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Copy pathRunoff&LeachingVul_10_11_2023.R
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Runoff&LeachingVul_10_11_2023.R
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library(terra)
library(aws.s3)
# point to s3 bucket
bucket <- get_bucket("s3-fpac-nrcs-dshub-prod")
#### Load Raster Inputs ####
# taxcode
# 1 = histosols
# 2 = non-histosols
taxcode <- vrt(list.files("~/SOLUS/Inputs/taxcode/", recursive=TRUE, full.names=TRUE), filename="./Inputs/taxcode_SSURGO_10_04_2023.vrt")
taxcode <- rast("./Inputs/taxcode_SSURGO_10_04_2023.vrt")
plot(taxcode, col=c('red', 'green'))
# wtcode
# 1 = apparent
# 2 = perched
# 3 = not rated
wtcode <- vrt(list.files("~/SOLUS/Inputs/wtcode/", recursive=TRUE, full.names=TRUE), filename="./Inputs/wtcode_SSURGO_10_04_2023.vrt")
wtcode <- rast("./Inputs/wtcode_SSURGO_10_04_2023.vrt")
plot(wtcode, col=c('red','green','grey'))
# wt_24in_flag. CART uses 61 cm for runoff and 76 for leaching. we are just going to use 24" (61 cm)
# 1 = water table <24"
# 2 = water table >24"
wt24in <- vrt(list.files("~/SOLUS/Inputs/wt_24in_flag/", recursive=TRUE, full.names=TRUE), filename="./Inputs/wt24in_SSURGO_10_04_2023.vrt")
wt24in <- rast("./Inputs/wt24in_SSURGO_10_04_2023.vrt")
plot(wt24in, col=c('red','green'))
# kwfactor
# < 0.24 = 1
# 0.24 < 0.28 = 2
# 0.28 < 0.32 = 3
# >= 0.32 = 4
kwfact <- vrt(list.files("~/SOLUS/Inputs/kfactor/", recursive=TRUE, full.names=TRUE), filename="./Inputs/kwcode_SSURGO_10_04_2023.vrt")
kwfact <- rast("./Inputs/kwcode_SSURGO_10_04_2023.vrt")
plot(kwfact, col=c("#ffffbf","#fee08b","#fc8d59","#d53e4f"))
# hsg
hsg <- vrt(list.files("~/SOLUS/Outputs/hsgtiles/", recursive=TRUE, full.names=TRUE), filename="./Inputs/hsg_SOLUS_10_04_2023.vrt")
hsg<- rast("./Inputs/hsg_SOLUS_10_04_2023.vrt")
# 1 = A
# 2 = B
# 3 = C
# 4 = D
# 5 = A/D
# 6 = B/D
# 7 = C/D
plot(hsg, col=c("#ffffbf","#fee08b","#fc8d59","#d53e4f","#4d9221","#998ec3","#3288bd"))
# slope
# not classified
# load .tif
slope <- rast("./Inputs/solus/covariates_SLPNED6.tif")
plot(slope)
# fragvol
# load frag vol tiffs
frag0cm <- rast("./Inputs/solus/fragvol_r_1x_0_cm_2D_QRFadj_bt.tif")
frag5cm <- rast("./Inputs/solus/fragvol_r_1x_5_cm_2D_QRFadj_bt.tif")
frag15cm <- rast("./Inputs/solus/fragvol_r_1x_15_cm_2D_QRFadj_bt.tif")
frag30cm <- rast("./Inputs/solus/fragvol_r_1x_30_cm_2D_QRFadj_bt.tif")
frag60cm <- rast("./Inputs/solus/fragvol_r_1x_60_cm_2D_QRFadj_bt.tif")
frag100cm <- rast("./Inputs/solus/fragvol_r_1x_100_cm_2D_QRFadj_bt.tif")
frag150cm <- rast("./Inputs/solus/fragvol_r_1x_150_cm_2D_QRFadj_bt.tif")
# average across 7 depth observations. Not sure of any other way since these arent layers
average.frags <- (frag0cm + frag5cm + frag15cm + frag30cm + frag60cm + frag100cm + frag150cm)/7
writeRaster(average.frags, "./Inputs/solus/averagefragvol_solus_0_150cm.tif")
# classify for interpretation
# 1 = <= 10
# 2 = >10 <= 30
# 3 = >30
average.frags[average.frags <= 10] <- 1
average.frags[average.frags > 10 & average.frags <= 30] <- 2
average.frags[average.frags > 30] <- 3
plot(average.frags)
writeRaster(average.frags, "./Inputs/fragvol0_150_solus_10_4_2023.tif")
fragvol <- rast("./Inputs/fragvol0_150_solus_10_4_2023.tif")
plot(fragvol, col=c("green","yellow","red"))
#### Stack Inputs and Make Tiles ####
# make empty raster with dimensions of SOLUS
empty_raster <- rast(xmin=xmin(hsg), xmax=xmax(hsg), ymin=ymin(hsg), ymax=ymax(hsg), ncols=ncol(hsg), nrows=nrow(hsg), res=res(hsg))
crs(empty_raster)<- crs(hsg)
# resample SSURGO rasters to SOLUS resolution
taxcode.resamp <- resample(taxcode, empty_raster, threads=TRUE, filename ="./Inputs/taxcode_resamp.tif", overwrite=TRUE, method="near")
wtcode.resamp <- resample(wtcode, empty_raster, threads=TRUE, filename ="./Inputs/wtcode_resamp.tif", overwrite=TRUE, method="near")
wt24in.resamp <- resample(wt24in, empty_raster, threads=TRUE, filename ="./Inputs/wt24in_resamp.tif", overwrite=TRUE, method="near")
kwfact.resamp <- resample(kwfact, empty_raster, threads=TRUE, filename ="./Inputs/kwfact_resamp.tif", overwrite=TRUE, method="near")
# edit min and maxs of wt24in and
taxcode.resamp <- rast("./Inputs/taxcode_resamp.tif")
wtcode.resamp <- rast("./Inputs/wtcode_resamp.tif")
wt24in.resamp <- rast("./Inputs/wt24in_resamp.tif")
kwfact.resamp <- rast("./Inputs/kwfact_resamp.tif")
# reclassify any values above max to max
wt24in.adj <- ifel(wt24in.resamp > 2, 2, wt24in.resamp, filename="./Inputs/wt24in_adj.tif", overwrite=TRUE)
kwfact.adj <- ifel(kwfact.resamp > 4, 4, kwfact.resamp, filename="./Inputs/kwfact_adj.tif", overwrite=TRUE)
wt24in.adj<- rast("./Inputs/wt24in_adj.tif")
ksfact.adj<- rast("./Inputs/kwfact_adj.tif")
# mask slope values that are outside SSURGO
slope.mask <-terra::mask(slope, wtcode.resamp, filename="./Inputs/SOLUS_slope_mask.tif")
hsg.mask <-terra::mask(hsg, wtcode.resamp, filename="./Inputs/SOLUS_hsg_mask.tif")
fragvol.mask <-terra::mask(fragvol, wtcode.resamp, filename="./Inputs/SOLUS_fragvol_mask.tif")
# load preprocessed inputs
wt24in.adj <- rast("./Inputs/wt24in_adj.tif")
ksfact.adj <- rast("./Inputs/kwfact_adj.tif")
slope.mask <- rast("./Inputs/SOLUS_slope_mask.tif")
hsg.mask <- rast("./Inputs/SOLUS_hsg_mask.tif")
fragvol.mask <- rast("./Inputs/SOLUS_fragvol_mask.tif")
# make stack with inputs
vul.inputs <- c(taxcode.resamp, wtcode.resamp, wt24in.adj, kwfact.adj, hsg, slope, fragvol)
names(vul.inputs) <-c("taxcode", "wtcode", "wt24in", "kwfact", "hsg", "slope", "fragvol")
writeRaster(vul.inputs, filename = "./Inputs/vulinputs.tif", overwrite=TRUE)
vul.inputs <- rast("./Inputs/vulinputs.tif")
# create grid for tiles
x <- rast(ncols=10, nrows=10, extent = ext(vul.inputs), crs = crs(vul.inputs))
# make tiles
makeTiles(vul.inputs, x, filename="./Inputs/vultiles/vultile_.tif")
#### Runoff Vulnerability ####
# runoff criteria
runoffvul <- function(wt24in, kwfact, slope, hsg, runoff){
# percher or apparent water table within 61 cm or 24" is 3=high. Since its perched or aparrent I jsut used the 24" SHWT raster
runoff[wt24in == 1] <- 3 # High
# Dual HSGs that are not drained are 3=high
runoff[wt24in > 1 & hsg >= 5] <- 3 # High
# HSG: A
runoff[wt24in > 1 & hsg == 1] <- 0 # Low
# HSG: B
runoff[wt24in > 1 & hsg == 2 & slope < 4] <- 0 # Low
runoff[wt24in > 1 & hsg == 2 & slope >=4 & slope <= 6 & kwfact <= 3] <- 1 # Mod
runoff[wt24in > 1 & hsg == 2 & slope >=4 & slope <= 6 & kwfact == 4] <- 2 # ModHigh
runoff[wt24in > 1 & hsg == 2 & slope > 6] <- 3 # High
# HSG: C
runoff[wt24in > 1 & hsg == 3 & slope < 2] <- 0 # Low
runoff[wt24in > 1 & hsg == 3 & slope >=2 & slope <= 6 & kwfact <= 2] <- 1 # Mod
runoff[wt24in > 1 & hsg == 3 & slope >=2 & slope <= 6 & kwfact >= 3] <- 2 # ModHigh
runoff[wt24in > 1 & hsg == 3 & slope > 6] <- 3 # High
# HSG: D
runoff[wt24in > 1 & hsg == 4 & slope < 2 & kwfact <= 2] <- 0 # Low
runoff[wt24in > 1 & hsg == 4 & slope < 2 & kwfact >= 3] <- 1 # Mod
runoff[wt24in > 1 & hsg == 4 & slope >=2 & slope <= 4] <- 2 # ModHigh
runoff[wt24in > 1 & hsg == 4 & slope > 4] <- 3 # High
return(runoff)
}
# Calculate Runoff Vulnerability
# list input tiles
inputtiles <- list.files("./Inputs/vultiles/", full.names = TRUE)
# run runoff function on tiles
# inputs (wtcode, wt24in, hsg, slope, kwfact, runoff)
for(i in 1:100){
x <- inputtiles[i]
vul.inputs <- rast(x)
# create band for output
runoff <- rast(xmin=xmin(vul.inputs), xmax=xmax(vul.inputs), ymin=ymin(vul.inputs), ymax=ymax(vul.inputs), ncols=ncol(vul.inputs), nrows=nrow(vul.inputs), res=100)
crs(runoff) <- crs(vul.inputs)
# add to stack and set name to hsg
vul.inputs <- c(vul.inputs, runoff)
names(vul.inputs)[7]<-"runoff"
lapp(c(vul.inputs$wt24in, vul.inputs$kwfact, vul.inputs$slope, vul.inputs$hsg, vul.inputs$runoff),
fun=runoffvul,
filename = paste0("./Outputs/runoffvul/runoffvultile_", i, ".tif"),
overwrite = TRUE,
wopt = list(datatype = "INT1U"))
}
#vrt
runoffvrt <- vrt(list.files("~/SOLUS/Outputs/runoffvul/", recursive=TRUE, full.names=TRUE), filename="~/SOLUS/Outputs/SOLUS_runoffvul_10_05_2023.vrt")
#plot
plot(runoffvrt, col=viridis::viridis(4))
# mask
runoffvrt.notcom<- terra::mask(runoffvrt, kwfact.adj)
writeRaster(runoffvrt.notcom, filename="./Outputs/SOLUS_srp_10_11_2023.tif")
# plot
plot(runoffvrt.notcom, col=viridis::viridis(4))
#### leaching Vulnerability ####
leachingvul <- function(taxcode, wt24in, hsg, slope, kwfact, fragvol, leaching){
# histosols is 3=high
leaching[taxcode == 1] <- 3 # High
# percher or appart water table within 61 cm or 24" is 3=high. Note: CART Criteria uses 76cm for leaching but we are going to use 61 since at this scale 15cm isnt much
leaching[taxcode == 2 & wt24in == 1] <- 3 # High
# Dual HSGs are 3=high
leaching[taxcode == 2 & wt24in > 1 & hsg >= 5] <- 3 # High
# HSG: A
leaching[taxcode == 2 & wt24in > 1 & hsg == 1 & slope > 12] <- 2 # ModHigh
leaching[taxcode == 2 & wt24in > 1 & hsg == 1 & slope < 12] <- 3 # High
# HSG: B
leaching[taxcode == 2 & wt24in > 1 & hsg == 2 & slope <= 12 & kwfact >= 2] <- 1 # Mod
leaching[taxcode == 2 & wt24in > 1 & hsg == 2 & slope > 12] <- 1 # Mod
leaching[taxcode == 2 & wt24in > 1 & hsg == 2 & slope >= 3 & slope <= 12 & kwfact == 1] <- 2 # ModHigh
leaching[taxcode == 2 & wt24in > 1 & hsg == 2 & slope < 3 & kwfact == 1] <- 3 # High
# HSG: C
leaching[taxcode == 2 & wt24in > 1 & hsg == 3] <- 1 #Mod
# HSG: D
leaching[taxcode == 2 & wt24in > 1 & hsg == 4] <- 0 #Low
# Coarse Fragment Corrections
# > 30% fragments
leaching[leaching == 0 & fragvol == 3] <- 2 #
leaching[leaching == 1 & fragvol == 3] <- 3
leaching[leaching == 2 & fragvol == 3] <- 3
# 10-30% fragments
leaching[leaching == 0 & fragvol == 2] <- 1
leaching[leaching == 1 & fragvol == 2] <- 2
leaching[leaching == 2 & fragvol == 2] <- 3
return(leaching)
}
# run leaching function on tiles
# inputs (taxcode, wtcode, wt24in, hsg, slope, kwfact, fragvol, leaching)
for(i in 1:100){
x <- inputtiles[i]
vul.inputs <- rast(x)
# create band for output
leaching <- rast(xmin=xmin(vul.inputs), xmax=xmax(vul.inputs), ymin=ymin(vul.inputs), ymax=ymax(vul.inputs), ncols=ncol(vul.inputs), nrows=nrow(vul.inputs), res=100)
crs(leaching) <- crs(vul.inputs)
# add to stack and set name to hsg
vul.inputs <- c(vul.inputs, leaching)
names(vul.inputs)[8]<-"leaching"
lapp(c(vul.inputs$taxcode, vul.inputs$wt24in, vul.inputs$hsg, vul.inputs$slope, vul.inputs$kwfact, vul.inputs$fragvol, vul.inputs$leaching),
fun=leachingvul,
filename = paste0("./Outputs/leachingvul/leachingvultile_", i, ".tif"),
overwrite = TRUE,
wopt = list(datatype = "INT1U"))
}
# .vrt
leachingvrt <- vrt(list.files("~/SOLUS/Outputs/leachingvul/", recursive=TRUE, full.names=TRUE), filename="~/SOLUS/Outputs/SOLUS_leachingvul_10_05_2023.vrt", overwrite=TRUE)
#plot
plot(leachingvrt, col=viridis::viridis(4))
writeRaster(leachingvrt, filename="./Outputs/SOLUS_nslp_10_11_2023.tif")
# TODO: see why on NOTCOM areas area already masked out with leaching
# load rast for masking
#kwfact.adj <- rast("./Inputs/kwfact_adj.tif")
# mask
#leachingvrt.notcom<- terra::mask(leachingvrt, kwfact.adj)
# plot
#plot(leachingvrt.notcom, col=viridis::viridis(4))