Added Documentation

Makefile

@@ -9,7 +9,7 @@ OGR:=ogr2ogr -f "PostgreSQL" PG:"dbname=${db} port=5433" -overwrite -a_srs epsg:
 
 .PHONY:import
 
-import: dau grid
+import: dau grid eto
 # This just rebuilds everytime
 dau: tar:=v2.0.0.tar.gz
 dau: url:=https://github.com/ucd-cws/dwr-dau/archive/v2.0.0.tar.gz
@@ -31,11 +31,25 @@ grid:
 	shp2pgsql -d -S -I -g boundary -s 3310 ${calsimetaw} ${schema}.calsimetaw | ${PG} > /dev/null
 	date > $@
 
+eto:rast:=${HOME}/ssj-weather/cimis/2015.wy/ETo.tif
+eto:
+	raster2pgsql -d ${rast} cimis.eto | ${PG} -f -
+	date > $@
+
 bbox: bbox:=/home/quinn/ssj-overview/ssj-delta-cu-bbox.geojson
 bbox:
 	${OGR} -nln overview.bbox ${bbox}
 	date > $@ 
 
+
+et_wy2015.tif:et_wy%.tif:
+	gdal_translate -of GTiff -co "COMPRESS=DEFLATE" \
+	  "PG:dbname='ssj' schema='calsimetaw' table='et_raster' mode='1' where='type=\\'et\\' and year=$*'" $@
+
+iet_wy2015.tif:iet_wy%.tif:
+	gdal_translate -of GTiff -co "COMPRESS=DEFLATE" \
+	  "PG:dbname='ssj' schema='calsimetaw' table='et_raster' mode='1' where='type=\'iet\' and year=$*'" $@
+
 # Getting DWR values
 #for i in `psql service=ssj -A -t --pset=footer -c 'with a as (select dwr_id,boundary,wkb_geometry,(st_area(st_intersection(g.boundary,b.wkb_geometry)))::int as area from calsimetaw.calsimetaw g join overview.bbox b on st_intersects(g.boundary,b.wkb_geometry)) select dwr_id from a where area > 0 order by dwr_id' `; do rsync ../weather/cimis_$i.csv . -v ; done
 

README.md

@@ -14,34 +14,64 @@ The CalSIMETAW program provides daily ET estimations for regions in California.
 
 Within each region, CalSIMETAW has estimates of Kc values for each of the DWR crop categories.  These values are supplied daily.  Each region also has estimates of the total area for each category for each DAUCO.
 
-CalSIMETAW provides daily Kc estimations for each crop categorie within each DAUCO.   In order to provide comparisons of the CalSIMETAW product with the other ET estimations in this project, the CalSIMETAW estimations need to be combined with higher resolution data to provide higher resolution ET estimates.   This includes Spatial CIMIS for estimations of ETo, and a landcover map, to determein
+In order to provide comparisons of the CalSIMETAW product with the other ET estimations in this project, the CalSIMETAW estimations need to be combined with higher resolution data to provide the ET estimates.   This includes Spatial CIMIS for estimations of ETo, and a landcover map, to determine where the crop categories reside.  
 
+The process is: 
+
+* Import CalSIMETAW Daily data for each commodity and dauco
+* Intersect CalSIMTAW Kc values with spatial CIMIS ETo to have estimated daily ET=Kc*ETo for every DAUCO, commodity and Spatial CIMIS pixel.
+* Aggregate Daily ET into monthly values, (average ET for each month)
+* Intersect Aggregated ET with Landuse type, to apply the appropriate commodity to each high resolution field/region.  The results has each field assigned it's average ET for each month
+* Export this as a 30m raster of 12 bands, each band representing the monthly average ET.  These can then be directly compared to the directly measured ET based methods.
+
+| Estimated Yearly ET |
+| --- |
+| ![Yearly ET][et] |
 
 ## [Results](./results)
 
-We calculate ET in a few ways.   Regardless of the method however, the outputs and intermediate products will be the same, the method provides monthy estimates of ET and Kc based on Spatial CIMIS ETo, CalSIMETAW ppt and the Kc curves supplied by DWR's CalSIMETAW program.
+We calculate our ET results in a few ways.   Regardless of the method however, the output will be the same, the method provides monthy estimates of ET based on Spatial CIMIS ETo, CalSIMETAW ppt and the Kc curves supplied by DWR's CalSIMETAW program.
 
-Because CalSIMETAW includes ET from precipication events, we will calculate monthly ET 
+One important component of CalSIMETAW is that it modifies it's Kc and ET based on precipication events that wet the surface.  We will also include monthly predictions of ET, that do not include this contribution.  This can be done using CalSIMETAW's iKc term.  This allows for 
 
 ETo is taken directly from the [Weather Repository].  The CalSIMETAW DAUCOs include precipitation events.  
 
 ### LandIQ Based
 
-DWR has contracted to produce the [LandIQ Landuse] dataset.  This dataset
-provides a *Level_2* classification that is nearly identical to the CalSIMETAW crop categories. We will use this vector layer to calculate prescriptive ET at the field scale.  For this effort, we will use vector processing of for the calculation. 
-
-
-
-Data | Monthly
----  | ---
-ET   | [results/nasa/wy2015/monthly]
-Kc   | [results/nasa/wy2015/monthly]
-
-To create the required 30m raster map, the resultant outputs where rasterized and exported.
+Data | Descriptionn | Monthly
+---  | --- | ---
+ET   | Average Monthly ET | [results/wy2015/monthly]
+iET   | Average Monthly Crop (no PPT) ET | [results/wy2015/monthly]
 
 [results/wy2015/monthly]: ./results/nasa/wy2015/monthly
 [LandIQ Landuse]: https://github.com/ssj-delta-cu/ssj-landuse
 
+DWR has contracted to produce the [LandIQ Landuse] dataset.  This dataset
+provides a *Level_2* classification that is nearly identical to the CalSIMETAW crop categories. 
+We used these polygons to calculate prescriptive ET at the field scale.  
+
+commodity | level_2 | commodity | level_2
+--- | --- | --- | ---
+Alfalfa   | Alfalfa  |     Pistachio | Pistachios
+Almonds   | Almonds  |        Potatoes  | Potatoes
+Cherries  | Cherries |        Rice      | Rice
+CitrusSubtrop  | Citrus |        Riparian  | Riparian
+Corn      | Corn  |        Riparian  | Floating Vegetation
+Cucurbits | Cucurbit |        Safflower | Safflower
+FALLOW    | Fallow |        OtherDeciduous | Semi-agricultural/ROW
+FALLOW    | Upland Herbaceous |      Sunflower | Sunflower
+Pasture   | Wet herbaceous/sub irrigated pasture |  Tomato    | Tomatoes
+Olives    | Olives       |   TruckCrops     | Truck Crops
+OtherDeciduous | Other Deciduous     |  Bushberries    | Bush Berries
+Pasture   | Pasture       |  UrbanLandscape | Urban
+Pasture   | Forage Grass      |  Vineyard  | Vineyards
+Turffarm  | Turf        |  Walnuts   | Walnuts
+Pears     | Pears        |  WaterSurface   | Water
+
+## Alternative Vegetation
+
+We could test the regional sensitivity of the CalSIMETAW method by comparing the results from a number of alternative landuse types.  Two additional Land-cover types are available.
+
 ### NASA Landcover Based
 
 NASA has provided the SSJ team with a vector data layer of landcover that includes [Cropland Data Layer] (CDL) crop types.  We will use this vector map to calculate ET.  For this effort, we will use vector processing of for the calculation.  We will also use the larger CalSIMETAW 4km grid for weather inputs.
@@ -51,9 +81,6 @@ Data | Daily | Monthly
 ET   | [results/nasa/wy2015/daily] | [results/nasa/wy2015/monthly]
 Kc   | [results/nasa/wy2015/daily] | [results/nasa/wy2015/monthly]
 
-To create the summarization tables, the vector maps were intersected with the [Island Regions].
-
-To create the required 30m raster map, the resultant outputs where rasterized and exported.
 
 [NASA Landcover]: https://github.com/ssj-delta-cu/ssj-nasa-landcover
 [results/nasa/wy2015/daily]: ./results/nasa/wy2015/daily
@@ -75,4 +102,5 @@ Kc   | [results/cdl/wy2015/daily] | [results/cdl/wy2015/monthly]
 
 [CUPS Users' Guide]: ./documentation/cups.pdf
 [Weather Repository]: https://github.com/ssj-delta-cu/ssj-weather/cimis
-
+[dauco]: dauco.png
+[et]: et.png