def main(files_DEM_dir, files_DEM, files_Basin, files_Runoff, files_Extraction, startdate, enddate, input_nc, resolution, Format_DEM_dir, Format_DEM, Format_Basin, Format_Runoff, Format_Extraction):
# Define a year to get the epsg and geo
Startdate_timestamp = pd.Timestamp(startdate)
year = Startdate_timestamp.year
############################## Drainage Direction #####################################
# Open Array DEM dir as netCDF
if Format_DEM_dir == "NetCDF":
file_DEM_dir = os.path.join(files_DEM_dir, "%d.nc" %year)
DataCube_DEM_dir = RC.Open_nc_array(file_DEM_dir, "Drainage_Direction")
geo_out_example, epsg_example, size_X_example, size_Y_example, size_Z_example, Time_example = RC.Open_nc_info(files_DEM_dir)
# Create memory file for reprojection
gland = DC.Save_as_MEM(DataCube_DEM_dir, geo_out_example, epsg_example)
dataset_example = file_name_DEM_dir = gland
# Open Array DEM dir as TIFF
if Format_DEM_dir == "TIFF":
file_name_DEM_dir = os.path.join(files_DEM_dir,"DIR_HydroShed_-_%s.tif" %resolution)
DataCube_DEM_dir = RC.Open_tiff_array(file_name_DEM_dir)
geo_out_example, epsg_example, size_X_example, size_Y_example = RC.Open_array_info(file_name_DEM_dir)
dataset_example = file_name_DEM_dir
# Calculate Area per pixel in m2
import watools.Functions.Start.Area_converter as AC
DataCube_Area = AC.Degrees_to_m2(file_name_DEM_dir)
################################## DEM ##########################################
# Open Array DEM as netCDF
if Format_DEM == "NetCDF":
file_DEM = os.path.join(files_DEM, "%d.nc" %year)
DataCube_DEM = RC.Open_nc_array(file_DEM, "Elevation")
# Open Array DEM as TIFF
if Format_DEM == "TIFF":
file_name_DEM = os.path.join(files_DEM,"DEM_HydroShed_m_%s.tif" %resolution)
destDEM = RC.reproject_dataset_example(file_name_DEM, dataset_example, method=1)
DataCube_DEM = destDEM.GetRasterBand(1).ReadAsArray()
################################ Landuse ##########################################
# Open Array Basin as netCDF
if Format_Basin == "NetCDF":
file_Basin = os.path.join(files_Basin, "%d.nc" %year)
DataCube_Basin = RC.Open_nc_array(file_Basin, "Landuse")
geo_out, epsg, size_X, size_Y, size_Z, Time = RC.Open_nc_info(file_Basin, "Landuse")
dest_basin = DC.Save_as_MEM(DataCube_Basin, geo_out, str(epsg))
destLU = RC.reproject_dataset_example(dest_basin, dataset_example, method=1)
DataCube_LU_CR = destLU.GetRasterBand(1).ReadAsArray()
DataCube_Basin = np.zeros([size_Y_example, size_X_example])
DataCube_Basin[DataCube_LU_CR > 0] = 1
# Open Array Basin as TIFF
if Format_Basin == "TIFF":
file_name_Basin = files_Basin
destLU = RC.reproject_dataset_example(file_name_Basin, dataset_example, method=1)
DataCube_LU_CR = destLU.GetRasterBand(1).ReadAsArray()
DataCube_Basin = np.zeros([size_Y_example, size_X_example])
DataCube_Basin[DataCube_LU_CR > 0] = 1
################################ Surface Runoff ##########################################
# Open Array runoff as netCDF
if Format_Runoff == "NetCDF":
DataCube_Runoff = RC.Open_ncs_array(files_Runoff, "Surface_Runoff", startdate, enddate)
size_Z_example = DataCube_Runoff.shape[0]
file_Runoff = os.path.join(files_Runoff, "%d.nc" %year)
geo_out, epsg, size_X, size_Y, size_Z, Time = RC.Open_nc_info(file_Runoff, "Surface_Runoff")
DataCube_Runoff_CR = np.ones([size_Z_example, size_Y_example, size_X_example]) * np.nan
for i in range(0, size_Z):
DataCube_Runoff_one = DataCube_Runoff[i,:,:]
dest_Runoff_one = DC.Save_as_MEM(DataCube_Runoff_one, geo_out, str(epsg))
dest_Runoff = RC.reproject_dataset_example(dest_Runoff_one, dataset_example, method=4)
DataCube_Runoff_CR[i,:,:] = dest_Runoff.GetRasterBand(1).ReadAsArray()
DataCube_Runoff_CR[:, DataCube_LU_CR == 0] = -9999
DataCube_Runoff_CR[DataCube_Runoff_CR < 0] = -9999
# Open Array runoff as TIFF
if Format_Runoff == "TIFF":
DataCube_Runoff_CR = RC.Get3Darray_time_series_monthly(files_Runoff, startdate, enddate, Example_data = dataset_example)
################################ Surface Withdrawal ##########################################
# Open Array Extraction as netCDF
if Format_Extraction == "NetCDF":
DataCube_Extraction = RC.Open_ncs_array(files_Extraction, "Surface_Withdrawal", startdate, enddate)
size_Z_example = DataCube_Extraction.shape[0]
file_Extraction = os.path.join(files_Extraction, "%d.nc" %year)
geo_out, epsg, size_X, size_Y, size_Z, Time = RC.Open_nc_info(file_Extraction, "Surface_Withdrawal")
DataCube_Extraction_CR = np.ones([size_Z_example, size_Y_example, size_X_example]) * np.nan
for i in range(0, size_Z):
DataCube_Extraction_one = DataCube_Extraction[i,:,:]
dest_Extraction_one = DC.Save_as_MEM(DataCube_Extraction_one, geo_out, str(epsg))
dest_Extraction = RC.reproject_dataset_example(dest_Extraction_one, dataset_example, method=4)
DataCube_Extraction_CR[i,:,:] = dest_Extraction.GetRasterBand(1).ReadAsArray()
DataCube_Extraction_CR[:, DataCube_LU_CR == 0] = -9999
DataCube_Extraction_CR[DataCube_Extraction_CR < 0] = -9999
# Open Array Extraction as TIFF
if Format_Extraction == "TIFF":
DataCube_Extraction_CR = RC.Get3Darray_time_series_monthly(files_Extraction, startdate, enddate, Example_data = dataset_example)
################################ Create input netcdf ##########################################
# Save data in one NetCDF file
geo_out_example = np.array(geo_out_example)
# Latitude and longitude
lon_ls = np.arange(size_X_example)*geo_out_example[1]+geo_out_example[0] + 0.5 * geo_out_example[1]
lat_ls = np.arange(size_Y_example)*geo_out_example[5]+geo_out_example[3] - 0.5 * geo_out_example[5]
lat_n = len(lat_ls)
lon_n = len(lon_ls)
# Create NetCDF file
nc_file = netCDF4.Dataset(input_nc, 'w')
nc_file.set_fill_on()
# Create dimensions
lat_dim = nc_file.createDimension('latitude', lat_n)
lon_dim = nc_file.createDimension('longitude', lon_n)
# Create NetCDF variables
crso = nc_file.createVariable('crs', 'i4')
crso.long_name = 'Lon/Lat Coords in WGS84'
crso.standard_name = 'crs'
crso.grid_mapping_name = 'latitude_longitude'
crso.projection = epsg_example
crso.longitude_of_prime_meridian = 0.0
crso.semi_major_axis = 6378137.0
crso.inverse_flattening = 298.257223563
crso.geo_reference = geo_out_example
lat_var = nc_file.createVariable('latitude', 'f8', ('latitude',))
lat_var.units = 'degrees_north'
lat_var.standard_name = 'latitude'
lat_var.pixel_size = geo_out_example[5]
lon_var = nc_file.createVariable('longitude', 'f8', ('longitude',))
lon_var.units = 'degrees_east'
lon_var.standard_name = 'longitude'
lon_var.pixel_size = geo_out_example[1]
Dates = pd.date_range(startdate,enddate,freq = 'MS')
time_or=np.zeros(len(Dates))
i = 0
for Date in Dates:
time_or[i] = Date.toordinal()
i += 1
nc_file.createDimension('time', None)
timeo = nc_file.createVariable('time', 'f4', ('time',))
timeo.units = 'Monthly'
timeo.standard_name = 'time'
# Variables
demdir_var = nc_file.createVariable('demdir', 'i',
('latitude', 'longitude'),
fill_value=-9999)
demdir_var.long_name = 'Flow Direction Map'
demdir_var.grid_mapping = 'crs'
dem_var = nc_file.createVariable('dem', 'f8',
('latitude', 'longitude'),
fill_value=-9999)
dem_var.long_name = 'Altitude'
dem_var.units = 'meters'
dem_var.grid_mapping = 'crs'
basin_var = nc_file.createVariable('basin', 'i',
('latitude', 'longitude'),
fill_value=-9999)
basin_var.long_name = 'Altitude'
basin_var.units = 'meters'
basin_var.grid_mapping = 'crs'
area_var = nc_file.createVariable('area', 'f8',
('latitude', 'longitude'),
fill_value=-9999)
area_var.long_name = 'area in squared meters'
area_var.units = 'squared_meters'
area_var.grid_mapping = 'crs'
runoff_var = nc_file.createVariable('Runoff_M', 'f8',
('time', 'latitude', 'longitude'),
fill_value=-9999)
runoff_var.long_name = 'Runoff'
runoff_var.units = 'm3/month'
runoff_var.grid_mapping = 'crs'
extraction_var = nc_file.createVariable('Extraction_M', 'f8',
('time', 'latitude', 'longitude'),
fill_value=-9999)
extraction_var.long_name = 'Surface water Extraction'
extraction_var.units = 'm3/month'
extraction_var.grid_mapping = 'crs'
# Load data
lat_var[:] = lat_ls
lon_var[:] = lon_ls
timeo[:] = time_or
# Static variables
demdir_var[:, :] = DataCube_DEM_dir[:, :]
dem_var[:, :] = DataCube_DEM[:, :]
basin_var[:, :] = DataCube_Basin[:, :]
area_var[:, :] = DataCube_Area[:, :]
for i in range(len(Dates)):
runoff_var[i,:,:] = DataCube_Runoff_CR[i,:,:]
for i in range(len(Dates)):
extraction_var[i,:,:] = DataCube_Extraction_CR[i,:,:]
# Close file
nc_file.close()
return()
def Calculate(Dir_Home, Basin, LU_file_name, P_Product, ET_Product,
LAI_Product, NDM_Product, Startdate, Enddate, Simulation):
"""
This functions is the main framework for calculating sheet 2.
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 ###################################
# import general python modules
import os
from netCDF4 import Dataset
from watools.General import raster_conversions as RC
from watools.General import data_conversions as DC
import watools.Functions.Two as Two
import watools.Functions.Start as Start
import watools.Generator.Sheet2 as Generate
######################### Set General Parameters ##############################
# 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(
LU_file_name, Dir_Home)
############## Cut dates into pieces if it is needed ######################
# Check the years that needs to be calculated
years = list(
range(int(Startdate.split('-')[0]),
int(Enddate.split('-')[0]) + 1))
for year in years:
# Create Start and End date for time chunk
Startdate_part = '%d-01-01' % int(year)
Enddate_part = '%s-12-31' % int(year)
# Create Directory
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)
# 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)
############################# Download Data ###################################
# Open variables in netcdf
fh = Dataset(nc_outname)
Variables_NC = [var for var in fh.variables]
fh.close()
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 "Rainy_Days" in Variables_NC:
Data_Path_P_Daily = Start.Download_Data.Precipitation_Daily(
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 "LAI" in Variables_NC:
Data_Path_LAI = Start.Download_Data.LAI(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']], Startdate_part,
Enddate_part, LAI_Product)
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 "Rainy_Days" in Variables_NC:
# Create Rainy Days based on daily CHIRPS
Data_Path_RD = Two.Rainy_Days.Calc_Rainy_Days(
Dir_Basin, Data_Path_P_Daily, Startdate_part, Enddate_part)
if not "LAI" in Variables_NC:
# Create monthly LAI
Start.Eightdaily_to_monthly_state.Nearest_Interpolate(
Data_Path_LAI, Startdate_part, Enddate_part)
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)
###################### 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
#_______________________________Rainy Days_________________________________
if not "Rainy_Days" in Variables_NC:
# Get the data of rainy days and save as nc
DataCube_RD = RC.Get3Darray_time_series_monthly(
Data_Path_RD,
Startdate_part,
Enddate_part,
Example_data=Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_RD, "Rainy_Days",
"amount_of_days", 0.01)
del DataCube_RD
#_______________________________Leaf Area Index____________________________
if not "LAI" in Variables_NC:
# Get the data of leave area index and save as nc
DataCube_LAI = RC.Get3Darray_time_series_monthly(
Data_Path_LAI,
Startdate_part,
Enddate_part,
Example_data=Example_dataset)
DC.Add_NC_Array_Variable(nc_outname, DataCube_LAI, "LAI", "m2-m-2",
0.01)
del DataCube_LAI
####################### Calculations Sheet 2 ##########################
if not ("Interception" in Variables_NC or "Transpiration"
in Variables_NC or "Evaporation" in Variables_NC):
DataCube_I, DataCube_T, DataCube_E = Two.SplitET.ITE(
Dir_Basin, nc_outname, Startdate_part, Enddate_part,
Simulation)
DC.Add_NC_Array_Variable(nc_outname, DataCube_I, "Interception",
"mm/month", 0.01)
DC.Add_NC_Array_Variable(nc_outname, DataCube_T, "Transpiration",
"mm/month", 0.01)
DC.Add_NC_Array_Variable(nc_outname, DataCube_E, "Evaporation",
"mm/month", 0.01)
del DataCube_I, DataCube_T, DataCube_E
######################### Create CSV 2 ################################
Dir_Basin_CSV = Generate.CSV.Create(Dir_Basin, Simulation, Basin,
Startdate_part, Enddate_part,
nc_outname, Example_dataset)
############################ Create Sheet 2 ###############################
Generate.PDF.Create(Dir_Basin, Basin, Simulation, Dir_Basin_CSV)
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 watools.General import raster_conversions as RC
from watools.General import data_conversions as DC
import watools.Functions.Four as Four
import watools.Functions.Start as Start
import watools.Generator.Sheet4 as Generate
import watools.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(list(
Moving_Window_Per_Class_dict.values()))
############## Cut dates into pieces if it is needed ######################
# Check the years that needs to be calculated
years = list(
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 list(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
#________________________Theta_Saturated_Topsoil___________________________
# 5.) Grey Water Footprint data
if not "Theta_Saturated_Topsoil" in Variables_NC:
# Get the data of grey water footprint and save as nc
ThetaSat_Filepath = os.path.join(
Dir_Basin, Data_Path_ThetaSat_topsoil,
"Theta_Saturated_Topsoil_HiHydroSoil.tif")
dest_ThetaSat = RC.reproject_dataset_example(ThetaSat_Filepath,
Example_dataset,
method=1)
DataCube_ThetaSat = dest_ThetaSat.GetRasterBand(1).ReadAsArray()
DC.Add_NC_Array_Static(nc_outname, DataCube_ThetaSat,
"Theta_Saturated_Topsoil", "fraction",
0.0001)
del DataCube_ThetaSat
####################### Calculations Sheet 4 ##############################
############## Cut dates into pieces if it is needed ######################
years = list(
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, 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 watools.General import raster_conversions as RC
from watools.General import data_conversions as DC
import watools.Functions.Five as Five
import watools.Functions.Start as Start
import watools.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(list(
Moving_Window_Per_Class_dict.values()))
############## Cut dates into pieces if it is needed ######################
# Check the years that needs to be calculated
years = list(
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 _______________________
if not "Fraction_Surface_Water_Supply" in Variables_NC:
DataCube_frac_sw = np.ones([size_Y, size_X]) * np.nan
import watools.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 = list(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 watools.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 Complete_3D_Array(nc_outname, Var, Startdate, Enddate,
Additional_Months_front, Additional_Months_tail,
Data_Path):
from netCDF4 import Dataset
import watools.General.raster_conversions as RC
# Define startdate and enddate with moving average
Startdate_Moving_Average = pd.Timestamp(Startdate) - pd.DateOffset(
months=Additional_Months_front)
Enddate_Moving_Average = pd.Timestamp(Enddate) + pd.DateOffset(
months=Additional_Months_tail)
Startdate_Moving_Average_String = '%d-%02d-%02d' % (
Startdate_Moving_Average.year, Startdate_Moving_Average.month,
Startdate_Moving_Average.day)
Enddate_Moving_Average_String = '%d-%02d-%02d' % (
Enddate_Moving_Average.year, Enddate_Moving_Average.month,
Enddate_Moving_Average.day)
# Extract moving average period before
Year_front = int(Startdate_Moving_Average.year)
filename_front = os.path.join(os.path.dirname(nc_outname),
"%d.nc" % Year_front)
Enddate_Front = pd.Timestamp(Startdate) - pd.DateOffset(days=1)
# Extract inside start and enddate
Array_main = RC.Open_nc_array(nc_outname, Var, Startdate, Enddate)
if Additional_Months_front > 0:
# Extract moving average period before
if os.path.exists(filename_front):
# Open variables in netcdf
fh = Dataset(filename_front)
Variables_NC = [var for var in fh.variables]
fh.close()
if Var in Variables_NC:
Array_front = RC.Open_nc_array(
filename_front, Var, Startdate_Moving_Average_String,
Enddate_Front)
else:
Array_front = RC.Get3Darray_time_series_monthly(
Data_Path, Startdate_Moving_Average_String, Enddate_Front,
nc_outname)
else:
Array_front = RC.Get3Darray_time_series_monthly(
Data_Path, Startdate_Moving_Average_String, Enddate_Front,
nc_outname)
# Merge dataset
Array_main = np.vstack([Array_front, Array_main])
if Additional_Months_tail > 0:
# Extract moving average period after
Year_tail = int(Enddate_Moving_Average.year)
filename_tail = os.path.join(os.path.dirname(nc_outname),
"%d.nc" % Year_tail)
Startdate_tail = pd.Timestamp(Enddate) + pd.DateOffset(days=1)
# Extract moving average period after
if os.path.exists(filename_tail):
# Open variables in netcdf
fh = Dataset(filename_tail)
Variables_NC = [var for var in fh.variables]
fh.close()
if Var in Variables_NC:
Array_tail = RC.Open_nc_array(filename_tail, Var,
Startdate_tail,
Enddate_Moving_Average_String)
else:
Array_tail = RC.Get3Darray_time_series_monthly(
Data_Path, Startdate_tail, Enddate_Moving_Average_String,
nc_outname)
else:
Array_tail = RC.Get3Darray_time_series_monthly(
Data_Path, Startdate_tail, Enddate_Moving_Average_String,
nc_outname)
# Merge dataset
Array_main = np.vstack([Array_main, Array_tail])
return (Array_main)
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 watools.General import raster_conversions as RC
from watools.General import data_conversions as DC
import watools.Functions.Three as Three
import watools.Functions.Two as Two
import watools.Functions.Start as Start
import watools.Functions.Four as Four
import watools.Generator.Sheet3 as Generate
import watools.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(list(
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_part,
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)
# Download additional time before the research time frame
if year == years[0]:
Data_Path_P_Monthly = Start.Download_Data.Precipitation(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']],
Startdate_Moving_Average_String, "%d-01-01" % year, P_Product)
Data_Path_ETref = Start.Download_Data.ETreference(
Dir_Basin, [Boundaries['Latmin'], Boundaries['Latmax']],
[Boundaries['Lonmin'], Boundaries['Lonmax']],
Startdate_Moving_Average_String, "%d-01-01" % year,
ETref_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 ()
