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# Name: Calculate_Stockpond_Consumptive_Use.py

# Author: Methodology developed by Caleb Mccurry, USBR and Troy Wirth, USBR; code by Kelly Meehan, USBR

# Created: 20200804

# Updated: 20200813

# Version: Created using Python 3.6.8

# Requires: ArcGIS Pro

# Notes: This script is intended to be used for a Script Tool within ArcGIS Pro; it is not intended as a stand-alone script.

# Description: This tool calculates stockpond consumptive use by multiplying net evapotranspiration by stockpond acreage

#----------------------------------------------------------------------------------------------

# Tool setup: The script tool's properties can be set as follows:

#

# Parameters tab:

# Output Geodatabase Workspace (Data Type) > Required (Type) > Input (Direction)

# PRISM Directory Workspace (Data Type) > Required (Type) > Input (Direction)

# State-HUC8 Feature Class Feature Class (Data Type) > Required (Type) > Input (Direction)

# Evapotranspiration Raster Raster Dataset (Data Type) > Required (Type) > Input (Direction)

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# This script will:

# 0. Set-up

# 1. Calculate annual mean precipitation per State-HUC8 from 1970 - 2018

# 2. Calculate mean evapotranspiration rate per State-HUC8

# 3. Calculate annual net evapotranspiration (in) per State-HUC8 from 1970 - 2018 using the formula: net evapotranspiration = (evapotranspiration (mm) - precipitation)/25.4

#----------------------------------------------------------------------------------------------

# 0. Set-up

# 0.0 Install necessary packages

import arcpy, os, fnmatch, re

#--------------------------------------------

# 0.1 Read in tool parameters

# User selects output directory file geodatabase

geodatabase = arcpy.GetParameterAsText(0)

# User selects directory with PRISM rasters

path_prism_directory = arcpy.GetParameterAsText(1)

# User selects original State-HUC8 feature class

fc_huc8_original = arcpy.GetParameterAsText(2)

# User selects evapotranspiration raster (Estimated Mean Monthly Evapotranspiration 1956 - 1970 raster) from https://cida.usgs.gov/thredds/ncss/mows/pe/dataset.html

raster_evap_original = arcpy.GetParameterAsText(3)

#--------------------------------------------

# 0.2 Set environment settings

# Set workspace to output directory

arcpy.env.workspace = geodatabase

#Overwrite output

arcpy.env.overwriteOutput = True

# 0.3 Check out Spatial Analyst Extension

arcpy.CheckOutExtension('Spatial')

# 0.4 Change working directory to output directory

os.chdir(geodatabase)

#----------------------------------------------------------------------------------------------

# 1. Calculate annual mean precipitation per State-HUC8 from 1970 - 2018

# Reproject State_HUC8 feature class to GCS NAD83 and PCS UTM12N

output_CRS = arcpy.SpatialReference('NAD 1983 UTM Zone 12N')

state_HUC8_NAD83_UTM12N = os.path.join(geodatabase, 'state_HUC8_NAD83_UTM12N')

arcpy.Project_management(in_dataset = fc_huc8_original, out_dataset = state_HUC8_NAD83_UTM12N, out_coor_system = output_CRS)

# Create a buffered version of the NAD83 UTM12N reprojected feature class

state_HUC8_NAD83_UTM12N_buffered = os.path.join(geodatabase, 'state_HUC8_NAD83_UTM12N_buffered')

arcpy.Buffer_analysis(in_features = state_HUC8_NAD83_UTM12N, out_feature_class = state_HUC8_NAD83_UTM12N_buffered, buffer_distance_or_field = '20000 Meters', dissolve_option = 'ALL')

# Create list of rasters by using a list comprehension to collect the yearly average raster while iterating through nested directories recursively

list_prism_rasters = [os.path.join(dirpath, f)

for dirpath, dirnames, filenames in os.walk(path_prism_directory)

for f in fnmatch.filter(filenames, 'PRISM_ppt_stable_4kmM?_????_bil.bil')]

list_prism_reprojected = []

for p in list_prism_rasters:

year = re.split('[_.]', os.path.basename(p))[4]

reprojected_raster = os.path.join(geodatabase, 'PRISM_' + year + '_NAD83_UTM12N')

arcpy.ProjectRaster_management(in_raster = p, out_raster = reprojected_raster, out_coor_system = output_CRS)

list_prism_reprojected.append(reprojected_raster)

# Generate a table of mean precipitation by State-HUC8 zone for each year; join values to State-HUC8 reprojected feature class

for r in list_prism_reprojected:

year = re.split('[_.]', os.path.basename(r))[1]

table_mean_precip = os.path.join(geodatabase, 'PRISM_' + year + '_Mean_Zonal_Precipitation')

arcpy.sa.ZonalStatisticsAsTable(in_zone_data = state_HUC8_NAD83_UTM12N, zone_field = 'OBJECTID', in_value_raster = r, out_table = table_mean_precip, statistics_type = 'MEAN')

arcpy.JoinField_management(in_data = state_HUC8_NAD83_UTM12N, in_field = 'OBJECTID', join_table = table_mean_precip, join_field = 'OBJECTID_1', fields = 'MEAN')

arcpy.AddField_management(in_table = state_HUC8_NAD83_UTM12N, field_name = 'ppt_mean_' + year, field_type = 'FLOAT')

arcpy.CalculateField_management(in_table = state_HUC8_NAD83_UTM12N, field = 'ppt_mean_' + year, expression = "!MEAN!", expression_type = 'PYTHON3')

arcpy.DeleteField_management(in_table = state_HUC8_NAD83_UTM12N, drop_field = 'MEAN')

#----------------------------------------------------------------------------------------------

# 2. Calculate mean evapotranspiration rate per State-HUC8

# Set snap raster environment setting (otherwise Extract by Mask may shift raster)

arcpy.env.snapRaster = raster_evap_original

# Extract Raster by Mask

raster_evap_clipped = os.path.join(geodatabase, os.path.splitext(os.path.basename(raster_evap_original))[0] + '_clipped')

out_evap_clipped = arcpy.sa.ExtractByMask(in_raster = raster_evap_original, in_mask_data = state_HUC8_NAD83_UTM12N_buffered)

out_evap_clipped.save(raster_evap_clipped)

# Reproject evapotranspiration raster to GCS NAD83 and PCS UTM12N

raster_evap_reprojected = os.path.join(geodatabase, 'Evapotranspiration_NAD83_UTM12N')

arcpy.ProjectRaster_management(in_raster = raster_evap_clipped, out_raster = raster_evap_reprojected, out_coor_system = output_CRS)

# Copy evapotranspiration raster to set NoData value to 0

raster_evap_nodata = os.path.join(geodatabase, 'Evapotranspiration_NAD83_UTM12N_NoData')

arcpy.CopyRaster_management(in_raster = raster_evap_reprojected, out_rasterdataset = raster_evap_nodata, nodata_value = 0)

# Resample evapotranspiration raster (otherwise Zonal Statistics as Table will return NULL values as cells would be too large to have a center in zones of small sizes)

raster_evap_resampled = os.path.join(geodatabase, 'Evapotranspiration_NAD83_UTM12N_NoData_Resample')

cell_size_x_result = arcpy.GetRasterProperties_management(in_raster = list_prism_reprojected[0], property_type = 'CELLSIZEX')

cell_size_x = cell_size_x_result.getOutput(0)

cell_size_y_result = arcpy.GetRasterProperties_management(in_raster = list_prism_reprojected[0], property_type = 'CELLSIZEY')

cell_size_y = cell_size_y_result.getOutput(0)

size_x_y = cell_size_x + ' ' + cell_size_y

arcpy.env.snapRaster = list_prism_reprojected[0]

arcpy.Resample_management(in_raster = raster_evap_nodata, out_raster = raster_evap_resampled, cell_size = size_x_y, resampling_type = 'BILINEAR')

# Generate a table of evapotranspiration rates per State-HUC8; join values to State-HUC8 reprojected feature class

table_mean_evap = os.path.join(geodatabase, 'Evapotranspiration_Mean_Rate')

arcpy.sa.ZonalStatisticsAsTable(in_zone_data = state_HUC8_NAD83_UTM12N, zone_field = 'OBJECTID', in_value_raster = raster_evap_resampled, out_table = table_mean_evap, statistics_type = 'MEAN')

# Join data

arcpy.JoinField_management(in_data = state_HUC8_NAD83_UTM12N, in_field = 'OBJECTID', join_table = table_mean_evap, join_field = 'OBJECTID_1', fields = 'MEAN')

field_evapotranspiration = 'Evapotranspiration_Mean_Rate'

arcpy.AddField_management(in_table = state_HUC8_NAD83_UTM12N, field_name = field_evapotranspiration, field_type = 'FLOAT')

arcpy.CalculateField_management(in_table = state_HUC8_NAD83_UTM12N, field = field_evapotranspiration, expression = "!MEAN!", expression_type = 'PYTHON3')

arcpy.DeleteField_management(in_table = state_HUC8_NAD83_UTM12N, drop_field = 'MEAN')

#----------------------------------------------------------------------------------------------

# 3. Calculate annual net evapotranspiration (inches) per State-HUC8 from 1970 - 2018 using the formula: net evapotranspiration = (evapotranspiration (millimeters) - precipitation)/25.4

fields_prism = [field.name for field in arcpy.ListFields(dataset = state_HUC8_NAD83_UTM12N, wild_card = 'ppt_mean_*')]

def calculate_net_evap_inches():

cursor.updateRow(row)

calculate_net_evap_inches()

# Export attribute table of State_HUC8 reprojected feature class

arcpy.management.CopyRows(in_rows = state_HUC8_NAD83_UTM12N, out_table = 'net_evapotranspiration.csv')