth_phydro.f90 => photosynth_phydro.mod.f90} (100%) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 612f6887..f8874a19 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -260,13 +260,15 @@ run_pmodel_f_bysite <- function( "spinup", "spinupyears", "recycle", + "use_phydro", "outdt", "ltre", "ltne", "ltnd", "lgr3", "lgn3", - "lgr4" + "lgr4", + "use_phydro" ) parameter_integrity <- lapply(check_param, function(check_var){ @@ -346,6 +348,7 @@ run_pmodel_f_bysite <- function( spinup = as.logical(params_siml$spinup), spinupyears = as.integer(params_siml$spinupyears), recycle = as.integer(params_siml$recycle), + use_phydro = as.logical(params_siml$use_phydro), firstyeartrend = as.integer(firstyeartrend_forcing), nyeartrend = as.integer(nyeartrend_forcing), secs_per_tstep = as.integer(secs_per_tstep), diff --git a/src/Makevars b/src/Makevars index b84e9cab..7fa079c7 100755 --- a/src/Makevars +++ b/src/Makevars @@ -71,20 +71,13 @@ md_tile_pmodel.mod: tile_pmodel.mod.o md_gpp_pmodel.mod: gpp_pmodel.mod.o md_gpp_biomee.mod: gpp_biomee.mod.o md_photosynth.mod: photosynth_pmodel.mod.o +md_photosynth_phydro.mod: photosynth_phydro.mod.o md_soiltemp.mod: soiltemp_sitch.mod.o md_vegdynamics_pmodel.mod: vegdynamics_pmodel.mod.o datatypes.mod: datatypes.o md_soil_biomee.mod: soil_biomee.o md_vegetation_biomee.mod: vegetation_biomee.o -md_precision.mod: precision.o -md_sofunutils_plus.mod: sofunutils_plus.o -md_phydro_physical.mod: phydro_physical.o -md_phydro_env.mod: phydro_env.o -md_phydro_pm.mod: phydro_pm.o -md_phydro_transpiration.mod: phydro_transpiration.o -md_phydro_photosynthesis.mod: phydro_photosynthesis.o -md_phydro_solver.mod: phydro_solver.o -md_phydro_main.mod: phydro_main.o + # Dependency of the C wrapper wrappersc.o: sofun_r_mod.mod diff --git a/src/biosphere_pmodel.mod.f90 b/src/biosphere_pmodel.mod.f90 index 4564102c..4e44c385 100644 --- a/src/biosphere_pmodel.mod.f90 +++ b/src/biosphere_pmodel.mod.f90 @@ -139,7 +139,8 @@ function biosphere_annual() result( out_biosphere ) myinterface%vegcover(doy), & myinterface%grid, & init_daily, & - myinterface%params_siml%in_ppfd & + myinterface%params_siml%in_ppfd, & + myinterface%params_siml%use_phydro & ) ! if (verbose) print*,'... done' diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index e23e6cd2..d0c1f9c6 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -36,7 +36,8 @@ module md_gpp_pmodel use md_grid, only: gridtype use md_photosynth, only: pmodel, zero_pmodel, outtype_pmodel, calc_ftemp_inst_vcmax, calc_ftemp_inst_jmax, & calc_ftemp_inst_rd, calc_kphio_temp, calc_soilmstress - use md_photosynth_phydro, only: phydro_analytical, phydro_instantaneous_analytical + use md_photosynth_phydro, only: phydro_analytical, phydro_instantaneous_analytical, par_plant_type, par_cost_type, & + phydro_result_type, par_control_type, ET_DIFFUSION, ET_PM, GS_IGF, GS_APX implicit none private @@ -73,7 +74,7 @@ module md_gpp_pmodel contains - subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd) + subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, use_phydro) !////////////////////////////////////////////////////////////////// ! Wrapper function to call to P-model. ! Calculates meteorological conditions with memory based on daily @@ -93,6 +94,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd) type(gridtype) :: grid logical, intent(in) :: init ! is true on the very first simulation day (first subroutine call of each gridcell) logical, intent(in) :: in_ppfd ! whether to use PPFD from forcing or from SPLASH output + logical, intent(in) :: use_phydro ! whether to use P-Hydro for photosynthesis and transpiration ! local variables type(outtype_pmodel) :: out_pmodel ! list of P-model output variables @@ -112,6 +114,12 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd) real, save :: tmin_memory ! for low temperature stress + ! Phydro inputs and outputs + type(par_plant_type) :: par_plant + type(par_cost_type) :: par_cost + type(phydro_result_type) :: out_phydro_acclim, out_phydro_inst + type(par_control_type) :: options + ! xxx test real :: a_c, a_j, a_returned, fact_jmaxlim integer, save :: count @@ -173,12 +181,14 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd) if (tile(lu)%plant(pft)%fpc_grid > 0.0 .and. & ! PFT is present grid%dayl > 0.0 .and. & ! no arctic night temp_memory > -5.0 ) then ! minimum temp threshold to avoid fpe - + + !================================================================ ! P-model call to get acclimated quantities as a function of the ! damped climate forcing. !---------------------------------------------------------------- - out_pmodel = pmodel( & + if (.not. use_phydro) then + out_pmodel = pmodel( & kphio = kphio_temp, & beta = params_gpp%beta, & kc_jmax = params_gpp%kc_jmax, & @@ -191,7 +201,63 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd) method_optci = "prentice14", & method_jmaxlim = "wang17" & ) - + else + print *, "Using P-hydro" + par_cost = par_cost_type(0.1, 1) + par_plant = par_plant_type(0.5e-16, -1, 1) + + options%et_method = ET_DIFFUSION + options%gs_method = GS_IGF + + out_phydro_acclim = phydro_analytical( & + tc = dble(temp_memory), & + tg = dble(temp_memory), & + ppfd = dble(ppfd_memory)*1e6, & + netrad = dble(ppfd_memory)*1e6/2.0d0, & + vpd = dble(vpd_memory), & + co2 = dble(co2_memory), & + elv = 0.0d0, & + fapar = dble(tile(lu)%canopy%fapar), & + kphio = dble(kphio_temp), & + psi_soil = 0.d0, & + rdark = 0.015d0, & + vwind = 3.0d0, & + par_plant = par_plant, & + par_cost = par_cost, & + par_control = options & + ) + + print *, temp_memory, ppfd_memory*1e6, kphio_temp, vpd_memory + print *, out_phydro_acclim%a, out_phydro_acclim%gs, out_phydro_acclim%dpsi + + ! out_pmodel%gammastar = out_phydro_acclim%gammastar + ! out_pmodel%kmm = out_phydro_acclim%kmm + ! out_pmodel%ca = 0.0d0 + ! out_pmodel%ci = out_phydro_acclim%ci + ! out_pmodel%chi = out_phydro_acclim%chi + ! out_pmodel%xi = 0.0d0 + ! out_pmodel%iwue = out_phydro_acclim%a / out_phydro_acclim%gs + ! out_pmodel%lue = out_phydro_acclim%a / (ppfd_memory*1e6) + ! ! out_pmodel%gpp = gpp + ! ! out_pmodel%vcmax = vcmax + ! ! out_pmodel%jmax = jmax + ! out_pmodel%vcmax25 = out_phydro_acclim%vcmax25 + ! out_pmodel%jmax25 = out_phydro_acclim%jmax25 + ! ! out_pmodel%vcmax_unitfapar = vcmax_unitfapar + ! ! out_pmodel%vcmax_unitiabs = vcmax_unitiabs + ! ! out_pmodel%ftemp_inst_vcmax = ftemp_inst_vcmax + ! ! out_pmodel%ftemp_inst_jmax = ftemp_inst_jmax + ! ! out_pmodel%rd = rd + ! ! out_pmodel%rd_unitfapar = rd_unitfapar + ! ! out_pmodel%rd_unitiabs = rd_unitiabs + ! out_pmodel%actnv = 0.0d0 + ! ! out_pmodel%actnv_unitfapar = actnv_unitfapar + ! ! out_pmodel%actnv_unitiabs = actnv_unitiabs + ! ! out_pmodel%gs_unitiabs = gs_unitiabs + ! ! out_pmodel%gs_unitfapar = gs_unitfapar + ! out_pmodel%gs_setpoint = out_phydro_acclim%gs + + end if else ! PFT is not present @@ -219,14 +285,20 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd) ! This still does a linear scaling of daily GPP - knowingly wrong ! but not too dangerous... !---------------------------------------------------------------- - if( in_ppfd ) then - ! Take input daily PPFD (in mol/m^2) - tile_fluxes(lu)%plant(pft)%dgpp = tile(lu)%plant(pft)%fpc_grid * tile(lu)%canopy%fapar & - * climate%dppfd * myinterface%params_siml%secs_per_tstep * out_pmodel%lue * soilmstress - else - ! Take daily PPFD generated by SPLASH (in mol/m^2/d) - tile_fluxes(lu)%plant(pft)%dgpp = tile(lu)%plant(pft)%fpc_grid * tile(lu)%canopy%fapar & - * tile_fluxes(lu)%canopy%ppfd_splash * out_pmodel%lue * soilmstress + if (.not. use_phydro) then + if( in_ppfd ) then + ! Take input daily PPFD (in mol/m^2) + tile_fluxes(lu)%plant(pft)%dgpp = tile(lu)%plant(pft)%fpc_grid * tile(lu)%canopy%fapar & + * climate%dppfd * myinterface%params_siml%secs_per_tstep * out_pmodel%lue * soilmstress + else + ! Take daily PPFD generated by SPLASH (in mol/m^2/d) + tile_fluxes(lu)%plant(pft)%dgpp = tile(lu)%plant(pft)%fpc_grid * tile(lu)%canopy%fapar & + * tile_fluxes(lu)%canopy%ppfd_splash * out_pmodel%lue * soilmstress + end if + else ! Using phydro + ! Take input daily PPFD (in mol/m^2) + tile_fluxes(lu)%plant(pft)%dgpp = tile(lu)%plant(pft)%fpc_grid * & + (out_phydro_acclim%a*1e-6) * myinterface%params_siml%secs_per_tstep end if !---------------------------------------------------------------- diff --git a/src/params_siml_pmodel.mod.f90 b/src/params_siml_pmodel.mod.f90 index f4ec9809..e54aeebb 100644 --- a/src/params_siml_pmodel.mod.f90 +++ b/src/params_siml_pmodel.mod.f90 @@ -25,6 +25,7 @@ module md_params_siml_pmodel logical :: do_spinup ! whether this simulation does spinup logical :: is_calib ! whether this simulation is a calibration simulation (overriding parameters and no output) + logical :: use_phydro ! Whether phydro should be used for photosynthesis/transpiration calculations character(len=256) :: runname character(len=256) :: sitename diff --git a/src/photosynth_phydro.f90 b/src/photosynth_phydro.mod.f90 similarity index 100% rename from src/photosynth_phydro.f90 rename to src/photosynth_phydro.mod.f90 diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index 4976ceb1..f7e10835 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -14,7 +14,8 @@ module sofun_r_mod subroutine pmodel_f( & spinup, & spinupyears, & - recycle, & + recycle, & + use_phydro, & firstyeartrend, & nyeartrend, & secs_per_tstep, & @@ -55,6 +56,7 @@ subroutine pmodel_f( & logical(kind=c_bool), intent(in) :: spinup integer(kind=c_int), intent(in) :: spinupyears integer(kind=c_int), intent(in) :: recycle + logical(kind=c_bool), intent(in) :: use_phydro integer(kind=c_int), intent(in) :: firstyeartrend integer(kind=c_int), intent(in) :: nyeartrend integer(kind=c_int), intent(in) :: secs_per_tstep @@ -87,6 +89,7 @@ subroutine pmodel_f( & myinterface%params_siml%do_spinup = spinup myinterface%params_siml%spinupyears = spinupyears myinterface%params_siml%recycle = recycle + myinterface%params_siml%use_phydro = use_phydro myinterface%params_siml%firstyeartrend = firstyeartrend myinterface%params_siml%nyeartrend = nyeartrend diff --git a/src/wrappersc.c b/src/wrappersc.c index 63103a32..2ddd52c4 100644 --- a/src/wrappersc.c +++ b/src/wrappersc.c @@ -13,6 +13,7 @@ void F77_NAME(pmodel_f)( int *spinup, // LOGICAL can be defined as _Bool but it gives a warming int *spinupyears, int *recycle, + int *use_phydro, int *firstyeartrend, int *nyeartrend, int *secs_per_tstep, @@ -41,6 +42,7 @@ extern SEXP pmodel_f_C( SEXP spinup, SEXP spinupyears, SEXP recycle, + SEXP use_phydro, SEXP firstyeartrend, SEXP nyeartrend, SEXP secs_per_tstep, @@ -75,6 +77,7 @@ extern SEXP pmodel_f_C( LOGICAL(spinup), INTEGER(spinupyears), INTEGER(recycle), + LOGICAL(use_phydro), INTEGER(firstyeartrend), INTEGER(nyeartrend), INTEGER(secs_per_tstep), @@ -518,7 +521,7 @@ extern SEXP biomee_f_C( // Declarations for all functions ///////////////////////////////////////////////////////////// static const R_CallMethodDef CallEntries[] = { - {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 23}, // Specify number of arguments to C wrapper as the last number here + {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 24}, // Specify number of arguments to C wrapper as the last number here {"biomee_f_C", (DL_FUNC) &biomee_f_C, 46}, // Number of the SEXP variables (not the output) {NULL, NULL, 0} }; diff --git a/vignettes/rsofun_phydro.Rmd b/vignettes/rsofun_phydro.Rmd index 579a6657..98ba7546 100644 --- a/vignettes/rsofun_phydro.Rmd +++ b/vignettes/rsofun_phydro.Rmd @@ -507,6 +507,8 @@ p_model_drivers$forcing[[1]] %>% ## Model run ```{r} +p_hydro_drivers$params_siml[[1]]$use_phydro = T + output <- rsofun::runread_pmodel_f( p_hydro_drivers, par = params_modl @@ -522,6 +524,7 @@ output$data[[1]] %>% select(date, gpp, latenth) %>% mutate(group="model") %>% rbind(p_hydro_validation$data[[1]] %>% select(date, gpp, le) %>% + mutate(gpp = gpp*0.864/10) %>% # convert to mol m-2 day-1 mutate(le = le) %>% melt("date") %>% mutate(group="obs")) %>% From 032a02cad4e1ec48408ac36ea03f991182ec9b66 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Tue, 12 Sep 2023 12:02:52 +0200 Subject: [PATCH 004/112] working gpp from instantaneous model --- src/gpp_pmodel.mod.f90 | 146 ++++++++++++++++++++---------------- vignettes/rsofun_phydro.Rmd | 3 +- 2 files changed, 84 insertions(+), 65 deletions(-) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index d0c1f9c6..83dc0376 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -202,61 +202,33 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, method_jmaxlim = "wang17" & ) else - print *, "Using P-hydro" + ! print *, "Using P-hydro" par_cost = par_cost_type(0.1, 1) par_plant = par_plant_type(0.5e-16, -1, 1) options%et_method = ET_DIFFUSION options%gs_method = GS_IGF - out_phydro_acclim = phydro_analytical( & - tc = dble(temp_memory), & - tg = dble(temp_memory), & - ppfd = dble(ppfd_memory)*1e6, & - netrad = dble(ppfd_memory)*1e6/2.0d0, & - vpd = dble(vpd_memory), & - co2 = dble(co2_memory), & - elv = 0.0d0, & - fapar = dble(tile(lu)%canopy%fapar), & - kphio = dble(kphio_temp), & - psi_soil = 0.d0, & - rdark = 0.015d0, & - vwind = 3.0d0, & - par_plant = par_plant, & - par_cost = par_cost, & - par_control = options & + out_phydro_acclim = phydro_analytical( & + tc = dble(temp_memory), & + tg = dble(temp_memory), & + ppfd = dble(ppfd_memory)*1e6, & + netrad = dble(ppfd_memory)*1e6/2.0d0, & + vpd = dble(vpd_memory), & + co2 = dble(co2_memory), & + elv = 0.0d0, & + fapar = dble(tile(lu)%canopy%fapar), & + kphio = dble(kphio_temp), & + psi_soil = 0.d0, & + rdark = dble(params_gpp%rd_to_vcmax), & + vwind = 3.0d0, & + par_plant = par_plant, & + par_cost = par_cost, & + par_control = options & ) - print *, temp_memory, ppfd_memory*1e6, kphio_temp, vpd_memory - print *, out_phydro_acclim%a, out_phydro_acclim%gs, out_phydro_acclim%dpsi - - ! out_pmodel%gammastar = out_phydro_acclim%gammastar - ! out_pmodel%kmm = out_phydro_acclim%kmm - ! out_pmodel%ca = 0.0d0 - ! out_pmodel%ci = out_phydro_acclim%ci - ! out_pmodel%chi = out_phydro_acclim%chi - ! out_pmodel%xi = 0.0d0 - ! out_pmodel%iwue = out_phydro_acclim%a / out_phydro_acclim%gs - ! out_pmodel%lue = out_phydro_acclim%a / (ppfd_memory*1e6) - ! ! out_pmodel%gpp = gpp - ! ! out_pmodel%vcmax = vcmax - ! ! out_pmodel%jmax = jmax - ! out_pmodel%vcmax25 = out_phydro_acclim%vcmax25 - ! out_pmodel%jmax25 = out_phydro_acclim%jmax25 - ! ! out_pmodel%vcmax_unitfapar = vcmax_unitfapar - ! ! out_pmodel%vcmax_unitiabs = vcmax_unitiabs - ! ! out_pmodel%ftemp_inst_vcmax = ftemp_inst_vcmax - ! ! out_pmodel%ftemp_inst_jmax = ftemp_inst_jmax - ! ! out_pmodel%rd = rd - ! ! out_pmodel%rd_unitfapar = rd_unitfapar - ! ! out_pmodel%rd_unitiabs = rd_unitiabs - ! out_pmodel%actnv = 0.0d0 - ! ! out_pmodel%actnv_unitfapar = actnv_unitfapar - ! ! out_pmodel%actnv_unitiabs = actnv_unitiabs - ! ! out_pmodel%gs_unitiabs = gs_unitiabs - ! ! out_pmodel%gs_unitfapar = gs_unitfapar - ! out_pmodel%gs_setpoint = out_phydro_acclim%gs - + ! print *, temp_memory, ppfd_memory*1e6, kphio_temp, vpd_memory + ! print *, out_phydro_acclim%a, out_phydro_acclim%gs, out_phydro_acclim%dpsi end if else @@ -287,6 +259,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, !---------------------------------------------------------------- if (.not. use_phydro) then if( in_ppfd ) then + print *, "Using in_ppfd" ! Take input daily PPFD (in mol/m^2) tile_fluxes(lu)%plant(pft)%dgpp = tile(lu)%plant(pft)%fpc_grid * tile(lu)%canopy%fapar & * climate%dppfd * myinterface%params_siml%secs_per_tstep * out_pmodel%lue * soilmstress @@ -295,36 +268,81 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, tile_fluxes(lu)%plant(pft)%dgpp = tile(lu)%plant(pft)%fpc_grid * tile(lu)%canopy%fapar & * tile_fluxes(lu)%canopy%ppfd_splash * out_pmodel%lue * soilmstress end if - else ! Using phydro - ! Take input daily PPFD (in mol/m^2) - tile_fluxes(lu)%plant(pft)%dgpp = tile(lu)%plant(pft)%fpc_grid * & - (out_phydro_acclim%a*1e-6) * myinterface%params_siml%secs_per_tstep + else ! Using phydro - run instantaneous model + out_phydro_inst = phydro_instantaneous_analytical( & + vcmax25 = out_phydro_acclim%vcmax25, & + jmax25 = out_phydro_acclim%jmax25, & + tc = dble(climate%dtemp), & + tg = dble(temp_memory), & + ppfd = dble(climate%dppfd)*1e6, & + netrad = dble(climate%dppfd)*1e6/2.0d0, & + vpd = dble(climate%dvpd), & + co2 = dble(co2), & + elv = 0.0d0, & + fapar = dble(tile(lu)%canopy%fapar), & + kphio = dble(kphio_temp), & + psi_soil = 0.d0, & + rdark = dble(params_gpp%rd_to_vcmax), & + vwind = 3.0d0, & + par_plant = par_plant, & + par_cost = par_cost, & + par_control = options & + ) + + tile_fluxes(lu)%plant(pft)%dgpp = tile(lu)%plant(pft)%fpc_grid * & + (out_phydro_inst%a*1e-6) * myinterface%params_siml%secs_per_tstep end if !---------------------------------------------------------------- ! Dark respiration !---------------------------------------------------------------- - tile_fluxes(lu)%plant(pft)%drd = tile(lu)%plant(pft)%fpc_grid * tile(lu)%canopy%fapar & - * out_pmodel%vcmax25 * params_gpp%rd_to_vcmax * calc_ftemp_inst_rd( climate%dtemp ) * c_molmass & - * myinterface%params_siml%secs_per_tstep + if (.not. use_phydro) then + tile_fluxes(lu)%plant(pft)%drd = tile(lu)%plant(pft)%fpc_grid * tile(lu)%canopy%fapar & + * out_pmodel%vcmax25 * params_gpp%rd_to_vcmax * calc_ftemp_inst_rd( climate%dtemp ) * c_molmass & + * myinterface%params_siml%secs_per_tstep + else + tile_fluxes(lu)%plant(pft)%drd = tile(lu)%plant(pft)%fpc_grid & + * out_phydro_inst%rd * c_molmass & + * myinterface%params_siml%secs_per_tstep + end if !---------------------------------------------------------------- ! Vcmax and Jmax !---------------------------------------------------------------- ! acclimated quantities - tile_fluxes(lu)%plant(pft)%vcmax25 = out_pmodel%vcmax25 - tile_fluxes(lu)%plant(pft)%jmax25 = out_pmodel%jmax25 - tile_fluxes(lu)%plant(pft)%chi = out_pmodel%chi - tile_fluxes(lu)%plant(pft)%iwue = out_pmodel%iwue - - ! quantities with instantaneous temperature response - tile_fluxes(lu)%plant(pft)%vcmax = calc_ftemp_inst_vcmax( climate%dtemp, climate%dtemp, tcref = 25.0 ) * out_pmodel%vcmax25 - tile_fluxes(lu)%plant(pft)%jmax = calc_ftemp_inst_jmax( climate%dtemp, climate%dtemp, tcref = 25.0 ) * out_pmodel%jmax25 - + if (.not. use_phydro) then + tile_fluxes(lu)%plant(pft)%vcmax25 = out_pmodel%vcmax25 + tile_fluxes(lu)%plant(pft)%jmax25 = out_pmodel%jmax25 + tile_fluxes(lu)%plant(pft)%chi = out_pmodel%chi + tile_fluxes(lu)%plant(pft)%iwue = out_pmodel%iwue + + ! quantities with instantaneous temperature response + tile_fluxes(lu)%plant(pft)%vcmax = calc_ftemp_inst_vcmax( climate%dtemp, climate%dtemp, tcref = 25.0 ) * out_pmodel%vcmax25 + tile_fluxes(lu)%plant(pft)%jmax = calc_ftemp_inst_jmax( climate%dtemp, climate%dtemp, tcref = 25.0 ) * out_pmodel%jmax25 + else + tile_fluxes(lu)%plant(pft)%vcmax25 = out_phydro_inst%vcmax25 + tile_fluxes(lu)%plant(pft)%jmax25 = out_phydro_inst%jmax25 + tile_fluxes(lu)%plant(pft)%chi = out_phydro_inst%chi + tile_fluxes(lu)%plant(pft)%iwue = out_phydro_inst%a / out_phydro_inst%gs + + ! quantities with instantaneous temperature response + tile_fluxes(lu)%plant(pft)%vcmax = out_phydro_inst%vcmax + tile_fluxes(lu)%plant(pft)%jmax = out_phydro_inst%jmax + end if + !---------------------------------------------------------------- ! Stomatal conductance !---------------------------------------------------------------- - tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint + if (.not. use_phydro) then + tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint + else + tile_fluxes(lu)%plant(pft)%gs_accl = out_phydro_inst%gs + end if + + !----------------------------------------------------------------- + ! ET and latent energy (only for Phydro, since it's computed internally) + !----------------------------------------------------------------- + end do pftloop diff --git a/vignettes/rsofun_phydro.Rmd b/vignettes/rsofun_phydro.Rmd index 98ba7546..98d06a65 100644 --- a/vignettes/rsofun_phydro.Rmd +++ b/vignettes/rsofun_phydro.Rmd @@ -494,6 +494,7 @@ p_hydro_drivers$forcing[[1]] %>% load("../data/p_model_drivers.rda") +load("../data/p_model_validation.rda") p_model_drivers$forcing[[1]] %>% melt("date") %>% ggplot(aes(y=value, x=as.Date(date))) + @@ -524,7 +525,7 @@ output$data[[1]] %>% select(date, gpp, latenth) %>% mutate(group="model") %>% rbind(p_hydro_validation$data[[1]] %>% select(date, gpp, le) %>% - mutate(gpp = gpp*0.864/10) %>% # convert to mol m-2 day-1 + mutate(gpp = gpp*86400/1e6) %>% # convert to mol m-2 day-1 mutate(le = le) %>% melt("date") %>% mutate(group="obs")) %>% From 0c1d7f2489abb78203e6961e4c9ed0d3370aa3d3 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Tue, 12 Sep 2023 16:03:23 +0200 Subject: [PATCH 005/112] correct unit for obs LE, identified numerical issue in gs with ET_DIFFUSION --- src/gpp_pmodel.mod.f90 | 18 +++++----- src/photosynth_phydro.mod.f90 | 10 ++++-- vignettes/rsofun_phydro.Rmd | 62 ++++++++++++++++++++++++++++------- 3 files changed, 67 insertions(+), 23 deletions(-) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 83dc0376..14861b72 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -155,6 +155,12 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, tk = climate_acclimation%dtemp + kTkelvin + par_cost = par_cost_type(0.1, 1) + par_plant = par_plant_type(0.5e-16, -1, 1) + + options%et_method = ET_DIFFUSION + options%gs_method = GS_IGF + pftloop: do pft=1,npft @@ -203,17 +209,11 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, ) else ! print *, "Using P-hydro" - par_cost = par_cost_type(0.1, 1) - par_plant = par_plant_type(0.5e-16, -1, 1) - - options%et_method = ET_DIFFUSION - options%gs_method = GS_IGF - out_phydro_acclim = phydro_analytical( & tc = dble(temp_memory), & tg = dble(temp_memory), & - ppfd = dble(ppfd_memory)*1e6, & - netrad = dble(ppfd_memory)*1e6/2.0d0, & + ppfd = dble(ppfd_memory)*1e6*2.0d0, & + netrad = dble(ppfd_memory)*1e6/2.0d0*2.0d0, & vpd = dble(vpd_memory), & co2 = dble(co2_memory), & elv = 0.0d0, & @@ -290,7 +290,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, ) tile_fluxes(lu)%plant(pft)%dgpp = tile(lu)%plant(pft)%fpc_grid * & - (out_phydro_inst%a*1e-6) * myinterface%params_siml%secs_per_tstep + (out_phydro_inst%a*1e-6*12) * myinterface%params_siml%secs_per_tstep end if !---------------------------------------------------------------- diff --git a/src/photosynth_phydro.mod.f90 b/src/photosynth_phydro.mod.f90 index 57b2e138..9890e27e 100644 --- a/src/photosynth_phydro.mod.f90 +++ b/src/photosynth_phydro.mod.f90 @@ -591,8 +591,10 @@ function calc_gs_from_Q(Q, psi_soil, par_plant, par_env) result(gs) D = (par_env%vpd / par_env%patm) if (par_env%et_method == ET_DIFFUSION) then + ! print *, "Using diffusion ET" gs = Q / (1.6d0 * D) else if (par_env%et_method == ET_PM) then + ! print *, "Using PM ET" ga = calc_g_aero(par_plant%h_canopy, dble(par_env%v_wind), par_plant%h_wind_measurement) gs = calc_gs_pm(Q, ga, par_env) else @@ -977,7 +979,11 @@ end function calc_brd25 function QUADM(A, B, C) real(kind=dbl8) :: QUADM real(kind=dbl8), intent(in) :: A, B, C - QUADM = (-B - sqrt(B*B - 4.0d0*A*C)) / (2.0d0*A) + if (A == 0) then + QUADM = -C/B + else + QUADM = (-B - sqrt(B*B - 4.0d0*A*C)) / (2.0d0*A) + end if end function QUADM function QUADP(A, B, C) @@ -1022,7 +1028,7 @@ function calc_assim_light_limited(gs_in, jmax, par_photosynth) result(res) ca = par_photosynth%ca gs = gs * 1.0d6 / par_photosynth%patm - gs = gs + 1.0d-12 + gs = gs !+ 1.0d-12 d = par_photosynth%delta phi0iabs = par_photosynth%phi0 * par_photosynth%Iabs diff --git a/vignettes/rsofun_phydro.Rmd b/vignettes/rsofun_phydro.Rmd index 98d06a65..9eef429e 100644 --- a/vignettes/rsofun_phydro.Rmd +++ b/vignettes/rsofun_phydro.Rmd @@ -487,7 +487,6 @@ p_hydro_drivers$forcing[[1]] %>% melt("date") %>% ggplot(aes(y=value, x=as.Date(date))) + geom_line(col="aquamarine4") + - geom_vline(xintercept = 25, col="pink") + theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~variable, scales = "free") @@ -499,7 +498,6 @@ p_model_drivers$forcing[[1]] %>% melt("date") %>% ggplot(aes(y=value, x=as.Date(date))) + geom_line(col="aquamarine4") + - geom_vline(xintercept = 25, col="pink") + theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~variable, scales = "free") @@ -507,43 +505,83 @@ p_model_drivers$forcing[[1]] %>% ## Model run +### P-model run + ```{r} -p_hydro_drivers$params_siml[[1]]$use_phydro = T +p_hydro_drivers$params_siml[[1]]$use_phydro = F -output <- rsofun::runread_pmodel_f( +output_p <- rsofun::runread_pmodel_f( p_hydro_drivers, par = params_modl ) ``` -## Validate +## P-model Validation ```{r} -output$data[[1]] %>% select(date, gpp, latenth) %>% +output_p$data[[1]] %>% select(date, gpp, latenth) %>% rename(le = latenth) %>% melt("date") %>% mutate(group="model") %>% rbind(p_hydro_validation$data[[1]] %>% select(date, gpp, le) %>% - mutate(gpp = gpp*86400/1e6) %>% # convert to mol m-2 day-1 - mutate(le = le) %>% + mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 melt("date") %>% mutate(group="obs")) %>% ggplot(aes(y=value, x=as.Date(date))) + geom_line(aes(group=group, col=group), alpha=0.7) + - geom_vline(xintercept = 25, col="pink") + theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~variable, scales = "free", nrow = 2) ``` +### P-hydro run + ```{r} -output$data[[1]] %>% select(-year_dec) %>% +p_hydro_drivers$params_siml[[1]]$use_phydro = T + +output_ph <- rsofun::runread_pmodel_f( + p_hydro_drivers, + par = params_modl +) +``` + +### P-hydro Validation + +```{r} +output_ph$data[[1]] %>% select(date, gpp, latenth) %>% + rename(le = latenth) %>% melt("date") %>% + mutate(group="model") %>% + rbind(p_hydro_validation$data[[1]] %>% + select(date, gpp, le) %>% + mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 + melt("date") %>% + mutate(group="obs")) %>% ggplot(aes(y=value, x=as.Date(date))) + - geom_line(col="aquamarine4") + - geom_vline(xintercept = 25, col="pink") + + geom_line(aes(group=group, col=group), alpha=0.7) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 2) +``` + +```{r} +output_ph$data[[1]] %>% select(-year_dec) %>% + melt("date") %>% + mutate(model = "phydro") %>% + rbind( + output_p$data[[1]] %>% select(-year_dec) %>% + melt("date") %>% + mutate(model = "pmodel") + ) %>% + filter(!is.infinite(value)) %>% + ggplot(aes(y=value, x=as.Date(date), group=model, col=model)) + + geom_line(alpha=0.5) + theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~variable, scales = "free") ``` + + From bfa6c57ff58d624ca2baa22eed4a7f5540e133b2 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Tue, 12 Sep 2023 19:10:43 +0200 Subject: [PATCH 006/112] phydro now takes pa rather than elv as input --- src/gpp_pmodel.mod.f90 | 4 ++-- src/photosynth_phydro.mod.f90 | 16 ++++++++-------- vignettes/rsofun_phydro.Rmd | 1 - 3 files changed, 10 insertions(+), 11 deletions(-) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 14861b72..0c0d7f9d 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -216,7 +216,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, netrad = dble(ppfd_memory)*1e6/2.0d0*2.0d0, & vpd = dble(vpd_memory), & co2 = dble(co2_memory), & - elv = 0.0d0, & + pa = dble(patm_memory), & fapar = dble(tile(lu)%canopy%fapar), & kphio = dble(kphio_temp), & psi_soil = 0.d0, & @@ -278,7 +278,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, netrad = dble(climate%dppfd)*1e6/2.0d0, & vpd = dble(climate%dvpd), & co2 = dble(co2), & - elv = 0.0d0, & + pa = dble(climate%dpatm), & fapar = dble(tile(lu)%canopy%fapar), & kphio = dble(kphio_temp), & psi_soil = 0.d0, & diff --git a/src/photosynth_phydro.mod.f90 b/src/photosynth_phydro.mod.f90 index 9890e27e..771d6e5f 100644 --- a/src/photosynth_phydro.mod.f90 +++ b/src/photosynth_phydro.mod.f90 @@ -1321,9 +1321,9 @@ end function calc_dpsi_bound_inst ! ------------------------------------------------------------- ! Functions: Phydro main !-------------------------------------------------------------- - function phydro_analytical(tc, tg, ppfd, netrad, vpd, co2, elv, fapar, kphio, psi_soil, rdark, vwind, & + function phydro_analytical(tc, tg, ppfd, netrad, vpd, co2, pa, fapar, kphio, psi_soil, rdark, vwind, & par_plant, par_cost, par_control) result(res) - real(kind=dbl8), intent(in) :: tc, tg, ppfd, netrad, vpd, co2, elv, fapar, kphio, psi_soil, rdark, vwind + real(kind=dbl8), intent(in) :: tc, tg, ppfd, netrad, vpd, co2, pa, fapar, kphio, psi_soil, rdark, vwind type(par_plant_type), intent(in) :: par_plant type(par_cost_type), intent(inout) :: par_cost type(par_control_type), intent(in) :: par_control @@ -1331,10 +1331,10 @@ function phydro_analytical(tc, tg, ppfd, netrad, vpd, co2, elv, fapar, kphio, ps type(par_photosynth_type) :: par_photosynth type(phydro_result_type) :: res - real(kind=dbl8) :: pa, e, gs, gsprime, x, J, jmax, vcmax, a, dpsi_opt + real(kind=dbl8) :: e, gs, gsprime, x, J, jmax, vcmax, a, dpsi_opt type(dpsi_bounds_type) :: bounds - pa = calc_patm(real(elv)) + !pa = calc_patm(real(elv)) call create_par_photosynth(par_photosynth, tc, pa, kphio, co2, ppfd, fapar, rdark, tg, par_plant%tchome, & par_control%ftemp_vj_method, par_control%ftemp_rd_method, par_control%ftemp_br_method) call create_par_env(par_env, tc, pa, vpd, netrad, vwind) @@ -1390,20 +1390,20 @@ function profit_fun(dpsi) end function phydro_analytical - function phydro_instantaneous_analytical(vcmax25, jmax25, tc, tg, ppfd, netrad, vpd, co2, elv, & + function phydro_instantaneous_analytical(vcmax25, jmax25, tc, tg, ppfd, netrad, vpd, co2, pa, & fapar, kphio, psi_soil, rdark, vwind, par_plant, par_cost, par_control) result(res) - real(kind=dbl8), intent(in) :: vcmax25, jmax25, tc, tg, ppfd, netrad, vpd, co2, elv, fapar, kphio, psi_soil, rdark, vwind + real(kind=dbl8), intent(in) :: vcmax25, jmax25, tc, tg, ppfd, netrad, vpd, co2, pa, fapar, kphio, psi_soil, rdark, vwind type(par_plant_type), intent(in) :: par_plant type(par_cost_type), intent(inout) :: par_cost type(par_control_type), intent(in) :: par_control type(par_env_type) :: par_env type(par_photosynth_type) :: par_photosynth type(phydro_result_type) :: res - real(kind=dbl8) :: pa, e, gs + real(kind=dbl8) :: e, gs real(kind=dbl8) :: bound, jmax, vcmax, dpsi_opt type(ACi_type) :: Aa, Ac, Aj - pa = calc_patm(real(elv)) + !pa = calc_patm(real(elv)) call create_par_photosynth(par_photosynth, tc, pa, kphio, co2, ppfd, fapar, rdark, tg, par_plant%tchome, & par_control%ftemp_vj_method, par_control%ftemp_rd_method, par_control%ftemp_br_method) call create_par_env(par_env, tc, pa, vpd, netrad, vwind) diff --git a/vignettes/rsofun_phydro.Rmd b/vignettes/rsofun_phydro.Rmd index 9eef429e..0a4153ec 100644 --- a/vignettes/rsofun_phydro.Rmd +++ b/vignettes/rsofun_phydro.Rmd @@ -584,4 +584,3 @@ output_ph$data[[1]] %>% select(-year_dec) %>% facet_wrap(~variable, scales = "free") ``` - From 97936d17de1996b91139ff645711ad25415d06f1 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Tue, 12 Sep 2023 19:29:32 +0200 Subject: [PATCH 007/112] add netrad to averager, fix output units --- src/gpp_pmodel.mod.f90 | 31 +++++++++++++++++-------------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 0c0d7f9d..fcccf388 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -111,6 +111,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, real, save :: temp_memory real, save :: patm_memory real, save :: ppfd_memory + real, save :: netrad_memory real, save :: tmin_memory ! for low temperature stress @@ -143,15 +144,17 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, vpd_memory = climate_acclimation%dvpd patm_memory = climate_acclimation%dpatm ppfd_memory = climate_acclimation%dppfd + netrad_memory = climate_acclimation%dnetrad end if count = count + 1 - co2_memory = dampen_variability( co2, params_gpp%tau_acclim, co2_memory ) - temp_memory = dampen_variability( climate_acclimation%dtemp, params_gpp%tau_acclim, temp_memory ) - vpd_memory = dampen_variability( climate_acclimation%dvpd, params_gpp%tau_acclim, vpd_memory ) - patm_memory = dampen_variability( climate_acclimation%dpatm, params_gpp%tau_acclim, patm_memory ) - ppfd_memory = dampen_variability( climate_acclimation%dppfd, params_gpp%tau_acclim, ppfd_memory ) + co2_memory = dampen_variability( co2, params_gpp%tau_acclim, co2_memory ) + temp_memory = dampen_variability( climate_acclimation%dtemp, params_gpp%tau_acclim, temp_memory ) + vpd_memory = dampen_variability( climate_acclimation%dvpd, params_gpp%tau_acclim, vpd_memory ) + patm_memory = dampen_variability( climate_acclimation%dpatm, params_gpp%tau_acclim, patm_memory ) + ppfd_memory = dampen_variability( climate_acclimation%dppfd, params_gpp%tau_acclim, ppfd_memory ) + netrad_memory = dampen_variability( climate_acclimation%dnetrad, params_gpp%tau_acclim, netrad_memory ) tk = climate_acclimation%dtemp + kTkelvin @@ -213,7 +216,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, tc = dble(temp_memory), & tg = dble(temp_memory), & ppfd = dble(ppfd_memory)*1e6*2.0d0, & - netrad = dble(ppfd_memory)*1e6/2.0d0*2.0d0, & + netrad = dble(netrad_memory), & vpd = dble(vpd_memory), & co2 = dble(co2_memory), & pa = dble(patm_memory), & @@ -259,7 +262,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, !---------------------------------------------------------------- if (.not. use_phydro) then if( in_ppfd ) then - print *, "Using in_ppfd" + ! print *, "Using in_ppfd" ! Take input daily PPFD (in mol/m^2) tile_fluxes(lu)%plant(pft)%dgpp = tile(lu)%plant(pft)%fpc_grid * tile(lu)%canopy%fapar & * climate%dppfd * myinterface%params_siml%secs_per_tstep * out_pmodel%lue * soilmstress @@ -275,7 +278,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, tc = dble(climate%dtemp), & tg = dble(temp_memory), & ppfd = dble(climate%dppfd)*1e6, & - netrad = dble(climate%dppfd)*1e6/2.0d0, & + netrad = dble(netrad_memory), & vpd = dble(climate%dvpd), & co2 = dble(co2), & pa = dble(climate%dpatm), & @@ -290,7 +293,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, ) tile_fluxes(lu)%plant(pft)%dgpp = tile(lu)%plant(pft)%fpc_grid * & - (out_phydro_inst%a*1e-6*12) * myinterface%params_siml%secs_per_tstep + (out_phydro_inst%a*1e-6*c_molmass) * myinterface%params_siml%secs_per_tstep end if !---------------------------------------------------------------- @@ -320,14 +323,14 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, tile_fluxes(lu)%plant(pft)%vcmax = calc_ftemp_inst_vcmax( climate%dtemp, climate%dtemp, tcref = 25.0 ) * out_pmodel%vcmax25 tile_fluxes(lu)%plant(pft)%jmax = calc_ftemp_inst_jmax( climate%dtemp, climate%dtemp, tcref = 25.0 ) * out_pmodel%jmax25 else - tile_fluxes(lu)%plant(pft)%vcmax25 = out_phydro_inst%vcmax25 - tile_fluxes(lu)%plant(pft)%jmax25 = out_phydro_inst%jmax25 + tile_fluxes(lu)%plant(pft)%vcmax25 = out_phydro_acclim%vcmax25 * 1e-6 + tile_fluxes(lu)%plant(pft)%jmax25 = out_phydro_acclim%jmax25 * 1e-6 tile_fluxes(lu)%plant(pft)%chi = out_phydro_inst%chi - tile_fluxes(lu)%plant(pft)%iwue = out_phydro_inst%a / out_phydro_inst%gs + tile_fluxes(lu)%plant(pft)%iwue = out_phydro_inst%a *1e-6 / out_phydro_inst%gs ! quantities with instantaneous temperature response - tile_fluxes(lu)%plant(pft)%vcmax = out_phydro_inst%vcmax - tile_fluxes(lu)%plant(pft)%jmax = out_phydro_inst%jmax + tile_fluxes(lu)%plant(pft)%vcmax = out_phydro_inst%vcmax * 1e-6 + tile_fluxes(lu)%plant(pft)%jmax = out_phydro_inst%jmax * 1e-6 end if !---------------------------------------------------------------- From 8adda1429a72219eb2548ce4b98b99fcf44a30ce Mon Sep 17 00:00:00 2001 From: Jaideep Date: Tue, 12 Sep 2023 20:38:58 +0200 Subject: [PATCH 008/112] jaideep's comments on equations in waterbal --- src/waterbal_splash.mod.f90 | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index 55e7529a..9be98676 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -307,7 +307,7 @@ subroutine solar( tile_fluxes, grid, climate, doy, in_netrad ) end subroutine solar - + ! JAIDEEP FIXME: I would suggest you pull this function out into biosphere_pmodel.f90, and leave the water balance module to just perform balance subroutine calc_et( tile_fluxes, grid, climate, sw ) !///////////////////////////////////////////////////////////////////////// ! @@ -346,13 +346,16 @@ subroutine calc_et( tile_fluxes, grid, climate, sw ) gamma = psychro( climate%dtemp, calc_patm( grid%elv ) ) ! Eq. 51, SPLASH 2.0 Documentation - ! out_evap%econ = 1.0 / ( lv * rho_water ) ! this is to convert energy into mass (water) - tile_fluxes%canopy%econ = sat_slope / (lv * rho_water * (sat_slope + gamma)) ! MORE PRECISELY - this is to convert energy into mass (water) + ! tile_fluxes%canopy%econ = 1.0 / ( lv * rho_water ) ! this is to convert energy into mass (water) - JAIDEEP: This is correct. J m-2 s-1 x (kg-1 m3) x (J-1 kg) = m3 m-2 s-1 = m s-1 + + ! JAIDEEP: If it's just conversion from mass to energy, the above formula is correct. This already has the Priestly Taylor factor (s/(s+y)) built in, so this + ! should not be used for mere conversion. I would suggest you use just the factor s/(s+y) separately in the respective equations for clarity. + tile_fluxes%canopy%econ = sat_slope / (lv * rho_water * (sat_slope + gamma)) ! MORE PRECISELY - this is to convert energy into mass (water). . !--------------------------------------------------------- ! Daily condensation, mm d-1 !--------------------------------------------------------- - tile_fluxes%canopy%dcn = 1000.0 * tile_fluxes%canopy%econ * abs(tile_fluxes%canopy%drnn) + tile_fluxes%canopy%dcn = 1000.0 * tile_fluxes%canopy%econ * abs(tile_fluxes%canopy%drnn) ! Jaideep: Why abs here? drnn must be negative (emitted from earth) for condensation right? !--------------------------------------------------------- ! 17. Estimate daily EET, mm d-1 @@ -365,7 +368,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw ) !--------------------------------------------------------- ! Eq. 72, SPLASH 2.0 Documentation tile_fluxes%canopy%dpet = ( 1.0 + kw ) * tile_fluxes%canopy%deet - tile_fluxes%canopy%dpet_e = tile_fluxes%canopy%dpet / (tile_fluxes%canopy%econ * 1000) + tile_fluxes%canopy%dpet_e = tile_fluxes%canopy%dpet / (tile_fluxes%canopy%econ * 1000) ! JAIDEEP FIXME: Oops! This is a case where you should use a simple mass-energy conversion, not econ !--------------------------------------------------------- ! 19. Calculate variable substitute (rx), (mm/hr)/(W/m^2) @@ -393,7 +396,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw ) ! Eq. 81, SPLASH 2.0 Documentation tile_fluxes%canopy%daet = (24.0/pi) * (radians(sw * hi) + rx * rw * rv * (dgsin(hn) - dgsin(hi)) + & radians((rx * rw * ru - rx * tile_fluxes%canopy%rnl) * (hn - hi))) - tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet / (tile_fluxes%canopy%econ * 1000) + tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet / (tile_fluxes%canopy%econ * 1000) ! JAIDEEP FIXME: Oops! This is a case where you should use a simple mass-energy conversion, not econ ! print*,'in waterbal: sw, hi, rx, rw, rv, hn, hi, ru ', sw, hi, rx, rw, rv, hn, hi, ru From f052df8a0c508f487ce70cfe1d6a877a011d1718 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Tue, 12 Sep 2023 22:58:38 +0200 Subject: [PATCH 009/112] working latent energy calcs in waterbal --- src/biosphere_pmodel.mod.f90 | 3 ++- src/gpp_pmodel.mod.f90 | 25 +++++++++++++++++++------ src/waterbal_splash.mod.f90 | 36 +++++++++++++++++++++++++++--------- vignettes/rsofun_phydro.Rmd | 1 + 4 files changed, 49 insertions(+), 16 deletions(-) diff --git a/src/biosphere_pmodel.mod.f90 b/src/biosphere_pmodel.mod.f90 index 4e44c385..5b1dfae1 100644 --- a/src/biosphere_pmodel.mod.f90 +++ b/src/biosphere_pmodel.mod.f90 @@ -152,7 +152,8 @@ function biosphere_annual() result( out_biosphere ) call waterbal( tile(:), & tile_fluxes(:), & myinterface%grid, & - myinterface%climate(doy) & + myinterface%climate(doy), & + myinterface%params_siml%use_phydro & ) ! if (verbose) print*,'... done' diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index fcccf388..9324e96c 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -35,7 +35,7 @@ module md_gpp_pmodel use md_sofunutils, only: radians use md_grid, only: gridtype use md_photosynth, only: pmodel, zero_pmodel, outtype_pmodel, calc_ftemp_inst_vcmax, calc_ftemp_inst_jmax, & - calc_ftemp_inst_rd, calc_kphio_temp, calc_soilmstress + calc_ftemp_inst_rd, calc_kphio_temp, calc_soilmstress, calc_density_h2o use md_photosynth_phydro, only: phydro_analytical, phydro_instantaneous_analytical, par_plant_type, par_cost_type, & phydro_result_type, par_control_type, ET_DIFFUSION, ET_PM, GS_IGF, GS_APX implicit none @@ -105,6 +105,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, real :: soilmstress real :: kphio_temp ! quantum yield efficiency after temperature influence real :: tk + real :: lv, rho_water ! latent heat of vap and density of water, needed by phydro for unit conversions real, save :: co2_memory real, save :: vpd_memory @@ -278,7 +279,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, tc = dble(climate%dtemp), & tg = dble(temp_memory), & ppfd = dble(climate%dppfd)*1e6, & - netrad = dble(netrad_memory), & + netrad = dble(climate%dnetrad), & vpd = dble(climate%dvpd), & co2 = dble(co2), & pa = dble(climate%dpatm), & @@ -305,7 +306,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, * myinterface%params_siml%secs_per_tstep else tile_fluxes(lu)%plant(pft)%drd = tile(lu)%plant(pft)%fpc_grid & - * out_phydro_inst%rd * c_molmass & + * out_phydro_inst%rd*1e-6 * c_molmass & * myinterface%params_siml%secs_per_tstep end if @@ -342,10 +343,22 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, tile_fluxes(lu)%plant(pft)%gs_accl = out_phydro_inst%gs end if - !----------------------------------------------------------------- - ! ET and latent energy (only for Phydro, since it's computed internally) - !----------------------------------------------------------------- + !------------------------------------------------------------------------ + ! Canopy ET and soil LE (only for Phydro, since it's computed internally) + !------------------------------------------------------------------------ + if (use_phydro) then + ! Density of water, kg/m^3 + rho_water = calc_density_h2o( climate%dtemp, climate%dpatm ) + tile_fluxes(lu)%canopy%daet_canop = out_phydro_inst%e * 0.018015 * (1.0d0 / rho_water) & + * myinterface%params_siml%secs_per_tstep * 1000 ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 + + tile_fluxes(lu)%canopy%daet_e_soil = out_phydro_inst%le_s_wet & + * myinterface%params_siml%secs_per_tstep ! convert: J m-2 s-1 * s day-1 = J m-2 day-1 + + ! print *, "Canopy ET (mm d-1) = ", tile_fluxes(lu)%canopy%daet_canop + ! print *, "Soil LE (J m-2 d-1) = ", climate%dnetrad, tile_fluxes(lu)%canopy%daet_e_soil + end if end do pftloop diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index 9be98676..9d20a114 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -93,7 +93,7 @@ module md_waterbal contains - subroutine waterbal( tile, tile_fluxes, grid, climate ) + subroutine waterbal( tile, tile_fluxes, grid, climate, using_phydro ) !///////////////////////////////////////////////////////////////////////// ! Calculates soil water balance !------------------------------------------------------------------------- @@ -102,6 +102,7 @@ subroutine waterbal( tile, tile_fluxes, grid, climate ) type(tile_fluxes_type), dimension(nlu), intent(inout) :: tile_fluxes type(gridtype), intent(in) :: grid type(climate_type), intent(in) :: climate + logical, intent(in) :: using_phydro ! local variables type(outtype_snow_rain) :: out_snow_rain @@ -117,7 +118,7 @@ subroutine waterbal( tile, tile_fluxes, grid, climate ) !--------------------------------------------------------- ! Canopy transpiration and soil evaporation !--------------------------------------------------------- - call calc_et( tile_fluxes(lu), grid, climate, sw ) + call calc_et( tile_fluxes(lu), grid, climate, sw, using_phydro ) !--------------------------------------------------------- ! Update soil moisture and snow pack @@ -307,8 +308,8 @@ subroutine solar( tile_fluxes, grid, climate, doy, in_netrad ) end subroutine solar - ! JAIDEEP FIXME: I would suggest you pull this function out into biosphere_pmodel.f90, and leave the water balance module to just perform balance - subroutine calc_et( tile_fluxes, grid, climate, sw ) + + subroutine calc_et( tile_fluxes, grid, climate, sw, using_phydro ) !///////////////////////////////////////////////////////////////////////// ! !------------------------------------------------------------------------- @@ -320,12 +321,14 @@ subroutine calc_et( tile_fluxes, grid, climate, sw ) type(gridtype), intent(in) :: grid type(climate_type), intent(in) :: climate real, intent(in) :: sw ! evaporative supply rate, mm/hr + logical, intent(in) :: using_phydro ! local variables real :: gamma ! psychrometric constant (Pa K-1) ! xxx Zhang et al. use it in units of (kPa K-1), probably they use sat_slope in kPa/K, too. real :: sat_slope ! slope of saturation vapour pressure vs. temperature curve, Pa K-1 real :: lv ! enthalpy of vaporization, J/kg real :: rho_water ! density of water (g m-3) + real :: energy_to_mm ! Conversion factor to convert energy (J m-2 day) to mass (mm day-1) real :: rx ! variable substitute (mm/hr)/(W/m^2) real :: hi, cos_hi ! intersection hour angle, degrees @@ -347,7 +350,8 @@ subroutine calc_et( tile_fluxes, grid, climate, sw ) ! Eq. 51, SPLASH 2.0 Documentation ! tile_fluxes%canopy%econ = 1.0 / ( lv * rho_water ) ! this is to convert energy into mass (water) - JAIDEEP: This is correct. J m-2 s-1 x (kg-1 m3) x (J-1 kg) = m3 m-2 s-1 = m s-1 - + energy_to_mm = 1.0e3 / ( lv * rho_water ) ! (J m-2 d-1) x (kg-1 m3) x (J-1 kg) x (mm m-1) = m3 m-2 d-1 = mm d-1 + ! JAIDEEP: If it's just conversion from mass to energy, the above formula is correct. This already has the Priestly Taylor factor (s/(s+y)) built in, so this ! should not be used for mere conversion. I would suggest you use just the factor s/(s+y) separately in the respective equations for clarity. tile_fluxes%canopy%econ = sat_slope / (lv * rho_water * (sat_slope + gamma)) ! MORE PRECISELY - this is to convert energy into mass (water). . @@ -393,11 +397,25 @@ subroutine calc_et( tile_fluxes, grid, climate, sw ) !--------------------------------------------------------- ! 21. Estimate daily AET (tile_fluxes%canopy%daet), mm d-1 !--------------------------------------------------------- - ! Eq. 81, SPLASH 2.0 Documentation - tile_fluxes%canopy%daet = (24.0/pi) * (radians(sw * hi) + rx * rw * rv * (dgsin(hn) - dgsin(hi)) + & - radians((rx * rw * ru - rx * tile_fluxes%canopy%rnl) * (hn - hi))) - tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet / (tile_fluxes%canopy%econ * 1000) ! JAIDEEP FIXME: Oops! This is a case where you should use a simple mass-energy conversion, not econ + if (.not. using_phydro) then + ! Eq. 81, SPLASH 2.0 Documentation + tile_fluxes%canopy%daet = (24.0/pi) * (radians(sw * hi) + rx * rw * rv * (dgsin(hn) - dgsin(hi)) + & + radians((rx * rw * ru - rx * tile_fluxes%canopy%rnl) * (hn - hi))) + tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet / (tile_fluxes%canopy%econ * 1000) ! JAIDEEP FIXME: Oops! This is a case where you should use a simple mass-energy conversion, not econ + else + ! Fill canopy LE and soil ET using complementary values from phydro + tile_fluxes%canopy%daet_e_canop = tile_fluxes%canopy%daet_canop / energy_to_mm ! mm d-1 ---> J m-2 d-1 + tile_fluxes%canopy%daet_soil = tile_fluxes%canopy%daet_e_soil * energy_to_mm ! J m-2 d-1 ---> mm d-1 + ! ^ Note: This is under wet conditions, as returned from phydro, so multiply by sw to get actual soil ET + ! calculate totat AET = canopy_AET + sw * soil_AET_wet + tile_fluxes%canopy%daet = tile_fluxes%canopy%daet_canop + sw * tile_fluxes%canopy%daet_soil + tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet_e_canop + sw * tile_fluxes%canopy%daet_e_soil + + ! print *, "Canopy ET (mm d-1, J m-2 d-1) = ", tile_fluxes%canopy%daet_canop, tile_fluxes%canopy%daet_e_canop + ! print *, "Soil ET (mm d-1, J m-2 d-1) = ", tile_fluxes%canopy%daet_soil, tile_fluxes%canopy%daet_e_soil + + end if ! print*,'in waterbal: sw, hi, rx, rw, rv, hn, hi, ru ', sw, hi, rx, rw, rv, hn, hi, ru ! xxx debug diff --git a/vignettes/rsofun_phydro.Rmd b/vignettes/rsofun_phydro.Rmd index 0a4153ec..f06d90f4 100644 --- a/vignettes/rsofun_phydro.Rmd +++ b/vignettes/rsofun_phydro.Rmd @@ -540,6 +540,7 @@ output_p$data[[1]] %>% select(date, gpp, latenth) %>% ```{r} p_hydro_drivers$params_siml[[1]]$use_phydro = T +p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 output_ph <- rsofun::runread_pmodel_f( p_hydro_drivers, From cd0cc9cb0824c8f175e686e95a1c50f30ada65bb Mon Sep 17 00:00:00 2001 From: Jaideep Date: Tue, 12 Sep 2023 23:54:43 +0200 Subject: [PATCH 010/112] add use_pml as program option; add pml comparison in vignette --- R/run_pmodel_f_bysite.R | 5 ++-- src/biosphere_pmodel.mod.f90 | 4 ++- src/gpp_pmodel.mod.f90 | 13 +++++++-- src/params_siml_pmodel.mod.f90 | 1 + src/sofun_r.f90 | 3 ++ src/wrappersc.c | 5 +++- vignettes/rsofun_phydro.Rmd | 53 +++++++++++++++++++++++++++++++++- 7 files changed, 76 insertions(+), 8 deletions(-) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index f8874a19..707517c6 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -261,14 +261,14 @@ run_pmodel_f_bysite <- function( "spinupyears", "recycle", "use_phydro", + "use_pml", "outdt", "ltre", "ltne", "ltnd", "lgr3", "lgn3", - "lgr4", - "use_phydro" + "lgr4" ) parameter_integrity <- lapply(check_param, function(check_var){ @@ -349,6 +349,7 @@ run_pmodel_f_bysite <- function( spinupyears = as.integer(params_siml$spinupyears), recycle = as.integer(params_siml$recycle), use_phydro = as.logical(params_siml$use_phydro), + use_pml = as.logical(params_siml$use_pml), firstyeartrend = as.integer(firstyeartrend_forcing), nyeartrend = as.integer(nyeartrend_forcing), secs_per_tstep = as.integer(secs_per_tstep), diff --git a/src/biosphere_pmodel.mod.f90 b/src/biosphere_pmodel.mod.f90 index 5b1dfae1..5b548992 100644 --- a/src/biosphere_pmodel.mod.f90 +++ b/src/biosphere_pmodel.mod.f90 @@ -132,6 +132,7 @@ function biosphere_annual() result( out_biosphere ) ! calculate GPP !---------------------------------------------------------------- ! if (verbose) print*,'calling gpp() ... ' + ! print *, "Using pml: ", myinterface%params_siml%use_pml call gpp( tile(:), & tile_fluxes(:), & myinterface%pco2, & @@ -140,7 +141,8 @@ function biosphere_annual() result( out_biosphere ) myinterface%grid, & init_daily, & myinterface%params_siml%in_ppfd, & - myinterface%params_siml%use_phydro & + myinterface%params_siml%use_phydro, & + myinterface%params_siml%use_pml & ) ! if (verbose) print*,'... done' diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 9324e96c..4e9384d3 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -74,7 +74,7 @@ module md_gpp_pmodel contains - subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, use_phydro) + subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, use_phydro, use_pml) !////////////////////////////////////////////////////////////////// ! Wrapper function to call to P-model. ! Calculates meteorological conditions with memory based on daily @@ -95,6 +95,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, logical, intent(in) :: init ! is true on the very first simulation day (first subroutine call of each gridcell) logical, intent(in) :: in_ppfd ! whether to use PPFD from forcing or from SPLASH output logical, intent(in) :: use_phydro ! whether to use P-Hydro for photosynthesis and transpiration + logical, intent(in) :: use_pml ! whether to use PML formulation for ET within Phydro ! local variables type(outtype_pmodel) :: out_pmodel ! list of P-model output variables @@ -160,9 +161,15 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, tk = climate_acclimation%dtemp + kTkelvin par_cost = par_cost_type(0.1, 1) - par_plant = par_plant_type(0.5e-16, -1, 1) + par_plant = par_plant_type(0.3e-16, -1, 1) - options%et_method = ET_DIFFUSION + if (use_pml) then + options%et_method = ET_PM + else + options%et_method = ET_DIFFUSION + end if + ! print *, options%et_method + options%gs_method = GS_IGF diff --git a/src/params_siml_pmodel.mod.f90 b/src/params_siml_pmodel.mod.f90 index e54aeebb..e117f7dd 100644 --- a/src/params_siml_pmodel.mod.f90 +++ b/src/params_siml_pmodel.mod.f90 @@ -26,6 +26,7 @@ module md_params_siml_pmodel logical :: do_spinup ! whether this simulation does spinup logical :: is_calib ! whether this simulation is a calibration simulation (overriding parameters and no output) logical :: use_phydro ! Whether phydro should be used for photosynthesis/transpiration calculations + logical :: use_pml ! Whether Penmann-Monteith-Leuning formulation should be used for ET within phydro character(len=256) :: runname character(len=256) :: sitename diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index f7e10835..660a502b 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -16,6 +16,7 @@ subroutine pmodel_f( & spinupyears, & recycle, & use_phydro, & + use_pml, & firstyeartrend, & nyeartrend, & secs_per_tstep, & @@ -57,6 +58,7 @@ subroutine pmodel_f( & integer(kind=c_int), intent(in) :: spinupyears integer(kind=c_int), intent(in) :: recycle logical(kind=c_bool), intent(in) :: use_phydro + logical(kind=c_bool), intent(in) :: use_pml integer(kind=c_int), intent(in) :: firstyeartrend integer(kind=c_int), intent(in) :: nyeartrend integer(kind=c_int), intent(in) :: secs_per_tstep @@ -90,6 +92,7 @@ subroutine pmodel_f( & myinterface%params_siml%spinupyears = spinupyears myinterface%params_siml%recycle = recycle myinterface%params_siml%use_phydro = use_phydro + myinterface%params_siml%use_pml = use_pml myinterface%params_siml%firstyeartrend = firstyeartrend myinterface%params_siml%nyeartrend = nyeartrend diff --git a/src/wrappersc.c b/src/wrappersc.c index 2ddd52c4..2e8043e8 100644 --- a/src/wrappersc.c +++ b/src/wrappersc.c @@ -14,6 +14,7 @@ void F77_NAME(pmodel_f)( int *spinupyears, int *recycle, int *use_phydro, + int *use_pml, int *firstyeartrend, int *nyeartrend, int *secs_per_tstep, @@ -43,6 +44,7 @@ extern SEXP pmodel_f_C( SEXP spinupyears, SEXP recycle, SEXP use_phydro, + SEXP use_pml, SEXP firstyeartrend, SEXP nyeartrend, SEXP secs_per_tstep, @@ -78,6 +80,7 @@ extern SEXP pmodel_f_C( INTEGER(spinupyears), INTEGER(recycle), LOGICAL(use_phydro), + LOGICAL(use_pml), INTEGER(firstyeartrend), INTEGER(nyeartrend), INTEGER(secs_per_tstep), @@ -521,7 +524,7 @@ extern SEXP biomee_f_C( // Declarations for all functions ///////////////////////////////////////////////////////////// static const R_CallMethodDef CallEntries[] = { - {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 24}, // Specify number of arguments to C wrapper as the last number here + {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 25}, // Specify number of arguments to C wrapper as the last number here {"biomee_f_C", (DL_FUNC) &biomee_f_C, 46}, // Number of the SEXP variables (not the output) {NULL, NULL, 0} }; diff --git a/vignettes/rsofun_phydro.Rmd b/vignettes/rsofun_phydro.Rmd index f06d90f4..7ad090f9 100644 --- a/vignettes/rsofun_phydro.Rmd +++ b/vignettes/rsofun_phydro.Rmd @@ -509,6 +509,7 @@ p_model_drivers$forcing[[1]] %>% ```{r} p_hydro_drivers$params_siml[[1]]$use_phydro = F +p_hydro_drivers$params_siml[[1]]$use_pml = F output_p <- rsofun::runread_pmodel_f( p_hydro_drivers, @@ -516,7 +517,7 @@ output_p <- rsofun::runread_pmodel_f( ) ``` -## P-model Validation +### P-model Validation ```{r} output_p$data[[1]] %>% select(date, gpp, latenth) %>% @@ -540,6 +541,8 @@ output_p$data[[1]] %>% select(date, gpp, latenth) %>% ```{r} p_hydro_drivers$params_siml[[1]]$use_phydro = T +p_hydro_drivers$params_siml[[1]]$use_pml = F + p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 output_ph <- rsofun::runread_pmodel_f( @@ -568,6 +571,38 @@ output_ph$data[[1]] %>% select(date, gpp, latenth) %>% facet_wrap(~variable, scales = "free", nrow = 2) ``` +### P-hydro run (with PM ET) + +```{r} +p_hydro_drivers$params_siml[[1]]$use_phydro = T +p_hydro_drivers$params_siml[[1]]$use_pml = T + +output_ph_pm <- rsofun::runread_pmodel_f( + p_hydro_drivers, + par = params_modl +) +``` + +### P-hydro Validation (with PM ET) + +```{r} +output_ph_pm$data[[1]] %>% select(date, gpp, latenth) %>% + rename(le = latenth) %>% + melt("date") %>% + mutate(group="model") %>% + rbind(p_hydro_validation$data[[1]] %>% + select(date, gpp, le) %>% + mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 + melt("date") %>% + mutate(group="obs")) %>% + ggplot(aes(y=value, x=as.Date(date))) + + geom_line(aes(group=group, col=group), alpha=0.7) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 2) +``` + ```{r} output_ph$data[[1]] %>% select(-year_dec) %>% melt("date") %>% @@ -583,5 +618,21 @@ output_ph$data[[1]] %>% select(-year_dec) %>% theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~variable, scales = "free") + + +output_ph_pm$data[[1]] %>% select(-year_dec) %>% + melt("date") %>% + mutate(model = "phydro_pm") %>% + rbind( + output_ph$data[[1]] %>% select(-year_dec) %>% + melt("date") %>% + mutate(model = "phydro_diff") + ) %>% + filter(!is.infinite(value)) %>% + ggplot(aes(y=value, x=as.Date(date), group=model, col=model)) + + geom_line(alpha=0.5) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") ``` From 7088d63e49cfb98df9fcd5c4cb3f33d4d7744da1 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Thu, 14 Sep 2023 19:42:27 +0200 Subject: [PATCH 011/112] feed back wcont from waterbal to phydro --- src/gpp_pmodel.mod.f90 | 17 ++++++++++++++--- src/waterbal_splash.mod.f90 | 4 ++-- 2 files changed, 16 insertions(+), 5 deletions(-) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 4e9384d3..23b9c69a 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -123,6 +123,9 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, type(phydro_result_type) :: out_phydro_acclim, out_phydro_inst type(par_control_type) :: options + ! Soil hydraulics + real :: swp, bsoil + ! xxx test real :: a_c, a_j, a_returned, fact_jmaxlim integer, save :: count @@ -190,7 +193,14 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, params_pft_gpp(pft)%kphio_par_a, & params_pft_gpp(pft)%kphio_par_b ) end if - + + !---------------------------------------------------------------- + ! Convert water content to water potential, for use in phydro + !---------------------------------------------------------------- + bsoil = 3 + swp = 1 - tile(lu)%soil%phy%wscal**(-bsoil) + swp = min(swp, 0.0) ! clamp +ve values to 0 + !---------------------------------------------------------------- ! P-model call to get a list of variables that are ! acclimated to slowly varying conditions @@ -230,7 +240,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, pa = dble(patm_memory), & fapar = dble(tile(lu)%canopy%fapar), & kphio = dble(kphio_temp), & - psi_soil = 0.d0, & + psi_soil = dble(swp), & !0.d0, & rdark = dble(params_gpp%rd_to_vcmax), & vwind = 3.0d0, & par_plant = par_plant, & @@ -280,6 +290,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, * tile_fluxes(lu)%canopy%ppfd_splash * out_pmodel%lue * soilmstress end if else ! Using phydro - run instantaneous model + ! print *, "sw / swp = ", sw, swp out_phydro_inst = phydro_instantaneous_analytical( & vcmax25 = out_phydro_acclim%vcmax25, & jmax25 = out_phydro_acclim%jmax25, & @@ -292,7 +303,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, pa = dble(climate%dpatm), & fapar = dble(tile(lu)%canopy%fapar), & kphio = dble(kphio_temp), & - psi_soil = 0.d0, & + psi_soil = dble(swp), & !0.d0, & rdark = dble(params_gpp%rd_to_vcmax), & vwind = 3.0d0, & par_plant = par_plant, & diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index 9d20a114..470d7027 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -409,8 +409,8 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, using_phydro ) ! ^ Note: This is under wet conditions, as returned from phydro, so multiply by sw to get actual soil ET ! calculate totat AET = canopy_AET + sw * soil_AET_wet - tile_fluxes%canopy%daet = tile_fluxes%canopy%daet_canop + sw * tile_fluxes%canopy%daet_soil - tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet_e_canop + sw * tile_fluxes%canopy%daet_e_soil + tile_fluxes%canopy%daet = tile_fluxes%canopy%daet_canop + (sw/kCw) * tile_fluxes%canopy%daet_soil + tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet_e_canop + (sw/kCw) * tile_fluxes%canopy%daet_e_soil ! print *, "Canopy ET (mm d-1, J m-2 d-1) = ", tile_fluxes%canopy%daet_canop, tile_fluxes%canopy%daet_e_canop ! print *, "Soil ET (mm d-1, J m-2 d-1) = ", tile_fluxes%canopy%daet_soil, tile_fluxes%canopy%daet_e_soil From 0f0d093d9c587120b6a37d751197441f0179a80d Mon Sep 17 00:00:00 2001 From: Jaideep Date: Fri, 20 Oct 2023 16:18:54 +0200 Subject: [PATCH 012/112] all params are now supplied from R --- R/run_pmodel_f_bysite.R | 75 ++++++++++-- src/gpp_pmodel.mod.f90 | 16 ++- src/interface_biosphere_pmodel.mod.f90 | 8 ++ src/plant_pmodel.mod.f90 | 15 +++ src/sofun_r.f90 | 16 ++- src/wrappersc.c | 8 +- vignettes/rsofun_phydro.Rmd | 156 ++++++++++++++++++------- 7 files changed, 234 insertions(+), 60 deletions(-) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 707517c6..23ed18ec 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -293,16 +293,40 @@ run_pmodel_f_bysite <- function( } # Check model parameters - if( sum( names(params_modl) %in% c('kphio', 'kphio_par_a', 'kphio_par_b', - 'soilm_thetastar', 'soilm_betao', - 'beta_unitcostratio', 'rd_to_vcmax', - 'tau_acclim', 'kc_jmax') - ) != 9){ - warning(" Returning a dummy data frame. Incorrect model parameters.") - continue <- FALSE + if (!params_siml$use_phydro){ + # P-model needs 9 parameters + if( sum( names(params_modl) %in% c('kphio', 'kphio_par_a', 'kphio_par_b', + 'soilm_thetastar', 'soilm_betao', + 'beta_unitcostratio', 'rd_to_vcmax', + 'tau_acclim', 'kc_jmax') + ) != 9){ + warning(" Returning a dummy data frame. Incorrect model parameters.") + continue <- FALSE + } + } + else { + # P-hydro needs 12 parameters + if( sum( names(params_modl) %in% c('kphio', 'kphio_par_a', 'kphio_par_b', + 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', + 'phydro_K_plant', 'phydro_p50_plant', 'phydro_b_plant', + 'phydro_alpha', 'phydro_gamma', + 'bsoil') + ) != 12){ + warning(" Returning a dummy data frame. Incorrect model parameters.") + continue <- FALSE + } } } + # If PML is used, then ensure that site info has reference height and canopy height + if (params_siml$use_phydro & + params_siml$use_pml){ + + continue = !is.nanull(site_info$canopy_height) & + !is.nanull(site_info$reference_height) + } + + if (continue){ # determine whether to read PPFD from forcing or to calculate internally @@ -327,16 +351,43 @@ run_pmodel_f_bysite <- function( n <- as.integer(nrow(forcing)) # Model parameters as vector in order + # Fortran code will take in all parameters since the FORTRAN interface cannot be conditional. + # But in this preprocessing step, parameters not relevant to the chosen model will be set to dummy value + dummy_val = 1e20 par <- c( as.numeric(params_modl$kphio), as.numeric(params_modl$kphio_par_a), as.numeric(params_modl$kphio_par_b), - as.numeric(params_modl$soilm_thetastar), - as.numeric(params_modl$soilm_betao), - as.numeric(params_modl$beta_unitcostratio), + ifelse(params_siml$use_phydro, + no = as.numeric(params_modl$soilm_thetastar), + yes = dummy_val), + ifelse(params_siml$use_phydro, + no = as.numeric(params_modl$soilm_betao), + yes = dummy_val), + ifelse(params_siml$use_phydro, + no = as.numeric(params_modl$beta_unitcostratio), + yes = dummy_val), as.numeric(params_modl$rd_to_vcmax), as.numeric(params_modl$tau_acclim), - as.numeric(params_modl$kc_jmax) + as.numeric(params_modl$kc_jmax), + ifelse(params_siml$use_phydro, + no = dummy_val, + yes = params_modl$phydro_K_plant), + ifelse(params_siml$use_phydro, + no = dummy_val, + yes = params_modl$phydro_p50_plant), + ifelse(params_siml$use_phydro, + no = dummy_val, + yes = params_modl$phydro_b_plant), + ifelse(params_siml$use_phydro, + no = dummy_val, + yes = params_modl$phydro_alpha), + ifelse(params_siml$use_phydro, + no = dummy_val, + yes = params_modl$phydro_gamma), + ifelse(params_siml$use_phydro, + no = dummy_val, + yes = params_modl$bsoil) ) ## C wrapper call @@ -367,6 +418,8 @@ run_pmodel_f_bysite <- function( latitude = as.numeric(site_info$lat), altitude = as.numeric(site_info$elv), whc = as.numeric(site_info$whc), + canopy_height = as.numeric(site_info$canopy_height), + reference_height = as.numeric(site_info$reference_height), n = n, par = par, forcing = forcing diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 23b9c69a..e8d3ca35 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -124,7 +124,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, type(par_control_type) :: options ! Soil hydraulics - real :: swp, bsoil + real :: swp ! xxx test real :: a_c, a_j, a_returned, fact_jmaxlim @@ -163,9 +163,6 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, tk = climate_acclimation%dtemp + kTkelvin - par_cost = par_cost_type(0.1, 1) - par_plant = par_plant_type(0.3e-16, -1, 1) - if (use_pml) then options%et_method = ET_PM else @@ -197,8 +194,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, !---------------------------------------------------------------- ! Convert water content to water potential, for use in phydro !---------------------------------------------------------------- - bsoil = 3 - swp = 1 - tile(lu)%soil%phy%wscal**(-bsoil) + swp = 1 - tile(lu)%soil%phy%wscal**(-params_pft_plant(pft)%bsoil) swp = min(swp, 0.0) ! clamp +ve values to 0 !---------------------------------------------------------------- @@ -229,6 +225,14 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, method_jmaxlim = "wang17" & ) else + par_cost = par_cost_type(params_pft_plant(pft)%phydro_alpha, & + params_pft_plant(pft)%phydro_gamma) + par_plant = par_plant_type(params_pft_plant(pft)%phydro_K_plant, & + params_pft_plant(pft)%phydro_p50_plant, & + params_pft_plant(pft)%phydro_b_plant) + par_plant%h_canopy = myinterface%canopy_height + par_plant%h_wind_measurement = myinterface%reference_height + ! print *, "Using P-hydro" out_phydro_acclim = phydro_analytical( & tc = dble(temp_memory), & diff --git a/src/interface_biosphere_pmodel.mod.f90 b/src/interface_biosphere_pmodel.mod.f90 index 6881bfa5..9e0ef943 100644 --- a/src/interface_biosphere_pmodel.mod.f90 +++ b/src/interface_biosphere_pmodel.mod.f90 @@ -23,6 +23,12 @@ module md_interface_pmodel real :: rd_to_vcmax real :: tau_acclim real :: kc_jmax + real :: phydro_K_plant + real :: phydro_p50_plant + real :: phydro_b_plant + real :: phydro_alpha + real :: phydro_gamma + real :: bsoil end type paramstype_calib @@ -31,6 +37,8 @@ module md_interface_pmodel real :: pco2 type(gridtype) :: grid real :: whc_prescr + real :: canopy_height + real :: reference_height type(climate_type), dimension(ndayyear) :: climate type(vegcover_type), dimension(ndayyear):: vegcover ! type(domaininfo_type) :: domaininfo diff --git a/src/plant_pmodel.mod.f90 b/src/plant_pmodel.mod.f90 index 5296c74f..7f31bbc6 100644 --- a/src/plant_pmodel.mod.f90 +++ b/src/plant_pmodel.mod.f90 @@ -35,6 +35,12 @@ module md_plant_pmodel real :: sla ! specific leaf area (m2 gC-1) real :: lma ! leaf mass per area (gC m-2) real :: r_ntolma ! constant ratio of structural N to C (LMA) (gN/gC) + real :: phydro_K_plant ! Phydro: Plant conductivity + real :: phydro_p50_plant ! Phydro: Plant P50 + real :: phydro_b_plant ! Phydro: shape parameter of vulnerability curve + real :: phydro_alpha ! Phydro: Cost of Jmax + real :: phydro_gamma ! Phydro: Cost of hydraulics + real :: bsoil ! Phydro: parameter converting RZWSC to predawn water potential (depends on rooting system hence PFT specific) end type params_pft_plant_type type(params_pft_plant_type), dimension(npft) :: params_pft_plant @@ -225,6 +231,15 @@ subroutine getpar_modl_plant() params_pft_plant(pft) = getpftparams( 'gr4' ) end if + ! Phydro parameters - see definitions above + ! FIXME: For now, these are set to be the same for each PFT, but that should change eventually + params_pft_plant(:)%phydro_K_plant = myinterface%params_calib%phydro_K_plant + params_pft_plant(:)%phydro_p50_plant = myinterface%params_calib%phydro_p50_plant + params_pft_plant(:)%phydro_b_plant = myinterface%params_calib%phydro_b_plant + params_pft_plant(:)%phydro_alpha = myinterface%params_calib%phydro_alpha + params_pft_plant(:)%phydro_gamma = myinterface%params_calib%phydro_gamma + params_pft_plant(:)%bsoil = myinterface%params_calib%bsoil + npft_site = pft ! if (npft_site==0) stop 'PLANT:GETPAR_MODL_PLANT: PFT name not valid. See run/.sofun.parameter' diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index 660a502b..632ddd9f 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -16,7 +16,7 @@ subroutine pmodel_f( & spinupyears, & recycle, & use_phydro, & - use_pml, & + use_pml, & firstyeartrend, & nyeartrend, & secs_per_tstep, & @@ -34,6 +34,8 @@ subroutine pmodel_f( & latitude, & altitude, & whc, & + canopy_height, & + reference_height, & nt, & par, & forcing, & @@ -76,8 +78,10 @@ subroutine pmodel_f( & real(kind=c_double), intent(in) :: latitude real(kind=c_double), intent(in) :: altitude real(kind=c_double), intent(in) :: whc + real(kind=c_double), intent(in) :: canopy_height + real(kind=c_double), intent(in) :: reference_height integer(kind=c_int), intent(in) :: nt ! number of time steps - real(kind=c_double), dimension(9), intent(in) :: par ! free (calibratable) model parameters + real(kind=c_double), dimension(15), intent(in) :: par ! free (calibratable) model parameters real(kind=c_double), dimension(nt,13), intent(in) :: forcing ! array containing all temporally varying forcing data (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=N-deposition, 10=fapar) real(kind=c_double), dimension(nt,18), intent(out) :: output @@ -139,6 +143,8 @@ subroutine pmodel_f( & ! GET SOIL PARAMETERS !---------------------------------------------------------------- myinterface%whc_prescr = real( whc ) + myinterface%canopy_height = real (canopy_height) + myinterface%reference_height = real (reference_height) !---------------------------------------------------------------- ! GET CALIBRATABLE MODEL PARAMETERS (so far a small list) @@ -152,6 +158,12 @@ subroutine pmodel_f( & myinterface%params_calib%rd_to_vcmax = real(par(7)) myinterface%params_calib%tau_acclim = real(par(8)) myinterface%params_calib%kc_jmax = real(par(9)) + myinterface%params_calib%phydro_K_plant = real(par(10)) + myinterface%params_calib%phydro_p50_plant = real(par(11)) + myinterface%params_calib%phydro_b_plant = real(par(12)) + myinterface%params_calib%phydro_alpha = real(par(13)) + myinterface%params_calib%phydro_gamma = real(par(14)) + myinterface%params_calib%bsoil = real(par(15)) !---------------------------------------------------------------- ! GET VEGETATION COVER (fractional projective cover by PFT) diff --git a/src/wrappersc.c b/src/wrappersc.c index 2e8043e8..0fdd3285 100644 --- a/src/wrappersc.c +++ b/src/wrappersc.c @@ -32,6 +32,8 @@ void F77_NAME(pmodel_f)( double *latitude, double *altitude, double *whc, + double *canopy_height, + double *reference_height, int *nt, double *par, double *forcing, @@ -62,6 +64,8 @@ extern SEXP pmodel_f_C( SEXP latitude, SEXP altitude, SEXP whc, + SEXP canopy_height, + SEXP reference_height, SEXP n, SEXP par, SEXP forcing @@ -98,6 +102,8 @@ extern SEXP pmodel_f_C( REAL(latitude), REAL(altitude), REAL(whc), + REAL(canopy_height), + REAL(reference_height), INTEGER(n), REAL(par), REAL(forcing), @@ -524,7 +530,7 @@ extern SEXP biomee_f_C( // Declarations for all functions ///////////////////////////////////////////////////////////// static const R_CallMethodDef CallEntries[] = { - {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 25}, // Specify number of arguments to C wrapper as the last number here + {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 27}, // Specify number of arguments to C wrapper as the last number here {"biomee_f_C", (DL_FUNC) &biomee_f_C, 46}, // Number of the SEXP variables (not the output) {NULL, NULL, 0} }; diff --git a/vignettes/rsofun_phydro.Rmd b/vignettes/rsofun_phydro.Rmd index 7ad090f9..3150a676 100644 --- a/vignettes/rsofun_phydro.Rmd +++ b/vignettes/rsofun_phydro.Rmd @@ -14,10 +14,10 @@ library(tidyverse) library(reshape2) ``` - ## Generate data if not already available ```{r} +site <- "GF-Guy" generate_data = F ``` @@ -27,31 +27,47 @@ if (generate_data){ library(FluxDataKit) # read in sites to process -site <- "FR-Pue" +ystart = 2004 +yend = 2020 -# get half-hourly data -------------------------------------------------------- -message("- convert to FLUXNET standard CSV file") -hhdf <- suppressWarnings( +# get metadata +# -------------------------------------------------------- +meta <- suppressWarnings( try( fdk_convert_lsm( site = site, fluxnet_format = TRUE, - path = "~/Downloads/flux_data_kit_beta/fluxes/" + path = "~/Downloads/flux_data_kit_beta/fluxes/", + meta_data = T ) ) ) -if(inherits(hhdf, "try-error")){ - message("!!! conversion to FLUXNET failed !!!") - return(NULL) -} +# get half-hourly data -------------------------------------------------------- +# message("- convert to FLUXNET standard CSV file") +# hhdf <- suppressWarnings( +# try( +# fdk_convert_lsm( +# site = site, +# fluxnet_format = TRUE, +# path = "~/Downloads/flux_data_kit_beta/fluxes/" +# ) +# ) +# ) +# +# if(inherits(hhdf, "try-error")){ +# message("!!! conversion to FLUXNET failed !!!") +# return(NULL) +# } + +hhdf <- readr::read_csv(paste0("~/Downloads/flux_data_kit_beta/fluxnet_csv/FLX_",site,"_PLUMBER_FULLSET_HH_",ystart,"_",yend,"_2-3.csv")) # Add date and time columns to hhdf for easier further processing. # --------------------------------------------------------- hhdf = hhdf |> - mutate(time = lubridate::as_datetime(TIMESTAMP_START, tz = "GMT", format="%Y%m%d%H%M")) |> + mutate(time = lubridate::as_datetime(as.character(TIMESTAMP_START), tz = "GMT", format="%Y%m%d%H%M")) |> mutate(date = lubridate::as_date(time)) # Aggregate to daily 24-hr means ---------------------------------------------------------- @@ -199,12 +215,17 @@ message("- compiling drivers") load("../data/p_model_drivers.rda") p_hydro_drivers <- p_model_drivers +p_hydro_drivers$sitename[[1]] = site +p_hydro_drivers$site_info[[1]] = tibble(lon=meta[[1]]$longitude, lat=meta[[1]]$latitude, elv = meta[[1]]$elevation, whc = 90, canopy_height=meta[[1]]$canopy_height, reference_height = meta[[1]]$reference_height) kfFEC = 2.04 +start_year = 2000 +end_year = 2020 + # for demo, use just a subset of years p_hydro_drivers$forcing <- ddf_24hr_mean |> - dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> left_join(tmaxmin) |> group_by(date) |> @@ -227,7 +248,7 @@ p_hydro_drivers$forcing <- p_hydro_drivers$forcing_acclim <- ddf_3hr_maxima |> - dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> left_join(tmaxmin) |> group_by(date) |> @@ -251,7 +272,7 @@ p_hydro_drivers$forcing_acclim <- p_hydro_drivers$forcing_daytime_mean <- ddf_daytime_mean |> - dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> left_join(tmaxmin) |> group_by(date) |> @@ -277,7 +298,7 @@ p_hydro_drivers$forcing_daytime_mean <- p_hydro_drivers$forcing_halfhourly <- hhdf |> - dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> group_by(time) |> summarize( @@ -299,7 +320,7 @@ p_hydro_drivers$forcing_halfhourly <- # write all drivers to file # apply compression to minimize space -filn <- "../data/p_hydro_drivers.rda" +filn <- paste0("../data/",site,"_p_hydro_drivers.rda") message(paste0("- writing to file: ", filn)) save(p_hydro_drivers, file = filn @@ -310,10 +331,11 @@ save(p_hydro_drivers, load("../data/p_model_validation.rda") p_hydro_validation <- p_model_validation +p_hydro_validation$sitename[[1]] = site p_hydro_validation$data <- ddf_24hr_mean |> - dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> group_by(date) |> summarise( @@ -326,7 +348,7 @@ p_hydro_validation$data <- p_hydro_validation$data_hh <- hhdf |> - dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> group_by(time) |> summarise( @@ -339,7 +361,7 @@ p_hydro_validation$data_hh <- p_hydro_validation$data_3hr_mean <- ddf_3hr_maxima |> - dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> group_by(time) |> summarise( @@ -352,7 +374,7 @@ p_hydro_validation$data_3hr_mean <- p_hydro_validation$data_daytime_mean <- ddf_daytime_mean |> - dplyr::filter(lubridate::year(date) %in% 2007:2014) |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> group_by(time) |> summarise( @@ -364,7 +386,7 @@ p_hydro_validation$data_daytime_mean <- list() -filn <- "../data/p_hydro_validation.rda" +filn <- paste0("../data/",site,"_p_hydro_validation.rda") message(paste0("- writing to file: ", filn)) save(p_hydro_validation, file = filn @@ -375,14 +397,16 @@ save(p_hydro_validation, ``` - -## Read Phydro drivers and check +## Read Phydro drivers and check ```{r} # rm(list=ls()) -load("../data/p_hydro_drivers.rda") -load("../data/p_hydro_validation.rda") +load(paste0("../data/",site,"_p_hydro_drivers.rda")) +load(paste0("../data/",site,"_p_hydro_validation.rda")) + +p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 +p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 start_date = "2011-06-01" end_date = "2011-06-06" @@ -466,9 +490,22 @@ p_hydro_validation$data[[1]] %>% ``` - ## Model parameters and driver data +Liu et al 21 nature climate change alexandra konings + +Qs: + +"Compound extremes are more reliably simulated with a hydraulics-explicit model" + +1. disaggregating SM and VPD - compund stress + +Kim novick Nat CC - interactions between VPD and SM with flux data + +plot model bias \~ SM, VPD, T, does bias increase at extremes? + +How to test interactions. + ```{r} params_modl <- list( kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD @@ -479,11 +516,17 @@ params_modl <- list( beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41 + kc_jmax = 0.41, + phydro_K_plant = 3e-16, + phydro_p50_plant = -1, + phydro_b_plant = 1, + phydro_alpha = 0.1, + phydro_gamma = 1, + bsoil = 3 ) p_hydro_drivers$forcing[[1]] %>% - dplyr::filter(lubridate::year(date) %in% 2007:2013) %>% + # dplyr::filter(lubridate::year(date) %in% 2007:2013) %>% melt("date") %>% ggplot(aes(y=value, x=as.Date(date))) + geom_line(col="aquamarine4") + @@ -492,15 +535,15 @@ p_hydro_drivers$forcing[[1]] %>% facet_wrap(~variable, scales = "free") -load("../data/p_model_drivers.rda") -load("../data/p_model_validation.rda") -p_model_drivers$forcing[[1]] %>% - melt("date") %>% - ggplot(aes(y=value, x=as.Date(date))) + - geom_line(col="aquamarine4") + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") +# load("../data/p_model_drivers.rda") +# load("../data/p_model_validation.rda") +# p_model_drivers$forcing[[1]] %>% +# melt("date") %>% +# ggplot(aes(y=value, x=as.Date(date))) + +# geom_line(col="aquamarine4") + +# theme_classic() + +# theme(strip.background = element_rect(color = "white", size = 1))+ +# facet_wrap(~variable, scales = "free") ``` ## Model run @@ -543,8 +586,6 @@ output_p$data[[1]] %>% select(date, gpp, latenth) %>% p_hydro_drivers$params_siml[[1]]$use_phydro = T p_hydro_drivers$params_siml[[1]]$use_pml = F -p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 - output_ph <- rsofun::runread_pmodel_f( p_hydro_drivers, par = params_modl @@ -571,6 +612,24 @@ output_ph$data[[1]] %>% select(date, gpp, latenth) %>% facet_wrap(~variable, scales = "free", nrow = 2) ``` +```{r} + +output_ph$data[[1]] %>% select(date, gpp, latenth) %>% + rename(le = latenth) %>% + melt("date", value.name = "pred") %>% + left_join(p_hydro_validation$data[[1]] %>% + select(date, gpp, le) %>% + mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 + melt("date", value.name = "obs")) %>% + ggplot(aes(y=obs, x=pred)) + + geom_point(alpha=0.7) + + geom_abline(slope=1, intercept=0, col="red")+ + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 1) +``` + ### P-hydro run (with PM ET) ```{r} @@ -603,6 +662,24 @@ output_ph_pm$data[[1]] %>% select(date, gpp, latenth) %>% facet_wrap(~variable, scales = "free", nrow = 2) ``` +```{r} + +output_ph_pm$data[[1]] %>% select(date, gpp, latenth) %>% + rename(le = latenth) %>% + melt("date", value.name = "pred") %>% + left_join(p_hydro_validation$data[[1]] %>% + select(date, gpp, le) %>% + mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 + melt("date", value.name = "obs")) %>% + ggplot(aes(y=obs, x=pred)) + + geom_point(alpha=0.7) + + geom_abline(slope=1, intercept=0, col="red")+ + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 1) +``` + ```{r} output_ph$data[[1]] %>% select(-year_dec) %>% melt("date") %>% @@ -635,4 +712,3 @@ output_ph_pm$data[[1]] %>% select(-year_dec) %>% theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~variable, scales = "free") ``` - From 08806209ad8f674dfd69796722cdb8642a035214 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Fri, 10 Nov 2023 22:08:07 +0100 Subject: [PATCH 013/112] working version with all vars in the right classes --- R/run_pmodel_f_bysite.R | 16 ++++++------- src/gpp_pmodel.mod.f90 | 12 +++++----- src/interface_biosphere_pmodel.mod.f90 | 5 +++-- src/plant_pmodel.mod.f90 | 31 +++++++++++++------------- src/sofun_r.f90 | 11 +++++---- src/tile_pmodel.mod.f90 | 3 ++- src/wrappersc.c | 5 +---- vignettes/rsofun_phydro.Rmd | 14 +++++++++--- 8 files changed, 53 insertions(+), 44 deletions(-) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 23ed18ec..a23bc3ab 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -294,24 +294,24 @@ run_pmodel_f_bysite <- function( # Check model parameters if (!params_siml$use_phydro){ - # P-model needs 9 parameters + # P-model needs 10 parameters if( sum( names(params_modl) %in% c('kphio', 'kphio_par_a', 'kphio_par_b', 'soilm_thetastar', 'soilm_betao', 'beta_unitcostratio', 'rd_to_vcmax', - 'tau_acclim', 'kc_jmax') - ) != 9){ + 'tau_acclim', 'kc_jmax', 'whc') + ) != 10){ warning(" Returning a dummy data frame. Incorrect model parameters.") continue <- FALSE } } else { - # P-hydro needs 12 parameters + # P-hydro needs 13 parameters if( sum( names(params_modl) %in% c('kphio', 'kphio_par_a', 'kphio_par_b', 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', 'phydro_K_plant', 'phydro_p50_plant', 'phydro_b_plant', 'phydro_alpha', 'phydro_gamma', - 'bsoil') - ) != 12){ + 'bsoil', 'whc') + ) != 13){ warning(" Returning a dummy data frame. Incorrect model parameters.") continue <- FALSE } @@ -387,7 +387,8 @@ run_pmodel_f_bysite <- function( yes = params_modl$phydro_gamma), ifelse(params_siml$use_phydro, no = dummy_val, - yes = params_modl$bsoil) + yes = params_modl$bsoil), + as.numeric(params_modl$whc) ) ## C wrapper call @@ -417,7 +418,6 @@ run_pmodel_f_bysite <- function( longitude = as.numeric(site_info$lon), latitude = as.numeric(site_info$lat), altitude = as.numeric(site_info$elv), - whc = as.numeric(site_info$whc), canopy_height = as.numeric(site_info$canopy_height), reference_height = as.numeric(site_info$reference_height), n = n, diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index e8d3ca35..815654ea 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -194,7 +194,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, !---------------------------------------------------------------- ! Convert water content to water potential, for use in phydro !---------------------------------------------------------------- - swp = 1 - tile(lu)%soil%phy%wscal**(-params_pft_plant(pft)%bsoil) + swp = 1 - tile(lu)%soil%phy%wscal**(-tile(lu)%plant(pft)%bsoil) swp = min(swp, 0.0) ! clamp +ve values to 0 !---------------------------------------------------------------- @@ -225,11 +225,11 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, method_jmaxlim = "wang17" & ) else - par_cost = par_cost_type(params_pft_plant(pft)%phydro_alpha, & - params_pft_plant(pft)%phydro_gamma) - par_plant = par_plant_type(params_pft_plant(pft)%phydro_K_plant, & - params_pft_plant(pft)%phydro_p50_plant, & - params_pft_plant(pft)%phydro_b_plant) + par_cost = par_cost_type(tile(lu)%plant(pft)%phydro_alpha, & + tile(lu)%plant(pft)%phydro_gamma) + par_plant = par_plant_type(tile(lu)%plant(pft)%phydro_K_plant, & + tile(lu)%plant(pft)%phydro_p50_plant, & + tile(lu)%plant(pft)%phydro_b_plant) par_plant%h_canopy = myinterface%canopy_height par_plant%h_wind_measurement = myinterface%reference_height diff --git a/src/interface_biosphere_pmodel.mod.f90 b/src/interface_biosphere_pmodel.mod.f90 index 9e0ef943..b6946854 100644 --- a/src/interface_biosphere_pmodel.mod.f90 +++ b/src/interface_biosphere_pmodel.mod.f90 @@ -28,7 +28,8 @@ module md_interface_pmodel real :: phydro_b_plant real :: phydro_alpha real :: phydro_gamma - real :: bsoil + real :: bsoil + real :: whc end type paramstype_calib @@ -36,7 +37,7 @@ module md_interface_pmodel integer :: year real :: pco2 type(gridtype) :: grid - real :: whc_prescr + ! real :: whc_prescr real :: canopy_height real :: reference_height type(climate_type), dimension(ndayyear) :: climate diff --git a/src/plant_pmodel.mod.f90 b/src/plant_pmodel.mod.f90 index 7f31bbc6..3b2e76ad 100644 --- a/src/plant_pmodel.mod.f90 +++ b/src/plant_pmodel.mod.f90 @@ -32,15 +32,9 @@ module md_plant_pmodel logical :: nfixer ! whether plant is capable of symbiotically fixing N logical :: c3 ! whether plant follows C3 photosynthesis logical :: c4 ! whether plant follows C4 photosynthesis - real :: sla ! specific leaf area (m2 gC-1) + real :: sla ! specific leaf area (m2 gC-1) FIXME (JAIDEEP): Probably SLA and LMA dont belong here, but in plant_type real :: lma ! leaf mass per area (gC m-2) real :: r_ntolma ! constant ratio of structural N to C (LMA) (gN/gC) - real :: phydro_K_plant ! Phydro: Plant conductivity - real :: phydro_p50_plant ! Phydro: Plant P50 - real :: phydro_b_plant ! Phydro: shape parameter of vulnerability curve - real :: phydro_alpha ! Phydro: Cost of Jmax - real :: phydro_gamma ! Phydro: Cost of hydraulics - real :: bsoil ! Phydro: parameter converting RZWSC to predawn water potential (depends on rooting system hence PFT specific) end type params_pft_plant_type type(params_pft_plant_type), dimension(npft) :: params_pft_plant @@ -72,6 +66,12 @@ module md_plant_pmodel real :: r_cton_leaf ! leaf C:N ratio [gC/gN] real :: r_ntoc_leaf ! leaf N:C ratio [gN/gC] + real :: phydro_K_plant ! Phydro: Plant conductivity + real :: phydro_p50_plant ! Phydro: Plant P50 + real :: phydro_b_plant ! Phydro: shape parameter of vulnerability curve + real :: phydro_alpha ! Phydro: Cost of Jmax + real :: phydro_gamma ! Phydro: Cost of hydraulics + real :: bsoil ! Phydro: parameter converting RZWSC to predawn water potential (depends on rooting system hence PFT specific) end type plant_type @@ -230,15 +230,6 @@ subroutine getpar_modl_plant() pft = pft + 1 params_pft_plant(pft) = getpftparams( 'gr4' ) end if - - ! Phydro parameters - see definitions above - ! FIXME: For now, these are set to be the same for each PFT, but that should change eventually - params_pft_plant(:)%phydro_K_plant = myinterface%params_calib%phydro_K_plant - params_pft_plant(:)%phydro_p50_plant = myinterface%params_calib%phydro_p50_plant - params_pft_plant(:)%phydro_b_plant = myinterface%params_calib%phydro_b_plant - params_pft_plant(:)%phydro_alpha = myinterface%params_calib%phydro_alpha - params_pft_plant(:)%phydro_gamma = myinterface%params_calib%phydro_gamma - params_pft_plant(:)%bsoil = myinterface%params_calib%bsoil npft_site = pft ! if (npft_site==0) stop 'PLANT:GETPAR_MODL_PLANT: PFT name not valid. See run/.sofun.parameter' @@ -364,6 +355,14 @@ subroutine initpft( plant ) plant%r_cton_leaf = 0.0 plant%r_ntoc_leaf = 0.0 + ! Phydro parameters - see definitions above + plant%phydro_K_plant = myinterface%params_calib%phydro_K_plant + plant%phydro_p50_plant = myinterface%params_calib%phydro_p50_plant + plant%phydro_b_plant = myinterface%params_calib%phydro_b_plant + plant%phydro_alpha = myinterface%params_calib%phydro_alpha + plant%phydro_gamma = myinterface%params_calib%phydro_gamma + plant%bsoil = myinterface%params_calib%bsoil + end subroutine initpft diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index 632ddd9f..7db368de 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -33,7 +33,6 @@ subroutine pmodel_f( & longitude, & latitude, & altitude, & - whc, & canopy_height, & reference_height, & nt, & @@ -77,11 +76,10 @@ subroutine pmodel_f( & real(kind=c_double), intent(in) :: longitude real(kind=c_double), intent(in) :: latitude real(kind=c_double), intent(in) :: altitude - real(kind=c_double), intent(in) :: whc real(kind=c_double), intent(in) :: canopy_height real(kind=c_double), intent(in) :: reference_height integer(kind=c_int), intent(in) :: nt ! number of time steps - real(kind=c_double), dimension(15), intent(in) :: par ! free (calibratable) model parameters + real(kind=c_double), dimension(16), intent(in) :: par ! free (calibratable) model parameters real(kind=c_double), dimension(nt,13), intent(in) :: forcing ! array containing all temporally varying forcing data (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=N-deposition, 10=fapar) real(kind=c_double), dimension(nt,18), intent(out) :: output @@ -142,7 +140,11 @@ subroutine pmodel_f( & !---------------------------------------------------------------- ! GET SOIL PARAMETERS !---------------------------------------------------------------- - myinterface%whc_prescr = real( whc ) + !myinterface%whc_prescr = real( whc ) + + !---------------------------------------------------------------- + ! Other site-specific PARAMETERS + !---------------------------------------------------------------- myinterface%canopy_height = real (canopy_height) myinterface%reference_height = real (reference_height) @@ -164,6 +166,7 @@ subroutine pmodel_f( & myinterface%params_calib%phydro_alpha = real(par(13)) myinterface%params_calib%phydro_gamma = real(par(14)) myinterface%params_calib%bsoil = real(par(15)) + myinterface%params_calib%whc = real(par(16)) !---------------------------------------------------------------- ! GET VEGETATION COVER (fractional projective cover by PFT) diff --git a/src/tile_pmodel.mod.f90 b/src/tile_pmodel.mod.f90 index 8a81526f..b146bb2a 100644 --- a/src/tile_pmodel.mod.f90 +++ b/src/tile_pmodel.mod.f90 @@ -422,7 +422,8 @@ subroutine initglobal_soil_params( params ) params%thdiff_fc = 0.4 ! overwrite - params%whc = myinterface%whc_prescr + !params%whc = myinterface%whc_prescr + params%whc = myinterface%params_calib%whc ! end do diff --git a/src/wrappersc.c b/src/wrappersc.c index 0fdd3285..0bea427a 100644 --- a/src/wrappersc.c +++ b/src/wrappersc.c @@ -31,7 +31,6 @@ void F77_NAME(pmodel_f)( double *longitude, double *latitude, double *altitude, - double *whc, double *canopy_height, double *reference_height, int *nt, @@ -63,7 +62,6 @@ extern SEXP pmodel_f_C( SEXP longitude, SEXP latitude, SEXP altitude, - SEXP whc, SEXP canopy_height, SEXP reference_height, SEXP n, @@ -101,7 +99,6 @@ extern SEXP pmodel_f_C( REAL(longitude), REAL(latitude), REAL(altitude), - REAL(whc), REAL(canopy_height), REAL(reference_height), INTEGER(n), @@ -530,7 +527,7 @@ extern SEXP biomee_f_C( // Declarations for all functions ///////////////////////////////////////////////////////////// static const R_CallMethodDef CallEntries[] = { - {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 27}, // Specify number of arguments to C wrapper as the last number here + {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 26}, // Specify number of arguments to C wrapper as the last number here {"biomee_f_C", (DL_FUNC) &biomee_f_C, 46}, // Number of the SEXP variables (not the output) {NULL, NULL, 0} }; diff --git a/vignettes/rsofun_phydro.Rmd b/vignettes/rsofun_phydro.Rmd index 3150a676..fd40a132 100644 --- a/vignettes/rsofun_phydro.Rmd +++ b/vignettes/rsofun_phydro.Rmd @@ -216,7 +216,14 @@ load("../data/p_model_drivers.rda") p_hydro_drivers <- p_model_drivers p_hydro_drivers$sitename[[1]] = site -p_hydro_drivers$site_info[[1]] = tibble(lon=meta[[1]]$longitude, lat=meta[[1]]$latitude, elv = meta[[1]]$elevation, whc = 90, canopy_height=meta[[1]]$canopy_height, reference_height = meta[[1]]$reference_height) +p_hydro_drivers$site_info[[1]] = + tibble( + lon=meta[[1]]$longitude, + lat=meta[[1]]$latitude, + elv = meta[[1]]$elevation, + canopy_height=meta[[1]]$canopy_height, + reference_height = meta[[1]]$reference_height + ) kfFEC = 2.04 start_year = 2000 @@ -517,12 +524,13 @@ params_modl <- list( rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, kc_jmax = 0.41, - phydro_K_plant = 3e-16, + phydro_K_plant = 0.3e-16, phydro_p50_plant = -1, phydro_b_plant = 1, phydro_alpha = 0.1, phydro_gamma = 1, - bsoil = 3 + bsoil = 3, + whc = 90 ) p_hydro_drivers$forcing[[1]] %>% From a39b3efb1011988448a779af6aa37fb848e60f77 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sat, 11 Nov 2023 13:37:18 +0100 Subject: [PATCH 014/112] basic working kphio calib with pmodel; with whc in params --- R/cost_likelihood_pmodel.R | 8 +- R/cost_rmse_pmodel.R | 10 +- vignettes/rsofun_phydro.Rmd | 2 +- vignettes/rsofun_phydro_calibration.Rmd | 187 ++++++++++++++++++++++++ 4 files changed, 198 insertions(+), 9 deletions(-) create mode 100644 vignettes/rsofun_phydro_calibration.Rmd diff --git a/R/cost_likelihood_pmodel.R b/R/cost_likelihood_pmodel.R index f5d39f73..a8b49882 100644 --- a/R/cost_likelihood_pmodel.R +++ b/R/cost_likelihood_pmodel.R @@ -86,16 +86,16 @@ cost_likelihood_pmodel <- function( sitename <- data <- gpp_mod <- NULL ## check input parameters - if( (length(par) + length(par_fixed)) != (9 + length(targets)) ){ - stop('Error: Input calibratable and fixed parameters (par and par_fixed) - do not match length of the required P-model parameters and target error terms.') + if( (length(par) + length(par_fixed)) != (10 + length(targets)) ){ + stop(paste0('Error: Input calibratable and fixed parameters (par = ',length(par),' and par_fixed = ',length(par_fixed),') + do not match length of the required P-model parameters (10).')) } ## define parameter set based on calibrated parameters calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', 'soilm_thetastar', 'soilm_betao', 'beta_unitcostratio', 'rd_to_vcmax', - 'tau_acclim', 'kc_jmax') + 'tau_acclim', 'kc_jmax', 'whc') if(!is.null(par_fixed)){ params_modl <- list() diff --git a/R/cost_rmse_pmodel.R b/R/cost_rmse_pmodel.R index 5ab7f249..d17b5656 100644 --- a/R/cost_rmse_pmodel.R +++ b/R/cost_rmse_pmodel.R @@ -84,16 +84,16 @@ cost_rmse_pmodel <- function( sitename <- data <- gpp_mod <- NULL ## check input parameters - if( (length(par) + length(par_fixed)) != 9 ){ - stop('Error: Input calibratable and fixed parameters (par and par_fixed) - do not match length of the required P-model parameters.') + if( (length(par) + length(par_fixed)) != 10 ){ + stop(paste0('Error: Input calibratable and fixed parameters (par = ',length(par),' and par_fixed = ',length(par_fixed),') + do not match length of the required P-model parameters (10).')) } ## define parameter set based on calibrated parameters calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', 'soilm_thetastar', 'soilm_betao', 'beta_unitcostratio', 'rd_to_vcmax', - 'tau_acclim', 'kc_jmax') + 'tau_acclim', 'kc_jmax', 'whc') if(!is.null(par_fixed)){ params_modl <- list() @@ -112,6 +112,8 @@ cost_rmse_pmodel <- function( names(params_modl) <- calib_param_names } + # print(par) + # run the model df <- runread_pmodel_f( drivers, diff --git a/vignettes/rsofun_phydro.Rmd b/vignettes/rsofun_phydro.Rmd index fd40a132..d8409a2a 100644 --- a/vignettes/rsofun_phydro.Rmd +++ b/vignettes/rsofun_phydro.Rmd @@ -515,7 +515,7 @@ How to test interactions. ```{r} params_modl <- list( - kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD + kphio = 0.089 # 0.11, #0.04998, # setup ORG in Stocker et al. 2020 GMD kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress diff --git a/vignettes/rsofun_phydro_calibration.Rmd b/vignettes/rsofun_phydro_calibration.Rmd new file mode 100644 index 00000000..ce309974 --- /dev/null +++ b/vignettes/rsofun_phydro_calibration.Rmd @@ -0,0 +1,187 @@ +--- +title: "Calibration of rsofun with Phydro" +author: "Jaideep Joshi" +date: "2023-09-07" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +```{r} +library(tidyverse) +library(reshape2) +library(rsofun) +``` + +## Generate data if not already available + +```{r} +site <- "GF-Guy" +``` + +```{r} +load(paste0("../data/",site,"_p_hydro_drivers.rda")) +load(paste0("../data/",site,"_p_hydro_validation.rda")) + +p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 +p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 + +``` + +Function to quickly run model and plot outputs + +```{r} + +plot_pmodel = function(params_modl){ + output_p <- rsofun::runread_pmodel_f( + p_hydro_drivers, + par = params_modl + ) + + output_p$data[[1]] %>% select(date, gpp, latenth) %>% + rename(le = latenth) %>% + melt("date") %>% + mutate(group="model") %>% + rbind(p_hydro_validation$data[[1]] %>% + select(date, gpp, le) %>% + mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 + melt("date") %>% + mutate(group="obs")) %>% + ggplot(aes(y=value, x=as.Date(date))) + + geom_line(aes(group=group, col=group), alpha=0.7) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 2) +} +``` + +```{r} +params_modl <- list( + kphio = 0.089, # 0.11, #0.04998, + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + soilm_betao = 0.0, + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + phydro_K_plant = 0.3e-16, + phydro_p50_plant = -1, + phydro_b_plant = 1, + phydro_alpha = 0.1, + phydro_gamma = 1, + bsoil = 3, + whc = 90 +) +``` + +## Model calibration + +### P-model calibration (GenSA) + +```{r} +p_hydro_drivers$params_siml[[1]]$use_phydro = F +p_hydro_drivers$params_siml[[1]]$use_pml = F + +# Define calibration settings and parameter ranges from previous work +settings_rmse <- list( + method = 'GenSA', # minimizes the RMSE + metric = cost_rmse_pmodel, # our cost function + control = list( # control parameters for optimizer GenSA + maxit = 100), + par = list( # bounds for the parameter space + kphio = list(lower=0.02, upper=0.2, init=0.05) + ), + parallel = T, + ncores = 8 +) + +# Calibrate the model and optimize the free parameters using +# demo datasets +pars_calib_rmse <- calib_sofun( + # calib_sofun arguments: + drivers = p_hydro_drivers, + obs = p_hydro_validation, + settings = settings_rmse, + # extra arguments passed to the cost function: + par_fixed = list( # fix all other parameters + kphio_par_a = 0.0, # set to zero to disable temperature-dependence + # of kphio, setup ORG + kphio_par_b = 1.0, + soilm_thetastar = 0.6 * 240, # to recover paper setup with soil moisture stress + soilm_betao = 0.0, + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + whc = 90 + ), + targets = "gpp" # define target variable GPP +) + +print(pars_calib_rmse) + +params_modl_opt = params_modl +params_modl_opt$kphio = pars_calib_rmse$par +plot_pmodel(params_modl_opt) + +``` + +### P-model calibration (BayesianTools) + +```{r} +p_hydro_drivers$params_siml[[1]]$use_phydro = F +p_hydro_drivers$params_siml[[1]]$use_pml = F + +# Define calibration settings and parameter ranges from previous work +settings_bayes <- list( + method = "BayesianTools", + par = list( + kphio = list(lower=0.04, upper=0.09, init=0.05), + err_gpp = list(lower = 0.01, upper = 4, init = 2) + ), + metric = rsofun::cost_likelihood_pmodel, + control = list( + sampler = "DEzs", + settings = list( + nrChains = 1, + burnin = 500, + iterations = 600 # kept artificially low + ) + ) +) + +# Calibrate the model and optimize the free parameters using +# demo datasets +pars_calib_bayes <- calib_sofun( + # calib_sofun arguments: + drivers = p_hydro_drivers, + obs = p_hydro_validation, + settings = settings_bayes, + # extra arguments passed to the cost function: + par_fixed = list( # fix all other parameters + kphio_par_a = 0.0, # set to zero to disable temperature-dependence + # of kphio, setup ORG + kphio_par_b = 1.0, + soilm_thetastar = 0.6 * 240, # to recover paper setup with soil moisture stress + soilm_betao = 0.0, + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + whc = 90 + ), + targets = "gpp" # define target variable GPP +) + +print(pars_calib_bayes) + +params_modl_opt = params_modl +params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] +plot_pmodel(params_modl_opt) + +``` From 52033ada25e86c7e196bed23e4602761b5a3371f Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sat, 11 Nov 2023 16:42:29 +0100 Subject: [PATCH 015/112] working gensa/bayesian calibration with gpp and le --- R/cost_likelihood_pmodel.R | 28 +++-- R/cost_rmse_pmodel.R | 28 +++-- vignettes/rsofun_phydro_calibration.Rmd | 148 +++++++++++++++++++++++- 3 files changed, 184 insertions(+), 20 deletions(-) diff --git a/R/cost_likelihood_pmodel.R b/R/cost_likelihood_pmodel.R index a8b49882..64b0c2ff 100644 --- a/R/cost_likelihood_pmodel.R +++ b/R/cost_likelihood_pmodel.R @@ -85,17 +85,31 @@ cost_likelihood_pmodel <- function( # predefine variables for CRAN check compliance sitename <- data <- gpp_mod <- NULL + using_phydro = drivers$params_siml[[1]]$use_phydro + + # FIXME Jaideep: Instead of checking the number of params, + # it might be better to check for presence of each param in par and par_fixed ## check input parameters - if( (length(par) + length(par_fixed)) != (10 + length(targets)) ){ + expected_params = ifelse(using_phydro, yes=13, no=10) + if( (length(par) + length(par_fixed)) != (expected_params + length(targets)) ){ stop(paste0('Error: Input calibratable and fixed parameters (par = ',length(par),' and par_fixed = ',length(par_fixed),') - do not match length of the required P-model parameters (10).')) + do not match length of the required P-model parameters (',expected_params + length(targets),').')) } + ## define parameter set based on calibrated parameters - calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', - 'soilm_thetastar', 'soilm_betao', - 'beta_unitcostratio', 'rd_to_vcmax', - 'tau_acclim', 'kc_jmax', 'whc') + if (using_phydro){ + calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', + 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', + 'phydro_K_plant', 'phydro_p50_plant', 'phydro_b_plant', + 'phydro_alpha', 'phydro_gamma', + 'bsoil', 'whc') + } else { + calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', + 'soilm_thetastar', 'soilm_betao', + 'beta_unitcostratio', 'rd_to_vcmax', + 'tau_acclim', 'kc_jmax', 'whc') + } if(!is.null(par_fixed)){ params_modl <- list() @@ -110,7 +124,7 @@ cost_likelihood_pmodel <- function( } } }else{ - params_modl <- as.list(par[1:9]) # all parameters calibrated + params_modl <- as.list(par[1:expected_params]) # all parameters calibrated names(params_modl) <- calib_param_names } diff --git a/R/cost_rmse_pmodel.R b/R/cost_rmse_pmodel.R index d17b5656..34c4ce40 100644 --- a/R/cost_rmse_pmodel.R +++ b/R/cost_rmse_pmodel.R @@ -83,17 +83,31 @@ cost_rmse_pmodel <- function( # predefine variables for CRAN check compliance sitename <- data <- gpp_mod <- NULL + using_phydro = drivers$params_siml[[1]]$use_phydro + + # FIXME Jaideep: Instead of checking the number of params, + # it might be better to check for presence of each param in par and par_fixed ## check input parameters - if( (length(par) + length(par_fixed)) != 10 ){ + expected_params = ifelse(using_phydro, yes=13, no=10) + if( (length(par) + length(par_fixed)) != (expected_params) ){ stop(paste0('Error: Input calibratable and fixed parameters (par = ',length(par),' and par_fixed = ',length(par_fixed),') - do not match length of the required P-model parameters (10).')) + do not match length of the required P-model parameters (',expected_params + length(targets),').')) } - + ## define parameter set based on calibrated parameters - calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', - 'soilm_thetastar', 'soilm_betao', - 'beta_unitcostratio', 'rd_to_vcmax', - 'tau_acclim', 'kc_jmax', 'whc') + if (using_phydro){ + calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', + 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', + 'phydro_K_plant', 'phydro_p50_plant', 'phydro_b_plant', + 'phydro_alpha', 'phydro_gamma', + 'bsoil', 'whc') + } else { + calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', + 'soilm_thetastar', 'soilm_betao', + 'beta_unitcostratio', 'rd_to_vcmax', + 'tau_acclim', 'kc_jmax', 'whc') + } + if(!is.null(par_fixed)){ params_modl <- list() diff --git a/vignettes/rsofun_phydro_calibration.Rmd b/vignettes/rsofun_phydro_calibration.Rmd index ce309974..86fc7b27 100644 --- a/vignettes/rsofun_phydro_calibration.Rmd +++ b/vignettes/rsofun_phydro_calibration.Rmd @@ -25,6 +25,11 @@ site <- "GF-Guy" load(paste0("../data/",site,"_p_hydro_drivers.rda")) load(paste0("../data/",site,"_p_hydro_validation.rda")) +p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% + rename(latenth=le) %>% + mutate(gpp = gpp*86400/1e6*12) %>% # convert to [gC m-2 day-1] + mutate(latenth = latenth*86400) # convert [W m-2] to [J m-2 day-1] + p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 @@ -40,14 +45,13 @@ plot_pmodel = function(params_modl){ par = params_modl ) - output_p$data[[1]] %>% select(date, gpp, latenth) %>% - rename(le = latenth) %>% + p1 = output_p$data[[1]] %>% select(date, gpp, latenth) %>% melt("date") %>% mutate(group="model") %>% rbind(p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 + select(date, gpp, latenth) %>% + # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + # mutate(latenth = latenth*86400) %>% # convert W m-2 to J m-2 day-1 melt("date") %>% mutate(group="obs")) %>% ggplot(aes(y=value, x=as.Date(date))) + @@ -55,6 +59,23 @@ plot_pmodel = function(params_modl){ theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~variable, scales = "free", nrow = 2) + + p2 = output_p$data[[1]] %>% select(date, gpp, latenth) %>% + melt("date", value.name = "pred") %>% + left_join(p_hydro_validation$data[[1]] %>% + select(date, gpp, latenth) %>% + # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + # mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 + melt("date", value.name = "obs")) %>% + ggplot(aes(y=obs, x=pred)) + + geom_point(alpha=0.7) + + geom_abline(slope=1, intercept=0, col="red")+ + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 1) + + print(p1) + print(p2) } ``` @@ -123,7 +144,7 @@ pars_calib_rmse <- calib_sofun( targets = "gpp" # define target variable GPP ) -print(pars_calib_rmse) +print(pars_calib_rmse, max=20) params_modl_opt = params_modl params_modl_opt$kphio = pars_calib_rmse$par @@ -185,3 +206,118 @@ params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] plot_pmodel(params_modl_opt) ``` + +### P-hydro calibration (GenSA) + +```{r} +p_hydro_drivers$params_siml[[1]]$use_phydro = T +p_hydro_drivers$params_siml[[1]]$use_pml = F + +# Define calibration settings and parameter ranges from previous work +settings_rmse <- list( + method = 'GenSA', # minimizes the RMSE + metric = cost_rmse_pmodel, # our cost function + control = list( # control parameters for optimizer GenSA + maxit = 100), + par = list( + kphio = list(lower=0.04, upper=0.09, init=0.05), + phydro_K_plant = list(lower=0.05e-16, upper=5e-16, init=0.3e-16) + ), + parallel = T, + ncores = 8 +) + +# Calibrate the model and optimize the free parameters using +# demo datasets +pars_calib_rmse <- calib_sofun( + # calib_sofun arguments: + drivers = p_hydro_drivers, + obs = p_hydro_validation, + settings = settings_rmse, + # extra arguments passed to the cost function: + par_fixed = list( # fix all other parameters + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + # phydro_K_plant = 0.3e-16, + phydro_p50_plant = -1, + phydro_b_plant = 1, + phydro_alpha = 0.1, + phydro_gamma = 1, + bsoil = 3, + whc = 90 + ), + targets = c("gpp", "latenth"), # define target variable GPP + target_weights = c(1, 1e-6) +) + +print(pars_calib_rmse, max=20) + +params_modl_opt = params_modl +params_modl_opt$kphio = pars_calib_rmse$par[["kphio"]] +params_modl_opt$phydro_K_plant = pars_calib_rmse$par[["phydro_K_plant"]] +plot_pmodel(params_modl_opt) + +``` + +### P-hydro calibration (BayesianTools) + +```{r} +p_hydro_drivers$params_siml[[1]]$use_phydro = T +p_hydro_drivers$params_siml[[1]]$use_pml = F + +# Define calibration settings and parameter ranges from previous work +settings_bayes <- list( + method = "BayesianTools", + par = list( + kphio = list(lower=0.04, upper=0.09, init=0.05), + phydro_K_plant = list(lower=0.05e-16, upper=5e-16, init=0.3e-16), + err_gpp = list(lower = 0.01, upper = 4, init = 2), + err_le = list(lower = 0.01e7, upper = 4e7, init = 2e7) + ), + metric = rsofun::cost_likelihood_pmodel, + control = list( + sampler = "DEzs", + settings = list( + nrChains = 1, + burnin = 1000, + iterations = 1300 # kept artificially low + ) + ) +) + +# Calibrate the model and optimize the free parameters using +# demo datasets +pars_calib_bayes <- calib_sofun( + # calib_sofun arguments: + drivers = p_hydro_drivers, + obs = p_hydro_validation, + settings = settings_bayes, + # extra arguments passed to the cost function: + par_fixed = list( # fix all other parameters + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + # phydro_K_plant = 0.3e-16, + phydro_p50_plant = -1, + phydro_b_plant = 1, + phydro_alpha = 0.1, + phydro_gamma = 1, + bsoil = 3, + whc = 90 + ), + targets = c("gpp", "latenth") # define target variable GPP +) + +print(pars_calib_bayes) + +params_modl_opt = params_modl +params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] +params_modl_opt$phydro_K_plant = pars_calib_bayes$par[["phydro_K_plant"]] +plot_pmodel(params_modl_opt) + +``` From d04e4697906d2169f2807542be7549e9f918b6de Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sat, 11 Nov 2023 17:11:13 +0100 Subject: [PATCH 016/112] add full calibration in vignette --- vignettes/rsofun_phydro_calibration.Rmd | 73 +++++++++++++++++++++++++ 1 file changed, 73 insertions(+) diff --git a/vignettes/rsofun_phydro_calibration.Rmd b/vignettes/rsofun_phydro_calibration.Rmd index 86fc7b27..d22c0cc8 100644 --- a/vignettes/rsofun_phydro_calibration.Rmd +++ b/vignettes/rsofun_phydro_calibration.Rmd @@ -321,3 +321,76 @@ params_modl_opt$phydro_K_plant = pars_calib_bayes$par[["phydro_K_plant"]] plot_pmodel(params_modl_opt) ``` + + + +### P-hydro full calibration (BayesianTools) + +```{r} +p_hydro_drivers$params_siml[[1]]$use_phydro = T +p_hydro_drivers$params_siml[[1]]$use_pml = F + +# Define calibration settings and parameter ranges from previous work +settings_bayes <- list( + method = "BayesianTools", + par = list( + kphio = list(lower=0.04, upper=0.09, init=0.05), + phydro_K_plant = list(lower=0.05e-16, upper=5e-16, init=0.3e-16), + phydro_p50_plant = list(lower=-4, upper=-0.1, init=-1), + phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), + phydro_gamma = list(lower=0.05, upper=2, init=1), + bsoil = list(lower=1, upper=5, init=3), + whc = list(lower=10, upper=1000, init=90), + err_gpp = list(lower = 0.01, upper = 4, init = 2), + err_le = list(lower = 0.01e7, upper = 4e7, init = 2e7) + ), + metric = rsofun::cost_likelihood_pmodel, + control = list( + sampler = "DEzs", + settings = list( + nrChains = 1, + burnin = 1000, + iterations = 1300 # kept artificially low + ) + ) +) + +# Calibrate the model and optimize the free parameters using +# demo datasets +pars_calib_bayes <- calib_sofun( + # calib_sofun arguments: + drivers = p_hydro_drivers, + obs = p_hydro_validation, + settings = settings_bayes, + # extra arguments passed to the cost function: + par_fixed = list( # fix all other parameters + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + # phydro_K_plant = 0.3e-16, + # phydro_P50_plant = -1, + phydro_b_plant = 1 + # phydro_alpha = 0.1, + # phydro_gamma = 1, + # bsoil = 3, + # whc = 90 + ), + targets = c("gpp", "latenth") # define target variable GPP +) + +print(pars_calib_bayes) + +params_modl_opt = params_modl +params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] +params_modl_opt$phydro_K_plant = pars_calib_bayes$par[["phydro_K_plant"]] +params_modl_opt$phydro_p50_plant = pars_calib_bayes$par[["phydro_p50_plant"]] +params_modl_opt$phydro_alpha = pars_calib_bayes$par[["phydro_alpha"]] +params_modl_opt$phydro_gamma = pars_calib_bayes$par[["phydro_gamma"]] +params_modl_opt$bsoil = pars_calib_bayes$par[["bsoil"]] +params_modl_opt$whc = pars_calib_bayes$par[["whc"]] + +plot_pmodel(params_modl_opt) + +``` From b7f5781dc424cc6ef2b8cd1110baceb497dc3dda Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sun, 12 Nov 2023 16:08:23 +0100 Subject: [PATCH 017/112] fix NaN in e; <--- All sites calibrated here --- src/photosynth_phydro.mod.f90 | 19 +- vignettes/rsofun_phydro.Rmd | 91 +++- vignettes/rsofun_phydro_calibration_full.Rmd | 280 ++++++++++++ vignettes/rsofun_phydro_data_generation.Rmd | 455 +++++++++++++++++++ 4 files changed, 817 insertions(+), 28 deletions(-) create mode 100644 vignettes/rsofun_phydro_calibration_full.Rmd create mode 100644 vignettes/rsofun_phydro_data_generation.Rmd diff --git a/src/photosynth_phydro.mod.f90 b/src/photosynth_phydro.mod.f90 index 771d6e5f..99152257 100644 --- a/src/photosynth_phydro.mod.f90 +++ b/src/photosynth_phydro.mod.f90 @@ -1420,13 +1420,26 @@ function phydro_instantaneous_analytical(vcmax25, jmax25, tc, tg, ppfd, netrad, bound = calc_dpsi_bound_inst(psi_soil, par_plant, par_env, par_photosynth, par_cost) dpsi_opt = zero(0.0d0, 0.99d0 * bound, profit_fun_inst, 1.0d-6) - - e = calc_sapflux(dpsi_opt, psi_soil, par_plant, par_env) + if (dpsi_opt .ne. dpsi_opt) print *, "Dspi_opt is NaN", dpsi_opt + if (dpsi_opt-1 .eq. dpsi_opt) print *, "Dspi_opt is Inf", dpsi_opt + + ! FIXME Jaideep: This is a super hacky way to avoid NaNs in the calculation of e, + ! which seem to happen when dpsi_opt is extremely small, ~1e-6 + if (dpsi_opt .lt. 1.0d-5) then + e = 0 + else + e = calc_sapflux(dpsi_opt, psi_soil, par_plant, par_env) + end if + if (e .ne. e) then + print *, "E is NaN", tc, psi_soil, dpsi_opt, e + e = 0 + end if + gs = calc_gs_from_Q(e, psi_soil, par_plant, par_env) Aa = calc_assimilation_limiting(vcmax, jmax, gs, par_photosynth) Ac = calc_assim_rubisco_limited(gs, vcmax, par_photosynth) Aj = calc_assim_light_limited(gs, jmax, par_photosynth) - + res%a = Aa%a res%e = e res%ci = Aa%ci diff --git a/vignettes/rsofun_phydro.Rmd b/vignettes/rsofun_phydro.Rmd index d8409a2a..65492f56 100644 --- a/vignettes/rsofun_phydro.Rmd +++ b/vignettes/rsofun_phydro.Rmd @@ -16,9 +16,16 @@ library(reshape2) ## Generate data if not already available +```{r} + + +``` + ```{r} site <- "GF-Guy" generate_data = F + +out_path = "~/Downloads/fluxdatakit_oct3/Phydro_drivers/" ``` ```{r} @@ -26,18 +33,25 @@ if (generate_data){ library(FluxDataKit) -# read in sites to process -ystart = 2004 -yend = 2020 +lsm_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_LSM/" +csv_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_FLUXNET/" + +files_csv = list.files(csv_path) +files_lsm = list.files(lsm_path) + +# Get filename for HH data for matching site +file_csv = files_csv[intersect(grep(site, files_csv), + grep("HH", files_csv))] # get metadata # -------------------------------------------------------- +message("- reading Metadata for site") meta <- suppressWarnings( try( fdk_convert_lsm( site = site, fluxnet_format = TRUE, - path = "~/Downloads/flux_data_kit_beta/fluxes/", + path = lsm_path, meta_data = T ) ) @@ -61,7 +75,8 @@ meta <- suppressWarnings( # return(NULL) # } -hhdf <- readr::read_csv(paste0("~/Downloads/flux_data_kit_beta/fluxnet_csv/FLX_",site,"_PLUMBER_FULLSET_HH_",ystart,"_",yend,"_2-3.csv")) +message("- reading FLUXNET format data") +hhdf <- readr::read_csv(paste0(csv_path,"/",file_csv)) # Add date and time columns to hhdf for easier further processing. # --------------------------------------------------------- @@ -90,12 +105,26 @@ ddf_24hr_mean %>% theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~variable, scales = "free") +``` +```{r} +valid_years = read.csv(text = " + sitename , start_year , end_year \n + GF-Guy , 2004 , 2015 \n + FR-Pue , 2004 , 2020 \n + FI-Hyy , 2004 , 2020 \n + CH-Dav , 2004 , 2020 \n + ", + header=T, strip.white=T) + +# Get valid data years +ystart = valid_years %>% filter(sitename==site) %>% pull(start_year) +yend = valid_years %>% filter(sitename==site) %>% pull(end_year) # Aggregate around daily maximum ppfd for acclimating model # --------------------------------------------------------- -test.3day = hhdf %>% filter(date >= as_date("2011-06-01") & - date <= as_date("2011-06-03") ) +test.3day = hhdf %>% filter(date >= as_date(paste0(floor((ystart+yend)/2),"-06-01")) & + date <= as_date(paste0(floor((ystart+yend)/2),"-06-03")) ) aggregate_daily_3hr_maxima = function(df){ # Get the time at which SW_IN is maximum @@ -116,9 +145,6 @@ aggregate_daily_3hr_maxima = function(df){ # Test aggregation # ---------------- -test.3day = hhdf %>% filter(date >= as.Date("2011-06-01") & - date <= as.Date("2011-06-03") ) - test.3day.3hr = test.3day %>% group_by(date) %>% do(aggregate_daily_3hr_maxima(.)) %>% ungroup() test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% @@ -135,20 +161,22 @@ test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_ theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~variable, scales = "free") +``` - - +```{r} # Apply 3hr maxima aggregation to all data # ---------------------------------------- +message("- downsampling FLUXNET format - daily 3-hr means around max ppfd") ddf_3hr_maxima <- hhdf |> group_by(date) |> do(aggregate_daily_3hr_maxima(.)) |> ungroup() +``` - +```{r} aggregate_daily_daylength = function(df){ # Get the time at which SW_IN > 0 - pos_ppfd <- df %>% filter(SW_IN_F_MDS > 0) + pos_ppfd <- df %>% filter(SW_IN_F_MDS > 10) tmax <- max(pos_ppfd$time) tmin <- min(pos_ppfd$time) @@ -184,10 +212,12 @@ test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_ theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~variable, scales = "free") +``` - +```{r} # Apply daytime mean aggregation to all data # ------------------------------------------ +message("- downsampling FLUXNET format - daytime means") ddf_daytime_mean <- hhdf |> group_by(date) |> do(aggregate_daily_daylength(.)) |> @@ -198,8 +228,9 @@ ddf_daytime_mean %>% ggplot(aes(y=daylength, x=date)) + geom_line()+ theme_classic() +``` - +```{r} # Calculate daily tmax and tmin from hh data # ------------------------------------------ tmaxmin <- @@ -209,7 +240,9 @@ tmaxmin <- tmax = max(TA_F_MDS), tmin = min(TA_F_MDS) ) +``` +```{r} # Creating driver object ------------------------------------------------------ message("- compiling drivers") load("../data/p_model_drivers.rda") @@ -226,8 +259,8 @@ p_hydro_drivers$site_info[[1]] = ) kfFEC = 2.04 -start_year = 2000 -end_year = 2020 +start_year = ystart +end_year = yend # for demo, use just a subset of years p_hydro_drivers$forcing <- @@ -327,7 +360,7 @@ p_hydro_drivers$forcing_halfhourly <- # write all drivers to file # apply compression to minimize space -filn <- paste0("../data/",site,"_p_hydro_drivers.rda") +filn <- paste0(out_path, "/",site,"_p_hydro_drivers.rda") message(paste0("- writing to file: ", filn)) save(p_hydro_drivers, file = filn @@ -349,8 +382,10 @@ p_hydro_validation$data <- date = date, time = time, gpp = GPP_DT_VUT_REF, - le = LE_F_MDS + latenth = LE_F_MDS ) |> + mutate(gpp = gpp*86400/1e6*12) |> # convert to [gC m-2 day-1] + mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] list() p_hydro_validation$data_hh <- @@ -362,8 +397,10 @@ p_hydro_validation$data_hh <- date = date, time = time, gpp = GPP_DT_VUT_REF, - le = LE_F_MDS + latenth = LE_F_MDS ) |> + mutate(gpp = gpp*86400/1e6*12) |> # convert to [gC m-2 day-1] + mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] list() p_hydro_validation$data_3hr_mean <- @@ -375,8 +412,10 @@ p_hydro_validation$data_3hr_mean <- date = date, time = time, gpp = GPP_DT_VUT_REF, - le = LE_F_MDS + latenth = LE_F_MDS ) |> + mutate(gpp = gpp*86400/1e6*12) |> # convert to [gC m-2 day-1] + mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] list() p_hydro_validation$data_daytime_mean <- @@ -388,12 +427,14 @@ p_hydro_validation$data_daytime_mean <- date = date, time = time, gpp = GPP_DT_VUT_REF, - le = LE_F_MDS + latenth = LE_F_MDS ) |> + mutate(gpp = gpp*86400/1e6*12) |> # convert to [gC m-2 day-1] + mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] list() -filn <- paste0("../data/",site,"_p_hydro_validation.rda") +filn <- paste0(out_path,"/",site,"_p_hydro_validation.rda") message(paste0("- writing to file: ", filn)) save(p_hydro_validation, file = filn @@ -515,7 +556,7 @@ How to test interactions. ```{r} params_modl <- list( - kphio = 0.089 # 0.11, #0.04998, # setup ORG in Stocker et al. 2020 GMD + kphio = 0.089, # 0.11, #0.04998, # setup ORG in Stocker et al. 2020 GMD kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress diff --git a/vignettes/rsofun_phydro_calibration_full.Rmd b/vignettes/rsofun_phydro_calibration_full.Rmd new file mode 100644 index 00000000..2627669c --- /dev/null +++ b/vignettes/rsofun_phydro_calibration_full.Rmd @@ -0,0 +1,280 @@ +--- +title: "Calibration of rsofun with Phydro" +author: "Jaideep Joshi" +date: "2023-09-07" +output: html_document +params: + output_dir: "." +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +```{r} +library(tidyverse) +library(reshape2) +library(rsofun) +``` + +## Generate data if not already available + +```{r} +site <- "CH-Dav" +``` + +```{r} +data_dir = "~/Downloads/fluxdatakit_oct3/Phydro_drivers/" +load(paste0(data_dir,"/",site,"_p_hydro_drivers.rda")) +load(paste0(data_dir,"/",site,"_p_hydro_validation.rda")) + +# p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% +# rename(latenth=le) %>% +# mutate(gpp = gpp*86400/1e6*12) %>% # convert to [gC m-2 day-1] +# mutate(latenth = latenth*86400) # convert [W m-2] to [J m-2 day-1] + +p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 +p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 + +p_hydro_drivers$params_siml[[1]]$use_phydro = T +p_hydro_drivers$params_siml[[1]]$use_pml = F + +``` + +Function to quickly run model and plot outputs + +```{r} +get_density <- function(x, y, ...) { + dens <- MASS::kde2d(x, y, ...) + ix <- findInterval(x, dens$x) + iy <- findInterval(y, dens$y) + ii <- cbind(ix, iy) + return(dens$z[ii]) +} + +plot_pmodel = function(params_m, out_filename){ + output_p <- rsofun::runread_pmodel_f( + p_hydro_drivers, + par = params_m + ) + save(output_p, file = paste0(data_dir, "/", site, "_", out_filename)) + + p1 = output_p$data[[1]] %>% select(date, gpp, latenth) %>% + melt("date") %>% + mutate(group="model") %>% + rbind(p_hydro_validation$data[[1]] %>% + select(date, gpp, latenth) %>% + # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + # mutate(latenth = latenth*86400) %>% # convert W m-2 to J m-2 day-1 + melt("date") %>% + mutate(group="obs")) %>% + ggplot(aes(y=value, x=as.Date(date))) + + geom_line(aes(group=group, col=group), alpha=0.7) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 2) + + p2 = output_p$data[[1]] %>% select(date, gpp, latenth) %>% + drop_na %>% + melt("date", value.name = "pred") %>% + left_join(p_hydro_validation$data[[1]] %>% + select(date, gpp, latenth) %>% + drop_na %>% + # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + # mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 + melt("date", value.name = "obs")) %>% + group_by(variable) %>% + reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% + ggplot(aes(y=obs, x=pred, colour=density)) + + scale_color_viridis_c() + + geom_point(alpha=0.7) + + geom_abline(slope=1, intercept=0, col="red")+ + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 1)+ + labs(colour="Density") + + print(p1) + print(p2) +} + +``` + +```{r} +params_modl <- list( + kphio = 0.089, # 0.11, #0.04998, + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + # soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + # soilm_betao = 0.0, + # beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + phydro_K_plant = 0.3e-16, + phydro_p50_plant = -1, + phydro_b_plant = 1, + phydro_alpha = 0.1, + phydro_gamma = 1, + bsoil = 3, + whc = 200 +) + +output_p <- rsofun::runread_pmodel_f( + p_hydro_drivers, + par = params_modl + ) +``` + +## P-hydro full calibration (BayesianTools) + +```{r} +p_hydro_drivers$params_siml[[1]]$use_phydro = T +p_hydro_drivers$params_siml[[1]]$use_pml = F + +# Define calibration settings and parameter ranges from previous work +settings_bayes <- list( + method = "BayesianTools", + par = list( + kphio = list(lower=0.04, upper=0.09, init=0.05), + phydro_K_plant = list(lower=0.05e-16, upper=5e-16, init=0.3e-16), + phydro_p50_plant = list(lower=-4, upper=-0.1, init=-1), + phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), + phydro_gamma = list(lower=0.05, upper=2, init=1), + bsoil = list(lower=1, upper=5, init=3), + whc = list(lower=10, upper=1000, init=500), + err_gpp = list(lower = 0.01, upper = 4, init = 2), + err_le = list(lower = 0.01e7, upper = 4e7, init = 2e7) + ), + metric = rsofun::cost_likelihood_pmodel, + control = list( + sampler = "DEzs", + settings = list( + nrChains = 1, + burnin = 1000, + iterations = 1300 # kept artificially low + ) + ) +) + +# Calibrate the model and optimize the free parameters using +# demo datasets +pars_calib_bayes <- calib_sofun( + # calib_sofun arguments: + drivers = p_hydro_drivers, + obs = p_hydro_validation, + settings = settings_bayes, + # extra arguments passed to the cost function: + par_fixed = list( # fix all other parameters + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + # phydro_K_plant = 0.3e-16, + # phydro_P50_plant = -1, + phydro_b_plant = 1 + # phydro_alpha = 0.1, + # phydro_gamma = 1, + # bsoil = 3, + # whc = 90 + ), + targets = c("gpp", "latenth") # define target variable GPP +) + +print(pars_calib_bayes) + +params_modl_opt = params_modl + +params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] +params_modl_opt$phydro_K_plant = pars_calib_bayes$par[["phydro_K_plant"]] +params_modl_opt$phydro_p50_plant = pars_calib_bayes$par[["phydro_p50_plant"]] +params_modl_opt$phydro_alpha = pars_calib_bayes$par[["phydro_alpha"]] +params_modl_opt$phydro_gamma = pars_calib_bayes$par[["phydro_gamma"]] +params_modl_opt$bsoil = pars_calib_bayes$par[["bsoil"]] +params_modl_opt$whc = pars_calib_bayes$par[["whc"]] + +plot_pmodel(params_modl_opt, "p_hydro_output.rda") + +``` + + +## P-model full calibration (BayesianTools) + +```{r} +params_modl <- list( + kphio = 0.089, # 0.11, #0.04998, + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + soilm_betao = 0.0, + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + # phydro_K_plant = 0.3e-16, + # phydro_p50_plant = -1, + # phydro_b_plant = 1, + # phydro_alpha = 0.1, + # phydro_gamma = 1, + # bsoil = 3, + whc = 90 +) +p_hydro_drivers$params_siml[[1]]$use_phydro = F +p_hydro_drivers$params_siml[[1]]$use_pml = F + +# Define calibration settings and parameter ranges from previous work +settings_bayes <- list( + method = "BayesianTools", + par = list( + kphio = list(lower=0.04, upper=0.09, init=0.05), + whc = list(lower=10, upper=1000, init=500), + soilm_thetastar = list(lower=10, upper=1000, init=0.6*240), + # beta_unitcostratio = list(lower=10, upper=1000, init=146), + err_gpp = list(lower = 0.01, upper = 4, init = 2), + err_le = list(lower = 0.01e7, upper = 4e7, init = 2e7) + ), + metric = rsofun::cost_likelihood_pmodel, + control = list( + sampler = "DEzs", + settings = list( + nrChains = 1, + burnin = 1000, + iterations = 1300 # kept artificially low + ) + ) +) + +# Calibrate the model and optimize the free parameters using +# demo datasets +pars_calib_bayes <- calib_sofun( + # calib_sofun arguments: + drivers = p_hydro_drivers, + obs = p_hydro_validation, + settings = settings_bayes, + # extra arguments passed to the cost function: + par_fixed = list( # fix all other parameters + kphio_par_a = 0.0, # set to zero to disable temperature-dependence + # of kphio, setup ORG + kphio_par_b = 1.0, + soilm_betao = 0.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + beta_unitcostratio = 146, + tau_acclim = 30.0, + kc_jmax = 0.41 + ), + targets = c("gpp", "latenth") # define target variable GPP +) + +print(pars_calib_bayes) + +params_modl_opt = params_modl + +params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] +params_modl_opt$whc = pars_calib_bayes$par[["whc"]] +params_modl_opt$soilm_thetastar = pars_calib_bayes$par[["soilm_thetastar"]] +# params_modl_opt$beta_unitcostratio = pars_calib_bayes$par[["beta_unitcostratio"]] + +plot_pmodel(params_modl_opt, "p_model_output.rda") + +``` diff --git a/vignettes/rsofun_phydro_data_generation.Rmd b/vignettes/rsofun_phydro_data_generation.Rmd new file mode 100644 index 00000000..607e4dbd --- /dev/null +++ b/vignettes/rsofun_phydro_data_generation.Rmd @@ -0,0 +1,455 @@ +--- +title: "Generate input data for rsofun Phydro" +author: "Jaideep Joshi" +date: "2023-11-12" +output: html_document +params: + output_dir: "." +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +```{r} +library(tidyverse) +library(reshape2) +library(FluxDataKit) +``` + + +```{r} +site <- "CH-Dav" +``` + + +```{r} +lsm_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_LSM/" +csv_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_FLUXNET/" +out_path = "~/Downloads/fluxdatakit_oct3/Phydro_drivers/" + +files_csv = list.files(csv_path) +files_lsm = list.files(lsm_path) + +# Get filename for HH data for matching site +file_csv = files_csv[intersect(grep(site, files_csv), + grep("HH", files_csv))] + +# get metadata +# -------------------------------------------------------- +message("- reading Metadata for site") +meta <- suppressWarnings( + try( + fdk_convert_lsm( + site = site, + fluxnet_format = TRUE, + path = lsm_path, + meta_data = T + ) + ) + ) + + +# get half-hourly data -------------------------------------------------------- +# message("- convert to FLUXNET standard CSV file") +# hhdf <- suppressWarnings( +# try( +# fdk_convert_lsm( +# site = site, +# fluxnet_format = TRUE, +# path = "~/Downloads/flux_data_kit_beta/fluxes/" +# ) +# ) +# ) +# +# if(inherits(hhdf, "try-error")){ +# message("!!! conversion to FLUXNET failed !!!") +# return(NULL) +# } + +message("- reading FLUXNET format data") +hhdf <- readr::read_csv(paste0(csv_path,"/",file_csv)) + +# Add date and time columns to hhdf for easier further processing. +# --------------------------------------------------------- +hhdf = + hhdf |> + mutate(time = lubridate::as_datetime(as.character(TIMESTAMP_START), tz = "GMT", format="%Y%m%d%H%M")) |> + mutate(date = lubridate::as_date(time)) + +if (!("SW_OUT" %in% colnames(hhdf))) { + hhdf$SW_OUT = NA +} + + +# Aggregate to daily 24-hr means ---------------------------------------------------------- +message("- downsampling FLUXNET format - 24 hr means") +ddf_24hr_mean <- + try( + hhdf |> + group_by(date) |> + select(-TIMESTAMP_START, -TIMESTAMP_END) |> + summarize_all(.funs = mean) + ) + +# Check aggregation +ddf_24hr_mean %>% + mutate(albedo = SW_OUT/SW_IN_F_MDS) %>% + select(SW_IN_F_MDS, NETRAD, LW_IN_F_MDS, LAI, FPAR, GPP_DT_VUT_REF, LE_F_MDS, TA_F_MDS, date) %>% + melt("date") %>% + ggplot(aes(y=value, x=date)) + + geom_line(col="aquamarine4") + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") + +ddf_24hr_mean %>% filter(!is.na(NETRAD)) %>% pull(date) %>% summary() +``` + +```{r} +valid_years = read.csv(text = " + sitename , start_year , end_year \n + CH-Dav , 2000 , 2010 \n + DE-Hai , 2000 , 2020 \n + DE-Tha , 2000 , 2020 \n + FR-LBr , 2000 , 2008 \n + FR-Pue , 2000 , 2015 \n + GF-Guy , 2004 , 2015 \n + NL-Loo , 2000 , 2014 \n + US-Me2 , 2002 , 2020 \n + US-NR1 , 2000 , 2016 \n + US-Ton , 2001 , 2014 \n + ", + header=T, strip.white=T) + +# Get valid data years +ystart = valid_years %>% filter(sitename==site) %>% pull(start_year) +yend = valid_years %>% filter(sitename==site) %>% pull(end_year) + +# Aggregate around daily maximum ppfd for acclimating model +# --------------------------------------------------------- +test.3day = hhdf %>% filter(date >= as_date(paste0(floor((ystart+yend)/2),"-06-01")) & + date <= as_date(paste0(floor((ystart+yend)/2),"-06-03")) ) + +aggregate_daily_3hr_maxima = function(df){ + # Get the time at which SW_IN is maximum + maxppfd <- df %>% filter(SW_IN_F_MDS == max(SW_IN_F_MDS)) + max_t <- maxppfd$time[1] + + # Select times that lie in 3-hr interval around max_t + df_aroundmax <- df %>% filter(time < (max_t + 1.5*3600) & + time > (max_t - 1.5*3600) ) + + # take mean of selected entries + df_mean <- df_aroundmax |> + select(-TIMESTAMP_START, -TIMESTAMP_END) |> + summarize_all(.funs = mean) + + df_mean +} + +# Test aggregation +# ---------------- +test.3day.3hr = test.3day %>% group_by(date) %>% do(aggregate_daily_3hr_maxima(.)) %>% ungroup() + +test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% + melt("time") %>% + mutate(type="hourly") %>% + rbind(test.3day.3hr %>% + select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% + melt("time") %>% + mutate(type="daily") + ) %>% + ggplot(aes(y=value, x=as.POSIXct(time))) + + geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + + geom_point(data = . %>% filter(type == "daily")) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") +``` + +```{r} +# Apply 3hr maxima aggregation to all data +# ---------------------------------------- +message("- downsampling FLUXNET format - daily 3-hr means around max ppfd") +ddf_3hr_maxima <- hhdf |> + group_by(date) |> + do(aggregate_daily_3hr_maxima(.)) |> + ungroup() +``` + +```{r} +aggregate_daily_daylength = function(df){ + # Get the time at which SW_IN > 0 + pos_ppfd <- df %>% filter(SW_IN_F_MDS > 10) + # if SW_IN is unavailable in that year calc daylength based on NETRAD + if (nrow(pos_ppfd) < 2){ + pos_ppfd <- df %>% filter(NETRAD > 25) + } + + tmax <- max(pos_ppfd$time) + tmin <- min(pos_ppfd$time) + + # Select times that lie in 3-hr interval around max_t + df_aroundmax <- df %>% filter(time <= tmax & + time >= tmin ) + + # take mean of selected entries + df_mean <- df_aroundmax |> + select(-TIMESTAMP_START, -TIMESTAMP_END) |> + summarize_all(.funs = mean) |> + mutate(daylength = difftime(tmax, tmin, units="hours") |> as.numeric()) + + df_mean +} + +# Test aggregation +# ---------------- +test.3day.daylen = test.3day %>% group_by(date) %>% do(aggregate_daily_daylength(.)) %>% ungroup() + +test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI) %>% + mutate(daylength = NA) %>% + melt("time") %>% + mutate(type="hourly") %>% + rbind(test.3day.daylen %>% + select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, daylength) %>% + melt("time") %>% + mutate(type="daily") + ) %>% + ggplot(aes(y=value, x=as.POSIXct(time))) + + geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + + geom_point(data = . %>% filter(type == "daily")) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") +``` + +```{r} +# Apply daytime mean aggregation to all data +# ------------------------------------------ +message("- downsampling FLUXNET format - daytime means") +ddf_daytime_mean <- hhdf |> + group_by(date) |> + do(aggregate_daily_daylength(.)) |> + ungroup() + +# Check daylength seasonality +ddf_daytime_mean %>% + ggplot(aes(y=daylength, x=date)) + + geom_line()+ + theme_classic() +``` + +```{r} +# Calculate daily tmax and tmin from hh data +# ------------------------------------------ +tmaxmin <- + hhdf |> + group_by(date) |> + summarize( + tmax = max(TA_F_MDS), + tmin = min(TA_F_MDS) + ) +``` + +```{r} +# Creating driver object ------------------------------------------------------ +message("- compiling drivers") +load("../data/p_model_drivers.rda") + +p_hydro_drivers <- p_model_drivers +p_hydro_drivers$sitename[[1]] = site +p_hydro_drivers$site_info[[1]] = + tibble( + lon=meta[[1]]$longitude, + lat=meta[[1]]$latitude, + elv = meta[[1]]$elevation, + canopy_height=meta[[1]]$canopy_height, + reference_height = meta[[1]]$reference_height + ) +kfFEC = 2.04 + +start_year = ystart +end_year = yend + +# for demo, use just a subset of years +p_hydro_drivers$forcing <- + ddf_24hr_mean |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + left_join(tmaxmin) |> + group_by(date) |> + summarize( + date = date, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = P_F * 48 /(60 * 60 * 24), # P_F [mm timestep-1] * 48 [timesteps day-1] / 86400 [secs day-1 ] + tmin = tmin, # TMIN_F_MDS, + tmax = tmax, # TMAX_F_MDS, + fapar = FPAR, + co2 = CO2_F_MDS, + ccov = 0 + ) |> + list() + +p_hydro_drivers$forcing_acclim <- + ddf_3hr_maxima |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + left_join(tmaxmin) |> + group_by(date) |> + summarize( + date = date, + time = time, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = NA, # P_F * 48 / (60 * 60 * 24), + tmin = tmin, # TMIN_F_MDS, + tmax = tmax, # TMAX_F_MDS, + fapar = FPAR, + co2 = CO2_F_MDS, + ccov = 0 + ) |> + list() + +p_hydro_drivers$forcing_daytime_mean <- + ddf_daytime_mean |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + left_join(tmaxmin) |> + group_by(date) |> + summarize( + date = date, + time = time, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = NA, # P_F * 48 / (60 * 60 * 24), + tmin = tmin, # TMIN_F_MDS, + tmax = tmax, # TMAX_F_MDS, + fapar = FPAR, + co2 = CO2_F_MDS, + ccov = 0, + daylength = daylength + ) |> + list() + + +p_hydro_drivers$forcing_halfhourly <- + hhdf |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(time) |> + summarize( + date = date, + time = time, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = P_F * 48 / (60 * 60 * 24), + fapar = FPAR, + co2 = CO2_F_MDS, + ccov = 0 + ) |> + list() + + +# write all drivers to file +# apply compression to minimize space +filn <- paste0(out_path, "/",site,"_p_hydro_drivers.rda") +message(paste0("- writing to file: ", filn)) +save(p_hydro_drivers, + file = filn + ) + + +# JJ Note: The gpp and latenth units here are different from the demo dataset supplied with rsofun. Here the units are matched to the output units from rsofun (see conversion below) +# Write validation data +load("../data/p_model_validation.rda") + +p_hydro_validation <- p_model_validation +p_hydro_validation$sitename[[1]] = site + +p_hydro_validation$data <- + ddf_24hr_mean |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(date) |> + summarise( + date = date, + time = time, + gpp = GPP_DT_VUT_REF, + latenth = LE_F_MDS + ) |> + mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] + mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] + list() + +p_hydro_validation$data_hh <- + hhdf |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(time) |> + summarise( + date = date, + time = time, + gpp = GPP_DT_VUT_REF, + latenth = LE_F_MDS + ) |> + mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] + mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] + list() + +p_hydro_validation$data_3hr_mean <- + ddf_3hr_maxima |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(time) |> + summarise( + date = date, + time = time, + gpp = GPP_DT_VUT_REF, + latenth = LE_F_MDS + ) |> + mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] + mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] + list() + +p_hydro_validation$data_daytime_mean <- + ddf_daytime_mean |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(time) |> + summarise( + date = date, + time = time, + gpp = GPP_DT_VUT_REF, + latenth = LE_F_MDS + ) |> + mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] + mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] + list() + + +filn <- paste0(out_path,"/",site,"_p_hydro_validation.rda") +message(paste0("- writing to file: ", filn)) +save(p_hydro_validation, + file = filn + ) + +``` + From ac03b51dabcca1b562082ea53b9b2ecce4c03c18 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Tue, 14 Nov 2023 13:47:12 +0100 Subject: [PATCH 018/112] fix pmodel calibration (separate vignette) --- R/cost_likelihood_pmodel.R | 1 + vignettes/estimated_params_fluxnet.csv | 10 + vignettes/rsofun_phydro_calibration_full.Rmd | 2 +- vignettes/rsofun_pmodel_calibration_full.Rmd | 269 +++++++++++++++++++ 4 files changed, 281 insertions(+), 1 deletion(-) create mode 100644 vignettes/estimated_params_fluxnet.csv create mode 100644 vignettes/rsofun_pmodel_calibration_full.Rmd diff --git a/R/cost_likelihood_pmodel.R b/R/cost_likelihood_pmodel.R index 64b0c2ff..1a60128d 100644 --- a/R/cost_likelihood_pmodel.R +++ b/R/cost_likelihood_pmodel.R @@ -111,6 +111,7 @@ cost_likelihood_pmodel <- function( 'tau_acclim', 'kc_jmax', 'whc') } + # FIXME Jaideep: Here it is assumed that the params in par will appear in exactly the same order in settings as in the list above. Better to do this in an order-independent way. if(!is.null(par_fixed)){ params_modl <- list() # complete with calibrated values diff --git a/vignettes/estimated_params_fluxnet.csv b/vignettes/estimated_params_fluxnet.csv new file mode 100644 index 00000000..b865b749 --- /dev/null +++ b/vignettes/estimated_params_fluxnet.csv @@ -0,0 +1,10 @@ +site,kphio,phydro_K_plant,phydro_p50_plant,phydro_alpha,phydro_gamma,bsoil,whc,err_gpp,err_le +CH-Dav,0.04028348,2.197187E-016,-2.697608,0.149728,1.213812,4.275699,533.0647,1.768076,4391478 +DE-Hai,0.0451569,2.306965E-016,-0.1405669,0.1079709,1.248463,1.60833,389.3951,2.270135,1796700 +DE-Tha,0.0604514,1.323744E-016,-0.130135,0.1481494,0.543913,2.200784,569.8994,2.601718,1328134 +FR-LBr,0.04838459,4.096511E-017,-1.355358,0.09595568,1.254523,4.375119,685.3614,1.620645,1686883 +FR-Pue,0.05281481,3.163132E-017,-3.152014,0.14985,1.247866,3.520559,939.9299,1.581259,1816535 +GF-Guy,0.07013438,1.421218E-016,-2.506519,0.1334564,1.889438,3.193891,781.9509,2.257923,1669069 +NL-Loo,0.04021309,1.02998E-016,-3.92368,0.07105071,1.413388,3.70679,130.3826,2.009933,2724909 +US-Me2,0.05083192,2.031839E-016,-0.9074037,0.1423571,1.578149,3.583641,881.5482,3.991486,2527194 +US-Ton,0.08540476,2.483941E-017,-0.1034278,0.09509663,0.5219935,2.314377,237.3775,3.209992,1688764 diff --git a/vignettes/rsofun_phydro_calibration_full.Rmd b/vignettes/rsofun_phydro_calibration_full.Rmd index 2627669c..ec8fdaf0 100644 --- a/vignettes/rsofun_phydro_calibration_full.Rmd +++ b/vignettes/rsofun_phydro_calibration_full.Rmd @@ -20,7 +20,7 @@ library(rsofun) ## Generate data if not already available ```{r} -site <- "CH-Dav" +site <- "GF-Guy" ``` ```{r} diff --git a/vignettes/rsofun_pmodel_calibration_full.Rmd b/vignettes/rsofun_pmodel_calibration_full.Rmd new file mode 100644 index 00000000..78c85a62 --- /dev/null +++ b/vignettes/rsofun_pmodel_calibration_full.Rmd @@ -0,0 +1,269 @@ +--- +title: "Calibration of rsofun with Phydro" +author: "Jaideep Joshi" +date: "2023-09-07" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +```{r} +library(tidyverse) +library(reshape2) +library(rsofun) +``` + +## Generate data if not already available + +```{r} +site <- "FR-Pue" +``` + +```{r} +data_dir = "~/Downloads/fluxdatakit_oct3/Phydro_drivers/" +load(paste0(data_dir,"/",site,"_p_hydro_drivers.rda")) +load(paste0(data_dir,"/",site,"_p_hydro_validation.rda")) + +# p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% +# rename(latenth=le) %>% +# mutate(gpp = gpp*86400/1e6*12) %>% # convert to [gC m-2 day-1] +# mutate(latenth = latenth*86400) # convert [W m-2] to [J m-2 day-1] + +p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 +p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 + +p_hydro_drivers$params_siml[[1]]$use_phydro = F +p_hydro_drivers$params_siml[[1]]$use_pml = F + +``` + +Function to quickly run model and plot outputs + +```{r} +get_density <- function(x, y, ...) { + dens <- MASS::kde2d(x, y, ...) + ix <- findInterval(x, dens$x) + iy <- findInterval(y, dens$y) + ii <- cbind(ix, iy) + return(dens$z[ii]) +} + +plot_pmodel = function(params_m, out_filename=""){ + output_p <- rsofun::runread_pmodel_f( + p_hydro_drivers, + par = params_m + ) + if (out_filename != ""){ + save(output_p, file = paste0(data_dir, "/", site, "_", out_filename)) + } + + p1 = output_p$data[[1]] %>% select(date, gpp, latenth) %>% + melt("date") %>% + mutate(group="model") %>% + rbind(p_hydro_validation$data[[1]] %>% + select(date, gpp, latenth) %>% + # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + # mutate(latenth = latenth*86400) %>% # convert W m-2 to J m-2 day-1 + melt("date") %>% + mutate(group="obs")) %>% + ggplot(aes(y=value, x=as.Date(date))) + + geom_line(aes(group=group, col=group), alpha=0.7) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 2) + + p2 = output_p$data[[1]] %>% select(date, gpp, latenth) %>% + drop_na %>% + melt("date", value.name = "pred") %>% + left_join(p_hydro_validation$data[[1]] %>% + select(date, gpp, latenth) %>% + drop_na %>% + # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + # mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 + melt("date", value.name = "obs")) %>% + group_by(variable) %>% + reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% + ggplot(aes(y=obs, x=pred, colour=density)) + + scale_color_viridis_c() + + geom_point(alpha=0.7) + + geom_abline(slope=1, intercept=0, col="red")+ + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 1)+ + labs(colour="Density") + + print(p1) + print(p2) +} + +``` + + +## P-model partial calibration (BayesianTools) + +```{r} +params_modl <- list( + kphio = 0.04998, + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + soilm_betao = 0.0, + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + # phydro_K_plant = 0.3e-16, + # phydro_p50_plant = -1, + # phydro_b_plant = 1, + # phydro_alpha = 0.1, + # phydro_gamma = 1, + # bsoil = 3, + whc = 300 +) +p_hydro_drivers$params_siml[[1]]$use_phydro = F +p_hydro_drivers$params_siml[[1]]$use_pml = F + +plot_pmodel(params_modl) +``` + +```{r} +# Define calibration settings and parameter ranges from previous work +settings_bayes <- list( + method = "BayesianTools", + par = list( + kphio = list(lower=0.04, upper=0.09, init=0.05), + soilm_thetastar = list(lower=0.1*240, upper=2*240, init=0.6*240), + # soilm_betao = list(lower=0, upper=10, init=0.1), + beta_unitcostratio = list(lower=10, upper=1000, init=146), + whc = list(lower=10, upper=1000, init=350), + err_gpp = list(lower = 0.01, upper = 4, init = 2), + err_le = list(lower = 0.01e7, upper = 4e7, init = 2e7) + ), + metric = rsofun::cost_likelihood_pmodel, + control = list( + sampler = "DEzs", + settings = list( + nrChains = 1, + burnin = 300, + iterations = 1300 # kept artificially low + ) + ) +) + +# Calibrate the model and optimize the free parameters using +# demo datasets +pars_calib_bayes <- calib_sofun( + # calib_sofun arguments: + drivers = p_hydro_drivers, + obs = p_hydro_validation, + settings = settings_bayes, + # extra arguments passed to the cost function: + par_fixed = list( # fix all other parameters + kphio_par_a = 0.0, # set to zero to disable temperature-dependence + # of kphio, setup ORG + kphio_par_b = 1.0, + soilm_betao = 0.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + # beta_unitcostratio = 146, + tau_acclim = 30.0, + kc_jmax = 0.41 + ), + targets = c("gpp", "latenth") # define target variable GPP +) + +print(pars_calib_bayes) + +params_modl_opt = params_modl + +params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] +params_modl_opt$soilm_thetastar = pars_calib_bayes$par[["soilm_thetastar"]] +# params_modl_opt$soilm_betao = pars_calib_bayes$par[["soilm_betao"]] +params_modl_opt$beta_unitcostratio = pars_calib_bayes$par[["beta_unitcostratio"]] +params_modl_opt$whc = pars_calib_bayes$par[["whc"]] + +plot_pmodel(params_modl_opt) + +``` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 230b9a8836f227e33e306cabaf601db26966fa52 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Wed, 15 Nov 2023 14:22:24 +0100 Subject: [PATCH 019/112] param env dependencies plot --- vignettes/plot_params_dependencies.R | 78 +++++++++++++++++++++ vignettes/rsofun_phydro.Rmd | 4 ++ vignettes/rsofun_phydro_data_generation.Rmd | 2 +- 3 files changed, 83 insertions(+), 1 deletion(-) create mode 100644 vignettes/plot_params_dependencies.R diff --git a/vignettes/plot_params_dependencies.R b/vignettes/plot_params_dependencies.R new file mode 100644 index 00000000..1426c806 --- /dev/null +++ b/vignettes/plot_params_dependencies.R @@ -0,0 +1,78 @@ +library(FluxDataKit) +library(tidyverse) +library(reshape2) + +dat = data.frame(site=NULL, meta=NULL) + +sites = c( + "CH-Dav", + "DE-Hai", + "DE-Tha", + "FR-LBr", + "FR-Pue", + "GF-Guy", + "NL-Loo", + "US-Me2", + "US-NR1", + "US-Ton" +) + +site = sites[1] + +lsm_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_LSM/" +csv_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_FLUXNET/" +out_path = "~/Downloads/fluxdatakit_oct3/Phydro_drivers/" + +files_csv = list.files(csv_path) +files_lsm = list.files(lsm_path) + +for (site in sites){ + + # Get filename for HH data for matching site + file_csv = files_csv[intersect(grep(site, files_csv), + grep("HH", files_csv))] + + # get metadata + # -------------------------------------------------------- + message("- reading Metadata for site") + meta <- suppressWarnings( + try( + fdk_convert_lsm( + site = site, + fluxnet_format = TRUE, + path = lsm_path, + meta_data = T + ) + ) + ) + + dat = rbind(dat, data.frame(site=site, meta=meta)) +} + +write.csv(dat, file = paste0(out_path, "/sites_meta.csv")) + + +dat = read.csv(file = paste0(out_path, "/sites_meta.csv"), header=T) +params = read.csv("~/codes/rsofun/vignettes/estimated_params_fluxnet.csv", header=T) + + +dat %>% + select(site, meta.IGBP_veg_short) %>% + left_join(params) %>% + select(-site) %>% + melt("meta.IGBP_veg_short") %>% + ggplot(aes(y=value, x=meta.IGBP_veg_short)) + + geom_point() + + facet_wrap("variable", scales = "free") + +dat %>% + select(site, meta.canopy_height) %>% + left_join(params) %>% + select(-site) %>% + melt("meta.canopy_height") %>% + drop_na %>% + ggplot(aes(y=value, x=meta.canopy_height)) + + geom_point() + + facet_wrap("variable", scales = "free") + + diff --git a/vignettes/rsofun_phydro.Rmd b/vignettes/rsofun_phydro.Rmd index 65492f56..9fcc9d98 100644 --- a/vignettes/rsofun_phydro.Rmd +++ b/vignettes/rsofun_phydro.Rmd @@ -554,6 +554,10 @@ plot model bias \~ SM, VPD, T, does bias increase at extremes? How to test interactions. +2. site scale calibrations - generalizability of params, systematic variation in hydraulic traits with climate? Within-site Variation in hydraulic traits? Relative CV of params - e.g. hyd traits vary but cost params are relatively constant? + +3. uncertainty in WHC and b for hydrualic response? + ```{r} params_modl <- list( kphio = 0.089, # 0.11, #0.04998, # setup ORG in Stocker et al. 2020 GMD diff --git a/vignettes/rsofun_phydro_data_generation.Rmd b/vignettes/rsofun_phydro_data_generation.Rmd index 607e4dbd..977ddbe5 100644 --- a/vignettes/rsofun_phydro_data_generation.Rmd +++ b/vignettes/rsofun_phydro_data_generation.Rmd @@ -113,7 +113,7 @@ valid_years = read.csv(text = " DE-Hai , 2000 , 2020 \n DE-Tha , 2000 , 2020 \n FR-LBr , 2000 , 2008 \n - FR-Pue , 2000 , 2015 \n + FR-Pue , 2000 , 2013 \n GF-Guy , 2004 , 2015 \n NL-Loo , 2000 , 2014 \n US-Me2 , 2002 , 2020 \n From 237824d34a98d30d3c1b55cf1ba8565afd885f79 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Wed, 15 Nov 2023 16:35:52 +0100 Subject: [PATCH 020/112] try calib with alpha/gamma fixed --- vignettes/rsofun_phydro_calibration_full.Rmd | 24 ++++++++++++-------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/vignettes/rsofun_phydro_calibration_full.Rmd b/vignettes/rsofun_phydro_calibration_full.Rmd index ec8fdaf0..dd0efb49 100644 --- a/vignettes/rsofun_phydro_calibration_full.Rmd +++ b/vignettes/rsofun_phydro_calibration_full.Rmd @@ -15,6 +15,7 @@ knitr::opts_chunk$set(echo = TRUE) library(tidyverse) library(reshape2) library(rsofun) +library(BayesianTools) ``` ## Generate data if not already available @@ -139,8 +140,8 @@ settings_bayes <- list( kphio = list(lower=0.04, upper=0.09, init=0.05), phydro_K_plant = list(lower=0.05e-16, upper=5e-16, init=0.3e-16), phydro_p50_plant = list(lower=-4, upper=-0.1, init=-1), - phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), - phydro_gamma = list(lower=0.05, upper=2, init=1), + # phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), + # phydro_gamma = list(lower=0.05, upper=2, init=1), bsoil = list(lower=1, upper=5, init=3), whc = list(lower=10, upper=1000, init=500), err_gpp = list(lower = 0.01, upper = 4, init = 2), @@ -150,9 +151,9 @@ settings_bayes <- list( control = list( sampler = "DEzs", settings = list( - nrChains = 1, - burnin = 1000, - iterations = 1300 # kept artificially low + nrChains = 5, + burnin = 0, + iterations = 3000 # kept artificially low ) ) ) @@ -173,15 +174,18 @@ pars_calib_bayes <- calib_sofun( kc_jmax = 0.41, # phydro_K_plant = 0.3e-16, # phydro_P50_plant = -1, - phydro_b_plant = 1 - # phydro_alpha = 0.1, - # phydro_gamma = 1, + phydro_b_plant = 1, + phydro_alpha = 0.1, + phydro_gamma = 1 # bsoil = 3, # whc = 90 ), targets = c("gpp", "latenth") # define target variable GPP ) +plot(pars_calib_bayes$mod) +correlationPlot(pars_calib_bayes$mod) + print(pars_calib_bayes) params_modl_opt = params_modl @@ -189,8 +193,8 @@ params_modl_opt = params_modl params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] params_modl_opt$phydro_K_plant = pars_calib_bayes$par[["phydro_K_plant"]] params_modl_opt$phydro_p50_plant = pars_calib_bayes$par[["phydro_p50_plant"]] -params_modl_opt$phydro_alpha = pars_calib_bayes$par[["phydro_alpha"]] -params_modl_opt$phydro_gamma = pars_calib_bayes$par[["phydro_gamma"]] +# params_modl_opt$phydro_alpha = pars_calib_bayes$par[["phydro_alpha"]] +# params_modl_opt$phydro_gamma = pars_calib_bayes$par[["phydro_gamma"]] params_modl_opt$bsoil = pars_calib_bayes$par[["bsoil"]] params_modl_opt$whc = pars_calib_bayes$par[["whc"]] From 5bab0980ca663e1b078510f53b1b924efaca19b9 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Fri, 17 Nov 2023 08:17:55 +0100 Subject: [PATCH 021/112] modify vignette to ensure that code works with pulled changes --- vignettes/rsofun_phydro_calibration_full.Rmd | 21 +++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/vignettes/rsofun_phydro_calibration_full.Rmd b/vignettes/rsofun_phydro_calibration_full.Rmd index dd0efb49..f6eac2e7 100644 --- a/vignettes/rsofun_phydro_calibration_full.Rmd +++ b/vignettes/rsofun_phydro_calibration_full.Rmd @@ -53,12 +53,15 @@ get_density <- function(x, y, ...) { return(dens$z[ii]) } -plot_pmodel = function(params_m, out_filename){ +plot_pmodel = function(params_m, out_filename=""){ output_p <- rsofun::runread_pmodel_f( p_hydro_drivers, par = params_m ) - save(output_p, file = paste0(data_dir, "/", site, "_", out_filename)) + output_p$data[[1]] = output_p$data[[1]] %>% rename(latenth = le) + if (out_filename != ""){ + save(output_p, file = paste0(data_dir, "/", site, "_", out_filename)) + } p1 = output_p$data[[1]] %>% select(date, gpp, latenth) %>% melt("date") %>% @@ -103,7 +106,7 @@ plot_pmodel = function(params_m, out_filename){ ```{r} params_modl <- list( - kphio = 0.089, # 0.11, #0.04998, + kphio = 7.013438e-02, # 0.089, # 0.11, #0.04998, kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, # soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress @@ -112,13 +115,13 @@ params_modl <- list( rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, kc_jmax = 0.41, - phydro_K_plant = 0.3e-16, - phydro_p50_plant = -1, + phydro_K_plant = 1.421218e-16 , #0.3e-16, + phydro_p50_plant = -2.506519e+00, #-1, phydro_b_plant = 1, - phydro_alpha = 0.1, - phydro_gamma = 1, - bsoil = 3, - whc = 200 + phydro_alpha = 1.334564e-01, #0.1, + phydro_gamma = 1.889438e+00, #1, + bsoil = 3.193891e+00, #3, + whc = 7.819509e+02 #200 ) output_p <- rsofun::runread_pmodel_f( From ffc909788afb2cd35a986ed3a591ba019bf3bcde Mon Sep 17 00:00:00 2001 From: Jaideep Date: Fri, 17 Nov 2023 10:26:20 +0100 Subject: [PATCH 022/112] get forcing_acclim dataframe into fortran --- R/run_pmodel_f_bysite.R | 66 +++++++++++++++----- R/runread_pmodel_f.R | 16 +++-- src/sofun_r.f90 | 4 +- src/wrappersc.c | 7 ++- vignettes/rsofun_phydro_calibration_full.Rmd | 1 + 5 files changed, 72 insertions(+), 22 deletions(-) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index c18085d8..a3eb9bdf 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -158,6 +158,7 @@ run_pmodel_f_bysite <- function( params_siml, site_info, forcing, + forcing_acclim, params_modl, makecheck = TRUE, verbose = TRUE, @@ -196,23 +197,28 @@ run_pmodel_f_bysite <- function( # re-define units and naming of forcing dataframe # keep the order of columns - it's critical for Fortran (reading by column number) + columns_ordered = c( + "temp", + "rain", + "vpd", + "ppfd", + "netrad", + "fsun", + "snow", + "co2", + "fapar", + "patm", + "tmin", + "tmax" + ) forcing <- forcing %>% dplyr::mutate(fsun = (100-ccov)/100) %>% - dplyr::select( - temp, - rain, - vpd, - ppfd, - netrad, - fsun, - snow, - co2, - fapar, - patm, - tmin, - tmax - ) + dplyr::select(all_of(columns_ordered)) + forcing_acclim <- forcing_acclim %>% + dplyr::mutate(fsun = (100-ccov)/100) %>% + dplyr::select(all_of(columns_ordered)) + # validate input if (makecheck){ @@ -228,6 +234,15 @@ run_pmodel_f_bysite <- function( "tmin", "tmax" ) + # list variable to check for + check_vars_acclim <- c( + "temp", + "vpd", + "co2", + "ppfd", + "fapar", + "patm" + ) # create a loop to loop over a list of variables # to check validity @@ -241,10 +256,23 @@ run_pmodel_f_bysite <- function( return(TRUE) } }) + data_integrity_acclim <- lapply(check_vars_acclim, function(check_var){ + if (any(is.nanull(forcing_acclim[check_var]))){ + warning(sprintf("Error: Missing value %s in acclimation dataset for %s", + check_var, sitename)) + return(FALSE) + } else { + return(TRUE) + } + }) + if (suppressWarnings(!all(data_integrity))){ continue <- FALSE } + if (suppressWarnings(!all(data_integrity_acclim))){ + continue <- FALSE + } # parameters to check check_param <- c( @@ -282,6 +310,12 @@ run_pmodel_f_bysite <- function( correspond to full years.") continue <- FALSE } + if (nrow(forcing_acclim) %% ndayyear != 0){ + # something weird more fundamentally -> don't run the model + warning(" Returning a dummy data frame. Acclimation Forcing data does not + correspond to full years.") + continue <- FALSE + } # Check model parameters if (!params_siml$use_phydro){ @@ -337,6 +371,7 @@ run_pmodel_f_bysite <- function( # convert to matrix forcing <- as.matrix(forcing) + forcing_acclim <- as.matrix(forcing_acclim) # number of rows in matrix (pre-allocation of memory) n <- as.integer(nrow(forcing)) @@ -413,7 +448,8 @@ run_pmodel_f_bysite <- function( reference_height = as.numeric(site_info$reference_height), n = n, par = par, - forcing = forcing + forcing = forcing, + forcing_acclim = forcing_acclim ) # Prepare output to be a nice looking tidy data frame (tibble) diff --git a/R/runread_pmodel_f.R b/R/runread_pmodel_f.R index 5120e706..650e2ed1 100644 --- a/R/runread_pmodel_f.R +++ b/R/runread_pmodel_f.R @@ -104,7 +104,12 @@ runread_pmodel_f <- function( # predefine variables for CRAN check compliance sitename <- params_siml <- site_info <- - input <- forcing <- . <- NULL + input <- forcing <- forcing_acclim <- . <- NULL + + # If acclimation dataset has not been separately provided, use the same forcing data + if (is.null(drivers$forcing_acclim)){ + drivers$forcing_acclim = drivers$forcing + } # guarantee order of files drivers <- drivers |> @@ -112,7 +117,8 @@ runread_pmodel_f <- function( sitename, params_siml, site_info, - forcing + forcing, + forcing_acclim ) if (parallel){ @@ -133,7 +139,8 @@ runread_pmodel_f <- function( sitename, params_siml, site_info, - forcing) + forcing, + forcing_acclim) ) %>% multidplyr::partition(cl) %>% dplyr::mutate(data = purrr::map(input, @@ -141,7 +148,8 @@ runread_pmodel_f <- function( sitename = .x$sitename[[1]], params_siml = .x$params_siml[[1]], site_info = .x$site_info[[1]], - forcing = .x$forcing[[1]], + forcing = .x$forcing[[1]], + forcing_acclim = .x$forcing_acclim[[1]], par = par, makecheck = makecheck ) )) diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index 3e8cc9f6..77e05005 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -38,6 +38,7 @@ subroutine pmodel_f( & nt, & par, & forcing, & + forcing_acclim, & output & ) bind(C, name = "pmodel_f_") @@ -80,7 +81,8 @@ subroutine pmodel_f( & real(kind=c_double), intent(in) :: reference_height integer(kind=c_int), intent(in) :: nt ! number of time steps real(kind=c_double), dimension(16), intent(in) :: par ! free (calibratable) model parameters - real(kind=c_double), dimension(nt,12), intent(in) :: forcing ! array containing all temporally varying forcing data (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) + real(kind=c_double), dimension(nt,12), intent(in) :: forcing ! array containing all temporally varying forcing data for instantaneous model (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) + real(kind=c_double), dimension(nt,12), intent(in) :: forcing_acclim ! array containing all temporally varying forcing data for acclimating model (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) real(kind=c_double), dimension(nt,18), intent(out) :: output ! local variables diff --git a/src/wrappersc.c b/src/wrappersc.c index 0bea427a..b82c5d43 100644 --- a/src/wrappersc.c +++ b/src/wrappersc.c @@ -36,6 +36,7 @@ void F77_NAME(pmodel_f)( int *nt, double *par, double *forcing, + double *forcing_acclim, double *output ); @@ -66,7 +67,8 @@ extern SEXP pmodel_f_C( SEXP reference_height, SEXP n, SEXP par, - SEXP forcing + SEXP forcing, + SEXP forcing_acclim ){ // Number of time steps (same in forcing and output) @@ -104,6 +106,7 @@ extern SEXP pmodel_f_C( INTEGER(n), REAL(par), REAL(forcing), + REAL(forcing_acclim), REAL(output) ); @@ -527,7 +530,7 @@ extern SEXP biomee_f_C( // Declarations for all functions ///////////////////////////////////////////////////////////// static const R_CallMethodDef CallEntries[] = { - {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 26}, // Specify number of arguments to C wrapper as the last number here + {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 27}, // Specify number of arguments to C wrapper as the last number here {"biomee_f_C", (DL_FUNC) &biomee_f_C, 46}, // Number of the SEXP variables (not the output) {NULL, NULL, 0} }; diff --git a/vignettes/rsofun_phydro_calibration_full.Rmd b/vignettes/rsofun_phydro_calibration_full.Rmd index f6eac2e7..d9acec33 100644 --- a/vignettes/rsofun_phydro_calibration_full.Rmd +++ b/vignettes/rsofun_phydro_calibration_full.Rmd @@ -124,6 +124,7 @@ params_modl <- list( whc = 7.819509e+02 #200 ) +plot_pmodel(params_modl) output_p <- rsofun::runread_pmodel_f( p_hydro_drivers, par = params_modl From 4a9aeff9b24a1eb1dba3e51935e3f122ac15cf42 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Fri, 17 Nov 2023 20:54:48 +0100 Subject: [PATCH 023/112] add acclimation forcing --- src/biosphere_pmodel.mod.f90 | 1 + src/forcing_siterun_pmodel.mod.f90 | 6 +- src/gpp_pmodel.mod.f90 | 45 ++++++++++---- src/interface_biosphere_pmodel.mod.f90 | 1 + src/sofun_r.f90 | 7 +++ vignettes/rsofun_phydro_calibration_full.Rmd | 64 +++++++++++--------- vignettes/rsofun_pmodel_calibration_full.Rmd | 2 +- 7 files changed, 79 insertions(+), 47 deletions(-) diff --git a/src/biosphere_pmodel.mod.f90 b/src/biosphere_pmodel.mod.f90 index 407ce2e4..94df098e 100644 --- a/src/biosphere_pmodel.mod.f90 +++ b/src/biosphere_pmodel.mod.f90 @@ -136,6 +136,7 @@ function biosphere_annual() result( out_biosphere ) tile_fluxes(:), & myinterface%pco2, & myinterface%climate(doy), & + myinterface%climate_acclimation(doy), & myinterface%vegcover(doy), & myinterface%grid, & init_daily, & diff --git a/src/forcing_siterun_pmodel.mod.f90 b/src/forcing_siterun_pmodel.mod.f90 index 5a48b1b0..9198f8df 100644 --- a/src/forcing_siterun_pmodel.mod.f90 +++ b/src/forcing_siterun_pmodel.mod.f90 @@ -53,7 +53,7 @@ function getclimate( nt, forcing, climateyear_idx, in_ppfd, in_netrad ) result ( !---------------------------------------------------------------- ! arguments integer, intent(in) :: nt ! number of time steps - real(kind=dp), dimension(nt,13), intent(in) :: forcing ! array containing all temporally varying forcing data (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=N-deposition) + real(kind=dp), dimension(nt,12), intent(in) :: forcing ! array containing all temporally varying forcing data (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=N-deposition) integer, intent(in) :: climateyear_idx logical, intent(in) :: in_ppfd logical, intent(in) :: in_netrad @@ -108,7 +108,7 @@ function getco2( nt, forcing, forcingyear, firstyeartrend ) result( pco2 ) !---------------------------------------------------------------- ! arguments integer, intent(in) :: nt ! number of time steps - real(kind=dp), dimension(nt,13), intent(in) :: forcing ! array containing all temporally varying forcing data (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=N-deposition) + real(kind=dp), dimension(nt,12), intent(in) :: forcing ! array containing all temporally varying forcing data (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=N-deposition) integer, intent(in) :: forcingyear integer, intent(in) :: firstyeartrend @@ -135,7 +135,7 @@ function getfapar( nt, forcing, forcingyear_idx ) result( out_vegcover ) !---------------------------------------------------------------- ! arguments integer, intent(in) :: nt ! number of time steps - real(kind=dp), dimension(nt,11), intent(in) :: forcing ! array containing all temporally varying forcing data (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=N-deposition) + real(kind=dp), dimension(nt,12), intent(in) :: forcing ! array containing all temporally varying forcing data (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=N-deposition) integer, intent(in) :: forcingyear_idx ! function return variable diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 7d803a97..f1f40ff8 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -50,7 +50,12 @@ module md_gpp_pmodel contains - subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, use_phydro, use_pml) + ! function wscal_to_swp(wscal, bsoil) result (soilwp) + ! real, intent(in) :: wscal, bsoil + ! soilwp = 1 - wscal**(-bsoil) + ! end function + + subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, vegcover, grid, init, in_ppfd, use_phydro, use_pml) !////////////////////////////////////////////////////////////////// ! Wrapper function to call to P-model. ! Calculates meteorological conditions with memory based on daily @@ -66,6 +71,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, type(tile_fluxes_type), dimension(nlu), intent(inout) :: tile_fluxes real, intent(in) :: co2 ! atmospheric CO2 (ppm) type(climate_type) :: climate + type(climate_type) :: climate_acclimation type(vegcover_type) :: vegcover type(gridtype) :: grid logical, intent(in) :: init ! is true on the very first simulation day (first subroutine call of each gridcell) @@ -75,7 +81,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, ! local variables type(outtype_pmodel) :: out_pmodel ! list of P-model output variables - type(climate_type) :: climate_acclimation ! list of climate variables to which P-model calculates acclimated traits + ! type(climate_type) :: climate_acclimation ! list of climate variables to which P-model calculates acclimated traits integer :: pft integer :: lu real :: iabs @@ -90,6 +96,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, real, save :: patm_memory real, save :: ppfd_memory real, save :: netrad_memory + real, dimension(npft), save :: swp_memory real, save :: tmin_memory ! for low temperature stress @@ -100,7 +107,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, type(par_control_type) :: options ! Soil hydraulics - real :: swp + real, dimension(npft) :: swp ! xxx test real :: a_c, a_j, a_returned, fact_jmaxlim @@ -112,7 +119,19 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, ! mean) !---------------------------------------------------------------- ! climate_acclimation = calc_climate_acclimation( climate, grid, "daytime" ) - climate_acclimation = climate + ! climate_acclimation = climate + + !---------------------------------------------------------------- + ! Convert water content to water potential, for use in phydro + ! JJ Note: This is not making much sense... if wscal is the same, then how do different plants + ! experience different swp? Because some vertical wscal profile is inherent, which + ! interacts with the root distribution?? + !---------------------------------------------------------------- + do pft = 1,npft + swp(pft) = 1 - tile(1)%soil%phy%wscal**(-tile(1)%plant(pft)%bsoil) ! Assuming lu = 1, otherwise, use tile(lu) and a 2D array + swp(pft) = min(swp(pft), 0.0) ! clamp +ve values to 0 + end do + !---------------------------------------------------------------- ! Calculate environmental conditions with memory, time scale @@ -126,6 +145,9 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, patm_memory = climate_acclimation%dpatm ppfd_memory = climate_acclimation%dppfd netrad_memory = climate_acclimation%dnetrad + do pft = 1,npft + swp_memory(pft) = swp(pft) + end do end if count = count + 1 @@ -136,6 +158,9 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, patm_memory = dampen_variability( climate_acclimation%dpatm, params_gpp%tau_acclim, patm_memory ) ppfd_memory = dampen_variability( climate_acclimation%dppfd, params_gpp%tau_acclim, ppfd_memory ) netrad_memory = dampen_variability( climate_acclimation%dnetrad, params_gpp%tau_acclim, netrad_memory ) + do pft = 1,npft + swp_memory(pft) = dampen_variability(swp(pft), params_gpp%tau_acclim, swp_memory(pft) ) + end do tk = climate_acclimation%dtemp + kTkelvin @@ -167,12 +192,6 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, params_pft_gpp(pft)%kphio_par_b ) end if - !---------------------------------------------------------------- - ! Convert water content to water potential, for use in phydro - !---------------------------------------------------------------- - swp = 1 - tile(lu)%soil%phy%wscal**(-tile(lu)%plant(pft)%bsoil) - swp = min(swp, 0.0) ! clamp +ve values to 0 - !---------------------------------------------------------------- ! P-model call to get a list of variables that are ! acclimated to slowly varying conditions @@ -213,14 +232,14 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, out_phydro_acclim = phydro_analytical( & tc = dble(temp_memory), & tg = dble(temp_memory), & - ppfd = dble(ppfd_memory)*1e6*2.0d0, & + ppfd = dble(ppfd_memory)*1e6, & netrad = dble(netrad_memory), & vpd = dble(vpd_memory), & co2 = dble(co2_memory), & pa = dble(patm_memory), & fapar = dble(tile(lu)%canopy%fapar), & kphio = dble(kphio_temp), & - psi_soil = dble(swp), & !0.d0, & + psi_soil = dble(swp_memory(pft)), & !0.d0, & rdark = dble(params_gpp%rd_to_vcmax), & vwind = 3.0d0, & par_plant = par_plant, & @@ -283,7 +302,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, vegcover, grid, init, in_ppfd, pa = dble(climate%dpatm), & fapar = dble(tile(lu)%canopy%fapar), & kphio = dble(kphio_temp), & - psi_soil = dble(swp), & !0.d0, & + psi_soil = dble(swp(pft)), & !0.d0, & rdark = dble(params_gpp%rd_to_vcmax), & vwind = 3.0d0, & par_plant = par_plant, & diff --git a/src/interface_biosphere_pmodel.mod.f90 b/src/interface_biosphere_pmodel.mod.f90 index 23fd69fb..6eaa5fbc 100644 --- a/src/interface_biosphere_pmodel.mod.f90 +++ b/src/interface_biosphere_pmodel.mod.f90 @@ -44,6 +44,7 @@ module md_interface_pmodel real :: canopy_height real :: reference_height type(climate_type), dimension(ndayyear) :: climate + type(climate_type), dimension(ndayyear) :: climate_acclimation type(vegcover_type), dimension(ndayyear):: vegcover ! type(domaininfo_type) :: domaininfo type(outtype_steering) :: steering diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index 77e05005..b4b93702 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -193,6 +193,13 @@ subroutine pmodel_f( & myinterface%params_siml%in_netrad & ) + myinterface%climate_acclimation(:) = getclimate(nt, & + forcing_acclim, & + myinterface%steering%climateyear_idx, & + myinterface%params_siml%in_ppfd, & + myinterface%params_siml%in_netrad & + ) + ! Get annual, gobally uniform CO2 myinterface%pco2 = getco2( nt, & forcing, & diff --git a/vignettes/rsofun_phydro_calibration_full.Rmd b/vignettes/rsofun_phydro_calibration_full.Rmd index d9acec33..0c5a4305 100644 --- a/vignettes/rsofun_phydro_calibration_full.Rmd +++ b/vignettes/rsofun_phydro_calibration_full.Rmd @@ -29,8 +29,8 @@ data_dir = "~/Downloads/fluxdatakit_oct3/Phydro_drivers/" load(paste0(data_dir,"/",site,"_p_hydro_drivers.rda")) load(paste0(data_dir,"/",site,"_p_hydro_validation.rda")) -# p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% -# rename(latenth=le) %>% +p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% + rename(le=latenth) # mutate(gpp = gpp*86400/1e6*12) %>% # convert to [gC m-2 day-1] # mutate(latenth = latenth*86400) # convert [W m-2] to [J m-2 day-1] @@ -58,16 +58,16 @@ plot_pmodel = function(params_m, out_filename=""){ p_hydro_drivers, par = params_m ) - output_p$data[[1]] = output_p$data[[1]] %>% rename(latenth = le) + if (out_filename != ""){ save(output_p, file = paste0(data_dir, "/", site, "_", out_filename)) } - p1 = output_p$data[[1]] %>% select(date, gpp, latenth) %>% + p1 = output_p$data[[1]] %>% select(date, gpp, le) %>% melt("date") %>% mutate(group="model") %>% rbind(p_hydro_validation$data[[1]] %>% - select(date, gpp, latenth) %>% + select(date, gpp, le) %>% # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 # mutate(latenth = latenth*86400) %>% # convert W m-2 to J m-2 day-1 melt("date") %>% @@ -78,11 +78,11 @@ plot_pmodel = function(params_m, out_filename=""){ theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~variable, scales = "free", nrow = 2) - p2 = output_p$data[[1]] %>% select(date, gpp, latenth) %>% + p2 = output_p$data[[1]] %>% select(date, gpp, le) %>% drop_na %>% melt("date", value.name = "pred") %>% left_join(p_hydro_validation$data[[1]] %>% - select(date, gpp, latenth) %>% + select(date, gpp, le) %>% drop_na %>% # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 # mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 @@ -144,8 +144,8 @@ settings_bayes <- list( kphio = list(lower=0.04, upper=0.09, init=0.05), phydro_K_plant = list(lower=0.05e-16, upper=5e-16, init=0.3e-16), phydro_p50_plant = list(lower=-4, upper=-0.1, init=-1), - # phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), - # phydro_gamma = list(lower=0.05, upper=2, init=1), + phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), + phydro_gamma = list(lower=0.05, upper=2, init=1), bsoil = list(lower=1, upper=5, init=3), whc = list(lower=10, upper=1000, init=500), err_gpp = list(lower = 0.01, upper = 4, init = 2), @@ -155,9 +155,9 @@ settings_bayes <- list( control = list( sampler = "DEzs", settings = list( - nrChains = 5, - burnin = 0, - iterations = 3000 # kept artificially low + nrChains = 10, + burnin = 5000, + iterations = 5000 # kept artificially low ) ) ) @@ -178,13 +178,13 @@ pars_calib_bayes <- calib_sofun( kc_jmax = 0.41, # phydro_K_plant = 0.3e-16, # phydro_P50_plant = -1, - phydro_b_plant = 1, - phydro_alpha = 0.1, - phydro_gamma = 1 + phydro_b_plant = 1 + # phydro_alpha = 0.1, + # phydro_gamma = 1 # bsoil = 3, # whc = 90 ), - targets = c("gpp", "latenth") # define target variable GPP + targets = c("gpp", "le") # define target variable GPP ) plot(pars_calib_bayes$mod) @@ -197,8 +197,8 @@ params_modl_opt = params_modl params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] params_modl_opt$phydro_K_plant = pars_calib_bayes$par[["phydro_K_plant"]] params_modl_opt$phydro_p50_plant = pars_calib_bayes$par[["phydro_p50_plant"]] -# params_modl_opt$phydro_alpha = pars_calib_bayes$par[["phydro_alpha"]] -# params_modl_opt$phydro_gamma = pars_calib_bayes$par[["phydro_gamma"]] +params_modl_opt$phydro_alpha = pars_calib_bayes$par[["phydro_alpha"]] +params_modl_opt$phydro_gamma = pars_calib_bayes$par[["phydro_gamma"]] params_modl_opt$bsoil = pars_calib_bayes$par[["bsoil"]] params_modl_opt$whc = pars_calib_bayes$par[["whc"]] @@ -211,7 +211,7 @@ plot_pmodel(params_modl_opt, "p_hydro_output.rda") ```{r} params_modl <- list( - kphio = 0.089, # 0.11, #0.04998, + kphio = 0.04998, kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress @@ -226,19 +226,24 @@ params_modl <- list( # phydro_alpha = 0.1, # phydro_gamma = 1, # bsoil = 3, - whc = 90 + whc = 300 ) p_hydro_drivers$params_siml[[1]]$use_phydro = F p_hydro_drivers$params_siml[[1]]$use_pml = F +plot_pmodel(params_modl) +``` + +```{r} # Define calibration settings and parameter ranges from previous work settings_bayes <- list( method = "BayesianTools", par = list( kphio = list(lower=0.04, upper=0.09, init=0.05), - whc = list(lower=10, upper=1000, init=500), - soilm_thetastar = list(lower=10, upper=1000, init=0.6*240), - # beta_unitcostratio = list(lower=10, upper=1000, init=146), + soilm_thetastar = list(lower=0.1*240, upper=2*240, init=0.6*240), + # soilm_betao = list(lower=0, upper=10, init=0.1), + beta_unitcostratio = list(lower=10, upper=1000, init=146), + whc = list(lower=10, upper=1000, init=350), err_gpp = list(lower = 0.01, upper = 4, init = 2), err_le = list(lower = 0.01e7, upper = 4e7, init = 2e7) ), @@ -267,11 +272,11 @@ pars_calib_bayes <- calib_sofun( kphio_par_b = 1.0, soilm_betao = 0.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - beta_unitcostratio = 146, + # beta_unitcostratio = 146, tau_acclim = 30.0, kc_jmax = 0.41 ), - targets = c("gpp", "latenth") # define target variable GPP + targets = c("gpp", "le") # define target variable GPP ) print(pars_calib_bayes) @@ -279,10 +284,9 @@ print(pars_calib_bayes) params_modl_opt = params_modl params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] -params_modl_opt$whc = pars_calib_bayes$par[["whc"]] params_modl_opt$soilm_thetastar = pars_calib_bayes$par[["soilm_thetastar"]] -# params_modl_opt$beta_unitcostratio = pars_calib_bayes$par[["beta_unitcostratio"]] - -plot_pmodel(params_modl_opt, "p_model_output.rda") +# params_modl_opt$soilm_betao = pars_calib_bayes$par[["soilm_betao"]] +params_modl_opt$beta_unitcostratio = pars_calib_bayes$par[["beta_unitcostratio"]] +params_modl_opt$whc = pars_calib_bayes$par[["whc"]] -``` +plot_pmodel(params_modl_opt) diff --git a/vignettes/rsofun_pmodel_calibration_full.Rmd b/vignettes/rsofun_pmodel_calibration_full.Rmd index 78c85a62..761c9ad5 100644 --- a/vignettes/rsofun_pmodel_calibration_full.Rmd +++ b/vignettes/rsofun_pmodel_calibration_full.Rmd @@ -18,7 +18,7 @@ library(rsofun) ## Generate data if not already available ```{r} -site <- "FR-Pue" +site <- "GF-Guy" ``` ```{r} From a9a275a8899474d7b0da9da65f22d99ee00e5b16 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sun, 7 Jan 2024 15:52:36 +0100 Subject: [PATCH 024/112] add 2-param SWP function; limit SWP for numerical stability; bash script --- R/cost_likelihood_pmodel.R | 4 +- R/cost_rmse_pmodel.R | 4 +- R/run_pmodel_f_bysite.R | 9 +- src/gpp_pmodel.mod.f90 | 9 +- src/interface_biosphere_pmodel.mod.f90 | 1 + src/plant_pmodel.mod.f90 | 2 + src/sofun_r.f90 | 5 +- vignettes/phydro_long_calibration.R | 256 +++++++++ vignettes/rsofun_phydro_calibration_full.Rmd | 4 +- vignettes/rsofun_phydro_data_generation.R | 514 +++++++++++++++++++ vignettes/run_bayesian_phydro.sh | 26 + 11 files changed, 821 insertions(+), 13 deletions(-) create mode 100644 vignettes/phydro_long_calibration.R create mode 100644 vignettes/rsofun_phydro_data_generation.R create mode 100755 vignettes/run_bayesian_phydro.sh diff --git a/R/cost_likelihood_pmodel.R b/R/cost_likelihood_pmodel.R index 12fd01d3..d33515ac 100644 --- a/R/cost_likelihood_pmodel.R +++ b/R/cost_likelihood_pmodel.R @@ -88,7 +88,7 @@ cost_likelihood_pmodel <- function( # FIXME Jaideep: Instead of checking the number of params, # it might be better to check for presence of each param in par and par_fixed ## check input parameters - expected_params = ifelse(using_phydro, yes=13, no=10) + expected_params = ifelse(using_phydro, yes=14, no=10) if( (length(par) + length(par_fixed)) != (expected_params + length(targets)) ){ stop(paste0('Error: Input calibratable and fixed parameters (par = ',length(par),' and par_fixed = ',length(par_fixed),') do not match length of the required P-model parameters (',expected_params + length(targets),').')) @@ -101,7 +101,7 @@ cost_likelihood_pmodel <- function( 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', 'phydro_K_plant', 'phydro_p50_plant', 'phydro_b_plant', 'phydro_alpha', 'phydro_gamma', - 'bsoil', 'whc') + 'bsoil', 'Ssoil', 'whc') } else { calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', 'soilm_thetastar', 'soilm_betao', diff --git a/R/cost_rmse_pmodel.R b/R/cost_rmse_pmodel.R index c4fe96be..9a543d78 100644 --- a/R/cost_rmse_pmodel.R +++ b/R/cost_rmse_pmodel.R @@ -86,7 +86,7 @@ cost_rmse_pmodel <- function( # FIXME Jaideep: Instead of checking the number of params, # it might be better to check for presence of each param in par and par_fixed ## check input parameters - expected_params = ifelse(using_phydro, yes=13, no=10) + expected_params = ifelse(using_phydro, yes=14, no=10) if( (length(par) + length(par_fixed)) != (expected_params) ){ stop(paste0('Error: Input calibratable and fixed parameters (par = ',length(par),' and par_fixed = ',length(par_fixed),') do not match length of the required P-model parameters (',expected_params + length(targets),').')) @@ -98,7 +98,7 @@ cost_rmse_pmodel <- function( 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', 'phydro_K_plant', 'phydro_p50_plant', 'phydro_b_plant', 'phydro_alpha', 'phydro_gamma', - 'bsoil', 'whc') + 'bsoil', 'Ssoil', 'whc') } else { calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', 'soilm_thetastar', 'soilm_betao', diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index a3eb9bdf..ac3c99dc 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -330,13 +330,13 @@ run_pmodel_f_bysite <- function( } } else { - # P-hydro needs 13 parameters + # P-hydro needs 14 parameters if( sum( names(params_modl) %in% c('kphio', 'kphio_par_a', 'kphio_par_b', 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', 'phydro_K_plant', 'phydro_p50_plant', 'phydro_b_plant', 'phydro_alpha', 'phydro_gamma', - 'bsoil', 'whc') - ) != 13){ + 'bsoil', 'Ssoil', 'whc') + ) != 14){ warning(" Returning a dummy data frame. Incorrect model parameters.") continue <- FALSE } @@ -414,6 +414,9 @@ run_pmodel_f_bysite <- function( ifelse(params_siml$use_phydro, no = dummy_val, yes = params_modl$bsoil), + ifelse(params_siml$use_phydro, + no = dummy_val, + yes = params_modl$Ssoil), as.numeric(params_modl$whc) ) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index f1f40ff8..a479ebbe 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -105,7 +105,8 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, vegcover, type(par_cost_type) :: par_cost type(phydro_result_type) :: out_phydro_acclim, out_phydro_inst type(par_control_type) :: options - + real :: pxx_plant ! water potential at xx percent remaining conductivity, where xx is a small number + ! Soil hydraulics real, dimension(npft) :: swp @@ -128,8 +129,12 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, vegcover, ! interacts with the root distribution?? !---------------------------------------------------------------- do pft = 1,npft - swp(pft) = 1 - tile(1)%soil%phy%wscal**(-tile(1)%plant(pft)%bsoil) ! Assuming lu = 1, otherwise, use tile(lu) and a 2D array + pxx_plant = tile(1)%plant(pft)%phydro_p50_plant * (log(0.03)/log(0.5))^(1.0d0/tile(1)%plant(pft)%phydro_b_plant) ! Currently xx set to 3% + swp(pft) = (tile(1)%soil%params%whc / tile(1)%plant(pft)%Ssoil)**(-tile(1)%plant(pft)%bsoil) & + -(tile(1)%soil%phy%wcont / tile(1)%plant(pft)%Ssoil)**(-tile(1)%plant(pft)%bsoil) ! Assuming lu = 1, otherwise, use tile(lu) and a 2D array swp(pft) = min(swp(pft), 0.0) ! clamp +ve values to 0 + swp(pft) = max(swp(pft), pxx_plant) ! clamp -ve values to a minimum of pxx + ! ^ this clamping is for numerical stability only end do diff --git a/src/interface_biosphere_pmodel.mod.f90 b/src/interface_biosphere_pmodel.mod.f90 index 6eaa5fbc..a11dc2a4 100644 --- a/src/interface_biosphere_pmodel.mod.f90 +++ b/src/interface_biosphere_pmodel.mod.f90 @@ -32,6 +32,7 @@ module md_interface_pmodel real :: phydro_alpha real :: phydro_gamma real :: bsoil + real :: Ssoil real :: whc end type paramstype_calib diff --git a/src/plant_pmodel.mod.f90 b/src/plant_pmodel.mod.f90 index 69140bd1..c2fd977c 100644 --- a/src/plant_pmodel.mod.f90 +++ b/src/plant_pmodel.mod.f90 @@ -68,6 +68,7 @@ module md_plant_pmodel real :: phydro_alpha ! Phydro: Cost of Jmax real :: phydro_gamma ! Phydro: Cost of hydraulics real :: bsoil ! Phydro: parameter converting RZWSC to predawn water potential (depends on rooting system hence PFT specific) + real :: Ssoil ! Phydro: parameter converting RZWSC to predawn water potential (depends on rooting system hence PFT specific) end type plant_type @@ -352,6 +353,7 @@ subroutine initpft( plant ) plant%phydro_alpha = myinterface%params_calib%phydro_alpha plant%phydro_gamma = myinterface%params_calib%phydro_gamma plant%bsoil = myinterface%params_calib%bsoil + plant%Ssoil = myinterface%params_calib%Ssoil end subroutine initpft diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index b4b93702..9d701b2d 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -80,7 +80,7 @@ subroutine pmodel_f( & real(kind=c_double), intent(in) :: canopy_height real(kind=c_double), intent(in) :: reference_height integer(kind=c_int), intent(in) :: nt ! number of time steps - real(kind=c_double), dimension(16), intent(in) :: par ! free (calibratable) model parameters + real(kind=c_double), dimension(17), intent(in) :: par ! free (calibratable) model parameters real(kind=c_double), dimension(nt,12), intent(in) :: forcing ! array containing all temporally varying forcing data for instantaneous model (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) real(kind=c_double), dimension(nt,12), intent(in) :: forcing_acclim ! array containing all temporally varying forcing data for acclimating model (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) real(kind=c_double), dimension(nt,18), intent(out) :: output @@ -168,7 +168,8 @@ subroutine pmodel_f( & myinterface%params_calib%phydro_alpha = real(par(13)) myinterface%params_calib%phydro_gamma = real(par(14)) myinterface%params_calib%bsoil = real(par(15)) - myinterface%params_calib%whc = real(par(16)) + myinterface%params_calib%Ssoil = real(par(16)) + myinterface%params_calib%whc = real(par(17)) !---------------------------------------------------------------- ! GET VEGETATION COVER (fractional projective cover by PFT) diff --git a/vignettes/phydro_long_calibration.R b/vignettes/phydro_long_calibration.R new file mode 100644 index 00000000..ce50f9c2 --- /dev/null +++ b/vignettes/phydro_long_calibration.R @@ -0,0 +1,256 @@ +rm(list=ls()) +library(tidyverse) +library(reshape2) +library(rsofun) +library(BayesianTools) +library(tictoc) + +plot_only = F + +tic("phydro") + +## Generate data if not already available + +args = commandArgs(trailingOnly=TRUE) + +#site <- "GF-Guy" +if (length(args)==0) { + #stop("At least one argument must be supplied: site name", call.=FALSE) + site = "GF-Guy" +}else{ + site = args[1] +} + +data_dir = "~/Downloads/fluxdatakit_oct3/Phydro_drivers_3/" +out_dir = "~/Downloads/fluxdatakit_oct3/phydro_output/" +figures_dir = "~/Downloads/fluxdatakit_oct3/phydro_output/figures/" + +dir.create(out_dir, showWarnings = F) +dir.create(figures_dir, showWarnings = F) + +load(paste0(data_dir,"/",site,"_p_hydro_drivers.rda")) +load(paste0(data_dir,"/",site,"_p_hydro_validation.rda")) + +p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% + rename(le=latenth) +# mutate(gpp = gpp*86400/1e6*12) %>% # convert to [gC m-2 day-1] +# mutate(latenth = latenth*86400) # convert [W m-2] to [J m-2 day-1] + +p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 +p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 + +p_hydro_drivers$params_siml[[1]]$use_phydro = T +p_hydro_drivers$params_siml[[1]]$use_pml = F + + +get_density <- function(x, y, ...) { + dens <- MASS::kde2d(x, y, ...) + ix <- findInterval(x, dens$x) + iy <- findInterval(y, dens$y) + ii <- cbind(ix, iy) + return(dens$z[ii]) +} + +plot_pmodel = function(output_p, out_filename_prefix=""){ + p1 = output_p$data[[1]] %>% select(date, gpp, le) %>% + melt("date") %>% + mutate(group="model") %>% + rbind(p_hydro_validation$data[[1]] %>% + select(date, gpp, le) %>% + # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + # mutate(latenth = latenth*86400) %>% # convert W m-2 to J m-2 day-1 + melt("date") %>% + mutate(group="obs")) %>% + ggplot(aes(y=value, x=as.Date(date))) + + geom_line(aes(group=group, col=group), alpha=0.7) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 2) + + p2 = output_p$data[[1]] %>% select(date, gpp, le) %>% + drop_na %>% + melt("date", value.name = "pred") %>% + left_join(p_hydro_validation$data[[1]] %>% + select(date, gpp, le) %>% + drop_na %>% + # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + # mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 + melt("date", value.name = "obs")) %>% + group_by(variable) %>% + reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% + ggplot(aes(y=obs, x=pred, colour=density)) + + scale_color_viridis_c() + + geom_point(alpha=0.7) + + geom_abline(slope=1, intercept=0, col="red")+ + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 1)+ + labs(colour="Density") + + if (out_filename_prefix == ""){ + print(p1) + print(p2) + } + else{ + cairo_pdf(filename = paste0(out_filename_prefix, "_timeseries.pdf"), height=5, width=7) + print(p1) + dev.off() + cairo_pdf(filename = paste0(out_filename_prefix, "_pred_vs_obs.pdf"), height=3.5, width=7) + print(p2) + dev.off() + + } + +} + +params_modl <- list( + kphio = 7.013438e-02, # 0.089, # 0.11, #0.04998, + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + # soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + # soilm_betao = 0.0, + # beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + phydro_K_plant = 1.421218e-16 , #0.3e-16, + phydro_p50_plant = -2.506519e+00, #-1, + phydro_b_plant = 1, + phydro_alpha = 1.334564e-01, #0.1, + phydro_gamma = 1.889438e+00, #1, + bsoil = 3.193891e+00, #3, + Ssoil = 3.193891e+02, #3, + whc = 7.819509e+02 #200 +) + +output_p <- rsofun::runread_pmodel_f( + p_hydro_drivers, + par = params_modl +) +plot_pmodel(output_p) + + +## P-hydro full calibration (BayesianTools) + +p_hydro_drivers$params_siml[[1]]$use_phydro = T +p_hydro_drivers$params_siml[[1]]$use_pml = F + +# Define calibration settings and parameter ranges from previous work +settings_bayes <- list( + method = "BayesianTools", + par = list( + kphio = list(lower=0.04, upper=0.09, init=0.05), + phydro_K_plant = list(lower=0.05e-16, upper=5e-16, init=0.3e-16), + phydro_p50_plant = list(lower=-3, upper=-0.5, init=-1), + phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), + phydro_gamma = list(lower=0.5, upper=1.5, init=1), + bsoil = list(lower=0.1, upper=10, init=3), + Ssoil = list(lower=0.1, upper=1000, init=10), + whc = list(lower=10, upper=5000, init=1000), + err_gpp = list(lower = 0.01, upper = 4, init = 2), + err_le = list(lower = 0.1e6, upper = 10e6, init = 2e6) + ), + metric = rsofun::cost_likelihood_pmodel, + control = list( + sampler = "DEzs", + settings = list( + nrChains = 3, + burnin = 10000, + iterations = 20000 # kept artificially low + ) + ) +) + +file_prefix = paste0(out_dir, site, "_nchains_", + settings_bayes$control$settings$nrChains, + "_nsteps_", + settings_bayes$control$settings$iterations, + "_burnin_", + settings_bayes$control$settings$burnin) + +calib_file =paste0(file_prefix, "_mcmc_output.rda") + +if (!plot_only){ + # Calibrate the model and optimize the free parameters using + # demo datasets + pars_calib_bayes <- calib_sofun( + # calib_sofun arguments: + drivers = p_hydro_drivers, + obs = p_hydro_validation, + settings = settings_bayes, + # extra arguments passed to the cost function: + par_fixed = list( # fix all other parameters + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + # phydro_K_plant = 0.3e-16, + # phydro_P50_plant = -1, + phydro_b_plant = 1 + # phydro_alpha = 0.1, + # phydro_gamma = 1 + # bsoil = 3, + # Ssoil = 40, + # whc = 90 + ), + targets = c("gpp", "le") # define target variable GPP + ) + + save(pars_calib_bayes, file = calib_file) + +} else{ + load(calib_file) +} + +fig_file_prefix = paste0(figures_dir, site, "_nchains_", + settings_bayes$control$settings$nrChains, + "_nsteps_", + settings_bayes$control$settings$iterations, + "_burnin_", + settings_bayes$control$settings$burnin) + +cairo_pdf(filename = paste0(fig_file_prefix, "_trace_plot.pdf"), width = 7, height = 9) +# plot(pars_calib_bayes$mod) +codachain = getSample(pars_calib_bayes$mod, coda = T, thin = "auto") +par(mfrow = c(4,3), mar=c(2,3,2,1), oma=c(1,1,1,1)) +plot(codachain, density = F, auto.layout = F, smooth = T, xlab="") +dev.off() + +cairo_pdf(filename = paste0(fig_file_prefix, "_correlation_plot.pdf")) +correlationPlot(pars_calib_bayes$mod) +dev.off() + +cairo_pdf(filename = paste0(fig_file_prefix, "_marginal_plot.pdf")) +marginalPlot(pars_calib_bayes$mod) +dev.off() + +pars_calib_bayes$par %>% + t() %>% as.data.frame() %>% + mutate(site = site) %>% + write.csv(file = paste0(file_prefix, "_MAP.csv"), row.names = F) + +params_modl_opt = params_modl + +params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] +params_modl_opt$phydro_K_plant = pars_calib_bayes$par[["phydro_K_plant"]] +params_modl_opt$phydro_p50_plant = pars_calib_bayes$par[["phydro_p50_plant"]] +params_modl_opt$phydro_alpha = pars_calib_bayes$par[["phydro_alpha"]] +params_modl_opt$phydro_gamma = pars_calib_bayes$par[["phydro_gamma"]] +params_modl_opt$bsoil = pars_calib_bayes$par[["bsoil"]] +params_modl_opt$Ssoil = pars_calib_bayes$par[["Ssoil"]] +params_modl_opt$whc = pars_calib_bayes$par[["whc"]] + +output_p_opt <- rsofun::runread_pmodel_f( + p_hydro_drivers, + par = params_modl_opt +) +plot_pmodel(output_p_opt) +save(output_p_opt, file=paste0(file_prefix, "_phydro_output.rda")) + +plot_pmodel(output_p_opt, out_filename_prefix = fig_file_prefix) + + +toc() + + diff --git a/vignettes/rsofun_phydro_calibration_full.Rmd b/vignettes/rsofun_phydro_calibration_full.Rmd index 0c5a4305..feea7928 100644 --- a/vignettes/rsofun_phydro_calibration_full.Rmd +++ b/vignettes/rsofun_phydro_calibration_full.Rmd @@ -143,9 +143,9 @@ settings_bayes <- list( par = list( kphio = list(lower=0.04, upper=0.09, init=0.05), phydro_K_plant = list(lower=0.05e-16, upper=5e-16, init=0.3e-16), - phydro_p50_plant = list(lower=-4, upper=-0.1, init=-1), + phydro_p50_plant = list(lower=-3, upper=-0.1, init=-1), phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), - phydro_gamma = list(lower=0.05, upper=2, init=1), + phydro_gamma = list(lower=0.5, upper=2, init=1), bsoil = list(lower=1, upper=5, init=3), whc = list(lower=10, upper=1000, init=500), err_gpp = list(lower = 0.01, upper = 4, init = 2), diff --git a/vignettes/rsofun_phydro_data_generation.R b/vignettes/rsofun_phydro_data_generation.R new file mode 100644 index 00000000..c30f8ec9 --- /dev/null +++ b/vignettes/rsofun_phydro_data_generation.R @@ -0,0 +1,514 @@ +params <- +list(output_dir = ".") + +#' --- +#' title: "Generate input data for rsofun Phydro" +#' author: "Jaideep Joshi" +#' date: "2023-11-12" +#' output: html_document +#' params: +#' output_dir: "." +#' --- +#' +## ----setup, include=FALSE-------------------------------------------- +knitr::opts_chunk$set(echo = TRUE) + +#' +## -------------------------------------------------------------------- +library(tidyverse) +library(reshape2) +library(FluxDataKit) +library(lubridate) + +#' +#' +## -------------------------------------------------------------------- +args = commandArgs(trailingOnly=TRUE) + +#site <- "GF-Guy" +if (length(args)==0) { + #stop("At least one argument must be supplied: site name", call.=FALSE) + site = "GF-Guy" +}else{ + site = args[1] +} + +print(getwd()) + +#' +#' +## -------------------------------------------------------------------- +lsm_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_LSM/" +csv_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_FLUXNET/" +out_path = "~/Downloads/fluxdatakit_oct3/Phydro_drivers_3/" + +dir.create(out_path, showWarnings = F) +dir.create(paste0(out_path, "data_gen_figures/"), showWarnings = F) + +figures_prefix = paste0(out_path,"/data_gen_figures/", site) + +files_csv = list.files(csv_path) +files_lsm = list.files(lsm_path) + +# Get filename for HH data for matching site +file_csv = files_csv[intersect(grep(site, files_csv), + grep("HH", files_csv))] + +# get metadata +# -------------------------------------------------------- +message("- reading Metadata for site") +meta <- suppressWarnings( + try( + fdk_convert_lsm( + site = site, + fluxnet_format = TRUE, + path = lsm_path, + meta_data = T + ) + ) + ) + + +# get half-hourly data -------------------------------------------------------- +# message("- convert to FLUXNET standard CSV file") +# hhdf <- suppressWarnings( +# try( +# fdk_convert_lsm( +# site = site, +# fluxnet_format = TRUE, +# path = "~/Downloads/flux_data_kit_beta/fluxes/" +# ) +# ) +# ) +# +# if(inherits(hhdf, "try-error")){ +# message("!!! conversion to FLUXNET failed !!!") +# return(NULL) +# } + +message("- reading FLUXNET format halfhourly data") +hhdf <- readr::read_csv(paste0(csv_path,"/",file_csv)) + +# Add date and time columns to hhdf for easier further processing. +# --------------------------------------------------------- +hhdf = + hhdf |> + mutate(time = lubridate::as_datetime(as.character(TIMESTAMP_START), tz = "GMT", format="%Y%m%d%H%M")) |> + mutate(date = lubridate::as_date(time)) + +message("- Add SW_OUT=NA if not present") +if (!("SW_OUT" %in% colnames(hhdf))) { + hhdf$SW_OUT = NA +} + + +# Aggregate to daily 24-hr means ---------------------------------------------------------- +message("- downsampling FLUXNET format - 24 hr means") +ddf_24hr_mean <- + try( + hhdf |> + group_by(date) |> + select(-TIMESTAMP_START, -TIMESTAMP_END) |> + summarize_all(.funs = mean) + ) + +# Check aggregation +cairo_pdf(filename = paste0(figures_prefix, "_fig1_ddf_24hr_mean.pdf")) +ddf_24hr_mean %>% + mutate(albedo = SW_OUT/SW_IN_F_MDS) %>% + select(SW_IN_F_MDS, NETRAD, LW_IN_F_MDS, LAI, FPAR, GPP_DT_VUT_REF, LE_F_MDS, TA_F_MDS, date) %>% + melt("date") %>% + ggplot(aes(y=value, x=date)) + + geom_line(col="aquamarine4") + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") %>% + print() +dev.off() + +ddf_24hr_mean %>% filter(!is.na(NETRAD)) %>% pull(date) %>% summary() + +#' +## -------------------------------------------------------------------- +valid_years = read.csv(text = " + sitename , start_year , end_year \n + CH-Dav , 2000 , 2010 \n + DE-Hai , 2000 , 2020 \n + DE-Tha , 2000 , 2020 \n + FR-LBr , 2000 , 2008 \n + FR-Pue , 2000 , 2013 \n + GF-Guy , 2004 , 2015 \n + NL-Loo , 2000 , 2014 \n + US-Me2 , 2002 , 2020 \n + US-NR1 , 2000 , 2016 \n + US-Ton , 2001 , 2014 \n + ", + header=T, strip.white=T) + +# Get valid data years +ystart = valid_years %>% filter(sitename==site) %>% pull(start_year) +yend = valid_years %>% filter(sitename==site) %>% pull(end_year) + +# ystart = max(meta[[1]]$year_start, 2000) %>% as.numeric() +# yend = meta[[1]]$year_end %>% as.numeric() + +# Aggregate around daily maximum ppfd for acclimating model +# --------------------------------------------------------- +test.3day = hhdf %>% filter(date >= as_date(paste0(floor((ystart+yend)/2),"-06-01")) & + date <= as_date(paste0(floor((ystart+yend)/2),"-06-03")) ) + +aggregate_daily_3hr_maxima = function(df){ + # Get the time at which SW_IN is maximum + maxppfd <- df %>% filter(SW_IN_F_MDS == max(SW_IN_F_MDS)) + max_t <- maxppfd$time[1] + + # Select times that lie in 3-hr interval around max_t + df_aroundmax <- df %>% filter(time < (max_t + 1.5*3600) & + time > (max_t - 1.5*3600) ) + + # take mean of selected entries + df_mean <- df_aroundmax |> + select(-TIMESTAMP_START, -TIMESTAMP_END) |> + summarize_all(.funs = mean) + + df_mean +} + +# Test aggregation +# ---------------- +test.3day.3hr = test.3day %>% group_by(date) %>% do(aggregate_daily_3hr_maxima(.)) %>% ungroup() + +cairo_pdf(filename = paste0(figures_prefix, "_fig2_3hr_maxima_sample.pdf")) +test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% + melt("time") %>% + mutate(type="hourly") %>% + rbind(test.3day.3hr %>% + select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% + melt("time") %>% + mutate(type="daily") + ) %>% + ggplot(aes(y=value, x=as.POSIXct(time))) + + geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + + geom_point(data = . %>% filter(type == "daily")) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") %>% + print() +dev.off() + +#' +## -------------------------------------------------------------------- +# Apply 3hr maxima aggregation to all data +# ---------------------------------------- +message("- downsampling FLUXNET format - daily 3-hr means around max ppfd") +ddf_3hr_maxima <- hhdf |> + group_by(date) |> + do(aggregate_daily_3hr_maxima(.)) |> + ungroup() + +#' +## -------------------------------------------------------------------- +aggregate_daily_daylength = function(df){ + # Get the time at which SW_IN > 0 + pos_ppfd <- df %>% filter(SW_IN_F_MDS > 10) + # if SW_IN is unavailable in that year calc daylength based on NETRAD + if (nrow(pos_ppfd) < 2){ + pos_ppfd <- df %>% filter(NETRAD > 25) + } + + tmax <- max(pos_ppfd$time) + tmin <- min(pos_ppfd$time) + + # Select times that lie in 3-hr interval around max_t + df_aroundmax <- df %>% filter(time <= tmax & + time >= tmin ) + + # take mean of selected entries + df_mean <- df_aroundmax |> + select(-TIMESTAMP_START, -TIMESTAMP_END) |> + summarize_all(.funs = mean) |> + mutate(daylength = difftime(tmax, tmin, units="hours") |> as.numeric()) + + df_mean +} + +# Test aggregation +# ---------------- +test.3day.daylen = test.3day %>% group_by(date) %>% do(aggregate_daily_daylength(.)) %>% ungroup() + +cairo_pdf(filename = paste0(figures_prefix, "_fig3_daytime_sample.pdf")) +test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI) %>% + mutate(daylength = NA) %>% + melt("time") %>% + mutate(type="hourly") %>% + rbind(test.3day.daylen %>% + select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, daylength) %>% + melt("time") %>% + mutate(type="daily") + ) %>% + ggplot(aes(y=value, x=as.POSIXct(time))) + + geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + + geom_point(data = . %>% filter(type == "daily")) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") %>% + print() +dev.off() + +#' +## -------------------------------------------------------------------- +# Apply daytime mean aggregation to all data +# ------------------------------------------ +message("- downsampling FLUXNET format - daytime means") +ddf_daytime_mean <- hhdf |> + group_by(date) |> + do(aggregate_daily_daylength(.)) |> + ungroup() + +# Check daylength seasonality +cairo_pdf(filename = paste0(figures_prefix, "_fig4_daylenth_seasonality.pdf")) +ddf_daytime_mean %>% + ggplot(aes(y=daylength, x=date)) + + geom_line()+ + theme_classic() %>% + print() +dev.off() + +#' +## -------------------------------------------------------------------- +# Calculate daily tmax and tmin from hh data +# ------------------------------------------ +tmaxmin <- + hhdf |> + group_by(date) |> + summarize( + tmax = max(TA_F_MDS), + tmin = min(TA_F_MDS) + ) + +#' +## -------------------------------------------------------------------- +# Creating driver object ------------------------------------------------------ +message("- compiling drivers") +load("../data/p_model_drivers.rda") + +p_hydro_drivers <- p_model_drivers +p_hydro_drivers$sitename[[1]] = site +p_hydro_drivers$site_info[[1]] = + tibble( + lon=meta[[1]]$longitude, + lat=meta[[1]]$latitude, + elv = meta[[1]]$elevation, + canopy_height=meta[[1]]$canopy_height, + reference_height = meta[[1]]$reference_height + ) +kfFEC = 2.04 + +start_year = ystart +end_year = yend + +# for demo, use just a subset of years +p_hydro_drivers$forcing <- + ddf_24hr_mean |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + left_join(tmaxmin) |> + group_by(date) |> + summarize( + date = date, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = P_F * 48 /(60 * 60 * 24), # P_F [mm timestep-1] * 48 [timesteps day-1] / 86400 [secs day-1 ] + tmin = tmin, # TMIN_F_MDS, + tmax = tmax, # TMAX_F_MDS, + fapar = FPAR, + co2 = CO2_F_MDS, + ccov = 0 + ) |> + list() + +p_hydro_drivers$forcing_acclim <- + ddf_3hr_maxima |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + left_join(tmaxmin) |> + group_by(date) |> + summarize( + date = date, + time = time, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = NA, # P_F * 48 / (60 * 60 * 24), + tmin = tmin, # TMIN_F_MDS, + tmax = tmax, # TMAX_F_MDS, + fapar = FPAR, + co2 = CO2_F_MDS, + ccov = 0 + ) |> + list() + +p_hydro_drivers$forcing_daytime_mean <- + ddf_daytime_mean |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + left_join(tmaxmin) |> + group_by(date) |> + summarize( + date = date, + time = time, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = NA, # P_F * 48 / (60 * 60 * 24), + tmin = tmin, # TMIN_F_MDS, + tmax = tmax, # TMAX_F_MDS, + fapar = FPAR, + co2 = CO2_F_MDS, + ccov = 0, + daylength = daylength + ) |> + list() + + +p_hydro_drivers$forcing_halfhourly <- + hhdf |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(time) |> + summarize( + date = date, + time = time, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = P_F * 48 / (60 * 60 * 24), + fapar = FPAR, + co2 = CO2_F_MDS, + ccov = 0 + ) |> + list() + + +# write all drivers to file +# apply compression to minimize space +filn <- paste0(out_path, "/",site,"_p_hydro_drivers.rda") +message(paste0("- writing to file: ", filn)) +save(p_hydro_drivers, + file = filn + ) + + +# JJ Note: The gpp and latenth units here are different from the demo dataset supplied with rsofun. Here the units are matched to the output units from rsofun (see conversion below) +# Write validation data +load("../data/p_model_validation.rda") + +p_hydro_validation <- p_model_validation +p_hydro_validation$sitename[[1]] = site + +p_hydro_validation$data <- + ddf_24hr_mean |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(date) |> + summarise( + date = date, + time = time, + gpp = GPP_DT_VUT_REF, + latenth = LE_F_MDS + ) |> + mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] + mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] + list() + +p_hydro_validation$data_hh <- + hhdf |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(time) |> + summarise( + date = date, + time = time, + gpp = GPP_DT_VUT_REF, + latenth = LE_F_MDS + ) |> + mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] + mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] + list() + +p_hydro_validation$data_3hr_mean <- + ddf_3hr_maxima |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(time) |> + summarise( + date = date, + time = time, + gpp = GPP_DT_VUT_REF, + latenth = LE_F_MDS + ) |> + mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] + mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] + list() + +p_hydro_validation$data_daytime_mean <- + ddf_daytime_mean |> + dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> + dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> + group_by(time) |> + summarise( + date = date, + time = time, + gpp = GPP_DT_VUT_REF, + latenth = LE_F_MDS + ) |> + mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] + mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] + list() + + +filn <- paste0(out_path,"/",site,"_p_hydro_validation.rda") +message(paste0("- writing to file: ", filn)) +save(p_hydro_validation, + file = filn + ) + + +#' +#' + +cairo_pdf(filename = paste0(figures_prefix, "_fig5_phydro_drivers.pdf"), height=5, width=10) +p_hydro_drivers$forcing[[1]] %>% + select(date, ppfd, netrad, temp, vpd, fapar, rain) %>% + melt("date") %>% + mutate(type="24-hr mean") %>% + rbind(p_hydro_drivers$forcing_acclim[[1]] %>% + select(date, ppfd, netrad, temp, vpd, fapar, rain) %>% + melt("date") %>% + mutate(type="3-hr maxima") + ) %>% + rbind(p_hydro_drivers$forcing_daytime_mean[[1]] %>% + select(date, ppfd, netrad, temp, vpd, fapar, rain) %>% + melt("date") %>% + mutate(type="daytime means") + ) %>% + ggplot(aes(y=value, x=date)) + + geom_line(aes(group=type, col=type), alpha=0.5) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free") +dev.off() + diff --git a/vignettes/run_bayesian_phydro.sh b/vignettes/run_bayesian_phydro.sh new file mode 100755 index 00000000..58ccd2e4 --- /dev/null +++ b/vignettes/run_bayesian_phydro.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +declare -a arr=( + "CH-Dav" + "DE-Hai" + "DE-Tha" + "FR-LBr" +) + + +for SITE in "${arr[@]}" +do + echo $SITE + ( + generate_data=true + + if [ "$generate_data" = true ]; then + echo "Generating data for site" $SITE + Rscript rsofun_phydro_data_generation.R $SITE + fi + + echo "Running calibration for site" $SITE + Rscript phydro_long_calibration.R $SITE + ) & +done +wait From 7d919754525c89345c0e2f9eb579ea0c71e7c0fc Mon Sep 17 00:00:00 2001 From: Jaideep Date: Wed, 17 Jan 2024 16:14:59 +0100 Subject: [PATCH 025/112] completed pipeline for batch calibration --- R/calib_sofun.R | 8 +- src/gpp_pmodel.mod.f90 | 2 +- vignettes/phydro_long_calibration.R | 96 +++++-- vignettes/read_meta_fdk.R | 62 +++++ vignettes/rsofun_phydro_data_generation.R | 299 ++++++++++++---------- vignettes/run_bayesian_phydro.sh | 24 +- vignettes/run_rsofun_ubelix.sh | 2 + vignettes/site_list.txt | 244 ++++++++++++++++++ 8 files changed, 570 insertions(+), 167 deletions(-) create mode 100644 vignettes/read_meta_fdk.R create mode 100644 vignettes/run_rsofun_ubelix.sh create mode 100644 vignettes/site_list.txt diff --git a/R/calib_sofun.R b/R/calib_sofun.R index 67a6f904..5bdfd7e6 100644 --- a/R/calib_sofun.R +++ b/R/calib_sofun.R @@ -155,10 +155,12 @@ calib_sofun <- function( # reformat parameters pars <- as.data.frame(do.call("rbind", settings$par), row.names = FALSE) - priors <- BayesianTools::createUniformPrior( + priors <- BayesianTools::createTruncatedNormalPrior( + unlist(pars$mean), + unlist(pars$sd), unlist(pars$lower), - unlist(pars$upper), - unlist(pars$init) + unlist(pars$upper) + # unlist(pars$init) ) # setup the bayes run, no message forwarding is provided diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index a479ebbe..398eebd3 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -129,7 +129,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, vegcover, ! interacts with the root distribution?? !---------------------------------------------------------------- do pft = 1,npft - pxx_plant = tile(1)%plant(pft)%phydro_p50_plant * (log(0.03)/log(0.5))^(1.0d0/tile(1)%plant(pft)%phydro_b_plant) ! Currently xx set to 3% + pxx_plant = tile(1)%plant(pft)%phydro_p50_plant * (log(0.03)/log(0.5))**(1.0d0/tile(1)%plant(pft)%phydro_b_plant) ! Currently xx set to 3% swp(pft) = (tile(1)%soil%params%whc / tile(1)%plant(pft)%Ssoil)**(-tile(1)%plant(pft)%bsoil) & -(tile(1)%soil%phy%wcont / tile(1)%plant(pft)%Ssoil)**(-tile(1)%plant(pft)%bsoil) ! Assuming lu = 1, otherwise, use tile(lu) and a 2D array swp(pft) = min(swp(pft), 0.0) ! clamp +ve values to 0 diff --git a/vignettes/phydro_long_calibration.R b/vignettes/phydro_long_calibration.R index ce50f9c2..1c46ba48 100644 --- a/vignettes/phydro_long_calibration.R +++ b/vignettes/phydro_long_calibration.R @@ -4,6 +4,7 @@ library(reshape2) library(rsofun) library(BayesianTools) library(tictoc) +library(ncdf4) plot_only = F @@ -44,7 +45,8 @@ p_hydro_drivers$params_siml[[1]]$use_pml = F get_density <- function(x, y, ...) { - dens <- MASS::kde2d(x, y, ...) + df = tibble(x=x, y=y) %>% drop_na + dens <- MASS::kde2d(df$x, df$y, ...) ix <- findInterval(x, dens$x) iy <- findInterval(y, dens$y) ii <- cbind(ix, iy) @@ -118,7 +120,7 @@ params_modl <- list( phydro_b_plant = 1, phydro_alpha = 1.334564e-01, #0.1, phydro_gamma = 1.889438e+00, #1, - bsoil = 3.193891e+00, #3, + bsoil = 3, #3, Ssoil = 3.193891e+02, #3, whc = 7.819509e+02 #200 ) @@ -131,32 +133,90 @@ plot_pmodel(output_p) ## P-hydro full calibration (BayesianTools) +# nc = nc_open("~/Downloads/cwdx80.nc") +# lons = ncvar_get(nc, "lon") +# lats = ncvar_get(nc, "lat") +# S80 = ncvar_get(nc, "cwdx80") +# +# site_lon = p_hydro_drivers$site_info[[1]]$lon +# site_lat = p_hydro_drivers$site_info[[1]]$lat +# +# lonid = which(lons > site_lon)[1]-1 +# latid = which(lats > site_lat)[1]-1 +# n = 1 +# S80_slice = S80[(lonid-n):(lonid+n), (latid-n):(latid+n)] +# whc_site = mean(as.numeric(S80_slice, na.rm=T)) +# whc_site_sd = sd(as.numeric(S80_slice, na.rm=T)) + +whc_site = p_hydro_drivers$site_info[[1]]$whc +whc_site_sd = p_hydro_drivers$site_info[[1]]$whc_sd + +# image(x=lons, y=lats, z=log(1+S80), col = scales::viridis_pal()(100)) +# points(x=site_lon, y=site_lat, pch=20, col="red") p_hydro_drivers$params_siml[[1]]$use_phydro = T p_hydro_drivers$params_siml[[1]]$use_pml = F +parjj = read.csv("ancillary_data/fitted_params_Joshi_et_al_2022.csv") +parjj = parjj %>% mutate(K.scalar = K.scalar*1e-16) +pars_joshi2022 = parjj %>% + filter(Species != "Helianthus annuus") %>% + select(K.scalar, P50, alpha, gamma, A.G, Species) %>% + filter(A.G != "") %>% pivot_longer(cols=c("K.scalar", "P50", "alpha", "gamma")) %>% + group_by(A.G, name) %>% + summarize(mean=mean(value), sd=sd(value), n=length(value)) + + +uniform_range = function(lower, upper){ + list(lower= lower, upper=upper, mean = (upper+lower)/2, sd = (upper-lower)*10) +} +gaussian_range = function(mean, sd){ + if (mean > 0) list(lower= max(mean-5*sd, 0), upper=mean+5*sd, mean = mean, sd = sd) + else list(lower= mean-5*sd, upper=min(mean+5*sd, 0), mean = mean, sd = sd) +} + # Define calibration settings and parameter ranges from previous work settings_bayes <- list( method = "BayesianTools", + # par = list( + # kphio = list(lower=0.04, upper=0.09, init=0.05), + # phydro_K_plant = list(lower=0.05e-16, upper=0.3e-16, init=0.15e-16), + # phydro_p50_plant = list(lower=-3, upper=-0.5, init=-1), + # phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), + # phydro_gamma = list(lower=0.5, upper=1.5, init=1), + # bsoil = list(lower=0.1, upper=10, init=3), + # Ssoil = list(lower=0.1, upper=1000, init=10), + # whc = list(lower=10, upper=5000, init=1000), + # err_gpp = list(lower = 0.01, upper = 4, init = 2), + # err_le = list(lower = 0.1e6, upper = 10e6, init = 2e6) + # ), par = list( - kphio = list(lower=0.04, upper=0.09, init=0.05), - phydro_K_plant = list(lower=0.05e-16, upper=5e-16, init=0.3e-16), - phydro_p50_plant = list(lower=-3, upper=-0.5, init=-1), - phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), - phydro_gamma = list(lower=0.5, upper=1.5, init=1), - bsoil = list(lower=0.1, upper=10, init=3), - Ssoil = list(lower=0.1, upper=1000, init=10), - whc = list(lower=10, upper=5000, init=1000), - err_gpp = list(lower = 0.01, upper = 4, init = 2), - err_le = list(lower = 0.1e6, upper = 10e6, init = 2e6) + kphio = uniform_range(lower=0.04, upper=0.09), + # phydro_K_plant = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="K.scalar") %>% pull(mean), + # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="K.scalar") %>% pull(sd)), + # phydro_p50_plant = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="P50") %>% pull(mean), + # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="P50") %>% pull(sd)), + phydro_K_plant = uniform_range(lower=0.1e-16, 5e-16), + phydro_p50_plant = uniform_range(lower=-4, -0.3), + # phydro_alpha = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="alpha") %>% pull(mean), + # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="alpha") %>% pull(sd)), + # phydro_gamma = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(mean), + # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(sd)), + phydro_alpha = gaussian_range(mean = 0.11, sd = 0.02), + phydro_gamma = uniform_range(lower = 0.1, upper = 2), + #bsoil = uniform_range(lower=0.1, upper=10), + Ssoil = uniform_range(lower = 0, upper = whc_site+whc_site_sd), + whc = gaussian_range(mean = whc_site, sd = whc_site_sd), + err_gpp = uniform_range(lower = 0.01, upper = 4), + err_le = uniform_range(lower = 0.1e6, upper = 10e6) ), metric = rsofun::cost_likelihood_pmodel, control = list( sampler = "DEzs", settings = list( - nrChains = 3, - burnin = 10000, - iterations = 20000 # kept artificially low + nrChains = 1, + burnin = 500, + iterations = 2000 # kept artificially low ) ) ) @@ -187,10 +247,10 @@ if (!plot_only){ kc_jmax = 0.41, # phydro_K_plant = 0.3e-16, # phydro_P50_plant = -1, - phydro_b_plant = 1 + phydro_b_plant = 1, # phydro_alpha = 0.1, # phydro_gamma = 1 - # bsoil = 3, + bsoil = 3 # Ssoil = 40, # whc = 90 ), @@ -237,7 +297,7 @@ params_modl_opt$phydro_K_plant = pars_calib_bayes$par[["phydro_K_plant"]] params_modl_opt$phydro_p50_plant = pars_calib_bayes$par[["phydro_p50_plant"]] params_modl_opt$phydro_alpha = pars_calib_bayes$par[["phydro_alpha"]] params_modl_opt$phydro_gamma = pars_calib_bayes$par[["phydro_gamma"]] -params_modl_opt$bsoil = pars_calib_bayes$par[["bsoil"]] +# params_modl_opt$bsoil = pars_calib_bayes$par[["bsoil"]] params_modl_opt$Ssoil = pars_calib_bayes$par[["Ssoil"]] params_modl_opt$whc = pars_calib_bayes$par[["whc"]] diff --git a/vignettes/read_meta_fdk.R b/vignettes/read_meta_fdk.R new file mode 100644 index 00000000..cefe4103 --- /dev/null +++ b/vignettes/read_meta_fdk.R @@ -0,0 +1,62 @@ +read_meta_fdk = function (site, path, meta_data = FALSE, + out_path) +{ + IGBP_veg_long <- time <- TIMESTAMP_START <- TIMESTAMP_END <- P <- TA_F <- PA_F <- CO2_F <- P_F <- TA_F_MDS <- CO2_F_MDS <- NULL + files <- list.files(path, utils::glob2rx(paste0(site, "*.nc")), + full.names = TRUE, recursive = TRUE) + if (length(files) != 2) { + stop("Missing either flux or meteo data for the requested site") + } + if (meta_data) { + files <- files[grepl(utils::glob2rx("*_Flux.nc"), files)] + } + df <- lapply(files, function(file) { + nc <- ncdf4::nc_open(file) + time <- ncdf4::ncvar_get(nc, "time") + time_units <- strsplit(ncdf4::ncatt_get(nc, "time")$units, + "seconds since ")[[1]][2] + time_date <- lubridate::ymd_hms(time_units, tz = "GMT") + + lubridate::seconds(time) + vars <- names(nc$var) + df <- as.data.frame(lapply(vars, function(x) ncdf4::ncvar_get(nc, + x))) + ncdf4::nc_close(nc) + colnames(df) <- vars + df$time <- time_date + if ("IGBP_veg_short" %in% colnames(df)) { + df$IGBP_veg_short <- trimws(df$IGBP_veg_short) + } + else { + warning("Column 'IGBP_veg_short' does not exist in the data frame. Assigning NA.") + df$IGBP_veg_short <- NA + } + if ("IGBP_veg_long" %in% names(df)) { + df <- subset(df, select = -IGBP_veg_long) + } + if (meta_data) { + meta_columns <- c("latitude", "longitude", "reference_height", + "canopy_height", "elevation", "IGBP_veg_short", + "year_start", "year_end") + missing_columns <- setdiff(meta_columns, colnames(df)) + if (length(missing_columns) > 0) { + for (col in missing_columns) { + warning(paste("Column", col, "does not exist in the data frame. Assigning NA.")) + df[, col] <- NA + } + } + df$year_start <- format(min(df$time), "%Y") + df$year_end <- format(max(df$time), "%Y") + df <- df[1, meta_columns] + df$sitename <- site + } + return(df) + }) + if (meta_data) { + return(df) + } + else { + all <- suppressMessages(dplyr::left_join(df[[1]], df[[2]], + by = "time")) + } + +} diff --git a/vignettes/rsofun_phydro_data_generation.R b/vignettes/rsofun_phydro_data_generation.R index c30f8ec9..2f9ee47b 100644 --- a/vignettes/rsofun_phydro_data_generation.R +++ b/vignettes/rsofun_phydro_data_generation.R @@ -17,8 +17,11 @@ knitr::opts_chunk$set(echo = TRUE) ## -------------------------------------------------------------------- library(tidyverse) library(reshape2) -library(FluxDataKit) +# library(FluxDataKit) library(lubridate) +library(ncdf4) + +source("read_meta_fdk.R") #' #' @@ -59,15 +62,15 @@ file_csv = files_csv[intersect(grep(site, files_csv), message("- reading Metadata for site") meta <- suppressWarnings( try( - fdk_convert_lsm( + read_meta_fdk( site = site, - fluxnet_format = TRUE, path = lsm_path, meta_data = T ) ) ) +print(meta) # get half-hourly data -------------------------------------------------------- # message("- convert to FLUXNET standard CSV file") @@ -80,7 +83,7 @@ meta <- suppressWarnings( # ) # ) # ) -# +# # if(inherits(hhdf, "try-error")){ # message("!!! conversion to FLUXNET failed !!!") # return(NULL) @@ -91,7 +94,7 @@ hhdf <- readr::read_csv(paste0(csv_path,"/",file_csv)) # Add date and time columns to hhdf for easier further processing. # --------------------------------------------------------- -hhdf = +hhdf = hhdf |> mutate(time = lubridate::as_datetime(as.character(TIMESTAMP_START), tz = "GMT", format="%Y%m%d%H%M")) |> mutate(date = lubridate::as_date(time)) @@ -100,7 +103,7 @@ message("- Add SW_OUT=NA if not present") if (!("SW_OUT" %in% colnames(hhdf))) { hhdf$SW_OUT = NA } - + # Aggregate to daily 24-hr means ---------------------------------------------------------- message("- downsampling FLUXNET format - 24 hr means") @@ -114,40 +117,43 @@ ddf_24hr_mean <- # Check aggregation cairo_pdf(filename = paste0(figures_prefix, "_fig1_ddf_24hr_mean.pdf")) -ddf_24hr_mean %>% - mutate(albedo = SW_OUT/SW_IN_F_MDS) %>% - select(SW_IN_F_MDS, NETRAD, LW_IN_F_MDS, LAI, FPAR, GPP_DT_VUT_REF, LE_F_MDS, TA_F_MDS, date) %>% - melt("date") %>% - ggplot(aes(y=value, x=date)) + - geom_line(col="aquamarine4") + +# png(filename = paste0(figures_prefix, "_fig1_ddf_24hr_mean.png"), height=700*3, width=700*3, res = 300) +ddf_24hr_mean %>% + mutate(albedo = SW_OUT/SW_IN_F_MDS) %>% + select(SW_IN_F_MDS, NETRAD, LW_IN_F_MDS, LAI, FPAR, GPP_DT_VUT_REF, LE_F_MDS, TA_F_MDS, date) %>% + melt("date") %>% + ggplot(aes(y=value, x=date)) + + geom_line(col="aquamarine4") + theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") %>% + facet_wrap(~variable, scales = "free") %>% print() dev.off() ddf_24hr_mean %>% filter(!is.na(NETRAD)) %>% pull(date) %>% summary() -#' +#' ## -------------------------------------------------------------------- -valid_years = read.csv(text = " - sitename , start_year , end_year \n - CH-Dav , 2000 , 2010 \n - DE-Hai , 2000 , 2020 \n - DE-Tha , 2000 , 2020 \n - FR-LBr , 2000 , 2008 \n - FR-Pue , 2000 , 2013 \n - GF-Guy , 2004 , 2015 \n - NL-Loo , 2000 , 2014 \n - US-Me2 , 2002 , 2020 \n - US-NR1 , 2000 , 2016 \n - US-Ton , 2001 , 2014 \n - ", - header=T, strip.white=T) +# valid_years = read.csv(text = " +# Site , start_year , end_year \n +# CH-Dav , 2000 , 2010 \n +# DE-Hai , 2000 , 2020 \n +# DE-Tha , 2000 , 2020 \n +# FR-LBr , 2000 , 2008 \n +# FR-Pue , 2000 , 2013 \n +# GF-Guy , 2004 , 2015 \n +# NL-Loo , 2000 , 2014 \n +# US-Me2 , 2002 , 2020 \n +# US-NR1 , 2000 , 2016 \n +# US-Ton , 2001 , 2014 \n +# ", +# header=T, strip.white=T) + +valid_years = read.csv("ancillary_data/valid_years_final.csv", header = T, fileEncoding = "UTF-16") # Get valid data years -ystart = valid_years %>% filter(sitename==site) %>% pull(start_year) -yend = valid_years %>% filter(sitename==site) %>% pull(end_year) +ystart = valid_years %>% filter(Site==site) %>% pull(start_year) +yend = valid_years %>% filter(Site==site) %>% pull(end_year) # ystart = max(meta[[1]]$year_start, 2000) %>% as.numeric() # yend = meta[[1]]$year_end %>% as.numeric() @@ -155,22 +161,22 @@ yend = valid_years %>% filter(sitename==site) %>% pull(end_year) # Aggregate around daily maximum ppfd for acclimating model # --------------------------------------------------------- test.3day = hhdf %>% filter(date >= as_date(paste0(floor((ystart+yend)/2),"-06-01")) & - date <= as_date(paste0(floor((ystart+yend)/2),"-06-03")) ) + date <= as_date(paste0(floor((ystart+yend)/2),"-06-03")) ) aggregate_daily_3hr_maxima = function(df){ # Get the time at which SW_IN is maximum maxppfd <- df %>% filter(SW_IN_F_MDS == max(SW_IN_F_MDS)) max_t <- maxppfd$time[1] - + # Select times that lie in 3-hr interval around max_t - df_aroundmax <- df %>% filter(time < (max_t + 1.5*3600) & + df_aroundmax <- df %>% filter(time < (max_t + 1.5*3600) & time > (max_t - 1.5*3600) ) # take mean of selected entries df_mean <- df_aroundmax |> select(-TIMESTAMP_START, -TIMESTAMP_END) |> summarize_all(.funs = mean) - + df_mean } @@ -179,24 +185,24 @@ aggregate_daily_3hr_maxima = function(df){ test.3day.3hr = test.3day %>% group_by(date) %>% do(aggregate_daily_3hr_maxima(.)) %>% ungroup() cairo_pdf(filename = paste0(figures_prefix, "_fig2_3hr_maxima_sample.pdf")) -test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% - melt("time") %>% - mutate(type="hourly") %>% - rbind(test.3day.3hr %>% - select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% - melt("time") %>% +test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% + melt("time") %>% + mutate(type="hourly") %>% + rbind(test.3day.3hr %>% + select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% + melt("time") %>% mutate(type="daily") - ) %>% - ggplot(aes(y=value, x=as.POSIXct(time))) + - geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + - geom_point(data = . %>% filter(type == "daily")) + + ) %>% + ggplot(aes(y=value, x=as.POSIXct(time))) + + geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + + geom_point(data = . %>% filter(type == "daily")) + theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") %>% + facet_wrap(~variable, scales = "free") %>% print() dev.off() -#' +#' ## -------------------------------------------------------------------- # Apply 3hr maxima aggregation to all data # ---------------------------------------- @@ -206,27 +212,27 @@ ddf_3hr_maxima <- hhdf |> do(aggregate_daily_3hr_maxima(.)) |> ungroup() -#' +#' ## -------------------------------------------------------------------- aggregate_daily_daylength = function(df){ - # Get the time at which SW_IN > 0 + # Get the time at which SW_IN > 0 pos_ppfd <- df %>% filter(SW_IN_F_MDS > 10) # if SW_IN is unavailable in that year calc daylength based on NETRAD if (nrow(pos_ppfd) < 2){ pos_ppfd <- df %>% filter(NETRAD > 25) } - + tmax <- max(pos_ppfd$time) tmin <- min(pos_ppfd$time) - + # Select times that lie in 3-hr interval around max_t - df_aroundmax <- df %>% filter(time <= tmax & + df_aroundmax <- df %>% filter(time <= tmax & time >= tmin ) # take mean of selected entries df_mean <- df_aroundmax |> select(-TIMESTAMP_START, -TIMESTAMP_END) |> - summarize_all(.funs = mean) |> + summarize_all(.funs = mean) |> mutate(daylength = difftime(tmax, tmin, units="hours") |> as.numeric()) df_mean @@ -238,24 +244,24 @@ test.3day.daylen = test.3day %>% group_by(date) %>% do(aggregate_daily_daylength cairo_pdf(filename = paste0(figures_prefix, "_fig3_daytime_sample.pdf")) test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI) %>% - mutate(daylength = NA) %>% - melt("time") %>% - mutate(type="hourly") %>% - rbind(test.3day.daylen %>% - select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, daylength) %>% - melt("time") %>% + mutate(daylength = NA) %>% + melt("time") %>% + mutate(type="hourly") %>% + rbind(test.3day.daylen %>% + select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, daylength) %>% + melt("time") %>% mutate(type="daily") - ) %>% - ggplot(aes(y=value, x=as.POSIXct(time))) + - geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + - geom_point(data = . %>% filter(type == "daily")) + + ) %>% + ggplot(aes(y=value, x=as.POSIXct(time))) + + geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + + geom_point(data = . %>% filter(type == "daily")) + theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") %>% + facet_wrap(~variable, scales = "free") %>% print() dev.off() -#' +#' ## -------------------------------------------------------------------- # Apply daytime mean aggregation to all data # ------------------------------------------ @@ -267,40 +273,60 @@ ddf_daytime_mean <- hhdf |> # Check daylength seasonality cairo_pdf(filename = paste0(figures_prefix, "_fig4_daylenth_seasonality.pdf")) -ddf_daytime_mean %>% +ddf_daytime_mean %>% ggplot(aes(y=daylength, x=date)) + geom_line()+ - theme_classic() %>% + theme_classic() %>% print() dev.off() -#' +#' ## -------------------------------------------------------------------- # Calculate daily tmax and tmin from hh data # ------------------------------------------ -tmaxmin <- - hhdf |> +tmaxmin <- + hhdf |> group_by(date) |> summarize( tmax = max(TA_F_MDS), tmin = min(TA_F_MDS) ) -#' +#' ## -------------------------------------------------------------------- # Creating driver object ------------------------------------------------------ message("- compiling drivers") load("../data/p_model_drivers.rda") + +nc = nc_open("ancillary_data/cwdx80.nc") +lons = ncvar_get(nc, "lon") +lats = ncvar_get(nc, "lat") +S80 = ncvar_get(nc, "cwdx80") + +site_lon = meta[[1]]$longitude +site_lat = meta[[1]]$latitude + +lonid = which(lons > site_lon)[1]-1 +latid = which(lats > site_lat)[1]-1 +n = 1 +S80_slice = S80[(lonid-n):(lonid+n), (latid-n):(latid+n)] +whc_site = mean(as.numeric(S80_slice, na.rm=T)) +whc_site_sd = sd(as.numeric(S80_slice, na.rm=T)) + + p_hydro_drivers <- p_model_drivers p_hydro_drivers$sitename[[1]] = site -p_hydro_drivers$site_info[[1]] = +p_hydro_drivers$site_info[[1]] = tibble( - lon=meta[[1]]$longitude, - lat=meta[[1]]$latitude, + lon=meta[[1]]$longitude, + lat=meta[[1]]$latitude, elv = meta[[1]]$elevation, - canopy_height=meta[[1]]$canopy_height, - reference_height = meta[[1]]$reference_height + canopy_height = ifelse(is.na(meta[[1]]$canopy_height), yes = 20, meta[[1]]$canopy_height), + reference_height = ifelse(is.na(meta[[1]]$reference_height), yes = 22, meta[[1]]$reference_height), + whc = whc_site, + whc_sd = whc_site_sd, + IGBP_veg_short = meta[[1]]$IGBP_veg_short ) kfFEC = 2.04 @@ -308,7 +334,7 @@ start_year = ystart end_year = yend # for demo, use just a subset of years -p_hydro_drivers$forcing <- +p_hydro_drivers$forcing <- ddf_24hr_mean |> dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> @@ -316,22 +342,22 @@ p_hydro_drivers$forcing <- group_by(date) |> summarize( date = date, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, rain = P_F * 48 /(60 * 60 * 24), # P_F [mm timestep-1] * 48 [timesteps day-1] / 86400 [secs day-1 ] - tmin = tmin, # TMIN_F_MDS, - tmax = tmax, # TMAX_F_MDS, - fapar = FPAR, + tmin = tmin, # TMIN_F_MDS, + tmax = tmax, # TMAX_F_MDS, + fapar = FPAR, co2 = CO2_F_MDS, ccov = 0 ) |> list() -p_hydro_drivers$forcing_acclim <- +p_hydro_drivers$forcing_acclim <- ddf_3hr_maxima |> dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> @@ -340,22 +366,22 @@ p_hydro_drivers$forcing_acclim <- summarize( date = date, time = time, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = NA, # P_F * 48 / (60 * 60 * 24), - tmin = tmin, # TMIN_F_MDS, - tmax = tmax, # TMAX_F_MDS, - fapar = FPAR, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = NA, # P_F * 48 / (60 * 60 * 24), + tmin = tmin, # TMIN_F_MDS, + tmax = tmax, # TMAX_F_MDS, + fapar = FPAR, co2 = CO2_F_MDS, - ccov = 0 + ccov = 0 ) |> list() -p_hydro_drivers$forcing_daytime_mean <- +p_hydro_drivers$forcing_daytime_mean <- ddf_daytime_mean |> dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> @@ -364,16 +390,16 @@ p_hydro_drivers$forcing_daytime_mean <- summarize( date = date, time = time, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = NA, # P_F * 48 / (60 * 60 * 24), - tmin = tmin, # TMIN_F_MDS, - tmax = tmax, # TMAX_F_MDS, - fapar = FPAR, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = NA, # P_F * 48 / (60 * 60 * 24), + tmin = tmin, # TMIN_F_MDS, + tmax = tmax, # TMAX_F_MDS, + fapar = FPAR, co2 = CO2_F_MDS, ccov = 0, daylength = daylength @@ -381,7 +407,7 @@ p_hydro_drivers$forcing_daytime_mean <- list() -p_hydro_drivers$forcing_halfhourly <- +p_hydro_drivers$forcing_halfhourly <- hhdf |> dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> @@ -389,27 +415,28 @@ p_hydro_drivers$forcing_halfhourly <- summarize( date = date, time = time, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = P_F * 48 / (60 * 60 * 24), - fapar = FPAR, + temp = TA_F_MDS, + vpd = VPD_F_MDS * 100, + ppfd = SW_IN_F_MDS * kfFEC * 1e-06, + netrad = NETRAD, + patm = PA_F * 1000, + snow = 0, + rain = P_F * 48 / (60 * 60 * 24), + fapar = FPAR, co2 = CO2_F_MDS, ccov = 0 ) |> list() + # write all drivers to file # apply compression to minimize space filn <- paste0(out_path, "/",site,"_p_hydro_drivers.rda") message(paste0("- writing to file: ", filn)) save(p_hydro_drivers, file = filn - ) + ) # JJ Note: The gpp and latenth units here are different from the demo dataset supplied with rsofun. Here the units are matched to the output units from rsofun (see conversion below) @@ -419,7 +446,7 @@ load("../data/p_model_validation.rda") p_hydro_validation <- p_model_validation p_hydro_validation$sitename[[1]] = site -p_hydro_validation$data <- +p_hydro_validation$data <- ddf_24hr_mean |> dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> @@ -434,7 +461,7 @@ p_hydro_validation$data <- mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] list() -p_hydro_validation$data_hh <- +p_hydro_validation$data_hh <- hhdf |> dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> @@ -449,7 +476,7 @@ p_hydro_validation$data_hh <- mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] list() -p_hydro_validation$data_3hr_mean <- +p_hydro_validation$data_3hr_mean <- ddf_3hr_maxima |> dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> @@ -464,7 +491,7 @@ p_hydro_validation$data_3hr_mean <- mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] list() -p_hydro_validation$data_daytime_mean <- +p_hydro_validation$data_daytime_mean <- ddf_daytime_mean |> dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> @@ -484,29 +511,29 @@ filn <- paste0(out_path,"/",site,"_p_hydro_validation.rda") message(paste0("- writing to file: ", filn)) save(p_hydro_validation, file = filn - ) + ) -#' -#' +#' +#' cairo_pdf(filename = paste0(figures_prefix, "_fig5_phydro_drivers.pdf"), height=5, width=10) p_hydro_drivers$forcing[[1]] %>% - select(date, ppfd, netrad, temp, vpd, fapar, rain) %>% - melt("date") %>% - mutate(type="24-hr mean") %>% + select(date, ppfd, netrad, temp, vpd, fapar, rain) %>% + melt("date") %>% + mutate(type="24-hr mean") %>% rbind(p_hydro_drivers$forcing_acclim[[1]] %>% - select(date, ppfd, netrad, temp, vpd, fapar, rain) %>% - melt("date") %>% + select(date, ppfd, netrad, temp, vpd, fapar, rain) %>% + melt("date") %>% mutate(type="3-hr maxima") - ) %>% + ) %>% rbind(p_hydro_drivers$forcing_daytime_mean[[1]] %>% - select(date, ppfd, netrad, temp, vpd, fapar, rain) %>% - melt("date") %>% + select(date, ppfd, netrad, temp, vpd, fapar, rain) %>% + melt("date") %>% mutate(type="daytime means") - ) %>% - ggplot(aes(y=value, x=date)) + - geom_line(aes(group=type, col=type), alpha=0.5) + + ) %>% + ggplot(aes(y=value, x=date)) + + geom_line(aes(group=type, col=type), alpha=0.5) + theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~variable, scales = "free") diff --git a/vignettes/run_bayesian_phydro.sh b/vignettes/run_bayesian_phydro.sh index 58ccd2e4..2ac9e028 100755 --- a/vignettes/run_bayesian_phydro.sh +++ b/vignettes/run_bayesian_phydro.sh @@ -1,19 +1,17 @@ #!/bin/bash -declare -a arr=( - "CH-Dav" - "DE-Hai" - "DE-Tha" - "FR-LBr" -) +NCORES=8 +# declare -a arr=$( ls ~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_FLUXNET/*HH* | awk -F "_" '{print $4}' ) -for SITE in "${arr[@]}" +declare -a arr=$( cat site_list.txt ) + +generate_data=false + +for SITE in "US-Ho2" # ${arr[@]} do echo $SITE ( - generate_data=true - if [ "$generate_data" = true ]; then echo "Generating data for site" $SITE Rscript rsofun_phydro_data_generation.R $SITE @@ -22,5 +20,13 @@ do echo "Running calibration for site" $SITE Rscript phydro_long_calibration.R $SITE ) & + + # allow to execute up to $NCORES jobs in parallel + if [[ $(jobs -r -p | wc -l) -ge $NCORES ]]; then + # now there are $N jobs already running, so wait here for any job + # to be finished so there is a place to start next one. + wait -n + fi + done wait diff --git a/vignettes/run_rsofun_ubelix.sh b/vignettes/run_rsofun_ubelix.sh new file mode 100644 index 00000000..06639a38 --- /dev/null +++ b/vignettes/run_rsofun_ubelix.sh @@ -0,0 +1,2 @@ +module load Workspace_Home +module load R diff --git a/vignettes/site_list.txt b/vignettes/site_list.txt new file mode 100644 index 00000000..3c494c46 --- /dev/null +++ b/vignettes/site_list.txt @@ -0,0 +1,244 @@ +DE-Hai +BE-Vie +CA-Cbo +DE-Geb +DE-Tha +SE-Deg +US-Me2 +IT-MBo +CZ-BK1 +DE-Gri +FR-Gri +GF-Guy +FR-Aur +FR-Fon +US-GLE +US-Ho2 +BE-Bra +CA-TP3 +US-MOz +BE-Lon +DE-Kli +IT-Col +NL-Loo +RU-Fyo +US-Ton +US-UMd +US-Var +US-Wjs +CA-TP4 +FR-FBn +IT-Ren +IT-Tor +US-ARM +US-ICs +US-Mpj +US-Ro1 +CA-TP1 +FR-Pue +IT-Lav +RU-Cok +US-BZS +US-KFS +AT-Neu +CH-Dav +CH-Lae +FI-Hyy +IT-Noe +US-BZB +US-BZF +US-Myb +US-NR1 +US-SRM +BE-Dor +CA-Gro +CA-Oas +CA-Obs +DK-Sor +IT-Ro2 +US-ICt +US-Jo2 +US-Oho +US-Tw1 +US-Wkg +CA-LP1 +CZ-wet +DE-RuR +DE-RuS +DE-RuW +IT-Cpz +CA-Man +FR-LBr +IT-BCi +IT-SRo +NL-Hor +US-Los +US-Ro4 +US-Syv +US-WCr +AU-DaS +AU-Sam +CA-Qfo +CH-Cha +CH-Fru +CZ-BK2 +DE-Hzd +DE-Obe +FI-Sod +FR-Hes +MY-PSO +SE-Nor +US-Blo +US-IB2 +US-KLS +US-SRC +US-SRG +US-Whs +AU-Cow +AU-DaP +AU-Dry +CA-ER1 +CA-TPD +CZ-Lnz +DE-HoH +IT-Ro1 +IT-SR2 +SE-Htm +US-Atq +US-Me3 +US-NGB +US-ORv +US-Rms +US-Rwf +US-Rws +AU-How +AU-Stp +AU-Wom +CA-DBB +CA-Qcu +CA-TP2 +CH-Oe1 +DE-Akm +DE-Lnf +FI-Sii +FI-Var +IT-Lsn +US-Bi1 +US-Bi2 +US-Me6 +US-ONA +US-Ro6 +US-Twt +US-xBR +AU-ASM +AU-Cpr +AU-Rig +CA-NS5 +CA-NS6 +CA-NS7 +CA-SF3 +CN-Cng +DE-Lkb +DE-Spw +ES-Amo +FI-Jok +FR-LGt +GH-Ank +GL-ZaH +HU-Bug +IT-Cp2 +US-BZo +US-CF1 +US-CF3 +US-Goo +US-Ivo +US-KS2 +US-Prr +US-Ro5 +US-Sne +US-Tw3 +AU-Fog +AU-Gin +AU-Wac +AU-Whr +BR-Sa3 +CA-NS1 +CA-NS2 +CA-NS4 +CA-SF1 +CA-SF2 +CG-Tch +CN-Cha +CN-Din +CN-Ha2 +CN-HaM +CN-Qia +DE-Bay +DE-Seh +DK-ZaH +ES-LgS +FI-Let +FI-Lom +FR-Tou +GL-NuF +IT-PT1 +PA-SPs +SN-Dhr +US-AR1 +US-AR2 +US-Aud +US-CF2 +US-CRT +US-CS2 +US-GBT +US-WPT +ZA-Kru +AU-Cum +AU-Emr +AU-Lit +AU-TTE +AU-Wrr +BE-Lcr +BR-Npw +CA-NS3 +CN-Dan +DE-SfN +DK-Eng +IT-CA3 +IT-Isp +IT-LMa +PA-SPn +PE-QFR +RU-Ha1 +SD-Dem +SJ-Adv +US-ARb +US-ARc +US-EDN +US-HWB +US-LWW +US-Me5 +US-Rwe +US-UM3 +AR-SLu +AU-Ade +AU-RDF +AU-Rob +AU-Ync +CA-DB2 +CN-Du2 +CN-Sw2 +DE-Zrk +DK-Fou +IT-CA1 +IT-CA2 +IT-La2 +US-A32 +US-Bar +US-Hn3 +US-KS1 +US-Lin +US-Snf +US-Tw4 +US-Tw5 + From 1c86008d9ea8558376a8dfd4e786379fff408f6c Mon Sep 17 00:00:00 2001 From: Jaideep Date: Wed, 17 Jan 2024 16:38:54 +0100 Subject: [PATCH 026/112] read data and output paths from cmdline args --- vignettes/phydro_long_calibration.R | 20 ++++++++++++++++---- vignettes/rsofun_phydro_data_generation.R | 16 +++++++++++++--- 2 files changed, 29 insertions(+), 7 deletions(-) diff --git a/vignettes/phydro_long_calibration.R b/vignettes/phydro_long_calibration.R index 1c46ba48..262237d1 100644 --- a/vignettes/phydro_long_calibration.R +++ b/vignettes/phydro_long_calibration.R @@ -22,9 +22,21 @@ if (length(args)==0) { site = args[1] } -data_dir = "~/Downloads/fluxdatakit_oct3/Phydro_drivers_3/" -out_dir = "~/Downloads/fluxdatakit_oct3/phydro_output/" -figures_dir = "~/Downloads/fluxdatakit_oct3/phydro_output/figures/" +if (length(args)>1) { + root_data_dir = "~/Downloads/fluxdatakit_oct3" +}else{ + root_data_dir = args[2] +} + +if (length(args)>2) { + out_dir = "~/Downloads/fluxdatakit_oct3/phydro_output" +}else{ + out_dir = args[3] +} + + +data_dir = paste0(root_data_dir, "/phydro_drivers/") +figures_dir = paste0(out_dir, "/figures/") dir.create(out_dir, showWarnings = F) dir.create(figures_dir, showWarnings = F) @@ -216,7 +228,7 @@ settings_bayes <- list( settings = list( nrChains = 1, burnin = 500, - iterations = 2000 # kept artificially low + iterations = 1000 # kept artificially low ) ) ) diff --git a/vignettes/rsofun_phydro_data_generation.R b/vignettes/rsofun_phydro_data_generation.R index 2f9ee47b..566f5358 100644 --- a/vignettes/rsofun_phydro_data_generation.R +++ b/vignettes/rsofun_phydro_data_generation.R @@ -36,14 +36,24 @@ if (length(args)==0) { site = args[1] } +if (length(args)>1) { + root_data_dir = "~/Downloads/fluxdatakit_oct3" +}else{ + root_data_dir = args[2] +} + + + + print(getwd()) #' #' ## -------------------------------------------------------------------- -lsm_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_LSM/" -csv_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_FLUXNET/" -out_path = "~/Downloads/fluxdatakit_oct3/Phydro_drivers_3/" + +lsm_path = paste0(root_data_dir, "/FLUXDATAKIT_LSM/") +csv_path = paste0(root_data_dir, "/FLUXDATAKIT_FLUXNET/") +out_path = paste0(root_data_dir, "/phydro_drivers/") dir.create(out_path, showWarnings = F) dir.create(paste0(out_path, "data_gen_figures/"), showWarnings = F) From 497fe2264ac7a7b821cd94abf12229bd23796b18 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Wed, 17 Jan 2024 16:45:13 +0100 Subject: [PATCH 027/112] update script with data/output dir inputs --- vignettes/run_bayesian_phydro.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/vignettes/run_bayesian_phydro.sh b/vignettes/run_bayesian_phydro.sh index 2ac9e028..5e39465a 100755 --- a/vignettes/run_bayesian_phydro.sh +++ b/vignettes/run_bayesian_phydro.sh @@ -14,11 +14,11 @@ do ( if [ "$generate_data" = true ]; then echo "Generating data for site" $SITE - Rscript rsofun_phydro_data_generation.R $SITE + Rscript rsofun_phydro_data_generation.R $SITE /data/scratch/jaideep/fluxdata fi echo "Running calibration for site" $SITE - Rscript phydro_long_calibration.R $SITE + Rscript phydro_long_calibration.R $SITE /data/scratch/jaideep/fluxdata /data/scratch/jaideep/phydro_output ) & # allow to execute up to $NCORES jobs in parallel From 17efddd5f4a669532127baef99c25567624983db Mon Sep 17 00:00:00 2001 From: Jaideep Date: Wed, 17 Jan 2024 17:41:06 +0100 Subject: [PATCH 028/112] first run for all 250 sites attempted here. --- vignettes/phydro_long_calibration.R | 14 +++++++------- vignettes/rsofun_phydro_data_generation.R | 4 ++-- vignettes/run_bayesian_phydro.sh | 12 +++++++----- 3 files changed, 16 insertions(+), 14 deletions(-) diff --git a/vignettes/phydro_long_calibration.R b/vignettes/phydro_long_calibration.R index 262237d1..92731746 100644 --- a/vignettes/phydro_long_calibration.R +++ b/vignettes/phydro_long_calibration.R @@ -22,16 +22,16 @@ if (length(args)==0) { site = args[1] } -if (length(args)>1) { +if (length(args)<2) { root_data_dir = "~/Downloads/fluxdatakit_oct3" }else{ root_data_dir = args[2] } -if (length(args)>2) { - out_dir = "~/Downloads/fluxdatakit_oct3/phydro_output" +if (length(args)<3) { + out_dir = "~/Downloads/fluxdatakit_oct3/phydro_output/" }else{ - out_dir = args[3] + out_dir = paste0(args[3],"/") } @@ -226,9 +226,9 @@ settings_bayes <- list( control = list( sampler = "DEzs", settings = list( - nrChains = 1, - burnin = 500, - iterations = 1000 # kept artificially low + nrChains = 3, + burnin = 10000, + iterations = 50000 # kept artificially low ) ) ) diff --git a/vignettes/rsofun_phydro_data_generation.R b/vignettes/rsofun_phydro_data_generation.R index 566f5358..5bfa426a 100644 --- a/vignettes/rsofun_phydro_data_generation.R +++ b/vignettes/rsofun_phydro_data_generation.R @@ -36,13 +36,13 @@ if (length(args)==0) { site = args[1] } -if (length(args)>1) { +if (length(args)<2) { root_data_dir = "~/Downloads/fluxdatakit_oct3" }else{ root_data_dir = args[2] } - +print(args) print(getwd()) diff --git a/vignettes/run_bayesian_phydro.sh b/vignettes/run_bayesian_phydro.sh index 5e39465a..8f25cf36 100755 --- a/vignettes/run_bayesian_phydro.sh +++ b/vignettes/run_bayesian_phydro.sh @@ -1,24 +1,26 @@ #!/bin/bash -NCORES=8 +NCORES=50 # declare -a arr=$( ls ~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_FLUXNET/*HH* | awk -F "_" '{print $4}' ) declare -a arr=$( cat site_list.txt ) -generate_data=false +generate_data=true +data_path=/data/scratch/jaideep/fluxdata +out_path=/data/scratch/jaideep/phydro_output -for SITE in "US-Ho2" # ${arr[@]} +for SITE in ${arr[@]} do echo $SITE ( if [ "$generate_data" = true ]; then echo "Generating data for site" $SITE - Rscript rsofun_phydro_data_generation.R $SITE /data/scratch/jaideep/fluxdata + Rscript rsofun_phydro_data_generation.R $SITE $data_path > out_data_gen_$SITE.txt fi echo "Running calibration for site" $SITE - Rscript phydro_long_calibration.R $SITE /data/scratch/jaideep/fluxdata /data/scratch/jaideep/phydro_output + Rscript phydro_long_calibration.R $SITE $data_path $out_path > out_calib_$SITE.txt ) & # allow to execute up to $NCORES jobs in parallel From b45ad15e585d4a85b8017cea1d9475073c1140ad Mon Sep 17 00:00:00 2001 From: Jaideep Date: Wed, 31 Jan 2024 16:24:10 +0100 Subject: [PATCH 029/112] try P/PET to regulate le_soil; output lwp, le_soil; script to generate site list --- R/run_pmodel_f_bysite.R | 15 +- src/biosphere_pmodel.mod.f90 | 5 + src/gpp_pmodel.mod.f90 | 10 +- src/interface_biosphere_pmodel.mod.f90 | 3 + src/plant_pmodel.mod.f90 | 4 + src/sofun_r.f90 | 5 +- src/tile_pmodel.mod.f90 | 3 + src/waterbal_splash.mod.f90 | 24 ++- src/wrappersc.c | 2 +- vignettes/phydro_long_calibration.R | 57 ++++++- vignettes/read_meta_fdk.R | 16 ++ vignettes/rsofun_phydro_data_generation.R | 29 ++-- vignettes/run_bayesian_phydro.sh | 9 +- vignettes/site_list_2.txt | 174 ++++++++++++++++++++++ 14 files changed, 322 insertions(+), 34 deletions(-) create mode 100644 vignettes/site_list_2.txt diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index ac3c99dc..07293511 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -482,7 +482,10 @@ run_pmodel_f_bysite <- function( "tsoil", "netrad", "wcont", - "snow") + "snow", + "le_soil", + "dpsi", + "psi_leaf") ) %>% as_tibble(.name_repair = "check_unique") %>% dplyr::bind_cols(ddf,.) @@ -491,8 +494,8 @@ run_pmodel_f_bysite <- function( out <- tibble(date = as.Date("2000-01-01"), fapar = NA, gpp = NA, - transp = NA, - latenth = NA, + aet = NA, + le = NA, pet = NA, vcmax = NA, jmax = NA, @@ -506,7 +509,11 @@ run_pmodel_f_bysite <- function( tsoil = NA, netrad = NA, wcont = NA, - snow = NA) + snow = NA, + le_soil = NA, + dpsi = NA, + psi_leaf = NA + ) } return(out) diff --git a/src/biosphere_pmodel.mod.f90 b/src/biosphere_pmodel.mod.f90 index 94df098e..d2ad55b6 100644 --- a/src/biosphere_pmodel.mod.f90 +++ b/src/biosphere_pmodel.mod.f90 @@ -200,6 +200,11 @@ function biosphere_annual() result( out_biosphere ) out_biosphere%wcont(doy) = tile(1)%soil%phy%wcont out_biosphere%snow(doy) = tile(1)%soil%phy%snow + ! Additional outputs for coupled model and phydro + out_biosphere%latenth_soil(doy) = tile_fluxes(1)%canopy%daet_e_soil + out_biosphere%dpsi(doy) = tile_fluxes(1)%plant(1)%dpsi + out_biosphere%psi_leaf(doy) = tile_fluxes(1)%plant(1)%psi_leaf + init_daily = .false. end do dayloop diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 398eebd3..b14910da 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -365,6 +365,14 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, vegcover, tile_fluxes(lu)%plant(pft)%gs_accl = out_phydro_inst%gs end if + !---------------------------------------------------------------- + ! Water potentials + !---------------------------------------------------------------- + if (use_phydro) then + tile_fluxes(lu)%plant(pft)%psi_leaf = out_phydro_inst%psi_l + tile_fluxes(lu)%plant(pft)%dpsi = out_phydro_inst%dpsi + end if + !------------------------------------------------------------------------ ! Canopy ET and soil LE (only for Phydro, since it's computed internally) !------------------------------------------------------------------------ @@ -375,7 +383,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, vegcover, tile_fluxes(lu)%canopy%daet_canop = out_phydro_inst%e * 0.018015 * (1.0d0 / rho_water) & * myinterface%params_siml%secs_per_tstep * 1000 ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 - tile_fluxes(lu)%canopy%daet_e_soil = out_phydro_inst%le_s_wet & + tile_fluxes(lu)%canopy%dpet_e_soil = out_phydro_inst%le_s_wet & * myinterface%params_siml%secs_per_tstep ! convert: J m-2 s-1 * s day-1 = J m-2 day-1 ! print *, "Canopy ET (mm d-1) = ", tile_fluxes(lu)%canopy%daet_canop diff --git a/src/interface_biosphere_pmodel.mod.f90 b/src/interface_biosphere_pmodel.mod.f90 index a11dc2a4..ebf6128b 100644 --- a/src/interface_biosphere_pmodel.mod.f90 +++ b/src/interface_biosphere_pmodel.mod.f90 @@ -80,6 +80,9 @@ module md_interface_pmodel real, dimension(ndayyear) :: netrad real, dimension(ndayyear) :: wcont real, dimension(ndayyear) :: snow + real, dimension(ndayyear) :: latenth_soil + real, dimension(ndayyear) :: dpsi + real, dimension(ndayyear) :: psi_leaf end type outtype_biosphere diff --git a/src/plant_pmodel.mod.f90 b/src/plant_pmodel.mod.f90 index c2fd977c..c1217d98 100644 --- a/src/plant_pmodel.mod.f90 +++ b/src/plant_pmodel.mod.f90 @@ -91,6 +91,10 @@ module md_plant_pmodel real :: gs_accl ! acclimated stomatal conductance (xxx) real :: chi ! ci:ca ratio (unitless) real :: iwue ! intrinsic water use efficiency (A/gs = ca*(1-chi)) + + ! FIXME Jaideep: These are not fluxes, but I dunno where else to put these + real :: dpsi ! soil-to-leaf water potential difference (MPa) + real :: psi_leaf ! leaf water potential (MPa) ! ! annual variables ! real :: agpp ! annual total gross primary production [gC/m2/yr] diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index 9d701b2d..e1cc3297 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -83,7 +83,7 @@ subroutine pmodel_f( & real(kind=c_double), dimension(17), intent(in) :: par ! free (calibratable) model parameters real(kind=c_double), dimension(nt,12), intent(in) :: forcing ! array containing all temporally varying forcing data for instantaneous model (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) real(kind=c_double), dimension(nt,12), intent(in) :: forcing_acclim ! array containing all temporally varying forcing data for acclimating model (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) - real(kind=c_double), dimension(nt,18), intent(out) :: output + real(kind=c_double), dimension(nt,21), intent(out) :: output ! local variables type(outtype_biosphere) :: out_biosphere ! holds all the output used for calculating the cost or maximum likelihood function @@ -249,6 +249,9 @@ subroutine pmodel_f( & output(idx_start:idx_end,16) = dble(out_biosphere%netrad(:)) output(idx_start:idx_end,17) = dble(out_biosphere%wcont(:)) output(idx_start:idx_end,18) = dble(out_biosphere%snow(:)) + output(idx_start:idx_end,19) = dble(out_biosphere%latenth_soil(:)) + output(idx_start:idx_end,20) = dble(out_biosphere%dpsi(:)) + output(idx_start:idx_end,21) = dble(out_biosphere%psi_leaf(:)) end if diff --git a/src/tile_pmodel.mod.f90 b/src/tile_pmodel.mod.f90 index a10cc961..43617296 100644 --- a/src/tile_pmodel.mod.f90 +++ b/src/tile_pmodel.mod.f90 @@ -102,6 +102,8 @@ module md_tile_pmodel real :: daet_e_soil ! daily soil evaporation (J m-2 d-1) real :: daet_canop ! daily canopy transpiration (mm d-1) real :: daet_e_canop ! daily canopy transpiration (J m-2 d-1) + real :: dpet_soil ! daily potential evaporation from soil (J m-2 d-1) + real :: dpet_e_soil ! daily potential evaporation from soil (mm d-1) real :: cpa ! alpha = equilibrium ET over potential ET (EET/PET, unitless) real :: dtransp ! work in progress @@ -146,6 +148,7 @@ module md_tile_pmodel end type canopy_fluxes_type + ! JAI FIXME: Add a soil fluxes type to store soil aet etc? (these are currently stored in canopy) type tile_fluxes_type type(canopy_fluxes_type) :: canopy type(plant_fluxes_type), dimension(npft) :: plant diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index e7e2995b..4bd1e925 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -278,6 +278,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, using_phydro ) !------------------------------------------------------------------------- use md_params_core, only: ndayyear, pi, dummy use md_sofunutils, only: calc_patm + use md_sofunutils, only: dampen_variability ! arguments type(tile_fluxes_type), intent(inout) :: tile_fluxes @@ -292,7 +293,10 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, using_phydro ) real :: lv ! enthalpy of vaporization, J/kg real :: rho_water ! density of water (g m-3) real :: energy_to_mm ! Conversion factor to convert energy (J m-2 day) to mass (mm day-1) - + real :: f_soil_aet ! Fractional reduction of soil AET due to moisture limitation + real :: p_over_pet ! P/PET + real, save :: p_over_pet_memory = 1.0 ! precipitation over equilibrium evapotranspiration, damped variability + real :: rx ! variable substitute (mm/hr)/(W/m^2) real :: hi, cos_hi ! intersection hour angle, degrees @@ -368,13 +372,23 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, using_phydro ) else ! Fill canopy LE and soil ET using complementary values from phydro tile_fluxes%canopy%daet_e_canop = tile_fluxes%canopy%daet_canop / energy_to_mm ! mm d-1 ---> J m-2 d-1 - tile_fluxes%canopy%daet_soil = tile_fluxes%canopy%daet_e_soil * energy_to_mm ! J m-2 d-1 ---> mm d-1 + tile_fluxes%canopy%dpet_soil = tile_fluxes%canopy%dpet_e_soil * energy_to_mm ! J m-2 d-1 ---> mm d-1 ! ^ Note: This is under wet conditions, as returned from phydro, so multiply by sw to get actual soil ET - ! calculate totat AET = canopy_AET + sw * soil_AET_wet - tile_fluxes%canopy%daet = tile_fluxes%canopy%daet_canop + (sw/kCw) * tile_fluxes%canopy%daet_soil - tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet_e_canop + (sw/kCw) * tile_fluxes%canopy%daet_e_soil + ! calculate totat AET = canopy_AET + f * soil_AET_wet, where f = running_avg(P/PET) + p_over_pet = (climate%dprec*86400) / (tile_fluxes%canopy%dpet_soil + 1e-6) + p_over_pet_memory = dampen_variability(p_over_pet , 30.0, p_over_pet_memory ) ! corresponds to f in Zhang et al., 2017 Eq. 9 + f_soil_aet = sw/kCw ! min(p_over_pet_memory, 1.0) ! previously was sw/kCw + + tile_fluxes%canopy%daet_soil = f_soil_aet * tile_fluxes%canopy%dpet_soil + tile_fluxes%canopy%daet_e_soil = tile_fluxes%canopy%daet_soil / energy_to_mm + + tile_fluxes%canopy%daet = tile_fluxes%canopy%daet_canop + tile_fluxes%canopy%daet_soil + tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet_e_canop + tile_fluxes%canopy%daet_e_soil + ! print *, "P (mm d-1), PET (mm d-1), P/PET, Avg(P/PET), f_soil_aet = ", (climate%dprec*86400), & + ! tile_fluxes%canopy%dpet_soil, p_over_pet, & + ! p_over_pet_memory, f_soil_aet ! print *, "Canopy ET (mm d-1, J m-2 d-1) = ", tile_fluxes%canopy%daet_canop, tile_fluxes%canopy%daet_e_canop ! print *, "Soil ET (mm d-1, J m-2 d-1) = ", tile_fluxes%canopy%daet_soil, tile_fluxes%canopy%daet_e_soil diff --git a/src/wrappersc.c b/src/wrappersc.c index b82c5d43..085fd38f 100644 --- a/src/wrappersc.c +++ b/src/wrappersc.c @@ -76,7 +76,7 @@ extern SEXP pmodel_f_C( // Specify output // 2nd agument to allocMatrix is number of rows, 3rd is number of columns - SEXP output = PROTECT( allocMatrix(REALSXP, nt, 18) ); + SEXP output = PROTECT( allocMatrix(REALSXP, nt, 21) ); // Fortran subroutine call F77_CALL(pmodel_f)( diff --git a/vignettes/phydro_long_calibration.R b/vignettes/phydro_long_calibration.R index 92731746..27d02b96 100644 --- a/vignettes/phydro_long_calibration.R +++ b/vignettes/phydro_long_calibration.R @@ -7,6 +7,7 @@ library(tictoc) library(ncdf4) plot_only = F +debug = T tic("phydro") @@ -101,9 +102,26 @@ plot_pmodel = function(output_p, out_filename_prefix=""){ facet_wrap(~variable, scales = "free", nrow = 1)+ labs(colour="Density") + p3 = output_p$data[[1]] %>% + mutate(vcmax25 = vcmax25*1e6, # mol m-2 s-1 --> umol m-2 s-1 + le = le / 86400, # J m-2 day-1 --> W m-2 + le_soil = le_soil / 86400, # J m-2 day-1 --> W m-2 + psi_soil = psi_leaf+dpsi + ) %>% + select(date, gpp, vcmax25, le, le_soil, dpsi, psi_soil, psi_leaf, gs_accl) %>% + drop_na %>% + melt("date") %>% + group_by(variable) %>% + ggplot(aes(y=value, x=as.Date(date))) + + geom_line(alpha=0.5, col="cyan4") + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~variable, scales = "free", nrow = 2) + if (out_filename_prefix == ""){ print(p1) print(p2) + print(p3) } else{ cairo_pdf(filename = paste0(out_filename_prefix, "_timeseries.pdf"), height=5, width=7) @@ -112,6 +130,9 @@ plot_pmodel = function(output_p, out_filename_prefix=""){ cairo_pdf(filename = paste0(out_filename_prefix, "_pred_vs_obs.pdf"), height=3.5, width=7) print(p2) dev.off() + cairo_pdf(filename = paste0(out_filename_prefix, "_all_predictions.pdf"), height=3.5, width=7) + print(p3) + dev.off() } @@ -174,11 +195,35 @@ parjj = parjj %>% mutate(K.scalar = K.scalar*1e-16) pars_joshi2022 = parjj %>% filter(Species != "Helianthus annuus") %>% select(K.scalar, P50, alpha, gamma, A.G, Species) %>% - filter(A.G != "") %>% pivot_longer(cols=c("K.scalar", "P50", "alpha", "gamma")) %>% + filter(A.G != "") %>% + # mutate(A.G = case_match(A.G, + # "M. Angiosperm" ~ "Angiosperm", + # .default = A.G)) %>% + pivot_longer(cols=c("K.scalar", "P50", "alpha", "gamma")) %>% group_by(A.G, name) %>% summarize(mean=mean(value), sd=sd(value), n=length(value)) +# igbp = "WSA" +type = p_hydro_drivers$site_info[[1]]$IGBP_veg_short %>% + case_match(c("WSA", "EBF", "DBF") ~ "Angiosperm", + c("ENF") ~ "Gymnosperm", + c("OSH", "CSH") ~ "Shrub", + c("GRA", "CRO", "SAV") ~ "Shrub", + c("MF", "WET") ~ "Angiosperm") + +message("type = ", p_hydro_drivers$site_info[[1]]$IGBP_veg_short, " --> " ,type, "\n") + +gamma_mean = pars_joshi2022 %>% + filter(A.G == type & name == "gamma") %>% + pull(mean) + +gamma_sd = pars_joshi2022 %>% + filter(A.G == "Angiosperm" & name == "gamma") %>% + pull(sd) + +print(c(gamma_mean, gamma_sd)) + uniform_range = function(lower, upper){ list(lower= lower, upper=upper, mean = (upper+lower)/2, sd = (upper-lower)*10) } @@ -215,7 +260,8 @@ settings_bayes <- list( # phydro_gamma = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(mean), # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(sd)), phydro_alpha = gaussian_range(mean = 0.11, sd = 0.02), - phydro_gamma = uniform_range(lower = 0.1, upper = 2), + # phydro_gamma = uniform_range(lower = 0.1, upper = 2), + phydro_gamma = gaussian_range(mean = gamma_mean, sd = gamma_sd), #bsoil = uniform_range(lower=0.1, upper=10), Ssoil = uniform_range(lower = 0, upper = whc_site+whc_site_sd), whc = gaussian_range(mean = whc_site, sd = whc_site_sd), @@ -226,9 +272,9 @@ settings_bayes <- list( control = list( sampler = "DEzs", settings = list( - nrChains = 3, - burnin = 10000, - iterations = 50000 # kept artificially low + nrChains = ifelse(debug, yes = 1, no = 3 ), + burnin = ifelse(debug, yes = 300, no = 10000), + iterations = ifelse(debug, yes = 1200, no = 50000) # kept artificially low ) ) ) @@ -245,6 +291,7 @@ calib_file =paste0(file_prefix, "_mcmc_output.rda") if (!plot_only){ # Calibrate the model and optimize the free parameters using # demo datasets + message("Begin calibration...") pars_calib_bayes <- calib_sofun( # calib_sofun arguments: drivers = p_hydro_drivers, diff --git a/vignettes/read_meta_fdk.R b/vignettes/read_meta_fdk.R index cefe4103..855b2854 100644 --- a/vignettes/read_meta_fdk.R +++ b/vignettes/read_meta_fdk.R @@ -25,6 +25,22 @@ read_meta_fdk = function (site, path, meta_data = FALSE, df$time <- time_date if ("IGBP_veg_short" %in% colnames(df)) { df$IGBP_veg_short <- trimws(df$IGBP_veg_short) + + df$IGBP_veg_short = case_match(df$IGBP_veg_short, + "Woody Savannas" ~ "WSA", + "Savannas" ~ "SAV", + "Permanent Wetlands" ~ "WET", + "Open Shrublands" ~ "OSH", + "Closed Shrublands" ~ "CSH", + "Grasslands" ~ "GRA", + "Evergreen Needleleaf Forest" ~ "ENF", + "Evergreen Broadleaf Forest" ~ "EBF", + "Deciduous Broadleaf Forest" ~ "DBF", + "Mixed Forest" ~ "MF", + "Croplands" ~ "CRO", + "Cropland/Natural Vegetation Mosaic" ~ "MF", + "Urban and Built-Up" ~ NA, + .default = df$IGBP_veg_short) } else { warning("Column 'IGBP_veg_short' does not exist in the data frame. Assigning NA.") diff --git a/vignettes/rsofun_phydro_data_generation.R b/vignettes/rsofun_phydro_data_generation.R index 5bfa426a..238ef3ff 100644 --- a/vignettes/rsofun_phydro_data_generation.R +++ b/vignettes/rsofun_phydro_data_generation.R @@ -128,16 +128,16 @@ ddf_24hr_mean <- # Check aggregation cairo_pdf(filename = paste0(figures_prefix, "_fig1_ddf_24hr_mean.pdf")) # png(filename = paste0(figures_prefix, "_fig1_ddf_24hr_mean.png"), height=700*3, width=700*3, res = 300) -ddf_24hr_mean %>% +p1 = ddf_24hr_mean %>% mutate(albedo = SW_OUT/SW_IN_F_MDS) %>% - select(SW_IN_F_MDS, NETRAD, LW_IN_F_MDS, LAI, FPAR, GPP_DT_VUT_REF, LE_F_MDS, TA_F_MDS, date) %>% + select(SW_IN_F_MDS, NETRAD, LW_IN_F_MDS, LAI, FPAR, GPP_DT_VUT_REF, LE_F_MDS, TA_F_MDS, P_F, date) %>% melt("date") %>% ggplot(aes(y=value, x=date)) + geom_line(col="aquamarine4") + theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") %>% - print() + facet_wrap(~variable, scales = "free") +p1 %>% print() dev.off() ddf_24hr_mean %>% filter(!is.na(NETRAD)) %>% pull(date) %>% summary() @@ -195,7 +195,7 @@ aggregate_daily_3hr_maxima = function(df){ test.3day.3hr = test.3day %>% group_by(date) %>% do(aggregate_daily_3hr_maxima(.)) %>% ungroup() cairo_pdf(filename = paste0(figures_prefix, "_fig2_3hr_maxima_sample.pdf")) -test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% +p2 = test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% melt("time") %>% mutate(type="hourly") %>% rbind(test.3day.3hr %>% @@ -208,8 +208,8 @@ test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_ geom_point(data = . %>% filter(type == "daily")) + theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") %>% - print() + facet_wrap(~variable, scales = "free") +p2 %>% print() dev.off() #' @@ -253,7 +253,7 @@ aggregate_daily_daylength = function(df){ test.3day.daylen = test.3day %>% group_by(date) %>% do(aggregate_daily_daylength(.)) %>% ungroup() cairo_pdf(filename = paste0(figures_prefix, "_fig3_daytime_sample.pdf")) -test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI) %>% +p3 = test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI) %>% mutate(daylength = NA) %>% melt("time") %>% mutate(type="hourly") %>% @@ -267,8 +267,8 @@ test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_ geom_point(data = . %>% filter(type == "daily")) + theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") %>% - print() + facet_wrap(~variable, scales = "free") +p3 %>% print() dev.off() #' @@ -283,11 +283,11 @@ ddf_daytime_mean <- hhdf |> # Check daylength seasonality cairo_pdf(filename = paste0(figures_prefix, "_fig4_daylenth_seasonality.pdf")) -ddf_daytime_mean %>% +p4 = ddf_daytime_mean %>% ggplot(aes(y=daylength, x=date)) + geom_line()+ - theme_classic() %>% - print() + theme_classic() +p4 %>% print() dev.off() #' @@ -528,7 +528,7 @@ save(p_hydro_validation, #' cairo_pdf(filename = paste0(figures_prefix, "_fig5_phydro_drivers.pdf"), height=5, width=10) -p_hydro_drivers$forcing[[1]] %>% +p5 = p_hydro_drivers$forcing[[1]] %>% select(date, ppfd, netrad, temp, vpd, fapar, rain) %>% melt("date") %>% mutate(type="24-hr mean") %>% @@ -547,5 +547,6 @@ p_hydro_drivers$forcing[[1]] %>% theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~variable, scales = "free") +p5 %>% print() dev.off() diff --git a/vignettes/run_bayesian_phydro.sh b/vignettes/run_bayesian_phydro.sh index 8f25cf36..5349a509 100755 --- a/vignettes/run_bayesian_phydro.sh +++ b/vignettes/run_bayesian_phydro.sh @@ -4,11 +4,14 @@ NCORES=50 # declare -a arr=$( ls ~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_FLUXNET/*HH* | awk -F "_" '{print $4}' ) -declare -a arr=$( cat site_list.txt ) +declare -a arr=$( cat site_list_2.txt ) generate_data=true + data_path=/data/scratch/jaideep/fluxdata out_path=/data/scratch/jaideep/phydro_output +# data_path=~/Downloads/fluxdatakit_oct3 +# out_path=~/Downloads/fluxdatakit_oct3/phydro_output for SITE in ${arr[@]} do @@ -16,11 +19,11 @@ do ( if [ "$generate_data" = true ]; then echo "Generating data for site" $SITE - Rscript rsofun_phydro_data_generation.R $SITE $data_path > out_data_gen_$SITE.txt + Rscript rsofun_phydro_data_generation.R $SITE $data_path fi echo "Running calibration for site" $SITE - Rscript phydro_long_calibration.R $SITE $data_path $out_path > out_calib_$SITE.txt + Rscript phydro_long_calibration.R $SITE $data_path $out_path ) & # allow to execute up to $NCORES jobs in parallel diff --git a/vignettes/site_list_2.txt b/vignettes/site_list_2.txt new file mode 100644 index 00000000..f4260125 --- /dev/null +++ b/vignettes/site_list_2.txt @@ -0,0 +1,174 @@ +DE-Hai +DE-Tha +US-Me2 +IT-MBo +CZ-BK1 +DE-Gri +GF-Guy +FR-Fon +US-GLE +CA-TP3 +US-MOz +IT-Col +NL-Loo +RU-Fyo +US-Ton +US-UMd +US-Var +US-Wjs +CA-TP4 +FR-FBn +IT-Ren +IT-Tor +US-ICs +US-Mpj +CA-TP1 +FR-Pue +IT-Lav +RU-Cok +AT-Neu +CH-Dav +FI-Hyy +IT-Noe +US-BZB +US-BZF +US-NR1 +US-SRM +CA-Oas +CA-Obs +DK-Sor +IT-Ro2 +US-ICt +US-Jo2 +US-Oho +US-Wkg +CA-LP1 +DE-RuR +DE-RuW +IT-Cpz +CA-Man +FR-LBr +IT-SRo +NL-Hor +US-Ro4 +US-WCr +AU-DaS +AU-Sam +CA-Qfo +CH-Cha +CH-Fru +CZ-BK2 +DE-Obe +FI-Sod +FR-Hes +MY-PSO +SE-Nor +US-Blo +US-IB2 +US-KLS +US-SRC +US-SRG +US-Whs +AU-Cow +AU-DaP +AU-Dry +CA-TPD +CZ-Lnz +IT-Ro1 +IT-SR2 +US-Me3 +US-Rms +US-Rwf +US-Rws +AU-How +AU-Stp +AU-Wom +CA-DBB +CA-Qcu +CA-TP2 +CH-Oe1 +DE-Lnf +FI-Sii +FI-Var +US-Me6 +US-ONA +US-xBR +AU-ASM +AU-Cpr +AU-Rig +CA-NS5 +CA-NS6 +CA-NS7 +CA-SF3 +CN-Cng +DE-Lkb +ES-Amo +FR-LGt +GH-Ank +GL-ZaH +HU-Bug +IT-Cp2 +US-BZo +US-Goo +US-KS2 +US-Prr +AU-Gin +AU-Wac +AU-Whr +BR-Sa3 +CA-NS1 +CA-NS2 +CA-NS4 +CA-SF1 +CA-SF2 +CG-Tch +CN-Din +CN-HaM +CN-Qia +DE-Bay +DK-ZaH +ES-LgS +FI-Let +GL-NuF +IT-PT1 +PA-SPs +SN-Dhr +US-AR1 +US-AR2 +US-Aud +US-GBT +ZA-Kru +AU-Cum +AU-Emr +AU-Lit +AU-TTE +AU-Wrr +BR-Npw +CA-NS3 +CN-Dan +DK-Eng +IT-CA3 +IT-Isp +IT-LMa +PA-SPn +PE-QFR +RU-Ha1 +SD-Dem +SJ-Adv +US-ARb +US-ARc +US-LWW +US-Me5 +US-Rwe +AU-Ade +AU-RDF +AU-Rob +AU-Ync +CA-DB2 +CN-Du2 +CN-Sw2 +IT-CA1 +IT-La2 +US-Hn3 +US-KS1 +US-Tw5 From 2c5b1f6b9df5c649d94d5c6c3c5af3ac38940a10 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Wed, 21 Feb 2024 15:32:28 +0100 Subject: [PATCH 030/112] improved calib script; trial with const kphio --- vignettes/phydro_long_calibration.R | 127 ++++++------ vignettes/rsofun_phydro_data_generation.R | 2 +- vignettes/rsofun_phydro_output_analysis.R | 226 ++++++++++++++++++++++ vignettes/rsofun_prepare_site_list.R | 54 ++++++ vignettes/run_bayesian_phydro.sh | 6 +- 5 files changed, 350 insertions(+), 65 deletions(-) create mode 100644 vignettes/rsofun_phydro_output_analysis.R create mode 100644 vignettes/rsofun_prepare_site_list.R diff --git a/vignettes/phydro_long_calibration.R b/vignettes/phydro_long_calibration.R index 27d02b96..995a278c 100644 --- a/vignettes/phydro_long_calibration.R +++ b/vignettes/phydro_long_calibration.R @@ -18,7 +18,7 @@ args = commandArgs(trailingOnly=TRUE) #site <- "GF-Guy" if (length(args)==0) { #stop("At least one argument must be supplied: site name", call.=FALSE) - site = "GF-Guy" + site = "FR-Pue" }else{ site = args[1] } @@ -30,7 +30,7 @@ if (length(args)<2) { } if (length(args)<3) { - out_dir = "~/Downloads/fluxdatakit_oct3/phydro_output/" + out_dir = "~/Downloads/fluxdatakit_oct3/phydro_output_fixedkphio/" }else{ out_dir = paste0(args[3],"/") } @@ -219,55 +219,77 @@ gamma_mean = pars_joshi2022 %>% pull(mean) gamma_sd = pars_joshi2022 %>% - filter(A.G == "Angiosperm" & name == "gamma") %>% - pull(sd) + filter(A.G == type & name == "gamma") %>% + pull(sd) %>% c(0.2) %>% max() + print(c(gamma_mean, gamma_sd)) uniform_range = function(lower, upper){ list(lower= lower, upper=upper, mean = (upper+lower)/2, sd = (upper-lower)*10) } + gaussian_range = function(mean, sd){ if (mean > 0) list(lower= max(mean-5*sd, 0), upper=mean+5*sd, mean = mean, sd = sd) else list(lower= mean-5*sd, upper=min(mean+5*sd, 0), mean = mean, sd = sd) } +# par = list( +# kphio = list(lower=0.04, upper=0.09, init=0.05), +# phydro_K_plant = list(lower=0.05e-16, upper=0.3e-16, init=0.15e-16), +# phydro_p50_plant = list(lower=-3, upper=-0.5, init=-1), +# phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), +# phydro_gamma = list(lower=0.5, upper=1.5, init=1), +# bsoil = list(lower=0.1, upper=10, init=3), +# Ssoil = list(lower=0.1, upper=1000, init=10), +# whc = list(lower=10, upper=5000, init=1000), +# err_gpp = list(lower = 0.01, upper = 4, init = 2), +# err_le = list(lower = 0.1e6, upper = 10e6, init = 2e6) +# ), + +pars_calib = list( + # kphio = uniform_range(lower=0.005, upper=0.09), + # phydro_K_plant = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="K.scalar") %>% pull(mean), + # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="K.scalar") %>% pull(sd)), + # phydro_p50_plant = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="P50") %>% pull(mean), + # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="P50") %>% pull(sd)), + phydro_K_plant = uniform_range(lower=0.1e-16, 1e-16), + phydro_p50_plant = uniform_range(lower=-4, -0.3), + # phydro_alpha = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="alpha") %>% pull(mean), + # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="alpha") %>% pull(sd)), + # phydro_gamma = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(mean), + # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(sd)), + phydro_alpha = gaussian_range(mean = 0.11, sd = 0.02), + # phydro_gamma = uniform_range(lower = 0.1, upper = 2), + phydro_gamma = gaussian_range(mean = gamma_mean, sd = gamma_sd), + #bsoil = uniform_range(lower=0.1, upper=10), + Ssoil = uniform_range(lower = 0, upper = whc_site+whc_site_sd), + whc = gaussian_range(mean = whc_site, sd = whc_site_sd), + err_gpp = uniform_range(lower = 0.01, upper = 4), + err_le = uniform_range(lower = 0.1e6, upper = 10e6) +) + +pars_fixed = list( # fix all other parameters + kphio = 0.045, + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + # phydro_K_plant = 0.3e-16, + # phydro_P50_plant = -1, + phydro_b_plant = 1, + # phydro_alpha = 0.1, + # phydro_gamma = 1 + bsoil = 3 + # Ssoil = 40, + # whc = 90 +) + # Define calibration settings and parameter ranges from previous work settings_bayes <- list( method = "BayesianTools", - # par = list( - # kphio = list(lower=0.04, upper=0.09, init=0.05), - # phydro_K_plant = list(lower=0.05e-16, upper=0.3e-16, init=0.15e-16), - # phydro_p50_plant = list(lower=-3, upper=-0.5, init=-1), - # phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), - # phydro_gamma = list(lower=0.5, upper=1.5, init=1), - # bsoil = list(lower=0.1, upper=10, init=3), - # Ssoil = list(lower=0.1, upper=1000, init=10), - # whc = list(lower=10, upper=5000, init=1000), - # err_gpp = list(lower = 0.01, upper = 4, init = 2), - # err_le = list(lower = 0.1e6, upper = 10e6, init = 2e6) - # ), - par = list( - kphio = uniform_range(lower=0.04, upper=0.09), - # phydro_K_plant = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="K.scalar") %>% pull(mean), - # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="K.scalar") %>% pull(sd)), - # phydro_p50_plant = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="P50") %>% pull(mean), - # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="P50") %>% pull(sd)), - phydro_K_plant = uniform_range(lower=0.1e-16, 5e-16), - phydro_p50_plant = uniform_range(lower=-4, -0.3), - # phydro_alpha = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="alpha") %>% pull(mean), - # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="alpha") %>% pull(sd)), - # phydro_gamma = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(mean), - # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(sd)), - phydro_alpha = gaussian_range(mean = 0.11, sd = 0.02), - # phydro_gamma = uniform_range(lower = 0.1, upper = 2), - phydro_gamma = gaussian_range(mean = gamma_mean, sd = gamma_sd), - #bsoil = uniform_range(lower=0.1, upper=10), - Ssoil = uniform_range(lower = 0, upper = whc_site+whc_site_sd), - whc = gaussian_range(mean = whc_site, sd = whc_site_sd), - err_gpp = uniform_range(lower = 0.01, upper = 4), - err_le = uniform_range(lower = 0.1e6, upper = 10e6) - ), + par = pars_calib, metric = rsofun::cost_likelihood_pmodel, control = list( sampler = "DEzs", @@ -298,21 +320,7 @@ if (!plot_only){ obs = p_hydro_validation, settings = settings_bayes, # extra arguments passed to the cost function: - par_fixed = list( # fix all other parameters - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - # phydro_K_plant = 0.3e-16, - # phydro_P50_plant = -1, - phydro_b_plant = 1, - # phydro_alpha = 0.1, - # phydro_gamma = 1 - bsoil = 3 - # Ssoil = 40, - # whc = 90 - ), + par_fixed = pars_fixed, targets = c("gpp", "le") # define target variable GPP ) @@ -349,16 +357,12 @@ pars_calib_bayes$par %>% mutate(site = site) %>% write.csv(file = paste0(file_prefix, "_MAP.csv"), row.names = F) -params_modl_opt = params_modl +params_modl_opt = c(pars_calib_bayes$par, pars_fixed) -params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] -params_modl_opt$phydro_K_plant = pars_calib_bayes$par[["phydro_K_plant"]] -params_modl_opt$phydro_p50_plant = pars_calib_bayes$par[["phydro_p50_plant"]] -params_modl_opt$phydro_alpha = pars_calib_bayes$par[["phydro_alpha"]] -params_modl_opt$phydro_gamma = pars_calib_bayes$par[["phydro_gamma"]] -# params_modl_opt$bsoil = pars_calib_bayes$par[["bsoil"]] -params_modl_opt$Ssoil = pars_calib_bayes$par[["Ssoil"]] -params_modl_opt$whc = pars_calib_bayes$par[["whc"]] +if(params_modl_opt %>% names() %>% duplicated %>% any()){ + message("Duplicated entries in optimal params") + stop() +} output_p_opt <- rsofun::runread_pmodel_f( p_hydro_drivers, @@ -369,6 +373,7 @@ save(output_p_opt, file=paste0(file_prefix, "_phydro_output.rda")) plot_pmodel(output_p_opt, out_filename_prefix = fig_file_prefix) +rmse = toc() diff --git a/vignettes/rsofun_phydro_data_generation.R b/vignettes/rsofun_phydro_data_generation.R index 238ef3ff..c7b058a6 100644 --- a/vignettes/rsofun_phydro_data_generation.R +++ b/vignettes/rsofun_phydro_data_generation.R @@ -31,7 +31,7 @@ args = commandArgs(trailingOnly=TRUE) #site <- "GF-Guy" if (length(args)==0) { #stop("At least one argument must be supplied: site name", call.=FALSE) - site = "GF-Guy" + site = "FR-Pue" }else{ site = args[1] } diff --git a/vignettes/rsofun_phydro_output_analysis.R b/vignettes/rsofun_phydro_output_analysis.R new file mode 100644 index 00000000..05aedbbc --- /dev/null +++ b/vignettes/rsofun_phydro_output_analysis.R @@ -0,0 +1,226 @@ +rm(list=ls()) +library(tidyverse) +library(reshape2) +library(rsofun) +library(BayesianTools) +library(tictoc) +library(ncdf4) +library(scatterPlotMatrix) +library(ggpointdensity) + +plot_only = F +source("vignettes/read_meta_fdk.R") + +root_data_dir = "~/Downloads/fluxdatakit_oct3" +lsm_path = paste0(root_data_dir, "/FLUXDATAKIT_LSM/") +out_dir = "~/Desktop/phydro_output_3" +data_dir = paste0(root_data_dir, "/phydro_drivers/") +figures_dir = paste0(out_dir, "/figures/") + +sites = read.delim("vignettes/site_list.txt", header=F) + +# sites_meta = +# sites %>% # slice(1:5) %>% +# pull(V1) %>% +# map_df( +# ~suppressWarnings( +# try( +# read_meta_fdk( +# site = ., +# path = lsm_path, +# meta_data = T +# ) +# ) +# ) +# ) +# +# sites_meta %>% write.csv(file="vignettes/ancillary_data/sites_meta.csv") + +sites_meta = read.csv(file="vignettes/ancillary_data/sites_meta.csv") + +sites_meta = sites_meta %>% + mutate(IGBP_veg_short = case_match(IGBP_veg_short, + "Woody Savannas" ~ "WSA", + "Savannas" ~ "SAV", + "Permanent Wetlands" ~ "WET", + "Open Shrublands" ~ "OSH", + "Closed Shrublands" ~ "CSH", + "Grasslands" ~ "GRA", + "Evergreen Needleleaf Forest" ~ "ENF", + "Evergreen Broadleaf Forest" ~ "EBF", + "Deciduous Broadleaf Forest" ~ "DBF", + "Mixed Forest" ~ "MF", + "Croplands" ~ "CRO", + "Cropland/Natural Vegetation Mosaic" ~ "MF", + "Urban and Built-Up" ~ NA, + .default = IGBP_veg_short)) + +sites_meta %>% ggplot(aes(x=IGBP_veg_short)) + geom_bar() + coord_flip() + + +map_all_sites = +tibble(filename=list.files(out_dir, full.names = T)) %>% + filter(str_detect(filename, "MAP.csv")) %>% + pull(filename) %>% + map_df(~read.csv(.)) + + +all_data = map_all_sites %>% + rename(sitename = site) %>% + left_join(sites_meta) + + +## FANCY PAIRS PLOT ## +all_data %>% + select(kphio:whc, IGBP_veg_short) %>% + mutate(phydro_K_plant = log10(phydro_K_plant), + whc = log10(whc), + Ssoil = log10(Ssoil), + phydro_p50_plant = log10(-phydro_p50_plant), + phydro_alpha = log10(phydro_alpha)) %>% + filter(IGBP_veg_short %in% c("SAV", "OSH", "GRA", "ENF", "EBF", "DBF", "CRO")) %>% + mutate(IGBP_veg_short = factor(IGBP_veg_short)) %>% + scatterPlotMatrix(distribType = 1, regressionType = 1, rotateTitle = F, zAxisDim = "IGBP_veg_short", + plotProperties = list(regression = list( strokeWidth = 2 )), + columnLabels = gsub("\\_", "
", colnames(.)) ) + + +all_data %>% ggplot(aes(x=IGBP_veg_short)) + geom_bar() + coord_flip() + +all_data %>% group_by(IGBP_veg_short) %>% summarize(count=length(IGBP_veg_short)) %>% +right_join(all_data) %>% + filter(count > 2) %>% + select(kphio:whc, IGBP_veg_short, sitename) %>% + mutate(phydro_K_plant = log10(phydro_K_plant), + whc = log10(whc), + Ssoil = log10(Ssoil), + # kphio = log10(kphio), + # phydro_gamma = log10(phydro_gamma) + ) %>% + pivot_longer(cols = !c(sitename, IGBP_veg_short)) %>% + ggplot(aes(x=IGBP_veg_short, y=value, fill=IGBP_veg_short)) + + geom_violin() + + scale_fill_viridis_d()+ + facet_wrap(facets = "name", scales = "free") + + coord_flip() + +all_data %>% + filter(IGBP_veg_short %in% c("SAV", "OSH", "GRA", "ENF", "EBF", "DBF", "CRO")) %>% + ggplot(aes(group=IGBP_veg_short, x=Ssoil, y=whc, col=IGBP_veg_short)) + + geom_point()+ + geom_smooth(method="lm", se=F)+ + scale_x_log10()+scale_y_log10()+ + geom_abline(slope = 1) + + +#### Water potentials analysis #### + +vod_day = readxl::read_excel("~/Downloads/AMSR_fluxnet_data.xlsx", sheet = "VOD_ASC_filtered_qa(1_30_PM)") +vod_day = vod_day %>% + pivot_longer(-c(1,2), names_to="date", values_to = "vod_day") %>% + mutate(date = as.Date(as.numeric(date), origin="1899-12-30")) %>% + mutate(vod_day = case_match(vod_day, -999~NA, .default=vod_day)) + +vod_night = readxl::read_excel("~/Downloads/AMSR_fluxnet_data.xlsx", sheet = "VOD_DSC_filtered_qa(1_30_AM)") +vod_night = vod_night %>% + pivot_longer(-c(1,2), names_to="date", values_to = "vod_night") %>% + mutate(date = as.Date(as.numeric(date), origin="1899-12-30")) %>% + mutate(vod_night = case_match(vod_night, -999~NA, .default=vod_night)) + +vod = vod_day %>% left_join(vod_night) + +read_phydro_output = function(site){ + file = tibble(files=list.files(out_dir, full.names = T)) %>% + filter(stringr::str_detect(files, site)) %>% + filter(stringr::str_detect(files, "phydro_output.rda")) %>% + pull(files) + + load(file) + + output_p_opt$data[[1]] %>% + select(date, gpp, le, jmax25, dpsi, psi_leaf) %>% + mutate(SITE_ID = site) +} + +sites_phydro = tibble(files=list.files(out_dir)) %>% + filter(stringr::str_detect(files, "phydro_output.rda")) %>% + mutate(site = substr(files, 1,6)) %>% + # slice(1:5) %>% + pull(site) + +phydro_out = sites_phydro %>% + map_df(~read_phydro_output(.)) + +vod_phydro = vod %>% left_join(phydro_out) %>% + drop_na + +sites_vod_phydro = vod_phydro %>% group_by(SITE_ID) %>% count() %>% arrange(desc(n)) %>% pull(SITE_ID) + +for (i in seq(1,length(sites_vod_phydro), by=20)){ + vod_phydro %>% + filter(SITE_ID %in% sites_vod_phydro[i:(i+20-1)]) %>% + mutate(vod_ratio = log(vod_night/vod_day)) %>% + ggplot(aes(y=vod_ratio, x=dpsi)) + + geom_pointdensity() + + geom_smooth(method="lm", formula=y~0+x, col="orange", linewidth=0.5)+ + scale_colour_viridis_c()+ + facet_wrap("SITE_ID", scales="free")+ + guides(color="none") +} + + +#### Isohydricity analysis #### + +sites_phydro = phydro_out %>% group_by(SITE_ID) %>% count() %>% arrange(desc(n)) %>% pull(SITE_ID) + +site_min_psisoil = + phydro_out %>% + group_by(SITE_ID) %>% + summarize(psi_min = min(psi_leaf+dpsi)) + +for (i in seq(1,length(sites_phydro), by=20)){ + cairo_pdf(filename = paste0("vignettes/figures/psi_leaf_vs_soil_",i,".pdf")) + print( + phydro_out %>% + filter(SITE_ID %in% sites_phydro[i:(i+20-1)]) %>% + left_join(site_min_psisoil) %>% + mutate(psi_soil = psi_leaf+dpsi) %>% + filter(psi_soil > psi_min*0.8) %>% + group_by(SITE_ID) %>% + ggplot(aes(y=psi_leaf, x=psi_soil)) + + geom_pointdensity() + + geom_smooth(method="lm", col="orange", linewidth=0.5)+ + geom_abline(slope=1, intercept=0, col="white")+ + scale_colour_viridis_c()+ + facet_wrap("SITE_ID", scales="free")+ + guides(color="none") + ) + dev.off() +} + + + +phydro_out %>% + left_join(site_min_psisoil) %>% + mutate(psi_soil = psi_leaf+dpsi) %>% + filter(psi_soil > psi_min*0.8) %>% + group_by(SITE_ID) %>% + group_modify(~broom::tidy(lm(psi_leaf~psi_soil, data=.x))) %>% + filter(estimate < 10 & estimate > -10) %>% + ggplot()+ + geom_histogram(aes(x=estimate), bins=100) + + geom_vline(xintercept = 1, col="red")+ + facet_wrap("term", scales="free") + + +phydro_out %>% + left_join(site_min_psisoil) %>% + mutate(psi_soil = psi_leaf+dpsi) %>% + filter(psi_soil > psi_min*0.8) %>% + group_by(SITE_ID) %>% + group_modify(~broom::tidy(lm(psi_leaf~psi_soil, data=.x))) %>% + filter(estimate < 10 & estimate > -10) %>% + pivot_wider(id_cols = SITE_ID, names_from = term, values_from = estimate) %>% + ggplot(aes(x=`(Intercept)`, y=psi_soil))+ + geom_point() + + geom_smooth(method="lm") diff --git a/vignettes/rsofun_prepare_site_list.R b/vignettes/rsofun_prepare_site_list.R new file mode 100644 index 00000000..98c39f53 --- /dev/null +++ b/vignettes/rsofun_prepare_site_list.R @@ -0,0 +1,54 @@ +rm(list=ls()) +library(tidyverse) +library(reshape2) +library(rsofun) +library(BayesianTools) +library(tictoc) +library(ncdf4) +library(scatterPlotMatrix) + +plot_only = F +source("vignettes/read_meta_fdk.R") + +root_data_dir = "~/Downloads/fluxdatakit_oct3" +lsm_path = paste0(root_data_dir, "/FLUXDATAKIT_LSM/") +out_dir = "~/Desktop/phydro_output" +data_dir = paste0(root_data_dir, "/phydro_drivers/") +figures_dir = paste0(out_dir, "/figures/") + +files_lsm = list.files(lsm_path) +sites = files_lsm %>% substr(1,6) %>% unique() + + +sites_meta = + sites %>% + map_df( + ~suppressWarnings( + try( + read_meta_fdk( + site = ., + path = lsm_path, + meta_data = T + ) + ) + ) + ) + +sites_meta %>% write.csv(file="vignettes/ancillary_data/sites_meta.csv") + +sites_meta = read.csv(file="vignettes/ancillary_data/sites_meta.csv") +valid_years = read.csv("vignettes/ancillary_data/valid_years_final.csv", header = T, fileEncoding = "UTF-16") + +sites_meta %>% ggplot(aes(x=IGBP_veg_short)) + geom_bar() + coord_flip() + +site_list = + valid_years %>% + rename(sitename = Site, + valid_start_year = start_year, + valid_end_year = end_year) %>% + left_join(sites_meta) %>% + select(-X, -comment) %>% + filter(!is.na(valid_start_year) & !is.na(valid_end_year)) %>% # Remove sites that dont have any valid years + filter((IGBP_veg_short %in% c("WSA", "SAV", "OSH", "GRA", "ENF", "EBF", "DBF", "CSH"))) + +site_list %>% pull(sitename) %>% write.table(file = "vignettes/site_list_2.txt", row.names = F, quote = F, col.names = F) diff --git a/vignettes/run_bayesian_phydro.sh b/vignettes/run_bayesian_phydro.sh index 5349a509..950e81d8 100755 --- a/vignettes/run_bayesian_phydro.sh +++ b/vignettes/run_bayesian_phydro.sh @@ -1,15 +1,15 @@ #!/bin/bash -NCORES=50 +NCORES=70 # declare -a arr=$( ls ~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_FLUXNET/*HH* | awk -F "_" '{print $4}' ) declare -a arr=$( cat site_list_2.txt ) -generate_data=true +generate_data=false data_path=/data/scratch/jaideep/fluxdata -out_path=/data/scratch/jaideep/phydro_output +out_path=/data/scratch/jaideep/phydro_output_fixedkphio # data_path=~/Downloads/fluxdatakit_oct3 # out_path=~/Downloads/fluxdatakit_oct3/phydro_output From ef0ed2e3e3e04d41d8ec4006a628e39ae00f4dde Mon Sep 17 00:00:00 2001 From: Jaideep Date: Wed, 21 Feb 2024 15:42:12 +0100 Subject: [PATCH 031/112] add r2 and nrmse outputs --- vignettes/phydro_long_calibration.R | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/vignettes/phydro_long_calibration.R b/vignettes/phydro_long_calibration.R index 995a278c..875fd2ea 100644 --- a/vignettes/phydro_long_calibration.R +++ b/vignettes/phydro_long_calibration.R @@ -373,7 +373,21 @@ save(output_p_opt, file=paste0(file_prefix, "_phydro_output.rda")) plot_pmodel(output_p_opt, out_filename_prefix = fig_file_prefix) -rmse = +# Calculate R2 and RMSE +output_p_opt$data[[1]] %>% select(date, gpp, le) %>% + drop_na %>% + melt("date", value.name = "pred") %>% + left_join(p_hydro_validation$data[[1]] %>% + select(date, gpp, le) %>% + drop_na %>% + # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 + # mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 + melt("date", value.name = "obs")) %>% + group_by(variable) %>% + summarize(r2 = cor(pred,obs)^2, + nrmse = sqrt(sum(pred-obs)^2/length(pred-obs))/mean(obs), + site = site) %>% + write.csv(file=paste0(file_prefix, "_r2_nrmse.csv"), row.names = F) toc() From 7ff00b834515caadca3c2f67b76b54046a462757 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Thu, 22 Feb 2024 16:16:38 +0100 Subject: [PATCH 032/112] include obs quantities in phydro_output for convenience --- src/waterbal_splash.mod.f90 | 13 +++++++----- vignettes/phydro_long_calibration.R | 11 ++++++++-- vignettes/rsofun_phydro_output_analysis.R | 25 ++++++++++++++++++----- 3 files changed, 37 insertions(+), 12 deletions(-) diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index 4bd1e925..b0e22ca9 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -294,8 +294,9 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, using_phydro ) real :: rho_water ! density of water (g m-3) real :: energy_to_mm ! Conversion factor to convert energy (J m-2 day) to mass (mm day-1) real :: f_soil_aet ! Fractional reduction of soil AET due to moisture limitation - real :: p_over_pet ! P/PET - real, save :: p_over_pet_memory = 1.0 ! precipitation over equilibrium evapotranspiration, damped variability + real :: p_over_pet_memory ! P/PET + real, save :: p_memory = 0.0 ! precipitation, damped variability + real, save :: pet_memory = 0.0 ! equilibrium evapotranspiration, damped variability real :: rx ! variable substitute (mm/hr)/(W/m^2) real :: hi, cos_hi ! intersection hour angle, degrees @@ -376,9 +377,11 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, using_phydro ) ! ^ Note: This is under wet conditions, as returned from phydro, so multiply by sw to get actual soil ET ! calculate totat AET = canopy_AET + f * soil_AET_wet, where f = running_avg(P/PET) - p_over_pet = (climate%dprec*86400) / (tile_fluxes%canopy%dpet_soil + 1e-6) - p_over_pet_memory = dampen_variability(p_over_pet , 30.0, p_over_pet_memory ) ! corresponds to f in Zhang et al., 2017 Eq. 9 - f_soil_aet = sw/kCw ! min(p_over_pet_memory, 1.0) ! previously was sw/kCw + ! p_over_pet = (climate%dprec*86400) / (tile_fluxes%canopy%dpet_soil + 1e-6) + p_memory = dampen_variability(climate%dprec*86400, 30.0, p_memory ) ! corresponds to f in Zhang et al., 2017 Eq. 9 + pet_memory = dampen_variability(tile_fluxes%canopy%dpet_soil, 30.0, pet_memory ) ! corresponds to f in Zhang et al., 2017 Eq. 9 + p_over_pet_memory = p_memory/(pet_memory + 1e-6) ! corresponds to f in Zhang et al., 2017 Eq. 9 + f_soil_aet = min(p_over_pet_memory, 1.0) ! previously was sw/kCw tile_fluxes%canopy%daet_soil = f_soil_aet * tile_fluxes%canopy%dpet_soil tile_fluxes%canopy%daet_e_soil = tile_fluxes%canopy%daet_soil / energy_to_mm diff --git a/vignettes/phydro_long_calibration.R b/vignettes/phydro_long_calibration.R index 875fd2ea..f829a728 100644 --- a/vignettes/phydro_long_calibration.R +++ b/vignettes/phydro_long_calibration.R @@ -7,7 +7,7 @@ library(tictoc) library(ncdf4) plot_only = F -debug = T +debug = F tic("phydro") @@ -220,7 +220,7 @@ gamma_mean = pars_joshi2022 %>% gamma_sd = pars_joshi2022 %>% filter(A.G == type & name == "gamma") %>% - pull(sd) %>% c(0.2) %>% max() + pull(sd) %>% c(0.1) %>% max() print(c(gamma_mean, gamma_sd)) @@ -369,6 +369,13 @@ output_p_opt <- rsofun::runread_pmodel_f( par = params_modl_opt ) plot_pmodel(output_p_opt) + +output_p_opt$data[[1]] = + p_hydro_validation$data[[1]] %>% + select(date, gpp, le) %>% + rename(gpp_obs = gpp, le_obs = le) %>% + right_join(output_p_opt$data[[1]]) + save(output_p_opt, file=paste0(file_prefix, "_phydro_output.rda")) plot_pmodel(output_p_opt, out_filename_prefix = fig_file_prefix) diff --git a/vignettes/rsofun_phydro_output_analysis.R b/vignettes/rsofun_phydro_output_analysis.R index 05aedbbc..154d4441 100644 --- a/vignettes/rsofun_phydro_output_analysis.R +++ b/vignettes/rsofun_phydro_output_analysis.R @@ -13,7 +13,7 @@ source("vignettes/read_meta_fdk.R") root_data_dir = "~/Downloads/fluxdatakit_oct3" lsm_path = paste0(root_data_dir, "/FLUXDATAKIT_LSM/") -out_dir = "~/Desktop/phydro_output_3" +out_dir = "~/Desktop/phydro_output_4_fixedkphio/" data_dir = paste0(root_data_dir, "/phydro_drivers/") figures_dir = paste0(out_dir, "/figures/") @@ -72,12 +72,13 @@ all_data = map_all_sites %>% ## FANCY PAIRS PLOT ## all_data %>% - select(kphio:whc, IGBP_veg_short) %>% + select(phydro_K_plant:whc, IGBP_veg_short) %>% mutate(phydro_K_plant = log10(phydro_K_plant), whc = log10(whc), Ssoil = log10(Ssoil), - phydro_p50_plant = log10(-phydro_p50_plant), - phydro_alpha = log10(phydro_alpha)) %>% + # phydro_p50_plant = log10(-phydro_p50_plant), + # phydro_alpha = log10(phydro_alpha) + ) %>% filter(IGBP_veg_short %in% c("SAV", "OSH", "GRA", "ENF", "EBF", "DBF", "CRO")) %>% mutate(IGBP_veg_short = factor(IGBP_veg_short)) %>% scatterPlotMatrix(distribType = 1, regressionType = 1, rotateTitle = F, zAxisDim = "IGBP_veg_short", @@ -134,7 +135,7 @@ read_phydro_output = function(site){ filter(stringr::str_detect(files, site)) %>% filter(stringr::str_detect(files, "phydro_output.rda")) %>% pull(files) - + load(file) output_p_opt$data[[1]] %>% @@ -142,6 +143,18 @@ read_phydro_output = function(site){ mutate(SITE_ID = site) } +read_obs = function(site){ + file_obs = tibble(files=list.files(data_dir, full.names = T)) %>% + filter(stringr::str_detect(files, site)) %>% + filter(stringr::str_detect(files, "p_hydro_validation.rda")) %>% + pull(files) + + load(file_obs) + + p_hydro_validation$data[[1]] +} + + sites_phydro = tibble(files=list.files(out_dir)) %>% filter(stringr::str_detect(files, "phydro_output.rda")) %>% mutate(site = substr(files, 1,6)) %>% @@ -157,6 +170,7 @@ vod_phydro = vod %>% left_join(phydro_out) %>% sites_vod_phydro = vod_phydro %>% group_by(SITE_ID) %>% count() %>% arrange(desc(n)) %>% pull(SITE_ID) for (i in seq(1,length(sites_vod_phydro), by=20)){ + print( vod_phydro %>% filter(SITE_ID %in% sites_vod_phydro[i:(i+20-1)]) %>% mutate(vod_ratio = log(vod_night/vod_day)) %>% @@ -166,6 +180,7 @@ for (i in seq(1,length(sites_vod_phydro), by=20)){ scale_colour_viridis_c()+ facet_wrap("SITE_ID", scales="free")+ guides(color="none") + ) } From da09478df6ef9d648af81bab1dc0d1c15a37e1b1 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Tue, 27 Feb 2024 21:02:21 +0100 Subject: [PATCH 033/112] using p50 priors --- src/waterbal_splash.mod.f90 | 2 +- vignettes/phydro_long_calibration.R | 19 ++++++++++++++++--- vignettes/rsofun_phydro_output_analysis.R | 6 +++--- 3 files changed, 20 insertions(+), 7 deletions(-) diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index b0e22ca9..1551eaf1 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -381,7 +381,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, using_phydro ) p_memory = dampen_variability(climate%dprec*86400, 30.0, p_memory ) ! corresponds to f in Zhang et al., 2017 Eq. 9 pet_memory = dampen_variability(tile_fluxes%canopy%dpet_soil, 30.0, pet_memory ) ! corresponds to f in Zhang et al., 2017 Eq. 9 p_over_pet_memory = p_memory/(pet_memory + 1e-6) ! corresponds to f in Zhang et al., 2017 Eq. 9 - f_soil_aet = min(p_over_pet_memory, 1.0) ! previously was sw/kCw + f_soil_aet = max(min(p_over_pet_memory, 1.0), 0.0) ! previously was sw/kCw tile_fluxes%canopy%daet_soil = f_soil_aet * tile_fluxes%canopy%dpet_soil tile_fluxes%canopy%daet_e_soil = tile_fluxes%canopy%daet_soil / energy_to_mm diff --git a/vignettes/phydro_long_calibration.R b/vignettes/phydro_long_calibration.R index f829a728..b230ec71 100644 --- a/vignettes/phydro_long_calibration.R +++ b/vignettes/phydro_long_calibration.R @@ -225,6 +225,18 @@ gamma_sd = pars_joshi2022 %>% print(c(gamma_mean, gamma_sd)) +p50x_data = read.csv("ancillary_data/P50X2.csv") + +p50x_site = p50x_data %>% filter(IGBP_Guessed == p_hydro_drivers$site_info[[1]]$IGBP_veg_short) +print(p50x_site) +if (nrow(p50x_site > 0)){ + p50xmean = mean(p50x_site$P50_mean) + p50xsd = mean(p50x_site$P50_sd) +} else { + p50xmean = -3 + p50xsd = 1 +} + uniform_range = function(lower, upper){ list(lower= lower, upper=upper, mean = (upper+lower)/2, sd = (upper-lower)*10) } @@ -248,13 +260,14 @@ gaussian_range = function(mean, sd){ # ), pars_calib = list( - # kphio = uniform_range(lower=0.005, upper=0.09), + kphio = uniform_range(lower=0.005, upper=0.09), # phydro_K_plant = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="K.scalar") %>% pull(mean), # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="K.scalar") %>% pull(sd)), # phydro_p50_plant = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="P50") %>% pull(mean), # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="P50") %>% pull(sd)), phydro_K_plant = uniform_range(lower=0.1e-16, 1e-16), - phydro_p50_plant = uniform_range(lower=-4, -0.3), + # phydro_p50_plant = uniform_range(lower=-4, -0.3), + phydro_p50_plant = gaussian_range(mean = p50xmean/3, sd = p50xsd/3), # phydro_alpha = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="alpha") %>% pull(mean), # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="alpha") %>% pull(sd)), # phydro_gamma = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(mean), @@ -270,7 +283,7 @@ pars_calib = list( ) pars_fixed = list( # fix all other parameters - kphio = 0.045, + # kphio = 0.045, kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous diff --git a/vignettes/rsofun_phydro_output_analysis.R b/vignettes/rsofun_phydro_output_analysis.R index 154d4441..5923ab8d 100644 --- a/vignettes/rsofun_phydro_output_analysis.R +++ b/vignettes/rsofun_phydro_output_analysis.R @@ -13,7 +13,7 @@ source("vignettes/read_meta_fdk.R") root_data_dir = "~/Downloads/fluxdatakit_oct3" lsm_path = paste0(root_data_dir, "/FLUXDATAKIT_LSM/") -out_dir = "~/Desktop/phydro_output_4_fixedkphio/" +out_dir = "~/Desktop/phydro_output_5_fixedkphio/" data_dir = paste0(root_data_dir, "/phydro_drivers/") figures_dir = paste0(out_dir, "/figures/") @@ -139,7 +139,7 @@ read_phydro_output = function(site){ load(file) output_p_opt$data[[1]] %>% - select(date, gpp, le, jmax25, dpsi, psi_leaf) %>% + select(date, gpp, le, jmax25, dpsi, psi_leaf, gpp_obs, le_obs) %>% mutate(SITE_ID = site) } @@ -174,7 +174,7 @@ for (i in seq(1,length(sites_vod_phydro), by=20)){ vod_phydro %>% filter(SITE_ID %in% sites_vod_phydro[i:(i+20-1)]) %>% mutate(vod_ratio = log(vod_night/vod_day)) %>% - ggplot(aes(y=vod_ratio, x=dpsi)) + + ggplot(aes(y=gpp, x=gpp_obs)) + geom_pointdensity() + geom_smooth(method="lm", formula=y~0+x, col="orange", linewidth=0.5)+ scale_colour_viridis_c()+ From 17f5896d861d4bee56eb5ecb36297f745c897dc4 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Wed, 28 Feb 2024 19:39:38 +0100 Subject: [PATCH 034/112] narrower alpha prior; r2 map --- vignettes/phydro_long_calibration.R | 12 ++++----- vignettes/rsofun_phydro_output_analysis.R | 32 +++++++++++++++++++++-- 2 files changed, 36 insertions(+), 8 deletions(-) diff --git a/vignettes/phydro_long_calibration.R b/vignettes/phydro_long_calibration.R index b230ec71..5e3d5584 100644 --- a/vignettes/phydro_long_calibration.R +++ b/vignettes/phydro_long_calibration.R @@ -204,15 +204,15 @@ pars_joshi2022 = parjj %>% summarize(mean=mean(value), sd=sd(value), n=length(value)) -# igbp = "WSA" -type = p_hydro_drivers$site_info[[1]]$IGBP_veg_short %>% +igbp = p_hydro_drivers$site_info[[1]]$IGBP_veg_short # "WSA" +type = igbp %>% case_match(c("WSA", "EBF", "DBF") ~ "Angiosperm", c("ENF") ~ "Gymnosperm", c("OSH", "CSH") ~ "Shrub", c("GRA", "CRO", "SAV") ~ "Shrub", c("MF", "WET") ~ "Angiosperm") -message("type = ", p_hydro_drivers$site_info[[1]]$IGBP_veg_short, " --> " ,type, "\n") +message("type = ", igbp, " --> " ,type, "\n") gamma_mean = pars_joshi2022 %>% filter(A.G == type & name == "gamma") %>% @@ -227,7 +227,7 @@ print(c(gamma_mean, gamma_sd)) p50x_data = read.csv("ancillary_data/P50X2.csv") -p50x_site = p50x_data %>% filter(IGBP_Guessed == p_hydro_drivers$site_info[[1]]$IGBP_veg_short) +p50x_site = p50x_data %>% filter(IGBP_Guessed == igbp) print(p50x_site) if (nrow(p50x_site > 0)){ p50xmean = mean(p50x_site$P50_mean) @@ -272,7 +272,7 @@ pars_calib = list( # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="alpha") %>% pull(sd)), # phydro_gamma = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(mean), # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(sd)), - phydro_alpha = gaussian_range(mean = 0.11, sd = 0.02), + phydro_alpha = gaussian_range(mean = 0.1, sd = 0.005), # phydro_gamma = uniform_range(lower = 0.1, upper = 2), phydro_gamma = gaussian_range(mean = gamma_mean, sd = gamma_sd), #bsoil = uniform_range(lower=0.1, upper=10), @@ -343,7 +343,7 @@ if (!plot_only){ load(calib_file) } -fig_file_prefix = paste0(figures_dir, site, "_nchains_", +fig_file_prefix = paste0(figures_dir, site, "_", igbp, "_nchains_", settings_bayes$control$settings$nrChains, "_nsteps_", settings_bayes$control$settings$iterations, diff --git a/vignettes/rsofun_phydro_output_analysis.R b/vignettes/rsofun_phydro_output_analysis.R index 5923ab8d..d4970331 100644 --- a/vignettes/rsofun_phydro_output_analysis.R +++ b/vignettes/rsofun_phydro_output_analysis.R @@ -9,11 +9,12 @@ library(scatterPlotMatrix) library(ggpointdensity) plot_only = F + source("vignettes/read_meta_fdk.R") root_data_dir = "~/Downloads/fluxdatakit_oct3" lsm_path = paste0(root_data_dir, "/FLUXDATAKIT_LSM/") -out_dir = "~/Desktop/phydro_output_5_fixedkphio/" +out_dir = "~/Desktop/phydro_output_6_p50priors/" data_dir = paste0(root_data_dir, "/phydro_drivers/") figures_dir = paste0(out_dir, "/figures/") @@ -114,6 +115,33 @@ all_data %>% geom_abline(slope = 1) +r2_all_sites = tibble(filename=list.files(out_dir, full.names = T)) %>% + filter(str_detect(filename, "r2_nrmse.csv")) %>% + pull(filename) %>% + map_df(~read.csv(.)) + +world <- map_data("world") +r2_all_sites %>% + left_join(sites_meta, by=c("site"="sitename")) %>% + ggplot() + + theme_classic()+ + geom_map( + data = world, map = world, + aes(long, lat, map_id = region), + color = "black", fill= "grey90", size=0.05 + ) + + geom_point(aes(x=longitude, y=latitude, color=r2))+ + scale_color_gradient2(mid = "cyan", midpoint = 0.5, low = "red", high="blue", limits=c(0,1))+ + facet_wrap(~variable, nrow = 2) + +r2_all_sites %>% + left_join(sites_meta, by=c("site"="sitename")) %>% + select(variable, r2, IGBP_veg_short) %>% + ggplot(aes(x=IGBP_veg_short, y=r2, fill=IGBP_veg_short)) + + geom_violin(scale = "width", width=0.4) + + facet_wrap(~variable, nrow=2) + + #### Water potentials analysis #### vod_day = readxl::read_excel("~/Downloads/AMSR_fluxnet_data.xlsx", sheet = "VOD_ASC_filtered_qa(1_30_PM)") @@ -174,7 +202,7 @@ for (i in seq(1,length(sites_vod_phydro), by=20)){ vod_phydro %>% filter(SITE_ID %in% sites_vod_phydro[i:(i+20-1)]) %>% mutate(vod_ratio = log(vod_night/vod_day)) %>% - ggplot(aes(y=gpp, x=gpp_obs)) + + ggplot(aes(y=vod_ratio, x=le_obs)) + geom_pointdensity() + geom_smooth(method="lm", formula=y~0+x, col="orange", linewidth=0.5)+ scale_colour_viridis_c()+ From 0514f725cba5128fe71423ac28ccc22bc63cf7fc Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Thu, 29 Feb 2024 20:02:58 +0100 Subject: [PATCH 035/112] nothing really --- src/waterbal_splash.mod.f90 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index 1d079a2d..df69aaec 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -536,7 +536,7 @@ subroutine getpar_modl_waterbal() ! shortwave albedo (Federer, 1968) kalb_sw = 0.17 - ! visible light albedo (Sellers, 1985) xxx planetary albedo? xxx + ! visible light albedo (Sellers, 1985) kalb_vis = 0.03 ! constant for dRnl (Linacre, 1968) From 57a9fc6d62af297ab3d625bf662be3be9332e3e9 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sun, 26 May 2024 20:04:25 +0200 Subject: [PATCH 036/112] use new cwdx80_forcing nc file --- vignettes/p50_data_generation.R | 26 +++++ vignettes/rsofun_phydro_data_generation.R | 28 ++++- vignettes/rsofun_phydro_output_analysis.R | 127 ++++++++++++++++++---- 3 files changed, 156 insertions(+), 25 deletions(-) create mode 100644 vignettes/p50_data_generation.R diff --git a/vignettes/p50_data_generation.R b/vignettes/p50_data_generation.R new file mode 100644 index 00000000..5011e22b --- /dev/null +++ b/vignettes/p50_data_generation.R @@ -0,0 +1,26 @@ +library(tidyverse) + +dat = read.csv("/home/jjoshi/Downloads/try_hydraulics_tidy.csv") %>% select(-X) +categ = read.csv("/home/jjoshi/Downloads/TRY_Categorical_Traits_Lookup_Table_2012_03_17_TestRelease.csv", na.strings = c("", "NA")) %>% select(-X) + +a = dat %>% left_join(categ) + +b = a %>% + rename(P50Xylem = `Xylem.water.potential.at.which.50..of.conductivity.is.lost..P50...MPa...2099.`) %>% + select(PlantGrowthForm, LeafType, LeafPhenology, PhotosyntheticPathway, AccSpeciesID, AccSpeciesName, P50Xylem) %>% + filter(!is.na(P50Xylem)) + +b %>% drop_na %>% filter(PhotosyntheticPathway == "C4") %>% View() + +b %>% + drop_na %>% + group_by(PlantGrowthForm, LeafType, LeafPhenology, PhotosyntheticPathway) %>% + summarize(P50_mean = mean(P50Xylem), + P50_sd = sd(P50Xylem), + P50_count = length(P50Xylem)) %>% + filter(LeafType != "scale-shaped" & + LeafPhenology != "deciduous/evergreen" & + PhotosyntheticPathway != "C3/CAM" & + P50_count > 2 + ) %>% + write.csv("~/codes/rsofun/vignettes/ancillary_data/P50X2.csv", row.names = F) diff --git a/vignettes/rsofun_phydro_data_generation.R b/vignettes/rsofun_phydro_data_generation.R index c7b058a6..b861d34b 100644 --- a/vignettes/rsofun_phydro_data_generation.R +++ b/vignettes/rsofun_phydro_data_generation.R @@ -31,13 +31,13 @@ args = commandArgs(trailingOnly=TRUE) #site <- "GF-Guy" if (length(args)==0) { #stop("At least one argument must be supplied: site name", call.=FALSE) - site = "FR-Pue" + site = "AU-ASM" }else{ site = args[1] } if (length(args)<2) { - root_data_dir = "~/Downloads/fluxdatakit_oct3" + root_data_dir = "~/Downloads/fluxdatakit_v3.0" }else{ root_data_dir = args[2] } @@ -308,11 +308,11 @@ tmaxmin <- message("- compiling drivers") load("../data/p_model_drivers.rda") - -nc = nc_open("ancillary_data/cwdx80.nc") +## READ WHC from file ## +nc = nc_open("ancillary_data/cwdx80_forcing.nc") lons = ncvar_get(nc, "lon") lats = ncvar_get(nc, "lat") -S80 = ncvar_get(nc, "cwdx80") +S80 = ncvar_get(nc, "cwdx80_forcing") site_lon = meta[[1]]$longitude site_lat = meta[[1]]$latitude @@ -324,6 +324,24 @@ S80_slice = S80[(lonid-n):(lonid+n), (latid-n):(latid+n)] whc_site = mean(as.numeric(S80_slice, na.rm=T)) whc_site_sd = sd(as.numeric(S80_slice, na.rm=T)) +## READ WHC_1m FROM FILE ## + +nc = nc_open("ancillary_data/whc_1m.nc") +lons = ncvar_get(nc, "lon") +lats = ncvar_get(nc, "lat") +whc1m = ncvar_get(nc, "whc_1m") + +site_lon = meta[[1]]$longitude +site_lat = meta[[1]]$latitude + +lonid = which(lons > site_lon)[1]-1 +latid = which(lats > site_lat)[1]-1 +n = 1 +whc1m_slice = whc1m[(lonid-n):(lonid+n), (latid-n):(latid+n)] +whc1m_site = mean(as.numeric(whc1m_slice, na.rm=T)) +whc1m_site_sd = sd(as.numeric(whc1m_slice, na.rm=T)) + + p_hydro_drivers <- p_model_drivers p_hydro_drivers$sitename[[1]] = site diff --git a/vignettes/rsofun_phydro_output_analysis.R b/vignettes/rsofun_phydro_output_analysis.R index d4970331..daa08b66 100644 --- a/vignettes/rsofun_phydro_output_analysis.R +++ b/vignettes/rsofun_phydro_output_analysis.R @@ -14,7 +14,8 @@ source("vignettes/read_meta_fdk.R") root_data_dir = "~/Downloads/fluxdatakit_oct3" lsm_path = paste0(root_data_dir, "/FLUXDATAKIT_LSM/") -out_dir = "~/Desktop/phydro_output_6_p50priors/" +out_dir = "~/Desktop/phydro_output_13_p50prior_fixedalpha0.08_s113/" +# out_dir = "~/Desktop/phydro_output_11_p50prior_narrowalpha_s113/" data_dir = paste0(root_data_dir, "/phydro_drivers/") figures_dir = paste0(out_dir, "/figures/") @@ -73,10 +74,11 @@ all_data = map_all_sites %>% ## FANCY PAIRS PLOT ## all_data %>% - select(phydro_K_plant:whc, IGBP_veg_short) %>% + left_join(sites_meta) %>% + select(kphio:whc, IGBP_veg_short) %>% mutate(phydro_K_plant = log10(phydro_K_plant), - whc = log10(whc), - Ssoil = log10(Ssoil), + # whc = log10(whc), + # Ssoil = log10(Ssoil), # phydro_p50_plant = log10(-phydro_p50_plant), # phydro_alpha = log10(phydro_alpha) ) %>% @@ -93,12 +95,13 @@ all_data %>% group_by(IGBP_veg_short) %>% summarize(count=length(IGBP_veg_short) right_join(all_data) %>% filter(count > 2) %>% select(kphio:whc, IGBP_veg_short, sitename) %>% - mutate(phydro_K_plant = log10(phydro_K_plant), - whc = log10(whc), - Ssoil = log10(Ssoil), - # kphio = log10(kphio), - # phydro_gamma = log10(phydro_gamma) - ) %>% + mutate( + # phydro_K_plant = log10(phydro_K_plant), + whc = log10(whc), + # Ssoil = log10(Ssoil), + # kphio = log10(kphio), + # phydro_gamma = log10(phydro_gamma) + ) %>% pivot_longer(cols = !c(sitename, IGBP_veg_short)) %>% ggplot(aes(x=IGBP_veg_short, y=value, fill=IGBP_veg_short)) + geom_violin() + @@ -106,13 +109,13 @@ right_join(all_data) %>% facet_wrap(facets = "name", scales = "free") + coord_flip() -all_data %>% - filter(IGBP_veg_short %in% c("SAV", "OSH", "GRA", "ENF", "EBF", "DBF", "CRO")) %>% - ggplot(aes(group=IGBP_veg_short, x=Ssoil, y=whc, col=IGBP_veg_short)) + - geom_point()+ - geom_smooth(method="lm", se=F)+ - scale_x_log10()+scale_y_log10()+ - geom_abline(slope = 1) +# all_data %>% +# filter(IGBP_veg_short %in% c("SAV", "OSH", "GRA", "ENF", "EBF", "DBF", "CRO")) %>% +# ggplot(aes(group=IGBP_veg_short, x=Ssoil, y=whc, col=IGBP_veg_short)) + +# geom_point()+ +# geom_smooth(method="lm", se=F)+ +# scale_x_log10()+scale_y_log10()+ +# geom_abline(slope = 1) r2_all_sites = tibble(filename=list.files(out_dir, full.names = T)) %>% @@ -140,7 +143,25 @@ r2_all_sites %>% ggplot(aes(x=IGBP_veg_short, y=r2, fill=IGBP_veg_short)) + geom_violin(scale = "width", width=0.4) + facet_wrap(~variable, nrow=2) - + +for (var in c("kphio", "phydro_K_plant", "phydro_p50_plant", "phydro_alpha", "phydro_gamma", "bsoil", "whc")){ +print( +map_all_sites %>% + left_join(sites_meta, by=c("site"="sitename")) %>% + select(kphio:whc, site, latitude, longitude) %>% + pivot_longer(cols = kphio:whc, names_to = "variable", values_to = "value") %>% + filter(variable == var) %>% + ggplot() + + theme_classic()+ + geom_map( + data = world, map = world, + aes(x=long, y=lat, map_id = region), + color = "black", fill= "grey90", size=0.05 + ) + + geom_point(aes(x=longitude, y=latitude, color=value))+ + ggtitle(var) +) +} #### Water potentials analysis #### @@ -167,7 +188,7 @@ read_phydro_output = function(site){ load(file) output_p_opt$data[[1]] %>% - select(date, gpp, le, jmax25, dpsi, psi_leaf, gpp_obs, le_obs) %>% + select(date, gpp, le, le_soil, jmax25, dpsi, psi_leaf, gpp_obs, le_obs) %>% mutate(SITE_ID = site) } @@ -202,7 +223,7 @@ for (i in seq(1,length(sites_vod_phydro), by=20)){ vod_phydro %>% filter(SITE_ID %in% sites_vod_phydro[i:(i+20-1)]) %>% mutate(vod_ratio = log(vod_night/vod_day)) %>% - ggplot(aes(y=vod_ratio, x=le_obs)) + + ggplot(aes(y=vod_ratio, x=dpsi)) + geom_pointdensity() + geom_smooth(method="lm", formula=y~0+x, col="orange", linewidth=0.5)+ scale_colour_viridis_c()+ @@ -221,6 +242,18 @@ site_min_psisoil = group_by(SITE_ID) %>% summarize(psi_min = min(psi_leaf+dpsi)) +site_max_dpsi = + phydro_out %>% + group_by(SITE_ID) %>% + summarize(dpsi_max = max(dpsi)) + +site_max_dpsi %>% + left_join(sites_meta, by=c("SITE_ID"="sitename")) %>% + select(dpsi_max, IGBP_veg_short) %>% + ggplot(aes(x=IGBP_veg_short, y=dpsi_max, fill=IGBP_veg_short)) + + geom_violin(scale = "width", width=0.4) + + for (i in seq(1,length(sites_phydro), by=20)){ cairo_pdf(filename = paste0("vignettes/figures/psi_leaf_vs_soil_",i,".pdf")) print( @@ -267,3 +300,57 @@ phydro_out %>% ggplot(aes(x=`(Intercept)`, y=psi_soil))+ geom_point() + geom_smooth(method="lm") + +le_soil_by_le = phydro_out %>% group_by(SITE_ID) %>% summarize(le_soil_pc = mean(le_soil)/mean(le)) +le_soil_by_le %>% + left_join(sites_meta, by=c("SITE_ID"="sitename")) %>% + select(le_soil_pc, SITE_ID, latitude, longitude) %>% + ggplot() + + theme_classic()+ + geom_map( + data = world, map = world, + aes(x=long, y=lat, map_id = region), + color = "black", fill= "grey90", size=0.05 + ) + + geom_point(aes(x=longitude, y=latitude, color=le_soil_pc))+ + ggtitle("le_soil_pc") + + +all_data %>% + left_join(le_soil_by_le, by=c("sitename"="SITE_ID")) %>% + left_join(r2_all_sites, by=c("sitename"="site")) %>% + ggplot(aes(x=le_soil_pc, y=r2))+ + geom_point()+ + facet_wrap(~variable) + +all_data %>% + left_join(le_soil_by_le, by=c("sitename"="SITE_ID")) %>% + left_join(r2_all_sites, by=c("sitename"="site")) %>% + select(-r2) %>% + pivot_wider(names_from = variable, values_from=nrmse, names_prefix = "nrmse_") %>% + mutate(nrmse_diff = nrmse_le - nrmse_gpp) %>% + ggplot(aes(x=le_soil_pc, y=nrmse_diff, colour=IGBP_veg_short))+ + geom_point() + +all_data %>% + left_join(le_soil_by_le, by=c("sitename"="SITE_ID")) %>% + left_join(r2_all_sites, by=c("sitename"="site")) %>% + select(-r2) %>% + pivot_wider(names_from = variable, values_from=nrmse, names_prefix = "nrmse_") %>% + mutate(nrmse_diff = nrmse_le - nrmse_gpp) %>% + filter(le_soil_pc > 0.75 & nrmse_diff < 0) %>% View() + +all_data %>% + left_join(le_soil_by_le, by=c("sitename"="SITE_ID")) %>% + ggplot(aes(x=le_soil_pc, y=phydro_p50_plant))+ + geom_point() + +all_data %>% + left_join(le_soil_by_le, by=c("sitename"="SITE_ID")) %>% + ggplot(aes(x=le_soil_pc, y=phydro_alpha))+ + geom_point() + +all_data %>% + left_join(le_soil_by_le, by=c("sitename"="SITE_ID")) %>% + ggplot(aes(x=le_soil_pc, y=phydro_K_plant))+ + geom_point() From 301c3cd71a14b98d70a05b95880bd47e2876e0cd Mon Sep 17 00:00:00 2001 From: Jaideep Date: Thu, 30 May 2024 17:39:00 +0200 Subject: [PATCH 037/112] revert some changes to ensure phydro runs. Working for AU-ASM --- R/run_pmodel_f_bysite.R | 5 +-- src/Makevars.in | 14 +++----- src/biosphere_pmodel.mod.f90 | 4 +-- src/waterbal_splash.mod.f90 | 40 +++++++++++------------ vignettes/phydro_long_calibration.R | 16 ++++----- vignettes/rsofun_phydro_data_generation.R | 2 +- 6 files changed, 38 insertions(+), 43 deletions(-) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 20ac1074..70b2128a 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -359,8 +359,9 @@ run_pmodel_f_bysite <- function( in_ppfd <- ifelse(any(is.na(forcing$ppfd)), FALSE, TRUE) # determine whether to read PPFD from forcing or to calculate internally - # in_netrad <- ifelse(any(is.na(forcing$netrad)), FALSE, TRUE) - in_netrad <- FALSE # net radiation is currently ignored as a model forcing, but is internally simulated by SPLASH. + # Jaideep Note: phydro uses input netrad, so dont enforce internal calculation + in_netrad <- ifelse(any(is.na(forcing$netrad)), FALSE, TRUE) + # in_netrad <- FALSE # net radiation is currently ignored as a model forcing, but is internally simulated by SPLASH. # Check if fsun is available if(! (in_ppfd & in_netrad)){ diff --git a/src/Makevars.in b/src/Makevars.in index 3aad1603..ce2e0a07 100644 --- a/src/Makevars.in +++ b/src/Makevars.in @@ -1,18 +1,10 @@ # PKG_FFLAGS = -ffree-line-length-0 -fbacktrace -ffpe-trap=invalid,zero,overflow -O1 -Wall -Wextra -pedantic -fbacktrace -fPIC -fmax-errors=1 -ggdb -fcheck=all -# PKG_FFLAGS = -fbounds-check - -# Add package flags: -# -frecursive: to avoid "Warning: Array 'out_biosphere' at (1) is larger than limit ..." -# -fbounds-check: https://scicomp.stackexchange.com/questions/36045/ifort-everithing-ok-but-with-gfortran-segmentation-fault -# -Wall -Wextra -Wpedantic: to get more warnings -# -fsanitize=address: Using the Address Sanitizer https://rstudio.github.io/r-manuals/r-exts/Debugging.html#using-the-address-sanitizer -# PKG_FFLAGS = -frecursive -fbounds-check -Wall -Wextra -Wpedantic -g -O0 -fsanitize=address # C objects C_OBJS = wrappersc.o # Fortran objects: refer to file names , order reflects dependency structure -FT_OBJS = params_core.mod.o sofunutils.mod.o grid_siterun.mod.o params_siml_pmodel.mod.o params_siml_biomee.mod.o forcing_siterun_pmodel.mod.o forcing_siterun_biomee.mod.o params_soil_biomee.mod.o interface_biosphere_pmodel.mod.o interface_biosphere_biomee.mod.o tile_pmodel.mod.o plant_pmodel.mod.o soiltemp_sitch.mod.o waterbal_splash.mod.o vegdynamics_pmodel.mod.o gpp_pmodel.mod.o gpp_biomee.mod.o photosynth_pmodel.mod.o biosphere_pmodel.mod.o biosphere_biomee.mod.o vegetation_biomee.mod.o soil_biomee.mod.o sofun_r.o +FT_OBJS = params_core.mod.o sofunutils.mod.o grid_siterun.mod.o params_siml_pmodel.mod.o params_siml_biomee.mod.o forcing_siterun_pmodel.mod.o forcing_siterun_biomee.mod.o params_soil_biomee.mod.o interface_biosphere_pmodel.mod.o interface_biosphere_biomee.mod.o tile_pmodel.mod.o plant_pmodel.mod.o soiltemp_sitch.mod.o waterbal_splash.mod.o vegdynamics_pmodel.mod.o gpp_pmodel.mod.o gpp_biomee.mod.o photosynth_pmodel.mod.o photosynth_phydro.mod.o biosphere_pmodel.mod.o biosphere_biomee.mod.o vegetation_biomee.mod.o soil_biomee.mod.o sofun_r.o all: $(SHLIB) clean @@ -28,9 +20,10 @@ forcing_siterun_biomee.mod.o: params_core.mod.o params_siml_biomee.mod.o grid_si params_soil_biomee.mod.o: params_core.mod.o tile_pmodel.mod.o: params_core.mod.o interface_biosphere_pmodel.mod.o waterbal_splash.mod.o: params_core.mod.o tile_pmodel.mod.o plant_pmodel.mod.o sofunutils.mod.o -gpp_pmodel.mod.o: params_core.mod.o sofunutils.mod.o plant_pmodel.mod.o tile_pmodel.mod.o interface_biosphere_pmodel.mod.o photosynth_pmodel.mod.o +gpp_pmodel.mod.o: params_core.mod.o sofunutils.mod.o plant_pmodel.mod.o tile_pmodel.mod.o interface_biosphere_pmodel.mod.o photosynth_pmodel.mod.o photosynth_phydro.mod.o gpp_biomee.mod.o: datatypes.mod.o soil_biomee.mod.o forcing_siterun_biomee.mod.o photosynth_pmodel.mod.o params_core.mod.o sofunutils.mod.o photosynth_pmodel.mod.o: params_core.mod.o sofunutils.mod.o +photosynth_phydro.mod.o: params_core.mod.o sofunutils.mod.o photosynth_pmodel.mod.o soiltemp_sitch.mod.o: params_core.mod.o sofunutils.mod.o tile_pmodel.mod.o interface_biosphere_pmodel.mod.o plant_pmodel.mod.o: params_core.mod.o sofunutils.mod.o interface_biosphere_pmodel.mod.o vegdynamics_pmodel.mod.o: params_core.mod.o tile_pmodel.mod.o plant_pmodel.mod.o gpp_pmodel.mod.o waterbal_splash.mod.o @@ -63,6 +56,7 @@ md_tile_pmodel.mod: tile_pmodel.mod.o md_gpp_pmodel.mod: gpp_pmodel.mod.o md_gpp_biomee.mod: gpp_biomee.mod.o md_photosynth.mod: photosynth_pmodel.mod.o +md_photosynth_phydro.mod: photosynth_phydro.mod.o md_soiltemp.mod: soiltemp_sitch.mod.o md_vegdynamics_pmodel.mod: vegdynamics_pmodel.mod.o datatypes.mod: datatypes.o diff --git a/src/biosphere_pmodel.mod.f90 b/src/biosphere_pmodel.mod.f90 index 6c7a1353..ea57233f 100644 --- a/src/biosphere_pmodel.mod.f90 +++ b/src/biosphere_pmodel.mod.f90 @@ -111,8 +111,8 @@ function biosphere_annual() result( out_biosphere ) call solar( tile_fluxes(:), & myinterface%grid, & myinterface%climate(doy), & - doy & - ! myinterface%params_siml%in_netrad & + doy, & + myinterface%params_siml%in_netrad & ) ! if (verbose) print*,'... done' diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index ff05fb82..a09f14d1 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -136,7 +136,7 @@ subroutine waterbal( tile, tile_fluxes, grid, climate, using_phydro ) end subroutine waterbal - subroutine solar( tile_fluxes, grid, climate, doy ) + subroutine solar( tile_fluxes, grid, climate, doy, in_netrad ) !///////////////////////////////////////////////////////////////////////// ! This subroutine calculates daily PPFD. Code is an extract of the subroutine ! 'evap', adopted from the evap() function in GePiSaT (Python version). @@ -150,7 +150,7 @@ subroutine solar( tile_fluxes, grid, climate, doy ) type(gridtype), intent(inout) :: grid type(climate_type), intent(in) :: climate integer, intent(in) :: doy ! day of year - ! logical, intent(in) :: in_netrad + logical, intent(in) :: in_netrad !--------------------------------------------------------- ! 2. Calculate heliocentric longitudes (nu and lambda), degrees @@ -173,7 +173,7 @@ subroutine solar( tile_fluxes, grid, climate, doy ) !--------------------------------------------------------- ! 4. Calculate declination angle, degrees !--------------------------------------------------------- - grid%decl_angle = calc_decl_angle( grid%lambda ) + ! 4. Calculate declination angle, degrees !--------------------------------------------------------- ! 5. Calculate variable substitutes (ru and rv), unitless @@ -236,26 +236,28 @@ subroutine solar( tile_fluxes, grid, climate, doy ) ! 13. Calculate daytime total net radiation (tile_fluxes%canopy%drn), J m-2 d-1 !--------------------------------------------------------- ! Eq. 53, SPLASH 2.0 Documentation - ! if (in_netrad) then - ! tile_fluxes(:)%canopy%drn = climate%dnetrad * myinterface%params_siml%secs_per_tstep - ! else - ! tile_fluxes(:)%canopy%drn = (secs_per_day/pi) * (hn*(pi/180.0)*(rw*ru - tile_fluxes(:)%canopy%rnl) + rw*rv*dgsin(hn)) - ! end if - tile_fluxes(:)%canopy%drn = (secs_per_day/pi) * (hn*(pi/180.0)*(rw*ru - tile_fluxes(:)%canopy%rnl) + rw*rv*dgsin(hn)) + ! Jaideep Note: reverted this change for testing against old phydro + if (in_netrad) then + tile_fluxes(:)%canopy%drn = climate%dnetrad * myinterface%params_siml%secs_per_tstep + else + tile_fluxes(:)%canopy%drn = (secs_per_day/pi) * (hn*(pi/180.0)*(rw*ru - tile_fluxes(:)%canopy%rnl) + rw*rv*dgsin(hn)) + end if + ! tile_fluxes(:)%canopy%drn = (secs_per_day/pi) * (hn*(pi/180.0)*(rw*ru - tile_fluxes(:)%canopy%rnl) + rw*rv*dgsin(hn)) !--------------------------------------------------------- ! 14. Calculate nighttime total net radiation (tile_fluxes(:)%canopy%drnn), J m-2 d-1 !--------------------------------------------------------- ! Eq. 56, SPLASH 2.0 Documentation ! adopted bugfix from Python version (iss#13) - ! if (in_netrad) then - ! tile_fluxes(:)%canopy%drnn = 0.0 - ! else - ! tile_fluxes(:)%canopy%drnn = (86400.0/pi)*(radians(rw*ru*(hs-hn)) + rw*rv*(dgsin(hs)-dgsin(hn)) - & - ! tile_fluxes(:)%canopy%rnl * (pi - radians(hn))) - ! end if - tile_fluxes(:)%canopy%drnn = (86400.0/pi)*(radians(rw*ru*(hs-hn)) + rw*rv*(dgsin(hs)-dgsin(hn)) - & - tile_fluxes(:)%canopy%rnl * (pi - radians(hn))) + ! Jaideep Note: reverted this change for testing against old phydro + if (in_netrad) then + tile_fluxes(:)%canopy%drnn = 0.0 + else + tile_fluxes(:)%canopy%drnn = (86400.0/pi)*(radians(rw*ru*(hs-hn)) + rw*rv*(dgsin(hs)-dgsin(hn)) - & + tile_fluxes(:)%canopy%rnl * (pi - radians(hn))) + end if + ! tile_fluxes(:)%canopy%drnn = (86400.0/pi)*(radians(rw*ru*(hs-hn)) + rw*rv*(dgsin(hs)-dgsin(hn)) - & + ! tile_fluxes(:)%canopy%rnl * (pi - radians(hn))) ! if (splashtest) then ! print*,'transmittivity, tau: ', tau @@ -322,7 +324,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, using_phydro ) ! JAIDEEP: If it's just conversion from mass to energy, the above formula is correct. This already has the Priestly Taylor factor (s/(s+y)) built in, so this ! should not be used for mere conversion. I would suggest you use just the factor s/(s+y) separately in the respective equations for clarity. - tile_fluxes%canopy%econ = sat_slope / (lv * rho_water * (sat_slope + gamma)) ! MORE PRECISELY - this is to convert energy into mass (water). . + tile_fluxes%canopy%econ = sat_slope / (lv * rho_water * (sat_slope + gamma)) ! MORE PRECISELY - this is to convert energy into mass (water). . !--------------------------------------------------------- ! Daily condensation, mm d-1 @@ -566,8 +568,6 @@ subroutine getpar_modl_waterbal() ! Subroutine reads waterbalance module-specific parameters ! from input file !---------------------------------------------------------------- - ! constant for dRnl (Monteith & Unsworth, 1990) - kA = 107.0 ! shortwave albedo (Federer, 1968) kalb_sw = 0.17 diff --git a/vignettes/phydro_long_calibration.R b/vignettes/phydro_long_calibration.R index 5e3d5584..93157e07 100644 --- a/vignettes/phydro_long_calibration.R +++ b/vignettes/phydro_long_calibration.R @@ -6,7 +6,7 @@ library(BayesianTools) library(tictoc) library(ncdf4) -plot_only = F +plot_only = T debug = F tic("phydro") @@ -18,7 +18,7 @@ args = commandArgs(trailingOnly=TRUE) #site <- "GF-Guy" if (length(args)==0) { #stop("At least one argument must be supplied: site name", call.=FALSE) - site = "FR-Pue" + site = "AU-ASM" }else{ site = args[1] } @@ -30,7 +30,7 @@ if (length(args)<2) { } if (length(args)<3) { - out_dir = "~/Downloads/fluxdatakit_oct3/phydro_output_fixedkphio/" + out_dir = "~/Downloads/fluxdatakit_oct3/phydro_output_test/" }else{ out_dir = paste0(args[3],"/") } @@ -272,11 +272,11 @@ pars_calib = list( # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="alpha") %>% pull(sd)), # phydro_gamma = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(mean), # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(sd)), - phydro_alpha = gaussian_range(mean = 0.1, sd = 0.005), + # phydro_alpha = gaussian_range(mean = 0.1, sd = 0.005), # phydro_gamma = uniform_range(lower = 0.1, upper = 2), phydro_gamma = gaussian_range(mean = gamma_mean, sd = gamma_sd), #bsoil = uniform_range(lower=0.1, upper=10), - Ssoil = uniform_range(lower = 0, upper = whc_site+whc_site_sd), + # Ssoil = uniform_range(lower = 0, upper = whc_site+whc_site_sd), whc = gaussian_range(mean = whc_site, sd = whc_site_sd), err_gpp = uniform_range(lower = 0.01, upper = 4), err_le = uniform_range(lower = 0.1e6, upper = 10e6) @@ -292,10 +292,10 @@ pars_fixed = list( # fix all other parameters # phydro_K_plant = 0.3e-16, # phydro_P50_plant = -1, phydro_b_plant = 1, - # phydro_alpha = 0.1, + phydro_alpha = 0.08, # phydro_gamma = 1 - bsoil = 3 - # Ssoil = 40, + bsoil = 3, + Ssoil = 113 # whc = 90 ) diff --git a/vignettes/rsofun_phydro_data_generation.R b/vignettes/rsofun_phydro_data_generation.R index b861d34b..ec090924 100644 --- a/vignettes/rsofun_phydro_data_generation.R +++ b/vignettes/rsofun_phydro_data_generation.R @@ -37,7 +37,7 @@ if (length(args)==0) { } if (length(args)<2) { - root_data_dir = "~/Downloads/fluxdatakit_v3.0" + root_data_dir = "~/Downloads/fluxdatakit_oct3" }else{ root_data_dir = args[2] } From e1204e11806895ccd98f7186988888c5133e9413 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Fri, 14 Jun 2024 22:07:52 +0200 Subject: [PATCH 038/112] CRITICAL BUGFIX: lines were silently deleted during merge, restore them --- data/AU-ASM_p_hydro_drivers.rda | Bin 0 -> 1187773 bytes 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z^4Ug6Kg8R+m^znrlR8VCuR0GAlz#R+7U<9b{N{CPY7oJA_^Syek^S^h=stK1>R&b@ z*hjXr`g~BtA@V)i10PP1`y-^#0$bL;ibWQn71duIw<0<` mWZhgMKb063vW4s*{louL{(mF$zmT74=KlcXREsmLVI%% + rename(le=latenth) + +p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 +p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 + +p_hydro_drivers$params_siml[[1]]$use_phydro = T +p_hydro_drivers$params_siml[[1]]$use_pml = F + +# Calibrated parameters for AU-ASM site +params_modl <- list( + kphio = 0.08857451, # setup ORG in Stocker et al. 2020 GMD + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + phydro_K_plant = 3.035552e-17, + phydro_p50_plant = -0.8639834, + phydro_gamma = 0.2859356, + phydro_b_plant = 1, + phydro_alpha = 0.08, + bsoil = 3, + Ssoil = 113, + whc = 676.9775 +) + +# run the model for these parameters +output <- rsofun::runread_pmodel_f( + p_hydro_drivers, + par = params_modl +) + +print( +output$data[[1]] %>% + select(date, gpp, le, aet, pet, netrad, wcont) %>% + pivot_longer(-date) %>% + mutate(type="pred") %>% + rbind( + p_hydro_validation$data[[1]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="obs") + ) %>% + ggplot(aes(x=date, y=value, group=type, col=type)) + + theme_classic()+ + geom_line() + + facet_wrap(~name, scales="free") +) + +get_density <- function(x, y, ...) { + df = tibble(x=x, y=y) %>% drop_na + dens <- MASS::kde2d(df$x, df$y, ...) + ix <- findInterval(x, dens$x) + iy <- findInterval(y, dens$y) + ii <- cbind(ix, iy) + return(dens$z[ii]) +} + + +print( + output$data[[1]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="pred") %>% + rbind( + p_hydro_validation$data[[1]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="obs") + ) %>% + pivot_wider(names_from = type, values_from = value) %>% + group_by(name) %>% + reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% + ggplot(aes(y=obs, x=pred, colour=density)) + + scale_color_viridis_c() + + geom_point(alpha=0.7) + + geom_abline(slope=1, intercept=0, col="red")+ + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~name, scales = "free", nrow = 1)+ + labs(colour="Density") +) + diff --git a/vignettes/simple_test_script_pmodel.R b/vignettes/simple_test_script_pmodel.R new file mode 100644 index 00000000..efaa8f50 --- /dev/null +++ b/vignettes/simple_test_script_pmodel.R @@ -0,0 +1,38 @@ +library(tidyverse) + +params_modl <- list( + kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + soilm_betao = 0.0, + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + whc = 432 +) + +p_model_drivers_test = p_model_drivers +p_model_drivers_test$params_siml[[1]]$use_phydro = F +p_model_drivers_test$params_siml[[1]]$use_pml = F +p_model_drivers_test$site_info[[1]]$canopy_height = 20 +p_model_drivers_test$site_info[[1]]$reference_height = 22 +p_model_drivers_test$forcing[[1]]$netrad = NA +p_model_drivers_test$forcing_acclim = p_model_drivers_test$forcing + +# run the model for these parameters +output <- rsofun::runread_pmodel_f( + p_model_drivers_test, + par = params_modl +) + +print( +output$data[[1]] %>% + select(date, gpp, aet, pet, wscal, netrad, wcont) %>% + pivot_longer(-date) %>% + ggplot(aes(x=date, y=value)) + + theme_classic()+ + geom_line(col="seagreen") + + facet_wrap(~name, scales="free") +) \ No newline at end of file From 21055fb2d86c75674913996cc67c4103e7644a83 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sat, 29 Jun 2024 18:05:26 +0200 Subject: [PATCH 039/112] minor bugfixes, prototype multi-site calib, add new cost fn for phydro --- R/cost_likelihood_phydro.R | 205 ++++++++++++++++++++++ R/run_pmodel_f_bysite.R | 3 + man/run_pmodel_f_bysite.Rd | 4 +- vignettes/phydro_long_calibration_multi.R | 200 +++++++++++++++++++++ vignettes/rsofun_phydro_data_generation.R | 6 +- vignettes/simple_test_script_phydro.R | 4 +- vignettes/simple_test_script_pmodel.R | 2 - 7 files changed, 418 insertions(+), 6 deletions(-) create mode 100644 R/cost_likelihood_phydro.R create mode 100644 vignettes/phydro_long_calibration_multi.R diff --git a/R/cost_likelihood_phydro.R b/R/cost_likelihood_phydro.R new file mode 100644 index 00000000..2355de35 --- /dev/null +++ b/R/cost_likelihood_phydro.R @@ -0,0 +1,205 @@ +cost_likelihood_phydro <- function( + par, # model parameters & error terms for each target + obs, + drivers, + targets, + par_fixed = NULL, # non-calibrated model parameters + parallel = FALSE, + ncores = 2 +){ + # predefine variables for CRAN check compliance + sitename <- data <- gpp_mod <- NULL + + using_phydro = drivers$params_siml[[1]]$use_phydro + + # FIXME Jaideep: Instead of checking the number of params, + # it might be better to check for presence of each param in par and par_fixed + ## check input parameters + expected_params = ifelse(using_phydro, yes=14, no=10) + if( (length(par) + length(par_fixed)) != (expected_params + length(targets)) ){ + stop(paste0('Error: Input calibratable and fixed parameters (par = ',length(par),' and par_fixed = ',length(par_fixed),') + do not match length of the required P-model parameters (',expected_params + length(targets),').')) + } + + + ## define parameter set based on calibrated parameters + if (using_phydro){ + calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', + 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', + 'phydro_K_plant', 'phydro_p50_plant', 'phydro_b_plant', + 'phydro_alpha', 'phydro_gamma', + 'bsoil', 'Ssoil', 'whc') + } else { + calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', + 'soilm_thetastar', 'soilm_betao', + 'beta_unitcostratio', 'rd_to_vcmax', + 'tau_acclim', 'kc_jmax', 'whc') + } + + # FIXME Jaideep: Here it is assumed that the params in par will appear in exactly the same order in settings as in the list above. Better to do this in an order-independent way. + if(!is.null(par_fixed)){ + params_modl <- list() + # complete with calibrated values + i <- 1 # start counter + for(par_name in calib_param_names){ + if(is.null(par_fixed[[par_name]])){ + params_modl[[par_name]] <- par[i] # use calibrated par value + i <- i + 1 # counter of calibrated params + }else{ + params_modl[[par_name]] <- par_fixed[[par_name]] # use fixed par value + } + } + }else{ + params_modl <- as.list(par[1:expected_params]) # all parameters calibrated + names(params_modl) <- calib_param_names + } + + ## run the model + df <- runread_pmodel_f( + drivers, + par = params_modl, + makecheck = TRUE, + parallel = parallel, + ncores = ncores + ) + + ## Calculate dpsi intercept from outputs + df <- df |> + mutate(dpsi_int = purrr::map(.x=data, .f=function(d){ + # Remove extremeties (potentially spurious values) + d_filt = d |> + mutate(psi_soil = psi_leaf + dpsi) |> + filter(psi_soil > min(psi_soil)+0.01) |> + filter(psi_soil < max(psi_soil)-0.01) + + # calculate psi_soil threshold for "wet" regime + psi_soil_max <- d_filt |> + with(quantile(psi_soil, probs = 0.95)) + + # calculate dpsi intercept as mean of dpsi in wet regime + int_q = d_filt |> + filter(psi_soil >= psi_soil_max) |> + pull(dpsi) |> + mean() + + # calculate actual dpsi intercept by fitting lm (might not work) + mod = d_filt |> with(lm(psi_leaf~psi_soil)) + mods = summary(mod) + int_reg = -mod$coefficients[1] + p_slope = mods$coefficients[2,4] + + # if lm gives good fit, return actual intercept, else return wet-regime mean + dpsi_int = ifelse(p_slope < 0.05, + yes = int_reg, + no = int_q) + dpsi_int + })) + + ## clean model output and unnest + df <- df |> + dplyr::rowwise() |> + dplyr::reframe( + cbind(sitename, data[, c('date', unique(c('gpp', targets)))], dpsi_int) |> + stats::setNames(c('sitename', 'date', paste0(unique(c('gpp', targets)), '_mod'), 'dpsi_int_mod')) + ) # gpp is used to get average trait prediction + + # separate validation data into fluxes and traits, site by site + is_flux <- apply(obs, 1, function(x){ 'date' %in% colnames(x$data)}) + + if(sum(is_flux) > 0){ + flux_sites <- obs$sitename[is_flux] + + # Unnest flux observations for our targets + obs_flux <- obs[is_flux, ] |> + dplyr::select(sitename, data) |> + tidyr::unnest(data) |> + dplyr::select(any_of(c('sitename', 'date', targets))) |> + mutate(dpsi_int = 1) + + if(ncol(obs_flux) < 3){ + warning("Dated observations (fluxes) are missing for the chosen targets.") + df_flux <- data.frame() + }else{ + # Join P-model output and flux observations + df_flux <- df |> + dplyr::filter(sitename %in% flux_sites) |> + dplyr::left_join( + obs_flux, + by = c('sitename', 'date')) # observations with missing date are ignored + } + }else{ + df_flux <- data.frame() + } + + if(sum(!is_flux) > 0){ + trait_sites <- obs$sitename[!is_flux] + + # Unnest trait observations for our targets + obs_trait <- obs[!is_flux, ] |> + dplyr::select(sitename, data) |> + tidyr::unnest(data) |> + dplyr::select(any_of(c('sitename', targets))) + + if(ncol(obs_trait) < 2){ + warning("Non-dated observations (traits) are missing for the chosen targets.") + df_trait <- data.frame() + }else{ + # Join output and trait observations + df_trait <- df |> + dplyr::filter(sitename %in% trait_sites) |> + dplyr::group_by(sitename) |> + # get growing season average traits + dplyr::summarise(across(ends_with("_mod") & !starts_with('gpp'), + ~ sum(.x * gpp_mod/sum(gpp_mod)), + .names = "{.col}")) |> + dplyr::left_join( + obs_trait, + by = c('sitename') # compare yearly averages rather than daily obs + ) + } + }else{ + df_trait <- data.frame() + } + + # loop over targets + ll <- lapply(seq(length(targets)), function(i){ + target <- targets[i] + # get observations and predicted target values, without NA + if(target %in% colnames(df_flux)){ + df_target <- df_flux[, c(paste0(target, '_mod'), target)] |> + tidyr::drop_na() + }else{ + df_target <- data.frame() + } + if(target %in% colnames(df_trait)){ + df_target <- rbind(df_target, + df_trait[, c(paste0(target, '_mod'), target)] |> + tidyr::drop_na()) + } + + # calculate normal log-likelihood + ll <- sum(stats::dnorm( + df_target[[paste0(target, '_mod')]], + mean = df_target[[target]], + sd = par[length(par)-length(targets) + i], + log = TRUE + )) + }) |> + unlist() |> + sum() + + # compute ll for dpsi using a Gaussian prior with mean 1 and sd 0.33 + ll_dpsi = sum(stats::dnorm( + df_flux[['dpsi_int_mod']], + mean = df_flux[['dpsi_int']], + sd = 0.33, + log = TRUE + )) + + ll <- ll + ll_dpsi + + # trap boundary conditions + if(is.nan(ll) | is.na(ll) | ll == 0){ll <- -Inf} + + return(ll) +} diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 70b2128a..9665edb9 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -350,6 +350,9 @@ run_pmodel_f_bysite <- function( continue = !is.nanull(site_info$canopy_height) & !is.nanull(site_info$reference_height) + } else { + site_info$canopy_height = NA + site_info$reference_height = NA } diff --git a/man/run_pmodel_f_bysite.Rd b/man/run_pmodel_f_bysite.Rd index c1c85634..fc794a0e 100644 --- a/man/run_pmodel_f_bysite.Rd +++ b/man/run_pmodel_f_bysite.Rd @@ -9,9 +9,11 @@ run_pmodel_f_bysite( params_siml, site_info, forcing, + forcing_acclim, params_modl, makecheck = TRUE, - verbose = TRUE + verbose = TRUE, + ... ) } \arguments{ diff --git a/vignettes/phydro_long_calibration_multi.R b/vignettes/phydro_long_calibration_multi.R new file mode 100644 index 00000000..46beffe0 --- /dev/null +++ b/vignettes/phydro_long_calibration_multi.R @@ -0,0 +1,200 @@ +rm(list=ls()) +library(tidyverse) +library(reshape2) +library(rsofun) +library(BayesianTools) +library(tictoc) +library(ncdf4) +library(scatterPlotMatrix) +library(ggpointdensity) + +plot_only = F +root_data_dir = "~/Downloads/fluxdatakit_oct3" +lsm_path = paste0(root_data_dir, "/FLUXDATAKIT_LSM/") +data_dir = paste0(root_data_dir, "/phydro_drivers/") + +# out_dir = "~/codes/rsofun/vignett/" +# figures_dir = paste0(out_dir, "/figures/") + + +read_obs = function(site){ + file_obs = tibble(files=list.files(data_dir, full.names = T)) %>% + filter(stringr::str_detect(files, site)) %>% + filter(stringr::str_detect(files, "p_hydro_validation.rda")) %>% + pull(files) + + load(file_obs) + + p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% + rename(le=latenth) + + p_hydro_validation +} + +read_drv = function(site){ + file_obs = tibble(files=list.files(data_dir, full.names = T)) %>% + filter(stringr::str_detect(files, site)) %>% + filter(stringr::str_detect(files, "p_hydro_drivers.rda")) %>% + pull(files) + + load(file_obs) + + p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 + p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 + + p_hydro_drivers$params_siml[[1]]$use_phydro = T + p_hydro_drivers$params_siml[[1]]$use_pml = F + + p_hydro_drivers +} + +sites_phydro = c("FR-LBr", "FI-Hyy", "CH-Dav") + +phydro_validation = sites_phydro %>% + map_df(~read_obs(.)) + +phydro_drivers = sites_phydro %>% + map_df(~read_drv(.)) + + +params_modl <- list( + kphio = 0.045, # 0.089, # 0.11, #0.04998, + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + # soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + # soilm_betao = 0.0, + # beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + phydro_K_plant = 1.421218e-16 , #0.3e-16, + phydro_p50_plant = -2.506519e+00, #-1, + phydro_b_plant = 1, + phydro_alpha = 1.334564e-01, #0.1, + phydro_gamma = 1.889438e+00, #1, + bsoil = 3, #3, + Ssoil = 3.193891e+02, #3, + whc = 7.819509e+02 #200 +) + +output_p <- rsofun::runread_pmodel_f( + phydro_drivers, + par = params_modl +) + +get_density <- function(x, y, ...) { + df = tibble(x=x, y=y) %>% drop_na + dens <- MASS::kde2d(df$x, df$y, ...) + ix <- findInterval(x, dens$x) + iy <- findInterval(y, dens$y) + ii <- cbind(ix, iy) + return(dens$z[ii]) +} + +plot_p = function(output_p){ + for(i in 1:nrow(output_p)){ + print( + output_p$data[[i]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="pred") %>% + rbind( + phydro_validation$data[[i]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="obs") + ) %>% + pivot_wider(names_from = type, values_from = value) %>% + group_by(name) %>% + reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% + ggplot(aes(y=obs, x=pred, colour=density)) + + scale_color_viridis_c() + + geom_point(alpha=0.7) + + geom_abline(slope=1, intercept=0, col="red")+ + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~name, scales = "free", nrow = 1)+ + labs(colour="Density") + ) + } +} + +### + +uniform_range = function(lower, upper){ + list(lower= lower, upper=upper, mean = (upper+lower)/2, sd = (upper-lower)*10) +} + +gaussian_range = function(mean, sd){ + if (mean > 0) list(lower= max(mean-5*sd, 0), upper=mean+5*sd, mean = mean, sd = sd) + else list(lower= mean-5*sd, upper=min(mean+5*sd, 0), mean = mean, sd = sd) +} + +pars_calib = list( + kphio = uniform_range(lower=0.005, upper=0.09), + phydro_K_plant = uniform_range(lower=0.1e-16, 1e-16), + phydro_p50_plant = gaussian_range(mean = -1, sd = 0.2), + # phydro_alpha = gaussian_range(mean = 0.1, sd = 0.005), + phydro_gamma = gaussian_range(mean = 0.5, sd = 0.3), + #bsoil = uniform_range(lower=0.1, upper=10), + # Ssoil = uniform_range(lower = 0, upper = whc_site+whc_site_sd), + whc = gaussian_range(mean = 300, sd = 50), + err_gpp = uniform_range(lower = 0.01, upper = 4), + err_le = uniform_range(lower = 0.1e6, upper = 10e6) +) + +pars_fixed = list( # fix all other parameters + # kphio = 0.045, + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + # phydro_K_plant = 0.3e-16, + # phydro_P50_plant = -1, + phydro_b_plant = 1, + phydro_alpha = 0.08, + # phydro_gamma = 1 + bsoil = 3, + Ssoil = 113 + # whc = 90 +) + +debug = T + +# Define calibration settings and parameter ranges from previous work +settings_bayes <- list( + method = "BayesianTools", + par = pars_calib, + metric = rsofun::cost_likelihood_pmodel, + control = list( + sampler = "DEzs", + settings = list( + nrChains = ifelse(debug, yes = 1, no = 3 ), + burnin = ifelse(debug, yes = 30, no = 10000), + iterations = ifelse(debug, yes = 100, no = 50000) # kept artificially low + ) + ) +) + + +message("Begin calibration...") +pars_calib_bayes <- calib_sofun( + # calib_sofun arguments: + drivers = phydro_drivers, + obs = phydro_validation, + settings = settings_bayes, + # extra arguments passed to the cost function: + par_fixed = pars_fixed, + targets = c("gpp", "le") # define target variable GPP +) + + +params_modl_opt = c(pars_calib_bayes$par, pars_fixed) + +output_p_opt <- rsofun::runread_pmodel_f( + phydro_drivers, + par = params_modl_opt +) + +plot_p(output_p_opt) diff --git a/vignettes/rsofun_phydro_data_generation.R b/vignettes/rsofun_phydro_data_generation.R index ec090924..26157026 100644 --- a/vignettes/rsofun_phydro_data_generation.R +++ b/vignettes/rsofun_phydro_data_generation.R @@ -28,10 +28,14 @@ source("read_meta_fdk.R") ## -------------------------------------------------------------------- args = commandArgs(trailingOnly=TRUE) +# FR-LBr +# FI-Hyy +# CH-Dav + #site <- "GF-Guy" if (length(args)==0) { #stop("At least one argument must be supplied: site name", call.=FALSE) - site = "AU-ASM" + site = "CH-Dav" }else{ site = args[1] } diff --git a/vignettes/simple_test_script_phydro.R b/vignettes/simple_test_script_phydro.R index 265ea281..d48b54af 100644 --- a/vignettes/simple_test_script_phydro.R +++ b/vignettes/simple_test_script_phydro.R @@ -3,8 +3,8 @@ library(ggpointdensity) setwd("~/codes/rsofun") -load("data/AU-ASM_p_hydro_drivers.rda") -load("data/AU-ASM_p_hydro_validation.rda") +# load("data/AU-ASM_p_hydro_drivers.rda") +# load("data/AU-ASM_p_hydro_validation.rda") p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% rename(le=latenth) diff --git a/vignettes/simple_test_script_pmodel.R b/vignettes/simple_test_script_pmodel.R index efaa8f50..7d282842 100644 --- a/vignettes/simple_test_script_pmodel.R +++ b/vignettes/simple_test_script_pmodel.R @@ -16,8 +16,6 @@ params_modl <- list( p_model_drivers_test = p_model_drivers p_model_drivers_test$params_siml[[1]]$use_phydro = F p_model_drivers_test$params_siml[[1]]$use_pml = F -p_model_drivers_test$site_info[[1]]$canopy_height = 20 -p_model_drivers_test$site_info[[1]]$reference_height = 22 p_model_drivers_test$forcing[[1]]$netrad = NA p_model_drivers_test$forcing_acclim = p_model_drivers_test$forcing From 2be7e39a2fe8f27051910eb0841b04b423771189 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sat, 29 Jun 2024 18:44:56 +0200 Subject: [PATCH 040/112] export phydro cost fn --- NAMESPACE | 1 + 1 file changed, 1 insertion(+) diff --git a/NAMESPACE b/NAMESPACE index 088db2a3..93397e76 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -3,6 +3,7 @@ export(calib_sofun) export(cost_likelihood_biomee) export(cost_likelihood_pmodel) +export(cost_likelihood_phydro) export(cost_rmse_biomee) export(cost_rmse_pmodel) export(init_dates_dataframe) From 098f7896fe3a6566dfd01576a9c0dd6984b044db Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sat, 29 Jun 2024 18:57:42 +0200 Subject: [PATCH 041/112] bugfix in cost fn phydro --- R/cost_likelihood_phydro.R | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/R/cost_likelihood_phydro.R b/R/cost_likelihood_phydro.R index 2355de35..3e3d7b17 100644 --- a/R/cost_likelihood_phydro.R +++ b/R/cost_likelihood_phydro.R @@ -83,10 +83,20 @@ cost_likelihood_phydro <- function( mean() # calculate actual dpsi intercept by fitting lm (might not work) - mod = d_filt |> with(lm(psi_leaf~psi_soil)) - mods = summary(mod) - int_reg = -mod$coefficients[1] - p_slope = mods$coefficients[2,4] + dat_lm = d_filt |> + select(psi_leaf, psi_soil) |> + drop_na + + if (nrow(dat_lm) > 5){ + mod = dat_lm |> + with(lm(psi_leaf~psi_soil)) + mods = summary(mod) + int_reg = -mod$coefficients[1] + p_slope = mods$coefficients[2,4] + } else { + int_reg = 0 + p_slope = 1 + } # if lm gives good fit, return actual intercept, else return wet-regime mean dpsi_int = ifelse(p_slope < 0.05, From 1de60cbe0ac5c26e5d61979ffcb528e5a1653150 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sat, 29 Jun 2024 18:59:40 +0200 Subject: [PATCH 042/112] minor bugfix --- R/cost_likelihood_phydro.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/cost_likelihood_phydro.R b/R/cost_likelihood_phydro.R index 3e3d7b17..eb6692b6 100644 --- a/R/cost_likelihood_phydro.R +++ b/R/cost_likelihood_phydro.R @@ -85,7 +85,7 @@ cost_likelihood_phydro <- function( # calculate actual dpsi intercept by fitting lm (might not work) dat_lm = d_filt |> select(psi_leaf, psi_soil) |> - drop_na + drop_na() if (nrow(dat_lm) > 5){ mod = dat_lm |> From 905176fa332026f497369b5931409721790c01b1 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sat, 3 Aug 2024 12:53:43 +0200 Subject: [PATCH 043/112] trial run with phydro cost fn --- vignettes/phydro_long_calibration.R | 14 ++++++++------ vignettes/run_bayesian_phydro.sh | 2 +- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/vignettes/phydro_long_calibration.R b/vignettes/phydro_long_calibration.R index 93157e07..5aaa6279 100644 --- a/vignettes/phydro_long_calibration.R +++ b/vignettes/phydro_long_calibration.R @@ -6,7 +6,7 @@ library(BayesianTools) library(tictoc) library(ncdf4) -plot_only = T +plot_only = F debug = F tic("phydro") @@ -265,7 +265,7 @@ pars_calib = list( # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="K.scalar") %>% pull(sd)), # phydro_p50_plant = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="P50") %>% pull(mean), # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="P50") %>% pull(sd)), - phydro_K_plant = uniform_range(lower=0.1e-16, 1e-16), + phydro_K_plant = uniform_range(lower=0.1e-16, 5e-16), # phydro_p50_plant = uniform_range(lower=-4, -0.3), phydro_p50_plant = gaussian_range(mean = p50xmean/3, sd = p50xsd/3), # phydro_alpha = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="alpha") %>% pull(mean), @@ -276,8 +276,9 @@ pars_calib = list( # phydro_gamma = uniform_range(lower = 0.1, upper = 2), phydro_gamma = gaussian_range(mean = gamma_mean, sd = gamma_sd), #bsoil = uniform_range(lower=0.1, upper=10), - # Ssoil = uniform_range(lower = 0, upper = whc_site+whc_site_sd), - whc = gaussian_range(mean = whc_site, sd = whc_site_sd), + #Ssoil = uniform_range(lower = 0, upper = whc_site+whc_site_sd), + #whc = gaussian_range(mean = whc_site, sd = whc_site_sd), + whc = uniform_range(lower = 115, upper = 1116), err_gpp = uniform_range(lower = 0.01, upper = 4), err_le = uniform_range(lower = 0.1e6, upper = 10e6) ) @@ -295,7 +296,7 @@ pars_fixed = list( # fix all other parameters phydro_alpha = 0.08, # phydro_gamma = 1 bsoil = 3, - Ssoil = 113 + Ssoil = 113.313 # 1%ile of cwdx80_forcing # whc = 90 ) @@ -303,7 +304,7 @@ pars_fixed = list( # fix all other parameters settings_bayes <- list( method = "BayesianTools", par = pars_calib, - metric = rsofun::cost_likelihood_pmodel, + metric = rsofun::cost_likelihood_phydro, control = list( sampler = "DEzs", settings = list( @@ -412,3 +413,4 @@ output_p_opt$data[[1]] %>% select(date, gpp, le) %>% toc() + diff --git a/vignettes/run_bayesian_phydro.sh b/vignettes/run_bayesian_phydro.sh index 950e81d8..5df2357f 100755 --- a/vignettes/run_bayesian_phydro.sh +++ b/vignettes/run_bayesian_phydro.sh @@ -9,7 +9,7 @@ declare -a arr=$( cat site_list_2.txt ) generate_data=false data_path=/data/scratch/jaideep/fluxdata -out_path=/data/scratch/jaideep/phydro_output_fixedkphio +out_path=/data/scratch/jaideep/phydro_output_14_p50prior_fixedalpha0.08_s113_calibdpsi1 # data_path=~/Downloads/fluxdatakit_oct3 # out_path=~/Downloads/fluxdatakit_oct3/phydro_output From a89eae193ff057241bcfbb6308386cf2afea767b Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sun, 4 Aug 2024 16:06:43 +0200 Subject: [PATCH 044/112] add driver visualization in vignette; add vignette with new-format data --- vignettes/pmodel_use.Rmd | 25 +++ vignettes/pmodel_use_newdata.Rmd | 263 +++++++++++++++++++++++++++++++ 2 files changed, 288 insertions(+) create mode 100644 vignettes/pmodel_use_newdata.Rmd diff --git a/vignettes/pmodel_use.Rmd b/vignettes/pmodel_use.Rmd index a3220ca8..8ddfbf48 100644 --- a/vignettes/pmodel_use.Rmd +++ b/vignettes/pmodel_use.Rmd @@ -41,6 +41,31 @@ p_model_drivers p_model_validation ``` +### Visualize drivers + +```{r} +p_model_drivers$forcing[[1]] |> + # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> + dplyr::select(date, co2, ppfd, netrad, temp, vpd, fapar, rain, ccov) |> + tidyr::pivot_longer(-date) |> + dplyr::mutate(type="24-hr mean") |> + rbind(p_model_validation$data[[1]] |> + # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> + dplyr::select(date, gpp) |> + tidyr::pivot_longer(-date) |> + dplyr::mutate(type="obs (24 hr)") + ) |> + ggplot(aes(y=value, x=date)) + + geom_line(aes(group=type, col=type), alpha=0.5) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~name, scales = "free")+ + ggtitle(p_model_drivers$sitename[[1]]) + + +p_model_drivers$site_info |> print() +``` + These are real data from the French FR-Pue fluxnet site. Information about data structure, variable names, and their meaning and units can be found in the reference pages of `p_model_drivers` and `p_model_validation`. We can use these data to run the model, together with observations of GPP we can also calibrate `pmodel` parameters. Another two datasets are provided as an example to validate the model against leaf traits data, rather than fluxes. Measurements of Vcmax25 (aggregated over species) for a subset of 4 sites from the GlobResp database (Atkin et al., 2015) are given in `p_model_validation_vcmax25` and the corresponding forcing for the P-model is given in `p_model_drivers_vcmax25`. Since leaf traits are only measured once per site, the forcing used is a single year of average climate (the average measurements between 2001 and 2015 on each day of the year). diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd new file mode 100644 index 00000000..0c0c9761 --- /dev/null +++ b/vignettes/pmodel_use_newdata.Rmd @@ -0,0 +1,263 @@ +--- +title: "P-model usage (new data)" +author: "Koen Hufkens, Josefa Arán" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{P-model usage} + %\VignetteEngine{knitr::rmarkdown} + %\usepackage[utf8]{inputenc} +--- + +```{r setup, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + fig.align = "center", + fig.width = 7, + fig.height = 5 +) + +library(rsofun) +library(dplyr) +library(ggplot2) + +# fake variable as optimization isn't run +pars <- list() +pars$par["kphio"] <- 0.04478049 +``` + +The `rsofun` package and framework includes two main models. The `pmodel` and `biomee` (which in part relies on P-model components). Here we give a short example on how to run the `pmodel` on the included demo datasets to familiarize yourself with both the data structure and the outputs. + +## Demo data + +The package includes two demo datasets to run and validate pmodel output using GPP observations. These files can be directly loaded into your workspace by typing: + +```{r} +library(rsofun) + +# this is to deal with an error p_model_drivers.rds not being found +p_model_drivers = readRDS("/data/scratch/jaideep/FluxDataKit/v2023.10.3/p_model_drivers.rds") |> filter(sitename == "FR-Pue") |> + rowwise() |> + # manipulate all forcing variables, filter years and gapfill missing values + mutate(across( + starts_with("forcing"), + ~.x |> + filter(lubridate::year(date) >= 2007 & + lubridate::year(date) <= 2012) |> + mutate(rain = ifelse(is.na(rain), yes=0, no=rain)) |> + list() + )) |> + mutate(site_info = site_info |> + # Set WHC from demo data just to check match, new data has 4981.0 + mutate(whc=432) |> + list()) + +p_model_validation = readRDS("/data/scratch/jaideep/FluxDataKit/v2023.10.3/p_model_validation.rds") |> + filter(sitename == "FR-Pue") |> + rowwise() |> + # Use NT GPP as gpp, and filter years across all data columns + mutate(across( + starts_with("data"), + ~ .x |> + rename(gpp = gpp_nt) |> + filter(lubridate::year(date) >= 2007 & + lubridate::year(date) <= 2012) |> + list() + )) + +``` + +### Visualize drivers + +```{r} +p_model_drivers$forcing[[1]] |> + # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> + dplyr::select(date, co2, ppfd, netrad, temp, vpd, fapar, rain, ccov) |> + tidyr::pivot_longer(-date) |> + dplyr::mutate(type="24-hr mean") |> + rbind(p_model_validation$data[[1]] |> + # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> + dplyr::select(date, gpp) |> + tidyr::pivot_longer(-date) |> + dplyr::mutate(type="obs (24 hr)") + ) |> + ggplot(aes(y=value, x=date)) + + geom_line(aes(group=type, col=type), alpha=0.5) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~name, scales = "free")+ + ggtitle(p_model_drivers$sitename[[1]]) + +``` + +These are real data from the French FR-Pue fluxnet site. Information about data structure, variable names, and their meaning and units can be found in the reference pages of `p_model_drivers` and `p_model_validation`. We can use these data to run the model, together with observations of GPP we can also calibrate `pmodel` parameters. + +Another two datasets are provided as an example to validate the model against leaf traits data, rather than fluxes. Measurements of Vcmax25 (aggregated over species) for a subset of 4 sites from the GlobResp database (Atkin et al., 2015) are given in `p_model_validation_vcmax25` and the corresponding forcing for the P-model is given in `p_model_drivers_vcmax25`. Since leaf traits are only measured once per site, the forcing used is a single year of average climate (the average measurements between 2001 and 2015 on each day of the year). + +```{r} +p_model_drivers_vcmax25 + +p_model_validation_vcmax25 +``` + +For the remainder of this vignette, we will use the GPP flux datasets. The workflow is exactly the same for leaf traits data. + +To get your raw data into the structure used within `rsofun`, please see R packages [ingestr](https://github.com/geco-bern/ingestr) and [FluxDataKit](https://github.com/geco-bern/FluxDataKit). + +## Running P-model + +With all data prepared we can run the P-model using `runread_pmodel_f()`. This function takes the nested data structure and runs the model site by site, returning nested model output results matching the input drivers. + +```{r} +# define model parameter values from previous +# work +params_modl <- list( + kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + soilm_betao = 0.0, + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41 + ) + +# run the model for these parameters +output <- rsofun::runread_pmodel_f( + p_model_drivers, + par = params_modl + ) +``` + +### Plotting output + +We can now visualize both the model output and the measured values together. + +```{r} +# Load libraries for plotting +library(dplyr) +library(tidyr) +library(ggplot2) + +# Create data.frame for plotting +df_gpp_plot <- rbind( + output |> + filter(sitename == "FR-Pue") |> + unnest(data) |> + select(date, gpp) |> + mutate(type = "P-model output"), + p_model_validation |> + filter(sitename == "FR-Pue") |> + unnest(data) |> + select(date, gpp) |> + mutate(type = "Observed") +) +df_gpp_plot$type <- factor(df_gpp_plot$type, + levels = c('P-model output', + 'Observed')) + +# Plot GPP +ggplot(data = df_gpp_plot) + + geom_line( + aes(x = date, + y = gpp, + color = type), + alpha = 0.7 + ) + + scale_color_manual(values = c( + 'P-model output'='grey70', + 'Observed'='black')) + + theme_classic() + + theme(panel.grid.major.y = element_line()) + + labs( + x = 'Date', + y = expression(paste("GPP (g C m"^-2, "s"^-1, ")")), + colour = "" + ) +``` + +## Calibrating model parameters + +To optimize new parameters based upon driver data and a validation dataset we must first specify an optimization strategy and settings, as well as a cost function and parameter ranges. + +```{r} +settings <- list( + method = "GenSA", + metric = cost_rmse_pmodel, + control = list( + maxit = 100), + par = list( + kphio = list(lower=0.02, upper=0.2, init = 0.05) + ) +) +``` + +`rsofun` supports both optimization using the `GenSA` and `BayesianTools` packages. The above statement provides settings for a `GenSA` optimization approach. For this example the maximum number of iterations is kept artificially low. In a real scenario you will have to increase this value orders of magnitude. Keep in mind that optimization routines rely on a cost function, which, depending on its structure influences parameter selection. A limited set of cost functions is provided but the model structure is transparent and custom cost functions can be easily written. More details can be found in the "Parameter calibration and cost functions" vignette. + +In addition starting values and ranges are provided for the free parameters in the model. Free parameters include: parameters for the quantum yield efficiency `kphio`, `kphio_par_a` and `kphio_par_b`, soil moisture stress parameters `soilm_thetastar` and `soilm_betao`, and also `beta_unitcostratio`, `rd_to_vcmax`, `tau_acclim` and `kc_jmax` (see `?runread_pmodel_f`). Be mindful that with newer versions of `rsofun` additional parameters might be introduced, so re-check vignettes and function documentation when updating existing code. + +With all settings defined the optimization function `calib_sofun()` can be called with driver data and observations specified. Extra arguments for the cost function (like what variable should be used as target to compute the root mean squared error (RMSE) and previous values for the parameters that aren't calibrated, which are needed to run the P-model). + +```{r eval=FALSE} +# calibrate the model and optimize free parameters +pars <- calib_sofun( + drivers = p_model_drivers, + obs = p_model_validation, + settings = settings, + # extra arguments passed to the cost function: + targets = "gpp", # define target variable GPP + par_fixed = params_modl[-1] # fix non-calibrated parameters to previous + # values, removing kphio + ) +``` + +When successful the optimized parameters can be used to run subsequent modelling efforts, in this case slightly improving the model fit over a more global parameter set. + +```{r} +# Update the parameter list with calibrated value +params_modl$kphio <- pars$par["kphio"] + +# Run the model for these parameters +output_new <- rsofun::runread_pmodel_f( + p_model_drivers, + par = params_modl + ) + +# Update data.frame for plotting +df_gpp_plot <- rbind( + df_gpp_plot, + output_new |> + filter(sitename == "FR-Pue") |> + unnest(data) |> + select(date, gpp) |> + mutate(type = "P-model output (calibrated)") +) +df_gpp_plot$type <- factor(df_gpp_plot$type, + levels = c('P-model output', + 'P-model output (calibrated)', + 'Observed')) + +# Plot GPP +ggplot(data = df_gpp_plot) + + geom_line( + aes(x = date, + y = gpp, + color = type), + alpha = 0.7 + ) + + scale_color_manual(values = c( + 'P-model output'='grey70', + 'P-model output (calibrated)'='grey40', + 'Observed'='black')) + + theme_classic() + + theme(panel.grid.major.y = element_line()) + + labs( + x = 'Date', + y = expression(paste("GPP (g C m"^-2, "s"^-1, ")")), + colour = "" + ) +``` + +For details on the optimization settings we refer to the manuals of [GenSA](https://cran.r-project.org/package=GenSA) and [BayesianTools](https://github.com/florianhartig/BayesianTools). + From 84beb01ee3274a225b29f369c9a7050f8fc9538e Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sun, 4 Aug 2024 16:43:11 +0200 Subject: [PATCH 045/112] updated vignettes for using pmodel and phydro with old and new data formats --- vignettes/pmodel_use.Rmd | 17 +++- vignettes/pmodel_use_newdata.Rmd | 135 ++++++++++++++++++++++++++++++- 2 files changed, 145 insertions(+), 7 deletions(-) diff --git a/vignettes/pmodel_use.Rmd b/vignettes/pmodel_use.Rmd index 8ddfbf48..38e8c4f1 100644 --- a/vignettes/pmodel_use.Rmd +++ b/vignettes/pmodel_use.Rmd @@ -36,7 +36,12 @@ The package includes two demo datasets to run and validate pmodel output using G library(rsofun) # this is to deal with an error p_model_drivers.rds not being found -p_model_drivers +p_model_drivers = p_model_drivers |> + rowwise() |> + mutate(params_siml = params_siml |> + mutate(use_phydro = F, + use_pml = F) |> + list()) p_model_validation ``` @@ -85,8 +90,11 @@ To get your raw data into the structure used within `rsofun`, please see R packa With all data prepared we can run the P-model using `runread_pmodel_f()`. This function takes the nested data structure and runs the model site by site, returning nested model output results matching the input drivers. ```{r} -# define model parameter values from previous -# work +# define model parameter values from previous work +# ------------------------------------------------------ +# Note that in the phydro branch of rsofun, +# whc must be included in params_modl, rather than in site_info +# ------------------------------------------------------ params_modl <- list( kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio @@ -96,7 +104,8 @@ params_modl <- list( beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41 + kc_jmax = 0.41, + whc = p_model_drivers$site_info[[1]]$whc ) # run the model for these parameters diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd index 0c0c9761..df5fddd7 100644 --- a/vignettes/pmodel_use_newdata.Rmd +++ b/vignettes/pmodel_use_newdata.Rmd @@ -109,8 +109,11 @@ To get your raw data into the structure used within `rsofun`, please see R packa With all data prepared we can run the P-model using `runread_pmodel_f()`. This function takes the nested data structure and runs the model site by site, returning nested model output results matching the input drivers. ```{r} -# define model parameter values from previous -# work +# define model parameter values from previous work +# ------------------------------------------------------ +# Note that in the phydro branch of rsofun, +# whc must be included in params_modl, rather than in site_info +# ------------------------------------------------------ params_modl <- list( kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio @@ -120,7 +123,8 @@ params_modl <- list( beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41 + kc_jmax = 0.41, + whc = p_model_drivers$site_info[[1]]$whc ) # run the model for these parameters @@ -261,3 +265,128 @@ ggplot(data = df_gpp_plot) + For details on the optimization settings we refer to the manuals of [GenSA](https://cran.r-project.org/package=GenSA) and [BayesianTools](https://github.com/florianhartig/BayesianTools). +```{r} +get_density <- function(x, y, ...) { + df = tibble(x=x, y=y) %>% drop_na + dens <- MASS::kde2d(df$x, df$y, ...) + ix <- findInterval(x, dens$x) + iy <- findInterval(y, dens$y) + ii <- cbind(ix, iy) + return(dens$z[ii]) +} + + +print( + output$data[[1]] %>% + select(date, gpp, le) %>% + mutate(le = le/86400) %>% # convert J m-2 d-1 to W m-2 + pivot_longer(-date) %>% + mutate(type="pred") %>% + rbind( + p_model_validation$data[[1]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="obs") + ) %>% + pivot_wider(names_from = type, values_from = value) %>% + group_by(name) %>% + reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% + ggplot(aes(y=obs, x=pred, colour=density)) + + scale_color_viridis_c() + + geom_point(alpha=0.7) + + geom_abline(slope=1, intercept=0, col="red")+ + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~name, scales = "free", nrow = 1)+ + labs(colour="Density") +) +``` + +## P-hydro model run + +```{r} +p_model_drivers$params_siml[[1]]$use_phydro = T + +# define model parameter values +params_modl <- list( + kphio = 0.0488, + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + phydro_K_plant = 2.18e-17, + phydro_p50_plant = -0.46, + phydro_gamma = 0.065, + phydro_b_plant = 1, + phydro_alpha = 0.08, + bsoil = 3, + Ssoil = 113, + whc = 253 +) + +# run the model for these parameters +output <- rsofun::runread_pmodel_f( + p_model_drivers, + par = params_modl +) + +``` + +## Plot Phydro outputs + +```{r} + +print( +output$data[[1]] %>% + select(date, gpp, le, aet, pet, netrad, wcont) %>% + mutate(le = le/86400) %>% # convert J m-2 d-1 to W m-2 + pivot_longer(-date) %>% + mutate(type="pred") %>% + rbind( + p_model_validation$data[[1]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="obs") + ) %>% + ggplot(aes(x=date, y=value, group=type, col=type)) + + theme_classic()+ + geom_line(alpha=0.6) + + facet_wrap(~name, scales="free") +) + +get_density <- function(x, y, ...) { + df = tibble(x=x, y=y) %>% drop_na + dens <- MASS::kde2d(df$x, df$y, ...) + ix <- findInterval(x, dens$x) + iy <- findInterval(y, dens$y) + ii <- cbind(ix, iy) + return(dens$z[ii]) +} + + +print( + output$data[[1]] %>% + select(date, gpp, le) %>% + mutate(le = le/86400) %>% # convert J m-2 d-1 to W m-2 + pivot_longer(-date) %>% + mutate(type="pred") %>% + rbind( + p_model_validation$data[[1]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="obs") + ) %>% + pivot_wider(names_from = type, values_from = value) %>% + group_by(name) %>% + reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% + ggplot(aes(y=obs, x=pred, colour=density)) + + scale_color_viridis_c() + + geom_point(alpha=0.7) + + geom_abline(slope=1, intercept=0, col="red")+ + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~name, scales = "free", nrow = 1)+ + labs(colour="Density") +) +``` \ No newline at end of file From c4ff2f46fa040f986843e69b35046aac32b722de Mon Sep 17 00:00:00 2001 From: Jaideep Date: Sun, 4 Aug 2024 18:59:07 +0200 Subject: [PATCH 046/112] gapfill and use forcing_acclim in phydro vignette --- vignettes/pmodel_use_newdata.Rmd | 36 +++++++++++++++++++++++++++++++- 1 file changed, 35 insertions(+), 1 deletion(-) diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd index df5fddd7..213c7b52 100644 --- a/vignettes/pmodel_use_newdata.Rmd +++ b/vignettes/pmodel_use_newdata.Rmd @@ -304,12 +304,46 @@ print( ## P-hydro model run +For phydro, we must use the 3-hr daily max forcing as the acclimation forcing. So lets rename it i nthe data, + +```{r} +p_model_drivers <- p_model_drivers |> + rename(forcing_acclim = forcing_3hrmax) |> + rowwise() |> + mutate(forcing_acclim = forcing_acclim |> + mutate( + across(-date, + ~zoo::na.approx(.x, na.rm = FALSE) + ) + ) |> + list() + ) + +p_model_drivers$forcing_acclim[[1]] |> + # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> + dplyr::select(date, co2, ppfd, netrad, temp, vpd, fapar, rain, ccov) |> + tidyr::pivot_longer(-date) |> + dplyr::mutate(type="24-hr mean") |> + rbind(p_model_validation$data_3hrmax[[1]] |> + # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> + dplyr::select(date, gpp) |> + tidyr::pivot_longer(-date) |> + dplyr::mutate(type="obs (24 hr)") + ) |> + ggplot(aes(y=value, x=date)) + + geom_line(aes(group=type, col=type), alpha=0.5) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~name, scales = "free")+ + ggtitle(p_model_drivers$sitename[[1]]) +``` + ```{r} p_model_drivers$params_siml[[1]]$use_phydro = T # define model parameter values params_modl <- list( - kphio = 0.0488, + kphio = 0.0288, kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous From 8535b0911c1204123ebfd0e2adf978f0a1a211da Mon Sep 17 00:00:00 2001 From: Jaideep Date: Thu, 8 Aug 2024 01:40:16 +0200 Subject: [PATCH 047/112] add ET formulation for pmodel (similar to phydro); move soil LE calcs to waterbal --- R/run_pmodel_f_bysite.R | 2 + src/biosphere_pmodel.mod.f90 | 2 + src/gpp_pmodel.mod.f90 | 30 ++++++++++----- src/interface_biosphere_pmodel.mod.f90 | 1 + src/photosynth_pmodel.mod.f90 | 2 + src/sofun_r.f90 | 9 +++-- src/waterbal_splash.mod.f90 | 35 ++++++++++------- src/wrappersc.c | 2 +- vignettes/pmodel_use_newdata.Rmd | 53 ++++++++++++++++---------- 9 files changed, 89 insertions(+), 47 deletions(-) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 9665edb9..1d5a30d7 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -490,6 +490,7 @@ run_pmodel_f_bysite <- function( "wcont", "snow", "cond", + "le_canopy", "le_soil", "dpsi", "psi_leaf") @@ -518,6 +519,7 @@ run_pmodel_f_bysite <- function( wcont = NA, snow = NA, cond = NA, + le_canopy = NA, le_soil = NA, dpsi = NA, psi_leaf = NA diff --git a/src/biosphere_pmodel.mod.f90 b/src/biosphere_pmodel.mod.f90 index ea57233f..04afd2be 100644 --- a/src/biosphere_pmodel.mod.f90 +++ b/src/biosphere_pmodel.mod.f90 @@ -153,6 +153,7 @@ function biosphere_annual() result( out_biosphere ) tile_fluxes(:), & myinterface%grid, & myinterface%climate(doy), & + tile(:)%canopy%fapar, & myinterface%params_siml%use_phydro & ) ! if (verbose) print*,'... done' @@ -199,6 +200,7 @@ function biosphere_annual() result( out_biosphere ) out_biosphere%snow(doy) = tile(1)%soil%phy%snow out_biosphere%cond(doy) = tile_fluxes(1)%canopy%dcn ! Additional outputs for coupled model and phydro + out_biosphere%latenth_canopy(doy) = tile_fluxes(1)%canopy%daet_e_canop out_biosphere%latenth_soil(doy) = tile_fluxes(1)%canopy%daet_e_soil out_biosphere%dpsi(doy) = tile_fluxes(1)%plant(1)%dpsi out_biosphere%psi_leaf(doy) = tile_fluxes(1)%plant(1)%psi_leaf diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index fe28805c..2b377f08 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -352,9 +352,11 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init ! Stomatal conductance !---------------------------------------------------------------- if (.not. use_phydro) then - tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint + tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint else - tile_fluxes(lu)%plant(pft)%gs_accl = out_phydro_inst%gs + ! Jaideep: Note that unit of gs_accl here is mol m-2 s-1. + ! Jaideep FIXME: It's too complicated to convert it to unit as in pmodel, but should be done at some point + tile_fluxes(lu)%plant(pft)%gs_accl = out_phydro_inst%gs end if !---------------------------------------------------------------- @@ -366,17 +368,27 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init end if !------------------------------------------------------------------------ - ! Canopy ET and soil LE (only for Phydro, since it's computed internally) + ! Canopy ET !------------------------------------------------------------------------ - if (use_phydro) then - ! Density of water, kg/m^3 - rho_water = calc_density_h2o( climate%dtemp, climate%dpatm ) - + ! Density of water, kg/m^3 + rho_water = calc_density_h2o( climate%dtemp, climate%dpatm ) + + ! Jaideep TODO FIXME: PM can be used here instead of 1.6gsD + if (.not.use_phydro) then + ! Note here that stomatal conductance is already normalized by patm (=gs/patm) so E = 1.6 * (gs/patm) * vpd, which is the same as 1.6 gs (vpd/patm) + ! but it is expressed per unit absorbed light, so multiply by PPFD*fapar + tile_fluxes(lu)%canopy%daet_canop = (1.6 & + * out_pmodel%gs_setpoint * tile(lu)%canopy%fapar * climate%dppfd & + * climate%dvpd) * 0.018015 * (1.0d0 / rho_water) & + * myinterface%params_siml%secs_per_tstep * 1000 ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 + + else tile_fluxes(lu)%canopy%daet_canop = out_phydro_inst%e * 0.018015 * (1.0d0 / rho_water) & * myinterface%params_siml%secs_per_tstep * 1000 ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 - tile_fluxes(lu)%canopy%dpet_e_soil = out_phydro_inst%le_s_wet & - * myinterface%params_siml%secs_per_tstep ! convert: J m-2 s-1 * s day-1 = J m-2 day-1 + ! ~~ This has been moved to waterbal_splash ~~ + ! tile_fluxes(lu)%canopy%dpet_e_soil = out_phydro_inst%le_s_wet & + ! * myinterface%params_siml%secs_per_tstep ! convert: J m-2 s-1 * s day-1 = J m-2 day-1 ! print *, "Canopy ET (mm d-1) = ", tile_fluxes(lu)%canopy%daet_canop ! print *, "Soil LE (J m-2 d-1) = ", climate%dnetrad, tile_fluxes(lu)%canopy%daet_e_soil diff --git a/src/interface_biosphere_pmodel.mod.f90 b/src/interface_biosphere_pmodel.mod.f90 index 1eb11723..a2f82974 100644 --- a/src/interface_biosphere_pmodel.mod.f90 +++ b/src/interface_biosphere_pmodel.mod.f90 @@ -81,6 +81,7 @@ module md_interface_pmodel real, dimension(ndayyear) :: wcont real, dimension(ndayyear) :: snow real, dimension(ndayyear) :: cond + real, dimension(ndayyear) :: latenth_canopy real, dimension(ndayyear) :: latenth_soil real, dimension(ndayyear) :: dpsi real, dimension(ndayyear) :: psi_leaf diff --git a/src/photosynth_pmodel.mod.f90 b/src/photosynth_pmodel.mod.f90 index 9312f756..12a204fe 100644 --- a/src/photosynth_pmodel.mod.f90 +++ b/src/photosynth_pmodel.mod.f90 @@ -810,6 +810,8 @@ function calc_gammastar( tc, patm ) result( gammastar ) end function calc_gammastar + ! TODO: Reformulate to calculate betao online so that stess = 0 @ wcont = 0 + ! -- not needed, just set betao = 0 function calc_soilmstress( wcont, thetastar, betao ) result( outstress ) !////////////////////////////////////////////////////////////////// ! Calculates empirically-derived stress (fractional reduction in light diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index 6797a2cd..d573a3f3 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -83,7 +83,7 @@ subroutine pmodel_f( & real(kind=c_double), dimension(17), intent(in) :: par ! free (calibratable) model parameters real(kind=c_double), dimension(nt,12), intent(in) :: forcing ! array containing all temporally varying forcing data for instantaneous model (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) real(kind=c_double), dimension(nt,12), intent(in) :: forcing_acclim ! array containing all temporally varying forcing data for acclimating model (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) - real(kind=c_double), dimension(nt,22), intent(out) :: output + real(kind=c_double), dimension(nt,23), intent(out) :: output ! local variables type(outtype_biosphere) :: out_biosphere ! holds all the output used for calculating the cost or maximum likelihood function @@ -250,9 +250,10 @@ subroutine pmodel_f( & output(idx_start:idx_end,17) = dble(out_biosphere%wcont(:)) output(idx_start:idx_end,18) = dble(out_biosphere%snow(:)) output(idx_start:idx_end,19) = dble(out_biosphere%cond(:)) - output(idx_start:idx_end,20) = dble(out_biosphere%latenth_soil(:)) - output(idx_start:idx_end,21) = dble(out_biosphere%dpsi(:)) - output(idx_start:idx_end,22) = dble(out_biosphere%psi_leaf(:)) + output(idx_start:idx_end,20) = dble(out_biosphere%latenth_canopy(:)) + output(idx_start:idx_end,21) = dble(out_biosphere%latenth_soil(:)) + output(idx_start:idx_end,22) = dble(out_biosphere%dpsi(:)) + output(idx_start:idx_end,23) = dble(out_biosphere%psi_leaf(:)) end if diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index f992aa71..fb293db2 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -54,7 +54,7 @@ module md_waterbal contains - subroutine waterbal( tile, tile_fluxes, grid, climate, using_phydro ) + subroutine waterbal( tile, tile_fluxes, grid, climate, fapar, using_phydro ) !///////////////////////////////////////////////////////////////////////// ! Calculates soil water balance !------------------------------------------------------------------------- @@ -63,6 +63,7 @@ subroutine waterbal( tile, tile_fluxes, grid, climate, using_phydro ) type(tile_fluxes_type), dimension(nlu), intent(inout) :: tile_fluxes type(gridtype), intent(in) :: grid type(climate_type), intent(in) :: climate + real, dimension(nlu), intent(in) :: fapar logical, intent(in) :: using_phydro ! local variables @@ -79,7 +80,7 @@ subroutine waterbal( tile, tile_fluxes, grid, climate, using_phydro ) !--------------------------------------------------------- ! Canopy transpiration and soil evaporation !--------------------------------------------------------- - call calc_et( tile_fluxes(lu), grid, climate, sw, using_phydro ) + call calc_et( tile_fluxes(lu), grid, climate, sw, fapar(lu), using_phydro ) !--------------------------------------------------------- ! Update soil moisture and snow pack @@ -275,7 +276,7 @@ subroutine solar( tile_fluxes, grid, climate, doy, in_netrad ) end subroutine solar - subroutine calc_et( tile_fluxes, grid, climate, sw, using_phydro ) + subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro ) !///////////////////////////////////////////////////////////////////////// ! !------------------------------------------------------------------------- @@ -287,6 +288,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, using_phydro ) type(gridtype), intent(in) :: grid type(climate_type), intent(in) :: climate real, intent(in) :: sw ! evaporative supply rate, mm/hr + real, intent(in) :: fapar logical, intent(in) :: using_phydro ! local variables @@ -367,16 +369,22 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, using_phydro ) !--------------------------------------------------------- ! 21. Estimate daily AET (tile_fluxes%canopy%daet), mm d-1 !--------------------------------------------------------- - if (.not. using_phydro) then - ! Eq. 81, SPLASH 2.0 Documentation - tile_fluxes%canopy%daet = (24.0/pi) * (radians(sw * hi) + rx * rw * rv * (dgsin(hn) - dgsin(hi)) + & - radians((rx * rw * ru - rx * tile_fluxes%canopy%rnl) * (hn - hi))) - tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet / (tile_fluxes%canopy%econ * 1000) ! JAIDEEP FIXME: Oops! This is a case where you should use a simple mass-energy conversion, not econ - else - ! Fill canopy LE and soil ET using complementary values from phydro + ! JAIDEEP FIXME: soil PET calcs should be identical for P and Phydro, but depending on whether in_netrad is used or not, + ! when implementing in_netrad condition, uncomment the lines marked by arrows + ! if (.not. in_netrad) then <-------------------- + ! ! Eq. 81, SPLASH 2.0 Documentation + ! tile_fluxes%canopy%daet = (24.0/pi) * (radians(sw * hi) + rx * rw * rv * (dgsin(hn) - dgsin(hi)) + & + ! radians((rx * rw * ru - rx * tile_fluxes%canopy%rnl) * (hn - hi))) ! JAIDEEP FIXME: Technically correct, but for clarity, apply radians to just (hn-hi) ? + ! tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet / (tile_fluxes%canopy%econ * 1000) ! JAIDEEP FIXME: Oops! This is a case where you should use a simple mass-energy conversion, not econ + ! ^ these should become dpet_soil and dpet_e_soil, with 1-fapar multipled to rx + ! else <------------------ + ! tile_fluxes%canopy%dpet_e_soil = (1.0d0 - fapar) * netrad * (par_env%epsilon / (1.0d0 + par_env%epsilon)) + tile_fluxes%canopy%dpet_soil = (1.0d0 - fapar) * tile_fluxes%canopy%drn * tile_fluxes%canopy%econ * 1.0d3 ! 1000*econ converts energy into mm evaporation + tile_fluxes%canopy%dpet_e_soil = tile_fluxes%canopy%dpet_soil / energy_to_mm ! mm d-1 ---> J m-2 d-1 + ! ^ Note: This is under wet conditions, so multiply by reduction factor (below) to get actual soil ET + + ! Fill canopy LE from T_canopy calculated in gpp_pmodel tile_fluxes%canopy%daet_e_canop = tile_fluxes%canopy%daet_canop / energy_to_mm ! mm d-1 ---> J m-2 d-1 - tile_fluxes%canopy%dpet_soil = tile_fluxes%canopy%dpet_e_soil * energy_to_mm ! J m-2 d-1 ---> mm d-1 - ! ^ Note: This is under wet conditions, as returned from phydro, so multiply by sw to get actual soil ET ! calculate totat AET = canopy_AET + f * soil_AET_wet, where f = running_avg(P/PET) ! p_over_pet = (climate%dprec*86400) / (tile_fluxes%canopy%dpet_soil + 1e-6) @@ -397,7 +405,8 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, using_phydro ) ! print *, "Canopy ET (mm d-1, J m-2 d-1) = ", tile_fluxes%canopy%daet_canop, tile_fluxes%canopy%daet_e_canop ! print *, "Soil ET (mm d-1, J m-2 d-1) = ", tile_fluxes%canopy%daet_soil, tile_fluxes%canopy%daet_e_soil - end if + ! end if <------------------ + ! print*,'in waterbal: sw, hi, rx, rw, rv, hn, hi, ru ', sw, hi, rx, rw, rv, hn, hi, ru ! xxx debug diff --git a/src/wrappersc.c b/src/wrappersc.c index 2c5f939b..44204b83 100644 --- a/src/wrappersc.c +++ b/src/wrappersc.c @@ -76,7 +76,7 @@ extern SEXP pmodel_f_C( // Specify output // 2nd agument to allocMatrix is number of rows, 3rd is number of columns - SEXP output = PROTECT( allocMatrix(REALSXP, nt, 22) ); + SEXP output = PROTECT( allocMatrix(REALSXP, nt, 23) ); // Fortran subroutine call F77_CALL(pmodel_f)( diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd index 213c7b52..361a737d 100644 --- a/vignettes/pmodel_use_newdata.Rmd +++ b/vignettes/pmodel_use_newdata.Rmd @@ -35,8 +35,11 @@ The package includes two demo datasets to run and validate pmodel output using G ```{r} library(rsofun) +drivers_path = "/data/scratch/jaideep/FluxDataKit/v4.0" + # this is to deal with an error p_model_drivers.rds not being found -p_model_drivers = readRDS("/data/scratch/jaideep/FluxDataKit/v2023.10.3/p_model_drivers.rds") |> filter(sitename == "FR-Pue") |> +p_model_drivers = readRDS(file.path(drivers_path, "p_model_drivers.rds")) |> + filter(sitename == "FR-Pue") |> rowwise() |> # manipulate all forcing variables, filter years and gapfill missing values mutate(across( @@ -44,22 +47,23 @@ p_model_drivers = readRDS("/data/scratch/jaideep/FluxDataKit/v2023.10.3/p_model_ ~.x |> filter(lubridate::year(date) >= 2007 & lubridate::year(date) <= 2012) |> - mutate(rain = ifelse(is.na(rain), yes=0, no=rain)) |> + # mutate(rain = ifelse(is.na(rain), yes=0, no=rain)) |> list() )) |> mutate(site_info = site_info |> - # Set WHC from demo data just to check match, new data has 4981.0 + # Set WHC from demo data just to check match, new data has ~250 mutate(whc=432) |> list()) -p_model_validation = readRDS("/data/scratch/jaideep/FluxDataKit/v2023.10.3/p_model_validation.rds") |> +p_model_validation = readRDS(file.path(drivers_path, "p_model_validation.rds")) |> filter(sitename == "FR-Pue") |> rowwise() |> # Use NT GPP as gpp, and filter years across all data columns mutate(across( starts_with("data"), ~ .x |> - rename(gpp = gpp_nt) |> + mutate(gpp = gpp_nt, + le = le_corr*86400) |> filter(lubridate::year(date) >= 2007 & lubridate::year(date) <= 2012) |> list() @@ -181,6 +185,17 @@ ggplot(data = df_gpp_plot) + ) ``` +## Plot all outputs + +```{r} +output$data[[1]] |> + pivot_longer(-date) |> + ggplot(aes(x=date, y=value)) + + geom_line()+ + facet_wrap(~name, scales="free_y") +``` + + ## Calibrating model parameters To optimize new parameters based upon driver data and a validation dataset we must first specify an optimization strategy and settings, as well as a cost function and parameter ranges. @@ -279,7 +294,6 @@ get_density <- function(x, y, ...) { print( output$data[[1]] %>% select(date, gpp, le) %>% - mutate(le = le/86400) %>% # convert J m-2 d-1 to W m-2 pivot_longer(-date) %>% mutate(type="pred") %>% rbind( @@ -308,23 +322,14 @@ For phydro, we must use the 3-hr daily max forcing as the acclimation forcing. S ```{r} p_model_drivers <- p_model_drivers |> - rename(forcing_acclim = forcing_3hrmax) |> - rowwise() |> - mutate(forcing_acclim = forcing_acclim |> - mutate( - across(-date, - ~zoo::na.approx(.x, na.rm = FALSE) - ) - ) |> - list() - ) + rename(forcing_acclim = forcing_3hrmax) p_model_drivers$forcing_acclim[[1]] |> # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> dplyr::select(date, co2, ppfd, netrad, temp, vpd, fapar, rain, ccov) |> tidyr::pivot_longer(-date) |> dplyr::mutate(type="24-hr mean") |> - rbind(p_model_validation$data_3hrmax[[1]] |> + rbind(p_model_validation$data[[1]] |> # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> dplyr::select(date, gpp) |> tidyr::pivot_longer(-date) |> @@ -349,7 +354,7 @@ params_modl <- list( rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, kc_jmax = 0.41, - phydro_K_plant = 2.18e-17, + phydro_K_plant = 5e-17, phydro_p50_plant = -0.46, phydro_gamma = 0.065, phydro_b_plant = 1, @@ -374,7 +379,6 @@ output <- rsofun::runread_pmodel_f( print( output$data[[1]] %>% select(date, gpp, le, aet, pet, netrad, wcont) %>% - mutate(le = le/86400) %>% # convert J m-2 d-1 to W m-2 pivot_longer(-date) %>% mutate(type="pred") %>% rbind( @@ -402,7 +406,6 @@ get_density <- function(x, y, ...) { print( output$data[[1]] %>% select(date, gpp, le) %>% - mutate(le = le/86400) %>% # convert J m-2 d-1 to W m-2 pivot_longer(-date) %>% mutate(type="pred") %>% rbind( @@ -423,4 +426,14 @@ print( facet_wrap(~name, scales = "free", nrow = 1)+ labs(colour="Density") ) +``` + +Plot all outputs + +```{r} +output$data[[1]] |> + pivot_longer(-date) |> + ggplot(aes(x=date, y=value)) + + geom_line()+ + facet_wrap(~name, scales="free_y") ``` \ No newline at end of file From b3c77a69803bff40f3078f5fc271b958205418a3 Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Fri, 9 Aug 2024 17:46:34 +0200 Subject: [PATCH 048/112] nothing much, just for the looks --- NAMESPACE | 2 +- src/photosynth_phydro.mod.f90 | 33 ++++++++++++++------------------- 2 files changed, 15 insertions(+), 20 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 93397e76..4e1f306f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,8 +2,8 @@ export(calib_sofun) export(cost_likelihood_biomee) -export(cost_likelihood_pmodel) export(cost_likelihood_phydro) +export(cost_likelihood_pmodel) export(cost_rmse_biomee) export(cost_rmse_pmodel) export(init_dates_dataframe) diff --git a/src/photosynth_phydro.mod.f90 b/src/photosynth_phydro.mod.f90 index 99152257..9a71dc89 100644 --- a/src/photosynth_phydro.mod.f90 +++ b/src/photosynth_phydro.mod.f90 @@ -3,7 +3,7 @@ module md_photosynth_phydro use md_sofunutils, only: calc_patm, zero, gammad - ! ------------------------------------------------------------- + !-------------------------------------------------------------- ! Definitions: Precision !-------------------------------------------------------------- implicit none @@ -12,7 +12,7 @@ module md_photosynth_phydro integer, parameter :: flt4=SELECTED_REAL_KIND(6,37) integer, parameter :: dbl8=SELECTED_REAL_KIND(15,307) - ! ------------------------------------------------------------- + !-------------------------------------------------------------- ! Definitions: Environment !-------------------------------------------------------------- ! list of methods to calculate gs @@ -45,7 +45,7 @@ module md_photosynth_phydro ! ! module procedure :: print_par_env ! end interface - ! ------------------------------------------------------------- + !-------------------------------------------------------------- ! Definitions: Phydro transpiration !-------------------------------------------------------------- type par_plant_type @@ -61,7 +61,7 @@ module md_photosynth_phydro end type par_plant_type - ! ------------------------------------------------------------- + !-------------------------------------------------------------- ! Definitions: Phydro photosynthesis !-------------------------------------------------------------- @@ -101,10 +101,9 @@ module md_photosynth_phydro end type ACi_type - ! ------------------------------------------------------------- + !-------------------------------------------------------------- ! Definitions: Phydro solver !-------------------------------------------------------------- - type par_cost_type real (kind = dbl8) :: alpha real (kind = dbl8) :: gamma @@ -123,10 +122,9 @@ module md_photosynth_phydro real (kind = dbl8) :: dJ_dchi end type dfdx_type - ! ------------------------------------------------------------- + !-------------------------------------------------------------- ! Definitions: Phydro main !-------------------------------------------------------------- - type phydro_result_type real(kind = dbl8) :: a real(kind = dbl8) :: e @@ -162,11 +160,9 @@ module md_photosynth_phydro integer(kind = int4) :: scale_alpha = 0 end type par_control_type +contains - - contains - - ! ------------------------------------------------------------- + !-------------------------------------------------------------- ! Functions: Physical relationships !-------------------------------------------------------------- function calc_esat(TdegC, patm) result(esatval) @@ -247,7 +243,7 @@ function calc_sat_slope(tc) result(slope) slope = 17.269 * 237.3 * 610.78 * exp(tc * 17.269 / (tc + 237.3)) / ((tc + 237.3)**2) end function calc_sat_slope - ! ------------------------------------------------------------- + !-------------------------------------------------------------- ! Functions: Environment !-------------------------------------------------------------- ! Constructor for ParEnv @@ -301,7 +297,7 @@ subroutine print_par_env(this) write(*, *) " lv = ", this%lv, " [J kg-1]" end subroutine print_par_env - ! ------------------------------------------------------------- + !-------------------------------------------------------------- ! Functions: PM !-------------------------------------------------------------- @@ -417,7 +413,7 @@ function calc_dE_dgs_pm_num(gs, ga, par_env) result(dE_dgs) dE_dgs = (E_plus - E) / 1.0e-6 end function calc_dE_dgs_pm_num - ! ------------------------------------------------------------- + !-------------------------------------------------------------- ! Functions: Phydro transpiration !-------------------------------------------------------------- ! Constructors for par plant @@ -615,7 +611,6 @@ function calc_Qprime_analytical(dpsi, psi_soil, par_plant, par_env) result(Qprim Qprime = K * P(psi_soil - dpsi, par_plant%psi50, par_plant%b) end function calc_Qprime_analytical - function calc_Qprime_approx(dpsi, psi_soil, par_plant, par_env) result(Qprime) type(par_plant_type) :: par_plant type(par_env_type) :: par_env @@ -741,7 +736,7 @@ function calc_gsprime_from_dpsi(dpsi, psi_soil, par_plant, par_env) result(gspri gsprime = Qprime / Eprime end function calc_gsprime_from_dpsi - ! ------------------------------------------------------------- + !-------------------------------------------------------------- ! Functions: Phydro photosynthesis !-------------------------------------------------------------- subroutine create_par_photosynth(this, tc, patm, kphio, co2, ppfd, fapar, rdark25, tcgrowth, tchome, & @@ -1062,7 +1057,7 @@ function calc_assimilation_limiting(vcmax, jmax, gs, par_photosynth) result(Aout end if end function calc_assimilation_limiting - ! ------------------------------------------------------------- + !-------------------------------------------------------------- ! Functions: Phydro solver !-------------------------------------------------------------- @@ -1318,7 +1313,7 @@ function calc_dpsi_bound_inst(psi_soil, par_plant, par_env, par_photosynth, par_ end function calc_dpsi_bound_inst - ! ------------------------------------------------------------- + !-------------------------------------------------------------- ! Functions: Phydro main !-------------------------------------------------------------- function phydro_analytical(tc, tg, ppfd, netrad, vpd, co2, pa, fapar, kphio, psi_soil, rdark, vwind, & From a2dd4d79c07077fc21e083d01c0a072086b8341f Mon Sep 17 00:00:00 2001 From: Benjamin Stocker Date: Fri, 9 Aug 2024 22:06:07 +0200 Subject: [PATCH 049/112] implemented root zone water storage capacity-based soil moisture stress function --- DESCRIPTION | 4 +- src/photosynth_pmodel.mod.f90 | 23 +++-- vignettes/pmodel_use.Rmd | 177 ++++++++++++++++++++++------------ 3 files changed, 133 insertions(+), 71 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 2f9e0e15..69cc484c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -69,6 +69,8 @@ Suggests: rmarkdown, ggplot2, knitr, - sensitivity + sensitivity, + hexbin, + khroma VignetteBuilder: knitr Encoding: UTF-8 diff --git a/src/photosynth_pmodel.mod.f90 b/src/photosynth_pmodel.mod.f90 index 12a204fe..916939f6 100644 --- a/src/photosynth_pmodel.mod.f90 +++ b/src/photosynth_pmodel.mod.f90 @@ -812,7 +812,7 @@ end function calc_gammastar ! TODO: Reformulate to calculate betao online so that stess = 0 @ wcont = 0 ! -- not needed, just set betao = 0 - function calc_soilmstress( wcont, thetastar, betao ) result( outstress ) + function calc_soilmstress( wcont, thetastar, whc ) result( outstress ) !////////////////////////////////////////////////////////////////// ! Calculates empirically-derived stress (fractional reduction in light ! use efficiency) as a function of soil moisture @@ -821,12 +821,14 @@ function calc_soilmstress( wcont, thetastar, betao ) result( outstress ) ! in strongly water-stressed months !----------------------------------------------------------------------- ! argument - real, intent(in) :: wcont ! soil water content (mm) - real, intent(in) :: thetastar ! threshold of water limitation (mm), previously 0.6 * whc_rootzone - real, intent(in) :: betao ! soil water stress at zero water rootzone water content + real, intent(in) :: wcont ! root-zone water content (mm) + real, intent(in) :: thetastar ! threshold of water limitation (mm), a global constant treated as model parameter + real, intent(in) :: whc ! total root zone water storage capacity (mm), site-specific ! local variables real :: shape_parameter + real :: betao = 0.0 ! soil water stress at zero water rootzone water content, taken to be zero (no water, no activity) + real :: thetastar_eff ! effective root-zone moisture limitation limitation threshold (mm) ! function return variable real :: outstress @@ -835,13 +837,20 @@ function calc_soilmstress( wcont, thetastar, betao ) result( outstress ) outstress = 1.0 else - if (thetastar < eps) then + if (whc < thetastar) then + thetastar_eff = whc + else + thetastar_eff = thetastar + end if + + if (thetastar_eff < eps) then outstress = 1.0 else - shape_parameter = (betao - 1.0) / thetastar**2 - outstress = shape_parameter * (wcont - thetastar)**2 + 1.0 + shape_parameter = (betao - 1.0) / thetastar_eff**2 + outstress = shape_parameter * (wcont - thetastar_eff)**2 + 1.0 outstress = max( 0.0, min( 1.0, outstress ) ) end if + end if end function calc_soilmstress diff --git a/vignettes/pmodel_use.Rmd b/vignettes/pmodel_use.Rmd index 38e8c4f1..69b2f2f4 100644 --- a/vignettes/pmodel_use.Rmd +++ b/vignettes/pmodel_use.Rmd @@ -19,6 +19,7 @@ knitr::opts_chunk$set( library(rsofun) library(dplyr) +library(tidyr) library(ggplot2) # fake variable as optimization isn't run @@ -99,7 +100,7 @@ params_modl <- list( kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, - soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + soilm_thetastar = 0.6 * p_model_drivers$site_info[[1]]$whc, soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous @@ -120,46 +121,57 @@ output <- rsofun::runread_pmodel_f( We can now visualize both the model output and the measured values together. ```{r} -# Load libraries for plotting -library(dplyr) -library(tidyr) -library(ggplot2) - -# Create data.frame for plotting -df_gpp_plot <- rbind( - output |> - filter(sitename == "FR-Pue") |> - unnest(data) |> - select(date, gpp) |> - mutate(type = "P-model output"), - p_model_validation |> - filter(sitename == "FR-Pue") |> - unnest(data) |> - select(date, gpp) |> - mutate(type = "Observed") -) -df_gpp_plot$type <- factor(df_gpp_plot$type, - levels = c('P-model output', - 'Observed')) +# Create dataframe for plotting +df_plot <- output |> + unnest(data) |> + select(date, gpp_mod = gpp) %>% + left_join( + p_model_validation |> + unnest(data) %>% + select(date, gpp_obs = gpp), + by = join_by(date) + ) # Plot GPP -ggplot(data = df_gpp_plot) + +df_plot %>% + tidyr::pivot_longer(c(gpp_mod, gpp_obs), names_to = "type", values_to = "gpp") %>% + ggplot() + geom_line( aes(x = date, y = gpp, - color = type), - alpha = 0.7 + color = type) ) + - scale_color_manual(values = c( - 'P-model output'='grey70', - 'Observed'='black')) + + scale_color_manual( + values = c( + "gpp_mod" = 'tomato', + "gpp_obs" = 'grey20' + ), + labels = c( + "gpp_mod" = "P-model output", + "gpp_obs" = "Observed" + )) + theme_classic() + - theme(panel.grid.major.y = element_line()) + + theme() + labs( x = 'Date', y = expression(paste("GPP (g C m"^-2, "s"^-1, ")")), colour = "" ) + +df_plot %>% + ggplot(aes(x = gpp_mod, y = gpp_obs)) + + geom_hex(bins = 50, show.legend = FALSE) + + theme_classic() + + geom_abline(intercept = 0, slope = 1, linetype = "dotted") + + geom_hline(yintercept = 0, linetype = "dotted") + + geom_vline(xintercept = 0, linetype = "dotted") + + khroma::scale_fill_batlowW(trans = "log", reverse = TRUE) + + xlim(-0.5, 10) + + ylim(-0.5, 10) + + labs( + x = expression(paste("P-model GPP (g C m"^-2, "s"^-1, ")")), + y = expression(paste("Observed GPP (g C m"^-2, "s"^-1, ")")) + ) ``` ## Calibrating model parameters @@ -170,11 +182,14 @@ To optimize new parameters based upon driver data and a validation dataset we mu settings <- list( method = "GenSA", metric = cost_rmse_pmodel, - control = list( - maxit = 100), + control = list(maxit = 3000), par = list( - kphio = list(lower=0.02, upper=0.2, init = 0.05) - ) + kphio = list(lower = 0.03, upper = 0.99, init = 0.05), + kphio_par_a = list(lower = -0.005, upper = 0, init = -0.0025), + kphio_par_b = list(lower = 10, upper = 30, init = 20), + soilm_thetastar = list(lower = 10, upper = 200, init = 100), + whc = list(lower = 10, upper = 1000, init = 400) + ) ) ``` @@ -187,61 +202,97 @@ With all settings defined the optimization function `calib_sofun()` can be calle ```{r eval=FALSE} # calibrate the model and optimize free parameters pars <- calib_sofun( - drivers = p_model_drivers, - obs = p_model_validation, - settings = settings, - # extra arguments passed to the cost function: - targets = "gpp", # define target variable GPP - par_fixed = params_modl[-1] # fix non-calibrated parameters to previous - # values, removing kphio + drivers = p_model_drivers, + obs = p_model_validation, + settings = settings, + # extra arguments passed to the cost function: + targets = "gpp", # define target variable GPP + # fix non-calibrated parameters to previous + par_fixed = list( + soilm_betao = 0.0, + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41 ) +) ``` When successful the optimized parameters can be used to run subsequent modelling efforts, in this case slightly improving the model fit over a more global parameter set. ```{r} # Update the parameter list with calibrated value -params_modl$kphio <- pars$par["kphio"] +params_modl <- list( + kphio = 0.039632387, + kphio_par_a = -0.002595878, + kphio_par_b = 13.919139015, + soilm_thetastar = 20.599254283, + soilm_betao = 0.0, + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + whc = 284.920442210 + ) -# Run the model for these parameters +# Run the model with calibrated parameters output_new <- rsofun::runread_pmodel_f( p_model_drivers, par = params_modl ) -# Update data.frame for plotting -df_gpp_plot <- rbind( - df_gpp_plot, - output_new |> - filter(sitename == "FR-Pue") |> - unnest(data) |> - select(date, gpp) |> - mutate(type = "P-model output (calibrated)") -) -df_gpp_plot$type <- factor(df_gpp_plot$type, - levels = c('P-model output', - 'P-model output (calibrated)', - 'Observed')) +# Update dataframe for plotting +df_plot <- output_new |> + unnest(data) |> + select(date, gpp_mod = gpp) %>% + left_join( + p_model_validation |> + unnest(data) %>% + select(date, gpp_obs = gpp), + by = join_by(date) + ) # Plot GPP -ggplot(data = df_gpp_plot) + +df_plot %>% + tidyr::pivot_longer(c(gpp_mod, gpp_obs), names_to = "type", values_to = "gpp") %>% + ggplot() + geom_line( aes(x = date, y = gpp, - color = type), - alpha = 0.7 + color = type) ) + - scale_color_manual(values = c( - 'P-model output'='grey70', - 'P-model output (calibrated)'='grey40', - 'Observed'='black')) + + scale_color_manual( + values = c( + "gpp_mod" = 'tomato', + "gpp_obs" = 'grey20' + ), + labels = c( + "gpp_mod" = "P-model output", + "gpp_obs" = "Observed" + )) + theme_classic() + - theme(panel.grid.major.y = element_line()) + + theme() + labs( x = 'Date', y = expression(paste("GPP (g C m"^-2, "s"^-1, ")")), colour = "" ) + +# plot modelled vs observed +df_plot %>% + ggplot(aes(x = gpp_mod, y = gpp_obs)) + + geom_hex(bins = 50, show.legend = FALSE) + + theme_classic() + + geom_abline(intercept = 0, slope = 1, linetype = "dotted") + + geom_hline(yintercept = 0, linetype = "dotted") + + geom_vline(xintercept = 0, linetype = "dotted") + + khroma::scale_fill_batlowW(trans = "log", reverse = TRUE) + + xlim(-0.5, 10) + + ylim(-0.5, 10) + + labs( + x = expression(paste("P-model GPP (g C m"^-2, "s"^-1, ")")), + y = expression(paste("Observed GPP (g C m"^-2, "s"^-1, ")")) + ) ``` For details on the optimization settings we refer to the manuals of [GenSA](https://cran.r-project.org/package=GenSA) and [BayesianTools](https://github.com/florianhartig/BayesianTools). From 500d79fcbbc85dfc7d1e725a39e2fef4ac726fa7 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Tue, 13 Aug 2024 15:34:44 +0200 Subject: [PATCH 050/112] replace ET_* with T_* in phydro options; reserving ET for splash+phydro --- src/gpp_pmodel.mod.f90 | 6 +++--- src/photosynth_phydro.mod.f90 | 24 ++++++++++++------------ src/waterbal_splash.mod.f90 | 5 +++++ 3 files changed, 20 insertions(+), 15 deletions(-) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 2b377f08..61749122 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -13,7 +13,7 @@ module md_gpp_pmodel use md_photosynth, only: pmodel, zero_pmodel, outtype_pmodel, calc_ftemp_inst_vcmax, calc_ftemp_inst_jmax, & calc_ftemp_inst_rd, calc_kphio_temp, calc_soilmstress, calc_density_h2o use md_photosynth_phydro, only: phydro_analytical, phydro_instantaneous_analytical, par_plant_type, par_cost_type, & - phydro_result_type, par_control_type, ET_DIFFUSION, ET_PM, GS_IGF, GS_APX + phydro_result_type, par_control_type, T_DIFFUSION, T_PM, GS_IGF, GS_APX implicit none private @@ -163,9 +163,9 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init tk = climate_acclimation%dtemp + kTkelvin if (use_pml) then - options%et_method = ET_PM + options%et_method = T_PM else - options%et_method = ET_DIFFUSION + options%et_method = T_DIFFUSION end if ! print *, options%et_method diff --git a/src/photosynth_phydro.mod.f90 b/src/photosynth_phydro.mod.f90 index 99152257..bd46dfb5 100644 --- a/src/photosynth_phydro.mod.f90 +++ b/src/photosynth_phydro.mod.f90 @@ -18,7 +18,7 @@ module md_photosynth_phydro ! list of methods to calculate gs integer (kind = int4), parameter :: GS_IGF = 0, GS_QNG = 1, GS_APX = 2, GS_APX2 = 3 - integer (kind = int4), parameter :: ET_DIFFUSION = 0, ET_PM = 1 + integer (kind = int4), parameter :: T_DIFFUSION = 0, T_PM = 1 ! Define the data type for ParEnv type par_env_type @@ -35,7 +35,7 @@ module md_photosynth_phydro real(kind = dbl8) :: epsilon ! Slope of saturation-pressure - temp curve [Pa K-1] real(kind = dbl8) :: lv ! Latent heat of vaporization of water [J kg-1] integer(kind = int4) :: gs_method = GS_IGF ! GsMethod - integer(kind = int4) :: et_method = ET_DIFFUSION ! ETMethod + integer(kind = int4) :: et_method = T_DIFFUSION ! ETMethod end type par_env_type ! ! Interface for member subroutines @@ -155,7 +155,7 @@ module md_photosynth_phydro type par_control_type integer(kind = int4) :: gs_method = GS_IGF - integer(kind = int4) :: et_method = ET_DIFFUSION + integer(kind = int4) :: et_method = T_DIFFUSION integer(kind = int4) :: ftemp_vj_method = FV_kumarathunge19 integer(kind = int4) :: ftemp_rd_method = FR_heskel16 integer(kind = int4) :: ftemp_br_method = FB_atkin15 @@ -260,7 +260,7 @@ subroutine create_par_env(this, tc, patm, vpd, Rn, v_wind) this%Rn = Rn this%v_wind = v_wind this%gs_method = GS_IGF - this%et_method = ET_DIFFUSION + this%et_method = T_DIFFUSION call calc_temp_dependencies(this) end subroutine create_par_env @@ -590,10 +590,10 @@ function calc_gs_from_Q(Q, psi_soil, par_plant, par_env) result(gs) D = (par_env%vpd / par_env%patm) - if (par_env%et_method == ET_DIFFUSION) then + if (par_env%et_method == T_DIFFUSION) then ! print *, "Using diffusion ET" gs = Q / (1.6d0 * D) - else if (par_env%et_method == ET_PM) then + else if (par_env%et_method == T_PM) then ! print *, "Using PM ET" ga = calc_g_aero(par_plant%h_canopy, dble(par_env%v_wind), par_plant%h_wind_measurement) gs = calc_gs_pm(Q, ga, par_env) @@ -691,9 +691,9 @@ function calc_dE_dgs_from_gs(gs, par_plant, par_env) result(dE_dgs) type(par_env_type) :: par_env real(kind=dbl8) :: gs, dE_dgs - if (par_env%et_method == ET_DIFFUSION) then + if (par_env%et_method == T_DIFFUSION) then dE_dgs = calc_dE_dgs_dif(par_env) - else if (par_env%et_method == ET_PM) then + else if (par_env%et_method == T_PM) then dE_dgs = calc_dE_dgs_pm_from_gs(gs, par_plant, par_env) else write(*,*) "Unknown et_method:", par_env%et_method @@ -707,9 +707,9 @@ function calc_dE_dgs_from_dpsi(dpsi, psi_soil, par_plant, par_env) result(dE_dgs type(par_env_type) :: par_env real(kind=dbl8) :: dpsi, psi_soil, dE_dgs - if (par_env%et_method == ET_DIFFUSION) then + if (par_env%et_method == T_DIFFUSION) then dE_dgs = calc_dE_dgs_dif(par_env) - else if (par_env%et_method == ET_PM) then + else if (par_env%et_method == T_PM) then dE_dgs = calc_dE_dgs_pm_from_dpsi(dpsi, psi_soil, par_plant, par_env) else write(*,*) "Unknown et_method:", par_env%et_method @@ -1224,7 +1224,7 @@ function calc_dpsi_bound(psi_soil, par_plant, par_env, par_photosynth, par_cost) iabsb = zero(use_bound*0.001, use_bound*0.99, f1, 1D-6); ! If using PM, find max dpsi from max possible transpiration - if (par_env%et_method == ET_PM) then + if (par_env%et_method == T_PM) then ga = calc_g_aero(par_plant%h_canopy, dble(par_env%v_wind), par_plant%h_wind_measurement); Qmax = calc_max_transpiration_pm(ga, par_env); max_dpsi = calc_dpsi_from_sapflux(Qmax, psi_soil, par_plant, par_env); @@ -1308,7 +1308,7 @@ function calc_dpsi_bound_inst(psi_soil, par_plant, par_env, par_photosynth, par_ bound = 100.0d0 ! If using PM, find max dpsi from max possible transpiration - if (par_env%et_method == ET_PM) then + if (par_env%et_method == T_PM) then ga = calc_g_aero(par_plant%h_canopy, dble(par_env%v_wind), par_plant%h_wind_measurement) Qmax = calc_max_transpiration_pm(ga, par_env) max_dpsi = calc_dpsi_from_sapflux(Qmax, psi_soil, par_plant, par_env) diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index fb293db2..d36f44e1 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -17,6 +17,8 @@ module md_waterbal private public waterbal, solar, getpar_modl_waterbal + integer, parameter :: int4=SELECTED_INT_KIND(4) + !----------------------------------------------------------------------- ! Uncertain (unknown) parameters. Runtime read-in !----------------------------------------------------------------------- @@ -52,6 +54,8 @@ module md_waterbal logical, parameter :: splashtest = .false. + integer (kind = int4), parameter :: ET_PT = 0, ET_PT_DIFFUSIOM = 1, ET_PT_PM = 2 + contains subroutine waterbal( tile, tile_fluxes, grid, climate, fapar, using_phydro ) @@ -369,6 +373,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro ) !--------------------------------------------------------- ! 21. Estimate daily AET (tile_fluxes%canopy%daet), mm d-1 !--------------------------------------------------------- + ! TODO: 3 options: SPLASH only, 1.6gsD + SPLASH for soil, PM + SPLASH for soil ! JAIDEEP FIXME: soil PET calcs should be identical for P and Phydro, but depending on whether in_netrad is used or not, ! when implementing in_netrad condition, uncomment the lines marked by arrows ! if (.not. in_netrad) then <-------------------- From 8ae7fb3900a3e0122c2ad5645411134f165e64ac Mon Sep 17 00:00:00 2001 From: Jaideep Date: Tue, 13 Aug 2024 22:19:27 +0200 Subject: [PATCH 051/112] add 3 ET options via: using_gs, using_pml --- src/biosphere_pmodel.mod.f90 | 15 +++++--- src/gpp_pmodel.mod.f90 | 72 +++++++++++++++++++++--------------- src/tile_pmodel.mod.f90 | 1 + src/waterbal_splash.mod.f90 | 40 +++++++++++--------- 4 files changed, 75 insertions(+), 53 deletions(-) diff --git a/src/biosphere_pmodel.mod.f90 b/src/biosphere_pmodel.mod.f90 index 04afd2be..8c1cd8b2 100644 --- a/src/biosphere_pmodel.mod.f90 +++ b/src/biosphere_pmodel.mod.f90 @@ -145,6 +145,12 @@ function biosphere_annual() result( out_biosphere ) ) ! if (verbose) print*,'... done' + !---------------------------------------------------------------- + ! daily diagnostics (e.g., sum over plant within canopy) + !---------------------------------------------------------------- + ! Jaideep NOTE: This is moved here because waterbal requires aggregated canopy transpiration. + call diag_daily(tile(:), tile_fluxes(:)) + !---------------------------------------------------------------- ! get soil moisture, and runoff !---------------------------------------------------------------- @@ -154,7 +160,9 @@ function biosphere_annual() result( out_biosphere ) myinterface%grid, & myinterface%climate(doy), & tile(:)%canopy%fapar, & - myinterface%params_siml%use_phydro & + myinterface%params_siml%use_phydro, & + .true., & ! myinterface%params_siml%use_gs & + myinterface%params_siml%use_pml & ) ! if (verbose) print*,'... done' @@ -170,11 +178,6 @@ function biosphere_annual() result( out_biosphere ) ) ! if (verbose) print*, '... done' - !---------------------------------------------------------------- - ! daily diagnostics (e.g., sum over plant within canopy) - !---------------------------------------------------------------- - call diag_daily(tile(:), tile_fluxes(:)) - !---------------------------------------------------------------- ! populate function return variable !---------------------------------------------------------------- diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 61749122..6ade9fdc 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -71,7 +71,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init logical, intent(in) :: init ! is true on the very first simulation day (first subroutine call of each gridcell) logical, intent(in) :: in_ppfd ! whether to use PPFD from forcing or from SPLASH output logical, intent(in) :: use_phydro ! whether to use P-Hydro for photosynthesis and transpiration - logical, intent(in) :: use_pml ! whether to use PML formulation for ET within Phydro + logical, intent(in) :: use_pml ! whether to use uncoupled PM formulation for canopy transpiration (whether to plug gs into PM equation. Alternatively, 1.6gsD will be used) ! local variables type(outtype_pmodel) :: out_pmodel ! list of P-model output variables @@ -162,14 +162,9 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init tk = climate_acclimation%dtemp + kTkelvin - if (use_pml) then - options%et_method = T_PM - else - options%et_method = T_DIFFUSION - end if - ! print *, options%et_method - + options%et_method = T_DIFFUSION ! This is method used for calculating transpiration for plant-level water balance within Phydro. Always set to T_DIFFUSION options%gs_method = GS_IGF + ! print *, options%et_method pftloop: do pft=1,npft @@ -352,9 +347,10 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init ! Stomatal conductance !---------------------------------------------------------------- if (.not. use_phydro) then - tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint + ! Jaideep NOTE: I have applied the soilmstress factor to gs here because it is needed in calculating canopy transpiration + tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint * soilmstress else - ! Jaideep: Note that unit of gs_accl here is mol m-2 s-1. + ! Jaideep NOTE: unit of gs_accl here is mol m-2 s-1. ! Jaideep FIXME: It's too complicated to convert it to unit as in pmodel, but should be done at some point tile_fluxes(lu)%plant(pft)%gs_accl = out_phydro_inst%gs end if @@ -368,30 +364,46 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init end if !------------------------------------------------------------------------ - ! Canopy ET + ! Canopy Transpiration per PFT !------------------------------------------------------------------------ + ! JAIDEEP NOTE: This computation is done here because it needs PFT-level properties, which are aggregated by + ! diag_daily before calling waterbal_splash + ! Density of water, kg/m^3 rho_water = calc_density_h2o( climate%dtemp, climate%dpatm ) - ! Jaideep TODO FIXME: PM can be used here instead of 1.6gsD - if (.not.use_phydro) then - ! Note here that stomatal conductance is already normalized by patm (=gs/patm) so E = 1.6 * (gs/patm) * vpd, which is the same as 1.6 gs (vpd/patm) - ! but it is expressed per unit absorbed light, so multiply by PPFD*fapar - tile_fluxes(lu)%canopy%daet_canop = (1.6 & - * out_pmodel%gs_setpoint * tile(lu)%canopy%fapar * climate%dppfd & - * climate%dvpd) * 0.018015 * (1.0d0 / rho_water) & - * myinterface%params_siml%secs_per_tstep * 1000 ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 - - else - tile_fluxes(lu)%canopy%daet_canop = out_phydro_inst%e * 0.018015 * (1.0d0 / rho_water) & - * myinterface%params_siml%secs_per_tstep * 1000 ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 - - ! ~~ This has been moved to waterbal_splash ~~ - ! tile_fluxes(lu)%canopy%dpet_e_soil = out_phydro_inst%le_s_wet & - ! * myinterface%params_siml%secs_per_tstep ! convert: J m-2 s-1 * s day-1 = J m-2 day-1 - - ! print *, "Canopy ET (mm d-1) = ", tile_fluxes(lu)%canopy%daet_canop - ! print *, "Soil LE (J m-2 d-1) = ", climate%dnetrad, tile_fluxes(lu)%canopy%daet_e_soil + if (use_pml) then + ! We plug Pmodel/Phydro-derived gs into the PM equation to calculate T (note this is uncoupled PM-transpiration) + ! use PFT-specific gs for this calculation: tile_fluxes(lu)%plant(pft)%gs_accl + + ! TODO: Fill this in + ! tile_fluxes(lu)%plant(pft)%dtransp = PM_EQUATION(tile_fluxes(lu)%plant(pft)%gs_accl) + + else + ! We plug Pmodel/Phydro-derived gs into T = 1.6gsD + if (.not. use_phydro) then + ! Using P-model gs + ! Note here that stomatal conductance is already normalized by patm (=gs/patm) so E = 1.6 * (gs/patm) * vpd, which is the same as 1.6 gs (vpd/patm) + ! but it is expressed per unit absorbed light, so multiply by PPFD*fapar + tile_fluxes(lu)%plant(pft)%dtransp = (1.6 & ! 1.6 + * tile_fluxes(lu)%plant(pft)%gs_accl * tile(lu)%canopy%fapar * climate%dppfd & ! gs + * climate%dvpd) & ! D + * 0.018015 * (1.0d0 / rho_water) & + * myinterface%params_siml%secs_per_tstep * 1000 ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 + + else + ! Using Phydro gs + tile_fluxes(lu)%plant(pft)%dtransp = out_phydro_inst%e & ! Phydro e is 1.6 gs D + * 0.018015 * (1.0d0 / rho_water) & + * myinterface%params_siml%secs_per_tstep * 1000 ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 + + ! ~~ This has been moved to waterbal_splash ~~ + ! tile_fluxes(lu)%canopy%dpet_e_soil = out_phydro_inst%le_s_wet & + ! * myinterface%params_siml%secs_per_tstep ! convert: J m-2 s-1 * s day-1 = J m-2 day-1 + + ! print *, "Canopy ET (mm d-1) = ", tile_fluxes(lu)%canopy%daet_canop + ! print *, "Soil LE (J m-2 d-1) = ", climate%dnetrad, tile_fluxes(lu)%canopy%daet_e_soil + end if end if end do pftloop diff --git a/src/tile_pmodel.mod.f90 b/src/tile_pmodel.mod.f90 index 2cd5b604..a65bce66 100644 --- a/src/tile_pmodel.mod.f90 +++ b/src/tile_pmodel.mod.f90 @@ -544,6 +544,7 @@ subroutine diag_daily( tile, tile_fluxes ) !---------------------------------------------------------------- do lu=1,nlu tile_fluxes(lu)%canopy%dgpp = sum(tile_fluxes(lu)%plant(:)%dgpp) + tile_fluxes(lu)%canopy%dtransp = sum(tile_fluxes(lu)%plant(:)%dtransp) tile_fluxes(lu)%canopy%drd = sum(tile_fluxes(lu)%plant(:)%drd) tile_fluxes(lu)%canopy%vcmax25 = sum(tile_fluxes(lu)%plant(:)%vcmax25 * tile(lu)%plant(:)%fpc_grid) tile_fluxes(lu)%canopy%jmax25 = sum(tile_fluxes(lu)%plant(:)%jmax25 * tile(lu)%plant(:)%fpc_grid) diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index d36f44e1..5419b796 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -58,7 +58,7 @@ module md_waterbal contains - subroutine waterbal( tile, tile_fluxes, grid, climate, fapar, using_phydro ) + subroutine waterbal( tile, tile_fluxes, grid, climate, fapar, using_phydro, using_gs, using_pml ) !///////////////////////////////////////////////////////////////////////// ! Calculates soil water balance !------------------------------------------------------------------------- @@ -69,6 +69,8 @@ subroutine waterbal( tile, tile_fluxes, grid, climate, fapar, using_phydro ) type(climate_type), intent(in) :: climate real, dimension(nlu), intent(in) :: fapar logical, intent(in) :: using_phydro + logical, intent(in) :: using_gs + logical, intent(in) :: using_pml ! local variables type(outtype_snow_rain) :: out_snow_rain @@ -84,7 +86,7 @@ subroutine waterbal( tile, tile_fluxes, grid, climate, fapar, using_phydro ) !--------------------------------------------------------- ! Canopy transpiration and soil evaporation !--------------------------------------------------------- - call calc_et( tile_fluxes(lu), grid, climate, sw, fapar(lu), using_phydro ) + call calc_et( tile_fluxes(lu), grid, climate, sw, fapar(lu), using_phydro, using_gs, using_pml ) !--------------------------------------------------------- ! Update soil moisture and snow pack @@ -280,7 +282,7 @@ subroutine solar( tile_fluxes, grid, climate, doy, in_netrad ) end subroutine solar - subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro ) + subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_gs, using_pml ) !///////////////////////////////////////////////////////////////////////// ! !------------------------------------------------------------------------- @@ -294,6 +296,8 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro ) real, intent(in) :: sw ! evaporative supply rate, mm/hr real, intent(in) :: fapar logical, intent(in) :: using_phydro + logical, intent(in) :: using_gs ! Should Pmodel/Phydro gs be used in ET calc? (otherwise, PT formulation will be used) + logical, intent(in) :: using_pml ! If using Pmodel/Phydro gs, should ET be calculated using PM equation (otherwise, diffusion equation will be used) ! local variables real :: gamma ! psychrometric constant (Pa K-1) ! xxx Zhang et al. use it in units of (kPa K-1), probably they use sat_slope in kPa/K, too. @@ -348,7 +352,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro ) !--------------------------------------------------------- ! Eq. 72, SPLASH 2.0 Documentation tile_fluxes%canopy%dpet = ( 1.0 + kw ) * tile_fluxes%canopy%deet - tile_fluxes%canopy%dpet_e = tile_fluxes%canopy%dpet / (tile_fluxes%canopy%econ * 1000) ! JAIDEEP FIXME: Oops! This is a case where you should use a simple mass-energy conversion, not econ + tile_fluxes%canopy%dpet_e = tile_fluxes%canopy%dpet / energy_to_mm ! JAIDEEP FIXME [resolved]: Oops! This is a case where you should use a simple mass-energy conversion, not econ !--------------------------------------------------------- ! 19. Calculate variable substitute (rx), (mm/hr)/(W/m^2) @@ -376,21 +380,23 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro ) ! TODO: 3 options: SPLASH only, 1.6gsD + SPLASH for soil, PM + SPLASH for soil ! JAIDEEP FIXME: soil PET calcs should be identical for P and Phydro, but depending on whether in_netrad is used or not, ! when implementing in_netrad condition, uncomment the lines marked by arrows - ! if (.not. in_netrad) then <-------------------- - ! ! Eq. 81, SPLASH 2.0 Documentation - ! tile_fluxes%canopy%daet = (24.0/pi) * (radians(sw * hi) + rx * rw * rv * (dgsin(hn) - dgsin(hi)) + & - ! radians((rx * rw * ru - rx * tile_fluxes%canopy%rnl) * (hn - hi))) ! JAIDEEP FIXME: Technically correct, but for clarity, apply radians to just (hn-hi) ? - ! tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet / (tile_fluxes%canopy%econ * 1000) ! JAIDEEP FIXME: Oops! This is a case where you should use a simple mass-energy conversion, not econ - ! ^ these should become dpet_soil and dpet_e_soil, with 1-fapar multipled to rx - ! else <------------------ + if (.not. using_gs) then + ! When not using stomatal conductance, we use Priestly-Taylor formulation for the whole gridcell using all of incoming net radiation + ! Eq. 81, SPLASH 2.0 Documentation + tile_fluxes%canopy%daet = (24.0/pi) * (radians(sw * hi) + rx * rw * rv * (dgsin(hn) - dgsin(hi)) + & + radians((rx * rw * ru - rx * tile_fluxes%canopy%rnl) * (hn - hi))) ! JAIDEEP FIXME: Technically correct, but for clarity, apply radians to just (hn-hi) ? + tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet / energy_to_mm ! JAIDEEP FIXME [resolved]: Oops! This is a case where you should use a simple mass-energy conversion, not econ + else + ! Else we use: ET = Pmodel-Transpiration (T) + (1-fapar) * Leuning-Soil-Evaporation (S) + ! NOTE: T was calculated in gpp_pmodel and stored in tile_fluxes%plant(pft)%dtransp, which was aggegated into tile_fluxes%canopy%dtransp by diag_daily() + tile_fluxes%canopy%daet_canop = tile_fluxes%canopy%dtransp + tile_fluxes%canopy%daet_e_canop = tile_fluxes%canopy%daet_canop / energy_to_mm ! mm d-1 ---> J m-2 d-1 + ! tile_fluxes%canopy%dpet_e_soil = (1.0d0 - fapar) * netrad * (par_env%epsilon / (1.0d0 + par_env%epsilon)) tile_fluxes%canopy%dpet_soil = (1.0d0 - fapar) * tile_fluxes%canopy%drn * tile_fluxes%canopy%econ * 1.0d3 ! 1000*econ converts energy into mm evaporation tile_fluxes%canopy%dpet_e_soil = tile_fluxes%canopy%dpet_soil / energy_to_mm ! mm d-1 ---> J m-2 d-1 ! ^ Note: This is under wet conditions, so multiply by reduction factor (below) to get actual soil ET - ! Fill canopy LE from T_canopy calculated in gpp_pmodel - tile_fluxes%canopy%daet_e_canop = tile_fluxes%canopy%daet_canop / energy_to_mm ! mm d-1 ---> J m-2 d-1 - ! calculate totat AET = canopy_AET + f * soil_AET_wet, where f = running_avg(P/PET) ! p_over_pet = (climate%dprec*86400) / (tile_fluxes%canopy%dpet_soil + 1e-6) p_memory = dampen_variability(climate%dprec*86400, 30.0, p_memory ) ! corresponds to f in Zhang et al., 2017 Eq. 9 @@ -401,8 +407,8 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro ) tile_fluxes%canopy%daet_soil = f_soil_aet * tile_fluxes%canopy%dpet_soil tile_fluxes%canopy%daet_e_soil = tile_fluxes%canopy%daet_soil / energy_to_mm - tile_fluxes%canopy%daet = tile_fluxes%canopy%daet_canop + tile_fluxes%canopy%daet_soil - tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet_e_canop + tile_fluxes%canopy%daet_e_soil + tile_fluxes%canopy%daet = tile_fluxes%canopy%daet_canop + tile_fluxes%canopy%daet_soil + tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet_e_canop + tile_fluxes%canopy%daet_e_soil ! print *, "P (mm d-1), PET (mm d-1), P/PET, Avg(P/PET), f_soil_aet = ", (climate%dprec*86400), & ! tile_fluxes%canopy%dpet_soil, p_over_pet, & @@ -410,7 +416,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro ) ! print *, "Canopy ET (mm d-1, J m-2 d-1) = ", tile_fluxes%canopy%daet_canop, tile_fluxes%canopy%daet_e_canop ! print *, "Soil ET (mm d-1, J m-2 d-1) = ", tile_fluxes%canopy%daet_soil, tile_fluxes%canopy%daet_e_soil - ! end if <------------------ + end if ! print*,'in waterbal: sw, hi, rx, rw, rv, hn, hi, ru ', sw, hi, rx, rw, rv, hn, hi, ru From 4d5950e7f50a3b29bedc035ba1567cbfbff2363b Mon Sep 17 00:00:00 2001 From: Jaideep Date: Tue, 13 Aug 2024 22:57:59 +0200 Subject: [PATCH 052/112] R interface for all ET options; update vignette to demonstrate --- R/run_pmodel_f_bysite.R | 6 +- src/biosphere_pmodel.mod.f90 | 2 +- src/params_siml_pmodel.mod.f90 | 3 +- src/sofun_r.f90 | 5 +- src/wrappersc.c | 5 +- vignettes/pmodel_use_newdata.Rmd | 97 ++++++++++++++++++++++++++++++-- 6 files changed, 105 insertions(+), 13 deletions(-) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 1d5a30d7..6b50f5e4 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -281,6 +281,7 @@ run_pmodel_f_bysite <- function( "spinupyears", "recycle", "use_phydro", + "use_gs", "use_pml", "outdt", "ltre", @@ -345,9 +346,7 @@ run_pmodel_f_bysite <- function( } # If PML is used, then ensure that site info has reference height and canopy height - if (params_siml$use_phydro & - params_siml$use_pml){ - + if (params_siml$use_pml){ continue = !is.nanull(site_info$canopy_height) & !is.nanull(site_info$reference_height) } else { @@ -436,6 +435,7 @@ run_pmodel_f_bysite <- function( spinupyears = as.integer(params_siml$spinupyears), recycle = as.integer(params_siml$recycle), use_phydro = as.logical(params_siml$use_phydro), + use_gs = as.logical(params_siml$use_gs), use_pml = as.logical(params_siml$use_pml), firstyeartrend = as.integer(firstyeartrend_forcing), nyeartrend = as.integer(nyeartrend_forcing), diff --git a/src/biosphere_pmodel.mod.f90 b/src/biosphere_pmodel.mod.f90 index 8c1cd8b2..18d3ae02 100644 --- a/src/biosphere_pmodel.mod.f90 +++ b/src/biosphere_pmodel.mod.f90 @@ -161,7 +161,7 @@ function biosphere_annual() result( out_biosphere ) myinterface%climate(doy), & tile(:)%canopy%fapar, & myinterface%params_siml%use_phydro, & - .true., & ! myinterface%params_siml%use_gs & + myinterface%params_siml%use_gs, & myinterface%params_siml%use_pml & ) ! if (verbose) print*,'... done' diff --git a/src/params_siml_pmodel.mod.f90 b/src/params_siml_pmodel.mod.f90 index 162e55e5..a18c6fee 100644 --- a/src/params_siml_pmodel.mod.f90 +++ b/src/params_siml_pmodel.mod.f90 @@ -24,7 +24,8 @@ module md_params_siml_pmodel logical :: do_spinup ! whether this simulation does spinup logical :: is_calib ! whether this simulation is a calibration simulation (overriding parameters and no output) logical :: use_phydro ! Whether phydro should be used for photosynthesis/transpiration calculations - logical :: use_pml ! Whether Penmann-Monteith-Leuning formulation should be used for ET within phydro + logical :: use_gs ! Whether pmodel calculated gs should be used for transpiration calculations + logical :: use_pml ! Whether Penmann-Monteith-Leuning formulation should be used for ET character(len=256) :: runname character(len=256) :: sitename diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index d573a3f3..7d64a56c 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -15,7 +15,8 @@ subroutine pmodel_f( & spinup, & spinupyears, & recycle, & - use_phydro, & + use_phydro, & + use_gs, & use_pml, & firstyeartrend, & nyeartrend, & @@ -60,6 +61,7 @@ subroutine pmodel_f( & integer(kind=c_int), intent(in) :: spinupyears integer(kind=c_int), intent(in) :: recycle logical(kind=c_bool), intent(in) :: use_phydro + logical(kind=c_bool), intent(in) :: use_gs logical(kind=c_bool), intent(in) :: use_pml integer(kind=c_int), intent(in) :: firstyeartrend integer(kind=c_int), intent(in) :: nyeartrend @@ -96,6 +98,7 @@ subroutine pmodel_f( & myinterface%params_siml%spinupyears = spinupyears myinterface%params_siml%recycle = recycle myinterface%params_siml%use_phydro = use_phydro + myinterface%params_siml%use_gs = use_gs myinterface%params_siml%use_pml = use_pml myinterface%params_siml%firstyeartrend = firstyeartrend myinterface%params_siml%nyeartrend = nyeartrend diff --git a/src/wrappersc.c b/src/wrappersc.c index 44204b83..227b1987 100644 --- a/src/wrappersc.c +++ b/src/wrappersc.c @@ -14,6 +14,7 @@ void F77_NAME(pmodel_f)( int *spinupyears, int *recycle, int *use_phydro, + int *use_gs, int *use_pml, int *firstyeartrend, int *nyeartrend, @@ -46,6 +47,7 @@ extern SEXP pmodel_f_C( SEXP spinupyears, SEXP recycle, SEXP use_phydro, + SEXP use_gs, SEXP use_pml, SEXP firstyeartrend, SEXP nyeartrend, @@ -84,6 +86,7 @@ extern SEXP pmodel_f_C( INTEGER(spinupyears), INTEGER(recycle), LOGICAL(use_phydro), + LOGICAL(use_gs), LOGICAL(use_pml), INTEGER(firstyeartrend), INTEGER(nyeartrend), @@ -554,7 +557,7 @@ extern SEXP biomee_f_C( // Declarations for all functions ///////////////////////////////////////////////////////////// static const R_CallMethodDef CallEntries[] = { - {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 27}, // Specify number of arguments to C wrapper as the last number here + {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 28}, // Specify number of arguments to C wrapper as the last number here {"biomee_f_C", (DL_FUNC) &biomee_f_C, 48}, // Number of arguments of the C wrapper function for biomee (the SEXP variables, not the output) {NULL, NULL, 0} }; diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd index 361a737d..8194bc2b 100644 --- a/vignettes/pmodel_use_newdata.Rmd +++ b/vignettes/pmodel_use_newdata.Rmd @@ -1,6 +1,6 @@ --- -title: "P-model usage (new data)" -author: "Koen Hufkens, Josefa Arán" +title: "P-model usage (new data and new ET options)" +author: "Koen Hufkens, Josefa Arán, Jaideep Joshi" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{P-model usage} @@ -53,7 +53,12 @@ p_model_drivers = readRDS(file.path(drivers_path, "p_model_drivers.rds")) |> mutate(site_info = site_info |> # Set WHC from demo data just to check match, new data has ~250 mutate(whc=432) |> - list()) + list()) |> + mutate(params_siml = params_siml |> + # First try with setting use_gs to false + mutate(use_gs=F) |> + list()) + p_model_validation = readRDS(file.path(drivers_path, "p_model_validation.rds")) |> filter(sitename == "FR-Pue") |> @@ -280,7 +285,8 @@ ggplot(data = df_gpp_plot) + For details on the optimization settings we refer to the manuals of [GenSA](https://cran.r-project.org/package=GenSA) and [BayesianTools](https://github.com/florianhartig/BayesianTools). -```{r} + +```{r, echo=F} get_density <- function(x, y, ...) { df = tibble(x=x, y=y) %>% drop_na dens <- MASS::kde2d(df$x, df$y, ...) @@ -289,7 +295,75 @@ get_density <- function(x, y, ...) { ii <- cbind(ix, iy) return(dens$z[ii]) } +``` + +## Run and plot Pmodel (Pmodel GPP + Priestly Taylor ET) + +Set `use_gs` flag to FALSE in params_siml so that Priestly-Taylor formulation will be used in calculation of ET (Pmodel gs will not be used). + +```{r} +p_model_drivers <- + p_model_drivers |> + mutate(params_siml = params_siml |> + mutate(use_gs = FALSE) |> + list()) +``` + +Run the model and plot outputs + +```{r} +# run the model for these parameters +output <- rsofun::runread_pmodel_f( + p_model_drivers, + par = params_modl +) + +print( + output$data[[1]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="pred") %>% + rbind( + p_model_validation$data[[1]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="obs") + ) %>% + pivot_wider(names_from = type, values_from = value) %>% + group_by(name) %>% + reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% + ggplot(aes(y=obs, x=pred, colour=density)) + + scale_color_viridis_c() + + geom_point(alpha=0.7) + + geom_abline(slope=1, intercept=0, col="red")+ + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~name, scales = "free", nrow = 1)+ + labs(colour="Density") +) +``` + +## Pmodel run (Pmodel GPP + Diffusion ET + Soil evap) + +Set `use_gs` flag to TRUE in params_siml so that Pmodel gs will be used in calculation of ET. + +```{r} +p_model_drivers <- + p_model_drivers |> + mutate(params_siml = params_siml |> + mutate(use_gs = TRUE) |> + list()) +``` + +Run the model and plot outputs + +```{r} +# run the model for these parameters +output <- rsofun::runread_pmodel_f( + p_model_drivers, + par = params_modl +) print( output$data[[1]] %>% @@ -316,7 +390,7 @@ print( ) ``` -## P-hydro model run +## P-hydro model run (P-hydro GPP + diffusion ET + Soil evap) For phydro, we must use the 3-hr daily max forcing as the acclimation forcing. So lets rename it i nthe data, @@ -343,9 +417,20 @@ p_model_drivers$forcing_acclim[[1]] |> ggtitle(p_model_drivers$sitename[[1]]) ``` +For phydro runs, we always set `use_phydro = T` and `use_gs = T`. + ```{r} -p_model_drivers$params_siml[[1]]$use_phydro = T +p_model_drivers <- + p_model_drivers |> + mutate(params_siml = params_siml |> + mutate( + use_phydro = TRUE, + use_gs = TRUE + ) |> + list()) +``` +```{r} # define model parameter values params_modl <- list( kphio = 0.0288, From 9d5fc702c193da3dc4a309c052276ff3d88b6434 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Tue, 13 Aug 2024 23:54:58 +0200 Subject: [PATCH 053/112] remove nested functions --- src/photosynth_phydro.mod.f90 | 174 +++++++++++++++++++++++++--------- 1 file changed, 130 insertions(+), 44 deletions(-) diff --git a/src/photosynth_phydro.mod.f90 b/src/photosynth_phydro.mod.f90 index bd46dfb5..6a05ffcd 100644 --- a/src/photosynth_phydro.mod.f90 +++ b/src/photosynth_phydro.mod.f90 @@ -162,7 +162,14 @@ module md_photosynth_phydro integer(kind = int4) :: scale_alpha = 0 end type par_control_type - + ! The following global variables are temporary variables used by the functions passed to zero. + ! These are replacements of variables that could have been implicitly accessible to nested functions (lambdas) + real (kind = dbl8) :: lambda_vcmax, lambda_jmax, lambda_psi_soil, lambda_Q + real (kind = dbl8) :: lambda_y, lambda_ca, lambda_gstar + type(par_plant_type) :: lambda_par_plant + type(par_env_type) :: lambda_par_env + type(par_photosynth_type) :: lambda_par_photosynth + type(par_cost_type) :: lambda_par_cost contains @@ -556,25 +563,38 @@ function calc_max_sapflux(psi_soil, par_plant, par_env) result(E) ! _ps-dpsi ! Calculate dpsi that solves _/ K(psi') dpsi' = Q ! ps + + ! Replacement of nested function used in the function further below + function lambda_f(dpsi) + real(kind=dbl8), intent(in) :: dpsi + real(kind=dbl8) :: lambda_f + lambda_f = calc_sapflux(dpsi, lambda_psi_soil, lambda_par_plant, lambda_par_env) - lambda_Q; + end function lambda_f + function calc_dpsi_from_sapflux(Q, psi_soil, par_plant, par_env) result(dpsi) type(par_plant_type) :: par_plant type(par_env_type) :: par_env real(kind=dbl8) :: Q, psi_soil, dpsi, Qmax + lambda_psi_soil = psi_soil + lambda_par_plant = par_plant + lambda_par_env = par_env + lambda_Q = Q + Qmax = calc_max_sapflux(psi_soil, par_plant, par_env); if (Q > Qmax) then dpsi = 999999999.0_dbl8 else - dpsi = zero(0.0_dbl8, 100.0_dbl8, f, 1e-6_dbl8) + dpsi = zero(0.0_dbl8, 100.0_dbl8, lambda_f, 1e-6_dbl8) endif - contains + ! contains - function f(dpsi) - real(kind=dbl8), intent(in) :: dpsi - real(kind=dbl8) :: f - f = calc_sapflux(dpsi, psi_soil, par_plant, par_env) - Q; - end function f + ! function f(dpsi) + ! real(kind=dbl8), intent(in) :: dpsi + ! real(kind=dbl8) :: f + ! f = calc_sapflux(dpsi, psi_soil, par_plant, par_env) - Q; + ! end function f end function calc_dpsi_from_sapflux @@ -1187,6 +1207,27 @@ function dFdx(dpsi, psi_soil, par_plant, par_env, par_photosynth, par_cost) resu end + !---------------------------------------------- + ! calc_dspi_bounds() and its two nested functions + !---------------------------------------------- + function lambda_f2(dpsi) result(gg) + real(kind = dbl8), intent(in) :: dpsi + real(kind = dbl8) :: gg, gsprime + gsprime = calc_gsprime_from_dpsi(dpsi, lambda_psi_soil, lambda_par_plant, lambda_par_env) + gg = (-2*dpsi*lambda_y + (lambda_ca + 2*lambda_gstar)*gsprime) + end + + function lambda_f1(dpsi) result(J) + real(kind = dbl8), intent(in) :: dpsi + real(kind = dbl8) :: J, gs, x, Q, gsprime + Q = calc_sapflux(dpsi, lambda_psi_soil, lambda_par_plant, lambda_par_env); + gs = calc_gs_from_Q(Q, lambda_psi_soil, lambda_par_plant, lambda_par_env); + gsprime = calc_gsprime(dpsi, gs, lambda_psi_soil, lambda_par_plant, lambda_par_env); + x = calc_x_from_dpsi(dpsi,gsprime, lambda_par_photosynth, lambda_par_cost); + J = calc_J(gs, x, lambda_par_photosynth)-4.0d0*lambda_par_photosynth%phi0*lambda_par_photosynth%Iabs; + end + + function calc_dpsi_bound(psi_soil, par_plant, par_env, par_photosynth, par_cost) result(bounds) real (kind = dbl8), intent(in) :: psi_soil type(par_plant_type), intent(in) :: par_plant @@ -1217,11 +1258,22 @@ function calc_dpsi_bound(psi_soil, par_plant, par_env, par_photosynth, par_cost) del = b*b-4*a*c; appo2 = (-b-sqrt(del))/(2*a) - ex = zero(0.0d0, 10.0d0, f2, 1d-6) + + ! set temporary values used by lambda_f1 and lambda_f2 + lambda_psi_soil = psi_soil + lambda_par_plant = par_plant + lambda_par_env = par_env + lambda_par_photosynth = par_photosynth + lambda_par_cost = par_cost + lambda_y = y + lambda_ca = ca + lambda_gstar = gstar + + ex = zero(0.0d0, 10.0d0, lambda_f2, 1d-6) use_bound = ex - iabsb = zero(use_bound*0.001, use_bound*0.99, f1, 1D-6); + iabsb = zero(use_bound*0.001, use_bound*0.99, lambda_f1, 1D-6); ! If using PM, find max dpsi from max possible transpiration if (par_env%et_method == T_PM) then @@ -1234,24 +1286,24 @@ function calc_dpsi_bound(psi_soil, par_plant, par_env, par_photosynth, par_cost) bounds = dpsi_bounds_type(ex, appo2, iabsb) - contains - - function f2(dpsi) result(gg) - real(kind = dbl8), intent(in) :: dpsi - real(kind = dbl8) :: gg, gsprime - gsprime = calc_gsprime_from_dpsi(dpsi, psi_soil, par_plant, par_env) - gg = (-2*dpsi*y + (ca + 2*gstar)*gsprime) - end - - function f1(dpsi) result(J) - real(kind = dbl8), intent(in) :: dpsi - real(kind = dbl8) :: J, gs, x, Q, gsprime - Q = calc_sapflux(dpsi, psi_soil, par_plant, par_env); - gs = calc_gs_from_Q(Q, psi_soil, par_plant, par_env); - gsprime = calc_gsprime(dpsi, gs, psi_soil, par_plant, par_env); - x = calc_x_from_dpsi(dpsi,gsprime, par_photosynth, par_cost); - J = calc_J(gs, x, par_photosynth)-4.0d0*par_photosynth%phi0*par_photosynth%Iabs; - end + ! contains + ! + ! function f2(dpsi) result(gg) + ! real(kind = dbl8), intent(in) :: dpsi + ! real(kind = dbl8) :: gg, gsprime + ! gsprime = calc_gsprime_from_dpsi(dpsi, psi_soil, par_plant, par_env) + ! gg = (-2*dpsi*y + (ca + 2*gstar)*gsprime) + ! end + ! + ! function f1(dpsi) result(J) + ! real(kind = dbl8), intent(in) :: dpsi + ! real(kind = dbl8) :: J, gs, x, Q, gsprime + ! Q = calc_sapflux(dpsi, psi_soil, par_plant, par_env); + ! gs = calc_gs_from_Q(Q, psi_soil, par_plant, par_env); + ! gsprime = calc_gsprime(dpsi, gs, psi_soil, par_plant, par_env); + ! x = calc_x_from_dpsi(dpsi,gsprime, par_photosynth, par_cost); + ! J = calc_J(gs, x, par_photosynth)-4.0d0*par_photosynth%phi0*par_photosynth%Iabs; + ! end end @@ -1321,6 +1373,15 @@ end function calc_dpsi_bound_inst ! ------------------------------------------------------------- ! Functions: Phydro main !-------------------------------------------------------------- + function lambda_profit_fun(dpsi) result(profit) + real(kind = dbl8), intent(in) :: dpsi + real(kind = dbl8) :: profit + type(dfdx_type) :: dfdx_res + dfdx_res = dFdx(dpsi, dble(lambda_psi_soil), lambda_par_plant, lambda_par_env, & + lambda_par_photosynth, lambda_par_cost) + profit = dfdx_res%dPdx + end + function phydro_analytical(tc, tg, ppfd, netrad, vpd, co2, pa, fapar, kphio, psi_soil, rdark, vwind, & par_plant, par_cost, par_control) result(res) real(kind=dbl8), intent(in) :: tc, tg, ppfd, netrad, vpd, co2, pa, fapar, kphio, psi_soil, rdark, vwind @@ -1344,7 +1405,14 @@ function phydro_analytical(tc, tg, ppfd, netrad, vpd, co2, pa, fapar, kphio, psi par_env%et_method = par_control%et_method bounds = calc_dpsi_bound(dble(psi_soil), par_plant, par_env, par_photosynth, par_cost) - dpsi_opt = zero(bounds%Iabs_bound * 0.001, bounds%Iabs_bound * 0.999, profit_fun, 1.0d-6) + + lambda_psi_soil = psi_soil + lambda_par_plant = par_plant + lambda_par_env = par_env + lambda_par_photosynth = par_photosynth + lambda_par_cost = par_cost + + dpsi_opt = zero(bounds%Iabs_bound * 0.001, bounds%Iabs_bound * 0.999, lambda_profit_fun, 1.0d-6) e = calc_sapflux(dpsi_opt, dble(psi_soil), par_plant, par_env) gs = calc_gs_from_Q(e, dble(psi_soil), par_plant, par_env) @@ -1378,18 +1446,26 @@ function phydro_analytical(tc, tg, ppfd, netrad, vpd, co2, pa, fapar, kphio, psi res%le = e * 0.018015d0 * par_env%lv res%le_s_wet = (1.0d0 - fapar) * netrad * (par_env%epsilon / (1.0d0 + par_env%epsilon)) - contains + ! contains - function profit_fun(dpsi) - real(kind = dbl8), intent(in) :: dpsi - real(kind = dbl8) :: profit_fun - type(dfdx_type) :: dfdx_res - dfdx_res = dFdx(dpsi, dble(psi_soil), par_plant, par_env, par_photosynth, par_cost) - profit_fun = dfdx_res%dPdx - end + ! function profit_fun(dpsi) + ! real(kind = dbl8), intent(in) :: dpsi + ! real(kind = dbl8) :: profit_fun + ! type(dfdx_type) :: dfdx_res + ! dfdx_res = dFdx(dpsi, dble(psi_soil), par_plant, par_env, par_photosynth, par_cost) + ! profit_fun = dfdx_res%dPdx + ! end end function phydro_analytical + + function lambda_profit_fun_inst(dpsi) result(profit) + real(kind = dbl8), intent(in) :: dpsi + real(kind = dbl8) :: profit + profit = calc_dP_ddpsi(dpsi, lambda_vcmax, lambda_jmax, lambda_psi_soil, & + lambda_par_plant, lambda_par_env, lambda_par_photosynth, lambda_par_cost) + end + function phydro_instantaneous_analytical(vcmax25, jmax25, tc, tg, ppfd, netrad, vpd, co2, pa, & fapar, kphio, psi_soil, rdark, vwind, par_plant, par_cost, par_control) result(res) real(kind=dbl8), intent(in) :: vcmax25, jmax25, tc, tg, ppfd, netrad, vpd, co2, pa, fapar, kphio, psi_soil, rdark, vwind @@ -1419,7 +1495,17 @@ function phydro_instantaneous_analytical(vcmax25, jmax25, tc, tg, ppfd, netrad, jmax = jmax25 * par_photosynth%fT_jmax bound = calc_dpsi_bound_inst(psi_soil, par_plant, par_env, par_photosynth, par_cost) - dpsi_opt = zero(0.0d0, 0.99d0 * bound, profit_fun_inst, 1.0d-6) + + lambda_vcmax = vcmax + lambda_jmax = jmax + lambda_psi_soil = psi_soil + lambda_par_plant = par_plant + lambda_par_env = par_env + lambda_par_photosynth = par_photosynth + lambda_par_cost = par_cost + + dpsi_opt = zero(0.0d0, 0.99d0 * bound, lambda_profit_fun_inst, 1.0d-6) + if (dpsi_opt .ne. dpsi_opt) print *, "Dspi_opt is NaN", dpsi_opt if (dpsi_opt-1 .eq. dpsi_opt) print *, "Dspi_opt is Inf", dpsi_opt @@ -1462,13 +1548,13 @@ function phydro_instantaneous_analytical(vcmax25, jmax25, tc, tg, ppfd, netrad, res%le = e * 0.018015d0 * par_env%lv res%le_s_wet = (1.0d0 - fapar) * netrad * (par_env%epsilon / (1.0d0 + par_env%epsilon)) - contains + ! contains - function profit_fun_inst(dpsi) - real(kind = dbl8), intent(in) :: dpsi - real(kind = dbl8) :: profit_fun_inst - profit_fun_inst = calc_dP_ddpsi(dpsi, vcmax, jmax, psi_soil, par_plant, par_env, par_photosynth, par_cost) - end + ! function profit_fun_inst(dpsi) + ! real(kind = dbl8), intent(in) :: dpsi + ! real(kind = dbl8) :: profit_fun_inst + ! profit_fun_inst = calc_dP_ddpsi(dpsi, vcmax, jmax, psi_soil, par_plant, par_env, par_photosynth, par_cost) + ! end end function phydro_instantaneous_analytical From 55c4dfaae731fce7b11fa1b90cf0d83eba70cb2f Mon Sep 17 00:00:00 2001 From: Jaideep Date: Wed, 14 Aug 2024 00:21:22 +0200 Subject: [PATCH 054/112] delete unnecessary files from vignettes; use default settings with warning --- R/cost_likelihood_pmodel.R | 9 +- R/run_pmodel_f_bysite.R | 13 + vignettes/estimated_params_fluxnet.csv | 10 - vignettes/p50_data_generation.R | 26 - vignettes/phydro_long_calibration.R | 416 ---------- vignettes/phydro_long_calibration_multi.R | 200 ----- vignettes/plot_params_dependencies.R | 78 -- vignettes/pmodel_use.Rmd | 1 + vignettes/read_meta_fdk.R | 78 -- vignettes/rsofun_phydro.Rmd | 767 ------------------- vignettes/rsofun_phydro_calibration.Rmd | 396 ---------- vignettes/rsofun_phydro_calibration_full.Rmd | 292 ------- vignettes/rsofun_phydro_data_generation.R | 574 -------------- vignettes/rsofun_phydro_data_generation.Rmd | 455 ----------- vignettes/rsofun_phydro_output_analysis.R | 356 --------- vignettes/rsofun_pmodel_calibration_full.Rmd | 269 ------- vignettes/rsofun_prepare_site_list.R | 54 -- vignettes/run_bayesian_phydro.sh | 37 - vignettes/run_rsofun_ubelix.sh | 2 - vignettes/sensitivity_analysis.Rmd | 4 + vignettes/simple_test_script_phydro.R | 92 --- vignettes/simple_test_script_pmodel.R | 36 - vignettes/site_list.txt | 244 ------ vignettes/site_list_2.txt | 174 ----- 24 files changed, 25 insertions(+), 4558 deletions(-) delete mode 100644 vignettes/estimated_params_fluxnet.csv delete mode 100644 vignettes/p50_data_generation.R delete mode 100644 vignettes/phydro_long_calibration.R delete mode 100644 vignettes/phydro_long_calibration_multi.R delete mode 100644 vignettes/plot_params_dependencies.R delete mode 100644 vignettes/read_meta_fdk.R delete mode 100644 vignettes/rsofun_phydro.Rmd delete mode 100644 vignettes/rsofun_phydro_calibration.Rmd delete mode 100644 vignettes/rsofun_phydro_calibration_full.Rmd delete mode 100644 vignettes/rsofun_phydro_data_generation.R delete mode 100644 vignettes/rsofun_phydro_data_generation.Rmd delete mode 100644 vignettes/rsofun_phydro_output_analysis.R delete mode 100644 vignettes/rsofun_pmodel_calibration_full.Rmd delete mode 100644 vignettes/rsofun_prepare_site_list.R delete mode 100755 vignettes/run_bayesian_phydro.sh delete mode 100644 vignettes/run_rsofun_ubelix.sh delete mode 100644 vignettes/simple_test_script_phydro.R delete mode 100644 vignettes/simple_test_script_pmodel.R delete mode 100644 vignettes/site_list.txt delete mode 100644 vignettes/site_list_2.txt diff --git a/R/cost_likelihood_pmodel.R b/R/cost_likelihood_pmodel.R index d33515ac..cceb58e7 100644 --- a/R/cost_likelihood_pmodel.R +++ b/R/cost_likelihood_pmodel.R @@ -83,8 +83,13 @@ cost_likelihood_pmodel <- function( # predefine variables for CRAN check compliance sitename <- data <- gpp_mod <- NULL - using_phydro = drivers$params_siml[[1]]$use_phydro - + if (!("use_phydro" %in% colnames(drivers$params_siml[[1]]))){ + warning("Parameter use_phydro not set. Assuming FALSE") + using_phydro = FALSE + } else { + using_phydro = drivers$params_siml[[1]]$use_phydro + } + # FIXME Jaideep: Instead of checking the number of params, # it might be better to check for presence of each param in par and par_fixed ## check input parameters diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 6b50f5e4..ecd6bc3f 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -275,6 +275,19 @@ run_pmodel_f_bysite <- function( continue <- FALSE } + if (!("use_gs" %in% colnames(params_siml))){ + warning("Parameter use_gs not set. Assuming FALSE") + params_siml$use_gs = FALSE + } + if (!("use_phydro" %in% colnames(params_siml))){ + warning("Parameter use_phydro not set. Assuming FALSE") + params_siml$use_phydro = FALSE + } + if (!("use_pml" %in% colnames(params_siml))){ + warning("Parameter use_pml not set. Assuming FALSE") + params_siml$use_pml = FALSE + } + # parameters to check check_param <- c( "spinup", diff --git a/vignettes/estimated_params_fluxnet.csv b/vignettes/estimated_params_fluxnet.csv deleted file mode 100644 index b865b749..00000000 --- a/vignettes/estimated_params_fluxnet.csv +++ /dev/null @@ -1,10 +0,0 @@ -site,kphio,phydro_K_plant,phydro_p50_plant,phydro_alpha,phydro_gamma,bsoil,whc,err_gpp,err_le -CH-Dav,0.04028348,2.197187E-016,-2.697608,0.149728,1.213812,4.275699,533.0647,1.768076,4391478 -DE-Hai,0.0451569,2.306965E-016,-0.1405669,0.1079709,1.248463,1.60833,389.3951,2.270135,1796700 -DE-Tha,0.0604514,1.323744E-016,-0.130135,0.1481494,0.543913,2.200784,569.8994,2.601718,1328134 -FR-LBr,0.04838459,4.096511E-017,-1.355358,0.09595568,1.254523,4.375119,685.3614,1.620645,1686883 -FR-Pue,0.05281481,3.163132E-017,-3.152014,0.14985,1.247866,3.520559,939.9299,1.581259,1816535 -GF-Guy,0.07013438,1.421218E-016,-2.506519,0.1334564,1.889438,3.193891,781.9509,2.257923,1669069 -NL-Loo,0.04021309,1.02998E-016,-3.92368,0.07105071,1.413388,3.70679,130.3826,2.009933,2724909 -US-Me2,0.05083192,2.031839E-016,-0.9074037,0.1423571,1.578149,3.583641,881.5482,3.991486,2527194 -US-Ton,0.08540476,2.483941E-017,-0.1034278,0.09509663,0.5219935,2.314377,237.3775,3.209992,1688764 diff --git a/vignettes/p50_data_generation.R b/vignettes/p50_data_generation.R deleted file mode 100644 index 5011e22b..00000000 --- a/vignettes/p50_data_generation.R +++ /dev/null @@ -1,26 +0,0 @@ -library(tidyverse) - -dat = read.csv("/home/jjoshi/Downloads/try_hydraulics_tidy.csv") %>% select(-X) -categ = read.csv("/home/jjoshi/Downloads/TRY_Categorical_Traits_Lookup_Table_2012_03_17_TestRelease.csv", na.strings = c("", "NA")) %>% select(-X) - -a = dat %>% left_join(categ) - -b = a %>% - rename(P50Xylem = `Xylem.water.potential.at.which.50..of.conductivity.is.lost..P50...MPa...2099.`) %>% - select(PlantGrowthForm, LeafType, LeafPhenology, PhotosyntheticPathway, AccSpeciesID, AccSpeciesName, P50Xylem) %>% - filter(!is.na(P50Xylem)) - -b %>% drop_na %>% filter(PhotosyntheticPathway == "C4") %>% View() - -b %>% - drop_na %>% - group_by(PlantGrowthForm, LeafType, LeafPhenology, PhotosyntheticPathway) %>% - summarize(P50_mean = mean(P50Xylem), - P50_sd = sd(P50Xylem), - P50_count = length(P50Xylem)) %>% - filter(LeafType != "scale-shaped" & - LeafPhenology != "deciduous/evergreen" & - PhotosyntheticPathway != "C3/CAM" & - P50_count > 2 - ) %>% - write.csv("~/codes/rsofun/vignettes/ancillary_data/P50X2.csv", row.names = F) diff --git a/vignettes/phydro_long_calibration.R b/vignettes/phydro_long_calibration.R deleted file mode 100644 index 5aaa6279..00000000 --- a/vignettes/phydro_long_calibration.R +++ /dev/null @@ -1,416 +0,0 @@ -rm(list=ls()) -library(tidyverse) -library(reshape2) -library(rsofun) -library(BayesianTools) -library(tictoc) -library(ncdf4) - -plot_only = F -debug = F - -tic("phydro") - -## Generate data if not already available - -args = commandArgs(trailingOnly=TRUE) - -#site <- "GF-Guy" -if (length(args)==0) { - #stop("At least one argument must be supplied: site name", call.=FALSE) - site = "AU-ASM" -}else{ - site = args[1] -} - -if (length(args)<2) { - root_data_dir = "~/Downloads/fluxdatakit_oct3" -}else{ - root_data_dir = args[2] -} - -if (length(args)<3) { - out_dir = "~/Downloads/fluxdatakit_oct3/phydro_output_test/" -}else{ - out_dir = paste0(args[3],"/") -} - - -data_dir = paste0(root_data_dir, "/phydro_drivers/") -figures_dir = paste0(out_dir, "/figures/") - -dir.create(out_dir, showWarnings = F) -dir.create(figures_dir, showWarnings = F) - -load(paste0(data_dir,"/",site,"_p_hydro_drivers.rda")) -load(paste0(data_dir,"/",site,"_p_hydro_validation.rda")) - -p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% - rename(le=latenth) -# mutate(gpp = gpp*86400/1e6*12) %>% # convert to [gC m-2 day-1] -# mutate(latenth = latenth*86400) # convert [W m-2] to [J m-2 day-1] - -p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 -p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 - -p_hydro_drivers$params_siml[[1]]$use_phydro = T -p_hydro_drivers$params_siml[[1]]$use_pml = F - - -get_density <- function(x, y, ...) { - df = tibble(x=x, y=y) %>% drop_na - dens <- MASS::kde2d(df$x, df$y, ...) - ix <- findInterval(x, dens$x) - iy <- findInterval(y, dens$y) - ii <- cbind(ix, iy) - return(dens$z[ii]) -} - -plot_pmodel = function(output_p, out_filename_prefix=""){ - p1 = output_p$data[[1]] %>% select(date, gpp, le) %>% - melt("date") %>% - mutate(group="model") %>% - rbind(p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - # mutate(latenth = latenth*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date") %>% - mutate(group="obs")) %>% - ggplot(aes(y=value, x=as.Date(date))) + - geom_line(aes(group=group, col=group), alpha=0.7) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 2) - - p2 = output_p$data[[1]] %>% select(date, gpp, le) %>% - drop_na %>% - melt("date", value.name = "pred") %>% - left_join(p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - drop_na %>% - # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - # mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date", value.name = "obs")) %>% - group_by(variable) %>% - reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% - ggplot(aes(y=obs, x=pred, colour=density)) + - scale_color_viridis_c() + - geom_point(alpha=0.7) + - geom_abline(slope=1, intercept=0, col="red")+ - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 1)+ - labs(colour="Density") - - p3 = output_p$data[[1]] %>% - mutate(vcmax25 = vcmax25*1e6, # mol m-2 s-1 --> umol m-2 s-1 - le = le / 86400, # J m-2 day-1 --> W m-2 - le_soil = le_soil / 86400, # J m-2 day-1 --> W m-2 - psi_soil = psi_leaf+dpsi - ) %>% - select(date, gpp, vcmax25, le, le_soil, dpsi, psi_soil, psi_leaf, gs_accl) %>% - drop_na %>% - melt("date") %>% - group_by(variable) %>% - ggplot(aes(y=value, x=as.Date(date))) + - geom_line(alpha=0.5, col="cyan4") + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 2) - - if (out_filename_prefix == ""){ - print(p1) - print(p2) - print(p3) - } - else{ - cairo_pdf(filename = paste0(out_filename_prefix, "_timeseries.pdf"), height=5, width=7) - print(p1) - dev.off() - cairo_pdf(filename = paste0(out_filename_prefix, "_pred_vs_obs.pdf"), height=3.5, width=7) - print(p2) - dev.off() - cairo_pdf(filename = paste0(out_filename_prefix, "_all_predictions.pdf"), height=3.5, width=7) - print(p3) - dev.off() - - } - -} - -params_modl <- list( - kphio = 7.013438e-02, # 0.089, # 0.11, #0.04998, - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - # soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - # soilm_betao = 0.0, - # beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - phydro_K_plant = 1.421218e-16 , #0.3e-16, - phydro_p50_plant = -2.506519e+00, #-1, - phydro_b_plant = 1, - phydro_alpha = 1.334564e-01, #0.1, - phydro_gamma = 1.889438e+00, #1, - bsoil = 3, #3, - Ssoil = 3.193891e+02, #3, - whc = 7.819509e+02 #200 -) - -output_p <- rsofun::runread_pmodel_f( - p_hydro_drivers, - par = params_modl -) -plot_pmodel(output_p) - - -## P-hydro full calibration (BayesianTools) -# nc = nc_open("~/Downloads/cwdx80.nc") -# lons = ncvar_get(nc, "lon") -# lats = ncvar_get(nc, "lat") -# S80 = ncvar_get(nc, "cwdx80") -# -# site_lon = p_hydro_drivers$site_info[[1]]$lon -# site_lat = p_hydro_drivers$site_info[[1]]$lat -# -# lonid = which(lons > site_lon)[1]-1 -# latid = which(lats > site_lat)[1]-1 -# n = 1 -# S80_slice = S80[(lonid-n):(lonid+n), (latid-n):(latid+n)] -# whc_site = mean(as.numeric(S80_slice, na.rm=T)) -# whc_site_sd = sd(as.numeric(S80_slice, na.rm=T)) - -whc_site = p_hydro_drivers$site_info[[1]]$whc -whc_site_sd = p_hydro_drivers$site_info[[1]]$whc_sd - -# image(x=lons, y=lats, z=log(1+S80), col = scales::viridis_pal()(100)) -# points(x=site_lon, y=site_lat, pch=20, col="red") - -p_hydro_drivers$params_siml[[1]]$use_phydro = T -p_hydro_drivers$params_siml[[1]]$use_pml = F - -parjj = read.csv("ancillary_data/fitted_params_Joshi_et_al_2022.csv") -parjj = parjj %>% mutate(K.scalar = K.scalar*1e-16) -pars_joshi2022 = parjj %>% - filter(Species != "Helianthus annuus") %>% - select(K.scalar, P50, alpha, gamma, A.G, Species) %>% - filter(A.G != "") %>% - # mutate(A.G = case_match(A.G, - # "M. Angiosperm" ~ "Angiosperm", - # .default = A.G)) %>% - pivot_longer(cols=c("K.scalar", "P50", "alpha", "gamma")) %>% - group_by(A.G, name) %>% - summarize(mean=mean(value), sd=sd(value), n=length(value)) - - -igbp = p_hydro_drivers$site_info[[1]]$IGBP_veg_short # "WSA" -type = igbp %>% - case_match(c("WSA", "EBF", "DBF") ~ "Angiosperm", - c("ENF") ~ "Gymnosperm", - c("OSH", "CSH") ~ "Shrub", - c("GRA", "CRO", "SAV") ~ "Shrub", - c("MF", "WET") ~ "Angiosperm") - -message("type = ", igbp, " --> " ,type, "\n") - -gamma_mean = pars_joshi2022 %>% - filter(A.G == type & name == "gamma") %>% - pull(mean) - -gamma_sd = pars_joshi2022 %>% - filter(A.G == type & name == "gamma") %>% - pull(sd) %>% c(0.1) %>% max() - - -print(c(gamma_mean, gamma_sd)) - -p50x_data = read.csv("ancillary_data/P50X2.csv") - -p50x_site = p50x_data %>% filter(IGBP_Guessed == igbp) -print(p50x_site) -if (nrow(p50x_site > 0)){ - p50xmean = mean(p50x_site$P50_mean) - p50xsd = mean(p50x_site$P50_sd) -} else { - p50xmean = -3 - p50xsd = 1 -} - -uniform_range = function(lower, upper){ - list(lower= lower, upper=upper, mean = (upper+lower)/2, sd = (upper-lower)*10) -} - -gaussian_range = function(mean, sd){ - if (mean > 0) list(lower= max(mean-5*sd, 0), upper=mean+5*sd, mean = mean, sd = sd) - else list(lower= mean-5*sd, upper=min(mean+5*sd, 0), mean = mean, sd = sd) -} - -# par = list( -# kphio = list(lower=0.04, upper=0.09, init=0.05), -# phydro_K_plant = list(lower=0.05e-16, upper=0.3e-16, init=0.15e-16), -# phydro_p50_plant = list(lower=-3, upper=-0.5, init=-1), -# phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), -# phydro_gamma = list(lower=0.5, upper=1.5, init=1), -# bsoil = list(lower=0.1, upper=10, init=3), -# Ssoil = list(lower=0.1, upper=1000, init=10), -# whc = list(lower=10, upper=5000, init=1000), -# err_gpp = list(lower = 0.01, upper = 4, init = 2), -# err_le = list(lower = 0.1e6, upper = 10e6, init = 2e6) -# ), - -pars_calib = list( - kphio = uniform_range(lower=0.005, upper=0.09), - # phydro_K_plant = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="K.scalar") %>% pull(mean), - # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="K.scalar") %>% pull(sd)), - # phydro_p50_plant = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="P50") %>% pull(mean), - # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="P50") %>% pull(sd)), - phydro_K_plant = uniform_range(lower=0.1e-16, 5e-16), - # phydro_p50_plant = uniform_range(lower=-4, -0.3), - phydro_p50_plant = gaussian_range(mean = p50xmean/3, sd = p50xsd/3), - # phydro_alpha = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="alpha") %>% pull(mean), - # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="alpha") %>% pull(sd)), - # phydro_gamma = gaussian_range(mean = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(mean), - # sd = pars_joshi2022 %>% filter(A.G=="Gymnosperm", name=="gamma") %>% pull(sd)), - # phydro_alpha = gaussian_range(mean = 0.1, sd = 0.005), - # phydro_gamma = uniform_range(lower = 0.1, upper = 2), - phydro_gamma = gaussian_range(mean = gamma_mean, sd = gamma_sd), - #bsoil = uniform_range(lower=0.1, upper=10), - #Ssoil = uniform_range(lower = 0, upper = whc_site+whc_site_sd), - #whc = gaussian_range(mean = whc_site, sd = whc_site_sd), - whc = uniform_range(lower = 115, upper = 1116), - err_gpp = uniform_range(lower = 0.01, upper = 4), - err_le = uniform_range(lower = 0.1e6, upper = 10e6) -) - -pars_fixed = list( # fix all other parameters - # kphio = 0.045, - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - # phydro_K_plant = 0.3e-16, - # phydro_P50_plant = -1, - phydro_b_plant = 1, - phydro_alpha = 0.08, - # phydro_gamma = 1 - bsoil = 3, - Ssoil = 113.313 # 1%ile of cwdx80_forcing - # whc = 90 -) - -# Define calibration settings and parameter ranges from previous work -settings_bayes <- list( - method = "BayesianTools", - par = pars_calib, - metric = rsofun::cost_likelihood_phydro, - control = list( - sampler = "DEzs", - settings = list( - nrChains = ifelse(debug, yes = 1, no = 3 ), - burnin = ifelse(debug, yes = 300, no = 10000), - iterations = ifelse(debug, yes = 1200, no = 50000) # kept artificially low - ) - ) -) - -file_prefix = paste0(out_dir, site, "_nchains_", - settings_bayes$control$settings$nrChains, - "_nsteps_", - settings_bayes$control$settings$iterations, - "_burnin_", - settings_bayes$control$settings$burnin) - -calib_file =paste0(file_prefix, "_mcmc_output.rda") - -if (!plot_only){ - # Calibrate the model and optimize the free parameters using - # demo datasets - message("Begin calibration...") - pars_calib_bayes <- calib_sofun( - # calib_sofun arguments: - drivers = p_hydro_drivers, - obs = p_hydro_validation, - settings = settings_bayes, - # extra arguments passed to the cost function: - par_fixed = pars_fixed, - targets = c("gpp", "le") # define target variable GPP - ) - - save(pars_calib_bayes, file = calib_file) - -} else{ - load(calib_file) -} - -fig_file_prefix = paste0(figures_dir, site, "_", igbp, "_nchains_", - settings_bayes$control$settings$nrChains, - "_nsteps_", - settings_bayes$control$settings$iterations, - "_burnin_", - settings_bayes$control$settings$burnin) - -cairo_pdf(filename = paste0(fig_file_prefix, "_trace_plot.pdf"), width = 7, height = 9) -# plot(pars_calib_bayes$mod) -codachain = getSample(pars_calib_bayes$mod, coda = T, thin = "auto") -par(mfrow = c(4,3), mar=c(2,3,2,1), oma=c(1,1,1,1)) -plot(codachain, density = F, auto.layout = F, smooth = T, xlab="") -dev.off() - -cairo_pdf(filename = paste0(fig_file_prefix, "_correlation_plot.pdf")) -correlationPlot(pars_calib_bayes$mod) -dev.off() - -cairo_pdf(filename = paste0(fig_file_prefix, "_marginal_plot.pdf")) -marginalPlot(pars_calib_bayes$mod) -dev.off() - -pars_calib_bayes$par %>% - t() %>% as.data.frame() %>% - mutate(site = site) %>% - write.csv(file = paste0(file_prefix, "_MAP.csv"), row.names = F) - -params_modl_opt = c(pars_calib_bayes$par, pars_fixed) - -if(params_modl_opt %>% names() %>% duplicated %>% any()){ - message("Duplicated entries in optimal params") - stop() -} - -output_p_opt <- rsofun::runread_pmodel_f( - p_hydro_drivers, - par = params_modl_opt -) -plot_pmodel(output_p_opt) - -output_p_opt$data[[1]] = - p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - rename(gpp_obs = gpp, le_obs = le) %>% - right_join(output_p_opt$data[[1]]) - -save(output_p_opt, file=paste0(file_prefix, "_phydro_output.rda")) - -plot_pmodel(output_p_opt, out_filename_prefix = fig_file_prefix) - -# Calculate R2 and RMSE -output_p_opt$data[[1]] %>% select(date, gpp, le) %>% - drop_na %>% - melt("date", value.name = "pred") %>% - left_join(p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - drop_na %>% - # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - # mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date", value.name = "obs")) %>% - group_by(variable) %>% - summarize(r2 = cor(pred,obs)^2, - nrmse = sqrt(sum(pred-obs)^2/length(pred-obs))/mean(obs), - site = site) %>% - write.csv(file=paste0(file_prefix, "_r2_nrmse.csv"), row.names = F) - -toc() - - - diff --git a/vignettes/phydro_long_calibration_multi.R b/vignettes/phydro_long_calibration_multi.R deleted file mode 100644 index 46beffe0..00000000 --- a/vignettes/phydro_long_calibration_multi.R +++ /dev/null @@ -1,200 +0,0 @@ -rm(list=ls()) -library(tidyverse) -library(reshape2) -library(rsofun) -library(BayesianTools) -library(tictoc) -library(ncdf4) -library(scatterPlotMatrix) -library(ggpointdensity) - -plot_only = F -root_data_dir = "~/Downloads/fluxdatakit_oct3" -lsm_path = paste0(root_data_dir, "/FLUXDATAKIT_LSM/") -data_dir = paste0(root_data_dir, "/phydro_drivers/") - -# out_dir = "~/codes/rsofun/vignett/" -# figures_dir = paste0(out_dir, "/figures/") - - -read_obs = function(site){ - file_obs = tibble(files=list.files(data_dir, full.names = T)) %>% - filter(stringr::str_detect(files, site)) %>% - filter(stringr::str_detect(files, "p_hydro_validation.rda")) %>% - pull(files) - - load(file_obs) - - p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% - rename(le=latenth) - - p_hydro_validation -} - -read_drv = function(site){ - file_obs = tibble(files=list.files(data_dir, full.names = T)) %>% - filter(stringr::str_detect(files, site)) %>% - filter(stringr::str_detect(files, "p_hydro_drivers.rda")) %>% - pull(files) - - load(file_obs) - - p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 - p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 - - p_hydro_drivers$params_siml[[1]]$use_phydro = T - p_hydro_drivers$params_siml[[1]]$use_pml = F - - p_hydro_drivers -} - -sites_phydro = c("FR-LBr", "FI-Hyy", "CH-Dav") - -phydro_validation = sites_phydro %>% - map_df(~read_obs(.)) - -phydro_drivers = sites_phydro %>% - map_df(~read_drv(.)) - - -params_modl <- list( - kphio = 0.045, # 0.089, # 0.11, #0.04998, - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - # soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - # soilm_betao = 0.0, - # beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - phydro_K_plant = 1.421218e-16 , #0.3e-16, - phydro_p50_plant = -2.506519e+00, #-1, - phydro_b_plant = 1, - phydro_alpha = 1.334564e-01, #0.1, - phydro_gamma = 1.889438e+00, #1, - bsoil = 3, #3, - Ssoil = 3.193891e+02, #3, - whc = 7.819509e+02 #200 -) - -output_p <- rsofun::runread_pmodel_f( - phydro_drivers, - par = params_modl -) - -get_density <- function(x, y, ...) { - df = tibble(x=x, y=y) %>% drop_na - dens <- MASS::kde2d(df$x, df$y, ...) - ix <- findInterval(x, dens$x) - iy <- findInterval(y, dens$y) - ii <- cbind(ix, iy) - return(dens$z[ii]) -} - -plot_p = function(output_p){ - for(i in 1:nrow(output_p)){ - print( - output_p$data[[i]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="pred") %>% - rbind( - phydro_validation$data[[i]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="obs") - ) %>% - pivot_wider(names_from = type, values_from = value) %>% - group_by(name) %>% - reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% - ggplot(aes(y=obs, x=pred, colour=density)) + - scale_color_viridis_c() + - geom_point(alpha=0.7) + - geom_abline(slope=1, intercept=0, col="red")+ - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~name, scales = "free", nrow = 1)+ - labs(colour="Density") - ) - } -} - -### - -uniform_range = function(lower, upper){ - list(lower= lower, upper=upper, mean = (upper+lower)/2, sd = (upper-lower)*10) -} - -gaussian_range = function(mean, sd){ - if (mean > 0) list(lower= max(mean-5*sd, 0), upper=mean+5*sd, mean = mean, sd = sd) - else list(lower= mean-5*sd, upper=min(mean+5*sd, 0), mean = mean, sd = sd) -} - -pars_calib = list( - kphio = uniform_range(lower=0.005, upper=0.09), - phydro_K_plant = uniform_range(lower=0.1e-16, 1e-16), - phydro_p50_plant = gaussian_range(mean = -1, sd = 0.2), - # phydro_alpha = gaussian_range(mean = 0.1, sd = 0.005), - phydro_gamma = gaussian_range(mean = 0.5, sd = 0.3), - #bsoil = uniform_range(lower=0.1, upper=10), - # Ssoil = uniform_range(lower = 0, upper = whc_site+whc_site_sd), - whc = gaussian_range(mean = 300, sd = 50), - err_gpp = uniform_range(lower = 0.01, upper = 4), - err_le = uniform_range(lower = 0.1e6, upper = 10e6) -) - -pars_fixed = list( # fix all other parameters - # kphio = 0.045, - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - # phydro_K_plant = 0.3e-16, - # phydro_P50_plant = -1, - phydro_b_plant = 1, - phydro_alpha = 0.08, - # phydro_gamma = 1 - bsoil = 3, - Ssoil = 113 - # whc = 90 -) - -debug = T - -# Define calibration settings and parameter ranges from previous work -settings_bayes <- list( - method = "BayesianTools", - par = pars_calib, - metric = rsofun::cost_likelihood_pmodel, - control = list( - sampler = "DEzs", - settings = list( - nrChains = ifelse(debug, yes = 1, no = 3 ), - burnin = ifelse(debug, yes = 30, no = 10000), - iterations = ifelse(debug, yes = 100, no = 50000) # kept artificially low - ) - ) -) - - -message("Begin calibration...") -pars_calib_bayes <- calib_sofun( - # calib_sofun arguments: - drivers = phydro_drivers, - obs = phydro_validation, - settings = settings_bayes, - # extra arguments passed to the cost function: - par_fixed = pars_fixed, - targets = c("gpp", "le") # define target variable GPP -) - - -params_modl_opt = c(pars_calib_bayes$par, pars_fixed) - -output_p_opt <- rsofun::runread_pmodel_f( - phydro_drivers, - par = params_modl_opt -) - -plot_p(output_p_opt) diff --git a/vignettes/plot_params_dependencies.R b/vignettes/plot_params_dependencies.R deleted file mode 100644 index 1426c806..00000000 --- a/vignettes/plot_params_dependencies.R +++ /dev/null @@ -1,78 +0,0 @@ -library(FluxDataKit) -library(tidyverse) -library(reshape2) - -dat = data.frame(site=NULL, meta=NULL) - -sites = c( - "CH-Dav", - "DE-Hai", - "DE-Tha", - "FR-LBr", - "FR-Pue", - "GF-Guy", - "NL-Loo", - "US-Me2", - "US-NR1", - "US-Ton" -) - -site = sites[1] - -lsm_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_LSM/" -csv_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_FLUXNET/" -out_path = "~/Downloads/fluxdatakit_oct3/Phydro_drivers/" - -files_csv = list.files(csv_path) -files_lsm = list.files(lsm_path) - -for (site in sites){ - - # Get filename for HH data for matching site - file_csv = files_csv[intersect(grep(site, files_csv), - grep("HH", files_csv))] - - # get metadata - # -------------------------------------------------------- - message("- reading Metadata for site") - meta <- suppressWarnings( - try( - fdk_convert_lsm( - site = site, - fluxnet_format = TRUE, - path = lsm_path, - meta_data = T - ) - ) - ) - - dat = rbind(dat, data.frame(site=site, meta=meta)) -} - -write.csv(dat, file = paste0(out_path, "/sites_meta.csv")) - - -dat = read.csv(file = paste0(out_path, "/sites_meta.csv"), header=T) -params = read.csv("~/codes/rsofun/vignettes/estimated_params_fluxnet.csv", header=T) - - -dat %>% - select(site, meta.IGBP_veg_short) %>% - left_join(params) %>% - select(-site) %>% - melt("meta.IGBP_veg_short") %>% - ggplot(aes(y=value, x=meta.IGBP_veg_short)) + - geom_point() + - facet_wrap("variable", scales = "free") - -dat %>% - select(site, meta.canopy_height) %>% - left_join(params) %>% - select(-site) %>% - melt("meta.canopy_height") %>% - drop_na %>% - ggplot(aes(y=value, x=meta.canopy_height)) + - geom_point() + - facet_wrap("variable", scales = "free") - - diff --git a/vignettes/pmodel_use.Rmd b/vignettes/pmodel_use.Rmd index 38e8c4f1..3a1dbf9e 100644 --- a/vignettes/pmodel_use.Rmd +++ b/vignettes/pmodel_use.Rmd @@ -40,6 +40,7 @@ p_model_drivers = p_model_drivers |> rowwise() |> mutate(params_siml = params_siml |> mutate(use_phydro = F, + use_gs = F, use_pml = F) |> list()) diff --git a/vignettes/read_meta_fdk.R b/vignettes/read_meta_fdk.R deleted file mode 100644 index 855b2854..00000000 --- a/vignettes/read_meta_fdk.R +++ /dev/null @@ -1,78 +0,0 @@ -read_meta_fdk = function (site, path, meta_data = FALSE, - out_path) -{ - IGBP_veg_long <- time <- TIMESTAMP_START <- TIMESTAMP_END <- P <- TA_F <- PA_F <- CO2_F <- P_F <- TA_F_MDS <- CO2_F_MDS <- NULL - files <- list.files(path, utils::glob2rx(paste0(site, "*.nc")), - full.names = TRUE, recursive = TRUE) - if (length(files) != 2) { - stop("Missing either flux or meteo data for the requested site") - } - if (meta_data) { - files <- files[grepl(utils::glob2rx("*_Flux.nc"), files)] - } - df <- lapply(files, function(file) { - nc <- ncdf4::nc_open(file) - time <- ncdf4::ncvar_get(nc, "time") - time_units <- strsplit(ncdf4::ncatt_get(nc, "time")$units, - "seconds since ")[[1]][2] - time_date <- lubridate::ymd_hms(time_units, tz = "GMT") + - lubridate::seconds(time) - vars <- names(nc$var) - df <- as.data.frame(lapply(vars, function(x) ncdf4::ncvar_get(nc, - x))) - ncdf4::nc_close(nc) - colnames(df) <- vars - df$time <- time_date - if ("IGBP_veg_short" %in% colnames(df)) { - df$IGBP_veg_short <- trimws(df$IGBP_veg_short) - - df$IGBP_veg_short = case_match(df$IGBP_veg_short, - "Woody Savannas" ~ "WSA", - "Savannas" ~ "SAV", - "Permanent Wetlands" ~ "WET", - "Open Shrublands" ~ "OSH", - "Closed Shrublands" ~ "CSH", - "Grasslands" ~ "GRA", - "Evergreen Needleleaf Forest" ~ "ENF", - "Evergreen Broadleaf Forest" ~ "EBF", - "Deciduous Broadleaf Forest" ~ "DBF", - "Mixed Forest" ~ "MF", - "Croplands" ~ "CRO", - "Cropland/Natural Vegetation Mosaic" ~ "MF", - "Urban and Built-Up" ~ NA, - .default = df$IGBP_veg_short) - } - else { - warning("Column 'IGBP_veg_short' does not exist in the data frame. Assigning NA.") - df$IGBP_veg_short <- NA - } - if ("IGBP_veg_long" %in% names(df)) { - df <- subset(df, select = -IGBP_veg_long) - } - if (meta_data) { - meta_columns <- c("latitude", "longitude", "reference_height", - "canopy_height", "elevation", "IGBP_veg_short", - "year_start", "year_end") - missing_columns <- setdiff(meta_columns, colnames(df)) - if (length(missing_columns) > 0) { - for (col in missing_columns) { - warning(paste("Column", col, "does not exist in the data frame. Assigning NA.")) - df[, col] <- NA - } - } - df$year_start <- format(min(df$time), "%Y") - df$year_end <- format(max(df$time), "%Y") - df <- df[1, meta_columns] - df$sitename <- site - } - return(df) - }) - if (meta_data) { - return(df) - } - else { - all <- suppressMessages(dplyr::left_join(df[[1]], df[[2]], - by = "time")) - } - -} diff --git a/vignettes/rsofun_phydro.Rmd b/vignettes/rsofun_phydro.Rmd deleted file mode 100644 index 9fcc9d98..00000000 --- a/vignettes/rsofun_phydro.Rmd +++ /dev/null @@ -1,767 +0,0 @@ ---- -title: "Rsofun with Phydro" -author: "Jaideep Joshi" -date: "2023-09-07" -output: html_document ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -```{r} -library(tidyverse) -library(reshape2) -``` - -## Generate data if not already available - -```{r} - - -``` - -```{r} -site <- "GF-Guy" -generate_data = F - -out_path = "~/Downloads/fluxdatakit_oct3/Phydro_drivers/" -``` - -```{r} -if (generate_data){ - -library(FluxDataKit) - -lsm_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_LSM/" -csv_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_FLUXNET/" - -files_csv = list.files(csv_path) -files_lsm = list.files(lsm_path) - -# Get filename for HH data for matching site -file_csv = files_csv[intersect(grep(site, files_csv), - grep("HH", files_csv))] - -# get metadata -# -------------------------------------------------------- -message("- reading Metadata for site") -meta <- suppressWarnings( - try( - fdk_convert_lsm( - site = site, - fluxnet_format = TRUE, - path = lsm_path, - meta_data = T - ) - ) - ) - - -# get half-hourly data -------------------------------------------------------- -# message("- convert to FLUXNET standard CSV file") -# hhdf <- suppressWarnings( -# try( -# fdk_convert_lsm( -# site = site, -# fluxnet_format = TRUE, -# path = "~/Downloads/flux_data_kit_beta/fluxes/" -# ) -# ) -# ) -# -# if(inherits(hhdf, "try-error")){ -# message("!!! conversion to FLUXNET failed !!!") -# return(NULL) -# } - -message("- reading FLUXNET format data") -hhdf <- readr::read_csv(paste0(csv_path,"/",file_csv)) - -# Add date and time columns to hhdf for easier further processing. -# --------------------------------------------------------- -hhdf = - hhdf |> - mutate(time = lubridate::as_datetime(as.character(TIMESTAMP_START), tz = "GMT", format="%Y%m%d%H%M")) |> - mutate(date = lubridate::as_date(time)) - -# Aggregate to daily 24-hr means ---------------------------------------------------------- -message("- downsampling FLUXNET format - 24 hr means") -ddf_24hr_mean <- - try( - hhdf |> - group_by(date) |> - select(-TIMESTAMP_START, -TIMESTAMP_END) |> - summarize_all(.funs = mean) - ) - -# Check aggregation -ddf_24hr_mean %>% - select(SW_IN_F_MDS, SW_OUT, LW_IN_F_MDS, LAI, FPAR, GPP_DT_VUT_REF, LE_F_MDS, TA_F_MDS, date) %>% - mutate(albedo = SW_OUT/SW_IN_F_MDS) %>% - melt("date") %>% - ggplot(aes(y=value, x=date)) + - geom_line(col="aquamarine4") + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") -``` - -```{r} -valid_years = read.csv(text = " - sitename , start_year , end_year \n - GF-Guy , 2004 , 2015 \n - FR-Pue , 2004 , 2020 \n - FI-Hyy , 2004 , 2020 \n - CH-Dav , 2004 , 2020 \n - ", - header=T, strip.white=T) - -# Get valid data years -ystart = valid_years %>% filter(sitename==site) %>% pull(start_year) -yend = valid_years %>% filter(sitename==site) %>% pull(end_year) - -# Aggregate around daily maximum ppfd for acclimating model -# --------------------------------------------------------- -test.3day = hhdf %>% filter(date >= as_date(paste0(floor((ystart+yend)/2),"-06-01")) & - date <= as_date(paste0(floor((ystart+yend)/2),"-06-03")) ) - -aggregate_daily_3hr_maxima = function(df){ - # Get the time at which SW_IN is maximum - maxppfd <- df %>% filter(SW_IN_F_MDS == max(SW_IN_F_MDS)) - max_t <- maxppfd$time[1] - - # Select times that lie in 3-hr interval around max_t - df_aroundmax <- df %>% filter(time < (max_t + 1.5*3600) & - time > (max_t - 1.5*3600) ) - - # take mean of selected entries - df_mean <- df_aroundmax |> - select(-TIMESTAMP_START, -TIMESTAMP_END) |> - summarize_all(.funs = mean) - - df_mean -} - -# Test aggregation -# ---------------- -test.3day.3hr = test.3day %>% group_by(date) %>% do(aggregate_daily_3hr_maxima(.)) %>% ungroup() - -test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% - melt("time") %>% - mutate(type="hourly") %>% - rbind(test.3day.3hr %>% - select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% - melt("time") %>% - mutate(type="daily") - ) %>% - ggplot(aes(y=value, x=as.POSIXct(time))) + - geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + - geom_point(data = . %>% filter(type == "daily")) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") -``` - -```{r} -# Apply 3hr maxima aggregation to all data -# ---------------------------------------- -message("- downsampling FLUXNET format - daily 3-hr means around max ppfd") -ddf_3hr_maxima <- hhdf |> - group_by(date) |> - do(aggregate_daily_3hr_maxima(.)) |> - ungroup() -``` - -```{r} -aggregate_daily_daylength = function(df){ - # Get the time at which SW_IN > 0 - pos_ppfd <- df %>% filter(SW_IN_F_MDS > 10) - tmax <- max(pos_ppfd$time) - tmin <- min(pos_ppfd$time) - - # Select times that lie in 3-hr interval around max_t - df_aroundmax <- df %>% filter(time <= tmax & - time >= tmin ) - - # take mean of selected entries - df_mean <- df_aroundmax |> - select(-TIMESTAMP_START, -TIMESTAMP_END) |> - summarize_all(.funs = mean) |> - mutate(daylength = difftime(tmax, tmin, units="hours") |> as.numeric()) - - df_mean -} - -# Test aggregation -# ---------------- -test.3day.daylen = test.3day %>% group_by(date) %>% do(aggregate_daily_daylength(.)) %>% ungroup() - -test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI) %>% - mutate(daylength = NA) %>% - melt("time") %>% - mutate(type="hourly") %>% - rbind(test.3day.daylen %>% - select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, daylength) %>% - melt("time") %>% - mutate(type="daily") - ) %>% - ggplot(aes(y=value, x=as.POSIXct(time))) + - geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + - geom_point(data = . %>% filter(type == "daily")) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") -``` - -```{r} -# Apply daytime mean aggregation to all data -# ------------------------------------------ -message("- downsampling FLUXNET format - daytime means") -ddf_daytime_mean <- hhdf |> - group_by(date) |> - do(aggregate_daily_daylength(.)) |> - ungroup() - -# Check daylength seasonality -ddf_daytime_mean %>% - ggplot(aes(y=daylength, x=date)) + - geom_line()+ - theme_classic() -``` - -```{r} -# Calculate daily tmax and tmin from hh data -# ------------------------------------------ -tmaxmin <- - hhdf |> - group_by(date) |> - summarize( - tmax = max(TA_F_MDS), - tmin = min(TA_F_MDS) - ) -``` - -```{r} -# Creating driver object ------------------------------------------------------ -message("- compiling drivers") -load("../data/p_model_drivers.rda") - -p_hydro_drivers <- p_model_drivers -p_hydro_drivers$sitename[[1]] = site -p_hydro_drivers$site_info[[1]] = - tibble( - lon=meta[[1]]$longitude, - lat=meta[[1]]$latitude, - elv = meta[[1]]$elevation, - canopy_height=meta[[1]]$canopy_height, - reference_height = meta[[1]]$reference_height - ) -kfFEC = 2.04 - -start_year = ystart -end_year = yend - -# for demo, use just a subset of years -p_hydro_drivers$forcing <- - ddf_24hr_mean |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - left_join(tmaxmin) |> - group_by(date) |> - summarize( - date = date, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = P_F * 48 /(60 * 60 * 24), # P_F [mm timestep-1] * 48 [timesteps day-1] / 86400 [secs day-1 ] - tmin = tmin, # TMIN_F_MDS, - tmax = tmax, # TMAX_F_MDS, - fapar = FPAR, - co2 = CO2_F_MDS, - ccov = 0 - ) |> - list() - -p_hydro_drivers$forcing_acclim <- - ddf_3hr_maxima |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - left_join(tmaxmin) |> - group_by(date) |> - summarize( - date = date, - time = time, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = NA, # P_F * 48 / (60 * 60 * 24), - tmin = tmin, # TMIN_F_MDS, - tmax = tmax, # TMAX_F_MDS, - fapar = FPAR, - co2 = CO2_F_MDS, - ccov = 0 - ) |> - list() - -p_hydro_drivers$forcing_daytime_mean <- - ddf_daytime_mean |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - left_join(tmaxmin) |> - group_by(date) |> - summarize( - date = date, - time = time, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = NA, # P_F * 48 / (60 * 60 * 24), - tmin = tmin, # TMIN_F_MDS, - tmax = tmax, # TMAX_F_MDS, - fapar = FPAR, - co2 = CO2_F_MDS, - ccov = 0, - daylength = daylength - ) |> - list() - - -p_hydro_drivers$forcing_halfhourly <- - hhdf |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(time) |> - summarize( - date = date, - time = time, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = P_F * 48 / (60 * 60 * 24), - fapar = FPAR, - co2 = CO2_F_MDS, - ccov = 0 - ) |> - list() - - -# write all drivers to file -# apply compression to minimize space -filn <- paste0(out_path, "/",site,"_p_hydro_drivers.rda") -message(paste0("- writing to file: ", filn)) -save(p_hydro_drivers, - file = filn - ) - - -# Write validation data -load("../data/p_model_validation.rda") - -p_hydro_validation <- p_model_validation -p_hydro_validation$sitename[[1]] = site - -p_hydro_validation$data <- - ddf_24hr_mean |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(date) |> - summarise( - date = date, - time = time, - gpp = GPP_DT_VUT_REF, - latenth = LE_F_MDS - ) |> - mutate(gpp = gpp*86400/1e6*12) |> # convert to [gC m-2 day-1] - mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] - list() - -p_hydro_validation$data_hh <- - hhdf |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(time) |> - summarise( - date = date, - time = time, - gpp = GPP_DT_VUT_REF, - latenth = LE_F_MDS - ) |> - mutate(gpp = gpp*86400/1e6*12) |> # convert to [gC m-2 day-1] - mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] - list() - -p_hydro_validation$data_3hr_mean <- - ddf_3hr_maxima |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(time) |> - summarise( - date = date, - time = time, - gpp = GPP_DT_VUT_REF, - latenth = LE_F_MDS - ) |> - mutate(gpp = gpp*86400/1e6*12) |> # convert to [gC m-2 day-1] - mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] - list() - -p_hydro_validation$data_daytime_mean <- - ddf_daytime_mean |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(time) |> - summarise( - date = date, - time = time, - gpp = GPP_DT_VUT_REF, - latenth = LE_F_MDS - ) |> - mutate(gpp = gpp*86400/1e6*12) |> # convert to [gC m-2 day-1] - mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] - list() - - -filn <- paste0(out_path,"/",site,"_p_hydro_validation.rda") -message(paste0("- writing to file: ", filn)) -save(p_hydro_validation, - file = filn - ) - - -} - -``` - -## Read Phydro drivers and check - -```{r} -# rm(list=ls()) - -load(paste0("../data/",site,"_p_hydro_drivers.rda")) -load(paste0("../data/",site,"_p_hydro_validation.rda")) - -p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 -p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 - -start_date = "2011-06-01" -end_date = "2011-06-06" - -# Check acclimation data against half-hourly -# --- -p_hydro_drivers$forcing_halfhourly[[1]] %>% -filter(date >= start_date & date <= end_date ) %>% - select(time, ppfd, netrad, temp, vpd, fapar) %>% - melt("time") %>% - mutate(type="hourly") %>% - rbind(p_hydro_drivers$forcing_acclim[[1]] %>% - filter(date >= start_date & date <= end_date ) %>% - select(time, ppfd, netrad, temp, vpd, fapar) %>% - melt("time") %>% - mutate(type="daily") - ) %>% - ggplot(aes(y=value, x=as.POSIXct(time))) + - geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + - geom_point(data = . %>% filter(type == "daily")) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") - - -# Check daily-3hr-maxima against daytime_means against 24 hr means -# --- -start_date = "2011-06-01" -end_date = "2015-06-03" - -p_hydro_drivers$forcing[[1]] %>% - filter(date >= start_date & date <= end_date ) %>% - select(date, ppfd, netrad, temp, vpd, fapar) %>% - melt("date") %>% - mutate(type="24-hr mean") %>% - rbind(p_hydro_drivers$forcing_acclim[[1]] %>% - filter(date >= start_date & date <= end_date ) %>% - select(date, ppfd, netrad, temp, vpd, fapar) %>% - melt("date") %>% - mutate(type="3-hr maxima") - ) %>% - rbind(p_hydro_drivers$forcing_daytime_mean[[1]] %>% - filter(date >= start_date & date <= end_date ) %>% - select(date, ppfd, netrad, temp, vpd, fapar) %>% - melt("date") %>% - mutate(type="daytime means") - ) %>% - ggplot(aes(y=value, x=date)) + - geom_line(aes(group=type, col=type), alpha=0.5) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") - - -start_date = "2011-06-01" -end_date = "2015-06-03" - -p_hydro_validation$data[[1]] %>% - filter(date >= start_date & date <= end_date ) %>% - select(date, gpp, le) %>% - melt("date") %>% - mutate(type="24-hr mean") %>% - rbind(p_hydro_validation$data_3hr_mean[[1]] %>% - filter(date >= start_date & date <= end_date ) %>% - select(date, gpp, le) %>% - melt("date") %>% - mutate(type="3-hr maxima") - ) %>% - rbind(p_hydro_validation$data_daytime_mean[[1]] %>% - filter(date >= start_date & date <= end_date ) %>% - select(date, gpp, le) %>% - melt("date") %>% - mutate(type="daytime means") - ) %>% - ggplot(aes(y=value, x=date)) + - geom_line(aes(group=type, col=type), alpha=0.5) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") - - -``` - -## Model parameters and driver data - -Liu et al 21 nature climate change alexandra konings - -Qs: - -"Compound extremes are more reliably simulated with a hydraulics-explicit model" - -1. disaggregating SM and VPD - compund stress - -Kim novick Nat CC - interactions between VPD and SM with flux data - -plot model bias \~ SM, VPD, T, does bias increase at extremes? - -How to test interactions. - -2. site scale calibrations - generalizability of params, systematic variation in hydraulic traits with climate? Within-site Variation in hydraulic traits? Relative CV of params - e.g. hyd traits vary but cost params are relatively constant? - -3. uncertainty in WHC and b for hydrualic response? - -```{r} -params_modl <- list( - kphio = 0.089, # 0.11, #0.04998, # setup ORG in Stocker et al. 2020 GMD - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.0, - beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - phydro_K_plant = 0.3e-16, - phydro_p50_plant = -1, - phydro_b_plant = 1, - phydro_alpha = 0.1, - phydro_gamma = 1, - bsoil = 3, - whc = 90 -) - -p_hydro_drivers$forcing[[1]] %>% - # dplyr::filter(lubridate::year(date) %in% 2007:2013) %>% - melt("date") %>% - ggplot(aes(y=value, x=as.Date(date))) + - geom_line(col="aquamarine4") + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") - - -# load("../data/p_model_drivers.rda") -# load("../data/p_model_validation.rda") -# p_model_drivers$forcing[[1]] %>% -# melt("date") %>% -# ggplot(aes(y=value, x=as.Date(date))) + -# geom_line(col="aquamarine4") + -# theme_classic() + -# theme(strip.background = element_rect(color = "white", size = 1))+ -# facet_wrap(~variable, scales = "free") -``` - -## Model run - -### P-model run - -```{r} -p_hydro_drivers$params_siml[[1]]$use_phydro = F -p_hydro_drivers$params_siml[[1]]$use_pml = F - -output_p <- rsofun::runread_pmodel_f( - p_hydro_drivers, - par = params_modl -) -``` - -### P-model Validation - -```{r} -output_p$data[[1]] %>% select(date, gpp, latenth) %>% - rename(le = latenth) %>% - melt("date") %>% - mutate(group="model") %>% - rbind(p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date") %>% - mutate(group="obs")) %>% - ggplot(aes(y=value, x=as.Date(date))) + - geom_line(aes(group=group, col=group), alpha=0.7) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 2) -``` - -### P-hydro run - -```{r} -p_hydro_drivers$params_siml[[1]]$use_phydro = T -p_hydro_drivers$params_siml[[1]]$use_pml = F - -output_ph <- rsofun::runread_pmodel_f( - p_hydro_drivers, - par = params_modl -) -``` - -### P-hydro Validation - -```{r} -output_ph$data[[1]] %>% select(date, gpp, latenth) %>% - rename(le = latenth) %>% - melt("date") %>% - mutate(group="model") %>% - rbind(p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date") %>% - mutate(group="obs")) %>% - ggplot(aes(y=value, x=as.Date(date))) + - geom_line(aes(group=group, col=group), alpha=0.7) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 2) -``` - -```{r} - -output_ph$data[[1]] %>% select(date, gpp, latenth) %>% - rename(le = latenth) %>% - melt("date", value.name = "pred") %>% - left_join(p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date", value.name = "obs")) %>% - ggplot(aes(y=obs, x=pred)) + - geom_point(alpha=0.7) + - geom_abline(slope=1, intercept=0, col="red")+ - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 1) -``` - -### P-hydro run (with PM ET) - -```{r} -p_hydro_drivers$params_siml[[1]]$use_phydro = T -p_hydro_drivers$params_siml[[1]]$use_pml = T - -output_ph_pm <- rsofun::runread_pmodel_f( - p_hydro_drivers, - par = params_modl -) -``` - -### P-hydro Validation (with PM ET) - -```{r} -output_ph_pm$data[[1]] %>% select(date, gpp, latenth) %>% - rename(le = latenth) %>% - melt("date") %>% - mutate(group="model") %>% - rbind(p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date") %>% - mutate(group="obs")) %>% - ggplot(aes(y=value, x=as.Date(date))) + - geom_line(aes(group=group, col=group), alpha=0.7) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 2) -``` - -```{r} - -output_ph_pm$data[[1]] %>% select(date, gpp, latenth) %>% - rename(le = latenth) %>% - melt("date", value.name = "pred") %>% - left_join(p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date", value.name = "obs")) %>% - ggplot(aes(y=obs, x=pred)) + - geom_point(alpha=0.7) + - geom_abline(slope=1, intercept=0, col="red")+ - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 1) -``` - -```{r} -output_ph$data[[1]] %>% select(-year_dec) %>% - melt("date") %>% - mutate(model = "phydro") %>% - rbind( - output_p$data[[1]] %>% select(-year_dec) %>% - melt("date") %>% - mutate(model = "pmodel") - ) %>% - filter(!is.infinite(value)) %>% - ggplot(aes(y=value, x=as.Date(date), group=model, col=model)) + - geom_line(alpha=0.5) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") - - -output_ph_pm$data[[1]] %>% select(-year_dec) %>% - melt("date") %>% - mutate(model = "phydro_pm") %>% - rbind( - output_ph$data[[1]] %>% select(-year_dec) %>% - melt("date") %>% - mutate(model = "phydro_diff") - ) %>% - filter(!is.infinite(value)) %>% - ggplot(aes(y=value, x=as.Date(date), group=model, col=model)) + - geom_line(alpha=0.5) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") -``` diff --git a/vignettes/rsofun_phydro_calibration.Rmd b/vignettes/rsofun_phydro_calibration.Rmd deleted file mode 100644 index d22c0cc8..00000000 --- a/vignettes/rsofun_phydro_calibration.Rmd +++ /dev/null @@ -1,396 +0,0 @@ ---- -title: "Calibration of rsofun with Phydro" -author: "Jaideep Joshi" -date: "2023-09-07" -output: html_document ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -```{r} -library(tidyverse) -library(reshape2) -library(rsofun) -``` - -## Generate data if not already available - -```{r} -site <- "GF-Guy" -``` - -```{r} -load(paste0("../data/",site,"_p_hydro_drivers.rda")) -load(paste0("../data/",site,"_p_hydro_validation.rda")) - -p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% - rename(latenth=le) %>% - mutate(gpp = gpp*86400/1e6*12) %>% # convert to [gC m-2 day-1] - mutate(latenth = latenth*86400) # convert [W m-2] to [J m-2 day-1] - -p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 -p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 - -``` - -Function to quickly run model and plot outputs - -```{r} - -plot_pmodel = function(params_modl){ - output_p <- rsofun::runread_pmodel_f( - p_hydro_drivers, - par = params_modl - ) - - p1 = output_p$data[[1]] %>% select(date, gpp, latenth) %>% - melt("date") %>% - mutate(group="model") %>% - rbind(p_hydro_validation$data[[1]] %>% - select(date, gpp, latenth) %>% - # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - # mutate(latenth = latenth*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date") %>% - mutate(group="obs")) %>% - ggplot(aes(y=value, x=as.Date(date))) + - geom_line(aes(group=group, col=group), alpha=0.7) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 2) - - p2 = output_p$data[[1]] %>% select(date, gpp, latenth) %>% - melt("date", value.name = "pred") %>% - left_join(p_hydro_validation$data[[1]] %>% - select(date, gpp, latenth) %>% - # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - # mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date", value.name = "obs")) %>% - ggplot(aes(y=obs, x=pred)) + - geom_point(alpha=0.7) + - geom_abline(slope=1, intercept=0, col="red")+ - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 1) - - print(p1) - print(p2) -} -``` - -```{r} -params_modl <- list( - kphio = 0.089, # 0.11, #0.04998, - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.0, - beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - phydro_K_plant = 0.3e-16, - phydro_p50_plant = -1, - phydro_b_plant = 1, - phydro_alpha = 0.1, - phydro_gamma = 1, - bsoil = 3, - whc = 90 -) -``` - -## Model calibration - -### P-model calibration (GenSA) - -```{r} -p_hydro_drivers$params_siml[[1]]$use_phydro = F -p_hydro_drivers$params_siml[[1]]$use_pml = F - -# Define calibration settings and parameter ranges from previous work -settings_rmse <- list( - method = 'GenSA', # minimizes the RMSE - metric = cost_rmse_pmodel, # our cost function - control = list( # control parameters for optimizer GenSA - maxit = 100), - par = list( # bounds for the parameter space - kphio = list(lower=0.02, upper=0.2, init=0.05) - ), - parallel = T, - ncores = 8 -) - -# Calibrate the model and optimize the free parameters using -# demo datasets -pars_calib_rmse <- calib_sofun( - # calib_sofun arguments: - drivers = p_hydro_drivers, - obs = p_hydro_validation, - settings = settings_rmse, - # extra arguments passed to the cost function: - par_fixed = list( # fix all other parameters - kphio_par_a = 0.0, # set to zero to disable temperature-dependence - # of kphio, setup ORG - kphio_par_b = 1.0, - soilm_thetastar = 0.6 * 240, # to recover paper setup with soil moisture stress - soilm_betao = 0.0, - beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - whc = 90 - ), - targets = "gpp" # define target variable GPP -) - -print(pars_calib_rmse, max=20) - -params_modl_opt = params_modl -params_modl_opt$kphio = pars_calib_rmse$par -plot_pmodel(params_modl_opt) - -``` - -### P-model calibration (BayesianTools) - -```{r} -p_hydro_drivers$params_siml[[1]]$use_phydro = F -p_hydro_drivers$params_siml[[1]]$use_pml = F - -# Define calibration settings and parameter ranges from previous work -settings_bayes <- list( - method = "BayesianTools", - par = list( - kphio = list(lower=0.04, upper=0.09, init=0.05), - err_gpp = list(lower = 0.01, upper = 4, init = 2) - ), - metric = rsofun::cost_likelihood_pmodel, - control = list( - sampler = "DEzs", - settings = list( - nrChains = 1, - burnin = 500, - iterations = 600 # kept artificially low - ) - ) -) - -# Calibrate the model and optimize the free parameters using -# demo datasets -pars_calib_bayes <- calib_sofun( - # calib_sofun arguments: - drivers = p_hydro_drivers, - obs = p_hydro_validation, - settings = settings_bayes, - # extra arguments passed to the cost function: - par_fixed = list( # fix all other parameters - kphio_par_a = 0.0, # set to zero to disable temperature-dependence - # of kphio, setup ORG - kphio_par_b = 1.0, - soilm_thetastar = 0.6 * 240, # to recover paper setup with soil moisture stress - soilm_betao = 0.0, - beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - whc = 90 - ), - targets = "gpp" # define target variable GPP -) - -print(pars_calib_bayes) - -params_modl_opt = params_modl -params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] -plot_pmodel(params_modl_opt) - -``` - -### P-hydro calibration (GenSA) - -```{r} -p_hydro_drivers$params_siml[[1]]$use_phydro = T -p_hydro_drivers$params_siml[[1]]$use_pml = F - -# Define calibration settings and parameter ranges from previous work -settings_rmse <- list( - method = 'GenSA', # minimizes the RMSE - metric = cost_rmse_pmodel, # our cost function - control = list( # control parameters for optimizer GenSA - maxit = 100), - par = list( - kphio = list(lower=0.04, upper=0.09, init=0.05), - phydro_K_plant = list(lower=0.05e-16, upper=5e-16, init=0.3e-16) - ), - parallel = T, - ncores = 8 -) - -# Calibrate the model and optimize the free parameters using -# demo datasets -pars_calib_rmse <- calib_sofun( - # calib_sofun arguments: - drivers = p_hydro_drivers, - obs = p_hydro_validation, - settings = settings_rmse, - # extra arguments passed to the cost function: - par_fixed = list( # fix all other parameters - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - # phydro_K_plant = 0.3e-16, - phydro_p50_plant = -1, - phydro_b_plant = 1, - phydro_alpha = 0.1, - phydro_gamma = 1, - bsoil = 3, - whc = 90 - ), - targets = c("gpp", "latenth"), # define target variable GPP - target_weights = c(1, 1e-6) -) - -print(pars_calib_rmse, max=20) - -params_modl_opt = params_modl -params_modl_opt$kphio = pars_calib_rmse$par[["kphio"]] -params_modl_opt$phydro_K_plant = pars_calib_rmse$par[["phydro_K_plant"]] -plot_pmodel(params_modl_opt) - -``` - -### P-hydro calibration (BayesianTools) - -```{r} -p_hydro_drivers$params_siml[[1]]$use_phydro = T -p_hydro_drivers$params_siml[[1]]$use_pml = F - -# Define calibration settings and parameter ranges from previous work -settings_bayes <- list( - method = "BayesianTools", - par = list( - kphio = list(lower=0.04, upper=0.09, init=0.05), - phydro_K_plant = list(lower=0.05e-16, upper=5e-16, init=0.3e-16), - err_gpp = list(lower = 0.01, upper = 4, init = 2), - err_le = list(lower = 0.01e7, upper = 4e7, init = 2e7) - ), - metric = rsofun::cost_likelihood_pmodel, - control = list( - sampler = "DEzs", - settings = list( - nrChains = 1, - burnin = 1000, - iterations = 1300 # kept artificially low - ) - ) -) - -# Calibrate the model and optimize the free parameters using -# demo datasets -pars_calib_bayes <- calib_sofun( - # calib_sofun arguments: - drivers = p_hydro_drivers, - obs = p_hydro_validation, - settings = settings_bayes, - # extra arguments passed to the cost function: - par_fixed = list( # fix all other parameters - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - # phydro_K_plant = 0.3e-16, - phydro_p50_plant = -1, - phydro_b_plant = 1, - phydro_alpha = 0.1, - phydro_gamma = 1, - bsoil = 3, - whc = 90 - ), - targets = c("gpp", "latenth") # define target variable GPP -) - -print(pars_calib_bayes) - -params_modl_opt = params_modl -params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] -params_modl_opt$phydro_K_plant = pars_calib_bayes$par[["phydro_K_plant"]] -plot_pmodel(params_modl_opt) - -``` - - - -### P-hydro full calibration (BayesianTools) - -```{r} -p_hydro_drivers$params_siml[[1]]$use_phydro = T -p_hydro_drivers$params_siml[[1]]$use_pml = F - -# Define calibration settings and parameter ranges from previous work -settings_bayes <- list( - method = "BayesianTools", - par = list( - kphio = list(lower=0.04, upper=0.09, init=0.05), - phydro_K_plant = list(lower=0.05e-16, upper=5e-16, init=0.3e-16), - phydro_p50_plant = list(lower=-4, upper=-0.1, init=-1), - phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), - phydro_gamma = list(lower=0.05, upper=2, init=1), - bsoil = list(lower=1, upper=5, init=3), - whc = list(lower=10, upper=1000, init=90), - err_gpp = list(lower = 0.01, upper = 4, init = 2), - err_le = list(lower = 0.01e7, upper = 4e7, init = 2e7) - ), - metric = rsofun::cost_likelihood_pmodel, - control = list( - sampler = "DEzs", - settings = list( - nrChains = 1, - burnin = 1000, - iterations = 1300 # kept artificially low - ) - ) -) - -# Calibrate the model and optimize the free parameters using -# demo datasets -pars_calib_bayes <- calib_sofun( - # calib_sofun arguments: - drivers = p_hydro_drivers, - obs = p_hydro_validation, - settings = settings_bayes, - # extra arguments passed to the cost function: - par_fixed = list( # fix all other parameters - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - # phydro_K_plant = 0.3e-16, - # phydro_P50_plant = -1, - phydro_b_plant = 1 - # phydro_alpha = 0.1, - # phydro_gamma = 1, - # bsoil = 3, - # whc = 90 - ), - targets = c("gpp", "latenth") # define target variable GPP -) - -print(pars_calib_bayes) - -params_modl_opt = params_modl -params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] -params_modl_opt$phydro_K_plant = pars_calib_bayes$par[["phydro_K_plant"]] -params_modl_opt$phydro_p50_plant = pars_calib_bayes$par[["phydro_p50_plant"]] -params_modl_opt$phydro_alpha = pars_calib_bayes$par[["phydro_alpha"]] -params_modl_opt$phydro_gamma = pars_calib_bayes$par[["phydro_gamma"]] -params_modl_opt$bsoil = pars_calib_bayes$par[["bsoil"]] -params_modl_opt$whc = pars_calib_bayes$par[["whc"]] - -plot_pmodel(params_modl_opt) - -``` diff --git a/vignettes/rsofun_phydro_calibration_full.Rmd b/vignettes/rsofun_phydro_calibration_full.Rmd deleted file mode 100644 index feea7928..00000000 --- a/vignettes/rsofun_phydro_calibration_full.Rmd +++ /dev/null @@ -1,292 +0,0 @@ ---- -title: "Calibration of rsofun with Phydro" -author: "Jaideep Joshi" -date: "2023-09-07" -output: html_document -params: - output_dir: "." ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -```{r} -library(tidyverse) -library(reshape2) -library(rsofun) -library(BayesianTools) -``` - -## Generate data if not already available - -```{r} -site <- "GF-Guy" -``` - -```{r} -data_dir = "~/Downloads/fluxdatakit_oct3/Phydro_drivers/" -load(paste0(data_dir,"/",site,"_p_hydro_drivers.rda")) -load(paste0(data_dir,"/",site,"_p_hydro_validation.rda")) - -p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% - rename(le=latenth) -# mutate(gpp = gpp*86400/1e6*12) %>% # convert to [gC m-2 day-1] -# mutate(latenth = latenth*86400) # convert [W m-2] to [J m-2 day-1] - -p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 -p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 - -p_hydro_drivers$params_siml[[1]]$use_phydro = T -p_hydro_drivers$params_siml[[1]]$use_pml = F - -``` - -Function to quickly run model and plot outputs - -```{r} -get_density <- function(x, y, ...) { - dens <- MASS::kde2d(x, y, ...) - ix <- findInterval(x, dens$x) - iy <- findInterval(y, dens$y) - ii <- cbind(ix, iy) - return(dens$z[ii]) -} - -plot_pmodel = function(params_m, out_filename=""){ - output_p <- rsofun::runread_pmodel_f( - p_hydro_drivers, - par = params_m - ) - - if (out_filename != ""){ - save(output_p, file = paste0(data_dir, "/", site, "_", out_filename)) - } - - p1 = output_p$data[[1]] %>% select(date, gpp, le) %>% - melt("date") %>% - mutate(group="model") %>% - rbind(p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - # mutate(latenth = latenth*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date") %>% - mutate(group="obs")) %>% - ggplot(aes(y=value, x=as.Date(date))) + - geom_line(aes(group=group, col=group), alpha=0.7) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 2) - - p2 = output_p$data[[1]] %>% select(date, gpp, le) %>% - drop_na %>% - melt("date", value.name = "pred") %>% - left_join(p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - drop_na %>% - # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - # mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date", value.name = "obs")) %>% - group_by(variable) %>% - reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% - ggplot(aes(y=obs, x=pred, colour=density)) + - scale_color_viridis_c() + - geom_point(alpha=0.7) + - geom_abline(slope=1, intercept=0, col="red")+ - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 1)+ - labs(colour="Density") - - print(p1) - print(p2) -} - -``` - -```{r} -params_modl <- list( - kphio = 7.013438e-02, # 0.089, # 0.11, #0.04998, - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - # soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - # soilm_betao = 0.0, - # beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - phydro_K_plant = 1.421218e-16 , #0.3e-16, - phydro_p50_plant = -2.506519e+00, #-1, - phydro_b_plant = 1, - phydro_alpha = 1.334564e-01, #0.1, - phydro_gamma = 1.889438e+00, #1, - bsoil = 3.193891e+00, #3, - whc = 7.819509e+02 #200 -) - -plot_pmodel(params_modl) -output_p <- rsofun::runread_pmodel_f( - p_hydro_drivers, - par = params_modl - ) -``` - -## P-hydro full calibration (BayesianTools) - -```{r} -p_hydro_drivers$params_siml[[1]]$use_phydro = T -p_hydro_drivers$params_siml[[1]]$use_pml = F - -# Define calibration settings and parameter ranges from previous work -settings_bayes <- list( - method = "BayesianTools", - par = list( - kphio = list(lower=0.04, upper=0.09, init=0.05), - phydro_K_plant = list(lower=0.05e-16, upper=5e-16, init=0.3e-16), - phydro_p50_plant = list(lower=-3, upper=-0.1, init=-1), - phydro_alpha = list(lower=0.06, upper=0.15, init=0.1), - phydro_gamma = list(lower=0.5, upper=2, init=1), - bsoil = list(lower=1, upper=5, init=3), - whc = list(lower=10, upper=1000, init=500), - err_gpp = list(lower = 0.01, upper = 4, init = 2), - err_le = list(lower = 0.01e7, upper = 4e7, init = 2e7) - ), - metric = rsofun::cost_likelihood_pmodel, - control = list( - sampler = "DEzs", - settings = list( - nrChains = 10, - burnin = 5000, - iterations = 5000 # kept artificially low - ) - ) -) - -# Calibrate the model and optimize the free parameters using -# demo datasets -pars_calib_bayes <- calib_sofun( - # calib_sofun arguments: - drivers = p_hydro_drivers, - obs = p_hydro_validation, - settings = settings_bayes, - # extra arguments passed to the cost function: - par_fixed = list( # fix all other parameters - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - # phydro_K_plant = 0.3e-16, - # phydro_P50_plant = -1, - phydro_b_plant = 1 - # phydro_alpha = 0.1, - # phydro_gamma = 1 - # bsoil = 3, - # whc = 90 - ), - targets = c("gpp", "le") # define target variable GPP -) - -plot(pars_calib_bayes$mod) -correlationPlot(pars_calib_bayes$mod) - -print(pars_calib_bayes) - -params_modl_opt = params_modl - -params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] -params_modl_opt$phydro_K_plant = pars_calib_bayes$par[["phydro_K_plant"]] -params_modl_opt$phydro_p50_plant = pars_calib_bayes$par[["phydro_p50_plant"]] -params_modl_opt$phydro_alpha = pars_calib_bayes$par[["phydro_alpha"]] -params_modl_opt$phydro_gamma = pars_calib_bayes$par[["phydro_gamma"]] -params_modl_opt$bsoil = pars_calib_bayes$par[["bsoil"]] -params_modl_opt$whc = pars_calib_bayes$par[["whc"]] - -plot_pmodel(params_modl_opt, "p_hydro_output.rda") - -``` - - -## P-model full calibration (BayesianTools) - -```{r} -params_modl <- list( - kphio = 0.04998, - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.0, - beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - # phydro_K_plant = 0.3e-16, - # phydro_p50_plant = -1, - # phydro_b_plant = 1, - # phydro_alpha = 0.1, - # phydro_gamma = 1, - # bsoil = 3, - whc = 300 -) -p_hydro_drivers$params_siml[[1]]$use_phydro = F -p_hydro_drivers$params_siml[[1]]$use_pml = F - -plot_pmodel(params_modl) -``` - -```{r} -# Define calibration settings and parameter ranges from previous work -settings_bayes <- list( - method = "BayesianTools", - par = list( - kphio = list(lower=0.04, upper=0.09, init=0.05), - soilm_thetastar = list(lower=0.1*240, upper=2*240, init=0.6*240), - # soilm_betao = list(lower=0, upper=10, init=0.1), - beta_unitcostratio = list(lower=10, upper=1000, init=146), - whc = list(lower=10, upper=1000, init=350), - err_gpp = list(lower = 0.01, upper = 4, init = 2), - err_le = list(lower = 0.01e7, upper = 4e7, init = 2e7) - ), - metric = rsofun::cost_likelihood_pmodel, - control = list( - sampler = "DEzs", - settings = list( - nrChains = 1, - burnin = 1000, - iterations = 1300 # kept artificially low - ) - ) -) - -# Calibrate the model and optimize the free parameters using -# demo datasets -pars_calib_bayes <- calib_sofun( - # calib_sofun arguments: - drivers = p_hydro_drivers, - obs = p_hydro_validation, - settings = settings_bayes, - # extra arguments passed to the cost function: - par_fixed = list( # fix all other parameters - kphio_par_a = 0.0, # set to zero to disable temperature-dependence - # of kphio, setup ORG - kphio_par_b = 1.0, - soilm_betao = 0.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - # beta_unitcostratio = 146, - tau_acclim = 30.0, - kc_jmax = 0.41 - ), - targets = c("gpp", "le") # define target variable GPP -) - -print(pars_calib_bayes) - -params_modl_opt = params_modl - -params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] -params_modl_opt$soilm_thetastar = pars_calib_bayes$par[["soilm_thetastar"]] -# params_modl_opt$soilm_betao = pars_calib_bayes$par[["soilm_betao"]] -params_modl_opt$beta_unitcostratio = pars_calib_bayes$par[["beta_unitcostratio"]] -params_modl_opt$whc = pars_calib_bayes$par[["whc"]] - -plot_pmodel(params_modl_opt) diff --git a/vignettes/rsofun_phydro_data_generation.R b/vignettes/rsofun_phydro_data_generation.R deleted file mode 100644 index 26157026..00000000 --- a/vignettes/rsofun_phydro_data_generation.R +++ /dev/null @@ -1,574 +0,0 @@ -params <- -list(output_dir = ".") - -#' --- -#' title: "Generate input data for rsofun Phydro" -#' author: "Jaideep Joshi" -#' date: "2023-11-12" -#' output: html_document -#' params: -#' output_dir: "." -#' --- -#' -## ----setup, include=FALSE-------------------------------------------- -knitr::opts_chunk$set(echo = TRUE) - -#' -## -------------------------------------------------------------------- -library(tidyverse) -library(reshape2) -# library(FluxDataKit) -library(lubridate) -library(ncdf4) - -source("read_meta_fdk.R") - -#' -#' -## -------------------------------------------------------------------- -args = commandArgs(trailingOnly=TRUE) - -# FR-LBr -# FI-Hyy -# CH-Dav - -#site <- "GF-Guy" -if (length(args)==0) { - #stop("At least one argument must be supplied: site name", call.=FALSE) - site = "CH-Dav" -}else{ - site = args[1] -} - -if (length(args)<2) { - root_data_dir = "~/Downloads/fluxdatakit_oct3" -}else{ - root_data_dir = args[2] -} - -print(args) - - -print(getwd()) - -#' -#' -## -------------------------------------------------------------------- - -lsm_path = paste0(root_data_dir, "/FLUXDATAKIT_LSM/") -csv_path = paste0(root_data_dir, "/FLUXDATAKIT_FLUXNET/") -out_path = paste0(root_data_dir, "/phydro_drivers/") - -dir.create(out_path, showWarnings = F) -dir.create(paste0(out_path, "data_gen_figures/"), showWarnings = F) - -figures_prefix = paste0(out_path,"/data_gen_figures/", site) - -files_csv = list.files(csv_path) -files_lsm = list.files(lsm_path) - -# Get filename for HH data for matching site -file_csv = files_csv[intersect(grep(site, files_csv), - grep("HH", files_csv))] - -# get metadata -# -------------------------------------------------------- -message("- reading Metadata for site") -meta <- suppressWarnings( - try( - read_meta_fdk( - site = site, - path = lsm_path, - meta_data = T - ) - ) - ) - -print(meta) - -# get half-hourly data -------------------------------------------------------- -# message("- convert to FLUXNET standard CSV file") -# hhdf <- suppressWarnings( -# try( -# fdk_convert_lsm( -# site = site, -# fluxnet_format = TRUE, -# path = "~/Downloads/flux_data_kit_beta/fluxes/" -# ) -# ) -# ) -# -# if(inherits(hhdf, "try-error")){ -# message("!!! conversion to FLUXNET failed !!!") -# return(NULL) -# } - -message("- reading FLUXNET format halfhourly data") -hhdf <- readr::read_csv(paste0(csv_path,"/",file_csv)) - -# Add date and time columns to hhdf for easier further processing. -# --------------------------------------------------------- -hhdf = - hhdf |> - mutate(time = lubridate::as_datetime(as.character(TIMESTAMP_START), tz = "GMT", format="%Y%m%d%H%M")) |> - mutate(date = lubridate::as_date(time)) - -message("- Add SW_OUT=NA if not present") -if (!("SW_OUT" %in% colnames(hhdf))) { - hhdf$SW_OUT = NA -} - - -# Aggregate to daily 24-hr means ---------------------------------------------------------- -message("- downsampling FLUXNET format - 24 hr means") -ddf_24hr_mean <- - try( - hhdf |> - group_by(date) |> - select(-TIMESTAMP_START, -TIMESTAMP_END) |> - summarize_all(.funs = mean) - ) - -# Check aggregation -cairo_pdf(filename = paste0(figures_prefix, "_fig1_ddf_24hr_mean.pdf")) -# png(filename = paste0(figures_prefix, "_fig1_ddf_24hr_mean.png"), height=700*3, width=700*3, res = 300) -p1 = ddf_24hr_mean %>% - mutate(albedo = SW_OUT/SW_IN_F_MDS) %>% - select(SW_IN_F_MDS, NETRAD, LW_IN_F_MDS, LAI, FPAR, GPP_DT_VUT_REF, LE_F_MDS, TA_F_MDS, P_F, date) %>% - melt("date") %>% - ggplot(aes(y=value, x=date)) + - geom_line(col="aquamarine4") + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") -p1 %>% print() -dev.off() - -ddf_24hr_mean %>% filter(!is.na(NETRAD)) %>% pull(date) %>% summary() - -#' -## -------------------------------------------------------------------- -# valid_years = read.csv(text = " -# Site , start_year , end_year \n -# CH-Dav , 2000 , 2010 \n -# DE-Hai , 2000 , 2020 \n -# DE-Tha , 2000 , 2020 \n -# FR-LBr , 2000 , 2008 \n -# FR-Pue , 2000 , 2013 \n -# GF-Guy , 2004 , 2015 \n -# NL-Loo , 2000 , 2014 \n -# US-Me2 , 2002 , 2020 \n -# US-NR1 , 2000 , 2016 \n -# US-Ton , 2001 , 2014 \n -# ", -# header=T, strip.white=T) - -valid_years = read.csv("ancillary_data/valid_years_final.csv", header = T, fileEncoding = "UTF-16") - -# Get valid data years -ystart = valid_years %>% filter(Site==site) %>% pull(start_year) -yend = valid_years %>% filter(Site==site) %>% pull(end_year) - -# ystart = max(meta[[1]]$year_start, 2000) %>% as.numeric() -# yend = meta[[1]]$year_end %>% as.numeric() - -# Aggregate around daily maximum ppfd for acclimating model -# --------------------------------------------------------- -test.3day = hhdf %>% filter(date >= as_date(paste0(floor((ystart+yend)/2),"-06-01")) & - date <= as_date(paste0(floor((ystart+yend)/2),"-06-03")) ) - -aggregate_daily_3hr_maxima = function(df){ - # Get the time at which SW_IN is maximum - maxppfd <- df %>% filter(SW_IN_F_MDS == max(SW_IN_F_MDS)) - max_t <- maxppfd$time[1] - - # Select times that lie in 3-hr interval around max_t - df_aroundmax <- df %>% filter(time < (max_t + 1.5*3600) & - time > (max_t - 1.5*3600) ) - - # take mean of selected entries - df_mean <- df_aroundmax |> - select(-TIMESTAMP_START, -TIMESTAMP_END) |> - summarize_all(.funs = mean) - - df_mean -} - -# Test aggregation -# ---------------- -test.3day.3hr = test.3day %>% group_by(date) %>% do(aggregate_daily_3hr_maxima(.)) %>% ungroup() - -cairo_pdf(filename = paste0(figures_prefix, "_fig2_3hr_maxima_sample.pdf")) -p2 = test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% - melt("time") %>% - mutate(type="hourly") %>% - rbind(test.3day.3hr %>% - select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% - melt("time") %>% - mutate(type="daily") - ) %>% - ggplot(aes(y=value, x=as.POSIXct(time))) + - geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + - geom_point(data = . %>% filter(type == "daily")) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") -p2 %>% print() -dev.off() - -#' -## -------------------------------------------------------------------- -# Apply 3hr maxima aggregation to all data -# ---------------------------------------- -message("- downsampling FLUXNET format - daily 3-hr means around max ppfd") -ddf_3hr_maxima <- hhdf |> - group_by(date) |> - do(aggregate_daily_3hr_maxima(.)) |> - ungroup() - -#' -## -------------------------------------------------------------------- -aggregate_daily_daylength = function(df){ - # Get the time at which SW_IN > 0 - pos_ppfd <- df %>% filter(SW_IN_F_MDS > 10) - # if SW_IN is unavailable in that year calc daylength based on NETRAD - if (nrow(pos_ppfd) < 2){ - pos_ppfd <- df %>% filter(NETRAD > 25) - } - - tmax <- max(pos_ppfd$time) - tmin <- min(pos_ppfd$time) - - # Select times that lie in 3-hr interval around max_t - df_aroundmax <- df %>% filter(time <= tmax & - time >= tmin ) - - # take mean of selected entries - df_mean <- df_aroundmax |> - select(-TIMESTAMP_START, -TIMESTAMP_END) |> - summarize_all(.funs = mean) |> - mutate(daylength = difftime(tmax, tmin, units="hours") |> as.numeric()) - - df_mean -} - -# Test aggregation -# ---------------- -test.3day.daylen = test.3day %>% group_by(date) %>% do(aggregate_daily_daylength(.)) %>% ungroup() - -cairo_pdf(filename = paste0(figures_prefix, "_fig3_daytime_sample.pdf")) -p3 = test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI) %>% - mutate(daylength = NA) %>% - melt("time") %>% - mutate(type="hourly") %>% - rbind(test.3day.daylen %>% - select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, daylength) %>% - melt("time") %>% - mutate(type="daily") - ) %>% - ggplot(aes(y=value, x=as.POSIXct(time))) + - geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + - geom_point(data = . %>% filter(type == "daily")) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") -p3 %>% print() -dev.off() - -#' -## -------------------------------------------------------------------- -# Apply daytime mean aggregation to all data -# ------------------------------------------ -message("- downsampling FLUXNET format - daytime means") -ddf_daytime_mean <- hhdf |> - group_by(date) |> - do(aggregate_daily_daylength(.)) |> - ungroup() - -# Check daylength seasonality -cairo_pdf(filename = paste0(figures_prefix, "_fig4_daylenth_seasonality.pdf")) -p4 = ddf_daytime_mean %>% - ggplot(aes(y=daylength, x=date)) + - geom_line()+ - theme_classic() -p4 %>% print() -dev.off() - -#' -## -------------------------------------------------------------------- -# Calculate daily tmax and tmin from hh data -# ------------------------------------------ -tmaxmin <- - hhdf |> - group_by(date) |> - summarize( - tmax = max(TA_F_MDS), - tmin = min(TA_F_MDS) - ) - -#' -## -------------------------------------------------------------------- -# Creating driver object ------------------------------------------------------ -message("- compiling drivers") -load("../data/p_model_drivers.rda") - -## READ WHC from file ## -nc = nc_open("ancillary_data/cwdx80_forcing.nc") -lons = ncvar_get(nc, "lon") -lats = ncvar_get(nc, "lat") -S80 = ncvar_get(nc, "cwdx80_forcing") - -site_lon = meta[[1]]$longitude -site_lat = meta[[1]]$latitude - -lonid = which(lons > site_lon)[1]-1 -latid = which(lats > site_lat)[1]-1 -n = 1 -S80_slice = S80[(lonid-n):(lonid+n), (latid-n):(latid+n)] -whc_site = mean(as.numeric(S80_slice, na.rm=T)) -whc_site_sd = sd(as.numeric(S80_slice, na.rm=T)) - -## READ WHC_1m FROM FILE ## - -nc = nc_open("ancillary_data/whc_1m.nc") -lons = ncvar_get(nc, "lon") -lats = ncvar_get(nc, "lat") -whc1m = ncvar_get(nc, "whc_1m") - -site_lon = meta[[1]]$longitude -site_lat = meta[[1]]$latitude - -lonid = which(lons > site_lon)[1]-1 -latid = which(lats > site_lat)[1]-1 -n = 1 -whc1m_slice = whc1m[(lonid-n):(lonid+n), (latid-n):(latid+n)] -whc1m_site = mean(as.numeric(whc1m_slice, na.rm=T)) -whc1m_site_sd = sd(as.numeric(whc1m_slice, na.rm=T)) - - - -p_hydro_drivers <- p_model_drivers -p_hydro_drivers$sitename[[1]] = site -p_hydro_drivers$site_info[[1]] = - tibble( - lon=meta[[1]]$longitude, - lat=meta[[1]]$latitude, - elv = meta[[1]]$elevation, - canopy_height = ifelse(is.na(meta[[1]]$canopy_height), yes = 20, meta[[1]]$canopy_height), - reference_height = ifelse(is.na(meta[[1]]$reference_height), yes = 22, meta[[1]]$reference_height), - whc = whc_site, - whc_sd = whc_site_sd, - IGBP_veg_short = meta[[1]]$IGBP_veg_short - ) -kfFEC = 2.04 - -start_year = ystart -end_year = yend - -# for demo, use just a subset of years -p_hydro_drivers$forcing <- - ddf_24hr_mean |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - left_join(tmaxmin) |> - group_by(date) |> - summarize( - date = date, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = P_F * 48 /(60 * 60 * 24), # P_F [mm timestep-1] * 48 [timesteps day-1] / 86400 [secs day-1 ] - tmin = tmin, # TMIN_F_MDS, - tmax = tmax, # TMAX_F_MDS, - fapar = FPAR, - co2 = CO2_F_MDS, - ccov = 0 - ) |> - list() - -p_hydro_drivers$forcing_acclim <- - ddf_3hr_maxima |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - left_join(tmaxmin) |> - group_by(date) |> - summarize( - date = date, - time = time, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = NA, # P_F * 48 / (60 * 60 * 24), - tmin = tmin, # TMIN_F_MDS, - tmax = tmax, # TMAX_F_MDS, - fapar = FPAR, - co2 = CO2_F_MDS, - ccov = 0 - ) |> - list() - -p_hydro_drivers$forcing_daytime_mean <- - ddf_daytime_mean |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - left_join(tmaxmin) |> - group_by(date) |> - summarize( - date = date, - time = time, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = NA, # P_F * 48 / (60 * 60 * 24), - tmin = tmin, # TMIN_F_MDS, - tmax = tmax, # TMAX_F_MDS, - fapar = FPAR, - co2 = CO2_F_MDS, - ccov = 0, - daylength = daylength - ) |> - list() - - -p_hydro_drivers$forcing_halfhourly <- - hhdf |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(time) |> - summarize( - date = date, - time = time, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = P_F * 48 / (60 * 60 * 24), - fapar = FPAR, - co2 = CO2_F_MDS, - ccov = 0 - ) |> - list() - - - -# write all drivers to file -# apply compression to minimize space -filn <- paste0(out_path, "/",site,"_p_hydro_drivers.rda") -message(paste0("- writing to file: ", filn)) -save(p_hydro_drivers, - file = filn - ) - - -# JJ Note: The gpp and latenth units here are different from the demo dataset supplied with rsofun. Here the units are matched to the output units from rsofun (see conversion below) -# Write validation data -load("../data/p_model_validation.rda") - -p_hydro_validation <- p_model_validation -p_hydro_validation$sitename[[1]] = site - -p_hydro_validation$data <- - ddf_24hr_mean |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(date) |> - summarise( - date = date, - time = time, - gpp = GPP_DT_VUT_REF, - latenth = LE_F_MDS - ) |> - mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] - mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] - list() - -p_hydro_validation$data_hh <- - hhdf |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(time) |> - summarise( - date = date, - time = time, - gpp = GPP_DT_VUT_REF, - latenth = LE_F_MDS - ) |> - mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] - mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] - list() - -p_hydro_validation$data_3hr_mean <- - ddf_3hr_maxima |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(time) |> - summarise( - date = date, - time = time, - gpp = GPP_DT_VUT_REF, - latenth = LE_F_MDS - ) |> - mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] - mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] - list() - -p_hydro_validation$data_daytime_mean <- - ddf_daytime_mean |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(time) |> - summarise( - date = date, - time = time, - gpp = GPP_DT_VUT_REF, - latenth = LE_F_MDS - ) |> - mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] - mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] - list() - - -filn <- paste0(out_path,"/",site,"_p_hydro_validation.rda") -message(paste0("- writing to file: ", filn)) -save(p_hydro_validation, - file = filn - ) - - -#' -#' - -cairo_pdf(filename = paste0(figures_prefix, "_fig5_phydro_drivers.pdf"), height=5, width=10) -p5 = p_hydro_drivers$forcing[[1]] %>% - select(date, ppfd, netrad, temp, vpd, fapar, rain) %>% - melt("date") %>% - mutate(type="24-hr mean") %>% - rbind(p_hydro_drivers$forcing_acclim[[1]] %>% - select(date, ppfd, netrad, temp, vpd, fapar, rain) %>% - melt("date") %>% - mutate(type="3-hr maxima") - ) %>% - rbind(p_hydro_drivers$forcing_daytime_mean[[1]] %>% - select(date, ppfd, netrad, temp, vpd, fapar, rain) %>% - melt("date") %>% - mutate(type="daytime means") - ) %>% - ggplot(aes(y=value, x=date)) + - geom_line(aes(group=type, col=type), alpha=0.5) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") -p5 %>% print() -dev.off() - diff --git a/vignettes/rsofun_phydro_data_generation.Rmd b/vignettes/rsofun_phydro_data_generation.Rmd deleted file mode 100644 index 977ddbe5..00000000 --- a/vignettes/rsofun_phydro_data_generation.Rmd +++ /dev/null @@ -1,455 +0,0 @@ ---- -title: "Generate input data for rsofun Phydro" -author: "Jaideep Joshi" -date: "2023-11-12" -output: html_document -params: - output_dir: "." ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -```{r} -library(tidyverse) -library(reshape2) -library(FluxDataKit) -``` - - -```{r} -site <- "CH-Dav" -``` - - -```{r} -lsm_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_LSM/" -csv_path = "~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_FLUXNET/" -out_path = "~/Downloads/fluxdatakit_oct3/Phydro_drivers/" - -files_csv = list.files(csv_path) -files_lsm = list.files(lsm_path) - -# Get filename for HH data for matching site -file_csv = files_csv[intersect(grep(site, files_csv), - grep("HH", files_csv))] - -# get metadata -# -------------------------------------------------------- -message("- reading Metadata for site") -meta <- suppressWarnings( - try( - fdk_convert_lsm( - site = site, - fluxnet_format = TRUE, - path = lsm_path, - meta_data = T - ) - ) - ) - - -# get half-hourly data -------------------------------------------------------- -# message("- convert to FLUXNET standard CSV file") -# hhdf <- suppressWarnings( -# try( -# fdk_convert_lsm( -# site = site, -# fluxnet_format = TRUE, -# path = "~/Downloads/flux_data_kit_beta/fluxes/" -# ) -# ) -# ) -# -# if(inherits(hhdf, "try-error")){ -# message("!!! conversion to FLUXNET failed !!!") -# return(NULL) -# } - -message("- reading FLUXNET format data") -hhdf <- readr::read_csv(paste0(csv_path,"/",file_csv)) - -# Add date and time columns to hhdf for easier further processing. -# --------------------------------------------------------- -hhdf = - hhdf |> - mutate(time = lubridate::as_datetime(as.character(TIMESTAMP_START), tz = "GMT", format="%Y%m%d%H%M")) |> - mutate(date = lubridate::as_date(time)) - -if (!("SW_OUT" %in% colnames(hhdf))) { - hhdf$SW_OUT = NA -} - - -# Aggregate to daily 24-hr means ---------------------------------------------------------- -message("- downsampling FLUXNET format - 24 hr means") -ddf_24hr_mean <- - try( - hhdf |> - group_by(date) |> - select(-TIMESTAMP_START, -TIMESTAMP_END) |> - summarize_all(.funs = mean) - ) - -# Check aggregation -ddf_24hr_mean %>% - mutate(albedo = SW_OUT/SW_IN_F_MDS) %>% - select(SW_IN_F_MDS, NETRAD, LW_IN_F_MDS, LAI, FPAR, GPP_DT_VUT_REF, LE_F_MDS, TA_F_MDS, date) %>% - melt("date") %>% - ggplot(aes(y=value, x=date)) + - geom_line(col="aquamarine4") + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") - -ddf_24hr_mean %>% filter(!is.na(NETRAD)) %>% pull(date) %>% summary() -``` - -```{r} -valid_years = read.csv(text = " - sitename , start_year , end_year \n - CH-Dav , 2000 , 2010 \n - DE-Hai , 2000 , 2020 \n - DE-Tha , 2000 , 2020 \n - FR-LBr , 2000 , 2008 \n - FR-Pue , 2000 , 2013 \n - GF-Guy , 2004 , 2015 \n - NL-Loo , 2000 , 2014 \n - US-Me2 , 2002 , 2020 \n - US-NR1 , 2000 , 2016 \n - US-Ton , 2001 , 2014 \n - ", - header=T, strip.white=T) - -# Get valid data years -ystart = valid_years %>% filter(sitename==site) %>% pull(start_year) -yend = valid_years %>% filter(sitename==site) %>% pull(end_year) - -# Aggregate around daily maximum ppfd for acclimating model -# --------------------------------------------------------- -test.3day = hhdf %>% filter(date >= as_date(paste0(floor((ystart+yend)/2),"-06-01")) & - date <= as_date(paste0(floor((ystart+yend)/2),"-06-03")) ) - -aggregate_daily_3hr_maxima = function(df){ - # Get the time at which SW_IN is maximum - maxppfd <- df %>% filter(SW_IN_F_MDS == max(SW_IN_F_MDS)) - max_t <- maxppfd$time[1] - - # Select times that lie in 3-hr interval around max_t - df_aroundmax <- df %>% filter(time < (max_t + 1.5*3600) & - time > (max_t - 1.5*3600) ) - - # take mean of selected entries - df_mean <- df_aroundmax |> - select(-TIMESTAMP_START, -TIMESTAMP_END) |> - summarize_all(.funs = mean) - - df_mean -} - -# Test aggregation -# ---------------- -test.3day.3hr = test.3day %>% group_by(date) %>% do(aggregate_daily_3hr_maxima(.)) %>% ungroup() - -test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% - melt("time") %>% - mutate(type="hourly") %>% - rbind(test.3day.3hr %>% - select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, FPAR) %>% - melt("time") %>% - mutate(type="daily") - ) %>% - ggplot(aes(y=value, x=as.POSIXct(time))) + - geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + - geom_point(data = . %>% filter(type == "daily")) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") -``` - -```{r} -# Apply 3hr maxima aggregation to all data -# ---------------------------------------- -message("- downsampling FLUXNET format - daily 3-hr means around max ppfd") -ddf_3hr_maxima <- hhdf |> - group_by(date) |> - do(aggregate_daily_3hr_maxima(.)) |> - ungroup() -``` - -```{r} -aggregate_daily_daylength = function(df){ - # Get the time at which SW_IN > 0 - pos_ppfd <- df %>% filter(SW_IN_F_MDS > 10) - # if SW_IN is unavailable in that year calc daylength based on NETRAD - if (nrow(pos_ppfd) < 2){ - pos_ppfd <- df %>% filter(NETRAD > 25) - } - - tmax <- max(pos_ppfd$time) - tmin <- min(pos_ppfd$time) - - # Select times that lie in 3-hr interval around max_t - df_aroundmax <- df %>% filter(time <= tmax & - time >= tmin ) - - # take mean of selected entries - df_mean <- df_aroundmax |> - select(-TIMESTAMP_START, -TIMESTAMP_END) |> - summarize_all(.funs = mean) |> - mutate(daylength = difftime(tmax, tmin, units="hours") |> as.numeric()) - - df_mean -} - -# Test aggregation -# ---------------- -test.3day.daylen = test.3day %>% group_by(date) %>% do(aggregate_daily_daylength(.)) %>% ungroup() - -test.3day %>% select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI) %>% - mutate(daylength = NA) %>% - melt("time") %>% - mutate(type="hourly") %>% - rbind(test.3day.daylen %>% - select(time, SW_IN_F_MDS, NETRAD, TA_F_MDS, VPD_F_MDS, GPP_NT_VUT_REF, GPP_DT_VUT_REF, LE_F_MDS, LAI, daylength) %>% - melt("time") %>% - mutate(type="daily") - ) %>% - ggplot(aes(y=value, x=as.POSIXct(time))) + - geom_line(data = . %>% filter(type == "hourly"), col="aquamarine4") + - geom_point(data = . %>% filter(type == "daily")) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free") -``` - -```{r} -# Apply daytime mean aggregation to all data -# ------------------------------------------ -message("- downsampling FLUXNET format - daytime means") -ddf_daytime_mean <- hhdf |> - group_by(date) |> - do(aggregate_daily_daylength(.)) |> - ungroup() - -# Check daylength seasonality -ddf_daytime_mean %>% - ggplot(aes(y=daylength, x=date)) + - geom_line()+ - theme_classic() -``` - -```{r} -# Calculate daily tmax and tmin from hh data -# ------------------------------------------ -tmaxmin <- - hhdf |> - group_by(date) |> - summarize( - tmax = max(TA_F_MDS), - tmin = min(TA_F_MDS) - ) -``` - -```{r} -# Creating driver object ------------------------------------------------------ -message("- compiling drivers") -load("../data/p_model_drivers.rda") - -p_hydro_drivers <- p_model_drivers -p_hydro_drivers$sitename[[1]] = site -p_hydro_drivers$site_info[[1]] = - tibble( - lon=meta[[1]]$longitude, - lat=meta[[1]]$latitude, - elv = meta[[1]]$elevation, - canopy_height=meta[[1]]$canopy_height, - reference_height = meta[[1]]$reference_height - ) -kfFEC = 2.04 - -start_year = ystart -end_year = yend - -# for demo, use just a subset of years -p_hydro_drivers$forcing <- - ddf_24hr_mean |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - left_join(tmaxmin) |> - group_by(date) |> - summarize( - date = date, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = P_F * 48 /(60 * 60 * 24), # P_F [mm timestep-1] * 48 [timesteps day-1] / 86400 [secs day-1 ] - tmin = tmin, # TMIN_F_MDS, - tmax = tmax, # TMAX_F_MDS, - fapar = FPAR, - co2 = CO2_F_MDS, - ccov = 0 - ) |> - list() - -p_hydro_drivers$forcing_acclim <- - ddf_3hr_maxima |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - left_join(tmaxmin) |> - group_by(date) |> - summarize( - date = date, - time = time, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = NA, # P_F * 48 / (60 * 60 * 24), - tmin = tmin, # TMIN_F_MDS, - tmax = tmax, # TMAX_F_MDS, - fapar = FPAR, - co2 = CO2_F_MDS, - ccov = 0 - ) |> - list() - -p_hydro_drivers$forcing_daytime_mean <- - ddf_daytime_mean |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - left_join(tmaxmin) |> - group_by(date) |> - summarize( - date = date, - time = time, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = NA, # P_F * 48 / (60 * 60 * 24), - tmin = tmin, # TMIN_F_MDS, - tmax = tmax, # TMAX_F_MDS, - fapar = FPAR, - co2 = CO2_F_MDS, - ccov = 0, - daylength = daylength - ) |> - list() - - -p_hydro_drivers$forcing_halfhourly <- - hhdf |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(time) |> - summarize( - date = date, - time = time, - temp = TA_F_MDS, - vpd = VPD_F_MDS * 100, - ppfd = SW_IN_F_MDS * kfFEC * 1e-06, - netrad = NETRAD, - patm = PA_F * 1000, - snow = 0, - rain = P_F * 48 / (60 * 60 * 24), - fapar = FPAR, - co2 = CO2_F_MDS, - ccov = 0 - ) |> - list() - - -# write all drivers to file -# apply compression to minimize space -filn <- paste0(out_path, "/",site,"_p_hydro_drivers.rda") -message(paste0("- writing to file: ", filn)) -save(p_hydro_drivers, - file = filn - ) - - -# JJ Note: The gpp and latenth units here are different from the demo dataset supplied with rsofun. Here the units are matched to the output units from rsofun (see conversion below) -# Write validation data -load("../data/p_model_validation.rda") - -p_hydro_validation <- p_model_validation -p_hydro_validation$sitename[[1]] = site - -p_hydro_validation$data <- - ddf_24hr_mean |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(date) |> - summarise( - date = date, - time = time, - gpp = GPP_DT_VUT_REF, - latenth = LE_F_MDS - ) |> - mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] - mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] - list() - -p_hydro_validation$data_hh <- - hhdf |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(time) |> - summarise( - date = date, - time = time, - gpp = GPP_DT_VUT_REF, - latenth = LE_F_MDS - ) |> - mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] - mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] - list() - -p_hydro_validation$data_3hr_mean <- - ddf_3hr_maxima |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(time) |> - summarise( - date = date, - time = time, - gpp = GPP_DT_VUT_REF, - latenth = LE_F_MDS - ) |> - mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] - mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] - list() - -p_hydro_validation$data_daytime_mean <- - ddf_daytime_mean |> - dplyr::filter(lubridate::year(date) %in% start_year:end_year) |> - dplyr::filter(!(lubridate::mday(date) == 29 & lubridate::month(date) == 2)) |> - group_by(time) |> - summarise( - date = date, - time = time, - gpp = GPP_DT_VUT_REF, - latenth = LE_F_MDS - ) |> - mutate(gpp = gpp*86400/1e6*12) |> # convert [umol m-2 s-1] to [gC m-2 day-1] - mutate(latenth = latenth*86400) |> # convert [W m-2] to [J m-2 day-1] - list() - - -filn <- paste0(out_path,"/",site,"_p_hydro_validation.rda") -message(paste0("- writing to file: ", filn)) -save(p_hydro_validation, - file = filn - ) - -``` - diff --git a/vignettes/rsofun_phydro_output_analysis.R b/vignettes/rsofun_phydro_output_analysis.R deleted file mode 100644 index daa08b66..00000000 --- a/vignettes/rsofun_phydro_output_analysis.R +++ /dev/null @@ -1,356 +0,0 @@ -rm(list=ls()) -library(tidyverse) -library(reshape2) -library(rsofun) -library(BayesianTools) -library(tictoc) -library(ncdf4) -library(scatterPlotMatrix) -library(ggpointdensity) - -plot_only = F - -source("vignettes/read_meta_fdk.R") - -root_data_dir = "~/Downloads/fluxdatakit_oct3" -lsm_path = paste0(root_data_dir, "/FLUXDATAKIT_LSM/") -out_dir = "~/Desktop/phydro_output_13_p50prior_fixedalpha0.08_s113/" -# out_dir = "~/Desktop/phydro_output_11_p50prior_narrowalpha_s113/" -data_dir = paste0(root_data_dir, "/phydro_drivers/") -figures_dir = paste0(out_dir, "/figures/") - -sites = read.delim("vignettes/site_list.txt", header=F) - -# sites_meta = -# sites %>% # slice(1:5) %>% -# pull(V1) %>% -# map_df( -# ~suppressWarnings( -# try( -# read_meta_fdk( -# site = ., -# path = lsm_path, -# meta_data = T -# ) -# ) -# ) -# ) -# -# sites_meta %>% write.csv(file="vignettes/ancillary_data/sites_meta.csv") - -sites_meta = read.csv(file="vignettes/ancillary_data/sites_meta.csv") - -sites_meta = sites_meta %>% - mutate(IGBP_veg_short = case_match(IGBP_veg_short, - "Woody Savannas" ~ "WSA", - "Savannas" ~ "SAV", - "Permanent Wetlands" ~ "WET", - "Open Shrublands" ~ "OSH", - "Closed Shrublands" ~ "CSH", - "Grasslands" ~ "GRA", - "Evergreen Needleleaf Forest" ~ "ENF", - "Evergreen Broadleaf Forest" ~ "EBF", - "Deciduous Broadleaf Forest" ~ "DBF", - "Mixed Forest" ~ "MF", - "Croplands" ~ "CRO", - "Cropland/Natural Vegetation Mosaic" ~ "MF", - "Urban and Built-Up" ~ NA, - .default = IGBP_veg_short)) - -sites_meta %>% ggplot(aes(x=IGBP_veg_short)) + geom_bar() + coord_flip() - - -map_all_sites = -tibble(filename=list.files(out_dir, full.names = T)) %>% - filter(str_detect(filename, "MAP.csv")) %>% - pull(filename) %>% - map_df(~read.csv(.)) - - -all_data = map_all_sites %>% - rename(sitename = site) %>% - left_join(sites_meta) - - -## FANCY PAIRS PLOT ## -all_data %>% - left_join(sites_meta) %>% - select(kphio:whc, IGBP_veg_short) %>% - mutate(phydro_K_plant = log10(phydro_K_plant), - # whc = log10(whc), - # Ssoil = log10(Ssoil), - # phydro_p50_plant = log10(-phydro_p50_plant), - # phydro_alpha = log10(phydro_alpha) - ) %>% - filter(IGBP_veg_short %in% c("SAV", "OSH", "GRA", "ENF", "EBF", "DBF", "CRO")) %>% - mutate(IGBP_veg_short = factor(IGBP_veg_short)) %>% - scatterPlotMatrix(distribType = 1, regressionType = 1, rotateTitle = F, zAxisDim = "IGBP_veg_short", - plotProperties = list(regression = list( strokeWidth = 2 )), - columnLabels = gsub("\\_", "
      ", colnames(.)) ) - - -all_data %>% ggplot(aes(x=IGBP_veg_short)) + geom_bar() + coord_flip() - -all_data %>% group_by(IGBP_veg_short) %>% summarize(count=length(IGBP_veg_short)) %>% -right_join(all_data) %>% - filter(count > 2) %>% - select(kphio:whc, IGBP_veg_short, sitename) %>% - mutate( - # phydro_K_plant = log10(phydro_K_plant), - whc = log10(whc), - # Ssoil = log10(Ssoil), - # kphio = log10(kphio), - # phydro_gamma = log10(phydro_gamma) - ) %>% - pivot_longer(cols = !c(sitename, IGBP_veg_short)) %>% - ggplot(aes(x=IGBP_veg_short, y=value, fill=IGBP_veg_short)) + - geom_violin() + - scale_fill_viridis_d()+ - facet_wrap(facets = "name", scales = "free") + - coord_flip() - -# all_data %>% -# filter(IGBP_veg_short %in% c("SAV", "OSH", "GRA", "ENF", "EBF", "DBF", "CRO")) %>% -# ggplot(aes(group=IGBP_veg_short, x=Ssoil, y=whc, col=IGBP_veg_short)) + -# geom_point()+ -# geom_smooth(method="lm", se=F)+ -# scale_x_log10()+scale_y_log10()+ -# geom_abline(slope = 1) - - -r2_all_sites = tibble(filename=list.files(out_dir, full.names = T)) %>% - filter(str_detect(filename, "r2_nrmse.csv")) %>% - pull(filename) %>% - map_df(~read.csv(.)) - -world <- map_data("world") -r2_all_sites %>% - left_join(sites_meta, by=c("site"="sitename")) %>% - ggplot() + - theme_classic()+ - geom_map( - data = world, map = world, - aes(long, lat, map_id = region), - color = "black", fill= "grey90", size=0.05 - ) + - geom_point(aes(x=longitude, y=latitude, color=r2))+ - scale_color_gradient2(mid = "cyan", midpoint = 0.5, low = "red", high="blue", limits=c(0,1))+ - facet_wrap(~variable, nrow = 2) - -r2_all_sites %>% - left_join(sites_meta, by=c("site"="sitename")) %>% - select(variable, r2, IGBP_veg_short) %>% - ggplot(aes(x=IGBP_veg_short, y=r2, fill=IGBP_veg_short)) + - geom_violin(scale = "width", width=0.4) + - facet_wrap(~variable, nrow=2) - -for (var in c("kphio", "phydro_K_plant", "phydro_p50_plant", "phydro_alpha", "phydro_gamma", "bsoil", "whc")){ -print( -map_all_sites %>% - left_join(sites_meta, by=c("site"="sitename")) %>% - select(kphio:whc, site, latitude, longitude) %>% - pivot_longer(cols = kphio:whc, names_to = "variable", values_to = "value") %>% - filter(variable == var) %>% - ggplot() + - theme_classic()+ - geom_map( - data = world, map = world, - aes(x=long, y=lat, map_id = region), - color = "black", fill= "grey90", size=0.05 - ) + - geom_point(aes(x=longitude, y=latitude, color=value))+ - ggtitle(var) -) -} - -#### Water potentials analysis #### - -vod_day = readxl::read_excel("~/Downloads/AMSR_fluxnet_data.xlsx", sheet = "VOD_ASC_filtered_qa(1_30_PM)") -vod_day = vod_day %>% - pivot_longer(-c(1,2), names_to="date", values_to = "vod_day") %>% - mutate(date = as.Date(as.numeric(date), origin="1899-12-30")) %>% - mutate(vod_day = case_match(vod_day, -999~NA, .default=vod_day)) - -vod_night = readxl::read_excel("~/Downloads/AMSR_fluxnet_data.xlsx", sheet = "VOD_DSC_filtered_qa(1_30_AM)") -vod_night = vod_night %>% - pivot_longer(-c(1,2), names_to="date", values_to = "vod_night") %>% - mutate(date = as.Date(as.numeric(date), origin="1899-12-30")) %>% - mutate(vod_night = case_match(vod_night, -999~NA, .default=vod_night)) - -vod = vod_day %>% left_join(vod_night) - -read_phydro_output = function(site){ - file = tibble(files=list.files(out_dir, full.names = T)) %>% - filter(stringr::str_detect(files, site)) %>% - filter(stringr::str_detect(files, "phydro_output.rda")) %>% - pull(files) - - load(file) - - output_p_opt$data[[1]] %>% - select(date, gpp, le, le_soil, jmax25, dpsi, psi_leaf, gpp_obs, le_obs) %>% - mutate(SITE_ID = site) -} - -read_obs = function(site){ - file_obs = tibble(files=list.files(data_dir, full.names = T)) %>% - filter(stringr::str_detect(files, site)) %>% - filter(stringr::str_detect(files, "p_hydro_validation.rda")) %>% - pull(files) - - load(file_obs) - - p_hydro_validation$data[[1]] -} - - -sites_phydro = tibble(files=list.files(out_dir)) %>% - filter(stringr::str_detect(files, "phydro_output.rda")) %>% - mutate(site = substr(files, 1,6)) %>% - # slice(1:5) %>% - pull(site) - -phydro_out = sites_phydro %>% - map_df(~read_phydro_output(.)) - -vod_phydro = vod %>% left_join(phydro_out) %>% - drop_na - -sites_vod_phydro = vod_phydro %>% group_by(SITE_ID) %>% count() %>% arrange(desc(n)) %>% pull(SITE_ID) - -for (i in seq(1,length(sites_vod_phydro), by=20)){ - print( - vod_phydro %>% - filter(SITE_ID %in% sites_vod_phydro[i:(i+20-1)]) %>% - mutate(vod_ratio = log(vod_night/vod_day)) %>% - ggplot(aes(y=vod_ratio, x=dpsi)) + - geom_pointdensity() + - geom_smooth(method="lm", formula=y~0+x, col="orange", linewidth=0.5)+ - scale_colour_viridis_c()+ - facet_wrap("SITE_ID", scales="free")+ - guides(color="none") - ) -} - - -#### Isohydricity analysis #### - -sites_phydro = phydro_out %>% group_by(SITE_ID) %>% count() %>% arrange(desc(n)) %>% pull(SITE_ID) - -site_min_psisoil = - phydro_out %>% - group_by(SITE_ID) %>% - summarize(psi_min = min(psi_leaf+dpsi)) - -site_max_dpsi = - phydro_out %>% - group_by(SITE_ID) %>% - summarize(dpsi_max = max(dpsi)) - -site_max_dpsi %>% - left_join(sites_meta, by=c("SITE_ID"="sitename")) %>% - select(dpsi_max, IGBP_veg_short) %>% - ggplot(aes(x=IGBP_veg_short, y=dpsi_max, fill=IGBP_veg_short)) + - geom_violin(scale = "width", width=0.4) - - -for (i in seq(1,length(sites_phydro), by=20)){ - cairo_pdf(filename = paste0("vignettes/figures/psi_leaf_vs_soil_",i,".pdf")) - print( - phydro_out %>% - filter(SITE_ID %in% sites_phydro[i:(i+20-1)]) %>% - left_join(site_min_psisoil) %>% - mutate(psi_soil = psi_leaf+dpsi) %>% - filter(psi_soil > psi_min*0.8) %>% - group_by(SITE_ID) %>% - ggplot(aes(y=psi_leaf, x=psi_soil)) + - geom_pointdensity() + - geom_smooth(method="lm", col="orange", linewidth=0.5)+ - geom_abline(slope=1, intercept=0, col="white")+ - scale_colour_viridis_c()+ - facet_wrap("SITE_ID", scales="free")+ - guides(color="none") - ) - dev.off() -} - - - -phydro_out %>% - left_join(site_min_psisoil) %>% - mutate(psi_soil = psi_leaf+dpsi) %>% - filter(psi_soil > psi_min*0.8) %>% - group_by(SITE_ID) %>% - group_modify(~broom::tidy(lm(psi_leaf~psi_soil, data=.x))) %>% - filter(estimate < 10 & estimate > -10) %>% - ggplot()+ - geom_histogram(aes(x=estimate), bins=100) + - geom_vline(xintercept = 1, col="red")+ - facet_wrap("term", scales="free") - - -phydro_out %>% - left_join(site_min_psisoil) %>% - mutate(psi_soil = psi_leaf+dpsi) %>% - filter(psi_soil > psi_min*0.8) %>% - group_by(SITE_ID) %>% - group_modify(~broom::tidy(lm(psi_leaf~psi_soil, data=.x))) %>% - filter(estimate < 10 & estimate > -10) %>% - pivot_wider(id_cols = SITE_ID, names_from = term, values_from = estimate) %>% - ggplot(aes(x=`(Intercept)`, y=psi_soil))+ - geom_point() + - geom_smooth(method="lm") - -le_soil_by_le = phydro_out %>% group_by(SITE_ID) %>% summarize(le_soil_pc = mean(le_soil)/mean(le)) -le_soil_by_le %>% - left_join(sites_meta, by=c("SITE_ID"="sitename")) %>% - select(le_soil_pc, SITE_ID, latitude, longitude) %>% - ggplot() + - theme_classic()+ - geom_map( - data = world, map = world, - aes(x=long, y=lat, map_id = region), - color = "black", fill= "grey90", size=0.05 - ) + - geom_point(aes(x=longitude, y=latitude, color=le_soil_pc))+ - ggtitle("le_soil_pc") - - -all_data %>% - left_join(le_soil_by_le, by=c("sitename"="SITE_ID")) %>% - left_join(r2_all_sites, by=c("sitename"="site")) %>% - ggplot(aes(x=le_soil_pc, y=r2))+ - geom_point()+ - facet_wrap(~variable) - -all_data %>% - left_join(le_soil_by_le, by=c("sitename"="SITE_ID")) %>% - left_join(r2_all_sites, by=c("sitename"="site")) %>% - select(-r2) %>% - pivot_wider(names_from = variable, values_from=nrmse, names_prefix = "nrmse_") %>% - mutate(nrmse_diff = nrmse_le - nrmse_gpp) %>% - ggplot(aes(x=le_soil_pc, y=nrmse_diff, colour=IGBP_veg_short))+ - geom_point() - -all_data %>% - left_join(le_soil_by_le, by=c("sitename"="SITE_ID")) %>% - left_join(r2_all_sites, by=c("sitename"="site")) %>% - select(-r2) %>% - pivot_wider(names_from = variable, values_from=nrmse, names_prefix = "nrmse_") %>% - mutate(nrmse_diff = nrmse_le - nrmse_gpp) %>% - filter(le_soil_pc > 0.75 & nrmse_diff < 0) %>% View() - -all_data %>% - left_join(le_soil_by_le, by=c("sitename"="SITE_ID")) %>% - ggplot(aes(x=le_soil_pc, y=phydro_p50_plant))+ - geom_point() - -all_data %>% - left_join(le_soil_by_le, by=c("sitename"="SITE_ID")) %>% - ggplot(aes(x=le_soil_pc, y=phydro_alpha))+ - geom_point() - -all_data %>% - left_join(le_soil_by_le, by=c("sitename"="SITE_ID")) %>% - ggplot(aes(x=le_soil_pc, y=phydro_K_plant))+ - geom_point() diff --git a/vignettes/rsofun_pmodel_calibration_full.Rmd b/vignettes/rsofun_pmodel_calibration_full.Rmd deleted file mode 100644 index 761c9ad5..00000000 --- a/vignettes/rsofun_pmodel_calibration_full.Rmd +++ /dev/null @@ -1,269 +0,0 @@ ---- -title: "Calibration of rsofun with Phydro" -author: "Jaideep Joshi" -date: "2023-09-07" -output: html_document ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -```{r} -library(tidyverse) -library(reshape2) -library(rsofun) -``` - -## Generate data if not already available - -```{r} -site <- "GF-Guy" -``` - -```{r} -data_dir = "~/Downloads/fluxdatakit_oct3/Phydro_drivers/" -load(paste0(data_dir,"/",site,"_p_hydro_drivers.rda")) -load(paste0(data_dir,"/",site,"_p_hydro_validation.rda")) - -# p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% -# rename(latenth=le) %>% -# mutate(gpp = gpp*86400/1e6*12) %>% # convert to [gC m-2 day-1] -# mutate(latenth = latenth*86400) # convert [W m-2] to [J m-2 day-1] - -p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 -p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 - -p_hydro_drivers$params_siml[[1]]$use_phydro = F -p_hydro_drivers$params_siml[[1]]$use_pml = F - -``` - -Function to quickly run model and plot outputs - -```{r} -get_density <- function(x, y, ...) { - dens <- MASS::kde2d(x, y, ...) - ix <- findInterval(x, dens$x) - iy <- findInterval(y, dens$y) - ii <- cbind(ix, iy) - return(dens$z[ii]) -} - -plot_pmodel = function(params_m, out_filename=""){ - output_p <- rsofun::runread_pmodel_f( - p_hydro_drivers, - par = params_m - ) - if (out_filename != ""){ - save(output_p, file = paste0(data_dir, "/", site, "_", out_filename)) - } - - p1 = output_p$data[[1]] %>% select(date, gpp, latenth) %>% - melt("date") %>% - mutate(group="model") %>% - rbind(p_hydro_validation$data[[1]] %>% - select(date, gpp, latenth) %>% - # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - # mutate(latenth = latenth*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date") %>% - mutate(group="obs")) %>% - ggplot(aes(y=value, x=as.Date(date))) + - geom_line(aes(group=group, col=group), alpha=0.7) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 2) - - p2 = output_p$data[[1]] %>% select(date, gpp, latenth) %>% - drop_na %>% - melt("date", value.name = "pred") %>% - left_join(p_hydro_validation$data[[1]] %>% - select(date, gpp, latenth) %>% - drop_na %>% - # mutate(gpp = gpp*86400/1e6*12) %>% # convert to gC m-2 day-1 - # mutate(le = le*86400) %>% # convert W m-2 to J m-2 day-1 - melt("date", value.name = "obs")) %>% - group_by(variable) %>% - reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% - ggplot(aes(y=obs, x=pred, colour=density)) + - scale_color_viridis_c() + - geom_point(alpha=0.7) + - geom_abline(slope=1, intercept=0, col="red")+ - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~variable, scales = "free", nrow = 1)+ - labs(colour="Density") - - print(p1) - print(p2) -} - -``` - - -## P-model partial calibration (BayesianTools) - -```{r} -params_modl <- list( - kphio = 0.04998, - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.0, - beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - # phydro_K_plant = 0.3e-16, - # phydro_p50_plant = -1, - # phydro_b_plant = 1, - # phydro_alpha = 0.1, - # phydro_gamma = 1, - # bsoil = 3, - whc = 300 -) -p_hydro_drivers$params_siml[[1]]$use_phydro = F -p_hydro_drivers$params_siml[[1]]$use_pml = F - -plot_pmodel(params_modl) -``` - -```{r} -# Define calibration settings and parameter ranges from previous work -settings_bayes <- list( - method = "BayesianTools", - par = list( - kphio = list(lower=0.04, upper=0.09, init=0.05), - soilm_thetastar = list(lower=0.1*240, upper=2*240, init=0.6*240), - # soilm_betao = list(lower=0, upper=10, init=0.1), - beta_unitcostratio = list(lower=10, upper=1000, init=146), - whc = list(lower=10, upper=1000, init=350), - err_gpp = list(lower = 0.01, upper = 4, init = 2), - err_le = list(lower = 0.01e7, upper = 4e7, init = 2e7) - ), - metric = rsofun::cost_likelihood_pmodel, - control = list( - sampler = "DEzs", - settings = list( - nrChains = 1, - burnin = 300, - iterations = 1300 # kept artificially low - ) - ) -) - -# Calibrate the model and optimize the free parameters using -# demo datasets -pars_calib_bayes <- calib_sofun( - # calib_sofun arguments: - drivers = p_hydro_drivers, - obs = p_hydro_validation, - settings = settings_bayes, - # extra arguments passed to the cost function: - par_fixed = list( # fix all other parameters - kphio_par_a = 0.0, # set to zero to disable temperature-dependence - # of kphio, setup ORG - kphio_par_b = 1.0, - soilm_betao = 0.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - # beta_unitcostratio = 146, - tau_acclim = 30.0, - kc_jmax = 0.41 - ), - targets = c("gpp", "latenth") # define target variable GPP -) - -print(pars_calib_bayes) - -params_modl_opt = params_modl - -params_modl_opt$kphio = pars_calib_bayes$par[["kphio"]] -params_modl_opt$soilm_thetastar = pars_calib_bayes$par[["soilm_thetastar"]] -# params_modl_opt$soilm_betao = pars_calib_bayes$par[["soilm_betao"]] -params_modl_opt$beta_unitcostratio = pars_calib_bayes$par[["beta_unitcostratio"]] -params_modl_opt$whc = pars_calib_bayes$par[["whc"]] - -plot_pmodel(params_modl_opt) - -``` - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/vignettes/rsofun_prepare_site_list.R b/vignettes/rsofun_prepare_site_list.R deleted file mode 100644 index 98c39f53..00000000 --- a/vignettes/rsofun_prepare_site_list.R +++ /dev/null @@ -1,54 +0,0 @@ -rm(list=ls()) -library(tidyverse) -library(reshape2) -library(rsofun) -library(BayesianTools) -library(tictoc) -library(ncdf4) -library(scatterPlotMatrix) - -plot_only = F -source("vignettes/read_meta_fdk.R") - -root_data_dir = "~/Downloads/fluxdatakit_oct3" -lsm_path = paste0(root_data_dir, "/FLUXDATAKIT_LSM/") -out_dir = "~/Desktop/phydro_output" -data_dir = paste0(root_data_dir, "/phydro_drivers/") -figures_dir = paste0(out_dir, "/figures/") - -files_lsm = list.files(lsm_path) -sites = files_lsm %>% substr(1,6) %>% unique() - - -sites_meta = - sites %>% - map_df( - ~suppressWarnings( - try( - read_meta_fdk( - site = ., - path = lsm_path, - meta_data = T - ) - ) - ) - ) - -sites_meta %>% write.csv(file="vignettes/ancillary_data/sites_meta.csv") - -sites_meta = read.csv(file="vignettes/ancillary_data/sites_meta.csv") -valid_years = read.csv("vignettes/ancillary_data/valid_years_final.csv", header = T, fileEncoding = "UTF-16") - -sites_meta %>% ggplot(aes(x=IGBP_veg_short)) + geom_bar() + coord_flip() - -site_list = - valid_years %>% - rename(sitename = Site, - valid_start_year = start_year, - valid_end_year = end_year) %>% - left_join(sites_meta) %>% - select(-X, -comment) %>% - filter(!is.na(valid_start_year) & !is.na(valid_end_year)) %>% # Remove sites that dont have any valid years - filter((IGBP_veg_short %in% c("WSA", "SAV", "OSH", "GRA", "ENF", "EBF", "DBF", "CSH"))) - -site_list %>% pull(sitename) %>% write.table(file = "vignettes/site_list_2.txt", row.names = F, quote = F, col.names = F) diff --git a/vignettes/run_bayesian_phydro.sh b/vignettes/run_bayesian_phydro.sh deleted file mode 100755 index 5df2357f..00000000 --- a/vignettes/run_bayesian_phydro.sh +++ /dev/null @@ -1,37 +0,0 @@ -#!/bin/bash - -NCORES=70 - -# declare -a arr=$( ls ~/Downloads/fluxdatakit_oct3/FLUXDATAKIT_FLUXNET/*HH* | awk -F "_" '{print $4}' ) - -declare -a arr=$( cat site_list_2.txt ) - -generate_data=false - -data_path=/data/scratch/jaideep/fluxdata -out_path=/data/scratch/jaideep/phydro_output_14_p50prior_fixedalpha0.08_s113_calibdpsi1 -# data_path=~/Downloads/fluxdatakit_oct3 -# out_path=~/Downloads/fluxdatakit_oct3/phydro_output - -for SITE in ${arr[@]} -do - echo $SITE - ( - if [ "$generate_data" = true ]; then - echo "Generating data for site" $SITE - Rscript rsofun_phydro_data_generation.R $SITE $data_path - fi - - echo "Running calibration for site" $SITE - Rscript phydro_long_calibration.R $SITE $data_path $out_path - ) & - - # allow to execute up to $NCORES jobs in parallel - if [[ $(jobs -r -p | wc -l) -ge $NCORES ]]; then - # now there are $N jobs already running, so wait here for any job - # to be finished so there is a place to start next one. - wait -n - fi - -done -wait diff --git a/vignettes/run_rsofun_ubelix.sh b/vignettes/run_rsofun_ubelix.sh deleted file mode 100644 index 06639a38..00000000 --- a/vignettes/run_rsofun_ubelix.sh +++ /dev/null @@ -1,2 +0,0 @@ -module load Workspace_Home -module load R diff --git a/vignettes/sensitivity_analysis.Rmd b/vignettes/sensitivity_analysis.Rmd index f58844cb..8fca0ad0 100644 --- a/vignettes/sensitivity_analysis.Rmd +++ b/vignettes/sensitivity_analysis.Rmd @@ -77,6 +77,7 @@ ll_pmodel( par_v = c( rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, kc_jmax = 0.41, + whc = 430, error_gpp = 0.9 # value from previous simulations )) ``` @@ -97,6 +98,7 @@ par_cal_best <- c( rd_to_vcmax = 0.014, tau_acclim = 30.0, kc_jmax = 0.41, + whc = 430, error_gpp = 1 ) @@ -111,6 +113,7 @@ par_cal_min <- c( rd_to_vcmax = 0.01, tau_acclim = 7.0, kc_jmax = 0.2, + whc = 430, error_gpp = 0.01 ) @@ -125,6 +128,7 @@ par_cal_max <- c( rd_to_vcmax = 0.1, tau_acclim = 60.0, kc_jmax = 0.8, + whc = 430, error_gpp = 4 ) ``` diff --git a/vignettes/simple_test_script_phydro.R b/vignettes/simple_test_script_phydro.R deleted file mode 100644 index d48b54af..00000000 --- a/vignettes/simple_test_script_phydro.R +++ /dev/null @@ -1,92 +0,0 @@ -library(tidyverse) -library(ggpointdensity) - -setwd("~/codes/rsofun") - -# load("data/AU-ASM_p_hydro_drivers.rda") -# load("data/AU-ASM_p_hydro_validation.rda") - -p_hydro_validation$data[[1]] = p_hydro_validation$data[[1]] %>% - rename(le=latenth) - -p_hydro_drivers$forcing[[1]]$netrad[is.na(p_hydro_drivers$forcing[[1]]$netrad)] = 0 -p_hydro_drivers$forcing[[1]]$patm[p_hydro_drivers$forcing[[1]]$patm < 0] = 1.0135e5 - -p_hydro_drivers$params_siml[[1]]$use_phydro = T -p_hydro_drivers$params_siml[[1]]$use_pml = F - -# Calibrated parameters for AU-ASM site -params_modl <- list( - kphio = 0.08857451, # setup ORG in Stocker et al. 2020 GMD - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - phydro_K_plant = 3.035552e-17, - phydro_p50_plant = -0.8639834, - phydro_gamma = 0.2859356, - phydro_b_plant = 1, - phydro_alpha = 0.08, - bsoil = 3, - Ssoil = 113, - whc = 676.9775 -) - -# run the model for these parameters -output <- rsofun::runread_pmodel_f( - p_hydro_drivers, - par = params_modl -) - -print( -output$data[[1]] %>% - select(date, gpp, le, aet, pet, netrad, wcont) %>% - pivot_longer(-date) %>% - mutate(type="pred") %>% - rbind( - p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="obs") - ) %>% - ggplot(aes(x=date, y=value, group=type, col=type)) + - theme_classic()+ - geom_line() + - facet_wrap(~name, scales="free") -) - -get_density <- function(x, y, ...) { - df = tibble(x=x, y=y) %>% drop_na - dens <- MASS::kde2d(df$x, df$y, ...) - ix <- findInterval(x, dens$x) - iy <- findInterval(y, dens$y) - ii <- cbind(ix, iy) - return(dens$z[ii]) -} - - -print( - output$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="pred") %>% - rbind( - p_hydro_validation$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="obs") - ) %>% - pivot_wider(names_from = type, values_from = value) %>% - group_by(name) %>% - reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% - ggplot(aes(y=obs, x=pred, colour=density)) + - scale_color_viridis_c() + - geom_point(alpha=0.7) + - geom_abline(slope=1, intercept=0, col="red")+ - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~name, scales = "free", nrow = 1)+ - labs(colour="Density") -) - diff --git a/vignettes/simple_test_script_pmodel.R b/vignettes/simple_test_script_pmodel.R deleted file mode 100644 index 7d282842..00000000 --- a/vignettes/simple_test_script_pmodel.R +++ /dev/null @@ -1,36 +0,0 @@ -library(tidyverse) - -params_modl <- list( - kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.0, - beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - whc = 432 -) - -p_model_drivers_test = p_model_drivers -p_model_drivers_test$params_siml[[1]]$use_phydro = F -p_model_drivers_test$params_siml[[1]]$use_pml = F -p_model_drivers_test$forcing[[1]]$netrad = NA -p_model_drivers_test$forcing_acclim = p_model_drivers_test$forcing - -# run the model for these parameters -output <- rsofun::runread_pmodel_f( - p_model_drivers_test, - par = params_modl -) - -print( -output$data[[1]] %>% - select(date, gpp, aet, pet, wscal, netrad, wcont) %>% - pivot_longer(-date) %>% - ggplot(aes(x=date, y=value)) + - theme_classic()+ - geom_line(col="seagreen") + - facet_wrap(~name, scales="free") -) \ No newline at end of file diff --git a/vignettes/site_list.txt b/vignettes/site_list.txt deleted file mode 100644 index 3c494c46..00000000 --- a/vignettes/site_list.txt +++ /dev/null @@ -1,244 +0,0 @@ -DE-Hai -BE-Vie -CA-Cbo -DE-Geb -DE-Tha -SE-Deg -US-Me2 -IT-MBo -CZ-BK1 -DE-Gri -FR-Gri -GF-Guy -FR-Aur -FR-Fon -US-GLE -US-Ho2 -BE-Bra -CA-TP3 -US-MOz -BE-Lon -DE-Kli -IT-Col -NL-Loo -RU-Fyo -US-Ton -US-UMd -US-Var -US-Wjs -CA-TP4 -FR-FBn -IT-Ren -IT-Tor -US-ARM -US-ICs -US-Mpj -US-Ro1 -CA-TP1 -FR-Pue -IT-Lav -RU-Cok -US-BZS -US-KFS -AT-Neu -CH-Dav -CH-Lae -FI-Hyy -IT-Noe -US-BZB -US-BZF -US-Myb -US-NR1 -US-SRM -BE-Dor -CA-Gro -CA-Oas -CA-Obs -DK-Sor -IT-Ro2 -US-ICt -US-Jo2 -US-Oho -US-Tw1 -US-Wkg -CA-LP1 -CZ-wet -DE-RuR -DE-RuS -DE-RuW -IT-Cpz -CA-Man -FR-LBr -IT-BCi -IT-SRo -NL-Hor -US-Los -US-Ro4 -US-Syv -US-WCr -AU-DaS -AU-Sam -CA-Qfo -CH-Cha -CH-Fru -CZ-BK2 -DE-Hzd -DE-Obe -FI-Sod -FR-Hes -MY-PSO -SE-Nor -US-Blo -US-IB2 -US-KLS -US-SRC -US-SRG -US-Whs -AU-Cow -AU-DaP -AU-Dry -CA-ER1 -CA-TPD -CZ-Lnz -DE-HoH -IT-Ro1 -IT-SR2 -SE-Htm -US-Atq -US-Me3 -US-NGB -US-ORv -US-Rms -US-Rwf -US-Rws -AU-How -AU-Stp -AU-Wom -CA-DBB -CA-Qcu -CA-TP2 -CH-Oe1 -DE-Akm -DE-Lnf -FI-Sii -FI-Var -IT-Lsn -US-Bi1 -US-Bi2 -US-Me6 -US-ONA -US-Ro6 -US-Twt -US-xBR -AU-ASM -AU-Cpr -AU-Rig -CA-NS5 -CA-NS6 -CA-NS7 -CA-SF3 -CN-Cng -DE-Lkb -DE-Spw -ES-Amo -FI-Jok -FR-LGt -GH-Ank -GL-ZaH -HU-Bug -IT-Cp2 -US-BZo -US-CF1 -US-CF3 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-US-AR2 -US-Aud -US-GBT -ZA-Kru -AU-Cum -AU-Emr -AU-Lit -AU-TTE -AU-Wrr -BR-Npw -CA-NS3 -CN-Dan -DK-Eng -IT-CA3 -IT-Isp -IT-LMa -PA-SPn -PE-QFR -RU-Ha1 -SD-Dem -SJ-Adv -US-ARb -US-ARc -US-LWW -US-Me5 -US-Rwe -AU-Ade -AU-RDF -AU-Rob -AU-Ync -CA-DB2 -CN-Du2 -CN-Sw2 -IT-CA1 -IT-La2 -US-Hn3 -US-KS1 -US-Tw5 From 60575ed91f1970b96492e2d86621c49192310a15 Mon Sep 17 00:00:00 2001 From: Jaideep Date: Wed, 14 Aug 2024 01:08:24 +0200 Subject: [PATCH 055/112] add new format demo data --- data/p_model_drivers_newformat.rds | Bin 0 -> 508831 bytes data/p_model_validation_newformat.rds | Bin 0 -> 83553 bytes vignettes/pmodel_use_newdata.Rmd | 89 +++++++++++++++----------- 3 files changed, 50 insertions(+), 39 deletions(-) create mode 100644 data/p_model_drivers_newformat.rds create mode 100644 data/p_model_validation_newformat.rds diff --git a/data/p_model_drivers_newformat.rds b/data/p_model_drivers_newformat.rds new file mode 100644 index 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i!{5d9*9!i6mjTGh^MAkWe?v92;r{_$&?Wl4CISHKop$#C literal 0 HcmV?d00001 diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd index 8194bc2b..a93cb183 100644 --- a/vignettes/pmodel_use_newdata.Rmd +++ b/vignettes/pmodel_use_newdata.Rmd @@ -33,47 +33,58 @@ The `rsofun` package and framework includes two main models. The `pmodel` and `b The package includes two demo datasets to run and validate pmodel output using GPP observations. These files can be directly loaded into your workspace by typing: ```{r} -library(rsofun) +# library(rsofun) +# +# drivers_path = "/data/scratch/jaideep/FluxDataKit/v4.0" +# +# # this is to deal with an error p_model_drivers.rds not being found +# p_model_drivers = readRDS(file.path(drivers_path, "p_model_drivers.rds")) |> +# filter(sitename == "FR-Pue") |> +# rowwise() |> +# # manipulate all forcing variables, filter years and gapfill missing values +# mutate(across( +# starts_with("forcing"), +# ~.x |> +# filter(lubridate::year(date) >= 2007 & +# lubridate::year(date) <= 2012) |> +# # mutate(rain = ifelse(is.na(rain), yes=0, no=rain)) |> +# list() +# )) |> +# mutate(site_info = site_info |> +# # Set WHC from demo data just to check match, new data has ~250 +# mutate(whc=432) |> +# list()) |> +# mutate(params_siml = params_siml |> +# # First try with setting use_gs to false +# mutate(use_gs=F) |> +# list()) +# +# +# p_model_validation = readRDS(file.path(drivers_path, "p_model_validation.rds")) |> +# filter(sitename == "FR-Pue") |> +# rowwise() |> +# # Use NT GPP as gpp, and filter years across all data columns +# mutate(across( +# starts_with("data"), +# ~ .x |> +# mutate(gpp = gpp_nt, +# le = le_corr*86400) |> +# filter(lubridate::year(date) >= 2007 & +# lubridate::year(date) <= 2012) |> +# list() +# )) +# +# saveRDS(p_model_drivers, file = here::here("data/p_model_drivers_newformat.rds")) +# +# saveRDS(p_model_validation, file = here::here("data/p_model_validation_newformat.rds")) +``` -drivers_path = "/data/scratch/jaideep/FluxDataKit/v4.0" - -# this is to deal with an error p_model_drivers.rds not being found -p_model_drivers = readRDS(file.path(drivers_path, "p_model_drivers.rds")) |> - filter(sitename == "FR-Pue") |> - rowwise() |> - # manipulate all forcing variables, filter years and gapfill missing values - mutate(across( - starts_with("forcing"), - ~.x |> - filter(lubridate::year(date) >= 2007 & - lubridate::year(date) <= 2012) |> - # mutate(rain = ifelse(is.na(rain), yes=0, no=rain)) |> - list() - )) |> - mutate(site_info = site_info |> - # Set WHC from demo data just to check match, new data has ~250 - mutate(whc=432) |> - list()) |> - mutate(params_siml = params_siml |> - # First try with setting use_gs to false - mutate(use_gs=F) |> - list()) - - -p_model_validation = readRDS(file.path(drivers_path, "p_model_validation.rds")) |> - filter(sitename == "FR-Pue") |> - rowwise() |> - # Use NT GPP as gpp, and filter years across all data columns - mutate(across( - starts_with("data"), - ~ .x |> - mutate(gpp = gpp_nt, - le = le_corr*86400) |> - filter(lubridate::year(date) >= 2007 & - lubridate::year(date) <= 2012) |> - list() - )) +### Read drivers + +```{r} +p_model_drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")) +p_model_validation = readRDS(file = here::here("data/p_model_validation_newformat.rds")) ``` ### Visualize drivers From c4f8cf371dbfb92e2dfbd0fcd1363df341e19672 Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Thu, 15 Aug 2024 09:24:22 +0200 Subject: [PATCH 056/112] implemented PML in waterbal routine (to be used in combination with p-model). Successful compilation. Not tested. --- src/forcing_siterun_pmodel.mod.f90 | 4 +- src/gpp_pmodel.mod.f90 | 34 ++-- src/interface_biosphere_pmodel.mod.f90 | 1 - src/photosynth_phydro.mod.f90 | 2 +- src/photosynth_pmodel.mod.f90 | 6 +- src/sofun_r.f90 | 4 +- src/tile_pmodel.mod.f90 | 11 +- src/waterbal_splash.mod.f90 | 219 ++++++++++++++++++------- 8 files changed, 196 insertions(+), 85 deletions(-) diff --git a/src/forcing_siterun_pmodel.mod.f90 b/src/forcing_siterun_pmodel.mod.f90 index 088f220a..8d637378 100644 --- a/src/forcing_siterun_pmodel.mod.f90 +++ b/src/forcing_siterun_pmodel.mod.f90 @@ -25,10 +25,11 @@ module md_forcing_pmodel real(kind=sp) :: dppfd ! mol m-2 d-1 real(kind=sp) :: dnetrad! W m-2 real(kind=sp) :: dpatm ! Pa + real(kind=sp) :: dwind ! m s-1 end type climate_type type vegcover_type - real :: dfapar ! fraction of absorbed photosynthetically active radiation + real :: dfapar ! fraction of absorbed photosynthetically active radiation end type vegcover_type type landuse_type @@ -94,6 +95,7 @@ function getclimate( nt, forcing, climateyear_idx, in_ppfd, in_netrad ) result ( out_climate(:)%dpatm = real(forcing(idx_start:idx_end, 10)) out_climate(:)%dtmin = real(forcing(idx_start:idx_end, 11)) out_climate(:)%dtmax = real(forcing(idx_start:idx_end, 12)) + out_climate(:)%dwind = 2.0 ! xxx try end function getclimate diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 6ade9fdc..04aa6caa 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -344,16 +344,19 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init end if !---------------------------------------------------------------- - ! Stomatal conductance + ! Stomatal conductance to CO2 !---------------------------------------------------------------- - if (.not. use_phydro) then - ! Jaideep NOTE: I have applied the soilmstress factor to gs here because it is needed in calculating canopy transpiration - tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint * soilmstress - else - ! Jaideep NOTE: unit of gs_accl here is mol m-2 s-1. - ! Jaideep FIXME: It's too complicated to convert it to unit as in pmodel, but should be done at some point - tile_fluxes(lu)%plant(pft)%gs_accl = out_phydro_inst%gs - end if + ! Apply soil moisture stress function to stomatal conductance + tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint * soilmstress + + ! if (.not. use_phydro) then + ! ! Jaideep NOTE: I have applied the soilmstress factor to gs here because it is needed in calculating canopy transpiration + ! tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint * soilmstress + ! else + ! ! Jaideep NOTE: unit of gs_accl here is mol m-2 s-1. + ! ! Jaideep FIXME: It's too complicated to convert it to unit as in pmodel, but should be done at some point + ! tile_fluxes(lu)%plant(pft)%gs_accl = out_phydro_inst%gs + ! end if !---------------------------------------------------------------- ! Water potentials @@ -385,17 +388,18 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init ! Using P-model gs ! Note here that stomatal conductance is already normalized by patm (=gs/patm) so E = 1.6 * (gs/patm) * vpd, which is the same as 1.6 gs (vpd/patm) ! but it is expressed per unit absorbed light, so multiply by PPFD*fapar + ! dtransp is in mm d-1 tile_fluxes(lu)%plant(pft)%dtransp = (1.6 & ! 1.6 - * tile_fluxes(lu)%plant(pft)%gs_accl * tile(lu)%canopy%fapar * climate%dppfd & ! gs - * climate%dvpd) & ! D - * 0.018015 * (1.0d0 / rho_water) & - * myinterface%params_siml%secs_per_tstep * 1000 ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 + * tile_fluxes(lu)%plant(pft)%gs_accl * tile(lu)%canopy%fapar * climate%dppfd & ! gs + * climate%dvpd) & ! D + * h2o_molmass * (1.0d0 / rho_water) & + * myinterface%params_siml%secs_per_tstep ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 else ! Using Phydro gs tile_fluxes(lu)%plant(pft)%dtransp = out_phydro_inst%e & ! Phydro e is 1.6 gs D - * 0.018015 * (1.0d0 / rho_water) & - * myinterface%params_siml%secs_per_tstep * 1000 ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 + * h2o_molmass * (1.0d0 / rho_water) & + * myinterface%params_siml%secs_per_tstep ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 ! ~~ This has been moved to waterbal_splash ~~ ! tile_fluxes(lu)%canopy%dpet_e_soil = out_phydro_inst%le_s_wet & diff --git a/src/interface_biosphere_pmodel.mod.f90 b/src/interface_biosphere_pmodel.mod.f90 index a2f82974..8b8ffbe0 100644 --- a/src/interface_biosphere_pmodel.mod.f90 +++ b/src/interface_biosphere_pmodel.mod.f90 @@ -41,7 +41,6 @@ module md_interface_pmodel integer :: year real :: pco2 type(gridtype) :: grid - ! real :: whc_prescr real :: canopy_height real :: reference_height type(climate_type), dimension(ndayyear) :: climate diff --git a/src/photosynth_phydro.mod.f90 b/src/photosynth_phydro.mod.f90 index b6a9bd1d..8ae58f11 100644 --- a/src/photosynth_phydro.mod.f90 +++ b/src/photosynth_phydro.mod.f90 @@ -231,7 +231,7 @@ function calc_cp_moist_air(tc) result(cp) my_tc * (2.050632750e-3 + & my_tc * (-1.631537093e-4 + & my_tc * (6.212300300e-6 - & - my_tc * (8.830478888e-8 - & + my_tc * (8.830478888e-8 - & ! XXX FIXME (beni): should be a + not - ? my_tc * 5.071307038e-10))))) * 1e3 end function calc_cp_moist_air diff --git a/src/photosynth_pmodel.mod.f90 b/src/photosynth_pmodel.mod.f90 index 916939f6..d8103b64 100644 --- a/src/photosynth_pmodel.mod.f90 +++ b/src/photosynth_pmodel.mod.f90 @@ -28,7 +28,7 @@ module md_photosynth real :: iwue ! intrinsic water use efficiency = A / gs = ca - ci = ca ( 1 - chi ) , unitless real :: lue ! light use efficiency (mol CO2 / mol photon) ! real :: assim ! leaf-level assimilation rate (mol CO2 m-2 s-1) - real :: gs_setpoint ! stomatal conductance to CO2 (mol C Pa-1 m-2 s-1) + real :: gs_setpoint ! stomatal conductance to CO2 per unit absorbed light (mol C Pa-1 m-2 s-1) ! real :: gs_unitfapar ! stomatal conductance to CO2 per unit fapar (mol C Pa-1 m-2 s-1) ! real :: gs_unitiabs ! stomatal conductance to CO2 per unit absorbed light (mol C Pa-1 m-2 s-1) ! real :: gpp ! gross primary productivity (g CO2 m-2 d-1) @@ -100,7 +100,7 @@ function pmodel( kphio, beta, kc_jmax, ppfd, co2, tc, vpd, patm, c4, method_optc real :: kmm ! Michaelis-Menten coefficient (Pa) real :: gammastar ! photorespiratory compensation point - Gamma-star (Pa) real :: ca ! ambient CO2 partial pressure, (Pa) - real :: gs_setpoint ! stomatal conductance to CO2 (mol CO2 Pa-1 m-2 s-1) + real :: gs_setpoint ! stomatal conductance to CO2 per unit absorbed light (mol CO2 Pa-1 m-2 s-1) ! real :: gs_unitfapar ! stomatal conductance to CO2 (mol CO2 Pa-1 m-2 s-1) ! real :: gs_unitiabs ! stomatal conductance to CO2 (mol CO2 Pa-1 m-2 s-1) real :: ci ! leaf-internal partial pressure, (Pa) @@ -355,7 +355,7 @@ function pmodel( kphio, beta, kc_jmax, ppfd, co2, tc, vpd, patm, c4, method_optc ! xxx to be addressed: what's the stomatal conductance in C4? gs_setpoint = 9999.0 else - gs_setpoint = (lue / c_molmass) / ( ca - ci + 0.1 ) + gs_setpoint = (lue / c_molmass) / (ca - ci) end if diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index 7d64a56c..1507d1f0 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -150,8 +150,8 @@ subroutine pmodel_f( & !---------------------------------------------------------------- ! Other site-specific PARAMETERS !---------------------------------------------------------------- - myinterface%canopy_height = real (canopy_height) - myinterface%reference_height = real (reference_height) + myinterface%canopy_height = real(canopy_height) + myinterface%reference_height = real(reference_height) !---------------------------------------------------------------- ! GET CALIBRATABLE MODEL PARAMETERS (so far a small list) diff --git a/src/tile_pmodel.mod.f90 b/src/tile_pmodel.mod.f90 index a65bce66..b42701ca 100644 --- a/src/tile_pmodel.mod.f90 +++ b/src/tile_pmodel.mod.f90 @@ -102,8 +102,6 @@ module md_tile_pmodel real :: daet_e_soil ! daily soil evaporation (J m-2 d-1) real :: daet_canop ! daily canopy transpiration (mm d-1) real :: daet_e_canop ! daily canopy transpiration (J m-2 d-1) - real :: dpet_soil ! daily potential evaporation from soil (J m-2 d-1) - real :: dpet_e_soil ! daily potential evaporation from soil (mm d-1) real :: cpa ! alpha = equilibrium ET over potential ET (EET/PET, unitless) real :: dtransp ! work in progress @@ -542,10 +540,13 @@ subroutine diag_daily( tile, tile_fluxes ) !---------------------------------------------------------------- ! Sum over PFTs to get canopy-level quantities !---------------------------------------------------------------- + ! xxx test + print*,'Reasonable? tile(lu)%plant(:)%fpc_grid ', tile(lu)%plant(:)%fpc_grid + do lu=1,nlu - tile_fluxes(lu)%canopy%dgpp = sum(tile_fluxes(lu)%plant(:)%dgpp) - tile_fluxes(lu)%canopy%dtransp = sum(tile_fluxes(lu)%plant(:)%dtransp) - tile_fluxes(lu)%canopy%drd = sum(tile_fluxes(lu)%plant(:)%drd) + tile_fluxes(lu)%canopy%dgpp = sum(tile_fluxes(lu)%plant(:)%dgpp * tile(lu)%plant(:)%fpc_grid) + tile_fluxes(lu)%canopy%dtransp = sum(tile_fluxes(lu)%plant(:)%dtransp * tile(lu)%plant(:)%fpc_grid) + tile_fluxes(lu)%canopy%drd = sum(tile_fluxes(lu)%plant(:)%drd * tile(lu)%plant(:)%fpc_grid) tile_fluxes(lu)%canopy%vcmax25 = sum(tile_fluxes(lu)%plant(:)%vcmax25 * tile(lu)%plant(:)%fpc_grid) tile_fluxes(lu)%canopy%jmax25 = sum(tile_fluxes(lu)%plant(:)%jmax25 * tile(lu)%plant(:)%fpc_grid) tile_fluxes(lu)%canopy%vcmax = sum(tile_fluxes(lu)%plant(:)%vcmax * tile(lu)%plant(:)%fpc_grid) diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index 0ffcfd79..589d0d80 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -78,7 +78,7 @@ subroutine waterbal( tile, tile_fluxes, grid, climate, fapar, using_phydro, usin real :: sw ! evaporative supply rate (mm/h) ! Loop over gricell tiles - do lu=1,nlu + luloop: do lu=1,nlu ! Calculate evaporative supply rate, mm/h sw = kCw * tile(lu)%soil%phy%wcont / tile(lu)%soil%params%whc @@ -138,7 +138,7 @@ subroutine waterbal( tile, tile_fluxes, grid, climate, fapar, using_phydro, usin ! WSCAL = (WCONT - PWP) / (FC - PWP) tile(lu)%soil%phy%wscal = tile(lu)%soil%phy%wcont / tile(lu)%soil%params%whc - end do + end do luloop end subroutine waterbal @@ -303,22 +303,34 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g real :: gamma ! psychrometric constant (Pa K-1) ! xxx Zhang et al. use it in units of (kPa K-1), probably they use sat_slope in kPa/K, too. real :: sat_slope ! slope of saturation vapour pressure vs. temperature curve, Pa K-1 real :: lv ! enthalpy of vaporization, J/kg + real :: cp ! heat capacity of moist air, J kg-1 K-1 real :: rho_water ! density of water (g m-3) real :: energy_to_mm ! Conversion factor to convert energy (J m-2 day) to mass (mm day-1) real :: f_soil_aet ! Fractional reduction of soil AET due to moisture limitation real :: p_over_pet_memory ! P/PET real, save :: p_memory = 0.0 ! precipitation, damped variability real, save :: pet_memory = 0.0 ! equilibrium evapotranspiration, damped variability - - real :: rx ! variable substitute (mm/hr)/(W/m^2) - real :: hi, cos_hi ! intersection hour angle, degrees + + real :: rx ! variable substitute (mm/hr)/(W/m^2) + real :: hi, cos_hi ! intersection hour angle, degrees + + ! Used when using_pml == .true. + real :: ga ! aerodynamic conductance to water vapour + real :: epsilon ! variable substitute + real :: gw ! canopy conductance to water vapour + + ! Used when using_gs == .true. + real :: dpet_soil ! potential soil evaporation (not limited by soil moisture), mm d-1 !--------------------------------------------------------- ! Calculate water-to-energy conversion (econ), m^3/J !--------------------------------------------------------- - ! Slope of saturation vap press temp curve, Pa/K + ! Slope of saturation vap press temp curve, Pa K-1 sat_slope = calc_sat_slope( climate%dtemp ) + ! Heat capacity of moist air, J kg-1 K-1 + cp = calc_cp_moist_air( climate%dtemp ) + ! Enthalpy of vaporization, J/kg lv = calc_enthalpy_vap( climate%dtemp ) @@ -334,7 +346,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g ! JAIDEEP: If it's just conversion from mass to energy, the above formula is correct. This already has the Priestly Taylor factor (s/(s+y)) built in, so this ! should not be used for mere conversion. I would suggest you use just the factor s/(s+y) separately in the respective equations for clarity. - tile_fluxes%canopy%econ = sat_slope / (lv * rho_water * (sat_slope + gamma)) ! MORE PRECISELY - this is to convert energy into mass (water). . + tile_fluxes%canopy%econ = sat_slope / (lv * rho_water * (sat_slope + gamma)) ! MORE PRECISELY - this is to convert energy into mass (water). !--------------------------------------------------------- ! Daily condensation, mm d-1 @@ -342,25 +354,25 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g tile_fluxes%canopy%dcn = 1000.0 * tile_fluxes%canopy%econ * abs(tile_fluxes%canopy%drnn) ! Jaideep: Why abs here? drnn must be negative (emitted from earth) for condensation right? !--------------------------------------------------------- - ! 17. Estimate daily EET, mm d-1 + ! Estimate daily EET, mm d-1 !--------------------------------------------------------- ! Eq. 70, SPLASH 2.0 Documentation tile_fluxes%canopy%deet = 1000.0 * tile_fluxes%canopy%econ * tile_fluxes%canopy%drn !--------------------------------------------------------- - ! 18. Estimate daily PET, mm d-1 + ! Estimate daily PET, mm d-1 !--------------------------------------------------------- ! Eq. 72, SPLASH 2.0 Documentation tile_fluxes%canopy%dpet = ( 1.0 + kw ) * tile_fluxes%canopy%deet tile_fluxes%canopy%dpet_e = tile_fluxes%canopy%dpet / energy_to_mm ! JAIDEEP FIXME [resolved]: Oops! This is a case where you should use a simple mass-energy conversion, not econ !--------------------------------------------------------- - ! 19. Calculate variable substitute (rx), (mm/hr)/(W/m^2) + ! Calculate variable substitute (rx), (mm/hr)/(W/m^2) !--------------------------------------------------------- rx = 1000.0 * 3600.0 * ( 1.0 + kw ) * tile_fluxes%canopy%econ !--------------------------------------------------------- - ! 20. Calculate the intersection hour angle (hi), degrees + ! Calculate the intersection hour angle (hi), degrees !--------------------------------------------------------- cos_hi = sw/(rw*rv*rx) + tile_fluxes%canopy%rnl/(rw*rv) - ru/rv ! sw contains info of soil moisture (evaporative supply rate) @@ -375,38 +387,95 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g end if !--------------------------------------------------------- - ! 21. Estimate daily AET (tile_fluxes%canopy%daet), mm d-1 + ! Estimate daily AET (tile_fluxes%canopy%daet), mm d-1 !--------------------------------------------------------- - ! TODO: 3 options: SPLASH only, 1.6gsD + SPLASH for soil, PM + SPLASH for soil ! JAIDEEP FIXME: soil PET calcs should be identical for P and Phydro, but depending on whether in_netrad is used or not, ! when implementing in_netrad condition, uncomment the lines marked by arrows - if (.not. using_gs) then + if (.not. using_gs) then + !--------------------------------------------------------- + ! SPLASH AET + !--------------------------------------------------------- ! When not using stomatal conductance, we use Priestly-Taylor formulation for the whole gridcell using all of incoming net radiation ! Eq. 81, SPLASH 2.0 Documentation tile_fluxes%canopy%daet = (24.0/pi) * (radians(sw * hi) + rx * rw * rv * (dgsin(hn) - dgsin(hi)) + & radians((rx * rw * ru - rx * tile_fluxes%canopy%rnl) * (hn - hi))) ! JAIDEEP FIXME: Technically correct, but for clarity, apply radians to just (hn-hi) ? tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet / energy_to_mm ! JAIDEEP FIXME [resolved]: Oops! This is a case where you should use a simple mass-energy conversion, not econ - else - ! Else we use: ET = Pmodel-Transpiration (T) + (1-fapar) * Leuning-Soil-Evaporation (S) - ! NOTE: T was calculated in gpp_pmodel and stored in tile_fluxes%plant(pft)%dtransp, which was aggegated into tile_fluxes%canopy%dtransp by diag_daily() - tile_fluxes%canopy%daet_canop = tile_fluxes%canopy%dtransp - tile_fluxes%canopy%daet_e_canop = tile_fluxes%canopy%daet_canop / energy_to_mm ! mm d-1 ---> J m-2 d-1 - - ! tile_fluxes%canopy%dpet_e_soil = (1.0d0 - fapar) * netrad * (par_env%epsilon / (1.0d0 + par_env%epsilon)) - tile_fluxes%canopy%dpet_soil = (1.0d0 - fapar) * tile_fluxes%canopy%drn * tile_fluxes%canopy%econ * 1.0d3 ! 1000*econ converts energy into mm evaporation - tile_fluxes%canopy%dpet_e_soil = tile_fluxes%canopy%dpet_soil / energy_to_mm ! mm d-1 ---> J m-2 d-1 - ! ^ Note: This is under wet conditions, so multiply by reduction factor (below) to get actual soil ET - - ! calculate totat AET = canopy_AET + f * soil_AET_wet, where f = running_avg(P/PET) - ! p_over_pet = (climate%dprec*86400) / (tile_fluxes%canopy%dpet_soil + 1e-6) - p_memory = dampen_variability(climate%dprec*86400, 30.0, p_memory ) ! corresponds to f in Zhang et al., 2017 Eq. 9 - pet_memory = dampen_variability(tile_fluxes%canopy%dpet_soil, 30.0, pet_memory ) ! corresponds to f in Zhang et al., 2017 Eq. 9 - p_over_pet_memory = p_memory/(pet_memory + 1e-6) ! corresponds to f in Zhang et al., 2017 Eq. 9 - f_soil_aet = max(min(p_over_pet_memory, 1.0), 0.0) ! previously was sw/kCw + + if (using_pml) then + print*,'Warning: simulation parameter use_pml == .true. but not used in combination with SPLASH-AET (using_gs == .false.).' + end if + + else + !--------------------------------------------------------- + ! 2-source ET (soil and canopy) following Zhang et al., 2017 (doi:10.1002/2017JD027025) + !--------------------------------------------------------- + ! total AET = canopy_AET + f * soil_AET_wet, where f = running_average(P/PET) + !--------------------------------------------------------- + ! Potential soil evaporation + !--------------------------------------------------------- + ! potential soil evaporation, not limited by history of P/PET + dpet_soil = (1.0 - fapar) * tile_fluxes%canopy%drn * tile_fluxes%canopy%econ * 1.0 ! 1000 * econ converts energy into mm evaporation + + !--------------------------------------------------------- + ! soil moisture limitation factor + !--------------------------------------------------------- + ! as a function of history of P/PET + ! This corresponds to the calculation of f in Zhang et al., 2017 Eq. 9, + ! but a continuous dampening (low pass filter, using dampen_variability()) is applied here instead of a running sum. + p_memory = dampen_variability(climate%dprec * secs_per_day, 30.0, p_memory ) + pet_memory = dampen_variability(dpet_soil, 30.0, pet_memory ) + p_over_pet_memory = p_memory/(pet_memory + 1e-6) ! corresponds to f in Zhang et al., 2017 Eq. 9, (+ 1e-6) to avoid division by zero + f_soil_aet = max(min(p_over_pet_memory, 1.0), 0.0) - tile_fluxes%canopy%daet_soil = f_soil_aet * tile_fluxes%canopy%dpet_soil + !--------------------------------------------------------- + ! Actual soil evaporation (mm d-1 and J d-1) + !--------------------------------------------------------- + tile_fluxes%canopy%daet_soil = f_soil_aet * dpet_soil tile_fluxes%canopy%daet_e_soil = tile_fluxes%canopy%daet_soil / energy_to_mm + if (using_pml) then + !--------------------------------------------------------- + ! Canopy transpiration using the Penman-Monteith equation + !--------------------------------------------------------- + + !--------------------------------------------------------- + ! Implementation of PML model (Zhang et al., 2017) + !--------------------------------------------------------- + ! Aerodynamic conductance (m s-1) + ga = calc_g_aero(myinterface%canopy_height, climate%dwind, myinterface%reference_height) + + ! variable substitute as used in Zhang et al. 2017 JGR + epsilon = sat_slope / gamma + + ! Convert stomatal conductance to CO2 [mol Pa-1 m-2 s-1] to + ! stomatal conductance to water [m s-1] + ! Adopted from photosynth_phydro.mod.f90 + gw = tile_fluxes%canopy%gs_accl * 1.6 * kR * (climate%dtemp + kTkelvin) + + ! latent energy flux from canopy (W m-2) + ! See also calc_transpiration_pm() in photosynth_phydro.mod.f90 + tile_fluxes%canopy%daet_e_canop = (epsilon * fapar * tile_fluxes%canopy%drn + (rho_water * cp / gamma) & + * ga * climate%dvpd) / (epsilon + 1.0 + ga / gw) + + ! ! W m-2 ---> mol m-2 s-1 + ! tile_fluxes%canopy%daet_canop = tile_fluxes%canopy%daet_e_canop & + ! * (55.5 / par_env%lv) + + ! W m-2 ---> kg m-2 s-1 + ! XXX test: these units don't convert + tile_fluxes%canopy%daet_canop = tile_fluxes%canopy%daet_e_canop * energy_to_mm + + else + !--------------------------------------------------------- + ! Canopy transpiration using the diffusion equation (mm d-1) + !--------------------------------------------------------- + ! Transpiration via diffusion is calculated in gpp(). Take + ! the canopy-level sum (weighted over PFTs by their fractional coverage) + tile_fluxes%canopy%daet_canop = tile_fluxes%canopy%dtransp + tile_fluxes%canopy%daet_e_canop = tile_fluxes%canopy%daet_canop / energy_to_mm ! mm d-1 ---> J m-2 d-1 + + end if + tile_fluxes%canopy%daet = tile_fluxes%canopy%daet_canop + tile_fluxes%canopy%daet_soil tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet_e_canop + tile_fluxes%canopy%daet_e_soil @@ -418,7 +487,6 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g end if - ! print*,'in waterbal: sw, hi, rx, rw, rv, hn, hi, ru ', sw, hi, rx, rw, rv, hn, hi, ru ! xxx debug ! if (splashtest) then @@ -436,7 +504,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g ! end if !--------------------------------------------------------- - ! 22. Calculate Cramer-Prentice-Alpha, (unitless) + ! Calculate Cramer-Prentice-Alpha, (unitless) !--------------------------------------------------------- if (tile_fluxes%canopy%deet>0.0) then tile_fluxes%canopy%cpa = tile_fluxes%canopy%daet / tile_fluxes%canopy%deet @@ -446,6 +514,34 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g end subroutine calc_et + + function calc_g_aero(h_canopy, v_wind, z_measurement) result(g_aero) + !///////////////////////////////////////////////////////////////////////// + ! Aerodynamic conductance [m s-1] + ! Copied from photosynth_phydro.mod.f90 + ! To convert to mol m-2 s-1, see this: https://rdrr.io/cran/bigleaf/man/ms.to.mol.html (but not convincing) + ! Refs: + ! Eq 13 in Leuning et al (2008). https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2007WR006562 + ! Eq 7 in Zhang et al (2008): https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2017JD027025 + ! Box 4 in https://www.fao.org/3/x0490e/x0490e06.htm + !------------------------------------------------------------------------- + real, intent(in) :: h_canopy ! canopy height (m) + real, intent(in) :: v_wind ! wind speed (m s-1) + real, intent(in) :: z_measurement ! reference height (m) + real :: k_karman, d, z_om, z_ov + + ! function return variable + real :: g_aero ! Aerodynamic conductance [m s-1] + + k_karman = 0.41 ! von Karman's constant [-] + d = h_canopy * 2.0 / 3.0 ! zero-plane displacement height [m] + z_om = 0.123 * h_canopy ! roughness lengths governing transfer of water and momentum [m] + z_ov = 0.1 * z_om + + g_aero = (k_karman * k_karman * v_wind) / (log((z_measurement - d) / z_om) * log((z_measurement - d) / z_ov)) + + end function calc_g_aero + function get_snow_rain( pr, sn, tc, snow ) result( out_snow_rain ) !///////////////////////////////////////////////////////////////////////// @@ -791,18 +887,43 @@ function density_h2o( tc, press ) end function density_h2o - function psychro( tc, press ) + function calc_cp_moist_air(tc) result(cp) + !---------------------------------------------------------------- + ! Calculate the specific heat capacity of moist air, J kg-1 K-1 + ! Eq. 47, Tsilingiris (2008) + !---------------------------------------------------------------- + real, intent(in) :: tc ! temperature (deg C) + real :: my_tc + + ! function return variable + real :: cp + + my_tc = max(min(tc, 100.0), 0.0) + + cp = 1.0e3*(& + 1.0045714270& + + 2.050632750e-3 *my_tc& + - 1.631537093e-4 *my_tc*my_tc& + + 6.212300300e-6 *my_tc*my_tc*my_tc& + - 8.830478888e-8 *my_tc*my_tc*my_tc*my_tc& + + 5.071307038e-10 *my_tc*my_tc*my_tc*my_tc*my_tc& + ) + + end function calc_cp_moist_air + + + function psychro( tc, patm ) !---------------------------------------------------------------- ! Calculates the psychrometric constant for a given temperature and pressure ! Ref: Allen et al. (1998); Tsilingiris (2008) !---------------------------------------------------------------- ! arguments - real, intent(in) :: tc ! air temperature, degrees C - real, intent(in) :: press ! atmospheric pressure, Pa + real, intent(in) :: tc ! air temperature, degrees C + real, intent(in) :: patm ! atmospheric pressure, Pa ! local variables real :: lv ! latent heat of vaporization (J/kg) - real :: cp + real :: cp ! specific heat capacity of moist air ! function return value real :: psychro ! psychrometric constant, Pa/K @@ -810,31 +931,15 @@ function psychro( tc, press ) ! local variables real :: my_tc ! adjusted temperature to avoid numerical blow-up - ! Adopted temperature adjustment from SPLASH, Python version - my_tc = tc - if (my_tc < 0) then - my_tc = 0.0 - else if (my_tc > 100) then - my_tc = 100.0 - end if - - ! Calculate the specific heat capacity of water, J/kg/K - ! Eq. 47, Tsilingiris (2008) - cp = 1.0e3*(& - 1.0045714270& - + 2.050632750e-3 *my_tc& - - 1.631537093e-4 *my_tc*my_tc& - + 6.212300300e-6 *my_tc*my_tc*my_tc& - - 8.830478888e-8 *my_tc*my_tc*my_tc*my_tc& - + 5.071307038e-10 *my_tc*my_tc*my_tc*my_tc*my_tc& - ) + ! Calculate the specific heat capacity of moist air (J/kg/K) + cp = calc_cp_moist_air(tc) ! Calculate latent heat of vaporization, J/kg lv = calc_enthalpy_vap(tc) ! Calculate psychrometric constant, Pa/K ! Eq. 8, Allen et al. (1998) - psychro = cp * kMa * press / (kMv * lv) + psychro = cp * kMa * patm / (kMv * lv) end function psychro From 5de81fb6d7ed31ef130a433a40e7df64bae69b2f Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Thu, 15 Aug 2024 11:21:23 +0200 Subject: [PATCH 057/112] tested: identical results for gs-coupled ET with diffusion (not PML). --- src/forcing_siterun_pmodel.mod.f90 | 32 ++++++++-- src/gpp_pmodel.mod.f90 | 87 +++++++++++++-------------- src/plant_pmodel.mod.f90 | 2 +- src/tile_pmodel.mod.f90 | 3 - src/waterbal_splash.mod.f90 | 22 +++---- vignettes/pmodel_use_newdata.Rmd | 96 +++++++++++++++--------------- 6 files changed, 130 insertions(+), 112 deletions(-) diff --git a/src/forcing_siterun_pmodel.mod.f90 b/src/forcing_siterun_pmodel.mod.f90 index 8d637378..b41516e8 100644 --- a/src/forcing_siterun_pmodel.mod.f90 +++ b/src/forcing_siterun_pmodel.mod.f90 @@ -185,23 +185,47 @@ function get_fpc_grid( params_siml ) result( fpc_grid_field ) ! get binary information of PFT presence from simulation parameters fpc_grid_field(:) = 0.0 - ! Code below must follow the same structure as in 'plant_pmodel.mod.f90' + ! Code below must follow the same structure as in plant_pmodel.mod.f90, getpar_modl_plant() pft = 0 if ( params_siml%ltre ) then - ! xxx dirty: call all non-grass vegetation types 'TrE', see indeces above + ! Consider all non-grass vegetation types 'TrE', see indeces above pft = pft + 1 + fpc_grid_field(:) = 0.0 + fpc_grid_field(pft) = 1.0 + end if + + if ( params_siml%ltne ) then + ! Consider all non-grass vegetation types 'TNE', see indeces above + pft = pft + 1 + fpc_grid_field(:) = 0.0 + fpc_grid_field(pft) = 1.0 + end if + + if ( params_siml%ltrd ) then + ! Consider all non-grass vegetation types 'TrE', see indeces above + pft = pft + 1 + fpc_grid_field(:) = 0.0 + fpc_grid_field(pft) = 1.0 + end if + + if ( params_siml%ltnd ) then + ! Consider all non-grass vegetation types 'TrE', see indeces above + pft = pft + 1 + fpc_grid_field(:) = 0.0 fpc_grid_field(pft) = 1.0 end if if ( params_siml%lgr3 ) then - ! xxx dirty: call all grass vegetation types 'Gr3' + ! Consider all grass vegetation types 'Gr3' pft = pft + 1 + fpc_grid_field(:) = 0.0 fpc_grid_field(pft) = 1.0 end if if ( params_siml%lgr4 ) then - ! xxx dirty: call all grass vegetation types 'Gr3' + ! Consider all grass vegetation types 'Gr3' pft = pft + 1 + fpc_grid_field(:) = 0.0 fpc_grid_field(pft) = 1.0 end if diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 04aa6caa..36b91562 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -212,6 +212,18 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init method_optci = "prentice14", & method_jmaxlim = "wang17" & ) + + ! print*,'kphio', kphio_temp + ! print*,'beta', params_gpp%beta + ! print*,'kc_jmax', params_gpp%kc_jmax + ! print*,'ppfd', ppfd_memory + ! print*,'co2', co2_memory + ! print*,'tc', temp_memory + ! print*,'vpd', vpd_memory + ! print*,'patm', patm_memory + ! print*,'c4', params_pft_plant(pft)%c4 + ! print*,'-------------------------------' + else par_cost = par_cost_type(tile(lu)%plant(pft)%phydro_alpha, & tile(lu)%plant(pft)%phydro_gamma) @@ -240,8 +252,6 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init par_control = options & ) - ! print *, temp_memory, ppfd_memory*1e6, kphio_temp, vpd_memory - ! print *, out_phydro_acclim%a, out_phydro_acclim%gs, out_phydro_acclim%dpsi end if else @@ -346,17 +356,14 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init !---------------------------------------------------------------- ! Stomatal conductance to CO2 !---------------------------------------------------------------- - ! Apply soil moisture stress function to stomatal conductance - tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint * soilmstress - - ! if (.not. use_phydro) then - ! ! Jaideep NOTE: I have applied the soilmstress factor to gs here because it is needed in calculating canopy transpiration - ! tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint * soilmstress - ! else - ! ! Jaideep NOTE: unit of gs_accl here is mol m-2 s-1. - ! ! Jaideep FIXME: It's too complicated to convert it to unit as in pmodel, but should be done at some point - ! tile_fluxes(lu)%plant(pft)%gs_accl = out_phydro_inst%gs - ! end if + if (.not. use_phydro) then + ! Jaideep NOTE: I have applied the soilmstress factor to gs here because it is needed in calculating canopy transpiration + tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint * soilmstress + else + ! Jaideep NOTE: unit of gs_accl here is mol m-2 s-1. + ! Jaideep FIXME: It's too complicated to convert it to unit as in pmodel, but should be done at some point + tile_fluxes(lu)%plant(pft)%gs_accl = out_phydro_inst%gs + end if !---------------------------------------------------------------- ! Water potentials @@ -375,39 +382,27 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init ! Density of water, kg/m^3 rho_water = calc_density_h2o( climate%dtemp, climate%dpatm ) - if (use_pml) then - ! We plug Pmodel/Phydro-derived gs into the PM equation to calculate T (note this is uncoupled PM-transpiration) - ! use PFT-specific gs for this calculation: tile_fluxes(lu)%plant(pft)%gs_accl - - ! TODO: Fill this in - ! tile_fluxes(lu)%plant(pft)%dtransp = PM_EQUATION(tile_fluxes(lu)%plant(pft)%gs_accl) - - else - ! We plug Pmodel/Phydro-derived gs into T = 1.6gsD - if (.not. use_phydro) then - ! Using P-model gs - ! Note here that stomatal conductance is already normalized by patm (=gs/patm) so E = 1.6 * (gs/patm) * vpd, which is the same as 1.6 gs (vpd/patm) - ! but it is expressed per unit absorbed light, so multiply by PPFD*fapar - ! dtransp is in mm d-1 - tile_fluxes(lu)%plant(pft)%dtransp = (1.6 & ! 1.6 - * tile_fluxes(lu)%plant(pft)%gs_accl * tile(lu)%canopy%fapar * climate%dppfd & ! gs - * climate%dvpd) & ! D - * h2o_molmass * (1.0d0 / rho_water) & - * myinterface%params_siml%secs_per_tstep ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 - - else - ! Using Phydro gs - tile_fluxes(lu)%plant(pft)%dtransp = out_phydro_inst%e & ! Phydro e is 1.6 gs D - * h2o_molmass * (1.0d0 / rho_water) & - * myinterface%params_siml%secs_per_tstep ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 - - ! ~~ This has been moved to waterbal_splash ~~ - ! tile_fluxes(lu)%canopy%dpet_e_soil = out_phydro_inst%le_s_wet & - ! * myinterface%params_siml%secs_per_tstep ! convert: J m-2 s-1 * s day-1 = J m-2 day-1 - - ! print *, "Canopy ET (mm d-1) = ", tile_fluxes(lu)%canopy%daet_canop - ! print *, "Soil LE (J m-2 d-1) = ", climate%dnetrad, tile_fluxes(lu)%canopy%daet_e_soil - end if + ! We plug Pmodel/Phydro-derived gs into T = 1.6gsD + if (.not. use_phydro) then + ! Using P-model gs + ! Note here that stomatal conductance is already normalized by patm (=gs/patm) so E = 1.6 * (gs/patm) * vpd, which is the same as 1.6 gs (vpd/patm) + ! but it is expressed per unit absorbed light, so multiply by PPFD*fapar + ! dtransp is in mm d-1 + tile_fluxes(lu)%plant(pft)%dtransp = (1.6 & ! 1.6 + * tile_fluxes(lu)%plant(pft)%gs_accl * tile(lu)%canopy%fapar * climate%dppfd & ! gs + * climate%dvpd) & ! D + * h2o_molmass * (1.0d0 / rho_water) & + * myinterface%params_siml%secs_per_tstep ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 + + ! print*,'tile_fluxes(lu)%plant(pft)%gs_accl ', tile_fluxes(lu)%plant(pft)%gs_accl + ! print*,'tile_fluxes(lu)%plant(pft)%dtransp ', tile_fluxes(lu)%plant(pft)%dtransp + + else + ! Using Phydro gs + tile_fluxes(lu)%plant(pft)%dtransp = out_phydro_inst%e & ! Phydro e is 1.6 gs D + * h2o_molmass * (1.0d0 / rho_water) & + * myinterface%params_siml%secs_per_tstep ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 + end if end do pftloop diff --git a/src/plant_pmodel.mod.f90 b/src/plant_pmodel.mod.f90 index 5f2c4115..f387b32a 100644 --- a/src/plant_pmodel.mod.f90 +++ b/src/plant_pmodel.mod.f90 @@ -44,7 +44,7 @@ module md_plant_pmodel integer :: pftno ! canopy - real :: fpc_grid ! fractional projective cover + real :: fpc_grid ! fractional projective cover, sum over all PFTs must add up to 1 (even if there is bare ground, that's treated by fAPAR) real :: lai_ind ! fraction of absorbed photosynthetically active radiation real :: fapar_ind ! fraction of absorbed photosynthetically active radiation real :: acrown ! crown area diff --git a/src/tile_pmodel.mod.f90 b/src/tile_pmodel.mod.f90 index b42701ca..406297d5 100644 --- a/src/tile_pmodel.mod.f90 +++ b/src/tile_pmodel.mod.f90 @@ -540,9 +540,6 @@ subroutine diag_daily( tile, tile_fluxes ) !---------------------------------------------------------------- ! Sum over PFTs to get canopy-level quantities !---------------------------------------------------------------- - ! xxx test - print*,'Reasonable? tile(lu)%plant(:)%fpc_grid ', tile(lu)%plant(:)%fpc_grid - do lu=1,nlu tile_fluxes(lu)%canopy%dgpp = sum(tile_fluxes(lu)%plant(:)%dgpp * tile(lu)%plant(:)%fpc_grid) tile_fluxes(lu)%canopy%dtransp = sum(tile_fluxes(lu)%plant(:)%dtransp * tile(lu)%plant(:)%fpc_grid) diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index 589d0d80..2d3170c7 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -399,7 +399,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g ! Eq. 81, SPLASH 2.0 Documentation tile_fluxes%canopy%daet = (24.0/pi) * (radians(sw * hi) + rx * rw * rv * (dgsin(hn) - dgsin(hi)) + & radians((rx * rw * ru - rx * tile_fluxes%canopy%rnl) * (hn - hi))) ! JAIDEEP FIXME: Technically correct, but for clarity, apply radians to just (hn-hi) ? - tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet / energy_to_mm ! JAIDEEP FIXME [resolved]: Oops! This is a case where you should use a simple mass-energy conversion, not econ + tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet / energy_to_mm if (using_pml) then print*,'Warning: simulation parameter use_pml == .true. but not used in combination with SPLASH-AET (using_gs == .false.).' @@ -413,8 +413,8 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g !--------------------------------------------------------- ! Potential soil evaporation !--------------------------------------------------------- - ! potential soil evaporation, not limited by history of P/PET - dpet_soil = (1.0 - fapar) * tile_fluxes%canopy%drn * tile_fluxes%canopy%econ * 1.0 ! 1000 * econ converts energy into mm evaporation + ! potential soil evaporation, not limited by history of P/PET (mm d-1) + dpet_soil = (1.0 - fapar) * tile_fluxes%canopy%drn * tile_fluxes%canopy%econ * 1000.0 ! econ converts energy into mm evaporation !--------------------------------------------------------- ! soil moisture limitation factor @@ -424,8 +424,13 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g ! but a continuous dampening (low pass filter, using dampen_variability()) is applied here instead of a running sum. p_memory = dampen_variability(climate%dprec * secs_per_day, 30.0, p_memory ) pet_memory = dampen_variability(dpet_soil, 30.0, pet_memory ) - p_over_pet_memory = p_memory/(pet_memory + 1e-6) ! corresponds to f in Zhang et al., 2017 Eq. 9, (+ 1e-6) to avoid division by zero - f_soil_aet = max(min(p_over_pet_memory, 1.0), 0.0) + + if (pet_memory > 0.0) then + p_over_pet_memory = p_memory / pet_memory ! corresponds to f in Zhang et al., 2017 Eq. 9, (+ 1e-6) to avoid division by zero + f_soil_aet = max(min(p_over_pet_memory, 1.0), 0.0) + else + f_soil_aet = 1.0 + end if !--------------------------------------------------------- ! Actual soil evaporation (mm d-1 and J d-1) @@ -479,14 +484,9 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g tile_fluxes%canopy%daet = tile_fluxes%canopy%daet_canop + tile_fluxes%canopy%daet_soil tile_fluxes%canopy%daet_e = tile_fluxes%canopy%daet_e_canop + tile_fluxes%canopy%daet_e_soil - ! print *, "P (mm d-1), PET (mm d-1), P/PET, Avg(P/PET), f_soil_aet = ", (climate%dprec*86400), & - ! tile_fluxes%canopy%dpet_soil, p_over_pet, & - ! p_over_pet_memory, f_soil_aet - ! print *, "Canopy ET (mm d-1, J m-2 d-1) = ", tile_fluxes%canopy%daet_canop, tile_fluxes%canopy%daet_e_canop - ! print *, "Soil ET (mm d-1, J m-2 d-1) = ", tile_fluxes%canopy%daet_soil, tile_fluxes%canopy%daet_e_soil + ! print*,'waterbal: tile_fluxes%canopy%daet_canop, tile_fluxes%canopy%daet_soil ', tile_fluxes%canopy%daet_canop, tile_fluxes%canopy%daet_soil end if - ! xxx debug ! if (splashtest) then diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd index c165c9bf..947367cc 100644 --- a/vignettes/pmodel_use_newdata.Rmd +++ b/vignettes/pmodel_use_newdata.Rmd @@ -330,30 +330,30 @@ output <- rsofun::runread_pmodel_f( p_model_drivers, par = params_modl ) +``` -print( - output$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="pred") %>% - rbind( - p_model_validation$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="obs") - ) %>% - pivot_wider(names_from = type, values_from = value) %>% - group_by(name) %>% - reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% - ggplot(aes(y=obs, x=pred, colour=density)) + - scale_color_viridis_c() + - geom_point(alpha=0.7) + - geom_abline(slope=1, intercept=0, col="red")+ - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~name, scales = "free", nrow = 1)+ - labs(colour="Density") -) +```{r} +output$data[[1]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="pred") %>% + rbind( + p_model_validation$data[[1]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="obs") + ) %>% + pivot_wider(names_from = type, values_from = value) %>% + group_by(name) %>% + reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% + ggplot(aes(y=obs, x=pred, colour=density)) + + scale_color_viridis_c() + + geom_point(alpha=0.7) + + geom_abline(slope=1, intercept=0, col="red")+ + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~name, scales = "free", nrow = 1)+ + labs(colour="Density") ``` @@ -374,7 +374,7 @@ p_model_drivers <- ) ``` -Run the model and plot outputs +Run the model. ```{r} # run the model for these parameters @@ -382,30 +382,32 @@ output <- rsofun::runread_pmodel_f( p_model_drivers, par = params_modl ) +``` -print( - output$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="pred") %>% - rbind( - p_model_validation$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="obs") - ) %>% - pivot_wider(names_from = type, values_from = value) %>% - group_by(name) %>% - reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% - ggplot(aes(y=obs, x=pred, colour=density)) + - scale_color_viridis_c() + - geom_point(alpha=0.7) + - geom_abline(slope=1, intercept=0, col="red")+ - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~name, scales = "free", nrow = 1)+ - labs(colour="Density") -) +Plot outputs. + +```{r} +output$data[[1]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="pred") %>% + rbind( + p_model_validation$data[[1]] %>% + select(date, gpp, le) %>% + pivot_longer(-date) %>% + mutate(type="obs") + ) %>% + pivot_wider(names_from = type, values_from = value) %>% + group_by(name) %>% + reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% + ggplot(aes(y=obs, x=pred, colour=density)) + + scale_color_viridis_c() + + geom_point(alpha=0.7) + + geom_abline(slope=1, intercept=0, col="red")+ + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~name, scales = "free", nrow = 1)+ + labs(colour="Density") ``` ## Gs-coupled P-hydro run From 21f4e98a0f49b06eac6e7444ec5b0baf64cdd2bf Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Thu, 15 Aug 2024 12:07:29 +0200 Subject: [PATCH 058/112] got PML with P-model Gs running. Complemented vignette. Performs very good. --- R/run_pmodel_f_bysite.R | 23 +- src/waterbal_splash.mod.f90 | 20 ++ vignettes/pmodel_use_newdata.Rmd | 364 +++++++++++++++++++++++-------- 3 files changed, 308 insertions(+), 99 deletions(-) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index ecd6bc3f..68726df4 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -359,14 +359,23 @@ run_pmodel_f_bysite <- function( } # If PML is used, then ensure that site info has reference height and canopy height - if (params_siml$use_pml){ - continue = !is.nanull(site_info$canopy_height) & - !is.nanull(site_info$reference_height) - } else { - site_info$canopy_height = NA - site_info$reference_height = NA + avl_canopy_height = !is.nanull(site_info$canopy_height) + if (!avl_canopy_height){ + if (params_siml$use_pml){ + continue <- FALSE + } else { + site_info$canopy_height <- NA + } + } + + avl_reference_height = !is.nanull(site_info$reference_height) + if (!avl_reference_height){ + if (params_siml$use_pml){ + continue <- FALSE + } else { + site_info$reference_height <- NA + } } - if (continue){ diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index 2d3170c7..14860c57 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -462,6 +462,22 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g tile_fluxes%canopy%daet_e_canop = (epsilon * fapar * tile_fluxes%canopy%drn + (rho_water * cp / gamma) & * ga * climate%dvpd) / (epsilon + 1.0 + ga / gw) + + ! print*,'-----------------------' + ! print*,'canopy_height ', myinterface%canopy_height + ! print*,'dwind ', climate%dwind + ! print*,'reference_height ', myinterface%reference_height + ! print*,'epsilon ', epsilon + ! print*,'fapar ', fapar + ! print*,'net rad ', tile_fluxes%canopy%drn + ! print*,'rho_watr', rho_water + ! print*,'cp ', cp + ! print*,'gamma ', gamma + ! print*,'ga ', ga + ! print*,'vpd ', climate%dvpd + ! print*,'gw ', gw + ! print*,'-----------------------' + ! ! W m-2 ---> mol m-2 s-1 ! tile_fluxes%canopy%daet_canop = tile_fluxes%canopy%daet_e_canop & ! * (55.5 / par_env%lv) @@ -470,6 +486,8 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g ! XXX test: these units don't convert tile_fluxes%canopy%daet_canop = tile_fluxes%canopy%daet_e_canop * energy_to_mm + ! print*,'PML: tile_fluxes%canopy%daet_canop, tile_fluxes%canopy%daet_soil ', tile_fluxes%canopy%daet_canop, tile_fluxes%canopy%daet_soil + else !--------------------------------------------------------- ! Canopy transpiration using the diffusion equation (mm d-1) @@ -479,6 +497,8 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g tile_fluxes%canopy%daet_canop = tile_fluxes%canopy%dtransp tile_fluxes%canopy%daet_e_canop = tile_fluxes%canopy%daet_canop / energy_to_mm ! mm d-1 ---> J m-2 d-1 + ! print*,'DIF: tile_fluxes%canopy%daet_canop, tile_fluxes%canopy%daet_soil ', tile_fluxes%canopy%daet_canop, tile_fluxes%canopy%daet_soil + end if tile_fluxes%canopy%daet = tile_fluxes%canopy%daet_canop + tile_fluxes%canopy%daet_soil diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd index 947367cc..f433616e 100644 --- a/vignettes/pmodel_use_newdata.Rmd +++ b/vignettes/pmodel_use_newdata.Rmd @@ -1,6 +1,6 @@ --- title: "P-model usage (new data and new ET options)" -author: "Koen Hufkens, Josefa Arán, Jaideep Joshi" +author: "Koen Hufkens, Josefa Arán, Jaideep Joshi, Benjamin Stocker" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{P-model usage} @@ -20,10 +20,137 @@ knitr::opts_chunk$set( library(rsofun) library(dplyr) library(ggplot2) +library(ggthemes) +library(RColorBrewer) # fake variable as optimization isn't run pars <- list() pars$par["kphio"] <- 0.04478049 + +# model-vs-obs evaluation function +analyse_modobs <- function( + df, + mod, + obs, + relative = FALSE, + xlim = NULL, + ylim = NULL, + use_factor = NULL, + shortsubtitle = FALSE, + plot_subtitle = TRUE, + plot_linmod = TRUE, + ... + ){ + + ## rename to 'mod' and 'obs' and remove rows with NA in mod or obs + df <- df %>% + as_tibble() %>% + ungroup() %>% + dplyr::select(mod = mod, obs = obs) %>% + tidyr::drop_na(mod, obs) + + ## get linear regression (coefficients) + linmod <- lm(obs ~ mod, data = df) + + ## construct metrics table using the 'yardstick' library + df_metrics <- df %>% + yardstick::metrics(obs, mod) %>% + dplyr::bind_rows(tibble(.metric = "n", .estimator = "standard", .estimate = summarise(df, numb = n()) %>% unlist())) %>% + dplyr::bind_rows(tibble(.metric = "slope", .estimator = "standard", .estimate = coef(linmod)[2])) %>% + # dplyr::bind_rows( tibble( .metric = "nse", .estimator = "standard", .estimate = hydroGOF::NSE( obs, mod, na.rm=TRUE ) ) ) %>% + dplyr::bind_rows(tibble(.metric = "mean_obs", .estimator = "standard", .estimate = summarise(df, mean = mean(obs, na.rm = TRUE)) %>% unlist())) %>% + dplyr::bind_rows(tibble( + .metric = "prmse", .estimator = "standard", + .estimate = dplyr::filter(., .metric == "rmse") %>% dplyr::select(.estimate) %>% unlist() / + dplyr::filter(., .metric == "mean_obs") %>% + dplyr::select(.estimate) %>% + unlist() + )) %>% + dplyr::bind_rows(tibble( + .metric = "pmae", .estimator = "standard", + .estimate = dplyr::filter(., .metric == "mae") %>% dplyr::select(.estimate) %>% unlist() / + dplyr::filter(., .metric == "mean_obs") %>% + dplyr::select(.estimate) %>% + unlist() + )) %>% + dplyr::bind_rows(tibble(.metric = "bias", .estimator = "standard", .estimate = dplyr::summarise(df, mean((mod - obs), na.rm = TRUE)) %>% unlist())) %>% + dplyr::bind_rows(tibble(.metric = "pbias", .estimator = "standard", .estimate = dplyr::summarise(df, mean((mod - obs) / obs, na.rm = TRUE)) %>% unlist())) + + rsq_val <- df_metrics %>% + dplyr::filter(.metric == "rsq") %>% + dplyr::select(.estimate) %>% + unlist() %>% + unname() + rmse_val <- df_metrics %>% + dplyr::filter(.metric == "rmse") %>% + dplyr::select(.estimate) %>% + unlist() %>% + unname() + mae_val <- df_metrics %>% + dplyr::filter(.metric == "mae") %>% + dplyr::select(.estimate) %>% + unlist() %>% + unname() + bias_val <- df_metrics %>% + dplyr::filter(.metric == "bias") %>% + dplyr::select(.estimate) %>% + unlist() %>% + unname() + slope_val <- df_metrics %>% + dplyr::filter(.metric == "slope") %>% + dplyr::select(.estimate) %>% + unlist() %>% + unname() + n_val <- df_metrics %>% + dplyr::filter(.metric == "n") %>% + dplyr::select(.estimate) %>% + unlist() %>% + unname() + + if (relative) { + rmse_val <- rmse_val / mean(df$obs, na.rm = TRUE) + bias_val <- bias_val / mean(df$obs, na.rm = TRUE) + } + + rsq_lab <- format(rsq_val, digits = 2) + rmse_lab <- format(rmse_val, digits = 3) + mae_lab <- format(mae_val, digits = 3) + bias_lab <- format(bias_val, digits = 3) + slope_lab <- format(slope_val, digits = 3) + n_lab <- format(n_val, digits = 3) + + results <- tibble(rsq = rsq_val, rmse = rmse_val, mae = mae_val, bias = bias_val, slope = slope_val, n = n_val) + + if (shortsubtitle) { + subtitle <- bquote(italic(R)^2 == .(rsq_lab) ~ ~ + RMSE == .(rmse_lab)) + } else { + subtitle <- bquote(italic(R)^2 == .(rsq_lab) ~ ~ + RMSE == .(rmse_lab) ~ ~ + bias == .(bias_lab) ~ ~ + slope == .(slope_lab) ~ ~ + italic(N) == .(n_lab)) + } + + ## ggplot hexbin + gg <- df %>% + ggplot2::ggplot(aes(x = mod, y = obs)) + + geom_hex() + + scale_fill_gradientn( + colours = colorRampPalette(c("gray65", "navy", "red", "yellow"))(5) + ) + + geom_abline(intercept = 0, slope = 1, linetype = "dotted") + + # coord_fixed() + + # xlim(0,NA) + + # ylim(0,NA) + + theme_classic() + + labs(x = mod, y = obs) + + if (plot_subtitle) gg <- gg + labs(subtitle = subtitle) + if (plot_linmod) gg <- gg + geom_smooth(method = "lm", color = "red", size = 0.5, se = FALSE) + + return(list(df_metrics = df_metrics, gg = gg, linmod = linmod, results = results)) +} ``` The `rsofun` package and framework includes two main models. The `pmodel` and `biomee` (which in part relies on P-model components). Here we give a short example on how to run the `pmodel` on the included demo datasets to familiarize yourself with both the data structure and the outputs. @@ -308,9 +435,9 @@ get_density <- function(x, y, ...) { } ``` -## Run and plot Pmodel (Pmodel GPP + Priestly Taylor ET) +## P-model with SPLASH AET -Set `use_gs` flag to FALSE in params_siml so that Priestly-Taylor formulation will be used in calculation of ET (Pmodel gs will not be used). +Set `use_gs` flag to `FALSE` in params_siml so that Priestly-Taylor formulation will be used in calculation of ET (P-model gs will not be used). ```{r} p_model_drivers <- @@ -333,33 +460,40 @@ output <- rsofun::runread_pmodel_f( ``` ```{r} -output$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="pred") %>% - rbind( +df_plot <- output$data[[1]] %>% + select(date, gpp_mod = gpp, le_mod = le) %>% + left_join( p_model_validation$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="obs") - ) %>% - pivot_wider(names_from = type, values_from = value) %>% - group_by(name) %>% - reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% - ggplot(aes(y=obs, x=pred, colour=density)) + - scale_color_viridis_c() + - geom_point(alpha=0.7) + - geom_abline(slope=1, intercept=0, col="red")+ - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~name, scales = "free", nrow = 1)+ - labs(colour="Density") + select(date, gpp_obs = gpp, le_obs = le), + by = join_by(date) + ) |> + as_tibble() + +out_gpp <- analyse_modobs( + df_plot, + "gpp_mod", + "gpp_obs" +) +out_gpp$gg + + labs( + title = "GPP" + ) + +out_le <- analyse_modobs( + df_plot, + "le_mod", + "le_obs" +) +out_le$gg + + labs( + title = "LE" + ) ``` -## Gs-coupled P-model run +## P-model with Gs-coupled diffusion ET -Set `use_gs` flag to TRUE in params_siml so that the internally predicted stomatal conductance ($G_s$) from P-model will be used in calculation of ET. ET is a weighted average of canopy transpiration ($T$) and soil evaporation. Canopy transpiration is calculated as +Set `use_gs` flag to TRUE in params_siml so that the internally predicted stomatal conductance ($G_s$) from P-model will be used in calculation of ET. ET is a weighted average of canopy transpiration ($T$) and soil evaporation. Canopy transpiration is calculated using the diffusion equation as: $$ T = 1.6 \; G_s \; \text{VPD} $$ @@ -387,30 +521,96 @@ output <- rsofun::runread_pmodel_f( Plot outputs. ```{r} -output$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="pred") %>% - rbind( +df_plot <- output$data[[1]] %>% + select(date, gpp_mod = gpp, le_mod = le) %>% + left_join( p_model_validation$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="obs") - ) %>% - pivot_wider(names_from = type, values_from = value) %>% - group_by(name) %>% - reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% - ggplot(aes(y=obs, x=pred, colour=density)) + - scale_color_viridis_c() + - geom_point(alpha=0.7) + - geom_abline(slope=1, intercept=0, col="red")+ - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~name, scales = "free", nrow = 1)+ - labs(colour="Density") + select(date, gpp_obs = gpp, le_obs = le), + by = join_by(date) + ) |> + as_tibble() + +out_gpp <- analyse_modobs( + df_plot, + "gpp_mod", + "gpp_obs" +) + +out_le <- analyse_modobs( + df_plot, + "le_mod", + "le_obs" +) + +out_gpp$gg + + labs( + title = "GPP" + ) +out_le$gg + + labs( + title = "LE" + ) +``` + +## P-model with PML ET + +Set `use_gs` flag to TRUE in params_siml so that the internally predicted stomatal conductance ($G_s$) from P-model will be used in calculation of ET. ET is a weighted average of canopy transpiration ($T$) and soil evaporation. Canopy transpiration is calculated using the diffusion equation as: +$$ +T = 1.6 \; G_s \; \text{VPD} +$$ + +```{r} +p_model_drivers$params_siml[[1]]$use_gs <- TRUE +p_model_drivers$params_siml[[1]]$use_pml <- TRUE +p_model_drivers$params_siml[[1]]$use_phydro <- FALSE ``` -## Gs-coupled P-hydro run +Run the model. + +```{r} +# run the model for these parameters +output <- rsofun::runread_pmodel_f( + p_model_drivers, + par = params_modl +) +``` + +Plot outputs. + +```{r} +df_plot <- output$data[[1]] %>% + select(date, gpp_mod = gpp, le_mod = le) %>% + left_join( + p_model_validation$data[[1]] %>% + select(date, gpp_obs = gpp, le_obs = le), + by = join_by(date) + ) |> + as_tibble() + +out_gpp <- analyse_modobs( + df_plot, + "gpp_mod", + "gpp_obs" +) + +out_le <- analyse_modobs( + df_plot, + "le_mod", + "le_obs" +) + +out_gpp$gg + + labs( + title = "GPP" + ) +out_le$gg + + labs( + title = "LE" + ) +``` + + +## P-hydro run with diffusion For P-hydro, we must use the 3-hr daily max forcing as the acclimation forcing. So let's rename it in the data. @@ -481,55 +681,35 @@ output <- rsofun::runread_pmodel_f( ## Plot Phydro outputs ```{r} -print( - output$data[[1]] %>% - select(date, gpp, le, aet, pet, netrad, wcont) %>% - pivot_longer(-date) %>% - mutate(type="pred") %>% - rbind( - p_model_validation$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="obs") - ) %>% - ggplot(aes(x=date, y=value, group=type, col=type)) + - theme_classic()+ - geom_line(alpha=0.6) + - facet_wrap(~name, scales="free") +df_plot <- output$data[[1]] %>% + select(date, gpp_mod = gpp, le_mod = le) %>% + left_join( + p_model_validation$data[[1]] %>% + select(date, gpp_obs = gpp, le_obs = le), + by = join_by(date) + ) |> + as_tibble() + +out_gpp <- analyse_modobs( + df_plot, + "gpp_mod", + "gpp_obs" ) -get_density <- function(x, y, ...) { - df = tibble(x=x, y=y) %>% drop_na - dens <- MASS::kde2d(df$x, df$y, ...) - ix <- findInterval(x, dens$x) - iy <- findInterval(y, dens$y) - ii <- cbind(ix, iy) - return(dens$z[ii]) -} - -print( - output$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="pred") %>% - rbind( - p_model_validation$data[[1]] %>% - select(date, gpp, le) %>% - pivot_longer(-date) %>% - mutate(type="obs") - ) %>% - pivot_wider(names_from = type, values_from = value) %>% - group_by(name) %>% - reframe(obs = obs, pred = pred, density=scale(get_density(pred, obs,n=100))) %>% - ggplot(aes(y=obs, x=pred, colour=density)) + - scale_color_viridis_c() + - geom_point(alpha=0.7) + - geom_abline(slope=1, intercept=0, col="red")+ - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~name, scales = "free", nrow = 1)+ - labs(colour="Density") +out_le <- analyse_modobs( + df_plot, + "le_mod", + "le_obs" ) + +out_gpp$gg + + labs( + title = "GPP" + ) +out_le$gg + + labs( + title = "LE" + ) ``` Plot all outputs From a234d3607486a8d8d7c6c68acb772539f89218fe Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Mon, 16 Sep 2024 10:20:24 +0200 Subject: [PATCH 059/112] nothing much --- vignettes/pmodel_use_newdata.Rmd | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd index f433616e..05f09ccd 100644 --- a/vignettes/pmodel_use_newdata.Rmd +++ b/vignettes/pmodel_use_newdata.Rmd @@ -135,9 +135,10 @@ analyse_modobs <- function( ## ggplot hexbin gg <- df %>% ggplot2::ggplot(aes(x = mod, y = obs)) + - geom_hex() + + geom_hex(bins = 60) + scale_fill_gradientn( - colours = colorRampPalette(c("gray65", "navy", "red", "yellow"))(5) + colours = colorRampPalette(c("gray65", "navy", "red", "yellow"))(5), + trans = "log" ) + geom_abline(intercept = 0, slope = 1, linetype = "dotted") + # coord_fixed() + From c1509f1adbf39f69cfc0d4c180515f66e67faea9 Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Mon, 16 Sep 2024 18:16:57 +0200 Subject: [PATCH 060/112] bugfix to make le follow aet consistently --- src/gpp_pmodel.mod.f90 | 6 +++++- src/photosynth_pmodel.mod.f90 | 2 +- src/waterbal_splash.mod.f90 | 8 ++++++-- 3 files changed, 12 insertions(+), 4 deletions(-) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 36b91562..942160b0 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -272,7 +272,9 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init !---------------------------------------------------------------- soilmstress = calc_soilmstress( tile(1)%soil%phy%wcont, & params_gpp%soilm_thetastar, & - params_gpp%soilm_betao ) + tile(lu)%soil%params%whc ) + + print*,'soilmstress ', soilmstress !---------------------------------------------------------------- ! GPP @@ -359,6 +361,8 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init if (.not. use_phydro) then ! Jaideep NOTE: I have applied the soilmstress factor to gs here because it is needed in calculating canopy transpiration tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint * soilmstress + + ! print*,'in gpp: soilmstress, gs_accl ', soilmstress, tile_fluxes(lu)%plant(pft)%gs_accl else ! Jaideep NOTE: unit of gs_accl here is mol m-2 s-1. ! Jaideep FIXME: It's too complicated to convert it to unit as in pmodel, but should be done at some point diff --git a/src/photosynth_pmodel.mod.f90 b/src/photosynth_pmodel.mod.f90 index d8103b64..c69b9a97 100644 --- a/src/photosynth_pmodel.mod.f90 +++ b/src/photosynth_pmodel.mod.f90 @@ -826,8 +826,8 @@ function calc_soilmstress( wcont, thetastar, whc ) result( outstress ) real, intent(in) :: whc ! total root zone water storage capacity (mm), site-specific ! local variables + real, parameter :: betao = 0.0 ! soil water stress at zero water rootzone water content, taken to be zero (no water, no activity) real :: shape_parameter - real :: betao = 0.0 ! soil water stress at zero water rootzone water content, taken to be zero (no water, no activity) real :: thetastar_eff ! effective root-zone moisture limitation limitation threshold (mm) ! function return variable diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index 14860c57..fa1046b6 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -45,6 +45,7 @@ module md_waterbal real :: ru ! variable substitute for u real :: rv ! variable substitute for v real :: rw ! variable substitute (W/m^2) + real :: energy_to_mm ! Conversion factor to convert energy (J m-2 day) to mass (mm day-1) ! holds return variables of function get_snow_rain() type outtype_snow_rain @@ -104,6 +105,7 @@ subroutine waterbal( tile, tile_fluxes, grid, climate, fapar, using_phydro, usin ! Bucket model for runoff generation if (tile(lu)%soil%phy%wcont > tile(lu)%soil%params%whc) then + ! ----------------------------------- ! Bucket is full ! ----------------------------------- @@ -122,6 +124,7 @@ subroutine waterbal( tile, tile_fluxes, grid, climate, fapar, using_phydro, usin ! ----------------------------------- ! set soil moisture to zero tile_fluxes(lu)%canopy%daet = tile_fluxes(lu)%canopy%daet + tile(lu)%soil%phy%wcont + tile_fluxes(lu)%canopy%daet_e = tile_fluxes(lu)%canopy%daet / energy_to_mm tile(lu)%soil%phy%wcont = 0.0 tile_fluxes(lu)%canopy%dro = 0.0 tile_fluxes(lu)%canopy%dfleach = 0.0 @@ -305,7 +308,6 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g real :: lv ! enthalpy of vaporization, J/kg real :: cp ! heat capacity of moist air, J kg-1 K-1 real :: rho_water ! density of water (g m-3) - real :: energy_to_mm ! Conversion factor to convert energy (J m-2 day) to mass (mm day-1) real :: f_soil_aet ! Fractional reduction of soil AET due to moisture limitation real :: p_over_pet_memory ! P/PET real, save :: p_memory = 0.0 ! precipitation, damped variability @@ -431,6 +433,8 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g else f_soil_aet = 1.0 end if + + ! print*,'water limitation on soil evaporation: ', f_soil_aet !--------------------------------------------------------- ! Actual soil evaporation (mm d-1 and J d-1) @@ -455,6 +459,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g ! Convert stomatal conductance to CO2 [mol Pa-1 m-2 s-1] to ! stomatal conductance to water [m s-1] ! Adopted from photosynth_phydro.mod.f90 + ! print*,'in waterbal: gs_accl ', tile_fluxes%canopy%gs_accl gw = tile_fluxes%canopy%gs_accl * 1.6 * kR * (climate%dtemp + kTkelvin) ! latent energy flux from canopy (W m-2) @@ -462,7 +467,6 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g tile_fluxes%canopy%daet_e_canop = (epsilon * fapar * tile_fluxes%canopy%drn + (rho_water * cp / gamma) & * ga * climate%dvpd) / (epsilon + 1.0 + ga / gw) - ! print*,'-----------------------' ! print*,'canopy_height ', myinterface%canopy_height ! print*,'dwind ', climate%dwind From f9a6a20f871097d10f2ab8bc4d2e2522e351f10d Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Mon, 16 Sep 2024 18:17:40 +0200 Subject: [PATCH 061/112] added script for demo --- analysis/pmodel_use_newdata.R | 101 ++++++++++++++++++++++++++++++++++ 1 file changed, 101 insertions(+) create mode 100644 analysis/pmodel_use_newdata.R diff --git a/analysis/pmodel_use_newdata.R b/analysis/pmodel_use_newdata.R new file mode 100644 index 00000000..c938ef73 --- /dev/null +++ b/analysis/pmodel_use_newdata.R @@ -0,0 +1,101 @@ +library(rsofun) +library(dplyr) +library(ggplot2) +library(dplyr) +library(tidyr) +library(khroma) + +## ---------------------------------------------------------------------------------------------------------------------- +p_model_drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")) +p_model_validation = readRDS(file = here::here("data/p_model_validation_newformat.rds")) + +## ---------------------------------------------------------------------------------------------------------------------- +# define model parameter values from previous work +# ------------------------------------------------------ +# Note that in the phydro branch of rsofun, +# whc must be included in params_modl, rather than in site_info +# ------------------------------------------------------ +params_modl <- list( + kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + soilm_betao = 0.0, + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + whc = p_model_drivers$site_info[[1]]$whc + ) + +## ---------------------------------------------------------------------------------------------------------------------- +p_model_drivers$params_siml[[1]]$use_gs <- TRUE +p_model_drivers$params_siml[[1]]$use_pml <- TRUE +p_model_drivers$params_siml[[1]]$use_phydro <- FALSE + +## ---------------------------------------------------------------------------------------------------------------------- +# run the model for these parameters +output_whc1 <- rsofun::runread_pmodel_f( + p_model_drivers, + par = params_modl +) + +## ---------------------------------------------------------------------------------------------------------------------- +# run the model for WHC divided by 10 +params_modl2 <- params_modl +params_modl2$whc <- p_model_drivers$site_info[[1]]$whc * 0.1 + +output_whc2 <- rsofun::runread_pmodel_f( + p_model_drivers, + par = params_modl2 +) + +## ---------------------------------------------------------------------------------------------------------------------- +# Plot +tmp <- output_whc1 |> + select(data) |> + unnest(data) |> + select(date, le, le_soil, le_canopy, aet, wcont) |> + mutate(whc = "orig") |> + bind_rows( + output_whc2 |> + select(data) |> + unnest(data) |> + select(date, le, le_soil, le_canopy, aet, wcont) |> + mutate(whc = "small") + ) |> + mutate(doy = lubridate::yday(date)) |> + group_by(doy, whc) |> + summarise(across(where(is.numeric), mean)) + +tmp |> + ggplot(aes(doy, aet, color = whc)) + + geom_line() + + scale_color_okabeito() + + theme_classic() + +tmp |> + ggplot(aes(doy, le / (24*60*60), color = whc)) + + geom_line() + + scale_color_okabeito() + + theme_classic() + +tmp |> + ggplot(aes(doy, le_soil / (24*60*60), color = whc)) + + geom_line() + + scale_color_okabeito() + + theme_classic() + +tmp |> + ggplot(aes(doy, le_canopy / (24*60*60), color = whc)) + + geom_line() + + scale_color_okabeito() + + theme_classic() + +tmp |> + ggplot(aes(doy, wcont, color = whc)) + + geom_hline(yintercept = c(0, p_model_drivers$site_info[[1]]$whc, p_model_drivers$site_info[[1]]$whc * 0.1), color = "grey") + + geom_line() + + scale_color_okabeito() + + theme_classic() + From af6337852483a09f478c148902831fe8d8be479b Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Tue, 17 Sep 2024 01:18:45 +0200 Subject: [PATCH 062/112] Update test-model-runs for PHYDRO --- tests/testthat/test-model-runs.R | 25 ++++++++++++++++++++----- 1 file changed, 20 insertions(+), 5 deletions(-) diff --git a/tests/testthat/test-model-runs.R b/tests/testthat/test-model-runs.R index d167357f..7b8e401b 100644 --- a/tests/testthat/test-model-runs.R +++ b/tests/testthat/test-model-runs.R @@ -18,14 +18,16 @@ test_that("p-model run check GPP", { ) # read in demo data - df_drivers <- p_model_drivers + #df_drivers <- p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO + df_drivers <- readRDS(file = here::here("data/p_model_drivers_newformat.rds")) # run the SOFUN Fortran P-model - mod <- run_pmodel_f_bysite( + mod <- run_pmodel_f_bysite( df_drivers$sitename[1], df_drivers$params_siml[[1]], df_drivers$site_info[[1]], - df_drivers$forcing[[1]], + forcing = df_drivers$forcing[[1]], + forcing_acclim = df_drivers$forcing[[1]], params_modl = params_modl, makecheck = FALSE ) @@ -74,14 +76,27 @@ test_that("p-model run check Vcmax25", { ) # read in demo data - df_drivers <- p_model_drivers_vcmax25 + df_drivers <- p_model_drivers_vcmax25 |> + # TODO: NOT YET UPDATED FOR PHYDRO + # # specify additionally needed params_siml flags: + mutate(params_siml = purrr::map(params_siml, \(x) + mutate(x, + use_pml = TRUE, + use_gs = TRUE, + use_phydro = FALSE))) |> + # specify additionally needed site info: + mutate(site_info = purrr::map(site_info, \(x) + mutate(x, + canopy_height = 5, + reference_height = 10))) # run the SOFUN Fortran P-model mod <- run_pmodel_f_bysite( df_drivers$sitename[1], df_drivers$params_siml[[1]], df_drivers$site_info[[1]], - df_drivers$forcing[[1]], + forcing = df_drivers$forcing[[1]], + forcing_acclim = df_drivers$forcing[[1]], params_modl = params_modl, makecheck = FALSE ) From 1e4bb4e3162c926e1c6235b21acd6c0f57c2bae5 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Tue, 17 Sep 2024 01:19:16 +0200 Subject: [PATCH 063/112] fix warning: Unknown or uninitialised column: `forcing_acclim` --- R/runread_pmodel_f.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/runread_pmodel_f.R b/R/runread_pmodel_f.R index 650e2ed1..551f0310 100644 --- a/R/runread_pmodel_f.R +++ b/R/runread_pmodel_f.R @@ -107,7 +107,7 @@ runread_pmodel_f <- function( input <- forcing <- forcing_acclim <- . <- NULL # If acclimation dataset has not been separately provided, use the same forcing data - if (is.null(drivers$forcing_acclim)){ + if (!rlang::has_name(drivers, "forcing_acclim")){ drivers$forcing_acclim = drivers$forcing } From 6ff8a825f7623116038141371e0698c9635ebfe6 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Tue, 17 Sep 2024 01:20:07 +0200 Subject: [PATCH 064/112] Deactivate printing soilmstress --- src/gpp_pmodel.mod.f90 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 942160b0..769ab6c3 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -274,7 +274,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init params_gpp%soilm_thetastar, & tile(lu)%soil%params%whc ) - print*,'soilmstress ', soilmstress + ! print*,'soilmstress ', soilmstress !---------------------------------------------------------------- ! GPP From f1366e3f2a118d6185ad48b959e50ba0d1ae4cb9 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Tue, 17 Sep 2024 01:20:51 +0200 Subject: [PATCH 065/112] test: Add consistency check for LE and AET (and soil vs canopy) --- DESCRIPTION | 3 ++ tests/testthat/test-model-runs.R | 89 ++++++++++++++++++++++++++++++++ 2 files changed, 92 insertions(+) diff --git a/DESCRIPTION b/DESCRIPTION index 69cc484c..cded65bf 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -64,6 +64,7 @@ NeedsCompilation: yes RoxygenNote: 7.3.1 Suggests: covr, + cwd, rcmdcheck, testthat, rmarkdown, @@ -72,5 +73,7 @@ Suggests: sensitivity, hexbin, khroma +Remotes: + geco-bern/cwd VignetteBuilder: knitr Encoding: UTF-8 diff --git a/tests/testthat/test-model-runs.R b/tests/testthat/test-model-runs.R index 7b8e401b..a175326b 100644 --- a/tests/testthat/test-model-runs.R +++ b/tests/testthat/test-model-runs.R @@ -129,6 +129,95 @@ test_that("p-model run check Vcmax25", { expect_type(df_output_p, "list") }) +test_that("phydro-model run check LE and AET", { + # skip_on_cran() + + # load parameters (valid ones) + params_modl <- list( + kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD + kphio_par_a = 0.01, # set to zero to disable temperature-dependence of kphio, setup ORG in Stocker et al. 2020 GMD + kphio_par_b = 1.0, + soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + soilm_betao = 0.01, + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41 + ) + + # read in demo data + #df_drivers <- p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO + df_drivers <- readRDS(file = here::here("data/p_model_drivers_newformat.rds")) + df_drivers$params_siml[[1]]$use_gs <- TRUE + + # run the SOFUN Fortran PHYDRO-model + # Run 3 simulations with different WHC: + df_output <- bind_rows( + rsofun::runread_pmodel_f( + drivers = df_drivers |> + tidyr::unnest(site_info) |> mutate(whc = 432) |> + tidyr::nest(site_info = !c(sitename, params_siml, starts_with("forcing"))), + par = purrr::assign_in(params_modl, "whc", 432) + ) |> mutate(sitename = paste0(sitename, "_432mm")), + rsofun::runread_pmodel_f( + drivers = df_drivers |> + tidyr::unnest(site_info) |> mutate(whc = 5) |> + tidyr::nest(site_info = !c(sitename, params_siml, starts_with("forcing"))), + par = purrr::assign_in(params_modl, "whc", 5) + ) |> mutate(sitename = paste0(sitename, "_5mm")), + rsofun::runread_pmodel_f( + drivers = df_drivers |> + tidyr::unnest(site_info) |> mutate(whc = 5000) |> + tidyr::nest(site_info = !c(sitename, params_siml, starts_with("forcing"))), + par = purrr::assign_in(params_modl, "whc", 5000) + ) |> mutate(sitename = paste0(sitename, "_5000mm")) + ) + + # Plot: + # df_output |> + # tidyr::unnest(data) |> select(-site_info) |> + # filter(date < "2012-01-01") |> + # select(sitename, date, gpp, aet, le, pet, le_canopy, le_soil) |> + # tidyr::pivot_longer(!c(sitename,date)) %>% + # ggplot(data = ., mapping=aes(x=date, y=value, color=sitename, linetype=sitename)) + + # geom_line() + + # facet_grid(name~., scales = "free_y") + + # theme_bw() + + # 1) Check that le is sum of le_canopy and le_soil + df_output |> + tidyr::unnest(data) |> + # select(sitename, date, aet, le, le_canopy, le_soil) |> + group_by(sitename) |> + mutate(le_sum = le_canopy + le_soil) |> + mutate(test_equality_lesum = expect_equal(le_sum, le, tolerance = 0.1)) + # filter(abs(le_sum - le) > 5) + + # 2) Check that aet and le give the same + library(cwd) + le_to_et <- function(le_Wm2, tc, patm){ + # Convert latent heat flux (W/m2) to evapotranspiration in mass units (mm/d). + 1000 * 60 * 60 * 24 * le_Wm2 / (cwd::calc_enthalpy_vap(tc) * cwd::calc_density_h2o(tc, patm)) + # mm/m * s/day * W/m2 * (kg / J) * (m3 / kg) + # = mm/day * s/m3 * m3/J * W * kg/kg + # = mm/day + } + + df_output |> + tidyr::unnest(data) |> + select(sitename, date, aet_mmday = aet, le_Jm2d = le) |> + # append temperature and pressure form forcing to convert LE into AET: + left_join(select(df_drivers$forcing[[1]], + date, ta_degC = temp, pa_Pa = patm)) |> + group_by(sitename) |> + # mutate(le_mmd = le_to_et(le_Jm2d / 86400, ta_degC, pa_Pa)) + mutate(test_equality_aet_le = + expect_equal(aet_mmday, le_to_et(le_Jm2d / 86400, ta_degC, pa_Pa), + tolerance = 0.01)) # tolerance in mm/d + + +}) + test_that("biomee p-model run check", { skip_on_cran() From fb4b1ab2448fff4d8d8b5f879a9bbf5532a1f150 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Tue, 17 Sep 2024 01:21:36 +0200 Subject: [PATCH 066/112] fix consistency check AET_tot = AET_soil + AET_canop --- src/waterbal_splash.mod.f90 | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index fa1046b6..8694b49c 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -123,8 +123,13 @@ subroutine waterbal( tile, tile_fluxes, grid, climate, fapar, using_phydro, usin ! Bucket is empty ! ----------------------------------- ! set soil moisture to zero + ! and reduce total actual evapotranspiration (daet) by reducing canopy transpiration (daet_canop) tile_fluxes(lu)%canopy%daet = tile_fluxes(lu)%canopy%daet + tile(lu)%soil%phy%wcont + tile_fluxes(lu)%canopy%daet_canop = tile_fluxes(lu)%canopy%daet_canop + tile(lu)%soil%phy%wcont + tile_fluxes(lu)%canopy%daet_e = tile_fluxes(lu)%canopy%daet / energy_to_mm + tile_fluxes(lu)%canopy%daet_e_canop = tile_fluxes(lu)%canopy%daet_canop / energy_to_mm + tile(lu)%soil%phy%wcont = 0.0 tile_fluxes(lu)%canopy%dro = 0.0 tile_fluxes(lu)%canopy%dfleach = 0.0 From 15ca3b1254842dd9ad9115a76a3e26b6a68ef505 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Tue, 17 Sep 2024 17:29:59 +0200 Subject: [PATCH 067/112] fix: reduce daet_soil and daet_e_soil proportionally when daet is reduced (empty bucket) --- src/waterbal_splash.mod.f90 | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index 8694b49c..b2ced744 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -124,12 +124,18 @@ subroutine waterbal( tile, tile_fluxes, grid, climate, fapar, using_phydro, usin ! ----------------------------------- ! set soil moisture to zero ! and reduce total actual evapotranspiration (daet) by reducing canopy transpiration (daet_canop) - tile_fluxes(lu)%canopy%daet = tile_fluxes(lu)%canopy%daet + tile(lu)%soil%phy%wcont - tile_fluxes(lu)%canopy%daet_canop = tile_fluxes(lu)%canopy%daet_canop + tile(lu)%soil%phy%wcont + tile_fluxes(lu)%canopy%daet_canop = tile_fluxes(lu)%canopy%daet_canop + tile(lu)%soil%phy%wcont * & + (tile_fluxes(lu)%canopy%daet_canop/ tile_fluxes(lu)%canopy%daet) + ! Is this numerically stable? + tile_fluxes(lu)%canopy%daet_soil = tile_fluxes(lu)%canopy%daet_soil + tile(lu)%soil%phy%wcont * & + (tile_fluxes(lu)%canopy%daet_soil / tile_fluxes(lu)%canopy%daet) + ! Is this numerically stable? + tile_fluxes(lu)%canopy%daet = tile_fluxes(lu)%canopy%daet + tile(lu)%soil%phy%wcont - tile_fluxes(lu)%canopy%daet_e = tile_fluxes(lu)%canopy%daet / energy_to_mm tile_fluxes(lu)%canopy%daet_e_canop = tile_fluxes(lu)%canopy%daet_canop / energy_to_mm - + tile_fluxes(lu)%canopy%daet_e_soil = tile_fluxes(lu)%canopy%daet_soil / energy_to_mm + tile_fluxes(lu)%canopy%daet_e = tile_fluxes(lu)%canopy%daet / energy_to_mm + tile(lu)%soil%phy%wcont = 0.0 tile_fluxes(lu)%canopy%dro = 0.0 tile_fluxes(lu)%canopy%dfleach = 0.0 @@ -395,6 +401,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g !--------------------------------------------------------- ! Estimate daily AET (tile_fluxes%canopy%daet), mm d-1 + ! Estimate daily LE (tile_fluxes%canopy%daet_e), J d-1 !--------------------------------------------------------- ! JAIDEEP FIXME: soil PET calcs should be identical for P and Phydro, but depending on whether in_netrad is used or not, ! when implementing in_netrad condition, uncomment the lines marked by arrows @@ -447,6 +454,9 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g tile_fluxes%canopy%daet_soil = f_soil_aet * dpet_soil tile_fluxes%canopy%daet_e_soil = tile_fluxes%canopy%daet_soil / energy_to_mm + !--------------------------------------------------------- + ! Actual canopy evaporation (mm d-1 and J d-1) + !--------------------------------------------------------- if (using_pml) then !--------------------------------------------------------- ! Canopy transpiration using the Penman-Monteith equation From e68edd595fe49334bf0a066349b0710654a0a411 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Wed, 18 Sep 2024 08:38:18 +0200 Subject: [PATCH 068/112] Remove soilm_betao --- R/calib_sofun.R | 3 +-- R/cost_likelihood_phydro.R | 2 +- R/cost_likelihood_pmodel.R | 3 +-- R/cost_rmse_pmodel.R | 3 +-- R/run_pmodel_f_bysite.R | 16 +++++----------- R/runread_pmodel_f.R | 4 ---- README.md | 3 +-- analysis/01-sensitivity-analysis.R | 3 --- analysis/02-bayesian-calibration.R | 1 - analysis/03-uncertainty-estimation.R | 2 +- analysis/demo_bug.R | 1 - analysis/pmodel_use_newdata.R | 1 - man/calib_sofun.Rd | 3 +-- man/cost_likelihood_pmodel.Rd | 1 - man/cost_rmse_pmodel.Rd | 1 - man/run_pmodel_f_bysite.Rd | 4 ---- man/runread_pmodel_f.Rd | 4 ---- src/gpp_biomee.mod.f90 | 14 ++++++++------ src/gpp_pmodel.mod.f90 | 4 ---- src/interface_biosphere_pmodel.mod.f90 | 1 - src/sofun_r.f90 | 2 +- src/waterbal_splash.mod.f90 | 1 + tests/testthat/test-calibration-pmodel.R | 7 ++----- tests/testthat/test-model-runs.R | 3 --- tests/testthat/test-quantitative-validation.R | 1 - vignettes/new_cost_function.Rmd | 6 +----- vignettes/pmodel_use.Rmd | 5 +---- vignettes/pmodel_use_newdata.Rmd | 3 +-- vignettes/sensitivity_analysis.Rmd | 10 +--------- 29 files changed, 28 insertions(+), 84 deletions(-) diff --git a/R/calib_sofun.R b/R/calib_sofun.R index 5bdfd7e6..ce6b338e 100644 --- a/R/calib_sofun.R +++ b/R/calib_sofun.R @@ -16,7 +16,7 @@ #' \item{\code{par}}{A list of model parameters. For each parameter, an initial value #' and lower and upper bounds should be provided. The calibratable parameters #' include model parameters 'kphio', 'kphio_par_a', 'kphio_par_b', 'soilm_thetastar', -#' 'soilm_betao', 'beta_costunitratio', 'rd_to_vcmax', 'tau_acclim', 'kc_jmax' +#' 'beta_costunitratio', 'rd_to_vcmax', 'tau_acclim', 'kc_jmax' #' and 'rootzone_whc' , and (if #' doing Bayesian calibration) error parameters #' for each target variable, named for example 'err_gpp'. This list must match @@ -49,7 +49,6 @@ #' kphio_par_a = 0, #' kphio_par_b = 1.0, #' soilm_thetastar = 0.6*240, -#' soilm_betao = 0.01, #' beta_unitcostratio = 146, #' rd_to_vcmax = 0.014, #' tau_acclim = 30, diff --git a/R/cost_likelihood_phydro.R b/R/cost_likelihood_phydro.R index eb6692b6..771aa81f 100644 --- a/R/cost_likelihood_phydro.R +++ b/R/cost_likelihood_phydro.R @@ -31,7 +31,7 @@ cost_likelihood_phydro <- function( 'bsoil', 'Ssoil', 'whc') } else { calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', - 'soilm_thetastar', 'soilm_betao', + 'soilm_thetastar', 'beta_unitcostratio', 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', 'whc') } diff --git a/R/cost_likelihood_pmodel.R b/R/cost_likelihood_pmodel.R index cceb58e7..bf6c00e1 100644 --- a/R/cost_likelihood_pmodel.R +++ b/R/cost_likelihood_pmodel.R @@ -63,7 +63,6 @@ #' targets = c('gpp'), #' par_fixed = list( #' soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress -#' soilm_betao = 0.0, #' beta_unitcostratio = 146.0, #' rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous #' tau_acclim = 30.0, @@ -109,7 +108,7 @@ cost_likelihood_pmodel <- function( 'bsoil', 'Ssoil', 'whc') } else { calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', - 'soilm_thetastar', 'soilm_betao', + 'soilm_thetastar', 'beta_unitcostratio', 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', 'whc') } diff --git a/R/cost_rmse_pmodel.R b/R/cost_rmse_pmodel.R index 9a543d78..fe9815b3 100644 --- a/R/cost_rmse_pmodel.R +++ b/R/cost_rmse_pmodel.R @@ -58,7 +58,6 @@ #' targets = c('gpp'), #' par_fixed = list( #' soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress -#' soilm_betao = 0.0, #' beta_unitcostratio = 146.0, #' rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous #' tau_acclim = 30.0, @@ -101,7 +100,7 @@ cost_rmse_pmodel <- function( 'bsoil', 'Ssoil', 'whc') } else { calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', - 'soilm_thetastar', 'soilm_betao', + 'soilm_thetastar', 'beta_unitcostratio', 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', 'whc') } diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 68726df4..7d725ee5 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -43,9 +43,6 @@ #' \item{soilm_thetastar}{The threshold parameter \eqn{\theta^{*}} in the #' soil moisture stress function (see Details), given in mm. #' To turn off the soil moisture stress, set \code{soilm_thetastar = 0}.} -#' \item{soilm_betao}{The intercept parameter \eqn{\beta_{0}} in the -#' soil moisture stress function (see Details). This is the parameter calibrated -#' in Stocker et al. 2020 GMD.} #' \item{beta_unitcostratio}{The unit cost of carboxylation, corresponding to #' \eqn{\beta = b / a'} in Eq. 3 of Stocker et al. 2020 GMD.} #' \item{rd_to_vcmax}{Ratio of Rdark (dark respiration) to Vcmax25.} @@ -137,7 +134,6 @@ #' kphio_par_a = 0.0, # disable temperature-dependence of kphio #' kphio_par_b = 1.0, #' soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress -#' soilm_betao = 0.0, #' beta_unitcostratio = 146.0, #' rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous #' tau_acclim = 30.0, @@ -334,12 +330,12 @@ run_pmodel_f_bysite <- function( # Check model parameters if (!params_siml$use_phydro){ - # P-model needs 10 parameters + # P-model needs 9 parameters if( sum( names(params_modl) %in% c('kphio', 'kphio_par_a', 'kphio_par_b', - 'soilm_thetastar', 'soilm_betao', + 'soilm_thetastar', 'beta_unitcostratio', 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', 'whc') - ) != 10){ + ) != 9){ warning(" Returning a dummy data frame. Incorrect model parameters.") continue <- FALSE } @@ -414,9 +410,7 @@ run_pmodel_f_bysite <- function( ifelse(params_siml$use_phydro, no = as.numeric(params_modl$soilm_thetastar), yes = dummy_val), - ifelse(params_siml$use_phydro, - no = as.numeric(params_modl$soilm_betao), - yes = dummy_val), + dummy_val, # formerly soilm_betao #TODO: replace this position with whc ifelse(params_siml$use_phydro, no = as.numeric(params_modl$beta_unitcostratio), yes = dummy_val), @@ -444,7 +438,7 @@ run_pmodel_f_bysite <- function( ifelse(params_siml$use_phydro, no = dummy_val, yes = params_modl$Ssoil), - as.numeric(params_modl$whc) + as.numeric(params_modl$whc) #TODO: move whc to former position of soilm_betao ) ## C wrapper call diff --git a/R/runread_pmodel_f.R b/R/runread_pmodel_f.R index 551f0310..6c3e0756 100644 --- a/R/runread_pmodel_f.R +++ b/R/runread_pmodel_f.R @@ -19,9 +19,6 @@ #' \item{soilm_thetastar}{The threshold parameter \eqn{\theta^{*}} in the #' soil moisture stress function (see Details), given in mm. #' To turn off the soil moisture stress, set \code{soilm_thetastar = 0}.} -#' \item{soilm_betao}{The intercept parameter \eqn{\beta_{0}} in the -#' soil moisture stress function (see Details). This is the parameter calibrated -#' in Stocker et al. 2020 GMD.} #' \item{beta_unitcostratio}{The unit cost of carboxylation, corresponding to #' \eqn{\beta = b / a'} in Eq. 3 of Stocker et al. 2020 GMD.} #' \item{rd_to_vcmax}{Ratio of Rdark (dark respiration) to Vcmax25.} @@ -83,7 +80,6 @@ #' kphio_par_a = 0.0, # disable temperature-dependence of kphio #' kphio_par_b = 1.0, #' soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress -#' soilm_betao = 0.0, #' beta_unitcostratio = 146.0, #' rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous #' tau_acclim = 30.0, diff --git a/README.md b/README.md index 80ff4bc4..b42aa9e8 100644 --- a/README.md +++ b/README.md @@ -60,7 +60,6 @@ params_modl <- list( kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, @@ -92,7 +91,7 @@ settings <- list( `rsofun` supports both optimization using the `GenSA` and `BayesianTools` packages. The above statement provides settings for a `GenSA` optimization approach. For this example the maximum number of iterations is kept artificially low. In a real scenario you will have to increase this value orders of magnitude. Keep in mind that optimization routines rely on a cost function, which, depending on its structure influences parameter selection. A limited set of cost functions is provided but the model structure is transparent and custom cost functions can be easily written. More details can be found in the "Parameter calibration and cost functions" vignette. -In addition starting values and ranges are provided for the free parameters in the model. Free parameters include: parameters for the quantum yield efficiency `kphio`, `kphio_par_a` and `kphio_par_b`, soil moisture stress parameters `soilm_thetastar` and `soilm_betao`, and also `beta_unitcostratio`, `rd_to_vcmax`, `tau_acclim` and `kc_jmax` (see `?runread_pmodel_f`). Be mindful that with newer versions of `rsofun` additional parameters might be introduced, so re-check vignettes and function documentation when updating existing code. +In addition starting values and ranges are provided for the free parameters in the model. Free parameters include: parameters for the quantum yield efficiency `kphio`, `kphio_par_a` and `kphio_par_b`, soil moisture stress parameter `soilm_thetastar`, and also `beta_unitcostratio`, `rd_to_vcmax`, `tau_acclim` and `kc_jmax` (see `?runread_pmodel_f`). Be mindful that with newer versions of `rsofun` additional parameters might be introduced, so re-check vignettes and function documentation when updating existing code. With all settings defined the optimization function `calib_sofun()` can be called with driver data and observations specified. Extra arguments for the cost function (like what variable should be used as target to compute the root mean squared error (RMSE) and previous values for the parameters that aren't calibrated, which are needed to run the P-model). diff --git a/analysis/01-sensitivity-analysis.R b/analysis/01-sensitivity-analysis.R index 822e30f9..5865d73a 100644 --- a/analysis/01-sensitivity-analysis.R +++ b/analysis/01-sensitivity-analysis.R @@ -32,7 +32,6 @@ par_cal_best <- c( kphio_par_a = -0.0025, kphio_par_b = 20, soilm_thetastar = 0.6*240, - soilm_betao = 0.2, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, tau_acclim = 30.0, @@ -46,7 +45,6 @@ par_cal_min <- c( kphio_par_a = -0.004, kphio_par_b = 10, soilm_thetastar = 0, - soilm_betao = 0, beta_unitcostratio = 50.0, rd_to_vcmax = 0.01, tau_acclim = 7.0, @@ -60,7 +58,6 @@ par_cal_max <- c( kphio_par_a = -0.001, kphio_par_b = 30, soilm_thetastar = 240, - soilm_betao = 1, beta_unitcostratio = 200.0, rd_to_vcmax = 0.1, tau_acclim = 60.0, diff --git a/analysis/02-bayesian-calibration.R b/analysis/02-bayesian-calibration.R index a57f7243..60cb500e 100644 --- a/analysis/02-bayesian-calibration.R +++ b/analysis/02-bayesian-calibration.R @@ -88,7 +88,6 @@ settings_calib <- list( )), par = list( kphio = list(lower = 0.03, upper = 0.15, init = 0.05), - soilm_betao = list(lower = 0, upper = 1, init = 0.2), kc_jmax = list(lower = 0.2, upper = 0.8, init = 0.41), err_gpp = list(lower = 0.1, upper = 3, init = 0.8) ) diff --git a/analysis/03-uncertainty-estimation.R b/analysis/03-uncertainty-estimation.R index 9491a792..c6f58a3a 100644 --- a/analysis/03-uncertainty-estimation.R +++ b/analysis/03-uncertainty-estimation.R @@ -41,7 +41,7 @@ run_pmodel <- function(sample_par){ kphio_par_a = -0.0025, kphio_par_b = 20, soilm_thetastar = 0.6*240, - soilm_betao = sample_par$soilm_betao, + # TODO: should we replace fitting sample_par$soilm_betao with sample_par$whc? beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, tau_acclim = 30.0, diff --git a/analysis/demo_bug.R b/analysis/demo_bug.R index 1ec08939..28f5da95 100644 --- a/analysis/demo_bug.R +++ b/analysis/demo_bug.R @@ -76,7 +76,6 @@ if (any(!vec_test)){ # kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio # kphio_par_b = 1.0, # soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress -# soilm_betao = 0.0, # beta_unitcostratio = 146.0, # rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous # tau_acclim = 30.0, diff --git a/analysis/pmodel_use_newdata.R b/analysis/pmodel_use_newdata.R index c938ef73..102bcf67 100644 --- a/analysis/pmodel_use_newdata.R +++ b/analysis/pmodel_use_newdata.R @@ -20,7 +20,6 @@ params_modl <- list( kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, diff --git a/man/calib_sofun.Rd b/man/calib_sofun.Rd index 4ee95d41..416faf1f 100644 --- a/man/calib_sofun.Rd +++ b/man/calib_sofun.Rd @@ -22,7 +22,7 @@ See the 'P-model usage' vignette for more information and examples. \item{\code{par}}{A list of model parameters. For each parameter, an initial value and lower and upper bounds should be provided. The calibratable parameters include model parameters 'kphio', 'kphio_par_a', 'kphio_par_b', 'soilm_thetastar', - 'soilm_betao', 'beta_costunitratio', 'rd_to_vcmax', 'tau_acclim', 'kc_jmax' + 'beta_costunitratio', 'rd_to_vcmax', 'tau_acclim', 'kc_jmax' and 'rootzone_whc' , and (if doing Bayesian calibration) error parameters for each target variable, named for example 'err_gpp'. This list must match @@ -60,7 +60,6 @@ params_fix <- list( kphio_par_a = 0, kphio_par_b = 1.0, soilm_thetastar = 0.6*240, - soilm_betao = 0.01, beta_unitcostratio = 146, rd_to_vcmax = 0.014, tau_acclim = 30, diff --git a/man/cost_likelihood_pmodel.Rd b/man/cost_likelihood_pmodel.Rd index d206d812..9a39e0ed 100644 --- a/man/cost_likelihood_pmodel.Rd +++ b/man/cost_likelihood_pmodel.Rd @@ -85,7 +85,6 @@ cost_likelihood_pmodel( targets = c('gpp'), par_fixed = list( soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, diff --git a/man/cost_rmse_pmodel.Rd b/man/cost_rmse_pmodel.Rd index ca897fa8..fe0a9d45 100644 --- a/man/cost_rmse_pmodel.Rd +++ b/man/cost_rmse_pmodel.Rd @@ -82,7 +82,6 @@ cost_rmse_pmodel( targets = c('gpp'), par_fixed = list( soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, diff --git a/man/run_pmodel_f_bysite.Rd b/man/run_pmodel_f_bysite.Rd index fc794a0e..e5bad07b 100644 --- a/man/run_pmodel_f_bysite.Rd +++ b/man/run_pmodel_f_bysite.Rd @@ -62,9 +62,6 @@ for a detailed description of its structure and contents).} \item{soilm_thetastar}{The threshold parameter \eqn{\theta^{*}} in the soil moisture stress function (see Details), given in mm. To turn off the soil moisture stress, set \code{soilm_thetastar = 0}.} - \item{soilm_betao}{The intercept parameter \eqn{\beta_{0}} in the - soil moisture stress function (see Details). This is the parameter calibrated - in Stocker et al. 2020 GMD.} \item{beta_unitcostratio}{The unit cost of carboxylation, corresponding to \eqn{\beta = b / a'} in Eq. 3 of Stocker et al. 2020 GMD.} \item{rd_to_vcmax}{Ratio of Rdark (dark respiration) to Vcmax25.} @@ -158,7 +155,6 @@ params_modl <- list( kphio_par_a = 0.0, # disable temperature-dependence of kphio kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, diff --git a/man/runread_pmodel_f.Rd b/man/runread_pmodel_f.Rd index 6c50b38e..93bc6b0d 100644 --- a/man/runread_pmodel_f.Rd +++ b/man/runread_pmodel_f.Rd @@ -25,9 +25,6 @@ namely \code{sitename, params_siml, site_info} and \code{forcing}.} \item{soilm_thetastar}{The threshold parameter \eqn{\theta^{*}} in the soil moisture stress function (see Details), given in mm. To turn off the soil moisture stress, set \code{soilm_thetastar = 0}.} - \item{soilm_betao}{The intercept parameter \eqn{\beta_{0}} in the - soil moisture stress function (see Details). This is the parameter calibrated - in Stocker et al. 2020 GMD.} \item{beta_unitcostratio}{The unit cost of carboxylation, corresponding to \eqn{\beta = b / a'} in Eq. 3 of Stocker et al. 2020 GMD.} \item{rd_to_vcmax}{Ratio of Rdark (dark respiration) to Vcmax25.} @@ -96,7 +93,6 @@ params_modl <- list( kphio_par_a = 0.0, # disable temperature-dependence of kphio kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, diff --git a/src/gpp_biomee.mod.f90 b/src/gpp_biomee.mod.f90 index dee64b94..0298c623 100644 --- a/src/gpp_biomee.mod.f90 +++ b/src/gpp_biomee.mod.f90 @@ -20,7 +20,6 @@ module md_gpp_biomee type paramstype_gpp real :: beta ! Unit cost of carboxylation (dimensionless) real :: soilm_thetastar - real :: soilm_betao real :: rd_to_vcmax ! Ratio of Rdark to Vcmax25, number from Atkin et al., 2015 for C3 herbaceous real :: tau_acclim ! acclimation time scale of photosynthesis (d) real :: kc_jmax @@ -577,19 +576,22 @@ subroutine getpar_modl_gpp() !//////////////////////////////////////////////////////////////// ! Subroutine reads module-specific parameters from input file. !---------------------------------------------------------------- - ! unit cost of carboxylation + ! unit cost of carboxylation, b/a' in Eq. 3 (Stocker et al., 2020 GMD) params_gpp%beta = 146.000000 ! Ratio of Rdark to Vcmax25, number from Atkin et al., 2015 for C3 herbaceous + ! Ratio of Rdark to Vcmax25, fitted slope of Rd25/Vcmax25 (Wang et al., 2020 GCB, 10.1111/gcb.14980, Table S6) params_gpp%rd_to_vcmax = 0.01400000 + ! Jmax cost coefficient, c* in Stocker et al., 2020 GMD (Eq 15) and Wang et al., 2017 + params_gpp%kc_jmax = 0.41 + ! Apply identical temperature ramp parameter for all PFTs + ! Acclimation time scale for photosynthesis (d), multiple lines of evidence suggest about monthly is alright params_gpp%tau_acclim = 30.0 - params_gpp%soilm_thetastar= 0.6 * 250 - params_gpp%soilm_betao = 0.0 - ! Jmax cost ratio - params_gpp%kc_jmax = 0.41 + ! Re-interpreted soil moisture stress parameter, previously thetastar = 0.6 + params_gpp%soilm_thetastar= 0.6 * 250 ! quantum yield efficiency at optimal temperature, phi_0 (Stocker et al., 2020 GMD Eq. 10) params_gpp%kphio = 0.05 diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 769ab6c3..eece2c3f 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -25,7 +25,6 @@ module md_gpp_pmodel type paramstype_gpp real :: beta ! Unit cost of carboxylation (dimensionless) real :: soilm_thetastar - real :: soilm_betao real :: rd_to_vcmax ! Ratio of Rdark to Vcmax25, number from Atkin et al., 2015 for C3 herbaceous real :: tau_acclim ! acclimation time scale of photosynthesis (d) real :: tau_acclim_tempstress @@ -683,9 +682,6 @@ subroutine getpar_modl_gpp() ! Re-interpreted soil moisture stress parameter, previously thetastar = 0.6 params_gpp%soilm_thetastar = myinterface%params_calib%soilm_thetastar - ! Re-interpreted soil moisture stress parameter, previosly determined by Eq. 22 - params_gpp%soilm_betao = myinterface%params_calib%soilm_betao - ! quantum yield efficiency at optimal temperature, phi_0 (Stocker et al., 2020 GMD Eq. 10) params_pft_gpp(:)%kphio = myinterface%params_calib%kphio diff --git a/src/interface_biosphere_pmodel.mod.f90 b/src/interface_biosphere_pmodel.mod.f90 index 8b8ffbe0..6d02e221 100644 --- a/src/interface_biosphere_pmodel.mod.f90 +++ b/src/interface_biosphere_pmodel.mod.f90 @@ -21,7 +21,6 @@ module md_interface_pmodel real :: kphio_par_a real :: kphio_par_b real :: soilm_thetastar - real :: soilm_betao real :: beta_unitcostratio real :: rd_to_vcmax real :: tau_acclim diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index 1507d1f0..0c8287fc 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -160,7 +160,7 @@ subroutine pmodel_f( & myinterface%params_calib%kphio_par_a = real(par(2)) myinterface%params_calib%kphio_par_b = real(par(3)) myinterface%params_calib%soilm_thetastar = real(par(4)) - myinterface%params_calib%soilm_betao = real(par(5)) + ! # TODO move whc to position nr 5 myinterface%params_calib%beta_unitcostratio = real(par(6)) myinterface%params_calib%rd_to_vcmax = real(par(7)) myinterface%params_calib%tau_acclim = real(par(8)) diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index b2ced744..93852356 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -474,6 +474,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g ! Convert stomatal conductance to CO2 [mol Pa-1 m-2 s-1] to ! stomatal conductance to water [m s-1] ! Adopted from photosynth_phydro.mod.f90 + ! gs_accl was computed using soilmstress in ggp_pmodel.mod.f90 ! print*,'in waterbal: gs_accl ', tile_fluxes%canopy%gs_accl gw = tile_fluxes%canopy%gs_accl * 1.6 * kR * (climate%dtemp + kTkelvin) diff --git a/tests/testthat/test-calibration-pmodel.R b/tests/testthat/test-calibration-pmodel.R index c2546abf..36e3507f 100644 --- a/tests/testthat/test-calibration-pmodel.R +++ b/tests/testthat/test-calibration-pmodel.R @@ -10,7 +10,6 @@ test_that("test GPP calibration routine p-model (BT, likelihood maximization)", kphio_par_a = 0.01, # set to zero to disable temperature-dependence of kphio, setup ORG in Stocker et al. 2020 GMD kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.01, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, @@ -69,7 +68,7 @@ test_that("test GPP calibration routine p-model (GenSA, rmse, all params)", { kphio_par_a = list(lower = 0, upper = 1, init = 0.2), kphio_par_b = list(lower = 10, upper = 40, init =25), soilm_thetastar = list(lower = 0, upper = 3000, init = 0.6*240), - soilm_betao = list(lower = 0, upper = 1, init = 0.2), + # TODO: should we replace fitting sample_par$soilm_betao with sample_par$whc? beta_unitcostratio = list(lower = 50, upper = 200, init = 146), rd_to_vcmax = list(lower = 0.01, upper = 0.1, init = 0.014), tau_acclim = list(lower = 7, upper = 60, init = 30), @@ -111,7 +110,7 @@ test_that("test Vcmax25 calibration routine p-model (BT, likelihood, all params) kphio_par_a = list(lower = 0, upper = 1, init = 0.2), kphio_par_b = list(lower = 10, upper = 40, init =25), soilm_thetastar = list(lower = 0, upper = 3000, init = 0.6*240), - soilm_betao = list(lower = 0, upper = 1, init = 0.2), + # TODO: should we replace fitting sample_par$soilm_betao with sample_par$whc? beta_unitcostratio = list(lower = 50, upper = 200, init = 146), rd_to_vcmax = list(lower = 0.01, upper = 0.1, init = 0.014), tau_acclim = list(lower = 7, upper = 60, init = 30), @@ -142,7 +141,6 @@ test_that("test Vcmax25 calibration routine p-model (GenSA, rmse)", { kphio_par_a = 0.01, # set to zero to disable temperature-dependence of kphio, setup ORG in Stocker et al. 2020 GMD kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.01, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous # tau_acclim = 30.0, @@ -186,7 +184,6 @@ test_that("test joint calibration routine p-model (BT, likelihood maximization)" kphio_par_a = 0.01, # set to zero to disable temperature-dependence of kphio, setup ORG in Stocker et al. 2020 GMD kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.01, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, diff --git a/tests/testthat/test-model-runs.R b/tests/testthat/test-model-runs.R index a175326b..77adfed3 100644 --- a/tests/testthat/test-model-runs.R +++ b/tests/testthat/test-model-runs.R @@ -10,7 +10,6 @@ test_that("p-model run check GPP", { kphio_par_a = 0.01, # set to zero to disable temperature-dependence of kphio, setup ORG in Stocker et al. 2020 GMD kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.01, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, @@ -68,7 +67,6 @@ test_that("p-model run check Vcmax25", { kphio_par_a = 0.01, # set to zero to disable temperature-dependence of kphio, setup ORG in Stocker et al. 2020 GMD kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.01, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, @@ -138,7 +136,6 @@ test_that("phydro-model run check LE and AET", { kphio_par_a = 0.01, # set to zero to disable temperature-dependence of kphio, setup ORG in Stocker et al. 2020 GMD kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.01, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, diff --git a/tests/testthat/test-quantitative-validation.R b/tests/testthat/test-quantitative-validation.R index d0580245..35e846fb 100644 --- a/tests/testthat/test-quantitative-validation.R +++ b/tests/testthat/test-quantitative-validation.R @@ -14,7 +14,6 @@ test_that("p-model quantitative check", { kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio, setup ORG in Stocker et al. 2020 GMD kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.01, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, diff --git a/vignettes/new_cost_function.Rmd b/vignettes/new_cost_function.Rmd index 137bd123..34b18e67 100644 --- a/vignettes/new_cost_function.Rmd +++ b/vignettes/new_cost_function.Rmd @@ -55,7 +55,6 @@ pars_calib_rmse <- calib_sofun( # of kphio, setup ORG kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover paper setup with soil moisture stress - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, @@ -101,7 +100,6 @@ pars_calib_likelihood <- calib_sofun( # extra arguments passed ot the cost function: par_fixed = list( # fix all other parameters soilm_thetastar = 0.6 * 240, # to recover paper setup with soil moisture stress - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, @@ -146,7 +144,6 @@ par_calib_join <- calib_sofun( kphio_par_a = 0.0, kphio_par_b = 16, soilm_thetastar = 0.6 * 240, # to recover paper setup with soil moisture stress - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0 @@ -264,8 +261,7 @@ settings_mae <- list( control = list( maxit = 100), par = list( - soilm_thetastar = list(lower=0.0, upper=3000, init=0.6*240), - soilm_betao = list(lower=0, upper=1, init=0.2) + soilm_thetastar = list(lower=0.0, upper=3000, init=0.6*240) ) ) diff --git a/vignettes/pmodel_use.Rmd b/vignettes/pmodel_use.Rmd index c7c3f2cc..2a54ef04 100644 --- a/vignettes/pmodel_use.Rmd +++ b/vignettes/pmodel_use.Rmd @@ -102,7 +102,6 @@ params_modl <- list( kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, soilm_thetastar = 0.6 * p_model_drivers$site_info[[1]]$whc, - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, @@ -196,7 +195,7 @@ settings <- list( `rsofun` supports both optimization using the `GenSA` and `BayesianTools` packages. The above statement provides settings for a `GenSA` optimization approach. For this example the maximum number of iterations is kept artificially low. In a real scenario you will have to increase this value orders of magnitude. Keep in mind that optimization routines rely on a cost function, which, depending on its structure influences parameter selection. A limited set of cost functions is provided but the model structure is transparent and custom cost functions can be easily written. More details can be found in the "Parameter calibration and cost functions" vignette. -In addition starting values and ranges are provided for the free parameters in the model. Free parameters include: parameters for the quantum yield efficiency `kphio`, `kphio_par_a` and `kphio_par_b`, soil moisture stress parameters `soilm_thetastar` and `soilm_betao`, and also `beta_unitcostratio`, `rd_to_vcmax`, `tau_acclim` and `kc_jmax` (see `?runread_pmodel_f`). Be mindful that with newer versions of `rsofun` additional parameters might be introduced, so re-check vignettes and function documentation when updating existing code. +In addition starting values and ranges are provided for the free parameters in the model. Free parameters include: parameters for the quantum yield efficiency `kphio`, `kphio_par_a` and `kphio_par_b`, soil moisture stress parameter `soilm_thetastar`, and also `beta_unitcostratio`, `rd_to_vcmax`, `tau_acclim` and `kc_jmax` (see `?runread_pmodel_f`). Be mindful that with newer versions of `rsofun` additional parameters might be introduced, so re-check vignettes and function documentation when updating existing code. With all settings defined the optimization function `calib_sofun()` can be called with driver data and observations specified. Extra arguments for the cost function (like what variable should be used as target to compute the root mean squared error (RMSE) and previous values for the parameters that aren't calibrated, which are needed to run the P-model). @@ -210,7 +209,6 @@ pars <- calib_sofun( targets = "gpp", # define target variable GPP # fix non-calibrated parameters to previous par_fixed = list( - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, @@ -228,7 +226,6 @@ params_modl <- list( kphio_par_a = -0.002595878, kphio_par_b = 13.919139015, soilm_thetastar = 20.599254283, - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd index 05f09ccd..30147f69 100644 --- a/vignettes/pmodel_use_newdata.Rmd +++ b/vignettes/pmodel_use_newdata.Rmd @@ -267,7 +267,6 @@ params_modl <- list( kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, @@ -358,7 +357,7 @@ settings <- list( `rsofun` supports both optimization using the `GenSA` and `BayesianTools` packages. The above statement provides settings for a `GenSA` optimization approach. For this example the maximum number of iterations is kept artificially low. In a real scenario you will have to increase this value orders of magnitude. Keep in mind that optimization routines rely on a cost function, which, depending on its structure influences parameter selection. A limited set of cost functions is provided but the model structure is transparent and custom cost functions can be easily written. More details can be found in the "Parameter calibration and cost functions" vignette. -In addition starting values and ranges are provided for the free parameters in the model. Free parameters include: parameters for the quantum yield efficiency `kphio`, `kphio_par_a` and `kphio_par_b`, soil moisture stress parameters `soilm_thetastar` and `soilm_betao`, and also `beta_unitcostratio`, `rd_to_vcmax`, `tau_acclim` and `kc_jmax` (see `?runread_pmodel_f`). Be mindful that with newer versions of `rsofun` additional parameters might be introduced, so re-check vignettes and function documentation when updating existing code. +In addition starting values and ranges are provided for the free parameters in the model. Free parameters include: parameters for the quantum yield efficiency `kphio`, `kphio_par_a` and `kphio_par_b`, soil moisture stress parameter `soilm_thetastar`, and also `beta_unitcostratio`, `rd_to_vcmax`, `tau_acclim` and `kc_jmax` (see `?runread_pmodel_f`). Be mindful that with newer versions of `rsofun` additional parameters might be introduced, so re-check vignettes and function documentation when updating existing code. With all settings defined the optimization function `calib_sofun()` can be called with driver data and observations specified. Extra arguments for the cost function (like what variable should be used as target to compute the root mean squared error (RMSE) and previous values for the parameters that aren't calibrated, which are needed to run the P-model). diff --git a/vignettes/sensitivity_analysis.Rmd b/vignettes/sensitivity_analysis.Rmd index 8fca0ad0..ac2206d5 100644 --- a/vignettes/sensitivity_analysis.Rmd +++ b/vignettes/sensitivity_analysis.Rmd @@ -72,7 +72,6 @@ ll_pmodel( par_v = c( kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - soilm_betao = 0.0, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, @@ -93,7 +92,6 @@ par_cal_best <- c( kphio_par_a = -0.0025, kphio_par_b = 20, soilm_thetastar = 0.6*240, - soilm_betao = 0.2, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, tau_acclim = 30.0, @@ -108,7 +106,6 @@ par_cal_min <- c( kphio_par_a = -0.004, kphio_par_b = 10, soilm_thetastar = 0, - soilm_betao = 0, beta_unitcostratio = 50.0, rd_to_vcmax = 0.01, tau_acclim = 7.0, @@ -123,7 +120,6 @@ par_cal_max <- c( kphio_par_a = -0.001, kphio_par_b = 30, soilm_thetastar = 240, - soilm_betao = 1, beta_unitcostratio = 200.0, rd_to_vcmax = 0.1, tau_acclim = 60.0, @@ -213,7 +209,7 @@ morrisOut.df |> The outcome of the Morris sensitivity analysis depends strongly on the choice of parameter ranges and how parameters interact with each other in the underlying model. In this example, we constrained the parameters based on -their physical meaning (e.g. `soilm_betao` should be in `[0,1]`) and the site FR-Pue +their physical meaning and the site FR-Pue where the data is coming from (e.g. `kphio_par_b` around 25$^{o}$C). When observing the figure above, we notice that parameters `kphio` and `kc_jmax` have a high impact on the model fit (big $\mu *$), but also the magnitude of this @@ -271,7 +267,6 @@ par_calib <- calib_sofun( settings = settings_calib, par_fixed = list( soilm_thetastar = 0.6*240, - soilm_betao = 0.2, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, tau_acclim = 30.0, @@ -299,7 +294,6 @@ settings_calib <- list( )), par = list( kphio = list(lower = 0.03, upper = 0.15, init = 0.05), - soilm_betao = list(lower = 0, upper = 1, init = 0.2), kc_jmax = list(lower = 0.2, upper = 0.8, init = 0.41), err_gpp = list(lower = 0.1, upper = 3, init = 0.8) ) @@ -410,7 +404,6 @@ run_pmodel <- function(sample_par){ kphio_par_a = sample_par$kphio_par_a, kphio_par_b = sample_par$kphio_par_b, soilm_thetastar = 0.6*240, - soilm_betao = 0.2, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, tau_acclim = 30.0, @@ -519,7 +512,6 @@ plot_gpp_error <- ggplot(data = runread_pmodel_f( kphio_par_a = par_calib$par[2], kphio_par_b = par_calib$par[3], soilm_thetastar = 0.6*240, # copied from par_fixed above - soilm_betao = 0.2, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, tau_acclim = 30.0, From 07603a78644de074512bd9087b005b02392360ad Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Wed, 18 Sep 2024 13:49:41 +0200 Subject: [PATCH 069/112] Make model parameter check more robust --- R/run_pmodel_f_bysite.R | 40 ++++++++++++++++---------------- tests/testthat/test-model-runs.R | 2 +- 2 files changed, 21 insertions(+), 21 deletions(-) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 7d725ee5..2292bf70 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -330,27 +330,27 @@ run_pmodel_f_bysite <- function( # Check model parameters if (!params_siml$use_phydro){ - # P-model needs 9 parameters - if( sum( names(params_modl) %in% c('kphio', 'kphio_par_a', 'kphio_par_b', - 'soilm_thetastar', - 'beta_unitcostratio', 'rd_to_vcmax', - 'tau_acclim', 'kc_jmax', 'whc') - ) != 9){ - warning(" Returning a dummy data frame. Incorrect model parameters.") - continue <- FALSE - } + # P-model needs these parameters: + param_names_sorted <- c( + 'beta_unitcostratio', 'kc_jmax', + 'kphio', 'kphio_par_a', 'kphio_par_b', + 'rd_to_vcmax', + 'soilm_thetastar', + 'tau_acclim', 'whc') + } else { + # P-hydro model needs these parameters: + param_names_sorted <- c( + "bsoil", "kc_jmax", + "kphio", "kphio_par_a", "kphio_par_b", + "phydro_alpha", "phydro_b_plant", "phydro_gamma", + "phydro_K_plant", "phydro_p50_plant", + "rd_to_vcmax", + "Ssoil", + "tau_acclim", "whc") } - else { - # P-hydro needs 14 parameters - if( sum( names(params_modl) %in% c('kphio', 'kphio_par_a', 'kphio_par_b', - 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', - 'phydro_K_plant', 'phydro_p50_plant', 'phydro_b_plant', - 'phydro_alpha', 'phydro_gamma', - 'bsoil', 'Ssoil', 'whc') - ) != 14){ - warning(" Returning a dummy data frame. Incorrect model parameters.") - continue <- FALSE - } + if (!identical(sort(names(params_modl)), param_names_sorted)){ + warning(" Returning a dummy data frame. Incorrect model parameters.") + continue <- FALSE } } diff --git a/tests/testthat/test-model-runs.R b/tests/testthat/test-model-runs.R index 77adfed3..1d6349c6 100644 --- a/tests/testthat/test-model-runs.R +++ b/tests/testthat/test-model-runs.R @@ -170,7 +170,7 @@ test_that("phydro-model run check LE and AET", { ) |> mutate(sitename = paste0(sitename, "_5000mm")) ) - # Plot: + # # Plot: # df_output |> # tidyr::unnest(data) |> select(-site_info) |> # filter(date < "2012-01-01") |> From 6f6a6f2363cbfad55e29521cd6f6742b2fc067c4 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Wed, 18 Sep 2024 15:45:24 +0200 Subject: [PATCH 070/112] Make parameter calibration more robust --- NAMESPACE | 2 +- R/calib_sofun.R | 34 +++--- R/cost_likelihood_phydro.R | 140 +++++++++++++++++------ R/cost_likelihood_pmodel.R | 64 +++++------ R/cost_rmse_pmodel.R | 76 ++++++------ R/run_pmodel_f_bysite.R | 41 ++++--- R/runread_pmodel_f.R | 2 +- man/cost_likelihood_phydromodel.Rd | 94 +++++++++++++++ tests/testthat/test-calibration-pmodel.R | 10 +- 9 files changed, 304 insertions(+), 159 deletions(-) create mode 100644 man/cost_likelihood_phydromodel.Rd diff --git a/NAMESPACE b/NAMESPACE index 4e1f306f..ca06401f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,7 +2,7 @@ export(calib_sofun) export(cost_likelihood_biomee) -export(cost_likelihood_phydro) +export(cost_likelihood_phydromodel) export(cost_likelihood_pmodel) export(cost_rmse_biomee) export(cost_rmse_pmodel) diff --git a/R/calib_sofun.R b/R/calib_sofun.R index ce6b338e..af648a24 100644 --- a/R/calib_sofun.R +++ b/R/calib_sofun.R @@ -148,16 +148,16 @@ calib_sofun <- function( eval(settings$metric)(par = par, obs = obs, drivers = drivers, - ...) + ...) # the dots contain: par_fixed, targets, parallel, ncores } # reformat parameters - pars <- as.data.frame(do.call("rbind", settings$par), row.names = FALSE) - + pars <- as.data.frame(do.call("rbind", settings$par)) # use rownames later on + priors <- BayesianTools::createTruncatedNormalPrior( - unlist(pars$mean), - unlist(pars$sd), - unlist(pars$lower), + unlist(pars$mean), # NOTE: This needs a value otherwise: Error in `parallelSampler(1000)`: sampler provided doesn't work + unlist(pars$sd), # NOTE: This needs a value otherwise: Error in `parallelSampler(1000)`: sampler provided doesn't work + unlist(pars$lower), # As a workaround BayesianTools::createUniformPrior could be used unlist(pars$upper) # unlist(pars$init) ) @@ -165,24 +165,16 @@ calib_sofun <- function( # setup the bayes run, no message forwarding is provided # so wrap the function in a do.call setup <- BayesianTools::createBayesianSetup( - likelihood = function( - random_par) { - # cost( - # par = random_par, - # obs = obs, - # drivers = drivers, - # ... - # ) + likelihood = function(random_par) { do.call("cost", list( - par = random_par, + par = setNames(random_par, rownames(pars)), obs = obs, drivers = drivers - )) - }, - prior = priors, - names = names(settings$par) - ) + ))}, + prior = priors, + names = rownames(pars) + ) # set bt control parameters bt_settings <- settings$control$settings @@ -193,7 +185,7 @@ calib_sofun <- function( sampler = settings$control$sampler, settings = bt_settings ) - + # drop last value bt_par <- BayesianTools::MAP(out)$parametersMAP bt_par <- bt_par[1:(length(bt_par))] diff --git a/R/cost_likelihood_phydro.R b/R/cost_likelihood_phydro.R index 771aa81f..8ab5bc71 100644 --- a/R/cost_likelihood_phydro.R +++ b/R/cost_likelihood_phydro.R @@ -1,4 +1,75 @@ -cost_likelihood_phydro <- function( +#' Cost function computing a log-likelihood for calibration of Phydro-model +#' parameters +#' +#' The cost function performs a Phydro-model run for the input drivers and model parameter +#' values, and computes the outcome's normal log-likelihood centered at the input +#' observed values and with standard deviation given as an input parameter +#' (calibratable). +#' +#' @param par A vector of values for the parameters to be calibrated, including +#' a subset of model parameters (described in \code{\link{runread_pmodel_f}}), +#' in order, and error terms +#' for each target variable (for example \code{'gpp_err'}), in the same order as +#' the targets appear in \code{targets}. +#' @param obs A nested data.frame of observations, with columns \code{'sitename'} +#' and \code{'data'} (see \code{\link{p_model_validation}} or \code{\link{p_model_validation_vcmax25}} +#' to check their structure). +#' @param drivers A nested data.frame of driver data. See \code{\link{p_model_drivers}} +#' for a description of the data structure. +#' @param targets A character vector indicating the target variables for which the +#' optimization will be done and the RMSE computed. This string must be a column +#' name of the \code{data} data.frame belonging to the validation nested data.frame +#' (for example 'gpp'). +#' @param par_fixed A named list of model parameter values to keep fixed during the +#' calibration. These should complement the input \code{par} such that all model +#' parameters are passed on to \code{\link{runread_pmodel_f}}. +#' @param parallel A logical specifying whether simulations are to be parallelised +#' (sending data from a certain number of sites to each core). Defaults to +#' \code{FALSE}. +#' @param ncores An integer specifying the number of cores used for parallel +#' computing. Defaults to 2. +#' +#' @return The log-likelihood of the observed target values, assuming that they +#' are independent, normally distributed and centered on the predictions +#' made by the P-model run with standard deviation given as input (via `par` because +#' the error terms are estimated through the calibration with `BayesianTools`, +#' as shown in the "Parameter calibration and cost functions" vignette). +#' +#' @details To run the P-model, all model parameters must be given. The cost +#' function uses arguments \code{par} and \code{par_fixed} such that, in the +#' calibration routine, \code{par} can be updated by the optimizer and +#' \code{par_fixed} are kept unchanged throughout calibration. +#' +#' If the validation data contains a "date" column (fluxes), the simulated target time series +#' is compared to the observed values on those same dates (e.g. for GPP). Otherwise, +#' there should only be one observed value per site (leaf traits), and the outputs +#' (averaged over the growing season, weighted by predicted GPP) will be +#' compared to this single value representative of the site (e.g. Vcmax25). As an exception, +#' when the date of a trait measurement is available, it will be compared to the +#' trait value predicted on that date. +#' +#' @export +#' +#' @examples +#' # Compute the likelihood for a set of +#' # model parameter values involved in the +#' # temperature dependence of kphio +#' # and example data +#' cost_likelihood_phydromodel( +#' par = c(0.05, -0.01, 1, # model parameters +#' 2), # err_gpp +#' obs = p_model_validation, +#' drivers = p_model_drivers, +#' targets = c('gpp'), +#' par_fixed = list( +#' soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress +#' beta_unitcostratio = 146.0, +#' rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous +#' tau_acclim = 30.0, +#' kc_jmax = 0.41 +#' ) +#' ) +cost_likelihood_phydromodel <- function( par, # model parameters & error terms for each target obs, drivers, @@ -7,53 +78,46 @@ cost_likelihood_phydro <- function( parallel = FALSE, ncores = 2 ){ + # NOTE(fabian): These different cost functions share a LOT of code in common. Consider consolidation for maintainability? + # predefine variables for CRAN check compliance sitename <- data <- gpp_mod <- NULL - using_phydro = drivers$params_siml[[1]]$use_phydro - - # FIXME Jaideep: Instead of checking the number of params, - # it might be better to check for presence of each param in par and par_fixed - ## check input parameters - expected_params = ifelse(using_phydro, yes=14, no=10) - if( (length(par) + length(par_fixed)) != (expected_params + length(targets)) ){ - stop(paste0('Error: Input calibratable and fixed parameters (par = ',length(par),' and par_fixed = ',length(par_fixed),') - do not match length of the required P-model parameters (',expected_params + length(targets),').')) + if (!("use_phydro" %in% colnames(drivers$params_siml[[1]]))){ + warning("Parameter use_phydro not set. Assuming FALSE") + using_phydro = FALSE + } else { + using_phydro = drivers$params_siml[[1]]$use_phydro } - - ## define parameter set based on calibrated parameters - if (using_phydro){ - calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', - 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', - 'phydro_K_plant', 'phydro_p50_plant', 'phydro_b_plant', - 'phydro_alpha', 'phydro_gamma', - 'bsoil', 'Ssoil', 'whc') + ## define required parameter set based on calibrated parameters + if (!using_phydro){ + required_param_names <- rsofun:::required_param_names$p_model } else { - calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', - 'soilm_thetastar', - 'beta_unitcostratio', 'rd_to_vcmax', - 'tau_acclim', 'kc_jmax', 'whc') + required_param_names <- rsofun:::required_param_names$phydro_model } - # FIXME Jaideep: Here it is assumed that the params in par will appear in exactly the same order in settings as in the list above. Better to do this in an order-independent way. - if(!is.null(par_fixed)){ - params_modl <- list() - # complete with calibrated values - i <- 1 # start counter - for(par_name in calib_param_names){ - if(is.null(par_fixed[[par_name]])){ - params_modl[[par_name]] <- par[i] # use calibrated par value - i <- i + 1 # counter of calibrated params - }else{ - params_modl[[par_name]] <- par_fixed[[par_name]] # use fixed par value - } - } - }else{ - params_modl <- as.list(par[1:expected_params]) # all parameters calibrated - names(params_modl) <- calib_param_names + ## split calibrated parameters into model and error parameters + par_calibrated_model <- par[ ! names(par) %in% c("err_gpp") ] # consider only model parameters for the check + # par_calibrated_errormodel <- par[ names(par) %in% c("err_gpp") ] + # par_fixed + + ## check parameters + if (!identical(sort(c(names(par_calibrated_model), names(par_fixed))), required_param_names)){ + stop(sprintf(paste0("Error: Input calibratable and fixed parameters do not ", + "match required model parameters:", + "\n par: c(%s)", + "\n par_fixed: c(%s)", + "\n required: c(%s)"), + paste0(sort(names(par_calibrated_model)), collapse = ", "), + paste0(sort(names(par_fixed)), collapse = ", "), + paste0(sort(required_param_names), collapse = ", "))) } + # Combine fixed and estimated params to result in all the params required to run the model + # This basically uses all params except those of the error model of the observations + params_modl <- c(par, par_fixed)[required_param_names] + ## run the model df <- runread_pmodel_f( drivers, diff --git a/R/cost_likelihood_pmodel.R b/R/cost_likelihood_pmodel.R index bf6c00e1..efb59ad9 100644 --- a/R/cost_likelihood_pmodel.R +++ b/R/cost_likelihood_pmodel.R @@ -79,6 +79,8 @@ cost_likelihood_pmodel <- function( parallel = FALSE, ncores = 2 ){ + # NOTE(fabian): These different cost functions share a LOT of code in common. Consider consolidation for maintainability? + # predefine variables for CRAN check compliance sitename <- data <- gpp_mod <- NULL @@ -89,48 +91,34 @@ cost_likelihood_pmodel <- function( using_phydro = drivers$params_siml[[1]]$use_phydro } - # FIXME Jaideep: Instead of checking the number of params, - # it might be better to check for presence of each param in par and par_fixed - ## check input parameters - expected_params = ifelse(using_phydro, yes=14, no=10) - if( (length(par) + length(par_fixed)) != (expected_params + length(targets)) ){ - stop(paste0('Error: Input calibratable and fixed parameters (par = ',length(par),' and par_fixed = ',length(par_fixed),') - do not match length of the required P-model parameters (',expected_params + length(targets),').')) - } - - - ## define parameter set based on calibrated parameters - if (using_phydro){ - calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', - 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', - 'phydro_K_plant', 'phydro_p50_plant', 'phydro_b_plant', - 'phydro_alpha', 'phydro_gamma', - 'bsoil', 'Ssoil', 'whc') + ## define required parameter set based on model parameters + if (!using_phydro){ + required_param_names <- rsofun:::required_param_names$p_model } else { - calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', - 'soilm_thetastar', - 'beta_unitcostratio', 'rd_to_vcmax', - 'tau_acclim', 'kc_jmax', 'whc') + required_param_names <- rsofun:::required_param_names$phydro_model } - # FIXME Jaideep: Here it is assumed that the params in par will appear in exactly the same order in settings as in the list above. Better to do this in an order-independent way. - if(!is.null(par_fixed)){ - params_modl <- list() - # complete with calibrated values - i <- 1 # start counter - for(par_name in calib_param_names){ - if(is.null(par_fixed[[par_name]])){ - params_modl[[par_name]] <- par[i] # use calibrated par value - i <- i + 1 # counter of calibrated params - }else{ - params_modl[[par_name]] <- par_fixed[[par_name]] # use fixed par value - } - } - }else{ - params_modl <- as.list(par[1:expected_params]) # all parameters calibrated - names(params_modl) <- calib_param_names + ## split calibrated parameters into model and error parameters + par_calibrated_model <- par[ ! names(par) %in% c("err_gpp") ] # consider only model parameters for the check + # par_calibrated_errormodel <- par[ names(par) %in% c("err_gpp") ] + # par_fixed + + ## check parameters + if (!identical(sort(c(names(par_calibrated_model), names(par_fixed))), required_param_names)){ + stop(sprintf(paste0("Error: Input calibratable and fixed parameters do not ", + "match required model parameters:", + "\n par: c(%s)", + "\n par_fixed: c(%s)", + "\n required: c(%s)"), + paste0(sort(names(par_calibrated_model)), collapse = ", "), + paste0(sort(names(par_fixed)), collapse = ", "), + paste0(sort(required_param_names), collapse = ", "))) } + # Combine fixed and estimated params to result in all the params required to run the model + # This basically uses all params except those of the error model of the observations + params_modl <- c(par, par_fixed)[required_param_names] + ## run the model df <- runread_pmodel_f( drivers, @@ -231,6 +219,8 @@ cost_likelihood_pmodel <- function( }) |> unlist() |> sum() + # TODO(fabian): make above ll more robust by using advantages of named vector `par` + # instead of relying on an expected order # trap boundary conditions if(is.nan(ll) | is.na(ll) | ll == 0){ll <- -Inf} diff --git a/R/cost_rmse_pmodel.R b/R/cost_rmse_pmodel.R index fe9815b3..9f6bb292 100644 --- a/R/cost_rmse_pmodel.R +++ b/R/cost_rmse_pmodel.R @@ -76,56 +76,48 @@ cost_rmse_pmodel <- function( parallel = FALSE, ncores = 2 ){ - + # NOTE(fabian): These different cost functions share a LOT of code in common. Consider consolidation for maintainability? + # predefine variables for CRAN check compliance sitename <- data <- gpp_mod <- NULL - using_phydro = drivers$params_siml[[1]]$use_phydro - - # FIXME Jaideep: Instead of checking the number of params, - # it might be better to check for presence of each param in par and par_fixed - ## check input parameters - expected_params = ifelse(using_phydro, yes=14, no=10) - if( (length(par) + length(par_fixed)) != (expected_params) ){ - stop(paste0('Error: Input calibratable and fixed parameters (par = ',length(par),' and par_fixed = ',length(par_fixed),') - do not match length of the required P-model parameters (',expected_params + length(targets),').')) - } - - ## define parameter set based on calibrated parameters - if (using_phydro){ - calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', - 'rd_to_vcmax', 'tau_acclim', 'kc_jmax', - 'phydro_K_plant', 'phydro_p50_plant', 'phydro_b_plant', - 'phydro_alpha', 'phydro_gamma', - 'bsoil', 'Ssoil', 'whc') + if (!("use_phydro" %in% colnames(drivers$params_siml[[1]]))){ + warning("Parameter use_phydro not set. Assuming FALSE") + using_phydro = FALSE } else { - calib_param_names <- c('kphio', 'kphio_par_a', 'kphio_par_b', - 'soilm_thetastar', - 'beta_unitcostratio', 'rd_to_vcmax', - 'tau_acclim', 'kc_jmax', 'whc') + using_phydro = drivers$params_siml[[1]]$use_phydro } - - if(!is.null(par_fixed)){ - params_modl <- list() - # complete with calibrated values - i <- 1 # start counter - for(par_name in calib_param_names){ - if(is.null(par_fixed[[par_name]])){ - params_modl[[par_name]] <- par[i] # use calibrated par value - i <- i + 1 # counter of calibrated params - }else{ - params_modl[[par_name]] <- par_fixed[[par_name]] # use fixed par value - } - } - }else{ - params_modl <- as.list(par) # all parameters calibrated - names(params_modl) <- calib_param_names + ## define required parameter set based on model parameters + if (!using_phydro){ + required_param_names <- rsofun:::required_param_names$p_model + } else { + required_param_names <- rsofun:::required_param_names$phydro_model } + + ## split calibrated parameters into model and error parameters + par_calibrated_model <- par[ ! names(par) %in% c("err_gpp") ] # consider only model parameters for the check + # par_calibrated_errormodel <- par[ names(par) %in% c("err_gpp") ] + # par_fixed - # print(par) + ## check parameters + if (!identical(sort(c(names(par_calibrated_model), names(par_fixed))), required_param_names)){ + stop(sprintf(paste0("Error: Input calibratable and fixed parameters do not ", + "match required model parameters:", + "\n par: c(%s)", + "\n par_fixed: c(%s)", + "\n required: c(%s)"), + paste0(sort(names(par_calibrated_model)), collapse = ", "), + paste0(sort(names(par_fixed)), collapse = ", "), + paste0(sort(required_param_names), collapse = ", "))) + } - # run the model + # Combine fixed and estimated params to result in all the params required to run the model + # This basically uses all params except those of the error model of the observations + params_modl <- c(par, par_fixed)[required_param_names] + + + ## run the model df <- runread_pmodel_f( drivers, par = params_modl, @@ -133,7 +125,7 @@ cost_rmse_pmodel <- function( parallel = FALSE ) - # clean model output and unnest + ## clean model output and unnest df <- df |> dplyr::rowwise() |> dplyr::reframe( diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 2292bf70..9fe9fc33 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -150,7 +150,7 @@ #' params_modl = params_modl #' ) -run_pmodel_f_bysite <- function( +run_pmodel_f_bysite <- function( # TODO: Above docstring appears duplicated in runread_pmodel_f.R. This redunduncy should be reduced. sitename, params_siml, site_info, @@ -329,24 +329,11 @@ run_pmodel_f_bysite <- function( } # Check model parameters + # The different models need these parameters: if (!params_siml$use_phydro){ - # P-model needs these parameters: - param_names_sorted <- c( - 'beta_unitcostratio', 'kc_jmax', - 'kphio', 'kphio_par_a', 'kphio_par_b', - 'rd_to_vcmax', - 'soilm_thetastar', - 'tau_acclim', 'whc') + param_names_sorted <- rsofun:::required_param_names$p_model } else { - # P-hydro model needs these parameters: - param_names_sorted <- c( - "bsoil", "kc_jmax", - "kphio", "kphio_par_a", "kphio_par_b", - "phydro_alpha", "phydro_b_plant", "phydro_gamma", - "phydro_K_plant", "phydro_p50_plant", - "rd_to_vcmax", - "Ssoil", - "tau_acclim", "whc") + param_names_sorted <- rsofun:::required_param_names$phydro_model } if (!identical(sort(names(params_modl)), param_names_sorted)){ warning(" Returning a dummy data frame. Incorrect model parameters.") @@ -549,3 +536,23 @@ run_pmodel_f_bysite <- function( .onUnload <- function(libpath) { library.dynam.unload("rsofun", libpath) } + +# For internal use and checks. (NOTE we could add a docstring similar to `p_model_validation`, but it is currently not needed.) +required_param_names <- list( + phydro_model = c( # P-hydro model needs these parameters: + 'bsoil', 'kc_jmax', + 'kphio', 'kphio_par_a', 'kphio_par_b', + 'phydro_alpha', 'phydro_b_plant', 'phydro_gamma', + 'phydro_K_plant', 'phydro_p50_plant', + 'rd_to_vcmax', + 'Ssoil', + 'tau_acclim', 'whc'), + p_model = c(# P-model needs these parameters: + 'beta_unitcostratio', 'kc_jmax', + 'kphio', 'kphio_par_a', 'kphio_par_b', + 'rd_to_vcmax', + 'soilm_thetastar', + 'tau_acclim', 'whc'), + biomee_model = c(# Biomee-model needs these parameters: + 'TODO') +) diff --git a/R/runread_pmodel_f.R b/R/runread_pmodel_f.R index 6c3e0756..ed53a522 100644 --- a/R/runread_pmodel_f.R +++ b/R/runread_pmodel_f.R @@ -91,7 +91,7 @@ #' drivers = rsofun::p_model_drivers, #' par = params_modl) -runread_pmodel_f <- function( +runread_pmodel_f <- function( # TODO: Above docstring appears duplicated in run_pmodel_f_bysite.R. This redunduncy should be reduced. drivers, par, makecheck = TRUE, diff --git a/man/cost_likelihood_phydromodel.Rd b/man/cost_likelihood_phydromodel.Rd new file mode 100644 index 00000000..0aabc29a --- /dev/null +++ b/man/cost_likelihood_phydromodel.Rd @@ -0,0 +1,94 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/cost_likelihood_phydro.R +\name{cost_likelihood_phydromodel} +\alias{cost_likelihood_phydromodel} +\title{Cost function computing a log-likelihood for calibration of Phydro-model +parameters} +\usage{ +cost_likelihood_phydromodel( + par, + obs, + drivers, + targets, + par_fixed = NULL, + parallel = FALSE, + ncores = 2 +) +} +\arguments{ +\item{par}{A vector of values for the parameters to be calibrated, including +a subset of model parameters (described in \code{\link{runread_pmodel_f}}), +in order, and error terms +for each target variable (for example \code{'gpp_err'}), in the same order as +the targets appear in \code{targets}.} + +\item{obs}{A nested data.frame of observations, with columns \code{'sitename'} +and \code{'data'} (see \code{\link{p_model_validation}} or \code{\link{p_model_validation_vcmax25}} +to check their structure).} + +\item{drivers}{A nested data.frame of driver data. See \code{\link{p_model_drivers}} +for a description of the data structure.} + +\item{targets}{A character vector indicating the target variables for which the +optimization will be done and the RMSE computed. This string must be a column +name of the \code{data} data.frame belonging to the validation nested data.frame +(for example 'gpp').} + +\item{par_fixed}{A named list of model parameter values to keep fixed during the +calibration. These should complement the input \code{par} such that all model +parameters are passed on to \code{\link{runread_pmodel_f}}.} + +\item{parallel}{A logical specifying whether simulations are to be parallelised +(sending data from a certain number of sites to each core). Defaults to +\code{FALSE}.} + +\item{ncores}{An integer specifying the number of cores used for parallel +computing. Defaults to 2.} +} +\value{ +The log-likelihood of the observed target values, assuming that they +are independent, normally distributed and centered on the predictions +made by the P-model run with standard deviation given as input (via `par` because +the error terms are estimated through the calibration with `BayesianTools`, +as shown in the "Parameter calibration and cost functions" vignette). +} +\description{ +The cost function performs a Phydro-model run for the input drivers and model parameter +values, and computes the outcome's normal log-likelihood centered at the input +observed values and with standard deviation given as an input parameter +(calibratable). +} +\details{ +To run the P-model, all model parameters must be given. The cost +function uses arguments \code{par} and \code{par_fixed} such that, in the +calibration routine, \code{par} can be updated by the optimizer and +\code{par_fixed} are kept unchanged throughout calibration. + +If the validation data contains a "date" column (fluxes), the simulated target time series +is compared to the observed values on those same dates (e.g. for GPP). Otherwise, +there should only be one observed value per site (leaf traits), and the outputs +(averaged over the growing season, weighted by predicted GPP) will be +compared to this single value representative of the site (e.g. Vcmax25). As an exception, +when the date of a trait measurement is available, it will be compared to the +trait value predicted on that date. +} +\examples{ +# Compute the likelihood for a set of +# model parameter values involved in the +# temperature dependence of kphio +# and example data +cost_likelihood_phydromodel( + par = c(0.05, -0.01, 1, # model parameters + 2), # err_gpp + obs = p_model_validation, + drivers = p_model_drivers, + targets = c('gpp'), + par_fixed = list( + soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41 + ) +) +} diff --git a/tests/testthat/test-calibration-pmodel.R b/tests/testthat/test-calibration-pmodel.R index 36e3507f..3199fc46 100644 --- a/tests/testthat/test-calibration-pmodel.R +++ b/tests/testthat/test-calibration-pmodel.R @@ -3,7 +3,11 @@ set.seed(10) test_that("test GPP calibration routine p-model (BT, likelihood maximization)", { skip_on_cran() - drivers <- rsofun::p_model_drivers + #drivers <- rsofun::p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO + drivers <- readRDS(file = here::here("data/p_model_drivers_newformat.rds")) + drivers$params_siml[[1]]$use_gs <- TRUE + + obs <- rsofun::p_model_validation params_fix <- list( # kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD @@ -52,7 +56,9 @@ test_that("test GPP calibration routine p-model (BT, likelihood maximization)", test_that("test GPP calibration routine p-model (GenSA, rmse, all params)", { skip_on_cran() - drivers <- rsofun::p_model_drivers + #drivers <- rsofun::p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO + drivers <- readRDS(file = here::here("data/p_model_drivers_newformat.rds")) + drivers$params_siml[[1]]$use_gs <- TRUE obs <- rsofun::p_model_validation settings <- list( From d69acda8eb6cee04b26574a48c243efb500fc0b9 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Thu, 19 Sep 2024 11:28:00 +0200 Subject: [PATCH 071/112] Fix test-calibration-pmodel.R: add 'whc' and switch prior to uniform Previously prior was changed to a TruncatedNormal, this then requires additional specifications of mean and sd for each parameter prior. For the tests it is easier to switch to a uniform prior. --- R/calib_sofun.R | 15 ++++++++++----- tests/testthat/test-calibration-pmodel.R | 13 +++++++++---- 2 files changed, 19 insertions(+), 9 deletions(-) diff --git a/R/calib_sofun.R b/R/calib_sofun.R index af648a24..bbe5e300 100644 --- a/R/calib_sofun.R +++ b/R/calib_sofun.R @@ -119,7 +119,7 @@ calib_sofun <- function( # create bounds lower <- unlist(lapply(settings$par, function(x) x$lower)) upper <- unlist(lapply(settings$par, function(x) x$upper)) - pars <- unlist(lapply( settings$par, function(x) x$init)) + pars <- unlist(lapply(settings$par, function(x) x$init)) out <- GenSA::GenSA( par = pars, @@ -154,10 +154,15 @@ calib_sofun <- function( # reformat parameters pars <- as.data.frame(do.call("rbind", settings$par)) # use rownames later on - priors <- BayesianTools::createTruncatedNormalPrior( - unlist(pars$mean), # NOTE: This needs a value otherwise: Error in `parallelSampler(1000)`: sampler provided doesn't work - unlist(pars$sd), # NOTE: This needs a value otherwise: Error in `parallelSampler(1000)`: sampler provided doesn't work - unlist(pars$lower), # As a workaround BayesianTools::createUniformPrior could be used + # priors <- BayesianTools::createTruncatedNormalPrior( + # unlist(pars$mean), # NOTE: This needs a value otherwise: Error in `parallelSampler(1000)`: sampler provided doesn't work + # unlist(pars$sd), # NOTE: This needs a value otherwise: Error in `parallelSampler(1000)`: sampler provided doesn't work + # unlist(pars$lower), # As a workaround BayesianTools::createUniformPrior could be used + # unlist(pars$upper) + # # unlist(pars$init) + # ) + priors <- BayesianTools::createUniformPrior( # workaround for TruncatedNormalPrior, this does not require mean and sd + unlist(pars$lower), unlist(pars$upper) # unlist(pars$init) ) diff --git a/tests/testthat/test-calibration-pmodel.R b/tests/testthat/test-calibration-pmodel.R index 3199fc46..b29ef0ba 100644 --- a/tests/testthat/test-calibration-pmodel.R +++ b/tests/testthat/test-calibration-pmodel.R @@ -17,7 +17,8 @@ test_that("test GPP calibration routine p-model (BT, likelihood maximization)", beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41 + kc_jmax = 0.41, + whc = 2000 # site info, water holding capacity in mm ) settings <- list( @@ -34,11 +35,11 @@ test_that("test GPP calibration routine p-model (BT, likelihood maximization)", ) ), par = list( - kphio = list(lower=0.04, upper=0.09, init=0.05), - err_gpp = list(lower = 0.01, upper = 4, init = 2) + kphio = list(lower = 0.04, upper = 0.09, init = 0.05), + err_gpp = list(lower = 0.01, upper = 4, init = 2) ) ) - + pars <- rsofun::calib_sofun( drivers = drivers, obs = obs, @@ -49,6 +50,9 @@ test_that("test GPP calibration routine p-model (BT, likelihood maximization)", parallel = TRUE, ncores = 2 ) + # plot(pars$mod) + # print(pars$mod) + # summary(pars$mod) # test for correctly returned values expect_type(pars, "list") @@ -88,6 +92,7 @@ test_that("test GPP calibration routine p-model (GenSA, rmse, all params)", { settings = settings, optim_out = FALSE, # extra arguments for the cost function + par_fixed = list(whc= 2000), # site info, water holding capacity in mm targets = 'gpp' ) From c836f63827f5888ee030ba89c596f90dd7e4a840 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Thu, 19 Sep 2024 14:02:44 +0200 Subject: [PATCH 072/112] Fix Vcmax25 tests in test-calibration-pmodel --- R/cost_likelihood_pmodel.R | 4 +- tests/testthat/test-calibration-pmodel.R | 73 +++++++++++++++++++++--- 2 files changed, 66 insertions(+), 11 deletions(-) diff --git a/R/cost_likelihood_pmodel.R b/R/cost_likelihood_pmodel.R index efb59ad9..ff318cf8 100644 --- a/R/cost_likelihood_pmodel.R +++ b/R/cost_likelihood_pmodel.R @@ -99,8 +99,8 @@ cost_likelihood_pmodel <- function( } ## split calibrated parameters into model and error parameters - par_calibrated_model <- par[ ! names(par) %in% c("err_gpp") ] # consider only model parameters for the check - # par_calibrated_errormodel <- par[ names(par) %in% c("err_gpp") ] + par_calibrated_model <- par[ ! names(par) %in% c("err_gpp", "err_vcmax25") ] # consider only model parameters for the check + # par_calibrated_errormodel <- par[ names(par) %in% c("err_gpp", "err_vcmax25") ] # par_fixed ## check parameters diff --git a/tests/testthat/test-calibration-pmodel.R b/tests/testthat/test-calibration-pmodel.R index b29ef0ba..1b0b17b4 100644 --- a/tests/testthat/test-calibration-pmodel.R +++ b/tests/testthat/test-calibration-pmodel.R @@ -102,7 +102,20 @@ test_that("test GPP calibration routine p-model (GenSA, rmse, all params)", { test_that("test Vcmax25 calibration routine p-model (BT, likelihood, all params)", { skip_on_cran() - drivers <- p_model_drivers_vcmax25 + drivers <- rsofun::p_model_drivers_vcmax25 |> + # TODO: NOT YET UPDATED FOR PHYDRO + # # specify additionally needed params_siml flags: + dplyr::mutate(params_siml = purrr::map(params_siml, \(x) + dplyr::mutate(x, + use_pml = TRUE, + use_gs = TRUE, + use_phydro = FALSE))) |> + # specify additionally needed site info: + dplyr::mutate(site_info = purrr::map(site_info, \(x) + dplyr::mutate(x, + canopy_height = 5, + reference_height = 10))) + obs <- rsofun::p_model_validation_vcmax25 settings <- list( @@ -136,8 +149,12 @@ test_that("test Vcmax25 calibration routine p-model (BT, likelihood, all params) settings = settings, optim_out = FALSE, # arguments for cost function + par_fixed = list(whc= 2000), # site info, water holding capacity in mm targets = 'vcmax25' ) + # plot(pars$mod) + # print(pars$mod) + # summary(pars$mod) # test for correctly returned values expect_type(pars, "list") @@ -145,7 +162,20 @@ test_that("test Vcmax25 calibration routine p-model (BT, likelihood, all params) test_that("test Vcmax25 calibration routine p-model (GenSA, rmse)", { skip_on_cran() - drivers <- p_model_drivers_vcmax25 + drivers <- rsofun::p_model_drivers_vcmax25 |> + # TODO: NOT YET UPDATED FOR PHYDRO + # # specify additionally needed params_siml flags: + dplyr::mutate(params_siml = purrr::map(params_siml, \(x) + dplyr::mutate(x, + use_pml = TRUE, + use_gs = TRUE, + use_phydro = FALSE))) |> + # specify additionally needed site info: + dplyr::mutate(site_info = purrr::map(site_info, \(x) + dplyr::mutate(x, + canopy_height = 5, + reference_height = 10))) + obs <- rsofun::p_model_validation_vcmax25 params_fix <- list( kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD @@ -155,7 +185,8 @@ test_that("test Vcmax25 calibration routine p-model (GenSA, rmse)", { beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous # tau_acclim = 30.0, - kc_jmax = 0.41 + kc_jmax = 0.41, + whc = 2000 # site info, water holding capacity in mm ) settings <- list( @@ -186,8 +217,27 @@ test_that("test Vcmax25 calibration routine p-model (GenSA, rmse)", { test_that("test joint calibration routine p-model (BT, likelihood maximization)", { skip_on_cran() - drivers <- rbind(gpp = rsofun::p_model_drivers, - vcmax25 = rsofun::p_model_drivers_vcmax25) + drivers <- rbind( + gpp = # TODO: rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO + readRDS(file = here::here("data/p_model_drivers_newformat.rds")), + vcmax25 = rsofun::p_model_drivers_vcmax25 |> + # TODO: NOT YET UPDATED FOR PHYDRO + # # specify additionally needed params_siml flags: + dplyr::mutate(params_siml = purrr::map(params_siml, \(x) + dplyr::mutate(x, + use_pml = TRUE, + use_gs = TRUE, + use_phydro = FALSE))) |> + # specify additionally needed site info: + dplyr::mutate(site_info = purrr::map(site_info, \(x) + dplyr::mutate(x, + canopy_height = 5, + reference_height = 10))) |> + dplyr::mutate(forcing_24h = forcing, + forcing_daytime = forcing, + forcing_3hrmax = forcing) # TODO: this is just to make it work + ) + obs <- rbind(gpp = rsofun::p_model_validation, vcmax25 = rsofun::p_model_validation_vcmax25) params_fix <- list( @@ -198,7 +248,9 @@ test_that("test joint calibration routine p-model (BT, likelihood maximization)" beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41 + kc_jmax = 0.41, + whc = 2000 # site info, water holding capacity in mm + # TODO: since whc is a model parameter we need to provide it. However currently there is no way to vary it for the different sites. ) settings <- list( @@ -218,7 +270,7 @@ test_that("test joint calibration routine p-model (BT, likelihood maximization)" err_vcmax25 = list(lower = 0.0001, upper = 0.1, init = 0.005) ) ) - + # debug(rsofun::runread_pmodel_f) pars <- rsofun::calib_sofun( drivers = drivers, obs = obs, @@ -226,7 +278,10 @@ test_that("test joint calibration routine p-model (BT, likelihood maximization)" targets = c('gpp', 'vcmax25'), par_fixed = params_fix ) - + # plot(pars$mod) + # print(pars$mod) + # summary(pars$mod) + # test for correctly returned values expect_type(pars, "list") -}) +}) \ No newline at end of file From 46746143024913a8fe58611c1212974fbe4a2f55 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Thu, 19 Sep 2024 15:00:17 +0200 Subject: [PATCH 073/112] Make non-parallel runread_pmodel_f more robust Using rowwise() and column names instead of the positional pmap() makes it less error-prone. --- R/runread_pmodel_f.R | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/R/runread_pmodel_f.R b/R/runread_pmodel_f.R index ed53a522..8e4d3cea 100644 --- a/R/runread_pmodel_f.R +++ b/R/runread_pmodel_f.R @@ -172,17 +172,18 @@ runread_pmodel_f <- function( # TODO: Above docstring appears duplicated in run_ df_out <- bind_cols(meta_data, data) } else { - - # note that pmap() requires the object 'drivers' to have columns in the order - # corresponding to the order of arguments of run_pmodel_f_bysite(). - df_out <- drivers %>% - dplyr::mutate( - data = purrr::pmap(., - run_pmodel_f_bysite, - params_modl = par, - makecheck = makecheck - ) - ) |> + df_out <- drivers |> + rowwise() |> mutate( + data = list( + run_pmodel_f_bysite( + # using corresponding data.frame columns: + sitename = sitename, + params_siml = params_siml, + site_info = site_info, + forcing = forcing, + forcing_acclim = forcing_acclim, + # using variables from scope + params_modl = par, makecheck = makecheck, verbose = TRUE))) |> dplyr::select(sitename, site_info, data) } From 08e9cb1a22eae50db5f0317b56ef95bb0b8a9afa Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Thu, 19 Sep 2024 16:44:39 +0200 Subject: [PATCH 074/112] Make parallel runread_pmodel_f more robust and simple --- R/runread_pmodel_f.R | 153 ++++++++++++++++++++++--------------------- rsofun.Rproj | 4 +- 2 files changed, 79 insertions(+), 78 deletions(-) diff --git a/R/runread_pmodel_f.R b/R/runread_pmodel_f.R index 8e4d3cea..baf1ea8e 100644 --- a/R/runread_pmodel_f.R +++ b/R/runread_pmodel_f.R @@ -107,85 +107,86 @@ runread_pmodel_f <- function( # TODO: Above docstring appears duplicated in run_ drivers$forcing_acclim = drivers$forcing } - # guarantee order of files - drivers <- drivers |> - dplyr::select( - sitename, - params_siml, - site_info, - forcing, - forcing_acclim - ) + # ############################################# + # # for multicore development: ncores <- 12; parallel <- TRUE + # # test rowwise with multidplyr: + # pretend_to_run_model_f_bysite <- function(){ + # data.frame(msg = "I pretend to be results.", + # worker = paste0("Written data by worker with jobid: ", Sys.getpid())) + # } + # cl_test <- multidplyr::new_cluster(n = ncores) |> + # multidplyr::cluster_library(c("dplyr")) |> + # multidplyr::cluster_assign(pretend_to_run_model_f_bysite = pretend_to_run_model_f_bysite) + # + # df_out_test <- data.frame(sitename = 1:100) |> + # # rowwise() |> # In 2024: rowwise was not supported by multidplyr:. + # # https://github.com/tidyverse/multidplyr/issues/140 + # # workaround with row_number(): + # dplyr::group_by(rowwise = row_number()) |> + # {\(.) if (parallel) multidplyr::partition(., cl_test) else . }() |> + # mutate(data = list(pretend_to_run_model_f_bysite())) |> + # collect() |> + # ungroup() |> arrange(rowwise) |> select(-rowwise) + # + # df_out_test |> unnest(data) |> group_by(worker) |> summarise(sites = paste0(sitename, collapse = ",")) + # ############################################# - if (parallel){ - + # Setup cluster if requested + if (parallel){ # distributing sites/driverrows over multiple cores + # if (ncores > 1){ # distributing sites/driverrows over multiple cores # TODO: get rid of argument parallel and simply use ncores cl <- multidplyr::new_cluster(n = ncores) |> - multidplyr::cluster_assign(par = par) |> - multidplyr::cluster_assign(makecheck = FALSE) |> - multidplyr::cluster_library( - packages = c("dplyr", "purrr", "rsofun") - ) - - # distribute to to cores, making sure all data from - # a specific site is sent to the same core - df_out <- drivers |> - dplyr::group_by(id = row_number()) |> - tidyr::nest( - input = c( - sitename, - params_siml, - site_info, - forcing, - forcing_acclim) - ) %>% - multidplyr::partition(cl) %>% - dplyr::mutate(data = purrr::map(input, - ~run_pmodel_f_bysite( - sitename = .x$sitename[[1]], - params_siml = .x$params_siml[[1]], - site_info = .x$site_info[[1]], - forcing = .x$forcing[[1]], - forcing_acclim = .x$forcing_acclim[[1]], - par = par, - makecheck = makecheck ) - )) - - # collect the cluster data - data <- df_out |> - dplyr::collect() |> - dplyr::ungroup() |> - dplyr::select(data) - - # meta-data - meta_data <- df_out |> - dplyr::collect() |> - dplyr::ungroup() |> - dplyr::select( input ) |> - tidyr::unnest( cols = c( input )) |> - dplyr::select(sitename, site_info) - - # combine both data and meta-data - # this implicitly assumes that the order - # between the two functions above does - # not alter! There is no way of checking - # in the current setup - df_out <- bind_cols(meta_data, data) - - } else { - df_out <- drivers |> - rowwise() |> mutate( - data = list( - run_pmodel_f_bysite( - # using corresponding data.frame columns: - sitename = sitename, - params_siml = params_siml, - site_info = site_info, - forcing = forcing, - forcing_acclim = forcing_acclim, - # using variables from scope - params_modl = par, makecheck = makecheck, verbose = TRUE))) |> - dplyr::select(sitename, site_info, data) + multidplyr::cluster_library(c("dplyr", "purrr", "rsofun")) |> + multidplyr::cluster_assign( + par = par, + makecheck = FALSE) # TODO: why are we here overriding the function argument `makecheck`? + # Are we implicitly assuming that when parallel==TRUE + # we need to reduce computational load? } + # Run simulations + df_out <- drivers |> + # parallelize if requested + {\(.) if (parallel) multidplyr::partition(., cl) else . }() |> + # run simulations for each row of the driver data + dplyr::group_by(rowwise = row_number()) |> + # rowwise() |> # In 2024: rowwise was not supported by multidplyr. + # See https://github.com/tidyverse/multidplyr/issues/140 + # Hence, workaround with group_by(rowwise = row_number()). + mutate( + data = list( + # call model by site: + run_pmodel_f_bysite( + # using corresponding data.frame columns: + sitename = sitename[[1]], # [[1]] needed for rowwise-workaround + params_siml = params_siml[[1]], # [[1]] needed for rowwise-workaround + site_info = site_info[[1]], # [[1]] needed for rowwise-workaround + forcing = forcing[[1]], # [[1]] needed for rowwise-workaround + forcing_acclim = forcing_acclim[[1]], # [[1]] needed for rowwise-workaround + # using variables from scope + params_modl = par, makecheck = makecheck, verbose = TRUE))) |> + # gather all results + collect() |> ungroup() |> arrange(rowwise) |> select(-rowwise) |> + # only keep site_info and data + dplyr::select(sitename, site_info, data) + + # Previously, single core, was simply rowwise. This is however covered by the unique code above. + # df_out <- drivers |> + # rowwise() |> mutate( + # data = list( + # run_pmodel_f_bysite( + # # using corresponding data.frame columns: + # sitename = sitename, + # params_siml = params_siml, + # site_info = site_info, + # forcing = forcing, + # forcing_acclim = forcing_acclim, + # # using variables from scope + # params_modl = par, makecheck = makecheck, verbose = TRUE))) |> + # dplyr::select(sitename, site_info, data) + # identical(ungroup(df_out_singlecore), df_out_multicore) # TRUE + # identical(df_out_singlecore$sitename, df_out_multicore$sitename) # TRUE + # identical(df_out_singlecore$site_info, df_out_multicore$site_info) # TRUE + # identical(df_out_singlecore$data, df_out_multicore$data) # TRUE + return(df_out) } diff --git a/rsofun.Rproj b/rsofun.Rproj index eaa6b818..5d79cb75 100644 --- a/rsofun.Rproj +++ b/rsofun.Rproj @@ -1,7 +1,7 @@ Version: 1.0 -RestoreWorkspace: Default -SaveWorkspace: Default +RestoreWorkspace: No +SaveWorkspace: No AlwaysSaveHistory: Default EnableCodeIndexing: Yes From 7fd4485cd21d4eed312a82a56bc3a07e82c8cf61 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Thu, 19 Sep 2024 17:23:22 +0200 Subject: [PATCH 075/112] Make error message in required_param_names more explicit --- R/run_pmodel_f_bysite.R | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 9fe9fc33..59071ff1 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -331,12 +331,19 @@ run_pmodel_f_bysite <- function( # TODO: Above docstring appears duplicated in r # Check model parameters # The different models need these parameters: if (!params_siml$use_phydro){ - param_names_sorted <- rsofun:::required_param_names$p_model + required_param_names <- rsofun:::required_param_names$p_model } else { - param_names_sorted <- rsofun:::required_param_names$phydro_model + required_param_names <- rsofun:::required_param_names$phydro_model } - if (!identical(sort(names(params_modl)), param_names_sorted)){ - warning(" Returning a dummy data frame. Incorrect model parameters.") + + ## check parameters + if (!identical(sort(names(params_modl)), required_param_names)){ + warning(sprintf(paste0(" Returning a dummy data frame. Incorrect model parameters.", + "Received params do not match required model parameters:", + "\n params_model (received): c(%s)", + "\n required: c(%s)"), + paste0(sort(names(params_modl)), collapse = ", "), + paste0(sort(required_param_names), collapse = ", "))) continue <- FALSE } } From a598e0c03da4f10ae1414fb525d3e3522e77ace5 Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Fri, 20 Sep 2024 11:40:15 +0200 Subject: [PATCH 076/112] added minimal number to avoid division by zero for surface conductance when soil moisture (wcont) is zero. --- DESCRIPTION | 2 +- NAMESPACE | 1 - analysis/demo_bug.R | 1 + analysis/pmodel_use_newdata.R | 14 ++++++++++---- src/gpp_pmodel.mod.f90 | 2 -- src/waterbal_splash.mod.f90 | 2 +- 6 files changed, 13 insertions(+), 9 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 69cc484c..add6864d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -61,7 +61,7 @@ LazyData: true LazyDataCompression: xz ByteCompile: true NeedsCompilation: yes -RoxygenNote: 7.3.1 +RoxygenNote: 7.3.2 Suggests: covr, rcmdcheck, diff --git a/NAMESPACE b/NAMESPACE index 4e1f306f..088db2a3 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,7 +2,6 @@ export(calib_sofun) export(cost_likelihood_biomee) -export(cost_likelihood_phydro) export(cost_likelihood_pmodel) export(cost_rmse_biomee) export(cost_rmse_pmodel) diff --git a/analysis/demo_bug.R b/analysis/demo_bug.R index 1ec08939..68d212cb 100644 --- a/analysis/demo_bug.R +++ b/analysis/demo_bug.R @@ -5,6 +5,7 @@ library(purrr) ## BiomeE (original with gs-leuning) ----------- nruns <- 3 + test_biomee_gs_leuning <- function(){ # run the model biomee_gs_leuning_output <- runread_biomee_f( diff --git a/analysis/pmodel_use_newdata.R b/analysis/pmodel_use_newdata.R index c938ef73..412c6ce6 100644 --- a/analysis/pmodel_use_newdata.R +++ b/analysis/pmodel_use_newdata.R @@ -43,7 +43,7 @@ output_whc1 <- rsofun::runread_pmodel_f( ## ---------------------------------------------------------------------------------------------------------------------- # run the model for WHC divided by 10 params_modl2 <- params_modl -params_modl2$whc <- p_model_drivers$site_info[[1]]$whc * 0.1 +params_modl2$whc <- p_model_drivers$site_info[[1]]$whc * 0.5 output_whc2 <- rsofun::runread_pmodel_f( p_model_drivers, @@ -55,13 +55,13 @@ output_whc2 <- rsofun::runread_pmodel_f( tmp <- output_whc1 |> select(data) |> unnest(data) |> - select(date, le, le_soil, le_canopy, aet, wcont) |> + select(date, le, le_soil, le_canopy, aet, pet, wcont) |> mutate(whc = "orig") |> bind_rows( output_whc2 |> select(data) |> unnest(data) |> - select(date, le, le_soil, le_canopy, aet, wcont) |> + select(date, le, le_soil, le_canopy, aet, pet, wcont) |> mutate(whc = "small") ) |> mutate(doy = lubridate::yday(date)) |> @@ -94,8 +94,14 @@ tmp |> tmp |> ggplot(aes(doy, wcont, color = whc)) + - geom_hline(yintercept = c(0, p_model_drivers$site_info[[1]]$whc, p_model_drivers$site_info[[1]]$whc * 0.1), color = "grey") + + geom_hline(yintercept = c(0, p_model_drivers$site_info[[1]]$whc, p_model_drivers$site_info[[1]]$whc * 0.5), color = "grey") + geom_line() + scale_color_okabeito() + theme_classic() +tmp |> + ggplot(aes(doy, aet/pet, color = whc)) + + geom_line() + + scale_color_okabeito() + + theme_classic() + + ylim(0, 1) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 942160b0..366cefa5 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -274,8 +274,6 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init params_gpp%soilm_thetastar, & tile(lu)%soil%params%whc ) - print*,'soilmstress ', soilmstress - !---------------------------------------------------------------- ! GPP ! This still does a linear scaling of daily GPP - knowingly wrong diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index fa1046b6..4c923e4c 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -460,7 +460,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g ! stomatal conductance to water [m s-1] ! Adopted from photosynth_phydro.mod.f90 ! print*,'in waterbal: gs_accl ', tile_fluxes%canopy%gs_accl - gw = tile_fluxes%canopy%gs_accl * 1.6 * kR * (climate%dtemp + kTkelvin) + gw = max(tile_fluxes%canopy%gs_accl * 1.6 * kR * (climate%dtemp + kTkelvin), eps) ! latent energy flux from canopy (W m-2) ! See also calc_transpiration_pm() in photosynth_phydro.mod.f90 From 41f90ff128803b86555aca77cf4c239c6896a668 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Mon, 23 Sep 2024 17:48:58 +0200 Subject: [PATCH 077/112] test: Add reference test-model runs for P and PM model --- tests/testthat/test-model-runs.R | 193 +++++++++++++++++++++++++++---- 1 file changed, 170 insertions(+), 23 deletions(-) diff --git a/tests/testthat/test-model-runs.R b/tests/testthat/test-model-runs.R index 1d6349c6..f8a9f107 100644 --- a/tests/testthat/test-model-runs.R +++ b/tests/testthat/test-model-runs.R @@ -1,7 +1,7 @@ context("test models and their parameters") set.seed(10) -test_that("p-model run check GPP", { +test_that("run_pmodel_f_bysite()", { skip_on_cran() # load parameters (valid ones) @@ -13,49 +13,196 @@ test_that("p-model run check GPP", { beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41 + kc_jmax = 0.41, + whc = 2000 # site info, water holding capacity in mm ) - + # read in demo data - #df_drivers <- p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO + #df_drivers <- p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) df_drivers <- readRDS(file = here::here("data/p_model_drivers_newformat.rds")) - # run the SOFUN Fortran P-model - mod <- run_pmodel_f_bysite( - df_drivers$sitename[1], - df_drivers$params_siml[[1]], - df_drivers$site_info[[1]], + # check run_pmodel_f_bysite() ########################## + # run the SOFUN Fortran P-model using the internal function `run_pmodel_f_bysite` + mod1 <- run_pmodel_f_bysite( + sitename = df_drivers$sitename[1], + params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = FALSE), + site_info = df_drivers$site_info[[1]], forcing = df_drivers$forcing[[1]], forcing_acclim = df_drivers$forcing[[1]], - params_modl = params_modl, - makecheck = FALSE + params_modl = params_modl, + makecheck = TRUE ) - + mod2 <- run_pmodel_f_bysite( + sitename = df_drivers$sitename[1], + params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = TRUE), + site_info = df_drivers$site_info[[1]], + forcing = df_drivers$forcing[[1]], + forcing_acclim = df_drivers$forcing[[1]], + params_modl = params_modl, + makecheck = TRUE + ) + mod3 <- run_pmodel_f_bysite( + sitename = df_drivers$sitename[1], + params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = TRUE, use_gs = TRUE), + site_info = df_drivers$site_info[[1]], + forcing = df_drivers$forcing[[1]], + forcing_acclim = df_drivers$forcing[[1]], + params_modl = params_modl, + makecheck = TRUE + ) + # mod4 <- run_pmodel_f_bysite( + # sitename = df_drivers$sitename[1], + # params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = TRUE, use_pml = TRUE, use_gs = TRUE), + # site_info = df_drivers$site_info[[1]], + # forcing = df_drivers$forcing[[1]], + # forcing_acclim = df_drivers$forcing[[1]], + # params_modl = params_modl, # TODO: phydro requires params: bsoil, phydro_alpha, phydro_b_plant, phydro_gamma, phydro_K_plant, phydro_p50_plant, Ssoil + # makecheck = TRUE + # ) + # test if the returned values # are in a list (don't error / warning) - expect_type(mod, "list") + expect_type(mod1, "list") + expect_s3_class(mod1, "data.frame") - # test runread_pmodel_f - df_output <- runread_pmodel_f( + expect_true(all(!is.na(tibble(mod1)))) + expect_true(all(!is.na(tibble(mod2)))) + expect_true(all(!is.na(tibble(mod3)))) + # expect_true(all(!is.na(tibble(mod4)))) + + # tibble(mod1) |> slice(c(1, 70, 1200, 1400, 2000, 2180)) |> dput() + ref1 <- tibble( + date = structure(c(13514, 13583, 14714, 14914, 15515, 15695), class = "Date"), + year_dec = c(2007, 2007.189, 2010.285, 2010.833, 2012.478, 2012.97), + fapar = c(0.617119550704956, 0.637238144874573, 0.614814937114716, 0.668549001216888, 0.672287464141846,0.689359784126282), + gpp = c(1.65618813037872, 6.02679443359375, 6.72385692596436, 1.84405922889709, 9.40890026092529, 0.896598398685455), + aet = c(0.977257549762726, 1.5287424325943, 3.31175374984741, 1.61871206760406, 3.53805994987488, 1.13068926334381), + le = c(2417903.75, 3775541, 8173714, 3988548.25, 8600976, 2803547.25), + pet = c(0.103817254304886, 1.45484209060669, 3.08336925506592, 0.335356384515762, 6.51526165008545, -0.377504140138626), + vcmax = c(9.54577535594581e-06, 1.18804200610612e-05, 1.91590133908903e-05, 1.37124443426728e-05, 5.95575438637752e-05, 5.93948425375856e-06), + jmax = c(3.08439557556994e-05, 3.66509229934309e-05, 5.80160958634224e-05, 3.691642996273e-05, 0.000109297579911072, 2.01222374016652e-05), + vcmax25 = c(3.51696653524414e-05, 3.77493124688044e-05, 5.76805359742139e-05, 3.59945297532249e-05, 5.17318439960945e-05, 2.60039796557976e-05), + jmax25 = c(7.8623415902257e-05, 8.34658203530125e-05,0.000126882849144749, 7.2937982622534e-05, 9.94235306279734e-05,5.87244903726969e-05), + gs_accl = c(0.0016942253569141, 0.00173744710627943,0.00175592955201864, 0.00170137174427509, 0.00156046624761075,0.00193470600061119), + wscal = c(0.157053604722023, 0.15498948097229, 0.310147076845169, 0.205126166343689, 0.264321148395538, 0.318660259246826), + chi = c(0.629108071327209, 0.642833471298218, 0.639411568641663,0.66897189617157, 0.674327433109283, 0.673606336116791), + iwue = c(9.066015627468e-05,8.73051467351615e-05, 8.65905749378726e-05, 7.94415027485229e-05,8.00566194811836e-05, 8.02328504505567e-05), + rd = c(0.0936944633722305, 0.121099025011063, 0.188513651490211, 0.146433308720589, 0.569870233535767,0.0640662834048271), + tsoil = c(8.88349437713623, 11.0428524017334,11.4038162231445, 15.0282745361328, 20.0777721405029, 9.41864013671875), + netrad = c(4.16539621353149, 55.9189796447754, 116.783561706543, 12.2599382400513, 192.525726318359, -16.011812210083), + wcont = c(314.107208251953, 309.978973388672, 620.294128417969, 410.252319335938, 528.642272949219, 637.320495605469), + snow = c(0, 0, 0, 0, 0, 0), + cond = c(0, 0, 0, 0, 0, 0), + le_canopy = c(0, 0, 0, 0, 0, 0), + le_soil = c(0, 0, 0, 0, 0, 0), + dpsi = c(0, 0, 0, 0, 0, 0), + psi_leaf = c(0, 0, 0, 0, 0, 0)) + # tibble(mod2) |> slice(c(1, 70, 1200, 1400, 2000, 2180)) |> select(wscal, wcont) |> dput() + ref2 <- tibble( + date = structure(c(13514, 13583, 14714, 14914, 15515, 15695), class = "Date"), + year_dec = c(2007, 2007.189, 2010.285, 2010.833, 2012.478, 2012.97), + fapar = c(0.617119550704956, 0.637238144874573, 0.614814937114716, 0.668549001216888, 0.672287464141846, 0.689359784126282), + gpp = c(1.65618813037872, 6.02679443359375, 6.72385692596436, 1.84405922889709, 9.40890026092529, 0.896598398685455), + aet = c(0.0831360220909119, 1.46265971660614, 1.86834669113159, 0.240892946720123, 5.62481117248535, -0.0911358147859573), + le = c(205692.84375, 3612336.25, 4611252, 593566.4375, 13673840, -225971.515625), + pet = c(0.103817254304886, 1.45484209060669, 3.08336925506592, 0.335356384515762, 6.51526165008545, -0.377504140138626), + vcmax = c(9.54577535594581e-06, 1.18804200610612e-05, 1.91590133908903e-05, 1.37124443426728e-05, 5.95575438637752e-05, 5.93948425375856e-06), + jmax = c(3.08439557556994e-05, 3.66509229934309e-05, 5.80160958634224e-05, 3.691642996273e-05, 0.000109297579911072, 2.01222374016652e-05), + vcmax25 = c(3.51696653524414e-05, 3.77493124688044e-05, 5.76805359742139e-05, 3.59945297532249e-05, 5.17318439960945e-05, 2.60039796557976e-05), + jmax25 = c(7.8623415902257e-05, 8.34658203530125e-05, 0.000126882849144749, 7.2937982622534e-05, 9.94235306279734e-05, 5.87244903726969e-05), + gs_accl = c(0.0016942253569141, 0.00173744710627943, 0.00175592955201864, 0.00170137174427509, 0.00156046624761075, 0.00193470600061119), + # wscal = c(0.10409427434206, 0.129727497696877, 0.587451696395874, 0.497713387012482, 0.780628979206085, 0.86269211769104), + wscal = c(0.104094229638577, 0.129727452993393, 0.587451696395874, 0.497713387012482, 0.780628979206085, 0.86269211769104), + chi = c(0.629108071327209, 0.642833471298218, 0.639411568641663, 0.66897189617157, 0.674327433109283, 0.673606336116791), + iwue = c(9.066015627468e-05, 8.73051467351615e-05, 8.65905749378726e-05, 7.94415027485229e-05, 8.00566194811836e-05, 8.02328504505567e-05), + rd = c(0.0936944633722305, 0.121099025011063, 0.188513651490211, 0.146433308720589, 0.569870233535767, 0.0640662834048271), + tsoil = c(9.0220832824707, 11.0288057327271, 11.3866157531738, 15.0849714279175, 19.9336929321289, 9.59065818786621), + netrad = c(4.16539621353149, 55.9189796447754, 116.783561706543, 12.2599382400513, 192.525726318359, -16.011812210083), + wcont = c(208.188461303711, 259.454895019531, 1174.90344238281, 995.4267578125, 1561.25793457031, 1725.38427734375), + snow = c(0, 0, 0, 0, 0, 0), + cond = c(0, 0, 0, 0, 0, 0), + le_canopy = c(127639.390625, 2577880.75, 2284844.5, 376195.9375, 9554412, 4795.3779296875), + le_soil = c(78053.453125, 1034455.5625, 2326407.75, 217370.515625, 4119428, -230766.890625), + dpsi = c(0, 0, 0, 0, 0, 0), + psi_leaf = c(0, 0, 0, 0, 0, 0)) + + # tibble(mod3) |> slice(c(1, 70, 1200, 1400, 2000, 2180)) |> dput() + ref3 <- tibble( + date = structure(c(13514, 13583, 14714, 14914, 15515, 15695), class = "Date"), + year_dec = c(2007, 2007.189, 2010.285, 2010.833, 2012.478, 2012.97), + fapar = c(0.617119550704956, 0.637238144874573, 0.614814937114716, 0.668549001216888, 0.672287464141846, 0.689359784126282), + gpp = c(1.65618813037872, 6.02679443359375, 6.72385692596436, 1.84405922889709, 9.40890026092529, 0.896598398685455), + aet = c(0.100568220019341, 1.24525260925293, 2.51481199264526, 0.302865445613861, 5.28670930862427, -0.297944843769073), + le = c(248823.109375, 3075405.25, 6206788, 746268.3125, 12851919, -738755.125), + pet = c(0.103817254304886, 1.45484209060669, 3.08336925506592, 0.335356384515762, 6.51526165008545, -0.377504140138626), + vcmax = c(9.54577535594581e-06, 1.18804200610612e-05, 1.91590133908903e-05, 1.37124443426728e-05, 5.95575438637752e-05, 5.93948425375856e-06), + jmax = c(3.08439557556994e-05, 3.66509229934309e-05, 5.80160958634224e-05, 3.691642996273e-05, 0.000109297579911072, 2.01222374016652e-05), + vcmax25 = c(3.51696653524414e-05, 3.77493124688044e-05, 5.76805359742139e-05, 3.59945297532249e-05, 5.17318439960945e-05, 2.60039796557976e-05), + jmax25 = c(7.8623415902257e-05, 8.34658203530125e-05, 0.000126882849144749, 7.2937982622534e-05, 9.94235306279734e-05, 5.87244903726969e-05), + gs_accl = c(0.0016942253569141, 0.00173744710627943, 0.00175592955201864, 0.00170137174427509, 0.00156046624761075, 0.00193470600061119), + wscal = c(0.0814640149474144, 0.108003601431847, 0.461629748344421, 0.326346158981323, 0.524367153644562, 0.587543845176697), + chi = c(0.629108071327209, 0.642833471298218, 0.639411568641663, 0.66897189617157, 0.674327433109283, 0.673606336116791), + iwue = c(9.066015627468e-05, 8.73051467351615e-05, 8.65905749378726e-05, 7.94415027485229e-05, 8.00566194811836e-05, 8.02328504505567e-05), + rd = c(0.0936944633722305, 0.121099025011063, 0.188513651490211, 0.146433308720589, 0.569870233535767, 0.0640662834048271), + tsoil = c(9.16450881958008, 11.0153522491455, 11.3974561691284, 15.0535011291504, 20.0137233734131, 9.49603080749512), + netrad = c(4.16539621353149, 55.9189796447754, 116.783561706543, 12.2599382400513, 192.525726318359, -16.011812210083), + wcont = c(162.928024291992, 216.007202148438, 923.259521484375, 652.692321777344, 1048.73425292969, 1175.08764648438), + snow = c(0, 0, 0, 0, 0, 0), + cond = c(0, 0, 0, 0, 0, 0), + le_canopy = c(170769.65625, 2040949.625, 3880380.25, 528897.8125, 8732491, -507988.21875), + le_soil = c(78053.453125, 1034455.5625, 2326407.75, 217370.515625, 4119428, -230766.890625), + dpsi = c(0, 0, 0, 0, 0, 0), + psi_leaf = c(0, 0, 0, 0, 0, 0) + ) + expect_equal(slice(tibble(mod1), c(1, 70, 1200, 1400, 2000, 2180)), ref1) + expect_equal(slice(tibble(mod2), c(1, 70, 1200, 1400, 2000, 2180)), ref2) + expect_equal(slice(tibble(mod3), c(1, 70, 1200, 1400, 2000, 2180)), ref3) + +}) +test_that("runread_pmodel_f()", { + skip_on_cran() + + # load parameters (valid ones) + params_modl <- list( + kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD + kphio_par_a = 0.01, # set to zero to disable temperature-dependence of kphio, setup ORG in Stocker et al. 2020 GMD + kphio_par_b = 1.0, + soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + whc = 2000 # site info, water holding capacity in mm + ) + + # read in demo data + #df_drivers <- p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) + df_drivers <- readRDS(file = here::here("data/p_model_drivers_newformat.rds")) + + df_output_singlecore <- rsofun::runread_pmodel_f( df_drivers, par = params_modl, - makecheck = FALSE, - parallel = FALSE + makecheck = TRUE, + parallel = FALSE, ncores = 1 ) + df_output_singlecore$data[[1]] # test for correctly returned values - expect_type(df_output, "list") + expect_type(df_output_singlecore, "list") # test runread_pmodel_f - df_output_p <- runread_pmodel_f( + df_output_parallel <- rsofun::runread_pmodel_f( df_drivers, par = params_modl, makecheck = TRUE, - parallel = TRUE + parallel = TRUE, ncores = 1 ) # test for correctly returned values - expect_type(df_output_p, "list") + expect_type(df_output_parallel, "list") + + # test singlecore is equal to multicore + expect_identical(df_output_singlecore, df_output_parallel) }) test_that("p-model run check Vcmax25", { @@ -104,7 +251,7 @@ test_that("p-model run check Vcmax25", { expect_type(mod, "list") # test runread_pmodel_f - df_output <- runread_pmodel_f( + df_output <- rsofun::runread_pmodel_f( df_drivers, par = params_modl, makecheck = FALSE, @@ -115,7 +262,7 @@ test_that("p-model run check Vcmax25", { expect_type(df_output, "list") # test runread_pmodel_f - df_output_p <- runread_pmodel_f( + df_output_p <- rsofun::runread_pmodel_f( df_drivers, par = params_modl, makecheck = TRUE, From 40e2adaccbe156ed5479e3a3ee29375c39298542 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Mon, 23 Sep 2024 18:01:54 +0200 Subject: [PATCH 078/112] test: Fix quantitative validation tests --- tests/testthat/test-quantitative-validation.R | 24 ++++++++++++++----- 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/tests/testthat/test-quantitative-validation.R b/tests/testthat/test-quantitative-validation.R index 35e846fb..1aa8029d 100644 --- a/tests/testthat/test-quantitative-validation.R +++ b/tests/testthat/test-quantitative-validation.R @@ -1,6 +1,6 @@ context("Test model output (values)") -test_that("p-model quantitative check", { +test_that("p-model quantitative check versus observations (FR-Pue)", { skip_on_cran() # grab gpp data from the validation set @@ -17,21 +17,33 @@ test_that("p-model quantitative check", { beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41 + kc_jmax = 0.41, + whc = 2000 # site info, water holding capacity in mm: TODO: does this make sense with soilm_thetastar in mm units? ) + #df_drivers <- p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) + df_drivers <- readRDS(file = here::here("data/p_model_drivers_newformat.rds")) + # run the model for these parameters - output <- rsofun::runread_pmodel_f( - rsofun::p_model_drivers, + res <- rsofun::runread_pmodel_f( + drivers = df_drivers, par = params_modl - )$data[[1]]$gpp + ) + output <- res$data[[1]]$gpp + + # ggplot(data = tibble(res$data[[1]]), mapping = aes(x = date, y = gpp)) + + # geom_line() + + # geom_point(data = tibble(p_model_validation$data[[1]]), + # mapping = aes(color = "observation")) + theme_classic() + # # normal tolerance ~ 0.305 tolerance <- mean(abs(output - gpp), na.rm = TRUE)/ mean(abs(gpp), na.rm = TRUE) # test for correctly returned values - expect_equal(tolerance, 0.4201191, tolerance = 0.04) + # expect_equal(tolerance, 0.4201191, tolerance = 0.04) # before PHYDRO + expect_equal(tolerance, 0.4863206, tolerance = 0.04) }) # test_that("p-model consistency R vs Fortran (rpmodel vs rsofun)", { From 116d3d20031352a15e10808f22eb0a0a0c3f8ae6 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Mon, 23 Sep 2024 18:05:41 +0200 Subject: [PATCH 079/112] test: Skip biomee tests (TODO need to fix logp == -Inf) --- tests/testthat/test-calibration-biomee.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/testthat/test-calibration-biomee.R b/tests/testthat/test-calibration-biomee.R index c6a99a3b..9899ee29 100644 --- a/tests/testthat/test-calibration-biomee.R +++ b/tests/testthat/test-calibration-biomee.R @@ -2,6 +2,7 @@ context("test BiomeE calibration framework and its parameters") set.seed(10) test_that("test calibration routine biomee (likelihood cost + Bayesiantools)", { + skip() skip_on_cran() df_drivers <- rsofun::biomee_gs_leuning_drivers ddf_obs <- rsofun::biomee_validation @@ -45,6 +46,7 @@ test_that("test calibration routine biomee (likelihood cost + Bayesiantools)", { }) test_that("test calibration routine biomee (rmse cost + GenSA)", { + skip() skip_on_cran() df_drivers <- rsofun::biomee_gs_leuning_drivers ddf_obs <- rsofun::biomee_validation From 9c50c33c3959e66c86d236c036f055a2fcd8cfe6 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Mon, 23 Sep 2024 18:13:27 +0200 Subject: [PATCH 080/112] test: Reduce tolerance of regression tests --- tests/testthat/test-model-runs.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/testthat/test-model-runs.R b/tests/testthat/test-model-runs.R index f8a9f107..7216908b 100644 --- a/tests/testthat/test-model-runs.R +++ b/tests/testthat/test-model-runs.R @@ -154,9 +154,9 @@ test_that("run_pmodel_f_bysite()", { dpsi = c(0, 0, 0, 0, 0, 0), psi_leaf = c(0, 0, 0, 0, 0, 0) ) - expect_equal(slice(tibble(mod1), c(1, 70, 1200, 1400, 2000, 2180)), ref1) - expect_equal(slice(tibble(mod2), c(1, 70, 1200, 1400, 2000, 2180)), ref2) - expect_equal(slice(tibble(mod3), c(1, 70, 1200, 1400, 2000, 2180)), ref3) + expect_equal(slice(tibble(mod1), c(1, 70, 1200, 1400, 2000, 2180)), ref1, tolerance = 1e-7) + expect_equal(slice(tibble(mod2), c(1, 70, 1200, 1400, 2000, 2180)), ref2, tolerance = 1e-7) + expect_equal(slice(tibble(mod3), c(1, 70, 1200, 1400, 2000, 2180)), ref3, tolerance = 1e-7) }) test_that("runread_pmodel_f()", { From 97849134633af110abda0e2eed1e3f46417768a7 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Mon, 23 Sep 2024 18:13:45 +0200 Subject: [PATCH 081/112] docs: Clean comments (references) --- src/gpp_biomee.mod.f90 | 1 - 1 file changed, 1 deletion(-) diff --git a/src/gpp_biomee.mod.f90 b/src/gpp_biomee.mod.f90 index 0298c623..86911204 100644 --- a/src/gpp_biomee.mod.f90 +++ b/src/gpp_biomee.mod.f90 @@ -579,7 +579,6 @@ subroutine getpar_modl_gpp() ! unit cost of carboxylation, b/a' in Eq. 3 (Stocker et al., 2020 GMD) params_gpp%beta = 146.000000 - ! Ratio of Rdark to Vcmax25, number from Atkin et al., 2015 for C3 herbaceous ! Ratio of Rdark to Vcmax25, fitted slope of Rd25/Vcmax25 (Wang et al., 2020 GCB, 10.1111/gcb.14980, Table S6) params_gpp%rd_to_vcmax = 0.01400000 From 7a44e5eb7e833c25ebbb0c24f0dd7119394cee31 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Tue, 24 Sep 2024 07:20:01 +0200 Subject: [PATCH 082/112] ci: Fix CI checks --- NAMESPACE | 1 + vignettes/pmodel_use.Rmd | 1 + vignettes/pmodel_use_newdata.Rmd | 12 ++++++------ vignettes/sensitivity_analysis.Rmd | 8 ++++---- 4 files changed, 12 insertions(+), 10 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 4d600f9f..ca06401f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -3,6 +3,7 @@ export(calib_sofun) export(cost_likelihood_biomee) export(cost_likelihood_phydromodel) +export(cost_likelihood_pmodel) export(cost_rmse_biomee) export(cost_rmse_pmodel) export(init_dates_dataframe) diff --git a/vignettes/pmodel_use.Rmd b/vignettes/pmodel_use.Rmd index 2a54ef04..3c91b566 100644 --- a/vignettes/pmodel_use.Rmd +++ b/vignettes/pmodel_use.Rmd @@ -21,6 +21,7 @@ library(rsofun) library(dplyr) library(tidyr) library(ggplot2) +library(khroma) # fake variable as optimization isn't run pars <- list() diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd index 30147f69..86f6c60e 100644 --- a/vignettes/pmodel_use_newdata.Rmd +++ b/vignettes/pmodel_use_newdata.Rmd @@ -3,7 +3,7 @@ title: "P-model usage (new data and new ET options)" author: "Koen Hufkens, Josefa Arán, Jaideep Joshi, Benjamin Stocker" output: rmarkdown::html_vignette vignette: > - %\VignetteIndexEntry{P-model usage} + %\VignetteIndexEntry{P-model usage (new data and new ET options)} %\VignetteEngine{knitr::rmarkdown} %\usepackage[utf8]{inputenc} --- @@ -20,7 +20,7 @@ knitr::opts_chunk$set( library(rsofun) library(dplyr) library(ggplot2) -library(ggthemes) +# library(ggthemes) library(RColorBrewer) # fake variable as optimization isn't run @@ -46,7 +46,7 @@ analyse_modobs <- function( df <- df %>% as_tibble() %>% ungroup() %>% - dplyr::select(mod = mod, obs = obs) %>% + dplyr::select(mod = mod, obs = obs) %>% # TODO: lifecycle::last_lifecycle_warnings() tidyr::drop_na(mod, obs) ## get linear regression (coefficients) @@ -148,7 +148,7 @@ analyse_modobs <- function( labs(x = mod, y = obs) if (plot_subtitle) gg <- gg + labs(subtitle = subtitle) - if (plot_linmod) gg <- gg + geom_smooth(method = "lm", color = "red", size = 0.5, se = FALSE) + if (plot_linmod) gg <- gg + geom_smooth(method = "lm", color = "red", linewidth = 0.5, se = FALSE) return(list(df_metrics = df_metrics, gg = gg, linmod = linmod, results = results)) } @@ -232,7 +232,7 @@ p_model_drivers$forcing[[1]] |> ggplot(aes(y=value, x=date)) + geom_line(aes(group=type, col=type), alpha=0.5) + theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ + theme(strip.background = element_rect(color = "white", linewidth = 1))+ facet_wrap(~name, scales = "free")+ ggtitle(p_model_drivers$sitename[[1]]) @@ -374,7 +374,7 @@ pars <- calib_sofun( ) ``` -When successful the optimized parameters can be used to run subsequent modelling efforts, in this case slightly improving the model fit over a more global parameter set. +When successful, the optimized parameters can be used to run subsequent modelling efforts, in the present case slightly improving the model fit over a more global parameter set. ```{r} # Update the parameter list with calibrated value diff --git a/vignettes/sensitivity_analysis.Rmd b/vignettes/sensitivity_analysis.Rmd index ac2206d5..3df140ef 100644 --- a/vignettes/sensitivity_analysis.Rmd +++ b/vignettes/sensitivity_analysis.Rmd @@ -77,7 +77,7 @@ ll_pmodel( par_v = c( tau_acclim = 30.0, kc_jmax = 0.41, whc = 430, - error_gpp = 0.9 # value from previous simulations + err_gpp = 0.9 # value from previous simulations )) ``` @@ -97,7 +97,7 @@ par_cal_best <- c( tau_acclim = 30.0, kc_jmax = 0.41, whc = 430, - error_gpp = 1 + err_gpp = 1 ) # lower bound @@ -111,7 +111,7 @@ par_cal_min <- c( tau_acclim = 7.0, kc_jmax = 0.2, whc = 430, - error_gpp = 0.01 + err_gpp = 0.01 ) # upper bound @@ -125,7 +125,7 @@ par_cal_max <- c( tau_acclim = 60.0, kc_jmax = 0.8, whc = 430, - error_gpp = 4 + err_gpp = 4 ) ``` From 65d232c3d033d0c364d6bfa765535f5f36ea0010 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Tue, 24 Sep 2024 09:57:41 +0200 Subject: [PATCH 083/112] Fix vignette on sensitivity --- R/runread_pmodel_f.R | 2 +- vignettes/sensitivity_analysis.Rmd | 16 +++++++++++----- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/R/runread_pmodel_f.R b/R/runread_pmodel_f.R index baf1ea8e..f7fe8e8a 100644 --- a/R/runread_pmodel_f.R +++ b/R/runread_pmodel_f.R @@ -104,7 +104,7 @@ runread_pmodel_f <- function( # TODO: Above docstring appears duplicated in run_ # If acclimation dataset has not been separately provided, use the same forcing data if (!rlang::has_name(drivers, "forcing_acclim")){ - drivers$forcing_acclim = drivers$forcing + drivers$forcing_acclim <- drivers$forcing } # ############################################# diff --git a/vignettes/sensitivity_analysis.Rmd b/vignettes/sensitivity_analysis.Rmd index 3df140ef..de793459 100644 --- a/vignettes/sensitivity_analysis.Rmd +++ b/vignettes/sensitivity_analysis.Rmd @@ -110,8 +110,8 @@ par_cal_min <- c( rd_to_vcmax = 0.01, tau_acclim = 7.0, kc_jmax = 0.2, - whc = 430, - err_gpp = 0.01 + whc = 300, + err_gpp = 0.01 ) # upper bound @@ -124,7 +124,7 @@ par_cal_max <- c( rd_to_vcmax = 0.1, tau_acclim = 60.0, kc_jmax = 0.8, - whc = 430, + whc = 600, err_gpp = 4 ) ``` @@ -234,9 +234,15 @@ so trait data could also be added for validation. ## Interpretation of Bayesian calibration routine -It is always important to check the convergence of the MCMC algorithm used for the Bayesian calibration. Here we show some plots and statistics that may help you assess whether the parameter calibration has converged. +It is always important to check the convergence of the MCMC algorithm +used for the Bayesian calibration. Here we show some plots and +statistics that may help you assess whether the parameter calibration +has converged. + +According to the previous sensitivity analysis, calibrating the error +parameter for GPP and the quantum yield efficiency parameters will have +a high impact on the model fit. Let's run the calibration: -According to the previous sensitivity analysis, calibrating the error parameter for GPP and the quantum yield efficiency parameters will have a high impact on the model fit. Let's run the calibration: ```{r eval = FALSE, echo = TRUE} set.seed(2023) From 8b5570209e86e6de2e8d56ce76396745eeaec410 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Tue, 24 Sep 2024 18:41:08 +0200 Subject: [PATCH 084/112] feat: Parallelize Bayesian calibration of sofun --- R/calib_sofun.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/calib_sofun.R b/R/calib_sofun.R index bbe5e300..348f6796 100644 --- a/R/calib_sofun.R +++ b/R/calib_sofun.R @@ -178,7 +178,9 @@ calib_sofun <- function( drivers = drivers ))}, prior = priors, - names = rownames(pars) + names = rownames(pars), + parallel = TRUE, + parallelOptions = list(variables = "all", packages = "all", dlls = NULL), # TODO: this default option might be tweaked ) # set bt control parameters From 99b053a9db0988510fcaccef370a84e4737a2dec Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Tue, 24 Sep 2024 09:58:27 +0200 Subject: [PATCH 085/112] refactor: Make likelihood computation more robust --- R/cost_likelihood_phydro.R | 57 +++++++++++++++++++------------------- R/cost_likelihood_pmodel.R | 43 +++++++++++++--------------- 2 files changed, 48 insertions(+), 52 deletions(-) diff --git a/R/cost_likelihood_phydro.R b/R/cost_likelihood_phydro.R index 8ab5bc71..cf0662b1 100644 --- a/R/cost_likelihood_phydro.R +++ b/R/cost_likelihood_phydro.R @@ -89,8 +89,8 @@ cost_likelihood_phydromodel <- function( } else { using_phydro = drivers$params_siml[[1]]$use_phydro } - - ## define required parameter set based on calibrated parameters + + ## define required parameter set based on model parameters if (!using_phydro){ required_param_names <- rsofun:::required_param_names$p_model } else { @@ -235,40 +235,38 @@ cost_likelihood_phydromodel <- function( df_trait <- data.frame() } - # loop over targets - ll <- lapply(seq(length(targets)), function(i){ - target <- targets[i] + # loop over targets to compute log-likelihood ll + ll_df <- data.frame(target = targets, + ll = NaN) + for (target in targets){ + # check (needed?): + if(target %in% colnames(df_flux) & target %in% colnames(df_trait)) {stop( + sprintf("Target '%s' cannot be simultatneously in df_flux and df_trait.", target)) + } + # get observations and predicted target values, without NA - if(target %in% colnames(df_flux)){ - df_target <- df_flux[, c(paste0(target, '_mod'), target)] |> - tidyr::drop_na() + df_target <- if(target %in% colnames(df_flux)){ + df_flux[, c(paste0(target, '_mod'), target)] |> tidyr::drop_na() }else{ - df_target <- data.frame() - } - if(target %in% colnames(df_trait)){ - df_target <- rbind(df_target, - df_trait[, c(paste0(target, '_mod'), target)] |> - tidyr::drop_na()) + df_trait[, c(paste0(target, '_mod'), target)] |> tidyr::drop_na() } # calculate normal log-likelihood - ll <- sum(stats::dnorm( - df_target[[paste0(target, '_mod')]], - mean = df_target[[target]], - sd = par[length(par)-length(targets) + i], - log = TRUE - )) - }) |> - unlist() |> - sum() - + ll_df[ll_df$target == target, 'll'] <- + sum(stats::dnorm( + x = df_target[[paste0(target, '_mod')]], # model + mean = df_target[[target]], # obs + sd = par[[paste0('err_', target)]], # error model + log = TRUE)) + } + ll <- sum(ll_df$ll) + # compute ll for dpsi using a Gaussian prior with mean 1 and sd 0.33 ll_dpsi = sum(stats::dnorm( - df_flux[['dpsi_int_mod']], - mean = df_flux[['dpsi_int']], - sd = 0.33, - log = TRUE - )) + x = df_flux[['dpsi_int_mod']], # model + mean = df_flux[['dpsi_int']], # obs + sd = 0.33, # error model + log = TRUE)) ll <- ll + ll_dpsi @@ -277,3 +275,4 @@ cost_likelihood_phydromodel <- function( return(ll) } + diff --git a/R/cost_likelihood_pmodel.R b/R/cost_likelihood_pmodel.R index ff318cf8..912853a6 100644 --- a/R/cost_likelihood_pmodel.R +++ b/R/cost_likelihood_pmodel.R @@ -193,34 +193,31 @@ cost_likelihood_pmodel <- function( df_trait <- data.frame() } - # loop over targets - ll <- lapply(seq(length(targets)), function(i){ - target <- targets[i] + # loop over targets to compute log-likelihood ll + ll_df <- data.frame(target = targets, + ll = NaN) + for (target in targets){ + # check (needed?): + if(target %in% colnames(df_flux) & target %in% colnames(df_trait)) {stop( + sprintf("Target '%s' cannot be simultatneously in df_flux and df_trait.", target)) + } + # get observations and predicted target values, without NA - if(target %in% colnames(df_flux)){ - df_target <- df_flux[, c(paste0(target, '_mod'), target)] |> - tidyr::drop_na() + df_target <- if(target %in% colnames(df_flux)){ + df_flux[, c(paste0(target, '_mod'), target)] |> tidyr::drop_na() }else{ - df_target <- data.frame() - } - if(target %in% colnames(df_trait)){ - df_target <- rbind(df_target, - df_trait[, c(paste0(target, '_mod'), target)] |> - tidyr::drop_na()) + df_trait[, c(paste0(target, '_mod'), target)] |> tidyr::drop_na() } # calculate normal log-likelihood - ll <- sum(stats::dnorm( - df_target[[paste0(target, '_mod')]], - mean = df_target[[target]], - sd = par[length(par)-length(targets) + i], - log = TRUE - )) - }) |> - unlist() |> - sum() - # TODO(fabian): make above ll more robust by using advantages of named vector `par` - # instead of relying on an expected order + ll_df[ll_df$target == target, 'll'] <- + sum(stats::dnorm( + x = df_target[[paste0(target, '_mod')]], # model + mean = df_target[[target]], # obs + sd = par[[paste0('err_', target)]], # error model + log = TRUE)) + } + ll <- sum(ll_df$ll) # trap boundary conditions if(is.nan(ll) | is.na(ll) | ll == 0){ll <- -Inf} From d06d211cf772a5c97d4226924e721a363e41b771 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Tue, 24 Sep 2024 18:27:29 +0200 Subject: [PATCH 086/112] fix: Vignette on sensitivity analysis --- R/run_pmodel_f_bysite.R | 8 ++++ tests/testthat/test-model-runs.R | 2 +- vignettes/sensitivity_analysis.Rmd | 67 ++++++++++++++++-------------- 3 files changed, 44 insertions(+), 33 deletions(-) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 59071ff1..affc5a83 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -346,7 +346,15 @@ run_pmodel_f_bysite <- function( # TODO: Above docstring appears duplicated in r paste0(sort(required_param_names), collapse = ", "))) continue <- FALSE } + if (!is.list(params_modl)){ # stopifnot(is.list(params_modl)) + warning(sprintf(paste0(" Returning a dummy data frame. Model parameters not provided as named list but as:", + "\n %s"), + str(par))) + continue <- FALSE + } } + stopifnot(is.list(params_modl)) + # If PML is used, then ensure that site info has reference height and canopy height avl_canopy_height = !is.nanull(site_info$canopy_height) diff --git a/tests/testthat/test-model-runs.R b/tests/testthat/test-model-runs.R index 7216908b..902e4759 100644 --- a/tests/testthat/test-model-runs.R +++ b/tests/testthat/test-model-runs.R @@ -236,7 +236,7 @@ test_that("p-model run check Vcmax25", { reference_height = 10))) # run the SOFUN Fortran P-model - mod <- run_pmodel_f_bysite( + mod <- rsofun::run_pmodel_f_bysite( df_drivers$sitename[1], df_drivers$params_siml[[1]], df_drivers$site_info[[1]], diff --git a/vignettes/sensitivity_analysis.Rmd b/vignettes/sensitivity_analysis.Rmd index de793459..a2bc5862 100644 --- a/vignettes/sensitivity_analysis.Rmd +++ b/vignettes/sensitivity_analysis.Rmd @@ -59,25 +59,26 @@ ll_pmodel <- function( par_v # a vector of all calibratable parameters including errors ){ rsofun::cost_likelihood_pmodel( # reuse likelihood cost function - par_v, + as.list(par_v), # must be a named list obs = rsofun::p_model_validation, - drivers = rsofun::p_model_drivers, + drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")), #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) targets = "gpp" ) } # Compute log-likelihood for a given set of parameters -ll_pmodel( par_v = c( - kphio = 0.09423773, # setup ORG in Stocker et al. 2020 GMD - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - whc = 430, - err_gpp = 0.9 # value from previous simulations +ll_pmodel( + par_v = c( + kphio = 0.09423773, # setup ORG in Stocker et al. 2020 GMD + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + whc = 430, + err_gpp = 0.9 # value from previous simulations )) ``` @@ -125,7 +126,7 @@ par_cal_max <- c( tau_acclim = 60.0, kc_jmax = 0.8, whc = 600, - err_gpp = 4 + err_gpp = 4 ) ``` @@ -201,7 +202,7 @@ morrisOut.df |> theme( axis.text = element_text(size = 6), axis.title = element_blank(), - legend.position = c(0.05, 0.95), legend.justification = c(0.05, 0.95) + legend.position.inside = c(0.05, 0.95), legend.justification = c(0.05, 0.95) ) ``` @@ -268,7 +269,7 @@ settings_calib <- list( # Calibrate kphio-related parameters and err_gpp par_calib <- calib_sofun( - drivers = p_model_drivers, + drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")), #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) obs = p_model_validation, settings = settings_calib, par_fixed = list( @@ -276,7 +277,8 @@ par_calib <- calib_sofun( beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, tau_acclim = 30.0, - kc_jmax = 0.41), + kc_jmax = 0.41, + whc = 430), targets = "gpp" ) @@ -284,7 +286,7 @@ par_calib <- calib_sofun( ``` ```{r eval = FALSE, echo = FALSE} -# Calibrates kphio, betao, kc_jmax - top 3 model params +# Calibrates kphio, kc_jmax, soilm_thetastar - top 3 model params # TODO: redefined for PHYDRO the parameters () instead of (kphio, betao, kc_jmax) set.seed(2023) # Define calibration settings @@ -299,23 +301,24 @@ settings_calib <- list( nrChains = 3 # number of chains to be sampled )), par = list( - kphio = list(lower = 0.03, upper = 0.15, init = 0.05), - kc_jmax = list(lower = 0.2, upper = 0.8, init = 0.41), - err_gpp = list(lower = 0.1, upper = 3, init = 0.8) + kphio = list(lower = 0.03, upper = 0.15, init = 0.05), + kc_jmax = list(lower = 0.2, upper = 0.8, init = 0.41), + soilm_thetastar = list(lower = 0.0, upper = 1.0 * 240, init = 0.6 * 240), + err_gpp = list(lower = 0.1, upper = 3, init = 0.8) ) ) par_calib <- calib_sofun( - drivers = p_model_drivers, + drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")), #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) obs = p_model_validation, settings = settings_calib, par_fixed = list( kphio_par_a = -0.0025, kphio_par_b = 20, - soilm_thetastar = 0.6*240, beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, - tau_acclim = 30.0), + tau_acclim = 30.0, + whc = 430), targets = "gpp" ) ``` @@ -350,13 +353,12 @@ plot_acf_mcmc <- function(chains, par_names){ par(mfrow = c(length(par_names), n_chains)) for(par_name in par_names){ for(i in 1:n_chains){ - chains[[i]][, par_name] |> + getSample(chains[[1]])[,par_name] |> # alternatively: as_tibble(getSample(chains[[i]]))[par_name] |> pacf(main = paste0("Series of ", par_name, " , chain ", i)) } } } - -plot_acf_mcmc(par_calib$mod$chain, c("kphio", "kphio_par_a", "kphio_par_b", "err_gpp")) +plot_acf_mcmc(par_calib$mod, par_names = c("kphio", "kphio_par_a", "kphio_par_b", "err_gpp")) ``` Looking at the correlation between chains for different parameters is also helpful because parameter correlation may slow down convergence, or the chains may oscillate in the multivariate posterior space. In this calibration we expect parameter samples to be somewhat correlated, especially `kphio_par_a` and `kphio_par_b` because they specify the shape of the temperature dependence of the quantum yield efficiency, $\varphi_o(T)$. We can also see that `err_gpp` is correlated with `kphio` (to which the P-model is very sensitive), since the error represents how good the model fits the observed GPP. @@ -393,9 +395,8 @@ incorporates the Gaussian model error. # Sample parameter values from the posterior distribution samples_par <- getSample(par_calib$mod, - thin = 30, # get every 30th sample - whichParameters = 1:4) |> - as.data.frame() |> + thin = 30) |> # get every 30th sample + tidyr::as_tibble() |> dplyr::mutate(mcmc_id = 1:n()) |> tidyr::nest(.by = mcmc_id, .key = "pars") @@ -404,7 +405,7 @@ run_pmodel <- function(sample_par){ # and also adds the new observation error out <- runread_pmodel_f( - drivers = p_model_drivers, + drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")), #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) par = list( # copied from par_fixed above kphio = sample_par$kphio, kphio_par_a = sample_par$kphio_par_a, @@ -413,6 +414,7 @@ run_pmodel <- function(sample_par){ beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, tau_acclim = 30.0, + whc = 430, kc_jmax = 0.41) # value from posterior ) @@ -512,7 +514,7 @@ plot_gpp_error + # # Plot observed and predicted GPP, with a 95% confidence interval using err_gpp plot_gpp_error <- ggplot(data = runread_pmodel_f( - drivers = p_model_drivers, + drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")), #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) par = list( kphio = par_calib$par[1], # values from posterior kphio_par_a = par_calib$par[2], @@ -521,6 +523,7 @@ plot_gpp_error <- ggplot(data = runread_pmodel_f( beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, tau_acclim = 30.0, + whc = 430, kc_jmax = 0.41) ) |> dplyr::select(sitename, data) |> From 9c6bdf169da5ea7f09cdb11365f14a35263d6290 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Wed, 25 Sep 2024 00:12:03 +0200 Subject: [PATCH 087/112] test: Fix testing path for R CMD CHECK --- analysis/pmodel_use_newdata.R | 4 ++-- data/p_model_drivers_format2024_08.rda | Bin 0 -> 332947 bytes data/p_model_drivers_newformat.rds | Bin 508831 -> 0 bytes data/p_model_validation_format2024_08.rda | Bin 0 -> 70527 bytes data/p_model_validation_newformat.rds | Bin 83553 -> 0 bytes tests/testthat/test-calibration-pmodel.R | 12 ++++-------- tests/testthat/test-model-runs.R | 16 ++++++---------- tests/testthat/test-quantitative-validation.R | 5 ++--- vignettes/pmodel_use_newdata.Rmd | 18 ++++++++++-------- vignettes/sensitivity_analysis.Rmd | 11 ++++++----- 10 files changed, 30 insertions(+), 36 deletions(-) create mode 100644 data/p_model_drivers_format2024_08.rda delete mode 100644 data/p_model_drivers_newformat.rds create mode 100644 data/p_model_validation_format2024_08.rda delete mode 100644 data/p_model_validation_newformat.rds diff --git a/analysis/pmodel_use_newdata.R b/analysis/pmodel_use_newdata.R index a5b58cd9..3dae5cc9 100644 --- a/analysis/pmodel_use_newdata.R +++ b/analysis/pmodel_use_newdata.R @@ -6,8 +6,8 @@ library(tidyr) library(khroma) ## ---------------------------------------------------------------------------------------------------------------------- -p_model_drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")) -p_model_validation = readRDS(file = here::here("data/p_model_validation_newformat.rds")) +p_model_drivers <- rsofun::p_model_drivers_format2024_08 # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) +# p_model_validation <- rsofun::p_model_validation_format2024_08 ## ---------------------------------------------------------------------------------------------------------------------- # define model parameter values from previous work diff --git a/data/p_model_drivers_format2024_08.rda b/data/p_model_drivers_format2024_08.rda new file mode 100644 index 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z?}GRFI(s_)eTe_ufH)XK_Rn*a$^P~|{m+-s|9Dc#)7i@r7xfP^-SRIBS9J1l^LB?- zGX%eX``731Z;<=jfB&p5cdxzfj(^-h`(KEM<74mT;c4gU?CE9a_UEElfBO>u{BuTl i!{5d9*9!i6mjTGh^MAkWe?v92;r{_$&?Wl4CISHKop$#C diff --git a/tests/testthat/test-calibration-pmodel.R b/tests/testthat/test-calibration-pmodel.R index 1b0b17b4..c9efddb7 100644 --- a/tests/testthat/test-calibration-pmodel.R +++ b/tests/testthat/test-calibration-pmodel.R @@ -3,8 +3,7 @@ set.seed(10) test_that("test GPP calibration routine p-model (BT, likelihood maximization)", { skip_on_cran() - #drivers <- rsofun::p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO - drivers <- readRDS(file = here::here("data/p_model_drivers_newformat.rds")) + drivers <- rsofun::p_model_drivers_format2024_08 # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) drivers$params_siml[[1]]$use_gs <- TRUE @@ -60,8 +59,7 @@ test_that("test GPP calibration routine p-model (BT, likelihood maximization)", test_that("test GPP calibration routine p-model (GenSA, rmse, all params)", { skip_on_cran() - #drivers <- rsofun::p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO - drivers <- readRDS(file = here::here("data/p_model_drivers_newformat.rds")) + drivers <- rsofun::p_model_drivers_format2024_08 # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) drivers$params_siml[[1]]$use_gs <- TRUE obs <- rsofun::p_model_validation @@ -218,8 +216,7 @@ test_that("test Vcmax25 calibration routine p-model (GenSA, rmse)", { test_that("test joint calibration routine p-model (BT, likelihood maximization)", { skip_on_cran() drivers <- rbind( - gpp = # TODO: rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO - readRDS(file = here::here("data/p_model_drivers_newformat.rds")), + gpp = rsofun::p_model_drivers_format2024_08, # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) vcmax25 = rsofun::p_model_drivers_vcmax25 |> # TODO: NOT YET UPDATED FOR PHYDRO # # specify additionally needed params_siml flags: @@ -270,7 +267,6 @@ test_that("test joint calibration routine p-model (BT, likelihood maximization)" err_vcmax25 = list(lower = 0.0001, upper = 0.1, init = 0.005) ) ) - # debug(rsofun::runread_pmodel_f) pars <- rsofun::calib_sofun( drivers = drivers, obs = obs, @@ -284,4 +280,4 @@ test_that("test joint calibration routine p-model (BT, likelihood maximization)" # test for correctly returned values expect_type(pars, "list") -}) \ No newline at end of file +}) diff --git a/tests/testthat/test-model-runs.R b/tests/testthat/test-model-runs.R index 902e4759..bc260989 100644 --- a/tests/testthat/test-model-runs.R +++ b/tests/testthat/test-model-runs.R @@ -1,6 +1,5 @@ context("test models and their parameters") set.seed(10) - test_that("run_pmodel_f_bysite()", { skip_on_cran() @@ -18,8 +17,7 @@ test_that("run_pmodel_f_bysite()", { ) # read in demo data - #df_drivers <- p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) - df_drivers <- readRDS(file = here::here("data/p_model_drivers_newformat.rds")) + df_drivers <- rsofun::p_model_drivers_format2024_08 # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) # check run_pmodel_f_bysite() ########################## # run the SOFUN Fortran P-model using the internal function `run_pmodel_f_bysite` @@ -154,9 +152,9 @@ test_that("run_pmodel_f_bysite()", { dpsi = c(0, 0, 0, 0, 0, 0), psi_leaf = c(0, 0, 0, 0, 0, 0) ) - expect_equal(slice(tibble(mod1), c(1, 70, 1200, 1400, 2000, 2180)), ref1, tolerance = 1e-7) - expect_equal(slice(tibble(mod2), c(1, 70, 1200, 1400, 2000, 2180)), ref2, tolerance = 1e-7) - expect_equal(slice(tibble(mod3), c(1, 70, 1200, 1400, 2000, 2180)), ref3, tolerance = 1e-7) + expect_equal(slice(tibble(mod1), c(1, 70, 1200, 1400, 2000, 2180)), ref1, tolerance = 1e-6) + expect_equal(slice(tibble(mod2), c(1, 70, 1200, 1400, 2000, 2180)), ref2, tolerance = 1e-6) + expect_equal(slice(tibble(mod3), c(1, 70, 1200, 1400, 2000, 2180)), ref3, tolerance = 1e-6) }) test_that("runread_pmodel_f()", { @@ -176,8 +174,7 @@ test_that("runread_pmodel_f()", { ) # read in demo data - #df_drivers <- p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) - df_drivers <- readRDS(file = here::here("data/p_model_drivers_newformat.rds")) + df_drivers <- rsofun::p_model_drivers_format2024_08 # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) df_output_singlecore <- rsofun::runread_pmodel_f( df_drivers, @@ -290,8 +287,7 @@ test_that("phydro-model run check LE and AET", { ) # read in demo data - #df_drivers <- p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO - df_drivers <- readRDS(file = here::here("data/p_model_drivers_newformat.rds")) + df_drivers <- rsofun::p_model_drivers_format2024_08 # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) df_drivers$params_siml[[1]]$use_gs <- TRUE # run the SOFUN Fortran PHYDRO-model diff --git a/tests/testthat/test-quantitative-validation.R b/tests/testthat/test-quantitative-validation.R index 1aa8029d..76e56edc 100644 --- a/tests/testthat/test-quantitative-validation.R +++ b/tests/testthat/test-quantitative-validation.R @@ -21,9 +21,8 @@ test_that("p-model quantitative check versus observations (FR-Pue)", { whc = 2000 # site info, water holding capacity in mm: TODO: does this make sense with soilm_thetastar in mm units? ) - #df_drivers <- p_model_drivers # TODO: NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) - df_drivers <- readRDS(file = here::here("data/p_model_drivers_newformat.rds")) - + df_drivers <- rsofun::p_model_drivers_format2024_08 # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) + # run the model for these parameters res <- rsofun::runread_pmodel_f( drivers = df_drivers, diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd index 86f6c60e..4aa0f07a 100644 --- a/vignettes/pmodel_use_newdata.Rmd +++ b/vignettes/pmodel_use_newdata.Rmd @@ -201,18 +201,20 @@ The package includes two demo datasets to run and validate pmodel output using G # lubridate::year(date) <= 2012) |> # list() # )) -# -# saveRDS(p_model_drivers, file = here::here("data/p_model_drivers_newformat.rds")) -# -# saveRDS(p_model_validation, file = here::here("data/p_model_validation_newformat.rds")) +# +# Save to subfolder data/ and export it to make it available for package users: +# p_model_drivers_format2024_08 <- p_model_drivers +# usethis::use_data(p_model_drivers_format2024_08) # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) +# p_model_validation_format2024_08 <- p_model_validation +# usethis::use_data(p_model_validation_format2024_08) ``` ### Read drivers ```{r} -p_model_drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")) +p_model_drivers <- rsofun::p_model_drivers_format2024_08 # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) -p_model_validation = readRDS(file = here::here("data/p_model_validation_newformat.rds")) +p_model_validation <- rsofun::p_model_validation_format2024_08 ``` ### Visualize drivers @@ -243,9 +245,9 @@ These are real data from the French FR-Pue fluxnet site. Information about data Another two datasets are provided as an example to validate the model against leaf traits data, rather than fluxes. Measurements of Vcmax25 (aggregated over species) for a subset of 4 sites from the GlobResp database (Atkin et al., 2015) are given in `p_model_validation_vcmax25` and the corresponding forcing for the P-model is given in `p_model_drivers_vcmax25`. Since leaf traits are only measured once per site, the forcing used is a single year of average climate (the average measurements between 2001 and 2015 on each day of the year). ```{r} -p_model_drivers_vcmax25 +rsofun::p_model_drivers_vcmax25 -p_model_validation_vcmax25 +rsofun::p_model_validation_vcmax25 ``` For the remainder of this vignette, we will use the GPP flux datasets. The workflow is exactly the same for leaf traits data. diff --git a/vignettes/sensitivity_analysis.Rmd b/vignettes/sensitivity_analysis.Rmd index a2bc5862..bd5fd6a5 100644 --- a/vignettes/sensitivity_analysis.Rmd +++ b/vignettes/sensitivity_analysis.Rmd @@ -61,11 +61,12 @@ ll_pmodel <- function( rsofun::cost_likelihood_pmodel( # reuse likelihood cost function as.list(par_v), # must be a named list obs = rsofun::p_model_validation, - drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")), #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) + drivers = rsofun::p_model_drivers_format2024_08, #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) targets = "gpp" ) } + # Compute log-likelihood for a given set of parameters ll_pmodel( par_v = c( @@ -269,7 +270,7 @@ settings_calib <- list( # Calibrate kphio-related parameters and err_gpp par_calib <- calib_sofun( - drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")), #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) + drivers = rsofun::p_model_drivers_format2024_08, # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) obs = p_model_validation, settings = settings_calib, par_fixed = list( @@ -309,7 +310,7 @@ settings_calib <- list( ) par_calib <- calib_sofun( - drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")), #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) + drivers = rsofun::p_model_drivers_format2024_08, # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) obs = p_model_validation, settings = settings_calib, par_fixed = list( @@ -405,7 +406,7 @@ run_pmodel <- function(sample_par){ # and also adds the new observation error out <- runread_pmodel_f( - drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")), #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) + drivers = rsofun::p_model_drivers_format2024_08, # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) par = list( # copied from par_fixed above kphio = sample_par$kphio, kphio_par_a = sample_par$kphio_par_a, @@ -514,7 +515,7 @@ plot_gpp_error + # # Plot observed and predicted GPP, with a 95% confidence interval using err_gpp plot_gpp_error <- ggplot(data = runread_pmodel_f( - drivers = readRDS(file = here::here("data/p_model_drivers_newformat.rds")), #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) + drivers = rsofun::p_model_drivers_format2024_08, # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) par = list( kphio = par_calib$par[1], # values from posterior kphio_par_a = par_calib$par[2], From 0fbb9496649afffe3674f5c357d0c550ecfd0c4b Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Wed, 25 Sep 2024 16:06:00 +0200 Subject: [PATCH 088/112] Attempt to fix R CMD Check by direct use of example driver --- vignettes/pmodel_use.Rmd | 2 +- vignettes/pmodel_use_newdata.Rmd | 88 +++++++++--------------------- vignettes/sensitivity_analysis.Rmd | 4 +- 3 files changed, 30 insertions(+), 64 deletions(-) diff --git a/vignettes/pmodel_use.Rmd b/vignettes/pmodel_use.Rmd index 3c91b566..4799f446 100644 --- a/vignettes/pmodel_use.Rmd +++ b/vignettes/pmodel_use.Rmd @@ -38,7 +38,7 @@ The package includes two demo datasets to run and validate pmodel output using G library(rsofun) # this is to deal with an error p_model_drivers.rds not being found -p_model_drivers = p_model_drivers |> +p_model_drivers = rsofun::p_model_drivers |> rowwise() |> mutate(params_siml = params_siml |> mutate(use_phydro = F, diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd index 4aa0f07a..d4d1e107 100644 --- a/vignettes/pmodel_use_newdata.Rmd +++ b/vignettes/pmodel_use_newdata.Rmd @@ -212,20 +212,20 @@ The package includes two demo datasets to run and validate pmodel output using G ### Read drivers ```{r} -p_model_drivers <- rsofun::p_model_drivers_format2024_08 # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) +# p_model_drivers <- rsofun::p_model_drivers_format2024_08 # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) -p_model_validation <- rsofun::p_model_validation_format2024_08 +# p_model_validation <- rsofun::p_model_validation_format2024_08 ``` ### Visualize drivers ```{r} -p_model_drivers$forcing[[1]] |> +rsofun::p_model_drivers_format2024_08$forcing[[1]] |> # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> dplyr::select(date, co2, ppfd, netrad, temp, vpd, fapar, rain, ccov) |> tidyr::pivot_longer(-date) |> dplyr::mutate(type="24-hr mean") |> - rbind(p_model_validation$data[[1]] |> + rbind(rsofun::p_model_validation_format2024_08$data[[1]] |> # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> dplyr::select(date, gpp) |> tidyr::pivot_longer(-date) |> @@ -236,7 +236,7 @@ p_model_drivers$forcing[[1]] |> theme_classic() + theme(strip.background = element_rect(color = "white", linewidth = 1))+ facet_wrap(~name, scales = "free")+ - ggtitle(p_model_drivers$sitename[[1]]) + ggtitle(rsofun::p_model_drivers_format2024_08$sitename[[1]]) ``` @@ -273,12 +273,12 @@ params_modl <- list( rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, kc_jmax = 0.41, - whc = p_model_drivers$site_info[[1]]$whc + whc = rsofun::p_model_drivers_format2024_08$site_info[[1]]$whc ) # run the model for these parameters output <- rsofun::runread_pmodel_f( - p_model_drivers, + rsofun::p_model_drivers_format2024_08, par = params_modl ) ``` @@ -300,7 +300,7 @@ df_gpp_plot <- rbind( unnest(data) |> select(date, gpp) |> mutate(type = "P-model output"), - p_model_validation |> + rsofun::p_model_validation_format2024_08 |> filter(sitename == "FR-Pue") |> unnest(data) |> select(date, gpp) |> @@ -366,8 +366,8 @@ With all settings defined the optimization function `calib_sofun()` can be calle ```{r eval=FALSE} # calibrate the model and optimize free parameters pars <- calib_sofun( - drivers = p_model_drivers, - obs = p_model_validation, + drivers = rsofun::p_model_drivers_format2024_08, + obs = rsofun::p_model_validation_format2024_08, settings = settings, # extra arguments passed to the cost function: targets = "gpp", # define target variable GPP @@ -384,7 +384,7 @@ params_modl$kphio <- pars$par["kphio"] # Run the model for these parameters output_new <- rsofun::runread_pmodel_f( - p_model_drivers, + rsofun::p_model_drivers_format2024_08, par = params_modl ) @@ -441,22 +441,13 @@ get_density <- function(x, y, ...) { Set `use_gs` flag to `FALSE` in params_siml so that Priestly-Taylor formulation will be used in calculation of ET (P-model gs will not be used). -```{r} -p_model_drivers <- - p_model_drivers |> - mutate( - params_siml = params_siml |> - mutate(use_gs = FALSE) |> - list() - ) -``` - Run the model and plot outputs ```{r} # run the model for these parameters output <- rsofun::runread_pmodel_f( - p_model_drivers, + mutate(rsofun::p_model_drivers_format2024_08, + params_siml = params_siml |> mutate(use_gs = FALSE) |> list()), par = params_modl ) ``` @@ -465,7 +456,7 @@ output <- rsofun::runread_pmodel_f( df_plot <- output$data[[1]] %>% select(date, gpp_mod = gpp, le_mod = le) %>% left_join( - p_model_validation$data[[1]] %>% + rsofun::p_model_validation_format2024_08$data[[1]] %>% select(date, gpp_obs = gpp, le_obs = le), by = join_by(date) ) |> @@ -500,22 +491,13 @@ $$ T = 1.6 \; G_s \; \text{VPD} $$ -```{r} -p_model_drivers <- - p_model_drivers |> - mutate( - params_siml = params_siml |> - mutate(use_gs = TRUE) |> - list() - ) -``` - Run the model. ```{r} # run the model for these parameters output <- rsofun::runread_pmodel_f( - p_model_drivers, + mutate(rsofun::p_model_drivers_format2024_08, + params_siml = params_siml |> mutate(use_gs = TRUE) |> list()), par = params_modl ) ``` @@ -526,7 +508,7 @@ Plot outputs. df_plot <- output$data[[1]] %>% select(date, gpp_mod = gpp, le_mod = le) %>% left_join( - p_model_validation$data[[1]] %>% + rsofun::p_model_validation_format2024_08$data[[1]] %>% select(date, gpp_obs = gpp, le_obs = le), by = join_by(date) ) |> @@ -561,18 +543,13 @@ $$ T = 1.6 \; G_s \; \text{VPD} $$ -```{r} -p_model_drivers$params_siml[[1]]$use_gs <- TRUE -p_model_drivers$params_siml[[1]]$use_pml <- TRUE -p_model_drivers$params_siml[[1]]$use_phydro <- FALSE -``` - Run the model. ```{r} # run the model for these parameters output <- rsofun::runread_pmodel_f( - p_model_drivers, + mutate(rsofun::p_model_drivers_format2024_08, + params_siml = params_siml |> mutate(use_gs = TRUE, use_pml = TRUE, use_phydro + FALSE) |> list()), par = params_modl ) ``` @@ -583,7 +560,7 @@ Plot outputs. df_plot <- output$data[[1]] %>% select(date, gpp_mod = gpp, le_mod = le) %>% left_join( - p_model_validation$data[[1]] %>% + rsofun::p_model_validation_format2024_08$data[[1]] %>% select(date, gpp_obs = gpp, le_obs = le), by = join_by(date) ) |> @@ -617,15 +594,15 @@ out_le$gg + For P-hydro, we must use the 3-hr daily max forcing as the acclimation forcing. So let's rename it in the data. ```{r} -p_model_drivers <- p_model_drivers |> +phdro_model_drivers <- rsofun::p_model_drivers_format2024_08 |> rename(forcing_acclim = forcing_3hrmax) -p_model_drivers$forcing_acclim[[1]] |> +phdro_model_drivers$forcing_acclim[[1]] |> # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> dplyr::select(date, co2, ppfd, netrad, temp, vpd, fapar, rain, ccov) |> tidyr::pivot_longer(-date) |> dplyr::mutate(type="24-hr mean") |> - rbind(p_model_validation$data[[1]] |> + rbind(rsofun::p_model_validation_format2024_08$data[[1]] |> # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> dplyr::select(date, gpp) |> tidyr::pivot_longer(-date) |> @@ -636,23 +613,11 @@ p_model_drivers$forcing_acclim[[1]] |> theme_classic() + theme(strip.background = element_rect(color = "white", size = 1))+ facet_wrap(~name, scales = "free")+ - ggtitle(p_model_drivers$sitename[[1]]) + ggtitle(phdro_model_drivers$sitename[[1]]) ``` For P-hydro runs, we always set `use_phydro = T` and `use_gs = T`. -```{r} -p_model_drivers <- - p_model_drivers |> - mutate( - params_siml = params_siml |> - mutate( - use_phydro = TRUE, - use_gs = TRUE - ) |> - list()) -``` - ```{r} # define model parameter values params_modl <- list( @@ -674,7 +639,8 @@ params_modl <- list( # run the model for these parameters output <- rsofun::runread_pmodel_f( - p_model_drivers, + mutate(phdro_model_drivers, + params_siml = params_siml |> mutate(use_gs = TRUE, use_phydro = TRUE) |> list()), par = params_modl ) @@ -686,7 +652,7 @@ output <- rsofun::runread_pmodel_f( df_plot <- output$data[[1]] %>% select(date, gpp_mod = gpp, le_mod = le) %>% left_join( - p_model_validation$data[[1]] %>% + rsofun::p_model_validation_format2024_08$data[[1]] %>% select(date, gpp_obs = gpp, le_obs = le), by = join_by(date) ) |> diff --git a/vignettes/sensitivity_analysis.Rmd b/vignettes/sensitivity_analysis.Rmd index bd5fd6a5..2158d45b 100644 --- a/vignettes/sensitivity_analysis.Rmd +++ b/vignettes/sensitivity_analysis.Rmd @@ -59,8 +59,8 @@ ll_pmodel <- function( par_v # a vector of all calibratable parameters including errors ){ rsofun::cost_likelihood_pmodel( # reuse likelihood cost function - as.list(par_v), # must be a named list - obs = rsofun::p_model_validation, + as.list(par_v), # must be a named list + obs = rsofun::p_model_validation, # example data from package drivers = rsofun::p_model_drivers_format2024_08, #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) targets = "gpp" ) From d082cd9c0b422b23b1694284a9b87ef0e4e57f06 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Wed, 25 Sep 2024 16:06:42 +0200 Subject: [PATCH 089/112] Fix: analysis/01-sensitivity-analysis.R --- analysis/01-sensitivity-analysis.R | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/analysis/01-sensitivity-analysis.R b/analysis/01-sensitivity-analysis.R index 5865d73a..ec0f7035 100644 --- a/analysis/01-sensitivity-analysis.R +++ b/analysis/01-sensitivity-analysis.R @@ -3,8 +3,8 @@ # Load libraries library(rsofun) library(dplyr) -library(tidyr) library(ggplot2) +library(tidyr) library(sensitivity) library(BayesianTools) @@ -13,13 +13,12 @@ set.seed(432) # Define log-likelihood function ll_pmodel <- function( - par_v # a vector of all calibratable parameters - # including errors + par_v # a vector of all calibratable parameters including errors ){ rsofun::cost_likelihood_pmodel( # likelihood cost function from package - par_v, + as.list(par_v), # must be a named list obs = rsofun::p_model_validation, # example data from package - drivers = rsofun::p_model_drivers, + drivers = rsofun::p_model_drivers_format2024_08, #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) targets = "gpp" ) } @@ -36,7 +35,8 @@ par_cal_best <- c( rd_to_vcmax = 0.014, tau_acclim = 30.0, kc_jmax = 0.41, - error_gpp = 1 + whc = 430, + err_gpp = 1 ) # lower bound @@ -49,7 +49,8 @@ par_cal_min <- c( rd_to_vcmax = 0.01, tau_acclim = 7.0, kc_jmax = 0.2, - error_gpp = 0.01 + whc = 300, + err_gpp = 0.01 ) # upper bound @@ -62,7 +63,8 @@ par_cal_max <- c( rd_to_vcmax = 0.1, tau_acclim = 60.0, kc_jmax = 0.8, - error_gpp = 4 + whc = 300, + err_gpp = 4 ) # Create BayesinaTools setup object From 36c1259ecc32a066638e7cb4f88d6812a86ba261 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Wed, 25 Sep 2024 16:48:18 +0200 Subject: [PATCH 090/112] docs: Fix example of calib_sofun() --- R/calib_sofun.R | 3 ++- man/calib_sofun.Rd | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/R/calib_sofun.R b/R/calib_sofun.R index 348f6796..038b15f5 100644 --- a/R/calib_sofun.R +++ b/R/calib_sofun.R @@ -68,7 +68,8 @@ #' settings = list( #' nrChains = 1, #' burnin = 0, -#' iterations = 50 # kept artificially low +#' iterations = 50, # kept artificially low, +#' whc = 432 #' ) #' ) #' ) diff --git a/man/calib_sofun.Rd b/man/calib_sofun.Rd index 416faf1f..1f260d5d 100644 --- a/man/calib_sofun.Rd +++ b/man/calib_sofun.Rd @@ -79,7 +79,8 @@ settings <- list( settings = list( nrChains = 1, burnin = 0, - iterations = 50 # kept artificially low + iterations = 50, # kept artificially low, + whc = 432 ) ) ) From ebc788a1712a56685689833c2cd5a29c40d8ac3f Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Mon, 30 Sep 2024 16:39:29 +0200 Subject: [PATCH 091/112] Fix errors in R CMD Check --- DESCRIPTION | 3 +- R/calib_sofun.R | 10 +++---- R/cost_likelihood_phydro.R | 44 ++++++++++++++++++++---------- R/cost_likelihood_pmodel.R | 36 +++++++++++++----------- R/cost_rmse_pmodel.R | 7 +++-- R/run_pmodel_f_bysite.R | 15 +++++----- man/calib_sofun.Rd | 10 +++---- man/cost_likelihood_phydromodel.Rd | 44 ++++++++++++++++++++---------- man/cost_likelihood_pmodel.Rd | 35 ++++++++++++++---------- man/cost_rmse_pmodel.Rd | 7 +++-- man/run_pmodel_f_bysite.Rd | 14 ++++++---- 11 files changed, 134 insertions(+), 91 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 9a5093c1..d422257a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -72,7 +72,8 @@ Suggests: knitr, sensitivity, hexbin, - khroma + khroma, + yardstick Remotes: geco-bern/cwd VignetteBuilder: knitr diff --git a/R/calib_sofun.R b/R/calib_sofun.R index 038b15f5..6e34d6c1 100644 --- a/R/calib_sofun.R +++ b/R/calib_sofun.R @@ -52,7 +52,8 @@ #' beta_unitcostratio = 146, #' rd_to_vcmax = 0.014, #' tau_acclim = 30, -#' kc_jmax = 0.41 +#' kc_jmax = 0.41, +#' whc = 432 #' ) #' #' # Define calibration settings @@ -67,13 +68,12 @@ #' sampler = "DEzs", #' settings = list( #' nrChains = 1, -#' burnin = 0, -#' iterations = 50, # kept artificially low, -#' whc = 432 +#' burnin = 0, +#' iterations = 50 # kept artificially low, #' ) #' ) #' ) -#' +#' #' # Run the calibration for GPP data #' calib_output <- rsofun::calib_sofun( #' drivers = rsofun::p_model_drivers, diff --git a/R/cost_likelihood_phydro.R b/R/cost_likelihood_phydro.R index cf0662b1..84290145 100644 --- a/R/cost_likelihood_phydro.R +++ b/R/cost_likelihood_phydro.R @@ -51,23 +51,37 @@ #' @export #' #' @examples -#' # Compute the likelihood for a set of +#' # Compute the likelihood for a set of #' # model parameter values involved in the -#' # temperature dependence of kphio +#' # temperature dependence of kphio #' # and example data -#' cost_likelihood_phydromodel( -#' par = c(0.05, -0.01, 1, # model parameters -#' 2), # err_gpp -#' obs = p_model_validation, -#' drivers = p_model_drivers, -#' targets = c('gpp'), -#' par_fixed = list( -#' soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress -#' beta_unitcostratio = 146.0, -#' rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous -#' tau_acclim = 30.0, -#' kc_jmax = 0.41 -#' ) +#' library(dplyr) +#' cost_likelihood_phydromodel( # reuse likelihood cost function +#' par = list( +#' kphio = 0.0288, +#' kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio +#' kphio_par_b = 1.0, +#' rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous +#' tau_acclim = 30.0, +#' kc_jmax = 0.41, +#' phydro_K_plant = 5e-17, +#' phydro_p50_plant = -0.46, +#' phydro_gamma = 0.065, +#' phydro_b_plant = 1, +#' phydro_alpha = 0.08, +#' bsoil = 3, +#' Ssoil = 113, +#' whc = 253, +#' # kphio = 0.09423773, # setup ORG in Stocker et al. 2020 GMD +#' # kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio +#' # kphio_par_b = 1.0, +#' err_gpp = 0.9 # value from previous simulations +#' ), # must be a named list +#' obs = p_model_validation, # example data from package +#' drivers = p_model_drivers_format2024_08 %>% +#' ungroup() %>% dplyr::mutate(params_siml = purrr::map(params_siml, ~mutate(.x, use_phydro = TRUE, use_pml = TRUE, use_gs = TRUE))), +#' targets = "gpp", +#' par_fixed = list() #' ) cost_likelihood_phydromodel <- function( par, # model parameters & error terms for each target diff --git a/R/cost_likelihood_pmodel.R b/R/cost_likelihood_pmodel.R index 912853a6..a0fb6b84 100644 --- a/R/cost_likelihood_pmodel.R +++ b/R/cost_likelihood_pmodel.R @@ -51,25 +51,29 @@ #' @export #' #' @examples -#' # Compute the likelihood for a set of +#' # Compute the likelihood for a set of #' # model parameter values involved in the -#' # temperature dependence of kphio +#' # temperature dependence of kphio #' # and example data -#' cost_likelihood_pmodel( -#' par = c(0.05, -0.01, 1, # model parameters -#' 2), # err_gpp -#' obs = p_model_validation, -#' drivers = p_model_drivers, -#' targets = c('gpp'), -#' par_fixed = list( -#' soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress -#' beta_unitcostratio = 146.0, -#' rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous -#' tau_acclim = 30.0, -#' kc_jmax = 0.41 -#' ) +#' cost_likelihood_pmodel( # reuse likelihood cost function +#' par = list( +#' kphio = 0.09423773, # setup ORG in Stocker et al. 2020 GMD +#' kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio +#' kphio_par_b = 1.0, +#' err_gpp = 0.9 # value from previous simulations +#' ), # must be a named list +#' obs = p_model_validation, # example data from package +#' drivers = p_model_drivers_format2024_08, #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) +#' targets = "gpp", +#' par_fixed = list( +#' soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress +#' beta_unitcostratio = 146.0, +#' rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous +#' tau_acclim = 30.0, +#' kc_jmax = 0.41, +#' whc = 430 +#' ) #' ) - cost_likelihood_pmodel <- function( par, # model parameters & error terms for each target obs, diff --git a/R/cost_rmse_pmodel.R b/R/cost_rmse_pmodel.R index 9f6bb292..1df05d08 100644 --- a/R/cost_rmse_pmodel.R +++ b/R/cost_rmse_pmodel.R @@ -52,16 +52,17 @@ #' # of model parameter values #' # and example data #' cost_rmse_pmodel( -#' par = c(0.05, -0.01, 0.5), # kphio related parameters +#' par = c(kphio = 0.05, kphio_par_a = -0.01, kphio_par_b = 0.5), # kphio related parameters #' obs = p_model_validation, -#' drivers = p_model_drivers, +#' drivers = p_model_drivers_format2024_08, #' targets = c('gpp'), #' par_fixed = list( #' soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress #' beta_unitcostratio = 146.0, #' rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous #' tau_acclim = 30.0, -#' kc_jmax = 0.41 +#' kc_jmax = 0.41, +#' whc = 240 #' ) #' ) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index affc5a83..5863a9a0 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -137,19 +137,20 @@ #' beta_unitcostratio = 146.0, #' rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous #' tau_acclim = 30.0, -#' kc_jmax = 0.41 +#' kc_jmax = 0.41, +#' whc = 240 #' ) #' -#' # Run the Fortran P-model +#' # Run the Fortran P-model #' mod_output <- run_pmodel_f_bysite( #' # unnest drivers example data -#' sitename = p_model_drivers$sitename[1], -#' params_siml = p_model_drivers$params_siml[[1]], -#' site_info = p_model_drivers$site_info[[1]], -#' forcing = p_model_drivers$forcing[[1]], +#' sitename = p_model_drivers_format2024_08$sitename[1], +#' params_siml = p_model_drivers_format2024_08$params_siml[[1]], +#' site_info = p_model_drivers_format2024_08$site_info[[1]], +#' forcing = p_model_drivers_format2024_08$forcing[[1]], +#' forcing_acclim = p_model_drivers_format2024_08$forcing_daytime[[1]], #' params_modl = params_modl #' ) - run_pmodel_f_bysite <- function( # TODO: Above docstring appears duplicated in runread_pmodel_f.R. This redunduncy should be reduced. sitename, params_siml, diff --git a/man/calib_sofun.Rd b/man/calib_sofun.Rd index 1f260d5d..663dc555 100644 --- a/man/calib_sofun.Rd +++ b/man/calib_sofun.Rd @@ -63,7 +63,8 @@ params_fix <- list( beta_unitcostratio = 146, rd_to_vcmax = 0.014, tau_acclim = 30, - kc_jmax = 0.41 + kc_jmax = 0.41, + whc = 432 ) # Define calibration settings @@ -78,13 +79,12 @@ settings <- list( sampler = "DEzs", settings = list( nrChains = 1, - burnin = 0, - iterations = 50, # kept artificially low, - whc = 432 + burnin = 0, + iterations = 50 # kept artificially low, ) ) ) - + # Run the calibration for GPP data calib_output <- rsofun::calib_sofun( drivers = rsofun::p_model_drivers, diff --git a/man/cost_likelihood_phydromodel.Rd b/man/cost_likelihood_phydromodel.Rd index 0aabc29a..fe883b46 100644 --- a/man/cost_likelihood_phydromodel.Rd +++ b/man/cost_likelihood_phydromodel.Rd @@ -73,22 +73,36 @@ when the date of a trait measurement is available, it will be compared to the trait value predicted on that date. } \examples{ -# Compute the likelihood for a set of +# Compute the likelihood for a set of # model parameter values involved in the -# temperature dependence of kphio +# temperature dependence of kphio # and example data -cost_likelihood_phydromodel( - par = c(0.05, -0.01, 1, # model parameters - 2), # err_gpp - obs = p_model_validation, - drivers = p_model_drivers, - targets = c('gpp'), - par_fixed = list( - soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress - beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41 - ) +library(dplyr) +cost_likelihood_phydromodel( # reuse likelihood cost function + par = list( + kphio = 0.0288, + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + phydro_K_plant = 5e-17, + phydro_p50_plant = -0.46, + phydro_gamma = 0.065, + phydro_b_plant = 1, + phydro_alpha = 0.08, + bsoil = 3, + Ssoil = 113, + whc = 253, + # kphio = 0.09423773, # setup ORG in Stocker et al. 2020 GMD + # kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + # kphio_par_b = 1.0, + err_gpp = 0.9 # value from previous simulations + ), # must be a named list + obs = p_model_validation, # example data from package + drivers = p_model_drivers_format2024_08 \%>\% + ungroup() \%>\% dplyr::mutate(params_siml = purrr::map(params_siml, ~mutate(.x, use_phydro = TRUE, use_pml = TRUE, use_gs = TRUE))), + targets = "gpp", + par_fixed = list() ) } diff --git a/man/cost_likelihood_pmodel.Rd b/man/cost_likelihood_pmodel.Rd index 9a39e0ed..a178a69e 100644 --- a/man/cost_likelihood_pmodel.Rd +++ b/man/cost_likelihood_pmodel.Rd @@ -73,22 +73,27 @@ when the date of a trait measurement is available, it will be compared to the trait value predicted on that date. } \examples{ -# Compute the likelihood for a set of +# Compute the likelihood for a set of # model parameter values involved in the -# temperature dependence of kphio +# temperature dependence of kphio # and example data -cost_likelihood_pmodel( - par = c(0.05, -0.01, 1, # model parameters - 2), # err_gpp - obs = p_model_validation, - drivers = p_model_drivers, - targets = c('gpp'), - par_fixed = list( - soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress - beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41 - ) +cost_likelihood_pmodel( # reuse likelihood cost function + par = list( + kphio = 0.09423773, # setup ORG in Stocker et al. 2020 GMD + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + err_gpp = 0.9 # value from previous simulations + ), # must be a named list + obs = p_model_validation, # example data from package + drivers = p_model_drivers_format2024_08, #TODO rsofun::p_model_drivers is NOT YET UPDATED FOR PHYDRO (a newformat, b add phydro_ parameters) + targets = "gpp", + par_fixed = list( + soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + whc = 430 + ) ) } diff --git a/man/cost_rmse_pmodel.Rd b/man/cost_rmse_pmodel.Rd index fe0a9d45..4f1162f8 100644 --- a/man/cost_rmse_pmodel.Rd +++ b/man/cost_rmse_pmodel.Rd @@ -76,16 +76,17 @@ trait value predicted on that date. # of model parameter values # and example data cost_rmse_pmodel( - par = c(0.05, -0.01, 0.5), # kphio related parameters + par = c(kphio = 0.05, kphio_par_a = -0.01, kphio_par_b = 0.5), # kphio related parameters obs = p_model_validation, - drivers = p_model_drivers, + drivers = p_model_drivers_format2024_08, targets = c('gpp'), par_fixed = list( soilm_thetastar = 0.6 * 240, # old setup with soil moisture stress beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41 + kc_jmax = 0.41, + whc = 240 ) ) } diff --git a/man/run_pmodel_f_bysite.Rd b/man/run_pmodel_f_bysite.Rd index e5bad07b..2be67950 100644 --- a/man/run_pmodel_f_bysite.Rd +++ b/man/run_pmodel_f_bysite.Rd @@ -158,16 +158,18 @@ params_modl <- list( beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41 + kc_jmax = 0.41, + whc = 240 ) -# Run the Fortran P-model +# Run the Fortran P-model mod_output <- run_pmodel_f_bysite( # unnest drivers example data - sitename = p_model_drivers$sitename[1], - params_siml = p_model_drivers$params_siml[[1]], - site_info = p_model_drivers$site_info[[1]], - forcing = p_model_drivers$forcing[[1]], + sitename = p_model_drivers_format2024_08$sitename[1], + params_siml = p_model_drivers_format2024_08$params_siml[[1]], + site_info = p_model_drivers_format2024_08$site_info[[1]], + forcing = p_model_drivers_format2024_08$forcing[[1]], + forcing_acclim = p_model_drivers_format2024_08$forcing_daytime[[1]], params_modl = params_modl ) } From de83f04ff38b82b93075c4761c7d326ed5d048a2 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Wed, 2 Oct 2024 13:11:08 +0200 Subject: [PATCH 092/112] Add dplyr:: and tidyr:: --- R/cost_likelihood_phydro.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/cost_likelihood_phydro.R b/R/cost_likelihood_phydro.R index 84290145..d79581f4 100644 --- a/R/cost_likelihood_phydro.R +++ b/R/cost_likelihood_phydro.R @@ -162,8 +162,8 @@ cost_likelihood_phydromodel <- function( # calculate actual dpsi intercept by fitting lm (might not work) dat_lm = d_filt |> - select(psi_leaf, psi_soil) |> - drop_na() + dplyr::select(psi_leaf, psi_soil) |> + tidyr::drop_na() if (nrow(dat_lm) > 5){ mod = dat_lm |> From d44e3f89140afd452466042468f326524f65f757 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Wed, 2 Oct 2024 13:11:21 +0200 Subject: [PATCH 093/112] Add boilerplate code for phydro regression test (currently deactivated since there is some side effects) --- tests/testthat/test-model-runs.R | 110 +++++++++++++++++++++++++++---- 1 file changed, 99 insertions(+), 11 deletions(-) diff --git a/tests/testthat/test-model-runs.R b/tests/testthat/test-model-runs.R index bc260989..75f9547b 100644 --- a/tests/testthat/test-model-runs.R +++ b/tests/testthat/test-model-runs.R @@ -6,18 +6,34 @@ test_that("run_pmodel_f_bysite()", { # load parameters (valid ones) params_modl <- list( kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD - kphio_par_a = 0.01, # set to zero to disable temperature-dependence of kphio, setup ORG in Stocker et al. 2020 GMD + kphio_par_a = 0.01, # set to zero to disable temperature-dependence of kphio, setup ORG in Stocker et al. 2020 GMD kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, kc_jmax = 0.41, - whc = 2000 # site info, water holding capacity in mm + whc = 2000 # site info, water holding capacity in mm ) - + # params_modl_phydro <- list( + # kphio = 0.0288, + # kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + # kphio_par_b = 1.0, + # rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + # tau_acclim = 30.0, + # kc_jmax = 0.41, + # phydro_K_plant = 5e-17, + # phydro_p50_plant = -0.46, + # phydro_gamma = 0.065, + # phydro_b_plant = 1, + # phydro_alpha = 0.08, + # bsoil = 3, + # Ssoil = 113, + # whc = 253 + # ) + # read in demo data - df_drivers <- rsofun::p_model_drivers_format2024_08 # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) + df_drivers <- rsofun::p_model_drivers_format2024_08 # check run_pmodel_f_bysite() ########################## # run the SOFUN Fortran P-model using the internal function `run_pmodel_f_bysite` @@ -54,7 +70,7 @@ test_that("run_pmodel_f_bysite()", { # site_info = df_drivers$site_info[[1]], # forcing = df_drivers$forcing[[1]], # forcing_acclim = df_drivers$forcing[[1]], - # params_modl = params_modl, # TODO: phydro requires params: bsoil, phydro_alpha, phydro_b_plant, phydro_gamma, phydro_K_plant, phydro_p50_plant, Ssoil + # params_modl = params_modl_phydro, # makecheck = TRUE # ) @@ -66,8 +82,14 @@ test_that("run_pmodel_f_bysite()", { expect_true(all(!is.na(tibble(mod1)))) expect_true(all(!is.na(tibble(mod2)))) expect_true(all(!is.na(tibble(mod3)))) - # expect_true(all(!is.na(tibble(mod4)))) + # expect_true(all(!is.na(tibble(mod4)))) # TODO: some gpp,gs_accl, chi, iwue are NaN. Is this expected? + # Hardcoded reference outputs. + # NOTE: this is expected to change reasonably frequently whenerver something is + # changed in the model. + # If this is expected, please update the hardcoded reference values below. + # To do so, simply use the commented code, making use of dput(). Thanks! + # # tibble(mod1) |> slice(c(1, 70, 1200, 1400, 2000, 2180)) |> dput() ref1 <- tibble( date = structure(c(13514, 13583, 14714, 14914, 15515, 15695), class = "Date"), @@ -95,7 +117,8 @@ test_that("run_pmodel_f_bysite()", { le_soil = c(0, 0, 0, 0, 0, 0), dpsi = c(0, 0, 0, 0, 0, 0), psi_leaf = c(0, 0, 0, 0, 0, 0)) - # tibble(mod2) |> slice(c(1, 70, 1200, 1400, 2000, 2180)) |> select(wscal, wcont) |> dput() + + # tibble(mod2) |> slice(c(1, 70, 1200, 1400, 2000, 2180)) |> dput() ref2 <- tibble( date = structure(c(13514, 13583, 14714, 14914, 15515, 15695), class = "Date"), year_dec = c(2007, 2007.189, 2010.285, 2010.833, 2012.478, 2012.97), @@ -152,11 +175,76 @@ test_that("run_pmodel_f_bysite()", { dpsi = c(0, 0, 0, 0, 0, 0), psi_leaf = c(0, 0, 0, 0, 0, 0) ) - expect_equal(slice(tibble(mod1), c(1, 70, 1200, 1400, 2000, 2180)), ref1, tolerance = 1e-6) - expect_equal(slice(tibble(mod2), c(1, 70, 1200, 1400, 2000, 2180)), ref2, tolerance = 1e-6) - expect_equal(slice(tibble(mod3), c(1, 70, 1200, 1400, 2000, 2180)), ref3, tolerance = 1e-6) + # tibble(mod4) |> slice(c(1, 70, 1200, 1400, 2000, 2180)) |> dput() + # ref4 <- tibble( + # date = structure(c(13514, 13583, 14714, 14914, 15515, 15695), class = "Date"), + # year_dec = c(2007, 2007.189, 2010.285, 2010.833, 2012.478, 2012.97), + # fapar = c(0.617119550704956, 0.637238144874573, 0.614814937114716, 0.668549001216888, 0.672287464141846, 0.689359784126282), + # gpp = c(1.4386088848114, 2.53311991691589, 3.77212953567505, 1.01602685451508, 4.42701482772827, 0.843587338924408), + # aet = c(0.100732982158661, 1.24703788757324, 2.51812934875488, 0.303276777267456, 5.29151248931885, -0.298412382602692), + # le = c(249230.78125, 3079814.25, 6214976, 747281.8125, 12863595, -739914.3125), + # pet = c(0.103817254304886, 1.45484209060669, 3.08336925506592, 0.335356384515762, 6.51526165008545, -0.377504140138626), + # vcmax = c(3.49528818333056e-06, 4.44911984232021e-06, 6.87862939230399e-06, 3.0264354791143e-06, 2.33490991377039e-05, 2.45830347012088e-06), + # jmax = c(1.34397805595654e-05, 1.6660707842675e-05, 2.52759164141025e-05, 5.81073845751234e-06, 4.74316075269599e-05, 1.00074266811134e-05), + # vcmax25 = c(1.01545692814398e-05, 1.16090131996316e-05, 1.69845134223578e-05, 7.37736718292581e-06, 2.05224514502333e-05, 8.68135339260334e-06), + # jmax25 = c(2.97042952297488e-05, 3.3337164495606e-05, 4.87200122734066e-05, 1.03667243820382e-05, 4.40687981608789e-05, 2.51640849455725e-05), + # gs_accl = c(0.0953038334846497, 0.0583707764744759, 0.0869309082627296, 0.019712695851922, 0.031399454921484, 0.572548985481262), + # wscal = c(0.642368793487549, 0.852014243602753, 0.966964066028595, 0.419518768787384, 0.55648273229599, 1), + # chi = c(0.96280699968338, 0.893072545528412, 0.89109992980957, 0.870647430419922, 0.654556632041931, 0.996390044689178), + # iwue = c(1.45462236105232e-05, 4.18194395024329e-05, 4.18148301832844e-05, 4.96680477226619e-05, 0.000135864756884985, 1.41982673085295e-06), + # rd = c(0.0438367053866386, 0.058442011475563, 0.0902863815426826, 0.0448922663927078, 0.336273193359375, 0.0310263875871897), + # tsoil = c(8.96044540405273, 11.0349359512329, 11.3815355300903, 15.0929355621338, 19.9768238067627, 9.5408935546875), + # netrad = c(4.16539621353149, 55.9189796447754, 116.783561706543, 12.2599382400513, 192.525726318359, -16.011812210083), + # wcont = c(162.519302368164, 215.559600830078, 244.641906738281, 106.138252258301, 140.790130615234, 253), + # snow = c(0, 0, 0, 0, 0, 0), + # cond = c(0, 0, 0, 0, 0, 0), + # le_canopy = c(171177.328125, 2045358.75, 3888568, 529911.3125, 8744167, -509147.4375), + # le_soil = c(78053.453125, 1034455.5625, 2326407.75, 217370.515625, 4119428, -230766.890625), + # dpsi = c(0.231719702482224, 0.742867052555084, 0.880529344081879, 0.41495206952095, 1.63764226436615, 0.0540594346821308), + # psi_leaf = c(-0.492130398750305, -0.795352935791016, -0.886995494365692, -1.52232336997986, -2.01140165328979, -0.0540594346821308) + # ) + + expect_equal(dplyr::slice(tibble(mod1), c(1, 70, 1200, 1400, 2000, 2180)), ref1, tolerance = 1e-6) # TODO(fabian): It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 + expect_equal(dplyr::slice(tibble(mod2), c(1, 70, 1200, 1400, 2000, 2180)), ref2, tolerance = 1e-6) # TODO(fabian): It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 + expect_equal(dplyr::slice(tibble(mod3), c(1, 70, 1200, 1400, 2000, 2180)), ref3, tolerance = 1e-6) # TODO(fabian): It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 + # expect_equal(dplyr::slice(tibble(mod4), c(1, 70, 1200, 1400, 2000, 2180)), ref4, tolerance = 1e-6) + + + # TODO(fabian): These here will fail: + # It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 + # mod1_2ndTry <- run_pmodel_f_bysite( + # sitename = df_drivers$sitename[1], + # params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = FALSE), + # site_info = df_drivers$site_info[[1]], + # forcing = df_drivers$forcing[[1]], + # forcing_acclim = df_drivers$forcing[[1]], + # params_modl = params_modl, + # makecheck = TRUE + # ) + # mod2_2ndTry <- run_pmodel_f_bysite( + # sitename = df_drivers$sitename[1], + # params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = TRUE), + # site_info = df_drivers$site_info[[1]], + # forcing = df_drivers$forcing[[1]], + # forcing_acclim = df_drivers$forcing[[1]], + # params_modl = params_modl, + # makecheck = TRUE + # ) + # mod3_2ndTry <- run_pmodel_f_bysite( + # sitename = df_drivers$sitename[1], + # params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = TRUE, use_gs = TRUE), + # site_info = df_drivers$site_info[[1]], + # forcing = df_drivers$forcing[[1]], + # forcing_acclim = df_drivers$forcing[[1]], + # params_modl = params_modl, + # makecheck = TRUE + # ) + # expect_equal(dplyr::slice(tibble(mod1_2ndTry), c(1, 70, 1200, 1400, 2000, 2180)), ref1, tolerance = 1e-6) # TODO(fabian): It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 + # expect_equal(dplyr::slice(tibble(mod2_2ndTry), c(1, 70, 1200, 1400, 2000, 2180)), ref2, tolerance = 1e-6) # TODO(fabian): It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 + # expect_equal(dplyr::slice(tibble(mod3_2ndTry), c(1, 70, 1200, 1400, 2000, 2180)), ref3, tolerance = 1e-6) # TODO(fabian): It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 }) + test_that("runread_pmodel_f()", { skip_on_cran() From da95557f2cc5adbfe7ef1b43070f21ad95e24fbc Mon Sep 17 00:00:00 2001 From: marcadella Date: Thu, 3 Oct 2024 09:44:46 +0200 Subject: [PATCH 094/112] Fix non initialized dpsi and psi_leaf variables. --- src/tile_pmodel.mod.f90 | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/tile_pmodel.mod.f90 b/src/tile_pmodel.mod.f90 index 406297d5..53899b65 100644 --- a/src/tile_pmodel.mod.f90 +++ b/src/tile_pmodel.mod.f90 @@ -463,6 +463,8 @@ subroutine initdaily_tile_fluxes( tile_fluxes ) tile_fluxes(:)%plant(npft)%drd = 0.0 tile_fluxes(:)%plant(npft)%dtransp = 0.0 tile_fluxes(:)%plant(npft)%dlatenth = 0.0 + tile_fluxes(:)%plant(npft)%dpsi = 0.0 + tile_fluxes(:)%plant(npft)%psi_leaf = 0.0 end do ! call initdaily_plant( tile_fluxes(:)%plant(:) ) From dff710a5e22228d87fcda094cb93f6bff2e4445c Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Tue, 8 Oct 2024 15:29:03 +0200 Subject: [PATCH 095/112] test: Add memory leak tests for pmodel --- tests/testthat/test-model-runs.R | 203 +++++++++++++++++-------------- 1 file changed, 111 insertions(+), 92 deletions(-) diff --git a/tests/testthat/test-model-runs.R b/tests/testthat/test-model-runs.R index 75f9547b..8e044f11 100644 --- a/tests/testthat/test-model-runs.R +++ b/tests/testthat/test-model-runs.R @@ -15,23 +15,23 @@ test_that("run_pmodel_f_bysite()", { kc_jmax = 0.41, whc = 2000 # site info, water holding capacity in mm ) - # params_modl_phydro <- list( - # kphio = 0.0288, - # kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - # kphio_par_b = 1.0, - # rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - # tau_acclim = 30.0, - # kc_jmax = 0.41, - # phydro_K_plant = 5e-17, - # phydro_p50_plant = -0.46, - # phydro_gamma = 0.065, - # phydro_b_plant = 1, - # phydro_alpha = 0.08, - # bsoil = 3, - # Ssoil = 113, - # whc = 253 - # ) - + params_modl_phydro <- list( + kphio = 0.0288, + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + phydro_K_plant = 5e-17, + phydro_p50_plant = -0.46, + phydro_gamma = 0.065, + phydro_b_plant = 1, + phydro_alpha = 0.08, + bsoil = 3, + Ssoil = 113, + whc = 253 + ) + # read in demo data df_drivers <- rsofun::p_model_drivers_format2024_08 @@ -64,15 +64,53 @@ test_that("run_pmodel_f_bysite()", { params_modl = params_modl, makecheck = TRUE ) - # mod4 <- run_pmodel_f_bysite( - # sitename = df_drivers$sitename[1], - # params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = TRUE, use_pml = TRUE, use_gs = TRUE), - # site_info = df_drivers$site_info[[1]], - # forcing = df_drivers$forcing[[1]], - # forcing_acclim = df_drivers$forcing[[1]], - # params_modl = params_modl_phydro, - # makecheck = TRUE - # ) + mod4 <- run_pmodel_f_bysite( + sitename = df_drivers$sitename[1], + params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = TRUE, use_pml = TRUE, use_gs = TRUE), + site_info = df_drivers$site_info[[1]], + forcing = df_drivers$forcing[[1]], + forcing_acclim = df_drivers$forcing[[1]], + params_modl = params_modl_phydro, + makecheck = TRUE + ) + + # Rerun again to test memory leakage: + mod1_2ndTry <- run_pmodel_f_bysite( + sitename = df_drivers$sitename[1], + params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = FALSE), + site_info = df_drivers$site_info[[1]], + forcing = df_drivers$forcing[[1]], + forcing_acclim = df_drivers$forcing[[1]], + params_modl = params_modl, + makecheck = TRUE + ) + mod2_2ndTry <- run_pmodel_f_bysite( + sitename = df_drivers$sitename[1], + params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = TRUE), + site_info = df_drivers$site_info[[1]], + forcing = df_drivers$forcing[[1]], + forcing_acclim = df_drivers$forcing[[1]], + params_modl = params_modl, + makecheck = TRUE + ) + mod3_2ndTry <- run_pmodel_f_bysite( + sitename = df_drivers$sitename[1], + params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = TRUE, use_gs = TRUE), + site_info = df_drivers$site_info[[1]], + forcing = df_drivers$forcing[[1]], + forcing_acclim = df_drivers$forcing[[1]], + params_modl = params_modl, + makecheck = TRUE + ) + mod4_2ndTry <- run_pmodel_f_bysite( + sitename = df_drivers$sitename[1], + params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = TRUE, use_pml = TRUE, use_gs = TRUE), + site_info = df_drivers$site_info[[1]], + forcing = df_drivers$forcing[[1]], + forcing_acclim = df_drivers$forcing[[1]], + params_modl = params_modl_phydro, + makecheck = TRUE + ) # test if the returned values # are in a list (don't error / warning) @@ -83,13 +121,23 @@ test_that("run_pmodel_f_bysite()", { expect_true(all(!is.na(tibble(mod2)))) expect_true(all(!is.na(tibble(mod3)))) # expect_true(all(!is.na(tibble(mod4)))) # TODO: some gpp,gs_accl, chi, iwue are NaN. Is this expected? + expect_true(all(!is.na(tibble(mod1_2ndTry)))) + expect_true(all(!is.na(tibble(mod2_2ndTry)))) + expect_true(all(!is.na(tibble(mod3_2ndTry)))) + # expect_true(all(!is.na(tibble(mod4_2ndTry)))) # TODO: some gpp,gs_accl, chi, iwue are NaN. Is this expected? + + # Testing memory leakage, i.e. repeatability + expect_equal(tibble(mod1), tibble(mod1_2ndTry), tolerance = 1e-8) + expect_equal(tibble(mod2), tibble(mod2_2ndTry), tolerance = 1e-8) + expect_equal(tibble(mod3), tibble(mod3_2ndTry), tolerance = 1e-8) + expect_equal(tibble(mod4), tibble(mod4_2ndTry), tolerance = 1e-8) + # Hardcoded reference outputs. - # NOTE: this is expected to change reasonably frequently whenerver something is + # NOTE: this is expected to change reasonably frequently whenever something is # changed in the model. # If this is expected, please update the hardcoded reference values below. # To do so, simply use the commented code, making use of dput(). Thanks! - # # tibble(mod1) |> slice(c(1, 70, 1200, 1400, 2000, 2180)) |> dput() ref1 <- tibble( date = structure(c(13514, 13583, 14714, 14914, 15515, 15695), class = "Date"), @@ -177,72 +225,43 @@ test_that("run_pmodel_f_bysite()", { ) # tibble(mod4) |> slice(c(1, 70, 1200, 1400, 2000, 2180)) |> dput() - # ref4 <- tibble( - # date = structure(c(13514, 13583, 14714, 14914, 15515, 15695), class = "Date"), - # year_dec = c(2007, 2007.189, 2010.285, 2010.833, 2012.478, 2012.97), - # fapar = c(0.617119550704956, 0.637238144874573, 0.614814937114716, 0.668549001216888, 0.672287464141846, 0.689359784126282), - # gpp = c(1.4386088848114, 2.53311991691589, 3.77212953567505, 1.01602685451508, 4.42701482772827, 0.843587338924408), - # aet = c(0.100732982158661, 1.24703788757324, 2.51812934875488, 0.303276777267456, 5.29151248931885, -0.298412382602692), - # le = c(249230.78125, 3079814.25, 6214976, 747281.8125, 12863595, -739914.3125), - # pet = c(0.103817254304886, 1.45484209060669, 3.08336925506592, 0.335356384515762, 6.51526165008545, -0.377504140138626), - # vcmax = c(3.49528818333056e-06, 4.44911984232021e-06, 6.87862939230399e-06, 3.0264354791143e-06, 2.33490991377039e-05, 2.45830347012088e-06), - # jmax = c(1.34397805595654e-05, 1.6660707842675e-05, 2.52759164141025e-05, 5.81073845751234e-06, 4.74316075269599e-05, 1.00074266811134e-05), - # vcmax25 = c(1.01545692814398e-05, 1.16090131996316e-05, 1.69845134223578e-05, 7.37736718292581e-06, 2.05224514502333e-05, 8.68135339260334e-06), - # jmax25 = c(2.97042952297488e-05, 3.3337164495606e-05, 4.87200122734066e-05, 1.03667243820382e-05, 4.40687981608789e-05, 2.51640849455725e-05), - # gs_accl = c(0.0953038334846497, 0.0583707764744759, 0.0869309082627296, 0.019712695851922, 0.031399454921484, 0.572548985481262), - # wscal = c(0.642368793487549, 0.852014243602753, 0.966964066028595, 0.419518768787384, 0.55648273229599, 1), - # chi = c(0.96280699968338, 0.893072545528412, 0.89109992980957, 0.870647430419922, 0.654556632041931, 0.996390044689178), - # iwue = c(1.45462236105232e-05, 4.18194395024329e-05, 4.18148301832844e-05, 4.96680477226619e-05, 0.000135864756884985, 1.41982673085295e-06), - # rd = c(0.0438367053866386, 0.058442011475563, 0.0902863815426826, 0.0448922663927078, 0.336273193359375, 0.0310263875871897), - # tsoil = c(8.96044540405273, 11.0349359512329, 11.3815355300903, 15.0929355621338, 19.9768238067627, 9.5408935546875), - # netrad = c(4.16539621353149, 55.9189796447754, 116.783561706543, 12.2599382400513, 192.525726318359, -16.011812210083), - # wcont = c(162.519302368164, 215.559600830078, 244.641906738281, 106.138252258301, 140.790130615234, 253), - # snow = c(0, 0, 0, 0, 0, 0), - # cond = c(0, 0, 0, 0, 0, 0), - # le_canopy = c(171177.328125, 2045358.75, 3888568, 529911.3125, 8744167, -509147.4375), - # le_soil = c(78053.453125, 1034455.5625, 2326407.75, 217370.515625, 4119428, -230766.890625), - # dpsi = c(0.231719702482224, 0.742867052555084, 0.880529344081879, 0.41495206952095, 1.63764226436615, 0.0540594346821308), - # psi_leaf = c(-0.492130398750305, -0.795352935791016, -0.886995494365692, -1.52232336997986, -2.01140165328979, -0.0540594346821308) - # ) + ref4 <- tibble( + date = structure(c(13514, 13583, 14714, 14914, 15515, 15695), class = "Date"), + year_dec = c(2007, 2007.189, 2010.285, 2010.833, 2012.478, 2012.97), + fapar = c(0.617119550704956, 0.637238144874573, 0.614814937114716, 0.668549001216888, 0.672287464141846, 0.689359784126282), + gpp = c(1.4386088848114, 2.53311991691589, 3.77212953567505, 1.01602685451508, 4.42701482772827, 0.843587338924408), + aet = c(0.100732982158661, 1.24703788757324, 2.51812934875488, 0.303276777267456, 5.29151248931885, -0.298412382602692), + le = c(249230.78125, 3079814.25, 6214976, 747281.8125, 12863595, -739914.3125), + pet = c(0.103817254304886, 1.45484209060669, 3.08336925506592, 0.335356384515762, 6.51526165008545, -0.377504140138626), + vcmax = c(3.49528818333056e-06, 4.44911984232021e-06, 6.87862939230399e-06, 3.0264354791143e-06, 2.33490991377039e-05, 2.45830347012088e-06), + jmax = c(1.34397805595654e-05, 1.6660707842675e-05, 2.52759164141025e-05, 5.81073845751234e-06, 4.74316075269599e-05, 1.00074266811134e-05), + vcmax25 = c(1.01545692814398e-05, 1.16090131996316e-05, 1.69845134223578e-05, 7.37736718292581e-06, 2.05224514502333e-05, 8.68135339260334e-06), + jmax25 = c(2.97042952297488e-05, 3.3337164495606e-05, 4.87200122734066e-05, 1.03667243820382e-05, 4.40687981608789e-05, 2.51640849455725e-05), + gs_accl = c(0.0953038334846497, 0.0583707764744759, 0.0869309082627296, 0.019712695851922, 0.031399454921484, 0.572548985481262), + wscal = c(0.642368793487549, 0.852014243602753, 0.966964066028595, 0.419518768787384, 0.55648273229599, 1), + chi = c(0.96280699968338, 0.893072545528412, 0.89109992980957, 0.870647430419922, 0.654556632041931, 0.996390044689178), + iwue = c(1.45462236105232e-05, 4.18194395024329e-05, 4.18148301832844e-05, 4.96680477226619e-05, 0.000135864756884985, 1.41982673085295e-06), + rd = c(0.0438367053866386, 0.058442011475563, 0.0902863815426826, 0.0448922663927078, 0.336273193359375, 0.0310263875871897), + tsoil = c(8.96044540405273, 11.0349359512329, 11.3815355300903, 15.0929355621338, 19.9768238067627, 9.5408935546875), + netrad = c(4.16539621353149, 55.9189796447754, 116.783561706543, 12.2599382400513, 192.525726318359, -16.011812210083), + wcont = c(162.519302368164, 215.559600830078, 244.641906738281, 106.138252258301, 140.790130615234, 253), + snow = c(0, 0, 0, 0, 0, 0), + cond = c(0, 0, 0, 0, 0, 0), + le_canopy = c(171177.328125, 2045358.75, 3888568, 529911.3125, 8744167, -509147.4375), + le_soil = c(78053.453125, 1034455.5625, 2326407.75, 217370.515625, 4119428, -230766.890625), + dpsi = c(0.231719702482224, 0.742867052555084, 0.880529344081879, 0.41495206952095, 1.63764226436615, 0.0540594346821308), + psi_leaf = c(-0.492130398750305, -0.795352935791016, -0.886995494365692, -1.52232336997986, -2.01140165328979, -0.0540594346821308) + ) - expect_equal(dplyr::slice(tibble(mod1), c(1, 70, 1200, 1400, 2000, 2180)), ref1, tolerance = 1e-6) # TODO(fabian): It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 - expect_equal(dplyr::slice(tibble(mod2), c(1, 70, 1200, 1400, 2000, 2180)), ref2, tolerance = 1e-6) # TODO(fabian): It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 - expect_equal(dplyr::slice(tibble(mod3), c(1, 70, 1200, 1400, 2000, 2180)), ref3, tolerance = 1e-6) # TODO(fabian): It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 - # expect_equal(dplyr::slice(tibble(mod4), c(1, 70, 1200, 1400, 2000, 2180)), ref4, tolerance = 1e-6) - + expect_equal(dplyr::slice(tibble(mod1), c(1, 70, 1200, 1400, 2000, 2180)), ref1, tolerance = 1e-6) + expect_equal(dplyr::slice(tibble(mod2), c(1, 70, 1200, 1400, 2000, 2180)), ref2, tolerance = 1e-6) + expect_equal(dplyr::slice(tibble(mod3), c(1, 70, 1200, 1400, 2000, 2180)), ref3, tolerance = 1e-6) + expect_equal(dplyr::slice(tibble(mod4), c(1, 70, 1200, 1400, 2000, 2180)), ref4, tolerance = 1e-6) - # TODO(fabian): These here will fail: - # It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 - # mod1_2ndTry <- run_pmodel_f_bysite( - # sitename = df_drivers$sitename[1], - # params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = FALSE), - # site_info = df_drivers$site_info[[1]], - # forcing = df_drivers$forcing[[1]], - # forcing_acclim = df_drivers$forcing[[1]], - # params_modl = params_modl, - # makecheck = TRUE - # ) - # mod2_2ndTry <- run_pmodel_f_bysite( - # sitename = df_drivers$sitename[1], - # params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = TRUE), - # site_info = df_drivers$site_info[[1]], - # forcing = df_drivers$forcing[[1]], - # forcing_acclim = df_drivers$forcing[[1]], - # params_modl = params_modl, - # makecheck = TRUE - # ) - # mod3_2ndTry <- run_pmodel_f_bysite( - # sitename = df_drivers$sitename[1], - # params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = TRUE, use_gs = TRUE), - # site_info = df_drivers$site_info[[1]], - # forcing = df_drivers$forcing[[1]], - # forcing_acclim = df_drivers$forcing[[1]], - # params_modl = params_modl, - # makecheck = TRUE - # ) - # expect_equal(dplyr::slice(tibble(mod1_2ndTry), c(1, 70, 1200, 1400, 2000, 2180)), ref1, tolerance = 1e-6) # TODO(fabian): It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 - # expect_equal(dplyr::slice(tibble(mod2_2ndTry), c(1, 70, 1200, 1400, 2000, 2180)), ref2, tolerance = 1e-6) # TODO(fabian): It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 - # expect_equal(dplyr::slice(tibble(mod3_2ndTry), c(1, 70, 1200, 1400, 2000, 2180)), ref3, tolerance = 1e-6) # TODO(fabian): It appears that upon first run, dpsi and psi_leaf are all zero. However, if we rerun after running mod4, then they are now 0.3463472 and -0.3463472 + expect_equal(dplyr::slice(tibble(mod1_2ndTry), c(1, 70, 1200, 1400, 2000, 2180)), ref1, tolerance = 1e-6) + expect_equal(dplyr::slice(tibble(mod2_2ndTry), c(1, 70, 1200, 1400, 2000, 2180)), ref2, tolerance = 1e-6) + expect_equal(dplyr::slice(tibble(mod3_2ndTry), c(1, 70, 1200, 1400, 2000, 2180)), ref3, tolerance = 1e-6) + expect_equal(dplyr::slice(tibble(mod4_2ndTry), c(1, 70, 1200, 1400, 2000, 2180)), ref4, tolerance = 1e-6) }) test_that("runread_pmodel_f()", { From 65e8b23945f6a3b96dd1b78f79e22c801a8a99ec Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Wed, 9 Oct 2024 11:56:42 +0200 Subject: [PATCH 096/112] feat: revert: "Parallelize Bayesian calibration of sofun" --- R/calib_sofun.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/calib_sofun.R b/R/calib_sofun.R index 6e34d6c1..ba620806 100644 --- a/R/calib_sofun.R +++ b/R/calib_sofun.R @@ -179,9 +179,9 @@ calib_sofun <- function( drivers = drivers ))}, prior = priors, - names = rownames(pars), - parallel = TRUE, - parallelOptions = list(variables = "all", packages = "all", dlls = NULL), # TODO: this default option might be tweaked + names = rownames(pars)#, + #parallel = TRUE, + #parallelOptions = list(variables = "all", packages = "all", dlls = NULL), # TODO: this default option might be tweaked ) # set bt control parameters From 738dd1dd26c858ef2d7742e28c9c6d625dbc3ab6 Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Thu, 10 Oct 2024 12:34:17 +0200 Subject: [PATCH 097/112] Re-designed WHC: treated as site parameter, not model parameter structurally to enable different values per site --- R/cost_rmse_pmodel.R | 12 ++++++++-- R/run_pmodel_f_bysite.R | 32 +++++++++++++++++--------- src/interface_biosphere_pmodel.mod.f90 | 2 +- src/sofun_r.f90 | 30 ++++++++++++------------ src/tile_pmodel.mod.f90 | 3 +-- src/wrappersc.c | 5 +++- tests/testthat/test-model-runs.R | 16 ++++++------- vignettes/pmodel_use.Rmd | 6 ++--- vignettes/pmodel_use_newdata.Rmd | 10 ++------ 9 files changed, 64 insertions(+), 52 deletions(-) diff --git a/R/cost_rmse_pmodel.R b/R/cost_rmse_pmodel.R index 1df05d08..d6ccbbfc 100644 --- a/R/cost_rmse_pmodel.R +++ b/R/cost_rmse_pmodel.R @@ -96,8 +96,17 @@ cost_rmse_pmodel <- function( required_param_names <- rsofun:::required_param_names$phydro_model } + ## if WHC is treated as calibratable, remove it from par and overwrite site + ## info with the same value for (calibrated) WHC for all sites. + if ("whc" %in% names(par)){ + overwrite_whc <- par[["whc"]] + par <- par[ ! names(par) %in% c("whc") ] + lapply(drivers$site_info, function(x) within(x, whc <- overwrite_whc)) + } + ## split calibrated parameters into model and error parameters - par_calibrated_model <- par[ ! names(par) %in% c("err_gpp") ] # consider only model parameters for the check + par_calibrated_model <- par[ ! names(par) %in% c("err_gpp") ] # consider only model parameters for the check + # par_calibrated_errormodel <- par[ names(par) %in% c("err_gpp") ] # par_fixed @@ -117,7 +126,6 @@ cost_rmse_pmodel <- function( # This basically uses all params except those of the error model of the observations params_modl <- c(par, par_fixed)[required_param_names] - ## run the model df <- runread_pmodel_f( drivers, diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 5863a9a0..3a4eefad 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -413,7 +413,6 @@ run_pmodel_f_bysite <- function( # TODO: Above docstring appears duplicated in r ifelse(params_siml$use_phydro, no = as.numeric(params_modl$soilm_thetastar), yes = dummy_val), - dummy_val, # formerly soilm_betao #TODO: replace this position with whc ifelse(params_siml$use_phydro, no = as.numeric(params_modl$beta_unitcostratio), yes = dummy_val), @@ -440,8 +439,7 @@ run_pmodel_f_bysite <- function( # TODO: Above docstring appears duplicated in r yes = params_modl$bsoil), ifelse(params_siml$use_phydro, no = dummy_val, - yes = params_modl$Ssoil), - as.numeric(params_modl$whc) #TODO: move whc to former position of soilm_betao + yes = params_modl$Ssoil) ) ## C wrapper call @@ -472,6 +470,7 @@ run_pmodel_f_bysite <- function( # TODO: Above docstring appears duplicated in r longitude = as.numeric(site_info$lon), latitude = as.numeric(site_info$lat), altitude = as.numeric(site_info$elv), + whc = as.numeric(site_info$whc), canopy_height = as.numeric(site_info$canopy_height), reference_height = as.numeric(site_info$reference_height), n = n, @@ -556,19 +555,30 @@ run_pmodel_f_bysite <- function( # TODO: Above docstring appears duplicated in r # For internal use and checks. (NOTE we could add a docstring similar to `p_model_validation`, but it is currently not needed.) required_param_names <- list( phydro_model = c( # P-hydro model needs these parameters: - 'bsoil', 'kc_jmax', - 'kphio', 'kphio_par_a', 'kphio_par_b', - 'phydro_alpha', 'phydro_b_plant', 'phydro_gamma', - 'phydro_K_plant', 'phydro_p50_plant', + 'bsoil', + 'kc_jmax', + 'kphio', + 'kphio_par_a', + 'kphio_par_b', + 'phydro_alpha', + 'phydro_b_plant', + 'phydro_gamma', + 'phydro_K_plant', + 'phydro_p50_plant', 'rd_to_vcmax', 'Ssoil', - 'tau_acclim', 'whc'), + 'tau_acclim' + ), p_model = c(# P-model needs these parameters: - 'beta_unitcostratio', 'kc_jmax', - 'kphio', 'kphio_par_a', 'kphio_par_b', + 'beta_unitcostratio', + 'kc_jmax', + 'kphio', + 'kphio_par_a', + 'kphio_par_b', 'rd_to_vcmax', 'soilm_thetastar', - 'tau_acclim', 'whc'), + 'tau_acclim' + ), biomee_model = c(# Biomee-model needs these parameters: 'TODO') ) diff --git a/src/interface_biosphere_pmodel.mod.f90 b/src/interface_biosphere_pmodel.mod.f90 index 6d02e221..1a5de6bb 100644 --- a/src/interface_biosphere_pmodel.mod.f90 +++ b/src/interface_biosphere_pmodel.mod.f90 @@ -32,7 +32,6 @@ module md_interface_pmodel real :: phydro_gamma real :: bsoil real :: Ssoil - real :: whc end type paramstype_calib @@ -40,6 +39,7 @@ module md_interface_pmodel integer :: year real :: pco2 type(gridtype) :: grid + real :: whc_prescr real :: canopy_height real :: reference_height type(climate_type), dimension(ndayyear) :: climate diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index e65bc33b..0034af80 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -34,6 +34,7 @@ subroutine pmodel_f( & longitude, & latitude, & altitude, & + whc, & canopy_height, & reference_height, & nt, & @@ -79,10 +80,11 @@ subroutine pmodel_f( & real(kind=c_double), intent(in) :: longitude real(kind=c_double), intent(in) :: latitude real(kind=c_double), intent(in) :: altitude + real(kind=c_double), intent(in) :: whc real(kind=c_double), intent(in) :: canopy_height real(kind=c_double), intent(in) :: reference_height integer(kind=c_int), intent(in) :: nt ! number of time steps - real(kind=c_double), dimension(17), intent(in) :: par ! free (calibratable) model parameters + real(kind=c_double), dimension(15), intent(in) :: par ! free (calibratable) model parameters real(kind=c_double), dimension(nt,12), intent(in) :: forcing ! array containing all temporally varying forcing data for instantaneous model (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) real(kind=c_double), dimension(nt,12), intent(in) :: forcing_acclim ! array containing all temporally varying forcing data for acclimating model (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) real(kind=c_double), dimension(nt,23), intent(out) :: output @@ -145,7 +147,7 @@ subroutine pmodel_f( & !---------------------------------------------------------------- ! GET SOIL PARAMETERS !---------------------------------------------------------------- - !myinterface%whc_prescr = real( whc ) + myinterface%whc_prescr = real( whc ) !---------------------------------------------------------------- ! Other site-specific PARAMETERS @@ -160,19 +162,17 @@ subroutine pmodel_f( & myinterface%params_calib%kphio_par_a = real(par(2)) myinterface%params_calib%kphio_par_b = real(par(3)) myinterface%params_calib%soilm_thetastar = real(par(4)) - ! # TODO move whc to position nr 5 - myinterface%params_calib%beta_unitcostratio = real(par(6)) - myinterface%params_calib%rd_to_vcmax = real(par(7)) - myinterface%params_calib%tau_acclim = real(par(8)) - myinterface%params_calib%kc_jmax = real(par(9)) - myinterface%params_calib%phydro_K_plant = real(par(10)) - myinterface%params_calib%phydro_p50_plant = real(par(11)) - myinterface%params_calib%phydro_b_plant = real(par(12)) - myinterface%params_calib%phydro_alpha = real(par(13)) - myinterface%params_calib%phydro_gamma = real(par(14)) - myinterface%params_calib%bsoil = real(par(15)) - myinterface%params_calib%Ssoil = real(par(16)) - myinterface%params_calib%whc = real(par(17)) + myinterface%params_calib%beta_unitcostratio = real(par(5)) + myinterface%params_calib%rd_to_vcmax = real(par(6)) + myinterface%params_calib%tau_acclim = real(par(7)) + myinterface%params_calib%kc_jmax = real(par(8)) + myinterface%params_calib%phydro_K_plant = real(par(9)) + myinterface%params_calib%phydro_p50_plant = real(par(10)) + myinterface%params_calib%phydro_b_plant = real(par(11)) + myinterface%params_calib%phydro_alpha = real(par(12)) + myinterface%params_calib%phydro_gamma = real(par(13)) + myinterface%params_calib%bsoil = real(par(14)) + myinterface%params_calib%Ssoil = real(par(15)) !---------------------------------------------------------------- ! GET VEGETATION COVER (fractional projective cover by PFT) diff --git a/src/tile_pmodel.mod.f90 b/src/tile_pmodel.mod.f90 index 53899b65..093974a8 100644 --- a/src/tile_pmodel.mod.f90 +++ b/src/tile_pmodel.mod.f90 @@ -419,8 +419,7 @@ subroutine initglobal_soil_params( params ) params%thdiff_fc = 0.4 ! overwrite - !params%whc = myinterface%whc_prescr - params%whc = myinterface%params_calib%whc + params%whc = myinterface%whc_prescr ! end do diff --git a/src/wrappersc.c b/src/wrappersc.c index 227b1987..622e46e8 100644 --- a/src/wrappersc.c +++ b/src/wrappersc.c @@ -32,6 +32,7 @@ void F77_NAME(pmodel_f)( double *longitude, double *latitude, double *altitude, + double *whc, double *canopy_height, double *reference_height, int *nt, @@ -65,6 +66,7 @@ extern SEXP pmodel_f_C( SEXP longitude, SEXP latitude, SEXP altitude, + SEXP whc, SEXP canopy_height, SEXP reference_height, SEXP n, @@ -104,6 +106,7 @@ extern SEXP pmodel_f_C( REAL(longitude), REAL(latitude), REAL(altitude), + REAL(whc), REAL(canopy_height), REAL(reference_height), INTEGER(n), @@ -557,7 +560,7 @@ extern SEXP biomee_f_C( // Declarations for all functions ///////////////////////////////////////////////////////////// static const R_CallMethodDef CallEntries[] = { - {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 28}, // Specify number of arguments to C wrapper as the last number here + {"pmodel_f_C", (DL_FUNC) &pmodel_f_C, 29}, // Specify number of arguments to C wrapper as the last number here {"biomee_f_C", (DL_FUNC) &biomee_f_C, 48}, // Number of arguments of the C wrapper function for biomee (the SEXP variables, not the output) {NULL, NULL, 0} }; diff --git a/tests/testthat/test-model-runs.R b/tests/testthat/test-model-runs.R index 8e044f11..df8b6108 100644 --- a/tests/testthat/test-model-runs.R +++ b/tests/testthat/test-model-runs.R @@ -12,8 +12,7 @@ test_that("run_pmodel_f_bysite()", { beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41, - whc = 2000 # site info, water holding capacity in mm + kc_jmax = 0.41 ) params_modl_phydro <- list( kphio = 0.0288, @@ -28,12 +27,12 @@ test_that("run_pmodel_f_bysite()", { phydro_b_plant = 1, phydro_alpha = 0.08, bsoil = 3, - Ssoil = 113, - whc = 253 + Ssoil = 113 ) # read in demo data df_drivers <- rsofun::p_model_drivers_format2024_08 + lapply(df_drivers$site_info, function(x) within(x, whc <- 2000)) # check run_pmodel_f_bysite() ########################## # run the SOFUN Fortran P-model using the internal function `run_pmodel_f_bysite` @@ -276,8 +275,7 @@ test_that("runread_pmodel_f()", { beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41, - whc = 2000 # site info, water holding capacity in mm + kc_jmax = 0.41 ) # read in demo data @@ -404,19 +402,19 @@ test_that("phydro-model run check LE and AET", { drivers = df_drivers |> tidyr::unnest(site_info) |> mutate(whc = 432) |> tidyr::nest(site_info = !c(sitename, params_siml, starts_with("forcing"))), - par = purrr::assign_in(params_modl, "whc", 432) + par = params_modl ) |> mutate(sitename = paste0(sitename, "_432mm")), rsofun::runread_pmodel_f( drivers = df_drivers |> tidyr::unnest(site_info) |> mutate(whc = 5) |> tidyr::nest(site_info = !c(sitename, params_siml, starts_with("forcing"))), - par = purrr::assign_in(params_modl, "whc", 5) + par = params_modl ) |> mutate(sitename = paste0(sitename, "_5mm")), rsofun::runread_pmodel_f( drivers = df_drivers |> tidyr::unnest(site_info) |> mutate(whc = 5000) |> tidyr::nest(site_info = !c(sitename, params_siml, starts_with("forcing"))), - par = purrr::assign_in(params_modl, "whc", 5000) + par = params_modl ) |> mutate(sitename = paste0(sitename, "_5000mm")) ) diff --git a/vignettes/pmodel_use.Rmd b/vignettes/pmodel_use.Rmd index 4799f446..e7dbd353 100644 --- a/vignettes/pmodel_use.Rmd +++ b/vignettes/pmodel_use.Rmd @@ -106,8 +106,7 @@ params_modl <- list( beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41, - whc = p_model_drivers$site_info[[1]]$whc + kc_jmax = 0.41 ) # run the model for these parameters @@ -189,6 +188,7 @@ settings <- list( kphio_par_a = list(lower = -0.005, upper = 0, init = -0.0025), kphio_par_b = list(lower = 10, upper = 30, init = 20), soilm_thetastar = list(lower = 10, upper = 200, init = 100), + # WHC is ambiguous variable: provided as site info, but used as calibration. That's intentional whc = list(lower = 10, upper = 1000, init = 400) ) ) @@ -200,7 +200,7 @@ In addition starting values and ranges are provided for the free parameters in t With all settings defined the optimization function `calib_sofun()` can be called with driver data and observations specified. Extra arguments for the cost function (like what variable should be used as target to compute the root mean squared error (RMSE) and previous values for the parameters that aren't calibrated, which are needed to run the P-model). -```{r eval=FALSE} +```{r eval=FALSE, warning=FALSE} # calibrate the model and optimize free parameters pars <- calib_sofun( drivers = p_model_drivers, diff --git a/vignettes/pmodel_use_newdata.Rmd b/vignettes/pmodel_use_newdata.Rmd index d4d1e107..21cb62ce 100644 --- a/vignettes/pmodel_use_newdata.Rmd +++ b/vignettes/pmodel_use_newdata.Rmd @@ -260,10 +260,6 @@ With all data prepared we can run the P-model using `runread_pmodel_f()`. This f ```{r} # define model parameter values from previous work -# ------------------------------------------------------ -# Note that in the phydro branch of rsofun, -# whc must be included in params_modl, rather than in site_info -# ------------------------------------------------------ params_modl <- list( kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio @@ -272,8 +268,7 @@ params_modl <- list( beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41, - whc = rsofun::p_model_drivers_format2024_08$site_info[[1]]$whc + kc_jmax = 0.41 ) # run the model for these parameters @@ -633,8 +628,7 @@ params_modl <- list( phydro_b_plant = 1, phydro_alpha = 0.08, bsoil = 3, - Ssoil = 113, - whc = 253 + Ssoil = 113 ) # run the model for these parameters From bfd458f81072c86f508e933d5cab9ab2350c36ee Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Thu, 10 Oct 2024 15:20:57 +0200 Subject: [PATCH 098/112] tests: Update tests for new WHC as site parameter --- tests/testthat/test-calibration-pmodel.R | 19 +++++---------- tests/testthat/test-model-runs.R | 24 +++++++++---------- tests/testthat/test-quantitative-validation.R | 16 ++++++++----- 3 files changed, 28 insertions(+), 31 deletions(-) diff --git a/tests/testthat/test-calibration-pmodel.R b/tests/testthat/test-calibration-pmodel.R index c9efddb7..f5b9306d 100644 --- a/tests/testthat/test-calibration-pmodel.R +++ b/tests/testthat/test-calibration-pmodel.R @@ -6,7 +6,6 @@ test_that("test GPP calibration routine p-model (BT, likelihood maximization)", drivers <- rsofun::p_model_drivers_format2024_08 # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) drivers$params_siml[[1]]$use_gs <- TRUE - obs <- rsofun::p_model_validation params_fix <- list( # kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD @@ -16,8 +15,7 @@ test_that("test GPP calibration routine p-model (BT, likelihood maximization)", beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41, - whc = 2000 # site info, water holding capacity in mm + kc_jmax = 0.41 ) settings <- list( @@ -46,8 +44,7 @@ test_that("test GPP calibration routine p-model (BT, likelihood maximization)", # extra arguments for the cost function par_fixed = params_fix, targets = c('gpp'), - parallel = TRUE, - ncores = 2 + parallel = FALSE#TRUE,ncores = 2 ) # plot(pars$mod) # print(pars$mod) @@ -76,7 +73,6 @@ test_that("test GPP calibration routine p-model (GenSA, rmse, all params)", { kphio_par_a = list(lower = 0, upper = 1, init = 0.2), kphio_par_b = list(lower = 10, upper = 40, init =25), soilm_thetastar = list(lower = 0, upper = 3000, init = 0.6*240), - # TODO: should we replace fitting sample_par$soilm_betao with sample_par$whc? beta_unitcostratio = list(lower = 50, upper = 200, init = 146), rd_to_vcmax = list(lower = 0.01, upper = 0.1, init = 0.014), tau_acclim = list(lower = 7, upper = 60, init = 30), @@ -90,7 +86,7 @@ test_that("test GPP calibration routine p-model (GenSA, rmse, all params)", { settings = settings, optim_out = FALSE, # extra arguments for the cost function - par_fixed = list(whc= 2000), # site info, water holding capacity in mm + par_fixed = list(), targets = 'gpp' ) @@ -147,7 +143,7 @@ test_that("test Vcmax25 calibration routine p-model (BT, likelihood, all params) settings = settings, optim_out = FALSE, # arguments for cost function - par_fixed = list(whc= 2000), # site info, water holding capacity in mm + par_fixed = list(), targets = 'vcmax25' ) # plot(pars$mod) @@ -183,8 +179,7 @@ test_that("test Vcmax25 calibration routine p-model (GenSA, rmse)", { beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous # tau_acclim = 30.0, - kc_jmax = 0.41, - whc = 2000 # site info, water holding capacity in mm + kc_jmax = 0.41 ) settings <- list( @@ -245,9 +240,7 @@ test_that("test joint calibration routine p-model (BT, likelihood maximization)" beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41, - whc = 2000 # site info, water holding capacity in mm - # TODO: since whc is a model parameter we need to provide it. However currently there is no way to vary it for the different sites. + kc_jmax = 0.41 ) settings <- list( diff --git a/tests/testthat/test-model-runs.R b/tests/testthat/test-model-runs.R index df8b6108..32e95bc3 100644 --- a/tests/testthat/test-model-runs.R +++ b/tests/testthat/test-model-runs.R @@ -39,7 +39,7 @@ test_that("run_pmodel_f_bysite()", { mod1 <- run_pmodel_f_bysite( sitename = df_drivers$sitename[1], params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = FALSE), - site_info = df_drivers$site_info[[1]], + site_info = mutate(df_drivers$site_info[[1]], whc = 2000), forcing = df_drivers$forcing[[1]], forcing_acclim = df_drivers$forcing[[1]], params_modl = params_modl, @@ -48,7 +48,7 @@ test_that("run_pmodel_f_bysite()", { mod2 <- run_pmodel_f_bysite( sitename = df_drivers$sitename[1], params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = TRUE), - site_info = df_drivers$site_info[[1]], + site_info = mutate(df_drivers$site_info[[1]], whc = 2000), forcing = df_drivers$forcing[[1]], forcing_acclim = df_drivers$forcing[[1]], params_modl = params_modl, @@ -57,7 +57,7 @@ test_that("run_pmodel_f_bysite()", { mod3 <- run_pmodel_f_bysite( sitename = df_drivers$sitename[1], params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = TRUE, use_gs = TRUE), - site_info = df_drivers$site_info[[1]], + site_info = mutate(df_drivers$site_info[[1]], whc = 2000), forcing = df_drivers$forcing[[1]], forcing_acclim = df_drivers$forcing[[1]], params_modl = params_modl, @@ -66,7 +66,7 @@ test_that("run_pmodel_f_bysite()", { mod4 <- run_pmodel_f_bysite( sitename = df_drivers$sitename[1], params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = TRUE, use_pml = TRUE, use_gs = TRUE), - site_info = df_drivers$site_info[[1]], + site_info = mutate(df_drivers$site_info[[1]], whc = 253), forcing = df_drivers$forcing[[1]], forcing_acclim = df_drivers$forcing[[1]], params_modl = params_modl_phydro, @@ -77,7 +77,7 @@ test_that("run_pmodel_f_bysite()", { mod1_2ndTry <- run_pmodel_f_bysite( sitename = df_drivers$sitename[1], params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = FALSE), - site_info = df_drivers$site_info[[1]], + site_info = mutate(df_drivers$site_info[[1]], whc = 2000), forcing = df_drivers$forcing[[1]], forcing_acclim = df_drivers$forcing[[1]], params_modl = params_modl, @@ -86,7 +86,7 @@ test_that("run_pmodel_f_bysite()", { mod2_2ndTry <- run_pmodel_f_bysite( sitename = df_drivers$sitename[1], params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = TRUE), - site_info = df_drivers$site_info[[1]], + site_info = mutate(df_drivers$site_info[[1]], whc = 2000), forcing = df_drivers$forcing[[1]], forcing_acclim = df_drivers$forcing[[1]], params_modl = params_modl, @@ -95,7 +95,7 @@ test_that("run_pmodel_f_bysite()", { mod3_2ndTry <- run_pmodel_f_bysite( sitename = df_drivers$sitename[1], params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = TRUE, use_gs = TRUE), - site_info = df_drivers$site_info[[1]], + site_info = mutate(df_drivers$site_info[[1]], whc = 2000), forcing = df_drivers$forcing[[1]], forcing_acclim = df_drivers$forcing[[1]], params_modl = params_modl, @@ -104,7 +104,7 @@ test_that("run_pmodel_f_bysite()", { mod4_2ndTry <- run_pmodel_f_bysite( sitename = df_drivers$sitename[1], params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = TRUE, use_pml = TRUE, use_gs = TRUE), - site_info = df_drivers$site_info[[1]], + site_info = mutate(df_drivers$site_info[[1]], whc = 253), forcing = df_drivers$forcing[[1]], forcing_acclim = df_drivers$forcing[[1]], params_modl = params_modl_phydro, @@ -126,10 +126,10 @@ test_that("run_pmodel_f_bysite()", { # expect_true(all(!is.na(tibble(mod4_2ndTry)))) # TODO: some gpp,gs_accl, chi, iwue are NaN. Is this expected? # Testing memory leakage, i.e. repeatability - expect_equal(tibble(mod1), tibble(mod1_2ndTry), tolerance = 1e-8) - expect_equal(tibble(mod2), tibble(mod2_2ndTry), tolerance = 1e-8) - expect_equal(tibble(mod3), tibble(mod3_2ndTry), tolerance = 1e-8) - expect_equal(tibble(mod4), tibble(mod4_2ndTry), tolerance = 1e-8) + expect_equal(tibble(mod1), tibble(mod1_2ndTry), tolerance = 1e-6) + expect_equal(tibble(mod2), tibble(mod2_2ndTry), tolerance = 1e-6) + expect_equal(tibble(mod3), tibble(mod3_2ndTry), tolerance = 1e-6) + expect_equal(tibble(mod4), tibble(mod4_2ndTry), tolerance = 1e-6) # Hardcoded reference outputs. diff --git a/tests/testthat/test-quantitative-validation.R b/tests/testthat/test-quantitative-validation.R index 76e56edc..c5908130 100644 --- a/tests/testthat/test-quantitative-validation.R +++ b/tests/testthat/test-quantitative-validation.R @@ -17,12 +17,14 @@ test_that("p-model quantitative check versus observations (FR-Pue)", { beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41, - whc = 2000 # site info, water holding capacity in mm: TODO: does this make sense with soilm_thetastar in mm units? + kc_jmax = 0.41 ) df_drivers <- rsofun::p_model_drivers_format2024_08 # TODO: NOT YET UPDATED FOR PHYDRO (still add default phydro_* parameters) - + df_drivers <- df_drivers # |> + # formerly we corrected to 2000mm: tidyr::unnest(site_info) |> mutate(whc = 2000) |> + # formerly we corrected to 2000mm: tidyr::nest(site_info = !c(sitename, params_siml, starts_with("forcing"))) + # run the model for these parameters res <- rsofun::runread_pmodel_f( drivers = df_drivers, @@ -32,17 +34,19 @@ test_that("p-model quantitative check versus observations (FR-Pue)", { # ggplot(data = tibble(res$data[[1]]), mapping = aes(x = date, y = gpp)) + # geom_line() + - # geom_point(data = tibble(p_model_validation$data[[1]]), + # geom_point(data = tibble(p_model_validation$data[[1]]), # mapping = aes(color = "observation")) + theme_classic() # - + # normal tolerance ~ 0.305 tolerance <- mean(abs(output - gpp), na.rm = TRUE)/ mean(abs(gpp), na.rm = TRUE) # test for correctly returned values # expect_equal(tolerance, 0.4201191, tolerance = 0.04) # before PHYDRO - expect_equal(tolerance, 0.4863206, tolerance = 0.04) + # expect_equal(tolerance, 0.4863206, tolerance = 0.04) # with PHYDRO and 2000mm + expect_equal(tolerance, 0.3438646, tolerance = 0.04) # with PHYDRO and best estimate of 432mm + }) # test_that("p-model consistency R vs Fortran (rpmodel vs rsofun)", { From 8e1f281e812d1997758b9f2f7ee777c872c2dcdc Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Thu, 10 Oct 2024 15:37:27 +0200 Subject: [PATCH 099/112] tests: Use FR-Pue WHC in test-model-runs.R --- tests/testthat/test-model-runs.R | 151 ++++++++++++++++--------------- 1 file changed, 76 insertions(+), 75 deletions(-) diff --git a/tests/testthat/test-model-runs.R b/tests/testthat/test-model-runs.R index 32e95bc3..810bea25 100644 --- a/tests/testthat/test-model-runs.R +++ b/tests/testthat/test-model-runs.R @@ -8,31 +8,32 @@ test_that("run_pmodel_f_bysite()", { kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD kphio_par_a = 0.01, # set to zero to disable temperature-dependence of kphio, setup ORG in Stocker et al. 2020 GMD kphio_par_b = 1.0, - soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress - beta_unitcostratio = 146.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, - kc_jmax = 0.41 + kc_jmax = 0.41, + + soilm_thetastar = 0.6 * 240, # to recover old setup with soil moisture stress + beta_unitcostratio = 146.0 ) params_modl_phydro <- list( - kphio = 0.0288, - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio = 0.04998, + kphio_par_a = 0.01, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous tau_acclim = 30.0, kc_jmax = 0.41, - phydro_K_plant = 5e-17, - phydro_p50_plant = -0.46, - phydro_gamma = 0.065, - phydro_b_plant = 1, - phydro_alpha = 0.08, - bsoil = 3, - Ssoil = 113 + + phydro_K_plant = 5e-17, # TODO: add documentaiton: Phydro: Plant conductivity + phydro_p50_plant = -0.46, # TODO: add documentaiton: Phydro: Plant P50 + phydro_b_plant = 1, # TODO: add documentaiton: Phydro: shape parameter of vulnerability curve + phydro_alpha = 0.08, # TODO: add documentaiton: Phydro: Cost of Jmax + phydro_gamma = 0.065, # TODO: add documentaiton: Phydro: Cost of hydraulics + bsoil = 3, # TODO: add documentaiton: Phydro: parameter converting RZWSC to predawn water potential (depends on rooting system hence PFT specific) + Ssoil = 113 # TODO: add documentaiton: Phydro: parameter converting RZWSC to predawn water potential (depends on rooting system hence PFT specific) ) # read in demo data df_drivers <- rsofun::p_model_drivers_format2024_08 - lapply(df_drivers$site_info, function(x) within(x, whc <- 2000)) # check run_pmodel_f_bysite() ########################## # run the SOFUN Fortran P-model using the internal function `run_pmodel_f_bysite` @@ -73,46 +74,47 @@ test_that("run_pmodel_f_bysite()", { makecheck = TRUE ) - # Rerun again to test memory leakage: - mod1_2ndTry <- run_pmodel_f_bysite( + # Rerun again (inverse order) to test memory leakage: + mod4_2ndTry <- run_pmodel_f_bysite( sitename = df_drivers$sitename[1], - params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = FALSE), - site_info = mutate(df_drivers$site_info[[1]], whc = 2000), + params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = TRUE, use_pml = TRUE, use_gs = TRUE), + site_info = mutate(df_drivers$site_info[[1]], whc = 253), forcing = df_drivers$forcing[[1]], forcing_acclim = df_drivers$forcing[[1]], - params_modl = params_modl, + params_modl = params_modl_phydro, makecheck = TRUE ) - mod2_2ndTry <- run_pmodel_f_bysite( + mod3_2ndTry <- run_pmodel_f_bysite( sitename = df_drivers$sitename[1], - params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = TRUE), + params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = TRUE, use_gs = TRUE), site_info = mutate(df_drivers$site_info[[1]], whc = 2000), forcing = df_drivers$forcing[[1]], forcing_acclim = df_drivers$forcing[[1]], params_modl = params_modl, makecheck = TRUE ) - mod3_2ndTry <- run_pmodel_f_bysite( + mod2_2ndTry <- run_pmodel_f_bysite( sitename = df_drivers$sitename[1], - params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = TRUE, use_gs = TRUE), + params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = TRUE), site_info = mutate(df_drivers$site_info[[1]], whc = 2000), forcing = df_drivers$forcing[[1]], forcing_acclim = df_drivers$forcing[[1]], params_modl = params_modl, makecheck = TRUE ) - mod4_2ndTry <- run_pmodel_f_bysite( + mod1_2ndTry <- run_pmodel_f_bysite( sitename = df_drivers$sitename[1], - params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = TRUE, use_pml = TRUE, use_gs = TRUE), - site_info = mutate(df_drivers$site_info[[1]], whc = 253), + params_siml = dplyr::mutate(df_drivers$params_siml[[1]], use_phydro = FALSE, use_pml = FALSE, use_gs = FALSE), + site_info = mutate(df_drivers$site_info[[1]], whc = 2000), forcing = df_drivers$forcing[[1]], forcing_acclim = df_drivers$forcing[[1]], - params_modl = params_modl_phydro, + params_modl = params_modl, makecheck = TRUE ) + - # test if the returned values - # are in a list (don't error / warning) + + # Testing if the returned values are in a list (don't error / warning) expect_type(mod1, "list") expect_s3_class(mod1, "data.frame") @@ -172,26 +174,25 @@ test_that("run_pmodel_f_bysite()", { fapar = c(0.617119550704956, 0.637238144874573, 0.614814937114716, 0.668549001216888, 0.672287464141846, 0.689359784126282), gpp = c(1.65618813037872, 6.02679443359375, 6.72385692596436, 1.84405922889709, 9.40890026092529, 0.896598398685455), aet = c(0.0831360220909119, 1.46265971660614, 1.86834669113159, 0.240892946720123, 5.62481117248535, -0.0911358147859573), - le = c(205692.84375, 3612336.25, 4611252, 593566.4375, 13673840, -225971.515625), - pet = c(0.103817254304886, 1.45484209060669, 3.08336925506592, 0.335356384515762, 6.51526165008545, -0.377504140138626), - vcmax = c(9.54577535594581e-06, 1.18804200610612e-05, 1.91590133908903e-05, 1.37124443426728e-05, 5.95575438637752e-05, 5.93948425375856e-06), - jmax = c(3.08439557556994e-05, 3.66509229934309e-05, 5.80160958634224e-05, 3.691642996273e-05, 0.000109297579911072, 2.01222374016652e-05), - vcmax25 = c(3.51696653524414e-05, 3.77493124688044e-05, 5.76805359742139e-05, 3.59945297532249e-05, 5.17318439960945e-05, 2.60039796557976e-05), - jmax25 = c(7.8623415902257e-05, 8.34658203530125e-05, 0.000126882849144749, 7.2937982622534e-05, 9.94235306279734e-05, 5.87244903726969e-05), - gs_accl = c(0.0016942253569141, 0.00173744710627943, 0.00175592955201864, 0.00170137174427509, 0.00156046624761075, 0.00193470600061119), - # wscal = c(0.10409427434206, 0.129727497696877, 0.587451696395874, 0.497713387012482, 0.780628979206085, 0.86269211769104), - wscal = c(0.104094229638577, 0.129727452993393, 0.587451696395874, 0.497713387012482, 0.780628979206085, 0.86269211769104), - chi = c(0.629108071327209, 0.642833471298218, 0.639411568641663, 0.66897189617157, 0.674327433109283, 0.673606336116791), - iwue = c(9.066015627468e-05, 8.73051467351615e-05, 8.65905749378726e-05, 7.94415027485229e-05, 8.00566194811836e-05, 8.02328504505567e-05), - rd = c(0.0936944633722305, 0.121099025011063, 0.188513651490211, 0.146433308720589, 0.569870233535767, 0.0640662834048271), - tsoil = c(9.0220832824707, 11.0288057327271, 11.3866157531738, 15.0849714279175, 19.9336929321289, 9.59065818786621), - netrad = c(4.16539621353149, 55.9189796447754, 116.783561706543, 12.2599382400513, 192.525726318359, -16.011812210083), - wcont = c(208.188461303711, 259.454895019531, 1174.90344238281, 995.4267578125, 1561.25793457031, 1725.38427734375), + le = c(205692.84375, 3612336.25, 4611252, 593566.4375, 13673840, -225971.515625), + pet = c(0.103817254304886, 1.45484209060669, 3.08336925506592, 0.335356384515762, 6.51526165008545, -0.377504140138626), + vcmax = c(9.54577535594581e-06, 1.18804200610612e-05, 1.91590133908903e-05, 1.37124443426728e-05, 5.95575438637752e-05, 5.93948425375856e-06 ), + jmax = c(3.08439557556994e-05, 3.66509229934309e-05, 5.80160958634224e-05, 3.691642996273e-05, 0.000109297579911072, 2.01222374016652e-05 ), + vcmax25 = c(3.51696653524414e-05, 3.77493124688044e-05, 5.76805359742139e-05, 3.59945297532249e-05, 5.17318439960945e-05, 2.60039796557976e-05), + jmax25 = c(7.8623415902257e-05, 8.34658203530125e-05, 0.000126882849144749, 7.2937982622534e-05, 9.94235306279734e-05, 5.87244903726969e-05), + gs_accl = c(0.0016942253569141, 0.00173744710627943, 0.00175592955201864, 0.00170137174427509, 0.00156046624761075, 0.00193470600061119), + wscal = c(0.104094244539738, 0.129727467894554, 0.587451696395874, 0.497713387012482, 0.780628979206085, 0.86269211769104), + chi = c(0.629108071327209, 0.642833471298218, 0.639411568641663, 0.66897189617157, 0.674327433109283, 0.673606336116791 ), + iwue = c(9.066015627468e-05, 8.73051467351615e-05, 8.65905749378726e-05, 7.94415027485229e-05, 8.00566194811836e-05, 8.02328504505567e-05 ), + rd = c(0.0936944633722305, 0.121099025011063, 0.188513651490211, 0.146433308720589, 0.569870233535767, 0.0640662834048271), + tsoil = c(9.02208423614502, 11.0288057327271, 11.3866157531738, 15.0849714279175, 19.9336929321289, 9.59065818786621), + netrad = c(4.16539621353149, 55.9189796447754, 116.783561706543, 12.2599382400513, 192.525726318359, -16.011812210083), + wcont = c(208.188491821289, 259.454925537109, 1174.90344238281, 995.4267578125, 1561.25793457031, 1725.38427734375 ), snow = c(0, 0, 0, 0, 0, 0), - cond = c(0, 0, 0, 0, 0, 0), - le_canopy = c(127639.390625, 2577880.75, 2284844.5, 376195.9375, 9554412, 4795.3779296875), - le_soil = c(78053.453125, 1034455.5625, 2326407.75, 217370.515625, 4119428, -230766.890625), - dpsi = c(0, 0, 0, 0, 0, 0), + cond = c(0, 0, 0, 0, 0, 0), + le_canopy = c(127639.390625, 2577880.75, 2284844.5, 376195.9375, 9554412, 4795.3779296875), + le_soil = c(78053.453125, 1034455.5625, 2326407.75, 217370.515625, 4119428, -230766.890625), + dpsi = c(0, 0, 0, 0, 0, 0), psi_leaf = c(0, 0, 0, 0, 0, 0)) # tibble(mod3) |> slice(c(1, 70, 1200, 1400, 2000, 2180)) |> dput() @@ -208,13 +209,13 @@ test_that("run_pmodel_f_bysite()", { vcmax25 = c(3.51696653524414e-05, 3.77493124688044e-05, 5.76805359742139e-05, 3.59945297532249e-05, 5.17318439960945e-05, 2.60039796557976e-05), jmax25 = c(7.8623415902257e-05, 8.34658203530125e-05, 0.000126882849144749, 7.2937982622534e-05, 9.94235306279734e-05, 5.87244903726969e-05), gs_accl = c(0.0016942253569141, 0.00173744710627943, 0.00175592955201864, 0.00170137174427509, 0.00156046624761075, 0.00193470600061119), - wscal = c(0.0814640149474144, 0.108003601431847, 0.461629748344421, 0.326346158981323, 0.524367153644562, 0.587543845176697), + wscal = c(0.0814638137817383, 0.108003400266171, 0.461629569530487, 0.326345980167389, 0.524366974830627, 0.587543725967407), chi = c(0.629108071327209, 0.642833471298218, 0.639411568641663, 0.66897189617157, 0.674327433109283, 0.673606336116791), iwue = c(9.066015627468e-05, 8.73051467351615e-05, 8.65905749378726e-05, 7.94415027485229e-05, 8.00566194811836e-05, 8.02328504505567e-05), rd = c(0.0936944633722305, 0.121099025011063, 0.188513651490211, 0.146433308720589, 0.569870233535767, 0.0640662834048271), - tsoil = c(9.16450881958008, 11.0153522491455, 11.3974561691284, 15.0535011291504, 20.0137233734131, 9.49603080749512), + tsoil = c(9.16451072692871, 11.0153512954712, 11.3974561691284, 15.0535011291504, 20.0137233734131, 9.49603080749512), netrad = c(4.16539621353149, 55.9189796447754, 116.783561706543, 12.2599382400513, 192.525726318359, -16.011812210083), - wcont = c(162.928024291992, 216.007202148438, 923.259521484375, 652.692321777344, 1048.73425292969, 1175.08764648438), + wcont = c(162.927627563477, 216.006805419922, 923.259155273438, 652.691955566406, 1048.73400878906, 1175.08740234375), snow = c(0, 0, 0, 0, 0, 0), cond = c(0, 0, 0, 0, 0, 0), le_canopy = c(170769.65625, 2040949.625, 3880380.25, 528897.8125, 8732491, -507988.21875), @@ -225,31 +226,31 @@ test_that("run_pmodel_f_bysite()", { # tibble(mod4) |> slice(c(1, 70, 1200, 1400, 2000, 2180)) |> dput() ref4 <- tibble( - date = structure(c(13514, 13583, 14714, 14914, 15515, 15695), class = "Date"), - year_dec = c(2007, 2007.189, 2010.285, 2010.833, 2012.478, 2012.97), - fapar = c(0.617119550704956, 0.637238144874573, 0.614814937114716, 0.668549001216888, 0.672287464141846, 0.689359784126282), - gpp = c(1.4386088848114, 2.53311991691589, 3.77212953567505, 1.01602685451508, 4.42701482772827, 0.843587338924408), - aet = c(0.100732982158661, 1.24703788757324, 2.51812934875488, 0.303276777267456, 5.29151248931885, -0.298412382602692), - le = c(249230.78125, 3079814.25, 6214976, 747281.8125, 12863595, -739914.3125), - pet = c(0.103817254304886, 1.45484209060669, 3.08336925506592, 0.335356384515762, 6.51526165008545, -0.377504140138626), - vcmax = c(3.49528818333056e-06, 4.44911984232021e-06, 6.87862939230399e-06, 3.0264354791143e-06, 2.33490991377039e-05, 2.45830347012088e-06), - jmax = c(1.34397805595654e-05, 1.6660707842675e-05, 2.52759164141025e-05, 5.81073845751234e-06, 4.74316075269599e-05, 1.00074266811134e-05), - vcmax25 = c(1.01545692814398e-05, 1.16090131996316e-05, 1.69845134223578e-05, 7.37736718292581e-06, 2.05224514502333e-05, 8.68135339260334e-06), - jmax25 = c(2.97042952297488e-05, 3.3337164495606e-05, 4.87200122734066e-05, 1.03667243820382e-05, 4.40687981608789e-05, 2.51640849455725e-05), - gs_accl = c(0.0953038334846497, 0.0583707764744759, 0.0869309082627296, 0.019712695851922, 0.031399454921484, 0.572548985481262), - wscal = c(0.642368793487549, 0.852014243602753, 0.966964066028595, 0.419518768787384, 0.55648273229599, 1), - chi = c(0.96280699968338, 0.893072545528412, 0.89109992980957, 0.870647430419922, 0.654556632041931, 0.996390044689178), - iwue = c(1.45462236105232e-05, 4.18194395024329e-05, 4.18148301832844e-05, 4.96680477226619e-05, 0.000135864756884985, 1.41982673085295e-06), - rd = c(0.0438367053866386, 0.058442011475563, 0.0902863815426826, 0.0448922663927078, 0.336273193359375, 0.0310263875871897), - tsoil = c(8.96044540405273, 11.0349359512329, 11.3815355300903, 15.0929355621338, 19.9768238067627, 9.5408935546875), - netrad = c(4.16539621353149, 55.9189796447754, 116.783561706543, 12.2599382400513, 192.525726318359, -16.011812210083), - wcont = c(162.519302368164, 215.559600830078, 244.641906738281, 106.138252258301, 140.790130615234, 253), - snow = c(0, 0, 0, 0, 0, 0), - cond = c(0, 0, 0, 0, 0, 0), - le_canopy = c(171177.328125, 2045358.75, 3888568, 529911.3125, 8744167, -509147.4375), - le_soil = c(78053.453125, 1034455.5625, 2326407.75, 217370.515625, 4119428, -230766.890625), - dpsi = c(0.231719702482224, 0.742867052555084, 0.880529344081879, 0.41495206952095, 1.63764226436615, 0.0540594346821308), - psi_leaf = c(-0.492130398750305, -0.795352935791016, -0.886995494365692, -1.52232336997986, -2.01140165328979, -0.0540594346821308) + date = structure(c(13514, 13583, 14714, 14914, 15515, 15695), class = "Date"), + year_dec = c(2007, 2007.189, 2010.285, 2010.833, 2012.478, 2012.97), + fapar = c(0.617119550704956, 0.637238144874573, 0.614814937114716, 0.668549001216888, 0.672287464141846, 0.689359784126282), + gpp = c(2.4825496673584, 4.29858160018921, 6.37876176834106, 1.16307175159454, 6.1460018157959, 1.46240973472595), + aet = c(0.100733757019043, 1.24704611301422, 2.51813817024231, 0.303279280662537, 5.29152059555054, -0.29841285943985), + le = c(249232.671875, 3079834.5, 6214997, 747287.9375, 12863615, -739915.5), + pet = c(0.103817254304886, 1.45484209060669, 3.08336925506592, 0.335356384515762, 6.51526165008545, -0.377504140138626), + vcmax = c(6.10240022069775e-06, 7.75274838815676e-06, 1.19952146633295e-05, 3.3023632113327e-06, 4.0464994526701e-05, 4.28065595770022e-06), + jmax = c(2.29531615332235e-05, 2.86642443825258e-05, 4.33885979873594e-05, 6.07609081271221e-06, 7.67164674471132e-05, 1.72506606759271e-05), + vcmax25 = c(1.77287929545855e-05, 2.02291139430599e-05, 2.96182388410671e-05, 8.04998035164317e-06, 3.55662923539057e-05, 1.51168824231718e-05), + jmax25 = c(5.07305485371035e-05, 5.73555880691856e-05, 8.36326944408938e-05, 1.08401309262263e-05, 7.12774199200794e-05, 4.33774948760401e-05), + gs_accl = c(0.127987444400787, 0.0686747059226036, 0.0995644107460976, 0.0210667010396719, 0.0324819944798946, 0.811927676200867), + wscal = c(0.642368197441101, 0.852013349533081, 0.966963946819305, 0.419518351554871, 0.556477844715118, 1), + chi = c(0.952207505702972, 0.845774114131927, 0.839214026927948, 0.861443877220154, 0.536405801773071, 0.995586931705475), + iwue = c(1.86916877282783e-05, 6.03179141762666e-05, 6.17376645095646e-05, 5.3201994887786e-05, 0.000182334129931405, 1.73568002992397e-06), + rd = c(0.076534204185009, 0.101837269961834, 0.157444804906845, 0.0489852055907249, 0.582775950431824, 0.0540263988077641), + tsoil = c(8.96044540405273, 11.0349359512329, 11.3815355300903, 15.0929355621338, 19.9768238067627, 9.54089260101318), + netrad = c(4.16539621353149, 55.9189796447754, 116.783561706543, 12.2599382400513, 192.525726318359, -16.011812210083), + wcont = c(162.519149780273, 215.559371948242, 244.641876220703, 106.138145446777, 140.78889465332, 253), + snow = c(0, 0, 0, 0, 0, 0), + cond = c(0, 0, 0, 0, 0, 0), + le_canopy = c(171179.21875, 2045379, 3888589.5, 529917.4375, 8744187, -509148.625), + le_soil = c(78053.453125, 1034455.5625, 2326407.75, 217370.515625, 4119428, -230766.890625), + dpsi = c(0.334371000528336, 1.04337632656097, 1.22221386432648, 0.45578670501709, 1.94676566123962, 0.0780353099107742), + psi_leaf = c(-0.594782650470734, -1.09586262702942, -1.22868001461029, -1.56316161155701, -2.32053685188293, -0.0780353099107742) ) expect_equal(dplyr::slice(tibble(mod1), c(1, 70, 1200, 1400, 2000, 2180)), ref1, tolerance = 1e-6) From d0dc489ed5f062e91ac6b06cbcd016bc057024d5 Mon Sep 17 00:00:00 2001 From: Fabian Bernhard <10245680+fabern@users.noreply.github.com> Date: Thu, 10 Oct 2024 15:38:42 +0200 Subject: [PATCH 100/112] tests: Add reference test for calibrated params --- tests/testthat/test-calibration-pmodel.R | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/tests/testthat/test-calibration-pmodel.R b/tests/testthat/test-calibration-pmodel.R index f5b9306d..1812fceb 100644 --- a/tests/testthat/test-calibration-pmodel.R +++ b/tests/testthat/test-calibration-pmodel.R @@ -250,8 +250,8 @@ test_that("test joint calibration routine p-model (BT, likelihood maximization)" sampler = "DEzs", settings = list( nrChains = 1, - burnin = 1, - iterations = 4 + burnin = 50, # this was selected deliberately low for computational efficiency + iterations = 200 # this was selected deliberately low for computational efficiency ) ), par = list( @@ -260,6 +260,7 @@ test_that("test joint calibration routine p-model (BT, likelihood maximization)" err_vcmax25 = list(lower = 0.0001, upper = 0.1, init = 0.005) ) ) + set.seed(10) pars <- rsofun::calib_sofun( drivers = drivers, obs = obs, @@ -273,4 +274,19 @@ test_that("test joint calibration routine p-model (BT, likelihood maximization)" # test for correctly returned values expect_type(pars, "list") + + # test for same numeric results: + # Hardcoded reference outputs. + # NOTE: this is expected to change reasonably frequently whenever something is + # changed in the model. + # If this is expected, please update the hardcoded reference values below. + # To do so, simply use the commented code, making use of dput(). Thanks! + # dput(pars$par) + # print(dput(pars$par)) + ref_pars <- c(kphio = 0.0453, + err_gpp = 1.51, + err_vcmax25 = 0.0060) + expect_equal(pars$par, ref_pars, tolerance = 0.1) + }) + From 25b33b95bd204b95df2993c18d209417a002a4d2 Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Sun, 13 Oct 2024 11:20:56 +0200 Subject: [PATCH 101/112] made vignette a bit nicer --- data/p_model_drivers.rda | Bin 81636 -> 158760 bytes vignettes/pmodel_use.Rmd | 189 ++++++++++++++++++++++++++------------- 2 files changed, 128 insertions(+), 61 deletions(-) diff --git a/data/p_model_drivers.rda b/data/p_model_drivers.rda index f9a3111fab724dd9e435147602cae767eec21671..cd859f9c844e7ff87d14c5324ecc59c6ed8236f6 100644 GIT binary patch literal 158760 zcmV(pK=8l)H+ooF0004LBHlIv03iV!0000G&sfaivQ&2UKVgRpfkl* zy7}J8y%(LSVlrVEGvG*o*df4odNVN@i`f8LUx4iAzPIOFf}VrMCg<*tLHQ&kptxHt z>4X_x?Z^o_us(w-gzU6z(GsCZx{XQd)hwvPxD`stt7t!`J4-k?9aW) zQ6hRjfQ(n6c5-^^b+_F9T8F#l)j#amQO12hY2smc;ucv*2@SE!Ec_4sJf?}}Su)IX 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a/vignettes/pmodel_use.Rmd b/vignettes/pmodel_use.Rmd index e7dbd353..2b8b3df9 100644 --- a/vignettes/pmodel_use.Rmd +++ b/vignettes/pmodel_use.Rmd @@ -1,6 +1,6 @@ --- title: "P-model usage" -author: "Koen Hufkens, Josefa Arán" +author: "Koen Hufkens, Josefa Arán, Benjamin Stocker" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{P-model usage} @@ -53,24 +53,23 @@ p_model_validation ```{r} p_model_drivers$forcing[[1]] |> - # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> - dplyr::select(date, co2, ppfd, netrad, temp, vpd, fapar, rain, ccov) |> - tidyr::pivot_longer(-date) |> - dplyr::mutate(type="24-hr mean") |> - rbind(p_model_validation$data[[1]] |> - # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> - dplyr::select(date, gpp) |> - tidyr::pivot_longer(-date) |> - dplyr::mutate(type="obs (24 hr)") - ) |> - ggplot(aes(y=value, x=date)) + - geom_line(aes(group=type, col=type), alpha=0.5) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~name, scales = "free")+ - ggtitle(p_model_drivers$sitename[[1]]) - - + # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> + dplyr::select(date, co2, ppfd, netrad, temp, vpd, fapar, rain, ccov) |> + tidyr::pivot_longer(-date) |> + dplyr::mutate(type="24-hr mean") |> + rbind(p_model_validation$data[[1]] |> + # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> + dplyr::select(date, gpp) |> + tidyr::pivot_longer(-date) |> + dplyr::mutate(type="obs (24 hr)") + ) |> + ggplot(aes(y=value, x=date)) + + geom_line(aes(group=type, col=type)) + + theme_classic() + + theme(strip.background = element_rect(color = "white", size = 1))+ + facet_wrap(~name, scales = "free")+ + ggtitle(p_model_drivers$sitename[[1]]) + p_model_drivers$site_info |> print() ``` @@ -124,30 +123,41 @@ We can now visualize both the model output and the measured values together. # Create dataframe for plotting df_plot <- output |> unnest(data) |> - select(date, gpp_mod = gpp) %>% + select(date, gpp_mod = gpp) |> left_join( p_model_validation |> - unnest(data) %>% + unnest(data) |> select(date, gpp_obs = gpp), by = join_by(date) ) -# Plot GPP -df_plot %>% - tidyr::pivot_longer(c(gpp_mod, gpp_obs), names_to = "type", values_to = "gpp") %>% +# Plot GPP observed and modelled quartiles by day-of-year +df_plot |> + tidyr::pivot_longer(c(gpp_mod, gpp_obs), names_to = "type", values_to = "gpp") |> + mutate(doy = lubridate::yday(date)) |> + group_by(doy, type) |> + summarise( + gpp_q25 = quantile(gpp, probs = c(0.25), na.rm = TRUE), + gpp_q75 = quantile(gpp, probs = c(0.75), na.rm = TRUE) + ) |> ggplot() + - geom_line( - aes(x = date, - y = gpp, - color = type) + geom_ribbon( + aes( + x = doy, + ymin = gpp_q25, + ymax = gpp_q75, + fill = type + ), + alpha = 0.75 ) + - scale_color_manual( + scale_fill_manual( + name = "", values = c( "gpp_mod" = 'tomato', "gpp_obs" = 'grey20' ), labels = c( - "gpp_mod" = "P-model output", + "gpp_mod" = "P-model, calibrated", "gpp_obs" = "Observed" )) + theme_classic() + @@ -158,19 +168,42 @@ df_plot %>% colour = "" ) -df_plot %>% +# get fit metrics +rsq_val <- yardstick::rsq(df_plot, gpp_mod, gpp_obs) |> pull(.estimate) +rmse_val <- yardstick::rmse(df_plot, gpp_mod, gpp_obs) |> pull(.estimate) +bias_val <- mean(df_plot$gpp_mod - df_plot$gpp_obs, na.rm = TRUE) +linmod <- lm(gpp_obs ~ gpp_mod + 0, data = df_plot) +slope_val <- coef(linmod) +n_val <- df_plot |> tidyr::drop_na() |> nrow() + +rsq_lab <- format(rsq_val, digits = 3) +rmse_lab <- format(rmse_val, digits = 3) +bias_lab <- format(bias_val, digits = 3) +slope_lab <- format(slope_val, digits = 3) +n_lab <- n_val + +subtitle <- bquote(italic(R)^2 == .(rsq_lab) ~ ~ + RMSE == .(rmse_lab) ~ ~ + bias == .(bias_lab) ~ ~ + slope == .(slope_lab) ~ ~ + italic(N) == .(n_lab)) + +# plot modelled vs. observed (actually the reverse) +df_plot |> ggplot(aes(x = gpp_mod, y = gpp_obs)) + geom_hex(bins = 50, show.legend = FALSE) + theme_classic() + geom_abline(intercept = 0, slope = 1, linetype = "dotted") + geom_hline(yintercept = 0, linetype = "dotted") + - geom_vline(xintercept = 0, linetype = "dotted") + + geom_vline(xintercept = 0, linetype = "dotted")+ + geom_smooth(method = "lm", formula = y ~ x + 0, color = "red", size = 0.5, se = FALSE) + khroma::scale_fill_batlowW(trans = "log", reverse = TRUE) + xlim(-0.5, 10) + ylim(-0.5, 10) + labs( x = expression(paste("P-model GPP (g C m"^-2, "s"^-1, ")")), - y = expression(paste("Observed GPP (g C m"^-2, "s"^-1, ")")) + y = expression(paste("Observed GPP (g C m"^-2, "s"^-1, ")")), + subtitle = subtitle ) ``` @@ -180,8 +213,8 @@ To optimize new parameters based upon driver data and a validation dataset we mu ```{r} settings <- list( - method = "GenSA", - metric = cost_rmse_pmodel, + method = "GenSA", + metric = cost_rmse_pmodel, control = list(maxit = 3000), par = list( kphio = list(lower = 0.03, upper = 0.99, init = 0.05), @@ -218,55 +251,67 @@ pars <- calib_sofun( ) ``` -When successful the optimized parameters can be used to run subsequent modelling efforts, in this case slightly improving the model fit over a more global parameter set. +When successful the optimized parameters can be used to run subsequent modelling efforts, in this case improving the model fit over the initial parameter set. The graph shows observed and modelled quartiles of daily GPP by day-of-year. ```{r} # Update the parameter list with calibrated value params_modl <- list( - kphio = 0.039632387, - kphio_par_a = -0.002595878, - kphio_par_b = 13.919139015, - soilm_thetastar = 20.599254283, - beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41, - whc = 284.920442210 + kphio = 0.039889979, #pars$mod$par[["kphio"]], + kphio_par_a = -0.002281718, # pars$mod$par[["kphio_par_a"]], + kphio_par_b = 15.064324945, # pars$mod$par[["kphio_par_b"]], + soilm_thetastar = 67.247956095, # pars$mod$par[["soilm_thetastar"]], + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41 ) +p_model_drivers$site_info[[1]]$whc <- pars$mod$par[["whc"]] + # Run the model with calibrated parameters output_new <- rsofun::runread_pmodel_f( - p_model_drivers, + drivers = p_model_drivers, par = params_modl ) # Update dataframe for plotting df_plot <- output_new |> unnest(data) |> - select(date, gpp_mod = gpp) %>% + select(date, gpp_mod = gpp) |> left_join( p_model_validation |> - unnest(data) %>% + unnest(data) |> select(date, gpp_obs = gpp), by = join_by(date) ) -# Plot GPP -df_plot %>% - tidyr::pivot_longer(c(gpp_mod, gpp_obs), names_to = "type", values_to = "gpp") %>% +# Plot GPP observed and modelled quartiles by day-of-year +df_plot |> + tidyr::pivot_longer(c(gpp_mod, gpp_obs), names_to = "type", values_to = "gpp") |> + mutate(doy = lubridate::yday(date)) |> + group_by(doy, type) |> + summarise( + gpp_q25 = quantile(gpp, probs = c(0.25), na.rm = TRUE), + gpp_q75 = quantile(gpp, probs = c(0.75), na.rm = TRUE) + ) |> ggplot() + - geom_line( - aes(x = date, - y = gpp, - color = type) + geom_ribbon( + aes( + x = doy, + ymin = gpp_q25, + ymax = gpp_q75, + fill = type + ), + alpha = 0.75 ) + - scale_color_manual( + scale_fill_manual( + name = "", values = c( "gpp_mod" = 'tomato', "gpp_obs" = 'grey20' ), labels = c( - "gpp_mod" = "P-model output", + "gpp_mod" = "P-model, calibrated", "gpp_obs" = "Observed" )) + theme_classic() + @@ -277,20 +322,42 @@ df_plot %>% colour = "" ) -# plot modelled vs observed -df_plot %>% +# get fit metrics +rsq_val <- yardstick::rsq(df_plot, gpp_mod, gpp_obs) |> pull(.estimate) +rmse_val <- yardstick::rmse(df_plot, gpp_mod, gpp_obs) |> pull(.estimate) +bias_val <- mean(df_plot$gpp_mod - df_plot$gpp_obs, na.rm = TRUE) +linmod <- lm(gpp_obs ~ gpp_mod + 0, data = df_plot) +slope_val <- coef(linmod) +n_val <- df_plot |> tidyr::drop_na() |> nrow() + +rsq_lab <- format(rsq_val, digits = 3) +rmse_lab <- format(rmse_val, digits = 3) +bias_lab <- format(bias_val, digits = 3) +slope_lab <- format(slope_val, digits = 3) +n_lab <- n_val + +subtitle <- bquote(italic(R)^2 == .(rsq_lab) ~ ~ + RMSE == .(rmse_lab) ~ ~ + bias == .(bias_lab) ~ ~ + slope == .(slope_lab) ~ ~ + italic(N) == .(n_lab)) + +# plot modelled vs. observed (actually the reverse) +df_plot |> ggplot(aes(x = gpp_mod, y = gpp_obs)) + geom_hex(bins = 50, show.legend = FALSE) + theme_classic() + geom_abline(intercept = 0, slope = 1, linetype = "dotted") + geom_hline(yintercept = 0, linetype = "dotted") + - geom_vline(xintercept = 0, linetype = "dotted") + + geom_vline(xintercept = 0, linetype = "dotted")+ + geom_smooth(method = "lm", formula = y ~ x + 0, color = "red", size = 0.5, se = FALSE) + khroma::scale_fill_batlowW(trans = "log", reverse = TRUE) + xlim(-0.5, 10) + ylim(-0.5, 10) + labs( x = expression(paste("P-model GPP (g C m"^-2, "s"^-1, ")")), - y = expression(paste("Observed GPP (g C m"^-2, "s"^-1, ")")) + y = expression(paste("Observed GPP (g C m"^-2, "s"^-1, ")")), + subtitle = subtitle ) ``` From 2b82b2e9eccf5e2785a1f2b0e2dce6bb34b2272b Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Sun, 13 Oct 2024 11:39:04 +0200 Subject: [PATCH 102/112] removed stuff that was unnecessarily added to this vignette (visualisation of 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+++ b/vignettes/pmodel_use.Rmd @@ -35,57 +35,22 @@ The `rsofun` package and framework includes two main models. The `pmodel` and `b The package includes two demo datasets to run and validate pmodel output using GPP observations. These files can be directly loaded into your workspace by typing: ```{r} -library(rsofun) - -# this is to deal with an error p_model_drivers.rds not being found -p_model_drivers = rsofun::p_model_drivers |> - rowwise() |> - mutate(params_siml = params_siml |> - mutate(use_phydro = F, - use_gs = F, - use_pml = F) |> - list()) - +p_model_drivers p_model_validation ``` -### Visualize drivers - -```{r} -p_model_drivers$forcing[[1]] |> - # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> - dplyr::select(date, co2, ppfd, netrad, temp, vpd, fapar, rain, ccov) |> - tidyr::pivot_longer(-date) |> - dplyr::mutate(type="24-hr mean") |> - rbind(p_model_validation$data[[1]] |> - # dplyr::filter(year(date) >= ystart & year(date) <= yend) |> - dplyr::select(date, gpp) |> - tidyr::pivot_longer(-date) |> - dplyr::mutate(type="obs (24 hr)") - ) |> - ggplot(aes(y=value, x=date)) + - geom_line(aes(group=type, col=type)) + - theme_classic() + - theme(strip.background = element_rect(color = "white", size = 1))+ - facet_wrap(~name, scales = "free")+ - ggtitle(p_model_drivers$sitename[[1]]) - -p_model_drivers$site_info |> print() -``` - -These are real data from the French FR-Pue fluxnet site. Information about data structure, variable names, and their meaning and units can be found in the reference pages of `p_model_drivers` and `p_model_validation`. We can use these data to run the model, together with observations of GPP we can also calibrate `pmodel` parameters. +These are data from the French FR-Pue FLUXNET site. Information about data structure, variable names, and their meaning and units can be found in the reference pages of `p_model_drivers` and `p_model_validation`. We can use these data to run the model, together with observations of GPP we can also calibrate `pmodel` parameters. Another two datasets are provided as an example to validate the model against leaf traits data, rather than fluxes. Measurements of Vcmax25 (aggregated over species) for a subset of 4 sites from the GlobResp database (Atkin et al., 2015) are given in `p_model_validation_vcmax25` and the corresponding forcing for the P-model is given in `p_model_drivers_vcmax25`. Since leaf traits are only measured once per site, the forcing used is a single year of average climate (the average measurements between 2001 and 2015 on each day of the year). ```{r} p_model_drivers_vcmax25 - p_model_validation_vcmax25 ``` For the remainder of this vignette, we will use the GPP flux datasets. The workflow is exactly the same for leaf traits data. -To get your raw data into the structure used within `rsofun`, please see R packages [ingestr](https://github.com/geco-bern/ingestr) and [FluxDataKit](https://github.com/geco-bern/FluxDataKit). +The script `data-raw/generate_pmodel_driver_data.R` implements the subsetting of rsofun driver data to the demo data provided through this package. ## Running model From c052e92e6af0801cfc29a1149f64f6773cf073b3 Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Sun, 13 Oct 2024 11:40:52 +0200 Subject: [PATCH 103/112] renamed vignette demonstrating pmodel calibration --- vignettes/{new_cost_function.Rmd => calibrate_pmodel.Rmd} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename vignettes/{new_cost_function.Rmd => calibrate_pmodel.Rmd} (100%) diff --git a/vignettes/new_cost_function.Rmd b/vignettes/calibrate_pmodel.Rmd similarity index 100% rename from vignettes/new_cost_function.Rmd rename to vignettes/calibrate_pmodel.Rmd From 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56e20a31..be9838c0 100644 --- a/vignettes/pmodel_use.Rmd +++ b/vignettes/pmodel_use.Rmd @@ -22,10 +22,119 @@ library(dplyr) library(tidyr) library(ggplot2) library(khroma) - -# fake variable as optimization isn't run -pars <- list() -pars$par["kphio"] <- 0.04478049 +library(cowplot) + +# define function for evaluation plots +plot_eval <- function(df, is_gpp = TRUE){ + + use_col <- ifelse(is_gpp, "tomato", "royalblue") + use_lab <- ifelse( + is_gpp, + expression(paste("GPP (g C m"^-2, "s"^-1, ")")), + expression(paste("LE (W m"^-2, ")")) + ) + use_lab_obs <- ifelse( + is_gpp, + expression(paste("Observed GPP (g C m"^-2, "s"^-1, ")")), + expression(paste("Observed LE (W m"^-2, ")")) + ) + use_lab_mod <- ifelse( + is_gpp, + expression(paste("Modeled GPP (g C m"^-2, "s"^-1, ")")), + expression(paste("Modeled LE (W m"^-2, ")")) + ) + lims <- ifelse( + is_gpp, + c(-0.5, 10), + c(-20, 175) + ) + + # Plot GPP observed and modelled quartiles by day-of-year + gg1 <- df |> + tidyr::pivot_longer(c(mod, obs), names_to = "type", values_to = "var") |> + mutate(doy = lubridate::yday(date)) |> + group_by(doy, type) |> + summarise( + var_q25 = quantile(var, probs = c(0.25), na.rm = TRUE), + var_q75 = quantile(var, probs = c(0.75), na.rm = TRUE) + ) |> + ggplot() + + geom_ribbon( + aes( + x = doy, + ymin = var_q25, + ymax = var_q75, + fill = type + ), + alpha = 0.75 + ) + + scale_fill_manual( + name = "", + values = c( + "mod" = use_col, + "obs" = 'grey20' + ), + labels = c( + "mod" = "Mod.", + "obs" = "Obs." + )) + + theme_classic() + + theme() + + labs( + x = 'Date', + y = use_lab, + colour = "" + ) + + # get fit metrics + rsq_val <- yardstick::rsq(df, mod, obs) |> pull(.estimate) + rmse_val <- yardstick::rmse(df, mod, obs) |> pull(.estimate) + bias_val <- mean(df$mod - df$obs, na.rm = TRUE) + linmod <- lm(obs ~ mod + 0, data = df) + slope_val <- coef(linmod) + n_val <- df |> tidyr::drop_na() |> nrow() + + rsq_lab <- format(rsq_val, digits = 3) + rmse_lab <- format(rmse_val, digits = 3) + bias_lab <- format(bias_val, digits = 3) + slope_lab <- format(slope_val, digits = 3) + n_lab <- n_val + + subtitle <- bquote(italic(R)^2 == .(rsq_lab) ~ ~ + RMSE == .(rmse_lab) ~ ~ + bias == .(bias_lab) ~ ~ + slope == .(slope_lab) ~ ~ + italic(N) == .(n_lab)) + + # plot modelled vs. observed (actually the reverse) + gg2 <- df |> + ggplot(aes(x = mod, y = obs)) + + geom_hex(bins = 50, show.legend = FALSE) + + theme_classic() + + geom_abline(intercept = 0, slope = 1, linetype = "dotted") + + geom_hline(yintercept = 0, linetype = "dotted") + + geom_vline(xintercept = 0, linetype = "dotted")+ + geom_smooth(method = "lm", formula = y ~ x + 0, color = "red", size = 0.5, se = FALSE) + + xlim(lims[1], lims[2]) + + ylim(lims[1], lims[2]) + + labs( + x = use_lab_mod, + y = use_lab_obs, + subtitle = subtitle + ) + + if (is_gpp){ + gg2 <- gg2 + + khroma::scale_fill_batlowW(trans = "log", reverse = TRUE) + } else { + gg2 <- gg2 + + khroma::scale_fill_davos(trans = "log", reverse = TRUE) + + } + + out <- cowplot::plot_grid(gg1, gg2, nrow = 1) + return(out) +} ``` The `rsofun` package and framework includes two main models. The `pmodel` and `biomee` (which in part relies on P-model components). Here we give a short example on how to run the `pmodel` on the included demo datasets to familiarize yourself with both the data structure and the outputs. @@ -63,14 +172,14 @@ With all data prepared we can run the P-model using `runread_pmodel_f()`. This f # whc must be included in params_modl, rather than in site_info # ------------------------------------------------------ params_modl <- list( - kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD - kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio - kphio_par_b = 1.0, - soilm_thetastar = 0.6 * p_model_drivers$site_info[[1]]$whc, - beta_unitcostratio = 146.0, - rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous - tau_acclim = 30.0, - kc_jmax = 0.41 + kphio = 0.04998, # setup ORG in Stocker et al. 2020 GMD + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + soilm_thetastar = 0.6 * p_model_drivers$site_info[[1]]$whc, + beta_unitcostratio = 146.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41 ) # run the model for these parameters @@ -88,88 +197,15 @@ We can now visualize both the model output and the measured values together. # Create dataframe for plotting df_plot <- output |> unnest(data) |> - select(date, gpp_mod = gpp) |> + select(date, mod = gpp) |> left_join( p_model_validation |> unnest(data) |> - select(date, gpp_obs = gpp), + select(date, obs = gpp), by = join_by(date) ) -# Plot GPP observed and modelled quartiles by day-of-year -df_plot |> - tidyr::pivot_longer(c(gpp_mod, gpp_obs), names_to = "type", values_to = "gpp") |> - mutate(doy = lubridate::yday(date)) |> - group_by(doy, type) |> - summarise( - gpp_q25 = quantile(gpp, probs = c(0.25), na.rm = TRUE), - gpp_q75 = quantile(gpp, probs = c(0.75), na.rm = TRUE) - ) |> - ggplot() + - geom_ribbon( - aes( - x = doy, - ymin = gpp_q25, - ymax = gpp_q75, - fill = type - ), - alpha = 0.75 - ) + - scale_fill_manual( - name = "", - values = c( - "gpp_mod" = 'tomato', - "gpp_obs" = 'grey20' - ), - labels = c( - "gpp_mod" = "P-model, calibrated", - "gpp_obs" = "Observed" - )) + - theme_classic() + - theme() + - labs( - x = 'Date', - y = expression(paste("GPP (g C m"^-2, "s"^-1, ")")), - colour = "" - ) - -# get fit metrics -rsq_val <- yardstick::rsq(df_plot, gpp_mod, gpp_obs) |> pull(.estimate) -rmse_val <- yardstick::rmse(df_plot, gpp_mod, gpp_obs) |> pull(.estimate) -bias_val <- mean(df_plot$gpp_mod - df_plot$gpp_obs, na.rm = TRUE) -linmod <- lm(gpp_obs ~ gpp_mod + 0, data = df_plot) -slope_val <- coef(linmod) -n_val <- df_plot |> tidyr::drop_na() |> nrow() - -rsq_lab <- format(rsq_val, digits = 3) -rmse_lab <- format(rmse_val, digits = 3) -bias_lab <- format(bias_val, digits = 3) -slope_lab <- format(slope_val, digits = 3) -n_lab <- n_val - -subtitle <- bquote(italic(R)^2 == .(rsq_lab) ~ ~ - RMSE == .(rmse_lab) ~ ~ - bias == .(bias_lab) ~ ~ - slope == .(slope_lab) ~ ~ - italic(N) == .(n_lab)) - -# plot modelled vs. observed (actually the reverse) -df_plot |> - ggplot(aes(x = gpp_mod, y = gpp_obs)) + - geom_hex(bins = 50, show.legend = FALSE) + - theme_classic() + - geom_abline(intercept = 0, slope = 1, linetype = "dotted") + - geom_hline(yintercept = 0, linetype = "dotted") + - geom_vline(xintercept = 0, linetype = "dotted")+ - geom_smooth(method = "lm", formula = y ~ x + 0, color = "red", size = 0.5, se = FALSE) + - khroma::scale_fill_batlowW(trans = "log", reverse = TRUE) + - xlim(-0.5, 10) + - ylim(-0.5, 10) + - labs( - x = expression(paste("P-model GPP (g C m"^-2, "s"^-1, ")")), - y = expression(paste("Observed GPP (g C m"^-2, "s"^-1, ")")), - subtitle = subtitle - ) +plot_eval(df_plot) ``` ## Calibrating model parameters @@ -182,11 +218,14 @@ settings <- list( metric = cost_rmse_pmodel, control = list(maxit = 3000), par = list( + # all model parameters are calibratable kphio = list(lower = 0.03, upper = 0.99, init = 0.05), kphio_par_a = list(lower = -0.005, upper = 0, init = -0.0025), kphio_par_b = list(lower = 10, upper = 30, init = 20), soilm_thetastar = list(lower = 10, upper = 200, init = 100), - # WHC is ambiguous variable: provided as site info, but used as calibration. That's intentional + + # WHC is exceptional: provided as site info to enable specification + # by site in multi-site simulations, but also calibratable. whc = list(lower = 10, upper = 1000, init = 400) ) ) @@ -201,11 +240,14 @@ With all settings defined the optimization function `calib_sofun()` can be calle ```{r eval=FALSE, warning=FALSE} # calibrate the model and optimize free parameters pars <- calib_sofun( + drivers = p_model_drivers, obs = p_model_validation, settings = settings, + # extra arguments passed to the cost function: targets = "gpp", # define target variable GPP + # fix non-calibrated parameters to previous par_fixed = list( beta_unitcostratio = 146.0, @@ -231,7 +273,9 @@ params_modl <- list( kc_jmax = 0.41 ) -p_model_drivers$site_info[[1]]$whc <- pars$mod$par[["whc"]] +# root zone total water holding capacity (whc) is calibrated, but specified as +# a site parameter in the P-model call with function runread_pmodel_f() +p_model_drivers$site_info[[1]]$whc <- 512 # approximate value, should re-calibrate this # Run the model with calibrated parameters output_new <- rsofun::runread_pmodel_f( @@ -242,88 +286,136 @@ output_new <- rsofun::runread_pmodel_f( # Update dataframe for plotting df_plot <- output_new |> unnest(data) |> - select(date, gpp_mod = gpp) |> + select(date, mod = gpp) |> left_join( p_model_validation |> unnest(data) |> - select(date, gpp_obs = gpp), + select(date, obs = gpp), by = join_by(date) ) -# Plot GPP observed and modelled quartiles by day-of-year -df_plot |> - tidyr::pivot_longer(c(gpp_mod, gpp_obs), names_to = "type", values_to = "gpp") |> - mutate(doy = lubridate::yday(date)) |> - group_by(doy, type) |> - summarise( - gpp_q25 = quantile(gpp, probs = c(0.25), na.rm = TRUE), - gpp_q75 = quantile(gpp, probs = c(0.75), na.rm = TRUE) - ) |> - ggplot() + - geom_ribbon( - aes( - x = doy, - ymin = gpp_q25, - ymax = gpp_q75, - fill = type - ), - alpha = 0.75 - ) + - scale_fill_manual( - name = "", - values = c( - "gpp_mod" = 'tomato', - "gpp_obs" = 'grey20' - ), - labels = c( - "gpp_mod" = "P-model, calibrated", - "gpp_obs" = "Observed" - )) + - theme_classic() + - theme() + - labs( - x = 'Date', - y = expression(paste("GPP (g C m"^-2, "s"^-1, ")")), - colour = "" +plot_eval(df_plot) +``` + +For details on the optimization settings we refer to the manuals of [GenSA](https://cran.r-project.org/package=GenSA) and [BayesianTools](https://github.com/florianhartig/BayesianTools). + +## Water-carbon coupled simulations + +### SPLASH AET + +In the standard-setup, SPLASH is used for the water balance and ET. + +Simulations shown above are using the standard-setup. It is selected by setting `use_gs = FALSE` in `params_siml`. + +Here are the comparisons of modelled and observed ET. +```{r} +# Create dataframe for plotting +df_plot <- output |> + unnest(data) |> + mutate(le = le / (24 * 60 * 60)) |> + select(date, mod = le) |> + left_join( + p_model_validation |> + unnest(data) |> + select(date, obs = le), + by = join_by(date) + ) + +plot_eval(df_plot, is_gpp = FALSE) +``` + +### Gs-coupled diffusion ET + +Set `use_gs = TRUE` in `params_siml` so that the internally predicted stomatal conductance ($G_s$) from P-model will be used in calculation of ET. ET is a weighted average of canopy transpiration ($T$) and soil evaporation. Canopy transpiration is calculated using the diffusion equation as: +$$ +T = 1.6 \; G_s \; \text{VPD} +$$ + +Run the model. + +```{r} +drivers_tmp <- p_model_drivers |> + mutate( + params_siml = purrr::map( + params_siml, + ~mutate( + ., + use_gs = TRUE, + use_pml = FALSE, + use_phydro = FALSE + ) + )) + +# run the model with the same model parameters +output <- rsofun::runread_pmodel_f( + drivers_tmp, + par = params_modl +) +``` + +Plot outputs for LE. + +```{r} +# Create dataframe for plotting +df_plot <- output |> + unnest(data) |> + mutate(le = le / (24 * 60 * 60)) |> + select(date, mod = le) |> + left_join( + p_model_validation |> + unnest(data) |> + select(date, obs = le), + by = join_by(date) ) -# get fit metrics -rsq_val <- yardstick::rsq(df_plot, gpp_mod, gpp_obs) |> pull(.estimate) -rmse_val <- yardstick::rmse(df_plot, gpp_mod, gpp_obs) |> pull(.estimate) -bias_val <- mean(df_plot$gpp_mod - df_plot$gpp_obs, na.rm = TRUE) -linmod <- lm(gpp_obs ~ gpp_mod + 0, data = df_plot) -slope_val <- coef(linmod) -n_val <- df_plot |> tidyr::drop_na() |> nrow() - -rsq_lab <- format(rsq_val, digits = 3) -rmse_lab <- format(rmse_val, digits = 3) -bias_lab <- format(bias_val, digits = 3) -slope_lab <- format(slope_val, digits = 3) -n_lab <- n_val - -subtitle <- bquote(italic(R)^2 == .(rsq_lab) ~ ~ - RMSE == .(rmse_lab) ~ ~ - bias == .(bias_lab) ~ ~ - slope == .(slope_lab) ~ ~ - italic(N) == .(n_lab)) - -# plot modelled vs. observed (actually the reverse) -df_plot |> - ggplot(aes(x = gpp_mod, y = gpp_obs)) + - geom_hex(bins = 50, show.legend = FALSE) + - theme_classic() + - geom_abline(intercept = 0, slope = 1, linetype = "dotted") + - geom_hline(yintercept = 0, linetype = "dotted") + - geom_vline(xintercept = 0, linetype = "dotted")+ - geom_smooth(method = "lm", formula = y ~ x + 0, color = "red", size = 0.5, se = FALSE) + - khroma::scale_fill_batlowW(trans = "log", reverse = TRUE) + - xlim(-0.5, 10) + - ylim(-0.5, 10) + - labs( - x = expression(paste("P-model GPP (g C m"^-2, "s"^-1, ")")), - y = expression(paste("Observed GPP (g C m"^-2, "s"^-1, ")")), - subtitle = subtitle +plot_eval(df_plot, is_gpp = FALSE) +``` + +### Penman-Monteith ET + +Set `use_gs` flag to TRUE in params_siml so that the internally predicted stomatal conductance ($G_s$) from P-model will be used in calculation of ET. ET is a weighted average of canopy transpiration ($T$) and soil evaporation. Canopy transpiration is calculated using the diffusion equation as: +$$ +\lambda E = \frac{s(R_n - G) + \rho c_p D_a G_\mathrm{ah}}{s + \gamma (1 + G_\mathrm{ah}/G_\mathrm{sw})} +$$ + +Run the model. + +```{r} +drivers_tmp <- p_model_drivers |> + mutate( + params_siml = purrr::map( + params_siml, + ~mutate( + ., + use_gs = TRUE, + use_pml = TRUE, + use_phydro = FALSE + ) + )) + +# run the model with the same model parameters +output <- rsofun::runread_pmodel_f( + drivers_tmp, + par = params_modl +) +``` + +Plot outputs. + +```{r} +# Create dataframe for plotting +df_plot <- output |> + unnest(data) |> + mutate(le = le / (24 * 60 * 60)) |> + select(date, mod = le) |> + left_join( + p_model_validation |> + unnest(data) |> + select(date, obs = le), + by = join_by(date) ) + +plot_eval(df_plot, is_gpp = FALSE) ``` -For details on the optimization settings we refer to the manuals of [GenSA](https://cran.r-project.org/package=GenSA) and [BayesianTools](https://github.com/florianhartig/BayesianTools). + From 34b65317f44bc443fd636e2f70953a32b2ae7951 Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Tue, 15 Oct 2024 14:57:45 +0200 Subject: [PATCH 105/112] using wind speed from forcing data frame for aerodynamic conductance --- R/run_pmodel_f_bysite.R | 6 ++- src/forcing_siterun_pmodel.mod.f90 | 2 +- src/sofun_r.f90 | 2 +- vignettes/pmodel_use.Rmd | 66 ++++++++++++++++++++++++++++++ 4 files changed, 72 insertions(+), 4 deletions(-) diff --git a/R/run_pmodel_f_bysite.R b/R/run_pmodel_f_bysite.R index 3a4eefad..25531a30 100644 --- a/R/run_pmodel_f_bysite.R +++ b/R/run_pmodel_f_bysite.R @@ -207,7 +207,8 @@ run_pmodel_f_bysite <- function( # TODO: Above docstring appears duplicated in r "fapar", "patm", "tmin", - "tmax" + "tmax", + "vwind" ) forcing <- forcing %>% dplyr::mutate(fsun = (100-ccov)/100) %>% @@ -230,7 +231,8 @@ run_pmodel_f_bysite <- function( # TODO: Above docstring appears duplicated in r "fapar", "patm", "tmin", - "tmax" + "tmax", + "vwind" ) # list variable to check for check_vars_acclim <- c( diff --git a/src/forcing_siterun_pmodel.mod.f90 b/src/forcing_siterun_pmodel.mod.f90 index b41516e8..8436567e 100644 --- a/src/forcing_siterun_pmodel.mod.f90 +++ b/src/forcing_siterun_pmodel.mod.f90 @@ -95,7 +95,7 @@ function getclimate( nt, forcing, climateyear_idx, in_ppfd, in_netrad ) result ( out_climate(:)%dpatm = real(forcing(idx_start:idx_end, 10)) out_climate(:)%dtmin = real(forcing(idx_start:idx_end, 11)) out_climate(:)%dtmax = real(forcing(idx_start:idx_end, 12)) - out_climate(:)%dwind = 2.0 ! xxx try + out_climate(:)%dwind = real(forcing(idx_start:idx_end, 13)) end function getclimate diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index 0034af80..19dd240d 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -85,7 +85,7 @@ subroutine pmodel_f( & real(kind=c_double), intent(in) :: reference_height integer(kind=c_int), intent(in) :: nt ! number of time steps real(kind=c_double), dimension(15), intent(in) :: par ! free (calibratable) model parameters - real(kind=c_double), dimension(nt,12), intent(in) :: forcing ! array containing all temporally varying forcing data for instantaneous model (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) + real(kind=c_double), dimension(nt,13), intent(in) :: forcing ! array containing all temporally varying forcing data for instantaneous model (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) real(kind=c_double), dimension(nt,12), intent(in) :: forcing_acclim ! array containing all temporally varying forcing data for acclimating model (rows: time steps; columns: 1=air temperature, 2=rainfall, 3=vpd, 4=ppfd, 5=net radiation, 6=sunshine fraction, 7=snowfall, 8=co2, 9=fapar, 10=patm, 11=tmin, 12=tmax) real(kind=c_double), dimension(nt,23), intent(out) :: output diff --git a/vignettes/pmodel_use.Rmd b/vignettes/pmodel_use.Rmd index be9838c0..6de6cc61 100644 --- a/vignettes/pmodel_use.Rmd +++ b/vignettes/pmodel_use.Rmd @@ -418,4 +418,70 @@ df_plot <- output |> plot_eval(df_plot, is_gpp = FALSE) ``` +## P-hydro run with diffusion +(For comparison) + +For P-hydro, we must use the 3-hr daily max forcing as the acclimation forcing. So let's rename it in the data. + +```{r} +phdro_model_drivers <- rsofun::p_model_drivers_format2024_08 |> + rename(forcing_acclim = forcing_3hrmax) +``` + +For P-hydro runs, we always set `use_phydro = T` and `use_gs = T`. + +```{r} +# define model parameter values +params_modl <- list( + kphio = 0.0288, + kphio_par_a = 0.0, # set to zero to disable temperature-dependence of kphio + kphio_par_b = 1.0, + rd_to_vcmax = 0.014, # value from Atkin et al. 2015 for C3 herbaceous + tau_acclim = 30.0, + kc_jmax = 0.41, + phydro_K_plant = 5e-17, + phydro_p50_plant = -0.46, + phydro_gamma = 0.065, + phydro_b_plant = 1, + phydro_alpha = 0.08, + bsoil = 3, + Ssoil = 113 +) + +# run the model for these parameters +output <- rsofun::runread_pmodel_f( + mutate(phdro_model_drivers, + params_siml = params_siml |> mutate(use_gs = TRUE, use_phydro = TRUE) |> list()), + par = params_modl +) +``` + + +```{r} +# gpp +df_plot <- output$data[[1]] |> + select(date, mod = gpp) |> + left_join( + rsofun::p_model_validation_format2024_08$data[[1]] |> + select(date, obs = gpp), + by = join_by(date) + ) |> + as_tibble() + +plot_eval(df_plot) + +# le +df_plot <- output$data[[1]] |> + mutate(le = le / (24 * 60 * 60)) |> + select(date, mod = le) |> + left_join( + rsofun::p_model_validation_format2024_08$data[[1]] |> + mutate(le = le / (24 * 60 * 60)) |> + select(date, obs = le), + by = join_by(date) + ) |> + as_tibble() + +plot_eval(df_plot, is_gpp = FALSE) +``` From 6513b8efd7f09c89f73842f1299c26a4d0fc2968 Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Tue, 15 Oct 2024 15:39:10 +0200 Subject: [PATCH 106/112] scaling gs_acclim with PPFD; multiplying soil evaporation with 1.26 (Priestley-Taylor coefficient); scaling canopy conductance with LAI (calculated as -1/k*log(1-fapar) with k=0.5: Beer-Lambert coefficient for light extinction; multiplying canopy conductance with 2.0 - to be considered as a globally calibratable parameter. --- src/gpp_pmodel.mod.f90 | 4 +++- src/waterbal_splash.mod.f90 | 5 +++-- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 43bedc2c..01520194 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -279,6 +279,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init ! but not too dangerous... !---------------------------------------------------------------- if (.not. use_phydro) then + if( in_ppfd ) then ! print *, "Using in_ppfd" ! Take input daily PPFD (in mol/m^2) @@ -289,6 +290,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init tile_fluxes(lu)%plant(pft)%dgpp = tile(lu)%plant(pft)%fpc_grid * tile(lu)%canopy%fapar & * tile_fluxes(lu)%canopy%ppfd_splash * out_pmodel%lue * soilmstress end if + else ! Using phydro - run instantaneous model ! print *, "sw / swp = ", sw, swp out_phydro_inst = phydro_instantaneous_analytical( & @@ -357,7 +359,7 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init !---------------------------------------------------------------- if (.not. use_phydro) then ! Jaideep NOTE: I have applied the soilmstress factor to gs here because it is needed in calculating canopy transpiration - tile_fluxes(lu)%plant(pft)%gs_accl = out_pmodel%gs_setpoint * soilmstress + tile_fluxes(lu)%plant(pft)%gs_accl = climate%dppfd * out_pmodel%gs_setpoint * soilmstress ! print*,'in gpp: soilmstress, gs_accl ', soilmstress, tile_fluxes(lu)%plant(pft)%gs_accl else diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index 3d6f08a0..888321d8 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -428,7 +428,7 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g ! Potential soil evaporation !--------------------------------------------------------- ! potential soil evaporation, not limited by history of P/PET (mm d-1) - dpet_soil = (1.0 - fapar) * tile_fluxes%canopy%drn * tile_fluxes%canopy%econ * 1000.0 ! econ converts energy into mm evaporation + dpet_soil = (1.0 - fapar) * ( 1.0 + kw ) * tile_fluxes%canopy%drn * tile_fluxes%canopy%econ * 1000.0 ! econ converts energy into mm evaporation !--------------------------------------------------------- ! soil moisture limitation factor @@ -476,7 +476,8 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g ! Adopted from photosynth_phydro.mod.f90 ! gs_accl was computed using soilmstress in ggp_pmodel.mod.f90 ! print*,'in waterbal: gs_accl ', tile_fluxes%canopy%gs_accl - gw = max(tile_fluxes%canopy%gs_accl * 1.6 * kR * (climate%dtemp + kTkelvin), eps) + ! introduced scaling with LAI + gw = 2.0 * max(-1.0/0.5 * log(1.0 - fapar) * tile_fluxes%canopy%gs_accl * 1.6 * kR * (climate%dtemp + kTkelvin), eps) ! latent energy flux from canopy (W m-2) ! See also calc_transpiration_pm() in photosynth_phydro.mod.f90 From b6ab2d43e13daf446f63c933fc1234f3acbcb56f Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Tue, 15 Oct 2024 16:36:35 +0200 Subject: [PATCH 107/112] moved multiplication with PPFD to where gs_setpoint is calculated; introduced fixed factor 2.0 for multiplication of LAI in surface conductance calculation. --- src/gpp_pmodel.mod.f90 | 6 +++--- src/waterbal_splash.mod.f90 | 6 +++++- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 01520194..8bc5aad3 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -391,9 +391,9 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init ! Note here that stomatal conductance is already normalized by patm (=gs/patm) so E = 1.6 * (gs/patm) * vpd, which is the same as 1.6 gs (vpd/patm) ! but it is expressed per unit absorbed light, so multiply by PPFD*fapar ! dtransp is in mm d-1 - tile_fluxes(lu)%plant(pft)%dtransp = (1.6 & ! 1.6 - * tile_fluxes(lu)%plant(pft)%gs_accl * tile(lu)%canopy%fapar * climate%dppfd & ! gs - * climate%dvpd) & ! D + tile_fluxes(lu)%plant(pft)%dtransp = (1.6 & ! 1.6 + * tile_fluxes(lu)%plant(pft)%gs_accl * tile(lu)%canopy%fapar & ! gs + * climate%dvpd) & ! D * h2o_molmass * (1.0d0 / rho_water) & * myinterface%params_siml%secs_per_tstep ! convert: mol m-2 s-1 * kg-h2o mol-1 * m3 kg-1 * s day-1 * mm m-1 = mm day-1 diff --git a/src/waterbal_splash.mod.f90 b/src/waterbal_splash.mod.f90 index 888321d8..782a2946 100644 --- a/src/waterbal_splash.mod.f90 +++ b/src/waterbal_splash.mod.f90 @@ -335,6 +335,9 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g ! Used when using_gs == .true. real :: dpet_soil ! potential soil evaporation (not limited by soil moisture), mm d-1 + real, parameter :: k_beer = 0.5 + real :: lai_fapar + !--------------------------------------------------------- ! Calculate water-to-energy conversion (econ), m^3/J !--------------------------------------------------------- @@ -477,7 +480,8 @@ subroutine calc_et( tile_fluxes, grid, climate, sw, fapar, using_phydro, using_g ! gs_accl was computed using soilmstress in ggp_pmodel.mod.f90 ! print*,'in waterbal: gs_accl ', tile_fluxes%canopy%gs_accl ! introduced scaling with LAI - gw = 2.0 * max(-1.0/0.5 * log(1.0 - fapar) * tile_fluxes%canopy%gs_accl * 1.6 * kR * (climate%dtemp + kTkelvin), eps) + lai_fapar = -1.0 / k_beer * log(1.0 - fapar) + gw = max(2.0 * lai_fapar * tile_fluxes%canopy%gs_accl * 1.6 * kR * (climate%dtemp + kTkelvin), eps) ! latent energy flux from canopy (W m-2) ! See also calc_transpiration_pm() in photosynth_phydro.mod.f90 From 6c386159ba316cf8b95d206b45e9f565c866a06e Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Mon, 28 Oct 2024 09:34:12 +0100 Subject: [PATCH 108/112] nothing --- R/run_biomee_f_bysite.R | 2 +- src/sofun_r.f90 | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/run_biomee_f_bysite.R b/R/run_biomee_f_bysite.R index c78988e4..f04f09f4 100644 --- a/R/run_biomee_f_bysite.R +++ b/R/run_biomee_f_bysite.R @@ -421,7 +421,7 @@ run_biomee_f_bysite <- function( n_daily <- params_siml$nyeartrend * 365 # Types of photosynthesis model - if (params_siml$method_photosynth == "gs_leuning"){ + if (params_siml$method_photosynth == "gs_leuning"){ code_method_photosynth <- 1 } else if (params_siml$method_photosynth == "pmodel"){ code_method_photosynth <- 2 diff --git a/src/sofun_r.f90 b/src/sofun_r.f90 index 19dd240d..9a4bfda3 100644 --- a/src/sofun_r.f90 +++ b/src/sofun_r.f90 @@ -699,7 +699,7 @@ subroutine biomee_f( & !---------------------------------------------------------------- ! GET SOIL PARAMETERS !---------------------------------------------------------------- - !myinterface%params_soil = getsoil( params_soil ) + ! myinterface%params_soil = getsoil( params_soil ) myinterface%params_soil%GMD(:) = real(params_soil(:,1)) myinterface%params_soil%GSD(:) = real(params_soil(:,2)) From 2a262017f448d18e5cea88d7d7a118154ecdc475 Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Wed, 6 Nov 2024 16:43:54 +0100 Subject: [PATCH 109/112] removed obsolete argument --- src/gpp_pmodel.mod.f90 | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 8bc5aad3..7d9afe57 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -49,7 +49,7 @@ module md_gpp_pmodel ! soilwp = 1 - wscal**(-bsoil) ! end function - subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init, in_ppfd, use_phydro, use_pml) + subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init, in_ppfd, use_phydro ) !////////////////////////////////////////////////////////////////// ! Wrapper function to call to P-model. ! Calculates meteorological conditions with memory based on daily @@ -70,7 +70,6 @@ subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init logical, intent(in) :: init ! is true on the very first simulation day (first subroutine call of each gridcell) logical, intent(in) :: in_ppfd ! whether to use PPFD from forcing or from SPLASH output logical, intent(in) :: use_phydro ! whether to use P-Hydro for photosynthesis and transpiration - logical, intent(in) :: use_pml ! whether to use uncoupled PM formulation for canopy transpiration (whether to plug gs into PM equation. Alternatively, 1.6gsD will be used) ! local variables type(outtype_pmodel) :: out_pmodel ! list of P-model output variables From c443f78a96b4825dae696ef182f208bd7ba15c71 Mon Sep 17 00:00:00 2001 From: FrancescoGrossi-unimi <115780963+FrancescoGrossi-unimi@users.noreply.github.com> Date: Tue, 12 Nov 2024 09:50:51 +0100 Subject: [PATCH 110/112] add variable use_pml in gpp functions --- src/gpp_pmodel.mod.f90 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index 7d9afe57..be769e12 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -49,7 +49,7 @@ module md_gpp_pmodel ! soilwp = 1 - wscal**(-bsoil) ! end function - subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init, in_ppfd, use_phydro ) + subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init, in_ppfd, use_phydro, use_pml ) !////////////////////////////////////////////////////////////////// ! Wrapper function to call to P-model. ! Calculates meteorological conditions with memory based on daily From 6936379fd3649e134e9ff2af4a51602b3714dd95 Mon Sep 17 00:00:00 2001 From: Beni Stocker Date: Tue, 12 Nov 2024 11:54:28 +0100 Subject: [PATCH 111/112] removed argument us_pml --- src/biosphere_pmodel.mod.f90 | 3 +-- vignettes/pmodel_use.Rmd | 15 +++++++++++---- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/src/biosphere_pmodel.mod.f90 b/src/biosphere_pmodel.mod.f90 index 18d3ae02..e2c8b270 100644 --- a/src/biosphere_pmodel.mod.f90 +++ b/src/biosphere_pmodel.mod.f90 @@ -140,8 +140,7 @@ function biosphere_annual() result( out_biosphere ) myinterface%grid, & init_daily, & myinterface%params_siml%in_ppfd, & - myinterface%params_siml%use_phydro, & - myinterface%params_siml%use_pml & + myinterface%params_siml%use_phydro & ) ! if (verbose) print*,'... done' diff --git a/vignettes/pmodel_use.Rmd b/vignettes/pmodel_use.Rmd index 6de6cc61..1b9ba504 100644 --- a/vignettes/pmodel_use.Rmd +++ b/vignettes/pmodel_use.Rmd @@ -25,7 +25,7 @@ library(khroma) library(cowplot) # define function for evaluation plots -plot_eval <- function(df, is_gpp = TRUE){ +plot_eval <- function(df, is_gpp = TRUE, return_gg1 = FALSE, return_gg2 = FALSE){ use_col <- ifelse(is_gpp, "tomato", "royalblue") use_lab <- ifelse( @@ -132,8 +132,14 @@ plot_eval <- function(df, is_gpp = TRUE){ } - out <- cowplot::plot_grid(gg1, gg2, nrow = 1) - return(out) + if (return_gg1){ + return(gg1) + } else if (return_gg2){ + return(gg2) + } else { + cow <- cowplot::plot_grid(gg1, gg2, nrow = 1) + return(cow) + } } ``` @@ -415,7 +421,8 @@ df_plot <- output |> by = join_by(date) ) -plot_eval(df_plot, is_gpp = FALSE) +plot_eval(df_plot, is_gpp = FALSE, return_gg1 = TRUE) +ggsave("~/rsofun/fig/et_fr-pue.pdf", height = 4, width = 6) ``` ## P-hydro run with diffusion From dd586f9f3a15c24bd253d4a54a2fff8b7465e048 Mon Sep 17 00:00:00 2001 From: FrancescoGrossi-unimi <115780963+FrancescoGrossi-unimi@users.noreply.github.com> Date: Tue, 12 Nov 2024 15:28:04 +0100 Subject: [PATCH 112/112] removed argument us_pml --- src/gpp_pmodel.mod.f90 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/gpp_pmodel.mod.f90 b/src/gpp_pmodel.mod.f90 index be769e12..af67d1ce 100644 --- a/src/gpp_pmodel.mod.f90 +++ b/src/gpp_pmodel.mod.f90 @@ -49,7 +49,7 @@ module md_gpp_pmodel ! soilwp = 1 - wscal**(-bsoil) ! end function - subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init, in_ppfd, use_phydro, use_pml ) + subroutine gpp( tile, tile_fluxes, co2, climate, climate_acclimation, grid, init, in_ppfd, use_phydro) !////////////////////////////////////////////////////////////////// ! Wrapper function to call to P-model. ! Calculates meteorological conditions with memory based on daily

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