def Calculate(WA_HOME_folder, Basin, P_Product, ET_Product, LAI_Product,
NDM_Product, NDVI_Product, ETref_Product, dict_crops,
dict_non_crops, Startdate, Enddate, Simulation):
"""
This functions is the main framework for calculating sheet 3.
Parameters
----------
Basin : str
Name of the basin
P_Product : str
Name of the rainfall product that will be used
ET_Product : str
Name of the evapotranspiration product that will be used
LAI_Product : str
Name of the LAI product that will be used
NDM_Product : str
Name of the NDM product that will be used
Startdate : str
Contains the start date of the model 'yyyy-mm-dd'
Enddate : str
Contains the end date of the model 'yyyy-mm-dd'
Simulation : int
Defines the simulation
"""
######################### Import WA modules ###################################
from wa.General import raster_conversions as RC
from wa.General import data_conversions as DC
import wa.Functions.Three as Three
import wa.Functions.Two as Two
import wa.Functions.Start as Start
import wa.Functions.Four as Four
import wa.Generator.Sheet3 as Generate
import wa.Functions.Start.Get_Dictionaries as GD
######################### Set General Parameters ##############################
# Check if there is a full year selected between Startdate and Enddate, otherwise Sheet 3 cannot be produced
try:
years_end = pd.date_range(Startdate, Enddate, freq="A").year
years_start = pd.date_range(Startdate, Enddate, freq="AS").year
if (len(years_start) == 0 or len(years_end) == 0):
print "Calculation period is less than a year, which is not possible for sheet 3"
quit
years = np.unique(np.append(years_end, years_start))
except:
print "Calculation period is less than a year, which is not possible for sheet 3"
quit
# Get environmental variable for the Home folder
if WA_HOME_folder == '':
WA_env_paths = os.environ["WA_HOME"].split(';')
Dir_Home = WA_env_paths[0]
else:
Dir_Home = WA_HOME_folder
# Create the Basin folder
Dir_Basin = os.path.join(Dir_Home, Basin)
output_dir = os.path.join(Dir_Basin, "Simulations",
"Simulation_%d" % Simulation)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Get the boundaries of the basin based on the shapefile of the watershed
# Boundaries, Shape_file_name_shp = Start.Boundaries.Determine(Basin)
Boundaries, Example_dataset = Start.Boundaries.Determine_LU_Based(
Basin, Dir_Home)
############################# Download Data ###################################
# Check the years that needs to be calculated
years = range(int(Startdate.split('-')[0]), int(Enddate.split('-')[0]) + 1)
# Find the maximum moving window value
ET_Blue_Green_Classes_dict, Moving_Window_Per_Class_dict = GD.get_bluegreen_classes(
version='1.0')
Additional_Months_tail = np.max(Moving_Window_Per_Class_dict.values())
for year in years:
# Create Start and End date for time chunk
Startdate_part = '%d-01-01' % int(year)
Enddate_part = '%s-12-31' % year
# Create .nc file if not exists
nc_outname = os.path.join(output_dir, "%d.nc" % year)
if not os.path.exists(nc_outname):
DC.Create_new_NC_file(nc_outname, Example_dataset, Basin)
#Set Startdate for moving average
if int(year) == int(years[0]):
Startdate_Moving_Average = pd.Timestamp(Startdate) - pd.DateOffset(
months=Additional_Months_tail)
Startdate_Moving_Average_String = Startdate_Moving_Average.strftime(
'%Y-%m-%d')
else:
Startdate_Moving_Average_String = Startdate_part
# Open variables in netcdf
fh = netCDF4.Dataset(nc_outname)
Variables_NC = [var for var in fh.variables]
fh.close()
# Download data
if not "Precipitation" in Variables_NC:
Data_Path_P_Monthly = Start.Download_Data.Precipitation(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']], Startdate_part,
Enddate_part, P_Product)
if not "Actual_Evapotransporation" in Variables_NC:
Data_Path_ET = Start.Download_Data.Evapotranspiration(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']], Startdate_part,
Enddate_part, ET_Product)
if not "Reference_Evapotranspiration" in Variables_NC:
Data_Path_ETref = Start.Download_Data.ETreference(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']],
Startdate_Moving_Average_String, Enddate_part, ETref_Product)
if not "NDVI" in Variables_NC:
Data_Path_NDVI = Start.Download_Data.NDVI(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']], Startdate_part,
Enddate_part)
if not "Normalized_Dry_Matter" in Variables_NC:
Data_Path_NPP = Start.Download_Data.NPP(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']], Startdate_part,
Enddate_part, NDM_Product)
Data_Path_GPP = Start.Download_Data.GPP(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']], Startdate_part,
Enddate_part, NDM_Product)
########################### Create input data #################################
if not "Normalized_Dry_Matter" in Variables_NC:
# Create NDM based on MOD17
if NDM_Product == 'MOD17':
# Create monthly GPP
Start.Eightdaily_to_monthly_state.Nearest_Interpolate(
Data_Path_GPP, Startdate_part, Enddate_part)
Data_Path_NDM = Two.Calc_NDM.NPP_GPP_Based(
Dir_Basin, Data_Path_GPP, Data_Path_NPP, Startdate_part,
Enddate_part)
if not "NDVI" in Variables_NC:
# Create monthly NDVI based on MOD13
if NDVI_Product == 'MOD13':
Start.Sixteendaily_to_monthly_state.Nearest_Interpolate(
Data_Path_NDVI, Startdate_part, Enddate_part)
###################### Save Data as netCDF files ##############################
#______________________________Precipitation_______________________________
# 1.) Precipitation data
if not "Precipitation" in Variables_NC:
# Get the data of Precipitation and save as nc
DataCube_Prec = RC.Get3Darray_time_series_monthly(
Data_Path_P_Monthly,
Startdate_part,
Enddate_part,
Example_data=Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_Prec,
"Precipitation", "mm/month", 0.01)
del DataCube_Prec
#_______________________________Evaporation________________________________
# 2.) Evapotranspiration data
if not "Actual_Evapotranspiration" in Variables_NC:
# Get the data of Evaporation and save as nc
DataCube_ET = RC.Get3Darray_time_series_monthly(
Data_Path_ET,
Startdate_part,
Enddate_part,
Example_data=Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ET,
"Actual_Evapotranspiration", "mm/month",
0.01)
del DataCube_ET
#___________________________Normalized Dry Matter__________________________
# 3.) Normalized Dry Matter
if not "Normalized_Dry_Matter" in Variables_NC:
# Get the data of Evaporation and save as nc
DataCube_NDM = RC.Get3Darray_time_series_monthly(
Data_Path_NDM,
Startdate_part,
Enddate_part,
Example_data=Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_NDM,
"Normalized_Dry_Matter", "kg_ha", 0.01)
del DataCube_NDM
#_______________________Reference Evaporation______________________________
# 4.) Reference Evapotranspiration data
if not "Reference_Evapotranspiration" in Variables_NC:
# Get the data of Precipitation and save as nc
DataCube_ETref = RC.Get3Darray_time_series_monthly(
Data_Path_ETref,
Startdate_part,
Enddate_part,
Example_data=Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ETref,
"Reference_Evapotranspiration",
"mm/month", 0.01)
del DataCube_ETref
#____________________________________NDVI__________________________________
# 4.) Reference Evapotranspiration data
if not "NDVI" in Variables_NC:
# Get the data of Precipitation and save as nc
DataCube_NDVI = RC.Get3Darray_time_series_monthly(
Data_Path_NDVI,
Startdate_part,
Enddate_part,
Example_data=Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_NDVI, "NDVI",
"Fraction", 0.0001)
del DataCube_NDVI
############################# Calculate Sheet 3 ###########################
#____________ Evapotranspiration data split in ETblue and ETgreen ____________
if not ("Blue_Evapotranspiration" in Variables_NC
or "Green_Evapotranspiration" in Variables_NC):
# Calculate Blue and Green ET
DataCube_ETblue, DataCube_ETgreen = Four.SplitET.Blue_Green(
Dir_Basin, nc_outname, ETref_Product, P_Product, Startdate,
Enddate)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ETblue,
"Blue_Evapotranspiration", "mm/month",
0.01)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ETgreen,
"Green_Evapotranspiration", "mm/month",
0.01)
del DataCube_ETblue, DataCube_ETgreen
#____________________________ Create the empty dictionaries ____________________________
# Create the dictionaries that are required for sheet 3
wp_y_irrigated_dictionary, wp_y_rainfed_dictionary, wp_y_non_crop_dictionary = GD.get_sheet3_empties(
)
#____________________________________ Fill in the dictionaries ________________________
# Fill in the crops dictionaries
wp_y_irrigated_dictionary, wp_y_rainfed_dictionary = Three.Fill_Dicts.Crop_Dictionaries(
wp_y_irrigated_dictionary, wp_y_rainfed_dictionary, dict_crops,
nc_outname, Dir_Basin)
# Fill in the non crops dictionaries
wp_y_non_crop_dictionary = Three.Fill_Dicts.Non_Crop_Dictionaries(
wp_y_non_crop_dictionary, dict_non_crops)
############################ Create CSV 3 #################################
csv_fh_a, csv_fh_b = Generate.CSV.Create(wp_y_irrigated_dictionary,
wp_y_rainfed_dictionary,
wp_y_non_crop_dictionary, Basin,
Simulation, year, Dir_Basin)
############################ Create Sheet 3 ###############################
Generate.PDF.Create(Dir_Basin, Basin, Simulation, csv_fh_a, csv_fh_b)
return ()
def Calculate(WA_HOME_folder, Basin, P_Product, ET_Product, ETref_Product, DEM_Product, Water_Occurence_Product, Inflow_Text_Files, WaterPIX_filename, Reservoirs_GEE_on_off, Supply_method, Startdate, Enddate, Simulation):
'''
This functions consists of the following sections:
1. Set General Parameters
2. Download Data
3. Convert the RAW data to NETCDF files
4. Run SurfWAT
'''
# import General modules
import os
import gdal
import numpy as np
import pandas as pd
from netCDF4 import Dataset
# import WA plus modules
from wa.General import raster_conversions as RC
from wa.General import data_conversions as DC
import wa.Functions.Five as Five
import wa.Functions.Start as Start
import wa.Functions.Start.Get_Dictionaries as GD
######################### 1. Set General Parameters ##############################
# Get environmental variable for the Home folder
if WA_HOME_folder == '':
WA_env_paths = os.environ["WA_HOME"].split(';')
Dir_Home = WA_env_paths[0]
else:
Dir_Home = WA_HOME_folder
# Create the Basin folder
Dir_Basin = os.path.join(Dir_Home, Basin)
output_dir = os.path.join(Dir_Basin, "Simulations", "Simulation_%d" %Simulation)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Get the boundaries of the basin based on the shapefile of the watershed
# Boundaries, Shape_file_name_shp = Start.Boundaries.Determine(Basin)
Boundaries, Example_dataset = Start.Boundaries.Determine_LU_Based(Basin, Dir_Home)
geo_out, proj, size_X, size_Y = RC.Open_array_info(Example_dataset)
# Define resolution of SRTM
Resolution = '15s'
# Find the maximum moving window value
ET_Blue_Green_Classes_dict, Moving_Window_Per_Class_dict = GD.get_bluegreen_classes(version = '1.0')
Additional_Months_tail = np.max(Moving_Window_Per_Class_dict.values())
############## Cut dates into pieces if it is needed ######################
# Check the years that needs to be calculated
years = range(int(Startdate.split('-')[0]),int(Enddate.split('-')[0]) + 1)
for year in years:
# Create .nc file if not exists
nc_outname = os.path.join(output_dir, "%d.nc" % year)
if not os.path.exists(nc_outname):
DC.Create_new_NC_file(nc_outname, Example_dataset, Basin)
# Open variables in netcdf
fh = Dataset(nc_outname)
Variables_NC = [var for var in fh.variables]
fh.close()
# Create Start and End date for time chunk
Startdate_part = '%d-01-01' %int(year)
Enddate_part = '%s-12-31' %int(year)
if int(year) == int(years[0]):
Startdate_Moving_Average = pd.Timestamp(Startdate) - pd.DateOffset(months = Additional_Months_tail)
Startdate_Moving_Average_String = Startdate_Moving_Average.strftime('%Y-%m-%d')
else:
Startdate_Moving_Average_String = Startdate_part
############################# 2. Download Data ###################################
# Download data
if not "Precipitation" in Variables_NC:
Data_Path_P_Monthly = Start.Download_Data.Precipitation(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Startdate_part, Enddate_part, P_Product)
if not "Actual_Evapotranspiration" in Variables_NC:
Data_Path_ET = Start.Download_Data.Evapotranspiration(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Startdate_part, Enddate_part, ET_Product)
if (WaterPIX_filename == "" or Supply_method == "Fraction") and not ("Reference_Evapotranspiration" in Variables_NC):
Data_Path_ETref = Start.Download_Data.ETreference(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Startdate_Moving_Average_String, Enddate_part, ETref_Product)
if Reservoirs_GEE_on_off == 1 and not ("Water_Occurrence" in Variables_NC):
Data_Path_JRC_occurrence = Start.Download_Data.JRC_occurrence(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Water_Occurence_Product)
input_JRC = os.path.join(Data_Path_JRC_occurrence, "JRC_Occurrence_percent.tif")
else:
input_JRC = None
# WaterPIX input
Data_Path_DEM_Dir = Start.Download_Data.DEM_Dir(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Resolution, DEM_Product)
Data_Path_DEM = Start.Download_Data.DEM(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Resolution, DEM_Product)
###################### 3. Convert the RAW data to NETCDF files ##############################
# The sequence of converting the data into netcdf is:
# Precipitation
# Evapotranspiration
# Reference Evapotranspiration
# DEM flow directions
#______________________________Precipitation_______________________________
# 1.) Precipitation data
if not "Precipitation" in Variables_NC:
# Get the data of Precipitation and save as nc
DataCube_Prec = RC.Get3Darray_time_series_monthly(Data_Path_P_Monthly, Startdate_part, Enddate_part, Example_data = Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_Prec, "Precipitation", "mm/month", 0.01)
del DataCube_Prec
#_______________________________Evaporation________________________________
# 2.) Evapotranspiration data
if not "Actual_Evapotranspiration" in Variables_NC:
# Get the data of Evaporation and save as nc
DataCube_ET = RC.Get3Darray_time_series_monthly(Data_Path_ET, Startdate_part, Enddate_part, Example_data = Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ET, "Actual_Evapotranspiration", "mm/month", 0.01)
del DataCube_ET
#_______________________Reference Evaporation______________________________
# 3.) Reference Evapotranspiration data
if (WaterPIX_filename == "" or Supply_method == "Fraction") and not ("Reference_Evapotranspiration" in Variables_NC):
# Get the data of Precipitation and save as nc
DataCube_ETref = RC.Get3Darray_time_series_monthly(Data_Path_ETref, Startdate_part, Enddate_part, Example_data = Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ETref, "Reference_Evapotranspiration", "mm/month", 0.01)
del DataCube_ETref
#____________________________fraction surface water _______________________
DataCube_frac_sw = np.ones([size_Y, size_X]) * np.nan
import wa.Functions.Start.Get_Dictionaries as GD
# Open LU dataset
DataCube_LU = RC.Open_nc_array(nc_outname, "Landuse")
# Get dictionaries and keys
lulc = GD.get_sheet5_classes()
lulc_dict = GD.get_sheet5_classes().keys()
consumed_frac_dict = GD.sw_supply_fractions()
for key in lulc_dict:
Numbers = lulc[key]
for LU_nmbr in Numbers:
DataCube_frac_sw[DataCube_LU==LU_nmbr] = consumed_frac_dict[key]
DC.Add_NC_Array_Static(nc_outname, DataCube_frac_sw, "Fraction_Surface_Water_Supply", "fraction", 0.01)
del DataCube_frac_sw, DataCube_LU
################### 4. Calculate Runoff (2 methods: a = Budyko and b = WaterPIX) #####################
################ 4a. Calculate Runoff based on Precipitation and Evapotranspiration ##################
if (Supply_method == "Fraction" and not "Surface_Runoff" in Variables_NC):
# Calculate runoff based on Budyko
DataCube_Runoff = Five.Fraction_Based.Calc_surface_runoff(Dir_Basin, nc_outname, Startdate_part, Enddate_part, Example_dataset, ETref_Product, P_Product)
# Save the runoff as netcdf
DC.Add_NC_Array_Variable(nc_outname, DataCube_Runoff, "Surface_Runoff", "mm/month", 0.01)
del DataCube_Runoff
###################### 4b. Get Runoff from WaterPIX ###########################
if (Supply_method == "WaterPIX" and not "Surface_Runoff" in Variables_NC):
# Get WaterPIX data
WaterPIX_Var = 'TotalRunoff_M'
DataCube_Runoff = Five.Read_WaterPIX.Get_Array(WaterPIX_filename, WaterPIX_Var, Example_dataset, Startdate_part, Enddate_part)
# Save the runoff as netcdf
DC.Add_NC_Array_Variable(nc_outname, DataCube_Runoff, "Surface_Runoff", "mm/month", 0.01)
del DataCube_Runoff
####################### 5. Calculate Extraction (2 methods: a = Fraction, b = WaterPIX) #############################
###################### 5a. Get extraction from fraction method by using budyko ###########################
if (Supply_method == "Fraction" and not "Surface_Withdrawal" in Variables_NC):
DataCube_surface_withdrawal = Five.Fraction_Based.Calc_surface_withdrawal(Dir_Basin, nc_outname, Startdate_part, Enddate_part, Example_dataset, ETref_Product, P_Product)
# Save the runoff as netcdf
DC.Add_NC_Array_Variable(nc_outname, DataCube_surface_withdrawal, "Surface_Withdrawal", "mm/month", 0.01)
del DataCube_surface_withdrawal
#################################### 5b. Get extraction from WaterPIX ####################################
if (Supply_method == "WaterPIX" and not "Surface_Withdrawal" in Variables_NC):
WaterPIX_Var = 'Supply_M'
DataCube_Supply = Five.Read_WaterPIX.Get_Array(WaterPIX_filename, WaterPIX_Var, Example_dataset, Startdate, Enddate)
# Open array with surface water fractions
DataCube_frac_sw = RC.Open_nc_array(nc_outname, "Fraction_Surface_Water_Supply")
# Total amount of ETblue taken out of rivers
DataCube_surface_withdrawal = DataCube_Supply * DataCube_frac_sw[None,:,:]
# Save the runoff as netcdf
DC.Add_NC_Array_Variable(nc_outname, DataCube_surface_withdrawal, "Surface_Withdrawal", "mm/month", 0.01)
del DataCube_surface_withdrawal
################################## 5. Run SurfWAT #####################################
import wa.Models.SurfWAT as SurfWAT
# Define formats of input data
Format_DEM = "TIFF" # or "TIFF"
Format_Runoff = "NetCDF" # or "TIFF"
Format_Extraction = "NetCDF" # or "TIFF"
Format_DEM_dir = "TIFF" # or "TIFF"
Format_Basin = "NetCDF" # or "TIFF"
# Give path (for tiff) or file (netcdf)
input_nc = os.path.join(Dir_Basin, "Simulations", "Simulation_%s"%Simulation,"SurfWAT_in_%d.nc" %year)
output_nc = os.path.join(Dir_Basin, "Simulations", "Simulation_%s"%Simulation,"SurfWAT_out_%d.nc" %year)
# Create Input File for SurfWAT
SurfWAT.Create_input_nc.main(Data_Path_DEM_Dir,
Data_Path_DEM,
os.path.dirname(nc_outname),
os.path.dirname(nc_outname),
os.path.dirname(nc_outname),
Startdate,
Enddate,
input_nc,
Resolution,
Format_DEM_dir,
Format_DEM,
Format_Basin,
Format_Runoff,
Format_Extraction)
# Run SurfWAT
SurfWAT.Run_SurfWAT.main(input_nc, output_nc, input_JRC, Inflow_Text_Files, Reservoirs_GEE_on_off)
'''
################################# Plot graph ##################################
# Draw graph
Five.Channel_Routing.Graph_DEM_Distance_Discharge(Discharge_dict_CR3, Distance_dict_CR2, DEM_dict_CR2, River_dict_CR2, Startdate, Enddate, Example_dataset)
######################## Change data to fit the LU data #######################
# Discharge
# Define info for the nc files
info = ['monthly','m3-month-1', ''.join([Startdate[5:7], Startdate[0:4]]) , ''.join([Enddate[5:7], Enddate[0:4]])]
Name_NC_Discharge = DC.Create_NC_name('DischargeEnd', Simulation, Dir_Basin, 5, info)
if not os.path.exists(Name_NC_Discharge):
# Get the data of Reference Evapotranspiration and save as nc
DataCube_Discharge_CR = DC.Convert_dict_to_array(River_dict_CR2, Discharge_dict_CR3, Example_dataset)
DC.Save_as_NC(Name_NC_Discharge, DataCube_Discharge_CR, 'Discharge_End_CR', Example_dataset, Startdate, Enddate, 'monthly')
del DataCube_Discharge_CR
'''
'''
# DEM
Name_NC_DEM = DC.Create_NC_name('DEM', Simulation, Dir_Basin, 5)
if not os.path.exists(Name_NC_DEM):
# Get the data of Reference Evapotranspiration and save as nc
DataCube_DEM_CR = RC.Open_nc_array(Name_NC_DEM_CR)
DataCube_DEM = RC.resize_array_example(DataCube_DEM_CR, LU_data, method=1)
DC.Save_as_NC(Name_NC_DEM, DataCube_DEM, 'DEM', LU_dataset)
del DataCube_DEM
# flow direction
Name_NC_DEM_Dir = DC.Create_NC_name('DEM_Dir', Simulation, Dir_Basin, 5)
if not os.path.exists(Name_NC_DEM_Dir):
# Get the data of Reference Evapotranspiration and save as nc
DataCube_DEM_Dir_CR = RC.Open_nc_array(Name_NC_DEM_Dir_CR)
DataCube_DEM_Dir = RC.resize_array_example(DataCube_DEM_Dir_CR, LU_data, method=1)
DC.Save_as_NC(Name_NC_DEM_Dir, DataCube_DEM_Dir, 'DEM_Dir', LU_dataset)
del DataCube_DEM_Dir
# Precipitation
# Define info for the nc files
info = ['monthly','mm', ''.join([Startdate[5:7], Startdate[0:4]]) , ''.join([Enddate[5:7], Enddate[0:4]])]
Name_NC_Prec = DC.Create_NC_name('Prec', Simulation, Dir_Basin, 5)
if not os.path.exists(Name_NC_Prec):
# Get the data of Reference Evapotranspiration and save as nc
DataCube_Prec = RC.Get3Darray_time_series_monthly(Dir_Basin, Data_Path_P_Monthly, Startdate, Enddate, LU_dataset)
DC.Save_as_NC(Name_NC_Prec, DataCube_Prec, 'Prec', LU_dataset, Startdate, Enddate, 'monthly', 0.01)
del DataCube_Prec
# Evapotranspiration
Name_NC_ET = DC.Create_NC_name('ET', Simulation, Dir_Basin, 5)
if not os.path.exists(Name_NC_ET):
# Get the data of Reference Evapotranspiration and save as nc
DataCube_ET = RC.Get3Darray_time_series_monthly(Dir_Basin, Data_Path_ET, Startdate, Enddate, LU_dataset)
DC.Save_as_NC(Name_NC_ET, DataCube_ET, 'ET', LU_dataset, Startdate, Enddate, 'monthly', 0.01)
del DataCube_ET
# Reference Evapotranspiration data
Name_NC_ETref = DC.Create_NC_name('ETref', Simulation, Dir_Basin, 5, info)
if not os.path.exists(Name_NC_ETref):
# Get the data of Reference Evapotranspiration and save as nc
DataCube_ETref = RC.Get3Darray_time_series_monthly(Dir_Basin, Data_Path_ETref, Startdate, Enddate, LU_dataset)
DC.Save_as_NC(Name_NC_ETref, DataCube_ETref, 'ETref', LU_dataset, Startdate, Enddate, 'monthly', 0.01)
del DataCube_ETref
# Rivers
Name_NC_Rivers = DC.Create_NC_name('Rivers', Simulation, Dir_Basin, 5, info)
if not os.path.exists(Name_NC_Rivers):
# Get the data of Reference Evapotranspiration and save as nc
Rivers_CR = RC.Open_nc_array(Name_NC_Rivers_CR)
DataCube_Rivers = RC.resize_array_example(Rivers_CR, LU_data)
DC.Save_as_NC(Name_NC_Rivers, DataCube_Rivers, 'Rivers', LU_dataset)
del DataCube_Rivers, Rivers_CR
# Discharge
# Define info for the nc files
info = ['monthly','m3', ''.join([Startdate[5:7], Startdate[0:4]]) , ''.join([Enddate[5:7], Enddate[0:4]])]
Name_NC_Routed_Discharge = DC.Create_NC_name('Routed_Discharge', Simulation, Dir_Basin, 5, info)
if not os.path.exists(Name_NC_Routed_Discharge):
# Get the data of Reference Evapotranspiration and save as nc
Routed_Discharge_CR = RC.Open_nc_array(Name_NC_Discharge)
DataCube_Routed_Discharge = RC.resize_array_example(Routed_Discharge_CR, LU_data)
DC.Save_as_NC(Name_NC_Routed_Discharge, DataCube_Routed_Discharge, 'Routed_Discharge', LU_dataset, Startdate, Enddate, 'monthly')
del DataCube_Routed_Discharge, Routed_Discharge_CR
# Get raster information
geo_out, proj, size_X, size_Y = RC.Open_array_info(Example_dataset)
Rivers = RC.Open_nc_array(Name_NC_Rivers_CR)
# Create ID Matrix
y,x = np.indices((size_Y, size_X))
ID_Matrix = np.int32(np.ravel_multi_index(np.vstack((y.ravel(),x.ravel())),(size_Y,size_X),mode='clip').reshape(x.shape)) + 1
# Get tiff array time dimension:
time_dimension = int(np.shape(Discharge_dict_CR3[0])[0])
# create an empty array
Result = np.zeros([time_dimension, size_Y, size_X])
for river_part in range(0,len(River_dict_CR2)):
for river_pixel in range(1,len(River_dict_CR2[river_part])):
river_pixel_ID = River_dict_CR2[river_part][river_pixel]
if len(np.argwhere(ID_Matrix == river_pixel_ID))>0:
row, col = np.argwhere(ID_Matrix == river_pixel_ID)[0][:]
Result[:,row,col] = Discharge_dict_CR3[river_part][:,river_pixel]
print(river_part)
Outflow = Discharge_dict_CR3[0][:,1]
for i in range(0,time_dimension):
output_name = r'C:/testmap/rtest_%s.tif' %i
Result_one = Result[i, :, :]
DC.Save_as_tiff(output_name, Result_one, geo_out, "WGS84")
import os
# Get environmental variable for the Home folder
WA_env_paths = os.environ["WA_HOME"].split(';')
Dir_Home = WA_env_paths[0]
# Create the Basin folder
Dir_Basin = os.path.join(Dir_Home, Basin)
info = ['monthly','m3-month-1', ''.join([Startdate[5:7], Startdate[0:4]]) , ''.join([Enddate[5:7], Enddate[0:4]])]
Name_Result = DC.Create_NC_name('DischargeEnd', Simulation, Dir_Basin, 5, info)
Result[np.logical_and(Result == 0.0, Rivers == 0.0)] = np.nan
DC.Save_as_NC(Name_Result, Result, 'DischargeEnd', Example_dataset, Startdate, Enddate, 'monthly')
'''
return()
def Calculate(WA_HOME_folder, Basin, P_Product, ET_Product, LAI_Product, ETref_Product, Runoff_Product, Startdate, Enddate, Simulation):
"""
This functions is the main framework for calculating sheet 4.
Parameters
----------
Basin : str
Name of the basin
P_Product : str
Name of the rainfall product that will be used
ET_Product : str
Name of the evapotranspiration product that will be used
LAI_Product : str
Name of the LAI product that will be used
Runoff_Product : str
Name of the Runoff product that will be used
Moving_Averiging_Length, int
Defines the length of the moving average
Startdate : str
Contains the start date of the model 'yyyy-mm-dd'
Enddate : str
Contains the end date of the model 'yyyy-mm-dd'
Simulation : int
Defines the simulation
"""
######################### Import WA modules ###################################
from wa.General import raster_conversions as RC
from wa.General import data_conversions as DC
import wa.Functions.Four as Four
import wa.Functions.Start as Start
import wa.Generator.Sheet4 as Generate
import wa.Functions.Start.Get_Dictionaries as GD
######################### Set General Parameters ##############################
# Get environmental variable for the Home folder
if WA_HOME_folder == '':
WA_env_paths = os.environ["WA_HOME"].split(';')
Dir_Home = WA_env_paths[0]
else:
Dir_Home = WA_HOME_folder
# Create the Basin folder
Dir_Basin = os.path.join(Dir_Home, Basin)
output_dir = os.path.join(Dir_Basin, "Simulations", "Simulation_%d" %Simulation)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Get the boundaries of the basin based on the shapefile of the watershed
# Boundaries, Shape_file_name_shp = Start.Boundaries.Determine(Basin)
Boundaries, Example_dataset = Start.Boundaries.Determine_LU_Based(Basin, Dir_Home)
# Find the maximum moving window value
ET_Blue_Green_Classes_dict, Moving_Window_Per_Class_dict = GD.get_bluegreen_classes(version = '1.0')
Additional_Months_tail = np.max(Moving_Window_Per_Class_dict.values())
############## Cut dates into pieces if it is needed ######################
# Check the years that needs to be calculated
years = range(int(Startdate.split('-')[0]),int(Enddate.split('-')[0]) + 1)
for year in years:
# Create .nc file if not exists
nc_outname = os.path.join(output_dir, "%d.nc" % year)
if not os.path.exists(nc_outname):
DC.Create_new_NC_file(nc_outname, Example_dataset, Basin)
# Open variables in netcdf
fh = Dataset(nc_outname)
Variables_NC = [var for var in fh.variables]
fh.close()
# Create Start and End date for time chunk
Startdate_part = '%d-01-01' %int(year)
Enddate_part = '%s-12-31' %int(year)
if int(year) == int(years[0]):
Startdate_Moving_Average = pd.Timestamp(Startdate) - pd.DateOffset(months = Additional_Months_tail)
Startdate_Moving_Average_String = Startdate_Moving_Average.strftime('%Y-%m-%d')
else:
Startdate_Moving_Average_String = Startdate_part
############################# Download Data ###################################
# Download data
if not "Precipitation" in Variables_NC:
Data_Path_P_Monthly = Start.Download_Data.Precipitation(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Startdate_part, Enddate_part, P_Product)
if not "Actual_Evapotranspiration" in Variables_NC:
Data_Path_ET = Start.Download_Data.Evapotranspiration(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Startdate_part, Enddate_part, ET_Product)
if not "Reference_Evapotranspiration" in Variables_NC:
Data_Path_ETref = Start.Download_Data.ETreference(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Startdate_Moving_Average_String, Enddate_part, ETref_Product)
if not "Grey_Water_Footprint" in Variables_NC:
Data_Path_GWF = Start.Download_Data.GWF(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']])
if not "Theta_Saturated_Topsoil" in Variables_NC:
Data_Path_ThetaSat_topsoil = Start.Download_Data.Soil_Properties(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Para = 'ThetaSat_TopSoil')
###################### Save Data as netCDF files ##############################
#______________________________Precipitation_______________________________
# 1.) Precipitation data
if not "Precipitation" in Variables_NC:
# Get the data of Precipitation and save as nc
DataCube_Prec = RC.Get3Darray_time_series_monthly(Data_Path_P_Monthly, Startdate_part, Enddate_part, Example_data = Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_Prec, "Precipitation", "mm/month", 0.01)
del DataCube_Prec
#_______________________Reference Evaporation______________________________
# 2.) Reference Evapotranspiration data
if not "Reference_Evapotranspiration" in Variables_NC:
# Get the data of Precipitation and save as nc
DataCube_ETref = RC.Get3Darray_time_series_monthly(Data_Path_ETref, Startdate_part, Enddate_part, Example_data = Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ETref, "Reference_Evapotranspiration", "mm/month", 0.01)
del DataCube_ETref
#_______________________________Evaporation________________________________
# 3.) Evapotranspiration data
if not "Actual_Evapotranspiration" in Variables_NC:
# Get the data of Evaporation and save as nc
DataCube_ET = RC.Get3Darray_time_series_monthly(Data_Path_ET, Startdate_part, Enddate_part, Example_data = Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ET, "Actual_Evapotranspiration", "mm/month", 0.01)
del DataCube_ET
#_____________________________________GWF__________________________________
# 4.) Grey Water Footprint data
if not "Grey_Water_Footprint" in Variables_NC:
# Get the data of grey water footprint and save as nc
GWF_Filepath = os.path.join(Dir_Basin, Data_Path_GWF, "Gray_Water_Footprint_Fraction.tif")
dest_GWF = RC.reproject_dataset_example(GWF_Filepath, Example_dataset, method=1)
DataCube_GWF = dest_GWF.GetRasterBand(1).ReadAsArray()
DC.Add_NC_Array_Static(nc_outname, DataCube_GWF, "Grey_Water_Footprint", "fraction", 0.0001)
del DataCube_GWF
####################### Calculations Sheet 4 ##############################
############## Cut dates into pieces if it is needed ######################
years = range(int(Startdate.split('-')[0]),int(Enddate.split('-')[0]) + 1)
for year in years:
if len(years) > 1.0:
if year is years[0]:
Startdate_part = Startdate
Enddate_part = '%s-12-31' %year
if year is years[-1]:
Startdate_part = '%s-01-01' %year
Enddate_part = Enddate
else:
Startdate_part = Startdate
Enddate_part = Enddate
#____________ Evapotranspiration data split in ETblue and ETgreen ____________
if not ("Blue_Evapotranspiration" in Variables_NC or "Green_Evapotranspiration" in Variables_NC):
# Calculate Blue and Green ET
DataCube_ETblue, DataCube_ETgreen = Four.SplitET.Blue_Green(Dir_Basin, nc_outname, ETref_Product, P_Product, Startdate, Enddate)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ETblue, "Blue_Evapotranspiration", "mm/month", 0.01)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ETgreen, "Green_Evapotranspiration", "mm/month", 0.01)
del DataCube_ETblue, DataCube_ETgreen
#____________ Calculate non-consumend and Total supply maps by using fractions and consumed maps (blue ET) ____________
if not ("Total_Supply" in Variables_NC or "Non_Consumed_Water" in Variables_NC):
# Do the calculations
DataCube_Total_Supply, DataCube_Non_Consumed = Four.Total_Supply.Fraction_Based(nc_outname, Startdate_part, Enddate_part)
# Save the Total Supply and non consumed data as NetCDF files
DC.Add_NC_Array_Variable(nc_outname, DataCube_Total_Supply, "Total_Supply", "mm/month", 0.01)
DC.Add_NC_Array_Variable(nc_outname, DataCube_Non_Consumed, "Non_Consumed_Water", "mm/month", 0.01)
del DataCube_Total_Supply, DataCube_Non_Consumed
#____________ Apply fractions over total supply to calculate gw and sw supply ____________
if not ("Total_Supply_Surface_Water" in Variables_NC or "Total_Supply_Ground_Water" in Variables_NC):
# Do the calculations
DataCube_Total_Supply_SW, DataCube_Total_Supply_GW = Four.SplitGW_SW_Supply.Fraction_Based(nc_outname, Startdate_part, Enddate_part)
# Save the Total Supply surface water and Total Supply ground water data as NetCDF files
DC.Add_NC_Array_Variable(nc_outname, DataCube_Total_Supply_SW, "Total_Supply_Surface_Water", "mm/month", 0.01)
DC.Add_NC_Array_Variable(nc_outname, DataCube_Total_Supply_GW, "Total_Supply_Ground_Water", "mm/month", 0.01)
del DataCube_Total_Supply_SW, DataCube_Total_Supply_GW
#____________ Apply gray water footprint fractions to calculated non recoverable flow based on the non consumed flow ____________
if not ("Non_Recovable_Flow" in Variables_NC or "Recovable_Flow" in Variables_NC):
# Calculate the non recovable flow and recovable flow by using Grey Water Footprint values
DataCube_NonRecovableFlow, Datacube_RecovableFlow = Four.SplitNonConsumed_NonRecov.GWF_Based(nc_outname, Startdate_part, Enddate_part)
# Get the data of Evaporation and save as nc
DC.Add_NC_Array_Variable(nc_outname, DataCube_NonRecovableFlow, "Non_Recovable_Flow", "mm/month", 0.01)
DC.Add_NC_Array_Variable(nc_outname, Datacube_RecovableFlow, "Recovable_Flow", "mm/month", 0.01)
del DataCube_NonRecovableFlow, Datacube_RecovableFlow
#____________Apply fractions to calculate the non recovarable SW/GW and recovarable SW/GW ____________
# 1. Non recovarable flow
if not ("Non_Recovable_Flow_Ground_Water" in Variables_NC or "Non_Recovable_Flow_Surface_Water" in Variables_NC):
# Calculate the non recovable return flow to ground and surface water
DataCube_NonRecovableFlow_Return_GW, Datacube_NonRecovableFlow_Return_SW = Four.SplitGW_SW_Return.Fraction_Based(nc_outname, "Non_Recovable_Flow", Startdate_part, Enddate_part)
# Get the data of Evaporation and save as nc
DC.Add_NC_Array_Variable(nc_outname, DataCube_NonRecovableFlow_Return_GW, "Non_Recovable_Flow_Ground_Water", "mm/month", 0.01)
DC.Add_NC_Array_Variable(nc_outname, Datacube_NonRecovableFlow_Return_SW, "Non_Recovable_Flow_Surface_Water", "mm/month", 0.01)
del DataCube_NonRecovableFlow_Return_GW, Datacube_NonRecovableFlow_Return_SW
# 2. Recovarable flow
if not ("Recovable_Flow_Ground_Water" in Variables_NC or "Recovable_Flow_Surface_Water" in Variables_NC):
# Calculate the non recovable return flow to ground and surface water
DataCube_RecovableFlow_Return_GW, Datacube_RecovableFlow_Return_SW = Four.SplitGW_SW_Return.Fraction_Based(nc_outname, "Recovable_Flow", Startdate_part, Enddate_part)
# Get the data of Evaporation and save as nc
DC.Add_NC_Array_Variable(nc_outname, DataCube_RecovableFlow_Return_GW, "Recovable_Flow_Ground_Water", "mm/month", 0.01)
DC.Add_NC_Array_Variable(nc_outname, Datacube_RecovableFlow_Return_SW, "Recovable_Flow_Surface_Water", "mm/month", 0.01)
del DataCube_RecovableFlow_Return_GW, Datacube_RecovableFlow_Return_SW
############################ Create CSV 4 #################################
Dir_Basin_CSV, Unit_front = Generate.CSV.Create(Dir_Basin, Simulation, Basin, Startdate_part, Enddate_part, nc_outname)
############################ Create Sheet 4 ###############################
Generate.PDF.Create(Dir_Basin, Basin, Simulation, Dir_Basin_CSV, Unit_front)
return()
def Calculate(WA_HOME_folder, Basin, P_Product, ET_Product, LAI_Product, NDM_Product, NDVI_Product, ETref_Product, dict_crops, dict_non_crops, Startdate, Enddate, Simulation):
"""
This functions is the main framework for calculating sheet 3.
Parameters
----------
Basin : str
Name of the basin
P_Product : str
Name of the rainfall product that will be used
ET_Product : str
Name of the evapotranspiration product that will be used
LAI_Product : str
Name of the LAI product that will be used
NDM_Product : str
Name of the NDM product that will be used
Startdate : str
Contains the start date of the model 'yyyy-mm-dd'
Enddate : str
Contains the end date of the model 'yyyy-mm-dd'
Simulation : int
Defines the simulation
"""
######################### Import WA modules ###################################
from wa.General import raster_conversions as RC
from wa.General import data_conversions as DC
import wa.Functions.Three as Three
import wa.Functions.Two as Two
import wa.Functions.Start as Start
import wa.Functions.Four as Four
import wa.Generator.Sheet3 as Generate
import wa.Functions.Start.Get_Dictionaries as GD
######################### Set General Parameters ##############################
# Check if there is a full year selected between Startdate and Enddate, otherwise Sheet 3 cannot be produced
try:
years_end = pd.date_range(Startdate,Enddate,freq="A").year
years_start = pd.date_range(Startdate,Enddate,freq="AS").year
if (len(years_start) == 0 or len(years_end) == 0):
print "Calculation period is less than a year, which is not possible for sheet 3"
quit
years = np.unique(np.append(years_end,years_start))
except:
print "Calculation period is less than a year, which is not possible for sheet 3"
quit
# Get environmental variable for the Home folder
if WA_HOME_folder == '':
WA_env_paths = os.environ["WA_HOME"].split(';')
Dir_Home = WA_env_paths[0]
else:
Dir_Home = WA_HOME_folder
# Create the Basin folder
Dir_Basin = os.path.join(Dir_Home, Basin)
output_dir = os.path.join(Dir_Basin, "Simulations", "Simulation_%d" %Simulation)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Get the boundaries of the basin based on the shapefile of the watershed
# Boundaries, Shape_file_name_shp = Start.Boundaries.Determine(Basin)
Boundaries, Example_dataset = Start.Boundaries.Determine_LU_Based(Basin, Dir_Home)
############################# Download Data ###################################
# Check the years that needs to be calculated
years = range(int(Startdate.split('-')[0]),int(Enddate.split('-')[0]) + 1)
# Find the maximum moving window value
ET_Blue_Green_Classes_dict, Moving_Window_Per_Class_dict = GD.get_bluegreen_classes(version = '1.0')
Additional_Months_tail = np.max(Moving_Window_Per_Class_dict.values())
for year in years:
# Create Start and End date for time chunk
Startdate_part = '%d-01-01' %int(year)
Enddate_part = '%s-12-31' %year
# Create .nc file if not exists
nc_outname = os.path.join(output_dir, "%d.nc" % year)
if not os.path.exists(nc_outname):
DC.Create_new_NC_file(nc_outname, Example_dataset, Basin)
#Set Startdate for moving average
if int(year) == int(years[0]):
Startdate_Moving_Average = pd.Timestamp(Startdate) - pd.DateOffset(months = Additional_Months_tail)
Startdate_Moving_Average_String = Startdate_Moving_Average.strftime('%Y-%m-%d')
else:
Startdate_Moving_Average_String = Startdate_part
# Open variables in netcdf
fh = netCDF4.Dataset(nc_outname)
Variables_NC = [var for var in fh.variables]
fh.close()
# Download data
if not "Precipitation" in Variables_NC:
Data_Path_P_Monthly = Start.Download_Data.Precipitation(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Startdate_part, Enddate_part, P_Product)
if not "Actual_Evapotransporation" in Variables_NC:
Data_Path_ET = Start.Download_Data.Evapotranspiration(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Startdate_part, Enddate_part, ET_Product)
if not "Reference_Evapotranspiration" in Variables_NC:
Data_Path_ETref = Start.Download_Data.ETreference(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Startdate_Moving_Average_String, Enddate_part, ETref_Product)
if not "NDVI" in Variables_NC:
Data_Path_NDVI = Start.Download_Data.NDVI(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Startdate_part, Enddate_part)
if not "Normalized_Dry_Matter" in Variables_NC:
Data_Path_NPP = Start.Download_Data.NPP(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Startdate_part, Enddate_part, NDM_Product)
Data_Path_GPP = Start.Download_Data.GPP(Dir_Basin, [Boundaries['Latmin'],Boundaries['Latmax']],[Boundaries['Lonmin'],Boundaries['Lonmax']], Startdate_part, Enddate_part, NDM_Product)
########################### Create input data #################################
if not "Normalized_Dry_Matter" in Variables_NC:
# Create NDM based on MOD17
if NDM_Product == 'MOD17':
# Create monthly GPP
Start.Eightdaily_to_monthly_state.Nearest_Interpolate(Data_Path_GPP, Startdate_part, Enddate_part)
Data_Path_NDM = Two.Calc_NDM.NPP_GPP_Based(Dir_Basin, Data_Path_GPP, Data_Path_NPP, Startdate_part, Enddate_part)
if not "NDVI" in Variables_NC:
# Create monthly NDVI based on MOD13
if NDVI_Product == 'MOD13':
Start.Sixteendaily_to_monthly_state.Nearest_Interpolate(Data_Path_NDVI, Startdate_part, Enddate_part)
###################### Save Data as netCDF files ##############################
#______________________________Precipitation_______________________________
# 1.) Precipitation data
if not "Precipitation" in Variables_NC:
# Get the data of Precipitation and save as nc
DataCube_Prec = RC.Get3Darray_time_series_monthly(Data_Path_P_Monthly, Startdate_part, Enddate_part, Example_data = Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_Prec, "Precipitation", "mm/month", 0.01)
del DataCube_Prec
#_______________________________Evaporation________________________________
# 2.) Evapotranspiration data
if not "Actual_Evapotranspiration" in Variables_NC:
# Get the data of Evaporation and save as nc
DataCube_ET = RC.Get3Darray_time_series_monthly(Data_Path_ET, Startdate_part, Enddate_part, Example_data = Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ET, "Actual_Evapotranspiration", "mm/month", 0.01)
del DataCube_ET
#___________________________Normalized Dry Matter__________________________
# 3.) Normalized Dry Matter
if not "Normalized_Dry_Matter" in Variables_NC:
# Get the data of Evaporation and save as nc
DataCube_NDM = RC.Get3Darray_time_series_monthly(Data_Path_NDM, Startdate_part, Enddate_part, Example_data = Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_NDM, "Normalized_Dry_Matter", "kg_ha", 0.01)
del DataCube_NDM
#_______________________Reference Evaporation______________________________
# 4.) Reference Evapotranspiration data
if not "Reference_Evapotranspiration" in Variables_NC:
# Get the data of Precipitation and save as nc
DataCube_ETref = RC.Get3Darray_time_series_monthly(Data_Path_ETref, Startdate_part, Enddate_part, Example_data = Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ETref, "Reference_Evapotranspiration", "mm/month", 0.01)
del DataCube_ETref
#____________________________________NDVI__________________________________
# 4.) Reference Evapotranspiration data
if not "NDVI" in Variables_NC:
# Get the data of Precipitation and save as nc
DataCube_NDVI = RC.Get3Darray_time_series_monthly(Data_Path_NDVI, Startdate_part, Enddate_part, Example_data = Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_NDVI, "NDVI", "Fraction", 0.0001)
del DataCube_NDVI
############################# Calculate Sheet 3 ###########################
#____________ Evapotranspiration data split in ETblue and ETgreen ____________
if not ("Blue_Evapotranspiration" in Variables_NC or "Green_Evapotranspiration" in Variables_NC):
# Calculate Blue and Green ET
DataCube_ETblue, DataCube_ETgreen = Four.SplitET.Blue_Green(Dir_Basin, nc_outname, ETref_Product, P_Product, Startdate, Enddate)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ETblue, "Blue_Evapotranspiration", "mm/month", 0.01)
DC.Add_NC_Array_Variable(nc_outname, DataCube_ETgreen, "Green_Evapotranspiration", "mm/month", 0.01)
del DataCube_ETblue, DataCube_ETgreen
#____________________________ Create the empty dictionaries ____________________________
# Create the dictionaries that are required for sheet 3
wp_y_irrigated_dictionary, wp_y_rainfed_dictionary, wp_y_non_crop_dictionary = GD.get_sheet3_empties()
#____________________________________ Fill in the dictionaries ________________________
# Fill in the crops dictionaries
wp_y_irrigated_dictionary, wp_y_rainfed_dictionary = Three.Fill_Dicts.Crop_Dictionaries(wp_y_irrigated_dictionary, wp_y_rainfed_dictionary, dict_crops, nc_outname, Dir_Basin)
# Fill in the non crops dictionaries
wp_y_non_crop_dictionary = Three.Fill_Dicts.Non_Crop_Dictionaries(wp_y_non_crop_dictionary, dict_non_crops)
############################ Create CSV 3 #################################
csv_fh_a, csv_fh_b = Generate.CSV.Create(wp_y_irrigated_dictionary, wp_y_rainfed_dictionary, wp_y_non_crop_dictionary, Basin, Simulation, year, Dir_Basin)
############################ Create Sheet 3 ###############################
Generate.PDF.Create(Dir_Basin, Basin, Simulation, csv_fh_a, csv_fh_b)
return()
def Blue_Green(Dir_Basin, nc_outname, ETref_Product, P_Product, Startdate,
Enddate):
"""
This functions split the evapotranspiration into green and blue evapotranspiration.
Parameters
----------
nc_outname : str
Path to the .nc file containing data
Moving_Averaging_Length: integer
Number defines the amount of months that are taken into account
Returns
-------
ET_Blue : array
Array[time, lat, lon] contains Blue Evapotranspiration
ET_Green : array
Array[time, lat, lon] contains Green Evapotranspiration
"""
import wa.General.raster_conversions as RC
import wa.Functions.Start.Get_Dictionaries as GD
# Input Parameters functions
scale = 1.1
# Open LU map for example
LU = RC.Open_nc_array(nc_outname, "Landuse")
# Define monthly dates
Dates = pd.date_range(Startdate, Enddate, freq='MS')
# Get moving window period
# Get dictionaries and keys for the moving average
ET_Blue_Green_Classes_dict, Moving_Window_Per_Class_dict = GD.get_bluegreen_classes(
version='1.0')
Classes = ET_Blue_Green_Classes_dict.keys()
Moving_Averages_Values_Array = np.ones(LU.shape) * np.nan
# Create array based on the dictionary that gives the Moving average tail for every pixel
for Class in Classes:
Values_Moving_Window_Class = Moving_Window_Per_Class_dict[Class]
for Values_Class in ET_Blue_Green_Classes_dict[Class]:
Moving_Averages_Values_Array[
LU == Values_Class] = Values_Moving_Window_Class
Additional_Months_front = int(np.nanmax(Moving_Averages_Values_Array))
Additional_Months_tail = 0
Start_period = Additional_Months_front
End_period = Additional_Months_tail * -1
########################### Extract ETref data #################################
if ETref_Product is 'WA_ETref':
# Define data path
Data_Path_ETref = os.path.join(Dir_Basin, 'ETref', 'Monthly')
else:
Data_Path_ETref = ETref_Product
ETref = Complete_3D_Array(nc_outname, 'Reference_Evapotranspiration',
Startdate, Enddate, Additional_Months_front,
Additional_Months_tail, Data_Path_ETref)
######################## Extract Precipitation data ########################
if (P_Product == "CHIRPS" or P_Product == "RFE" or P_Product == "TRMM"):
# Define data path
Data_Path_P = os.path.join(Dir_Basin, 'Precipitation', P_Product,
'Monthly')
else:
Data_Path_P = P_Product
P = Complete_3D_Array(nc_outname, 'Precipitation', Startdate, Enddate,
Additional_Months_front, Additional_Months_tail,
Data_Path_P)
########################## Extract actET data ##############################
ET = RC.Open_nc_array(nc_outname, "Actual_Evapotranspiration", Startdate,
Enddate)
############ Create average ETref and P using moving window ################
ETref_Ave = np.ones([len(Dates),
int(LU.shape[0]),
int(LU.shape[1])]) * np.nan
P_Ave = np.ones([len(Dates), int(LU.shape[0]), int(LU.shape[1])]) * np.nan
if End_period == 0:
P_period = P[Start_period:, :, :]
ETref_period = ETref[Start_period:, :, :]
else:
P_period = P[Start_period:End_period, :, :]
ETref_period = ETref[Start_period:End_period, :, :]
# Loop over the different moving average tails
for One_Value in np.unique(Moving_Window_Per_Class_dict.values()):
# If there is no moving average is 1 than use the value of the original ETref or P
if One_Value == 1:
Values_Ave_ETref = ETref[int(ETref.shape[0]) - len(Dates):, :, :]
Values_Ave_P = P[int(ETref.shape[0]) - len(Dates):, :, :]
# If there is tail, apply moving average over the whole datacube
else:
Values_Ave_ETref_tot = RC.Moving_average(ETref, One_Value - 1, 0)
Values_Ave_P_tot = RC.Moving_average(P, One_Value - 1, 0)
Values_Ave_ETref = Values_Ave_ETref_tot[
int(Values_Ave_ETref_tot.shape[0]) - len(Dates):, :, :]
Values_Ave_P = Values_Ave_P_tot[int(Values_Ave_P_tot.shape[0]) -
len(Dates):, :, :]
# Only add the data where the corresponding tail corresponds with the one_value
ETref_Ave[:, Moving_Averages_Values_Array ==
One_Value] = Values_Ave_ETref[:,
Moving_Averages_Values_Array ==
One_Value]
P_Ave[:, Moving_Averages_Values_Array ==
One_Value] = Values_Ave_P[:, Moving_Averages_Values_Array ==
One_Value]
##################### Calculate ET blue and green ###########################
# Mask out the nan values(if one of the parameters is nan, then they are all nan)
mask = np.any([
np.isnan(LU) *
np.ones([len(Dates), int(LU.shape[0]),
int(LU.shape[1])]) == 1,
np.isnan(ET),
np.isnan(ETref[int(ETref.shape[0]) - len(Dates):, :, :]),
np.isnan(P[int(ETref.shape[0]) - len(Dates):, :, :]),
np.isnan(P_Ave),
np.isnan(ETref_Ave)
],
axis=0)
ETref_period[mask] = ETref_Ave[mask] = ET[mask] = P_period[mask] = P_Ave[
mask] = np.nan
phi = ETref_Ave / P_Ave
# Calculate Budyko-index
Budyko = scale * np.sqrt(phi * np.tanh(1 / phi) * (1 - np.exp(-phi)))
# Calculate ET green
ETgreen_DataCube = np.minimum(
Budyko * P[int(ETref.shape[0]) - len(Dates):, :, :], ET)
# Calculate ET blue
ETblue_DataCube = ET - ETgreen_DataCube
return (np.array(ETblue_DataCube), np.array(ETgreen_DataCube))
def Blue_Green(Dir_Basin, nc_outname, ETref_Product, P_Product, Startdate, Enddate):
"""
This functions split the evapotranspiration into green and blue evapotranspiration.
Parameters
----------
nc_outname : str
Path to the .nc file containing data
Moving_Averaging_Length: integer
Number defines the amount of months that are taken into account
Returns
-------
ET_Blue : array
Array[time, lat, lon] contains Blue Evapotranspiration
ET_Green : array
Array[time, lat, lon] contains Green Evapotranspiration
"""
import wa.General.raster_conversions as RC
import wa.Functions.Start.Get_Dictionaries as GD
# Input Parameters functions
scale = 1.1
# Open LU map for example
LU = RC.Open_nc_array(nc_outname, "Landuse")
# Define monthly dates
Dates = pd.date_range(Startdate, Enddate, freq = 'MS')
# Get moving window period
# Get dictionaries and keys for the moving average
ET_Blue_Green_Classes_dict, Moving_Window_Per_Class_dict = GD.get_bluegreen_classes(version = '1.0')
Classes = ET_Blue_Green_Classes_dict.keys()
Moving_Averages_Values_Array = np.ones(LU.shape) * np.nan
# Create array based on the dictionary that gives the Moving average tail for every pixel
for Class in Classes:
Values_Moving_Window_Class = Moving_Window_Per_Class_dict[Class]
for Values_Class in ET_Blue_Green_Classes_dict[Class]:
Moving_Averages_Values_Array[LU == Values_Class] = Values_Moving_Window_Class
Additional_Months_front = int(np.nanmax(Moving_Averages_Values_Array))
Additional_Months_tail = 0
Start_period = Additional_Months_front
End_period = Additional_Months_tail * -1
########################### Extract ETref data #################################
if ETref_Product is 'WA_ETref':
# Define data path
Data_Path_ETref = os.path.join(Dir_Basin, 'ETref', 'Monthly')
else:
Data_Path_ETref = ETref_Product
ETref = Complete_3D_Array(nc_outname, 'Reference_Evapotranspiration', Startdate, Enddate, Additional_Months_front, Additional_Months_tail, Data_Path_ETref)
######################## Extract Precipitation data ########################
if (P_Product == "CHIRPS" or P_Product == "RFE"):
# Define data path
Data_Path_P = os.path.join(Dir_Basin, 'Precipitation', P_Product, 'Monthly')
else:
Data_Path_P = P_Product
P = Complete_3D_Array(nc_outname, 'Precipitation', Startdate, Enddate, Additional_Months_front, Additional_Months_tail, Data_Path_P)
########################## Extract actET data ##############################
ET = RC.Open_nc_array(nc_outname, "Actual_Evapotranspiration", Startdate, Enddate)
############ Create average ETref and P using moving window ################
ETref_Ave = np.ones([len(Dates),int(LU.shape[0]),int(LU.shape[1])]) * np.nan
P_Ave = np.ones([len(Dates),int(LU.shape[0]),int(LU.shape[1])]) * np.nan
if End_period == 0:
P_period = P[Start_period:,:,:]
ETref_period = ETref[Start_period:,:,:]
else:
P_period = P[Start_period:End_period,:,:]
ETref_period = ETref[Start_period:End_period,:,:]
# Loop over the different moving average tails
for One_Value in np.unique(Moving_Window_Per_Class_dict.values()):
# If there is no moving average is 1 than use the value of the original ETref or P
if One_Value == 1:
Values_Ave_ETref = ETref[int(ETref.shape[0])-len(Dates):,:,:]
Values_Ave_P = P[int(ETref.shape[0])-len(Dates):,:,:]
# If there is tail, apply moving average over the whole datacube
else:
Values_Ave_ETref_tot = RC.Moving_average(ETref, One_Value - 1, 0)
Values_Ave_P_tot = RC.Moving_average(P, One_Value - 1, 0)
Values_Ave_ETref = Values_Ave_ETref_tot[int(Values_Ave_ETref_tot.shape[0])-len(Dates):,:,:]
Values_Ave_P = Values_Ave_P_tot[int(Values_Ave_P_tot.shape[0])-len(Dates):,:,:]
# Only add the data where the corresponding tail corresponds with the one_value
ETref_Ave[:,Moving_Averages_Values_Array == One_Value] = Values_Ave_ETref[:,Moving_Averages_Values_Array == One_Value]
P_Ave[:,Moving_Averages_Values_Array == One_Value] = Values_Ave_P[:,Moving_Averages_Values_Array == One_Value]
##################### Calculate ET blue and green ###########################
# Mask out the nan values(if one of the parameters is nan, then they are all nan)
mask = np.any([np.isnan(LU)*np.ones([len(Dates),int(LU.shape[0]),int(LU.shape[1])])==1, np.isnan(ET), np.isnan(ETref[int(ETref.shape[0])-len(Dates):,:,:]), np.isnan(P[int(ETref.shape[0])-len(Dates):,:,:]), np.isnan(P_Ave), np.isnan(ETref_Ave)],axis=0)
ETref_period[mask] = ETref_Ave[mask] = ET[mask] = P_period[mask] = P_Ave[mask] = np.nan
phi = ETref_Ave / P_Ave
# Calculate Budyko-index
Budyko = scale * np.sqrt(phi*np.tanh(1/phi)*(1-np.exp(-phi)))
# Calculate ET green
ETgreen_DataCube = np.minimum(Budyko*P[int(ETref.shape[0])-len(Dates):,:,:],ET)
# Calculate ET blue
ETblue_DataCube = ET - ETgreen_DataCube
return(np.array(ETblue_DataCube), np.array(ETgreen_DataCube))