Update DETAW documentation.

detaw/README.md

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+# ssj-detaw
+Application of the DETAW calculation of ET, using supplied ETc and ETo daily values 
+for each DeTAW sub-region
+
+Lead: Tariq Kadir
+
+This method provides estimations of ET based on the DeTAW model which within the Bay Delta Service Area.  The DeTAW model supplies daily estimates of ET and ET for each of 163 sub-areas within the delta service region.
+
+The DeTAW estimates are prescriptive.  That means primarily that ET is estimated by predicting what each crop category would require rather than measure that directly.  Expected ET for each crop type are calibrated to expected Kc values that have been derived from satellite estimates using the SeBAL method.
+
+## Methodology
+
+DeTAW provides daily ET estimations for the Delta Service Region in California.  The Service region is divided into 163 regions.  Each daily DeTAW ET measurement is provided at the sub-area level.
+
+Within each sub-area DeTAW as ET and ETo estimations for each of the DWR crop categories.  Each sub-area also has estimates of the total area for each crop category.
+
+In order to provide comparisons of the DeTAW product with the other ET estimations in this project, DeTAW 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: 
+
+* Export DeTAW output dss data as CSV files.
+* Import CSV files into a database for daily ET and ETo estimations for each crop category over each 
+* Estimate daily DeTAW Kc values with spatial CIMIS ETo to obtain estimated daily ET=Kc*ETo for every sub-area, crop category 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 result has each field assigned the 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 | July Raster ET 
+| --- | ---
+| ![Yearly ET][et] | ![July Raster][jul]
+
+[jul]:july.png
+[aug]:raster.png
+[et]:detaw.png
+
+### Export DSS Data / Import into Database
+
+DeTAW output data comes in a set of DSS files.  In order to use these
+data, a certain set of data are extracted from these files.  This is
+down by extracting into CSV, the ETc and ETo entries from all the
+files delivered to UCD.  These were then manipulated into a more
+standard set of inputs for a Postgres table for both etc and eto.
+
+### Estimate Kc / ET for 30M pixels
+
+The following steps are used to create the high resolution images from
+the DeTAW data.  First, the Spatial CIMIS ETo data for Water Year
+21015 (wy2105), are imported.  Each sub-area is then intersected with
+each cimis pixel. These are the individual Kc regions.
+
+We then create a view, _model_output_ that divides ET by ETo for every
+sub-area,crop and date.  This gives us a Kc for every sub-area
+
+We then sum _model_output_ Kc * ETo for each sub-area pixel
+intersection, over each month to get an ET estimate for every
+pixel/sub-area combination.  We have these for every crop type as well.
+
+Then, for each pixel in the 30m region, we determine which
+pixel/sub-area it is in, and then associate the ET for the specified
+crop type for each region.
+
+Note, unlike CalSIMETAW were we could calculate non-precipitation
+events.  We cannot do the same with DeTAW, since we have no
+independant Kc estimate to use in that case.
+
+
+## [Results](./results)
+
+ETo is taken directly from the [Weather Repository].
+
+### LandIQ Based
+
+Data | Description | Monthly
+---  | --- | ---
+ET   | Average Monthly 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_1* classification that is nearly identical to the DeTAW crop categories. 
+We used these polygons to calculate prescriptive ET at the field scale.  
+
+ detaw_num | abbrev |       detaw       |               level_2                
+ --------- | ------ | ----------------- | -------------------------------------
+         1 | UR     | Urban             | Urban
+         2 | PA     | Pasture           | Pasture
+         2 | PA     | Pasture           | Turf
+         3 | AL     | Alfalfa           | Alfalfa
+         4 | FI     | Field             | Sunflower
+         4 | FI     | Field             | Corn
+         4 | FI     | Field             | Safflower
+         6 | GR     | Grain             | Forage Grass
+         7 | RI     | Rice              | Rice
+         8 | TR     | Truck             | Cucurbit
+         8 | TR     | Truck             | Bush Berries
+         8 | TR     | Truck             | Potatoes
+         8 | TR     | Truck             | Truck Crops
+         9 | TO     | Tomato            | Tomatoes
+        10 | OR     | Orchards          | Pears
+        10 | OR     | Orchards          | Almonds
+        10 | OR     | Orchards          | Cherries
+        10 | OR     | Orchards          | Citrus
+        10 | OR     | Orchards          | Olives
+        10 | OR     | Orchards          | Other Deciduous
+        10 | OR     | Orchards          | Pistachios
+        10 | OR     | Orchards          | Walnuts
+        11 | VI     | Vineyards         | Vineyards
+        12 | NR     | Native Riparian   | Riparian
+        13 | NV     | Native Vegetation | Semi-agricultural/ROW
+        13 | NV     | Native Vegetation | Fallow
+        13 | NV     | Native Vegetation | Wet herbaceous/sub irrigated pasture
+        13 | NV     | Native Vegetation | Upland Herbaceous
+        15 | WS     | Water             | Floating Vegetation
+        15 | WS     | Water             | Water