def Convert_dict_to_array(River_dict, Array_dict, Reference_data):
import numpy as np
import os
import watools.General.raster_conversions as RC
if os.path.splitext(Reference_data)[-1] == '.nc':
# Get raster information
geo_out, proj, size_X, size_Y, size_Z, Time = RC.Open_nc_info(
Reference_data)
else:
# Get raster information
geo_out, proj, size_X, size_Y = RC.Open_array_info(Reference_data)
# 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(Array_dict[0])[0])
# create an empty array
DataCube = np.ones([time_dimension, size_Y, size_X]) * np.nan
for river_part in range(0, len(River_dict)):
for river_pixel in range(1, len(River_dict[river_part])):
river_pixel_ID = River_dict[river_part][river_pixel]
if len(np.argwhere(ID_Matrix == river_pixel_ID)) > 0:
row, col = np.argwhere(ID_Matrix == river_pixel_ID)[0][:]
DataCube[:, row, col] = Array_dict[river_part][:, river_pixel]
return (DataCube)
def Run(input_nc, output_nc, input_JRC):
# Define names
#Name_py_Discharge_dict_CR2 = os.path.join(Dir_Basin, 'Simulations', 'Simulation_%d' %Simulation, 'Sheet_5', 'Discharge_dict_CR2_simulation%d.npy' %(Simulation))
#Name_py_River_dict_CR2 = os.path.join(Dir_Basin, 'Simulations', 'Simulation_%d' %Simulation, 'Sheet_5', 'River_dict_CR2_simulation%d.npy' %(Simulation))
#Name_py_DEM_dict_CR2 = os.path.join(Dir_Basin, 'Simulations', 'Simulation_%d' %Simulation, 'Sheet_5', 'DEM_dict_CR2_simulation%d.npy' %(Simulation))
#Name_py_Distance_dict_CR2 = os.path.join(Dir_Basin, 'Simulations', 'Simulation_%d' %Simulation, 'Sheet_5', 'Distance_dict_CR2_simulation%d.npy' %(Simulation))
#if not (os.path.exists(Name_py_Discharge_dict_CR2) and os.path.exists(Name_py_River_dict_CR2) and os.path.exists(Name_py_DEM_dict_CR2) and os.path.exists(Name_py_Distance_dict_CR2)):
# Copy dicts as starting adding reservoir
import watools.General.raster_conversions as RC
import numpy as np
from datetime import date
Discharge_dict_CR2 = RC.Open_nc_dict(output_nc, "dischargedict_dynamic")
DEM_dataset = RC.Open_nc_array(input_nc, "dem")
time = RC.Open_nc_array(output_nc, "time")
Startdate = date.fromordinal(time[0])
Enddate = date.fromordinal(time[-1])
# Define names for reservoirs calculations
#Name_py_Diff_Water_Volume = os.path.join(Dir_Basin,'Simulations','Simulation_%d' %Simulation, 'Sheet_5','Diff_Water_Volume_CR2_simulation%d.npy' %(Simulation))
#Name_py_Regions = os.path.join(Dir_Basin,'Simulations','Simulation_%d' %Simulation, 'Sheet_5','Regions_simulation%d.npy' %(Simulation))
geo_out, proj, size_X, size_Y = RC.Open_array_info(input_JRC)
Boundaries = dict()
Boundaries['Lonmin'] = geo_out[0]
Boundaries['Lonmax'] = geo_out[0] + size_X * geo_out[1]
Boundaries['Latmin'] = geo_out[3] + size_Y * geo_out[5]
Boundaries['Latmax'] = geo_out[3]
Regions = Calc_Regions(input_nc, output_nc, input_JRC, Boundaries)
Amount_months = len(Discharge_dict_CR2[0])
Diff_Water_Volume = np.zeros([len(Regions), Amount_months, 3])
reservoir = 0
for region in Regions:
popt = Find_Area_Volume_Relation(region, input_JRC, input_nc)
Area_Reservoir_Values = GEE_calc_reservoir_area(
region, Startdate, Enddate)
Diff_Water_Volume[reservoir, :, :] = Calc_Diff_Storage(
Area_Reservoir_Values, popt)
reservoir += 1
################# 7.3 Add storage reservoirs and change outflows ##################
Discharge_dict_CR2, River_dict_CR2, DEM_dict_CR2, Distance_dict_CR2 = Add_Reservoirs(
output_nc, Diff_Water_Volume, Regions)
return (Discharge_dict_CR2, River_dict_CR2, DEM_dict_CR2,
Distance_dict_CR2)
def Calc_Property(Dir, latlim, lonlim, SL):
import watools
# Define level
if SL == "sl3":
level = "Topsoil"
elif SL == "sl6":
level = "Subsoil"
# check if you need to download
filename_out_thetasat = os.path.join(Dir, 'SoilGrids', 'Theta_Sat' ,'Theta_Sat2_%s_SoilGrids_kg-kg.tif' %level)
if not os.path.exists(filename_out_thetasat):
if SL == "sl3":
watools.Products.SoilGrids.Theta_Sat2.Topsoil(Dir, latlim, lonlim)
elif SL == "sl6":
watools.Products.SoilGrids.Theta_Sat2.Subsoil(Dir, latlim, lonlim)
filedir_out_thetafc = os.path.join(Dir, 'SoilGrids', 'Theta_FC')
if not os.path.exists(filedir_out_thetafc):
os.makedirs(filedir_out_thetafc)
# Define theta field capacity output
filename_out_thetafc = os.path.join(filedir_out_thetafc, 'Theta_FC2_%s_SoilGrids_cm3-cm3.tif' %level)
if not os.path.exists(filename_out_thetafc):
# Get info layer
geo_out, proj, size_X, size_Y = RC.Open_array_info(filename_out_thetasat)
# Open dataset
theta_sat = RC.Open_tiff_array(filename_out_thetasat)
# Calculate theta field capacity
theta_FC = np.ones(theta_sat.shape) * -9999
theta_FC = np.where(theta_sat < 0.301, 0.042, np.arccosh(theta_sat + 0.7) - 0.32 * (theta_sat + 0.7) + 0.2)
# Save as tiff
DC.Save_as_tiff(filename_out_thetafc, theta_FC, geo_out, proj)
return
def fuel_wood(output_folder, lu_fh, AREA, ndm_fhs, fraction_fhs, ndmdates):
"""
Calculate natural livestock feed production
INPUTS
----------
lu_fh : str
filehandle for land use map
ndm_fhs: nd array
array of filehandles of NDM maps
abv_grnd_biomass_ratio: dict
dictionnary 'LULC':[above ground biomass]
"""
Data_Path_Fuel = "Fuel"
out_folder = os.path.join(output_folder, Data_Path_Fuel)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
area_ha = AREA * 100
LULC = RC.Open_tiff_array(lu_fh)
geo_out, proj, size_X, size_Y = RC.Open_array_info(lu_fh)
fuel_classes = [1, 8, 9, 10, 11, 12, 13]
fuel_mask = np.zeros(LULC.shape)
for fc in fuel_classes:
fuel_mask[np.where(LULC == fc)] = 1
fuel_fhs_landscape = []
fuel_fhs_incremental = []
for d in range(len(ndm_fhs)):
ndm_fh = ndm_fhs[d]
fraction_fh = fraction_fhs[d]
yield_fract = RC.Open_tiff_array(fraction_fh)
date1 = ndmdates[d]
year = '%d' % date1.year
month = '%02d' % date1.month
# year = ndm_fh[-14:-10]
# month = ndm_fh[-9:-7]
out_fh_l = out_folder + '\\fuel_prod_landscape_%s_%s.tif' % (year,
month)
out_fh_i = out_folder + '\\fuel_prod_incremental_%s_%s.tif' % (year,
month)
NDM = becgis.open_as_array(ndm_fh, nan_values=True)
NDM_fuel_incremental = NDM * .05 * fuel_mask * yield_fract * area_ha / 1e6
NDM_fuel_landscape = NDM * .05 * fuel_mask * (
1 - yield_fract) * area_ha / 1e6
DC.Save_as_tiff(out_fh_i, NDM_fuel_incremental, geo_out)
DC.Save_as_tiff(out_fh_l, NDM_fuel_landscape, geo_out)
fuel_fhs_landscape.append(out_fh_l)
fuel_fhs_incremental.append(out_fh_i)
return fuel_fhs_landscape, fuel_fhs_incremental
def monthly_to_yearly(in_files):
#month_range = pd.date_range(start= state_date, end= end_date, freq= 'MS').strftime("%Y.%m").tolist()
#print(month_range)
month_list = [
'01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'
]
month_word = [
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',
'Nov', 'Dec'
]
files = glob.glob(in_files)
#print(files)
# Get array information and define projection
geo_out, proj, size_X, size_Y = RC.Open_array_info(files[0])
if int(proj.split('"')[-2]) == 4326:
proj = "WGS84"
count = 0
for i in month_list:
files_list = []
data = []
for file in files:
if re.search(".*\." + i + "\.01.*", file):
files_list.append(file)
for j in files_list:
photo = Image.open(j)
month = np.array(photo)
data.append(month)
#print(data)
arr_month = np.array(data)
month_avg = np.average(arr_month, axis=1)
#print(month_avg)
#print(month_avg.shape)
# Save tiff file
for m_index, m_word in zip(month_list, month_word):
if m_index == i:
DC.Save_as_tiff(
r"D:\chapter3analysis\precipitation\Average\{}.tif".format(
m_word), month_avg, geo_out, proj)
print(month_word[count])
count += 1
def recycle(output_folder, et_bg_fhs, recy_ratio, lu_fh, et_type):
Data_Path_rec = "temp_et_recycle"
out_folder = os.path.join(output_folder, Data_Path_rec)
geo_out, proj, size_X, size_Y = RC.Open_array_info(lu_fh)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
recycle_fhs = []
for et_fh in et_bg_fhs:
out_fh = out_folder + "\\recycled_et_" + et_type + et_fh[
-11:-4] + ".tif"
et = becgis.open_as_array(et_fh, nan_values=True)
et_recy = et * recy_ratio
DC.Save_as_tiff(out_fh, et_recy, geo_out)
recycle_fhs.append(out_fh)
return recycle_fhs
def split_yield(output_folder, p_fhs, et_blue_fhs, et_green_fhs,
ab=(1.0, 1.0)):
Data_Path_split = "split_y"
out_folder = os.path.join(output_folder, Data_Path_split)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
sp_yield_fhs = []
geo_out, proj, size_X, size_Y = RC.Open_array_info(p_fhs[0])
for m in range(len(p_fhs)):
out_fh = out_folder + '\\split_yield' + et_blue_fhs[m][-12:]
P = RC.Open_tiff_array(p_fhs[m])
ETBLUE = RC.Open_tiff_array(et_blue_fhs[m])
ETGREEN = RC.Open_tiff_array(et_green_fhs[m])
etbfraction = ETBLUE / (ETBLUE + ETGREEN)
pfraction = P / np.nanmax(P)
fraction = sh3.split_Yield(pfraction, etbfraction, ab[0], ab[1])
DC.Save_as_tiff(out_fh, fraction, geo_out)
sp_yield_fhs.append(out_fh)
return sp_yield_fhs
def lapse_rate(Dir, temperature_map, DEMmap):
"""
This function downscales the GLDAS temperature map by using the DEM map
Keyword arguments:
temperature_map -- 'C:/' path to the temperature map
DEMmap -- 'C:/' path to the DEM map
"""
# calculate average altitudes corresponding to T resolution
dest = RC.reproject_dataset_example(DEMmap, temperature_map, method=4)
DEM_ave_out_name = os.path.join(Dir, 'HydroSHED', 'DEM', 'DEM_ave.tif')
geo_out, proj, size_X, size_Y = RC.Open_array_info(temperature_map)
DEM_ave_data = dest.GetRasterBand(1).ReadAsArray()
DC.Save_as_tiff(DEM_ave_out_name, DEM_ave_data, geo_out, proj)
dest = None
# determine lapse-rate [degress Celcius per meter]
lapse_rate_number = 0.0065
# open maps as numpy arrays
dest = RC.reproject_dataset_example(DEM_ave_out_name, DEMmap, method=2)
dem_avg = dest.GetRasterBand(1).ReadAsArray()
dem_avg[dem_avg < 0] = 0
dest = None
# Open the temperature dataset
dest = RC.reproject_dataset_example(temperature_map, DEMmap, method=2)
T = dest.GetRasterBand(1).ReadAsArray()
dest = None
# Open Demmap
demmap = RC.Open_tiff_array(DEMmap)
dem_avg[demmap <= 0] = 0
demmap[demmap == -32768] = np.nan
# calculate first part
T = T + ((dem_avg - demmap) * lapse_rate_number)
return T
def Degrees_to_m2(Reference_data):
"""
This functions calculated the area of each pixel in squared meter.
Parameters
----------
Reference_data: str
Path to a tiff file or nc file or memory file of which the pixel area must be defined
Returns
-------
area_in_m2: array
Array containing the area of each pixel in squared meters
"""
try:
# Get the extension of the example data
filename, file_extension = os.path.splitext(Reference_data)
# Get raster information
if str(file_extension) == '.tif':
geo_out, proj, size_X, size_Y = RC.Open_array_info(Reference_data)
elif str(file_extension) == '.nc':
geo_out, epsg, size_X, size_Y, size_Z, Time = RC.Open_nc_info(
Reference_data)
except:
geo_out = Reference_data.GetGeoTransform()
size_X = Reference_data.RasterXSize()
size_Y = Reference_data.RasterYSize()
# Calculate the difference in latitude and longitude in meters
dlat, dlon = Calc_dlat_dlon(geo_out, size_X, size_Y)
# Calculate the area in squared meters
area_in_m2 = dlat * dlon
return (area_in_m2)
def monthly_to_yearly(state_date, end_date, in_files, out_file):
month_range = pd.date_range(start=state_date, end=end_date,
freq='MS').strftime("%Y.%m").tolist()
#print(month_range)
files_list = []
data = []
files = glob.glob(in_files)
#print(files)
# Get array information and define projection
geo_out, proj, size_X, size_Y = RC.Open_array_info(files[0])
if int(proj.split('"')[-2]) == 4326:
proj = "WGS84"
for i in month_range:
if 'P_CHIRPS.v2.0_mm-month-1_monthly_' + i + '.01.tif' in files:
files_list.append('P_CHIRPS.v2.0_mm-month-1_monthly_' + i +
'.01.tif')
else:
print("No such file")
for j in files_list:
photo = Image.open(j)
month = np.array(photo)
data.append(month)
#print(data)
arr_year = np.array(data)
#print(year_sum)
year_sum = arr_year.sum(axis=0)
# Save tiff file
DC.Save_as_tiff(out_file, year_sum, geo_out, proj)
def Calc_surface_withdrawal(Dir_Basin, nc_outname, Startdate, Enddate,
Example_dataset, ETref_Product, P_Product):
from netCDF4 import Dataset
import watools.Functions.Four as Four
import watools.General.raster_conversions as RC
# Open variables in netcdf
fh = Dataset(nc_outname)
Variables_NC = [var for var in fh.variables]
fh.close()
# Open or calculate Blue Evapotranspiration
if not "Blue_Evapotranspiration" in Variables_NC:
# Calc ET blue and green
DataCube_ETblue, DataCube_ETgreen = Four.SplitET.Blue_Green(
Dir_Basin, nc_outname, ETref_Product, P_Product, Startdate,
Enddate)
else:
DataCube_ETblue = RC.Open_nc_array(nc_outname,
"Blue_Evapotranspiration",
Startdate, Enddate)
# Open data array info based on example data
geo_out, epsg, size_X, size_Y = RC.Open_array_info(Example_dataset)
# 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_ETblue * DataCube_frac_sw[
None, :, :]
return (DataCube_surface_withdrawal)
def adjust_P(Dir, pressure_map, DEMmap):
"""
This function downscales the GLDAS air pressure map by using the DEM map
Keyword arguments:
pressure_map -- 'C:/' path to the pressure map
DEMmap -- 'C:/' path to the DEM map
"""
# calculate average latitudes
destDEMave = RC.reproject_dataset_example(DEMmap, pressure_map, method=4)
DEM_ave_out_name = os.path.join(Dir, 'HydroSHED', 'DEM', 'DEM_ave.tif')
geo_out, proj, size_X, size_Y = RC.Open_array_info(pressure_map)
DEM_ave_data = destDEMave.GetRasterBand(1).ReadAsArray()
DC.Save_as_tiff(DEM_ave_out_name, DEM_ave_data, geo_out, proj)
# open maps as numpy arrays
dest = RC.reproject_dataset_example(DEM_ave_out_name, DEMmap, method=2)
dem_avg = dest.GetRasterBand(1).ReadAsArray()
dest = None
# open maps as numpy arrays
dest = RC.reproject_dataset_example(pressure_map, DEMmap, method=2)
P = dest.GetRasterBand(1).ReadAsArray()
dest = None
demmap = RC.Open_tiff_array(DEMmap)
dem_avg[demmap <= 0] = 0
demmap[demmap == -32768] = np.nan
# calculate second part
P = P + (101.3 * ((293 - 0.0065 * (demmap - dem_avg)) / 293)**5.26 - 101.3)
os.remove(DEM_ave_out_name)
return P
def main(Dir, Startdate = '', Enddate = '',
latlim = [-60, 60], lonlim = [-180, 180], pixel_size = False, cores = False, LANDSAF = 0, SourceLANDSAF= '', Waitbar = 1):
"""
This function downloads TRMM3B43 V7 (monthly) data
Keyword arguments:
Dir -- 'C:/file/to/path/'
Startdate -- 'yyyy-mm-dd'
Enddate -- 'yyyy-mm-dd'
latlim -- [ymin, ymax] (values must be between -50 and 50)
lonlim -- [xmin, xmax] (values must be between -180 and 180)
cores -- The number of cores used to run the routine.
It can be 'False' to avoid using parallel computing
routines.
Waitbar -- 1 (Default) will print the waitbar
"""
print('Create monthly Reference ET data for period %s till %s' %(Startdate, Enddate))
# An array of monthly dates which will be calculated
Dates = pd.date_range(Startdate,Enddate,freq = 'MS')
# Create Waitbar
if Waitbar == 1:
import watools.Functions.Start.WaitbarConsole as WaitbarConsole
total_amount = len(Dates)
amount = 0
WaitbarConsole.printWaitBar(amount, total_amount, prefix = 'Progress:', suffix = 'Complete', length = 50)
# Calculate the ETref day by day for every month
for Date in Dates:
# Collect date data
Y=Date.year
M=Date.month
Mday=calendar.monthrange(Y,M)[1]
Days=pd.date_range(Date,Date+pd.Timedelta(days=Mday),freq='D')
StartTime=Date.strftime('%Y')+'-'+Date.strftime('%m')+ '-01'
EndTime=Date.strftime('%Y')+'-'+Date.strftime('%m')+'-'+str(Mday)
# Get ETref on daily basis
daily(Dir=Dir, Startdate=StartTime,Enddate=EndTime,latlim=latlim, lonlim=lonlim, pixel_size = pixel_size, cores=cores, LANDSAF=LANDSAF, SourceLANDSAF=SourceLANDSAF, Waitbar = 0)
# Load DEM
if not pixel_size:
nameDEM='DEM_HydroShed_m_3s.tif'
DEMmap=os.path.join(Dir,'HydroSHED','DEM',nameDEM )
else:
DEMmap=os.path.join(Dir,'HydroSHED','DEM','DEM_HydroShed_m_reshaped_for_ETref.tif')
# Get some geo-data to save results
geo_ET, proj, size_X, size_Y = RC.Open_array_info(DEMmap)
dataMonth=np.zeros([size_Y,size_X])
for Day in Days[:-1]:
DirDay=os.path.join(Dir,'ETref','Daily','ETref_mm-day-1_daily_' + Day.strftime('%Y.%m.%d') + '.tif')
dataDay=gdal.Open(DirDay)
Dval=dataDay.GetRasterBand(1).ReadAsArray().astype(np.float32)
Dval[Dval<0]=0
dataMonth=dataMonth+Dval
dataDay=None
# make geotiff file
output_folder_month=os.path.join(Dir,'ETref','Monthly')
if os.path.exists(output_folder_month)==False:
os.makedirs(output_folder_month)
DirMonth=os.path.join(output_folder_month,'ETref_mm-month-1_monthly_'+Date.strftime('%Y.%m.%d') + '.tif')
# Create the tiff file
DC.Save_as_tiff(DirMonth,dataMonth, geo_ET, proj)
# Create Waitbar
if Waitbar == 1:
amount += 1
WaitbarConsole.printWaitBar(amount, total_amount, prefix = 'Progress:', suffix = 'Complete', length = 50)
def Save_as_NC(namenc,
DataCube,
Var,
Reference_filename,
Startdate='',
Enddate='',
Time_steps='',
Scaling_factor=1):
"""
This function save the array as a netcdf file
Keyword arguments:
namenc -- string, complete path of the output file with .nc extension
DataCube -- [array], dataset of the nc file, can be a 2D or 3D array [time, lat, lon], must be same size as reference data
Var -- string, the name of the variable
Reference_filename -- string, complete path to the reference file name
Startdate -- 'YYYY-mm-dd', needs to be filled when you want to save a 3D array, defines the Start datum of the dataset
Enddate -- 'YYYY-mm-dd', needs to be filled when you want to save a 3D array, defines the End datum of the dataset
Time_steps -- 'monthly' or 'daily', needs to be filled when you want to save a 3D array, defines the timestep of the dataset
Scaling_factor -- number, scaling_factor of the dataset, default = 1
"""
# Import modules
import watools.General.raster_conversions as RC
from netCDF4 import Dataset
if not os.path.exists(namenc):
# Get raster information
geo_out, proj, size_X, size_Y = RC.Open_array_info(Reference_filename)
# Create the lat/lon rasters
lon = np.arange(size_X) * geo_out[1] + geo_out[0] - 0.5 * geo_out[1]
lat = np.arange(size_Y) * geo_out[5] + geo_out[3] - 0.5 * geo_out[5]
# Create the nc file
nco = Dataset(namenc, 'w', format='NETCDF4_CLASSIC')
nco.description = '%s data' % Var
# Create dimensions, variables and attributes:
nco.createDimension('longitude', size_X)
nco.createDimension('latitude', size_Y)
# Create time dimension if the parameter is time dependent
if Startdate is not '':
if Time_steps == 'monthly':
Dates = pd.date_range(Startdate, Enddate, freq='MS')
if Time_steps == 'daily':
Dates = pd.date_range(Startdate, Enddate, freq='D')
time_or = np.zeros(len(Dates))
i = 0
for Date in Dates:
time_or[i] = Date.toordinal()
i += 1
nco.createDimension('time', None)
timeo = nco.createVariable('time', 'f4', ('time', ))
timeo.units = '%s' % Time_steps
timeo.standard_name = 'time'
# Create the lon variable
lono = nco.createVariable('longitude', 'f8', ('longitude', ))
lono.standard_name = 'longitude'
lono.units = 'degrees_east'
lono.pixel_size = geo_out[1]
# Create the lat variable
lato = nco.createVariable('latitude', 'f8', ('latitude', ))
lato.standard_name = 'latitude'
lato.units = 'degrees_north'
lato.pixel_size = geo_out[5]
# Create container variable for CRS: lon/lat WGS84 datum
crso = nco.createVariable('crs', 'i4')
crso.long_name = 'Lon/Lat Coords in WGS84'
crso.grid_mapping_name = 'latitude_longitude'
crso.projection = proj
crso.longitude_of_prime_meridian = 0.0
crso.semi_major_axis = 6378137.0
crso.inverse_flattening = 298.257223563
crso.geo_reference = geo_out
# Create the data variable
if Startdate is not '':
preco = nco.createVariable('%s' % Var,
'f8', ('time', 'latitude', 'longitude'),
zlib=True,
least_significant_digit=1)
timeo[:] = time_or
else:
preco = nco.createVariable('%s' % Var,
'f8', ('latitude', 'longitude'),
zlib=True,
least_significant_digit=1)
# Set the data variable information
preco.scale_factor = Scaling_factor
preco.add_offset = 0.00
preco.grid_mapping = 'crs'
preco.set_auto_maskandscale(False)
# Set the lat/lon variable
lono[:] = lon
lato[:] = lat
# Set the data variable
if Startdate is not '':
for i in range(len(Dates)):
preco[i, :, :] = DataCube[i, :, :] * 1. / np.float(
Scaling_factor)
else:
preco[:, :] = DataCube[:, :] * 1. / np.float(Scaling_factor)
nco.close()
return ()
def Calc_Regions(input_nc, output_nc, input_JRC, Boundaries):
import numpy as np
import watools.General.raster_conversions as RC
sensitivity = 700 # 900 is less sensitive 1 is very sensitive
# Get JRC array and information
Array_JRC_occ = RC.Open_tiff_array(input_JRC)
Geo_out, proj, size_X, size_Y = RC.Open_array_info(input_JRC)
# Get Basin boundary based on LU
Array_LU = RC.Open_nc_array(input_nc, "basin")
LU_array = RC.resize_array_example(Array_LU, Array_JRC_occ)
basin_array = np.zeros(np.shape(LU_array))
basin_array[LU_array > 0] = 1
del LU_array
# find all pixels with water occurence
Array_JRC_occ[basin_array < 1] = 0
Array_JRC_occ[Array_JRC_occ < 30] = 0
Array_JRC_occ[Array_JRC_occ >= 30] = 1
del basin_array
# sum larger areas to find lakes
x_size = np.round(int(np.shape(Array_JRC_occ)[0]) / 30)
y_size = np.round(int(np.shape(Array_JRC_occ)[1]) / 30)
sum_array = np.zeros([x_size, y_size])
for i in range(0, len(sum_array)):
for j in range(0, len(sum_array[1])):
sum_array[i, j] = np.sum(Array_JRC_occ[i * 30:(i + 1) * 30,
j * 30:(j + 1) * 30])
del Array_JRC_occ
lakes = np.argwhere(sum_array >= sensitivity)
lake_info = np.zeros([1, 4])
i = 0
k = 1
# find all neighboring pixels
for lake in lakes:
added = 0
for j in range(0, k):
if (lake[0] >= lake_info[j, 0] and lake[0] <= lake_info[j, 1]
and lake[1] >= lake_info[j, 2]
and lake[1] <= lake_info[j, 3]):
lake_info[j, 0] = np.maximum(
np.minimum(lake_info[j, 0], lake[0] - 8), 0)
lake_info[j, 1] = np.minimum(
np.maximum(lake_info[j, 1], lake[0] + 8), x_size)
lake_info[j, 2] = np.maximum(
np.minimum(lake_info[j, 2], lake[1] - 8), 0)
lake_info[j, 3] = np.minimum(
np.maximum(lake_info[j, 3], lake[1] + 8), y_size)
added = 1
if added == 0:
lake_info_one = np.zeros([4])
lake_info_one[0] = np.maximum(0, lake[0] - 8)
lake_info_one[1] = np.minimum(x_size, lake[0] + 8)
lake_info_one[2] = np.maximum(0, lake[1] - 8)
lake_info_one[3] = np.minimum(y_size, lake[1] + 8)
lake_info = np.append(lake_info, lake_info_one)
lake_info = np.resize(lake_info, (k + 1, 4))
k += 1
# merge all overlaping regions
p = 0
lake_info_end = np.zeros([1, 4])
for i in range(1, k):
added = 0
lake_info_one = lake_info[i, :]
lake_y_region = list(
range(int(lake_info_one[0]), int(lake_info_one[1] + 1)))
lake_x_region = list(
range(int(lake_info_one[2]), int(lake_info_one[3] + 1)))
for j in range(0, p + 1):
if len(lake_y_region) + len(
list(
range(int(lake_info_end[j, 0]),
int(lake_info_end[j, 1] + 1)))
) is not len(
np.unique(
np.append(
lake_y_region,
list(
range(int(lake_info_end[j, 0]),
int(lake_info_end[j, 1] + 1)))))
) and len(lake_x_region) + len(
list(
range(int(lake_info_end[j, 2]),
int(lake_info_end[j, 3] + 1)))) is not len(
np.unique(
np.append(
lake_x_region,
list(
range(
int(lake_info_end[j, 2]),
int(lake_info_end[j, 3] +
1)))))):
lake_info_end[j, 0] = np.min(
np.unique(
np.append(
lake_y_region,
list(
range(int(lake_info_end[j, 0]),
int(lake_info_end[j, 1] + 1))))))
lake_info_end[j, 1] = np.max(
np.unique(
np.append(
lake_y_region,
list(
range(int(lake_info_end[j, 0]),
int(lake_info_end[j, 1] + 1))))))
lake_info_end[j, 2] = np.min(
np.unique(
np.append(
lake_x_region,
list(
range(int(lake_info_end[j, 2]),
int(lake_info_end[j, 3] + 1))))))
lake_info_end[j, 3] = np.max(
np.unique(
np.append(
lake_x_region,
list(
range(int(lake_info_end[j, 2]),
int(lake_info_end[j, 3] + 1))))))
added = 1
if added == 0:
lake_info_one = lake_info[i, :]
lake_info_end = np.append(lake_info_end, lake_info_one)
lake_info_end = np.resize(lake_info_end, (p + 2, 4))
p += 1
# calculate the area
Regions = np.zeros([p, 4])
pixel_x_size = Geo_out[1] * 30
pixel_y_size = Geo_out[5] * 30
for region in range(1, p + 1):
Regions[region - 1,
0] = Geo_out[0] + pixel_x_size * lake_info_end[region, 2]
Regions[region - 1,
1] = Geo_out[0] + pixel_x_size * (lake_info_end[region, 3] + 1)
Regions[region - 1,
2] = Geo_out[3] + pixel_y_size * (lake_info_end[region, 1] + 1)
Regions[region - 1,
3] = Geo_out[3] + pixel_y_size * lake_info_end[region, 0]
return (Regions)
def livestock_feed(output_folder, lu_fh, AREA, ndm_fhs, feed_dict, live_feed,
cattle_fh, fraction_fhs, ndmdates):
"""
Calculate natural livestock feed production
INPUTS
----------
lu_fh : str
filehandle for land use map
ndm_fhs: nd array
array of filehandles of NDM maps
ndm_dates: nd array
array of dates for NDM maps
feed_dict: dict
dictionnary 'pasture class':[list of LULC]
feed_pct: dict
dictionnary 'pasture class':[percent available as feed]
cattle_fh : str
filehandle for cattle map
"""
Data_Path_Feed = "Feed"
out_folder = os.path.join(output_folder, Data_Path_Feed)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
area_ha = AREA * 100
LULC = RC.Open_tiff_array(lu_fh)
# cattle = RC.Open_tiff_array(cattle_fh)
geo_out, proj, size_X, size_Y = RC.Open_array_info(lu_fh)
f_pct = np.zeros(LULC.shape)
for lu_type in list(feed_dict.keys()):
classes = feed_dict[lu_type]
mask = np.logical_or.reduce([LULC == value for value in classes])
f_pct[mask] = live_feed[lu_type]
feed_fhs_landscape = []
feed_fhs_incremental = []
for d in range(len(ndm_fhs)):
ndm_fh = ndm_fhs[d]
fraction_fh = fraction_fhs[d]
date1 = ndmdates[d]
year = '%d' % date1.year
month = '%02d' % date1.month
yield_fract = RC.Open_tiff_array(fraction_fh)
out_fh_l = out_folder + '\\feed_prod_landscape_%s_%s.tif' % (year,
month)
out_fh_i = out_folder + '\\feed_prod_incremental_%s_%s.tif' % (year,
month)
# out_fh2 = out_folder+'\\Feed_prod_pH_%s_%s.tif' %(year, month)
NDM = becgis.open_as_array(ndm_fh, nan_values=True)
NDM_feed = NDM * f_pct
NDM_feed_incremental = NDM_feed * yield_fract * area_ha / 1e6
NDM_feed_landscape = (NDM_feed * (1 - yield_fract)) * area_ha / 1e6
DC.Save_as_tiff(out_fh_l, NDM_feed_landscape, geo_out)
DC.Save_as_tiff(out_fh_i, NDM_feed_incremental, geo_out)
# NDM_feed_perHead = NDM_feed / cattle
# DC.Save_as_tiff(out_fh2, NDM_feed, geo_out)
feed_fhs_landscape.append(out_fh_l)
feed_fhs_incremental.append(out_fh_i)
return feed_fhs_landscape, feed_fhs_incremental
def ETref(Date, args):
"""
This function starts to calculate ETref (daily) data based on Hydroshed, GLDAS, and (CFSR/LANDSAF) in parallel or single core
Keyword arguments:
Date -- panda timestamp
args -- includes all the parameters that are needed for the ETref
"""
# unpack the arguments
[Dir, lonlim, latlim, pixel_size, LANDSAF] = args
# Set the paths
nameTmin = 'Tair-min_GLDAS-NOAH_C_daily_' + Date.strftime(
'%Y.%m.%d') + ".tif"
tmin_str = os.path.join(Dir, 'Weather_Data', 'Model', 'GLDAS', 'daily',
'tair_f_inst', 'min', nameTmin)
nameTmax = 'Tair-max_GLDAS-NOAH_C_daily_' + Date.strftime(
'%Y.%m.%d') + ".tif"
tmax_str = os.path.join(Dir, 'Weather_Data', 'Model', 'GLDAS', 'daily',
'tair_f_inst', 'max', nameTmax)
nameHumid = 'Hum_GLDAS-NOAH_kg-kg_daily_' + Date.strftime(
'%Y.%m.%d') + ".tif"
humid_str = os.path.join(Dir, 'Weather_Data', 'Model', 'GLDAS', 'daily',
'qair_f_inst', 'mean', nameHumid)
namePress = 'P_GLDAS-NOAH_kpa_daily_' + Date.strftime('%Y.%m.%d') + ".tif"
press_str = os.path.join(Dir, 'Weather_Data', 'Model', 'GLDAS', 'daily',
'psurf_f_inst', 'mean', namePress)
nameWind = 'W_GLDAS-NOAH_m-s-1_daily_' + Date.strftime('%Y.%m.%d') + ".tif"
wind_str = os.path.join(Dir, 'Weather_Data', 'Model', 'GLDAS', 'daily',
'wind_f_inst', 'mean', nameWind)
if LANDSAF == 1:
nameShortClearname = 'ShortWave_Clear_Daily_W-m2_' + Date.strftime(
'%Y-%m-%d') + '.tif'
input2_str = os.path.join(Dir, 'Landsaf_Clipped',
'Shortwave_Clear_Sky', nameShortClearname)
nameShortNetname = 'ShortWave_Net_Daily_W-m2_' + Date.strftime(
'%Y-%m-%d') + '.tif'
input1_str = os.path.join(Dir, 'Landsaf_Clipped', 'Shortwave_Net',
nameShortNetname)
input3_str = 'not'
else:
if Date < pd.Timestamp(pd.datetime(2011, 4, 1)):
nameDownLong = 'DLWR_CFSR_W-m2_' + Date.strftime(
'%Y.%m.%d') + ".tif"
input2_str = os.path.join(Dir, 'Radiation', 'CFSR', nameDownLong)
nameDownShort = 'DSWR_CFSR_W-m2_' + Date.strftime(
'%Y.%m.%d') + ".tif"
input1_str = os.path.join(Dir, 'Radiation', 'CFSR', nameDownShort)
nameUpLong = 'ULWR_CFSR_W-m2_' + Date.strftime('%Y.%m.%d') + ".tif"
input3_str = os.path.join(Dir, 'Radiation', 'CFSR', nameUpLong)
else:
nameDownLong = 'DLWR_CFSRv2_W-m2_' + Date.strftime(
'%Y.%m.%d') + ".tif"
input2_str = os.path.join(Dir, 'Radiation', 'CFSRv2', nameDownLong)
nameDownShort = 'DSWR_CFSRv2_W-m2_' + Date.strftime(
'%Y.%m.%d') + ".tif"
input1_str = os.path.join(Dir, 'Radiation', 'CFSRv2',
nameDownShort)
nameUpLong = 'ULWR_CFSRv2_W-m2_' + Date.strftime(
'%Y.%m.%d') + ".tif"
input3_str = os.path.join(Dir, 'Radiation', 'CFSRv2', nameUpLong)
# The day of year
DOY = Date.dayofyear
# Load DEM
if not pixel_size:
DEMmap_str = os.path.join(Dir, 'HydroSHED', 'DEM',
'DEM_HydroShed_m_3s.tif')
else:
DEMmap_str = os.path.join(Dir, 'HydroSHED', 'DEM',
'DEM_HydroShed_m_3s.tif')
dest, ulx, lry, lrx, uly, epsg_to = RC.reproject_dataset_epsg(
DEMmap_str, pixel_spacing=pixel_size, epsg_to=4326, method=2)
DEMmap_str = os.path.join(Dir, 'HydroSHED', 'DEM',
'DEM_HydroShed_m_reshaped_for_ETref.tif')
DEM_data = dest.GetRasterBand(1).ReadAsArray()
geo_dem = [ulx, pixel_size, 0.0, uly, 0.0, -pixel_size]
DC.Save_as_tiff(name=DEMmap_str,
data=DEM_data,
geo=geo_dem,
projection='4326')
# Calc ETref
ETref = calc_ETref(Dir, tmin_str, tmax_str, humid_str, press_str, wind_str,
input1_str, input2_str, input3_str, DEMmap_str, DOY)
# Make directory for the MODIS ET data
output_folder = os.path.join(Dir, 'ETref', 'Daily')
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# Create the output names
NameETref = 'ETref_mm-day-1_daily_' + Date.strftime('%Y.%m.%d') + '.tif'
NameEnd = os.path.join(output_folder, NameETref)
# Collect geotiff information
geo_out, proj, size_X, size_Y = RC.Open_array_info(DEMmap_str)
# Create daily ETref tiff files
DC.Save_as_tiff(name=NameEnd, data=ETref, geo=geo_out, projection=proj)
def DownloadData(output_folder, latlim, lonlim, parameter, resolution):
"""
This function downloads DEM data from HydroSHED
Keyword arguments:
output_folder -- directory of the result
latlim -- [ymin, ymax] (values must be between -50 and 50)
lonlim -- [xmin, xmax] (values must be between -180 and 180)
Resample -- 1 = The data will be resampled to 0.001 degree spatial
resolution
-- 0 = The data will have the same pixel size as the data obtained
from the internet
"""
# Define parameter depedent variables
if parameter == "dir_3s":
para_name = "DIR"
unit = "-"
resolution = '3s'
parameter = 'dir'
if parameter == "dem_3s":
para_name = "DEM"
unit = "m"
resolution = '3s'
parameter = 'dem'
if parameter == "dir_15s":
para_name = "DIR"
unit = "-"
resolution = '15s'
parameter = 'dir'
if parameter == "dem_15s":
para_name = "DEM"
unit = "m"
resolution = '15s'
parameter = 'dem'
# converts the latlim and lonlim into names of the tiles which must be
# downloaded
if resolution == '3s':
name, rangeLon, rangeLat = Find_Document_Names(latlim, lonlim,
parameter)
# Memory for the map x and y shape (starts with zero)
size_X_tot = 0
size_Y_tot = 0
if resolution == '15s':
name = Find_Document_names_15s(latlim, lonlim, parameter, resolution)
nameResults = []
# Create a temporary folder for processing
output_folder_trash = os.path.join(output_folder, "Temp")
if not os.path.exists(output_folder_trash):
os.makedirs(output_folder_trash)
# Download, extract, and converts all the files to tiff files
for nameFile in name:
try:
# Download the data from
# http://earlywarning.usgs.gov/hydrodata/
output_file, file_name = Download_Data(nameFile,
output_folder_trash,
parameter, para_name,
resolution)
# extract zip data
DC.Extract_Data(output_file, output_folder_trash)
# Converts the data with a adf extention to a tiff extension.
# The input is the file name and in which directory the data must be stored
file_name_tiff = file_name.split('.')[0] + '_trans_temporary.tif'
file_name_extract = file_name.split('_')[0:3]
if resolution == '3s':
file_name_extract2 = file_name_extract[
0] + '_' + file_name_extract[1]
if resolution == '15s':
file_name_extract2 = file_name_extract[
0] + '_' + file_name_extract[1] + '_15s'
input_adf = os.path.join(output_folder_trash, file_name_extract2,
file_name_extract2, 'hdr.adf')
output_tiff = os.path.join(output_folder_trash, file_name_tiff)
# convert data from adf to a tiff file
output_tiff = DC.Convert_adf_to_tiff(input_adf, output_tiff)
geo_out, proj, size_X, size_Y = RC.Open_array_info(output_tiff)
if int(size_X) != int(6000) or int(size_Y) != int(6000):
data = np.ones((6000, 6000)) * -9999
# Create the latitude bound
Vfile = str(nameFile)[1:3]
SignV = str(nameFile)[0]
SignVer = 1
# If the sign before the filename is a south sign than latitude is negative
if SignV is "s":
SignVer = -1
Bound2 = int(SignVer) * int(Vfile)
# Create the longitude bound
Hfile = str(nameFile)[4:7]
SignH = str(nameFile)[3]
SignHor = 1
# If the sign before the filename is a west sign than longitude is negative
if SignH is "w":
SignHor = -1
Bound1 = int(SignHor) * int(Hfile)
Expected_X_min = Bound1
Expected_Y_max = Bound2 + 5
Xid_start = int(
np.round((geo_out[0] - Expected_X_min) / geo_out[1]))
Xid_end = int(
np.round(
((geo_out[0] + size_X * geo_out[1]) - Expected_X_min) /
geo_out[1]))
Yid_start = int(
np.round((Expected_Y_max - geo_out[3]) / (-geo_out[5])))
Yid_end = int(
np.round((Expected_Y_max - (geo_out[3] +
(size_Y * geo_out[5]))) /
(-geo_out[5])))
data[Yid_start:Yid_end,
Xid_start:Xid_end] = RC.Open_tiff_array(output_tiff)
if np.max(data) == 255:
data[data == 255] = -9999
data[data < -9999] = -9999
geo_in = [
Bound1, 0.00083333333333333, 0.0,
int(Bound2 + 5), 0.0, -0.0008333333333333333333
]
# save chunk as tiff file
DC.Save_as_tiff(name=output_tiff,
data=data,
geo=geo_in,
projection="WGS84")
except:
if resolution == '3s':
# If tile not exist create a replacing zero tile (sea tiles)
output = nameFile.split('.')[0] + "_trans_temporary.tif"
output_tiff = os.path.join(output_folder_trash, output)
file_name = nameFile
data = np.ones((6000, 6000)) * -9999
data = data.astype(np.float32)
# Create the latitude bound
Vfile = str(file_name)[1:3]
SignV = str(file_name)[0]
SignVer = 1
# If the sign before the filename is a south sign than latitude is negative
if SignV is "s":
SignVer = -1
Bound2 = int(SignVer) * int(Vfile)
# Create the longitude bound
Hfile = str(file_name)[4:7]
SignH = str(file_name)[3]
SignHor = 1
# If the sign before the filename is a west sign than longitude is negative
if SignH is "w":
SignHor = -1
Bound1 = int(SignHor) * int(Hfile)
# Geospatial data for the tile
geo_in = [
Bound1, 0.00083333333333333, 0.0,
int(Bound2 + 5), 0.0, -0.0008333333333333333333
]
# save chunk as tiff file
DC.Save_as_tiff(name=output_tiff,
data=data,
geo=geo_in,
projection="WGS84")
if resolution == '15s':
print('no 15s data is in dataset')
if resolution == '3s':
# clip data
Data, Geo_data = RC.clip_data(output_tiff, latlim, lonlim)
size_Y_out = int(np.shape(Data)[0])
size_X_out = int(np.shape(Data)[1])
# Total size of the product so far
size_Y_tot = int(size_Y_tot + size_Y_out)
size_X_tot = int(size_X_tot + size_X_out)
if nameFile is name[0]:
Geo_x_end = Geo_data[0]
Geo_y_end = Geo_data[3]
else:
Geo_x_end = np.min([Geo_x_end, Geo_data[0]])
Geo_y_end = np.max([Geo_y_end, Geo_data[3]])
# create name for chunk
FileNameEnd = "%s_temporary.tif" % (nameFile)
nameForEnd = os.path.join(output_folder_trash, FileNameEnd)
nameResults.append(str(nameForEnd))
# save chunk as tiff file
DC.Save_as_tiff(name=nameForEnd,
data=Data,
geo=Geo_data,
projection="WGS84")
if resolution == '3s':
#size_X_end = int(size_X_tot) #!
#size_Y_end = int(size_Y_tot) #!
size_X_end = int(size_X_tot / len(rangeLat)) + 1 #!
size_Y_end = int(size_Y_tot / len(rangeLon)) + 1 #!
# Define the georeference of the end matrix
geo_out = [Geo_x_end, Geo_data[1], 0, Geo_y_end, 0, Geo_data[5]]
latlim_out = [geo_out[3] + geo_out[5] * size_Y_end, geo_out[3]]
lonlim_out = [geo_out[0], geo_out[0] + geo_out[1] * size_X_end]
# merge chunk together resulting in 1 tiff map
datasetTot = Merge_DEM(latlim_out, lonlim_out, nameResults, size_Y_end,
size_X_end)
datasetTot[datasetTot < -9999] = -9999
if resolution == '15s':
output_file_merged = os.path.join(output_folder_trash, 'merged.tif')
datasetTot, geo_out = Merge_DEM_15s(output_folder_trash,
output_file_merged, latlim, lonlim)
# name of the end result
output_DEM_name = "%s_HydroShed_%s_%s.tif" % (para_name, unit, resolution)
Save_name = os.path.join(output_folder, output_DEM_name)
# Make geotiff file
DC.Save_as_tiff(name=Save_name,
data=datasetTot,
geo=geo_out,
projection="WGS84")
os.chdir(output_folder)
# Delete the temporary folder
shutil.rmtree(output_folder_trash)
def Merge_DEM_15s(output_folder_trash, output_file_merged, latlim, lonlim):
os.chdir(output_folder_trash)
tiff_files = glob.glob('*.tif')
resolution_geo = []
lonmin = lonlim[0]
lonmax = lonlim[1]
latmin = latlim[0]
latmax = latlim[1]
resolution_geo = 0.00416667
size_x_tot = int(np.round((lonmax - lonmin) / resolution_geo))
size_y_tot = int(np.round((latmax - latmin) / resolution_geo))
data_tot = np.ones([size_y_tot, size_x_tot]) * -9999.
for tiff_file in tiff_files:
inFile = os.path.join(output_folder_trash, tiff_file)
geo, proj, size_X, size_Y = RC.Open_array_info(inFile)
resolution_geo = geo[1]
lonmin_one = geo[0]
lonmax_one = geo[0] + size_X * geo[1]
latmin_one = geo[3] + size_Y * geo[5]
latmax_one = geo[3]
if lonmin_one < lonmin:
lonmin_clip = lonmin
else:
lonmin_clip = lonmin_one
if lonmax_one > lonmax:
lonmax_clip = lonmax
else:
lonmax_clip = lonmax_one
if latmin_one < latmin:
latmin_clip = latmin
else:
latmin_clip = latmin_one
if latmax_one > latmax:
latmax_clip = latmax
else:
latmax_clip = latmax_one
size_x_clip = int(
np.round((lonmax_clip - lonmin_clip) / resolution_geo))
size_y_clip = int(
np.round((latmax_clip - latmin_clip) / resolution_geo))
inFile = os.path.join(output_folder_trash, tiff_file)
geo, proj, size_X, size_Y = RC.Open_array_info(inFile)
Data = RC.Open_tiff_array(inFile)
lonmin_tiff = geo[0]
latmax_tiff = geo[3]
lon_tiff_position = int(
np.round((lonmin_clip - lonmin_tiff) / resolution_geo))
lat_tiff_position = int(
np.round((latmax_tiff - latmax_clip) / resolution_geo))
lon_data_tot_position = int(
np.round((lonmin_clip - lonmin) / resolution_geo))
lat_data_tot_position = int(
np.round((latmax - latmax_clip) / resolution_geo))
Data[Data < -9999.] = -9999.
data_tot[lat_data_tot_position:lat_data_tot_position + size_y_clip,
lon_data_tot_position:lon_data_tot_position +
size_x_clip][data_tot[
lat_data_tot_position:lat_data_tot_position + size_y_clip,
lon_data_tot_position:lon_data_tot_position +
size_x_clip] == -9999] = Data[
lat_tiff_position:lat_tiff_position + size_y_clip,
lon_tiff_position:lon_tiff_position +
size_x_clip][data_tot[
lat_data_tot_position:lat_data_tot_position +
size_y_clip,
lon_data_tot_position:lon_data_tot_position +
size_x_clip] == -9999]
geo_out = [lonmin, resolution_geo, 0.0, latmax, 0.0, -1 * resolution_geo]
geo_out = tuple(geo_out)
data_tot[data_tot < -9999.] = -9999.
return (data_tot, geo_out)
def main(Dir,
Startdate='',
Enddate='',
latlim=[-50, 50],
lonlim=[-180, 180],
cores=False,
Waitbar=1):
"""
This function downloads RFE V2.0 (monthly) data
Keyword arguments:
Dir -- 'C:/file/to/path/'
Startdate -- 'yyyy-mm-dd'
Enddate -- 'yyyy-mm-dd'
latlim -- [ymin, ymax] (values must be between -50 and 50)
lonlim -- [xmin, xmax] (values must be between -180 and 180)
cores -- The number of cores used to run the routine.
It can be 'False' to avoid using parallel computing
routines.
Waitbar -- 1 (Default) will print a waitbar
"""
# Download data
print('\nDownload monthly RFE precipitation data for period %s till %s' %
(Startdate, Enddate))
# Check variables
if not Startdate:
Startdate = pd.Timestamp('2001-01-01')
if not Enddate:
Enddate = pd.Timestamp('Now')
Dates = pd.date_range(Startdate, Enddate, freq='MS')
# Make directory
output_folder = os.path.join(Dir, 'Precipitation', 'RFE', 'Monthly/')
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# Create Waitbar
if Waitbar == 1:
import watools.Functions.Start.WaitbarConsole as WaitbarConsole
total_amount = len(Dates)
amount = 0
WaitbarConsole.printWaitBar(amount,
total_amount,
prefix='Progress:',
suffix='Complete',
length=50)
for Date in Dates:
month = Date.month
year = Date.year
end_day = calendar.monthrange(year, month)[1]
Startdate_one_month = '%s-%02s-01' % (year, month)
Enddate_one_month = '%s-%02s-%02s' % (year, month, end_day)
DownloadData(Dir, Startdate_one_month, Enddate_one_month, latlim,
lonlim, 0, cores)
Dates_daily = pd.date_range(Startdate_one_month,
Enddate_one_month,
freq='D')
# Make directory
input_folder_daily = os.path.join(Dir, 'Precipitation', 'RFE',
'Daily/')
i = 0
for Date_daily in Dates_daily:
file_name = 'P_RFE.v2.0_mm-day-1_daily_%s.%02s.%02s.tif' % (
Date_daily.strftime('%Y'), Date_daily.strftime('%m'),
Date_daily.strftime('%d'))
file_name_daily_path = os.path.join(input_folder_daily, file_name)
if os.path.exists(file_name_daily_path):
if Date_daily == Dates_daily[i]:
Raster_monthly = RC.Open_tiff_array(file_name_daily_path)
else:
Raster_monthly += RC.Open_tiff_array(file_name_daily_path)
else:
if Date_daily == Dates_daily[i]:
i += 1
geo_out, proj, size_X, size_Y = RC.Open_array_info(
file_name_daily_path)
file_name = 'P_RFE.v2.0_mm-month-1_monthly_%s.%02s.01.tif' % (
Date.strftime('%Y'), Date.strftime('%m'))
file_name_output = os.path.join(output_folder, file_name)
DC.Save_as_tiff(file_name_output,
Raster_monthly,
geo_out,
projection="WGS84")
if Waitbar == 1:
amount += 1
WaitbarConsole.printWaitBar(amount,
total_amount,
prefix='Progress:',
suffix='Complete',
length=50)
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 Nearest_Interpolate(Dir_in, Startdate, Enddate, Dir_out=None):
"""
This functions calculates monthly tiff files based on the 16 daily tiff files. (will calculate the average)
Parameters
----------
Dir_in : str
Path to the input data
Startdate : str
Contains the start date of the model 'yyyy-mm-dd'
Enddate : str
Contains the end date of the model 'yyyy-mm-dd'
Dir_out : str
Path to the output data, default is same as Dir_in
"""
# import WA+ modules
import watools.General.data_conversions as DC
import watools.General.raster_conversions as RC
# Change working directory
os.chdir(Dir_in)
# Find all eight daily files
files = glob.glob('*16-daily*.tif')
# Create array with filename and keys (DOY and year) of all the 8 daily files
i = 0
DOY_Year = np.zeros([len(files), 3])
for File in files:
# Get the time characteristics from the filename
year = File.split('.')[-4][-4:]
month = File.split('.')[-3]
day = File.split('.')[-2]
# Create pandas Timestamp
date_file = '%s-%02s-%02s' % (year, month, day)
Datum = pd.Timestamp(date_file)
# Get day of year
DOY = Datum.strftime('%j')
# Save data in array
DOY_Year[i, 0] = i
DOY_Year[i, 1] = DOY
DOY_Year[i, 2] = year
# Loop over files
i += 1
# Check enddate:
Enddate_split = Enddate.split('-')
month_range = calendar.monthrange(int(Enddate_split[0]),
int(Enddate_split[1]))[1]
Enddate = '%d-%02d-%02d' % (int(Enddate_split[0]), int(
Enddate_split[1]), month_range)
# Check startdate:
Startdate_split = Startdate.split('-')
Startdate = '%d-%02d-01' % (int(Startdate_split[0]), int(
Startdate_split[1]))
# Define end and start date
Dates = pd.date_range(Startdate, Enddate, freq='MS')
DatesEnd = pd.date_range(Startdate, Enddate, freq='M')
# Get array information and define projection
geo_out, proj, size_X, size_Y = RC.Open_array_info(files[0])
if int(proj.split('"')[-2]) == 4326:
proj = "WGS84"
# Get the No Data Value
dest = gdal.Open(files[0])
NDV = dest.GetRasterBand(1).GetNoDataValue()
# Loop over months and create monthly tiff files
i = 0
for date in Dates:
# Get Start and end DOY of the current month
DOY_month_start = date.strftime('%j')
DOY_month_end = DatesEnd[i].strftime('%j')
# Search for the files that are between those DOYs
year = date.year
DOYs = DOY_Year[DOY_Year[:, 2] == year]
DOYs_oneMonth = DOYs[np.logical_and(
(DOYs[:, 1] + 16) >= int(DOY_month_start),
DOYs[:, 1] <= int(DOY_month_end))]
# Create empty arrays
Monthly = np.zeros([size_Y, size_X])
Weight_tot = np.zeros([size_Y, size_X])
Data_one_month = np.ones([size_Y, size_X]) * np.nan
# Loop over the files that are within the DOYs
for EightDays in DOYs_oneMonth[:, 1]:
# Calculate the amount of days in this month of each file
Weight = np.ones([size_Y, size_X])
# For start of month
if np.min(DOYs_oneMonth[:, 1]) == EightDays:
Weight = Weight * int(EightDays + 16 - int(DOY_month_start))
# For end of month
elif np.max(DOYs_oneMonth[:, 1]) == EightDays:
Weight = Weight * (int(DOY_month_end) - EightDays + 1)
# For the middle of the month
else:
Weight = Weight * 16
row = DOYs_oneMonth[np.argwhere(
DOYs_oneMonth[:, 1] == EightDays)[0][0], :][0]
# Open the array of current file
input_name = os.path.join(Dir_in, files[int(row)])
Data = RC.Open_tiff_array(input_name)
# Remove NDV
Weight[Data == NDV] = 0
Data[Data == NDV] = np.nan
# Multiply weight time data (per day)
Data = Data * Weight
# Calculate the total weight and data
Weight_tot += Weight
Monthly[~np.isnan(Data)] += Data[~np.isnan(Data)]
# Go to next month
i += 1
# Calculate the average
Data_one_month[Weight_tot != 0.] = Monthly[
Weight_tot != 0.] / Weight_tot[Weight_tot != 0.]
# Define output directory
if Dir_out == None:
Dir_out = Dir_in
# Define output name
output_name = os.path.join(
Dir_out, files[int(row)].replace('16-daily', 'monthly'))
output_name = output_name[:-9] + '%02d.01.tif' % (date.month)
# Save tiff file
DC.Save_as_tiff(output_name, Data_one_month, geo_out, proj)
return
def CollectLANDSAF(SourceLANDSAF, Dir, Startdate, Enddate, latlim, lonlim):
"""
This function collects and clip LANDSAF data
Keyword arguments:
SourceLANDSAF -- 'C:/' path to the LANDSAF source data (The directory includes SIS and SID)
Dir -- 'C:/' path to the WA map
Startdate -- 'yyyy-mm-dd'
Enddate -- 'yyyy-mm-dd'
latlim -- [ymin, ymax] (values must be between -60 and 60)
lonlim -- [xmin, xmax] (values must be between -180 and 180)
"""
# Make an array of the days of which the ET is taken
Dates = pd.date_range(Startdate, Enddate, freq='D')
# make directories
SISdir = os.path.join(Dir, 'Landsaf_Clipped', 'SIS')
if os.path.exists(SISdir) is False:
os.makedirs(SISdir)
SIDdir = os.path.join(Dir, 'Landsaf_Clipped', 'SID')
if os.path.exists(SIDdir) is False:
os.makedirs(SIDdir)
ShortwaveBasin(SourceLANDSAF,
Dir,
latlim,
lonlim,
Dates=[Startdate, Enddate])
DEMmap_str = os.path.join(Dir, 'HydroSHED', 'DEM',
'DEM_HydroShed_m_3s.tif')
geo_out, proj, size_X, size_Y = RC.Open_array_info(DEMmap_str)
# Open DEM map
demmap = RC.Open_tiff_array(DEMmap_str)
demmap[demmap < 0] = 0
# make lat and lon arrays)
dlat = geo_out[5]
dlon = geo_out[1]
lat = geo_out[3] + (np.arange(size_Y) + 0.5) * dlat
lon = geo_out[0] + (np.arange(size_X) + 0.5) * dlon
for date in Dates:
# day of year
day = date.dayofyear
Horizontal, Sloping, sinb, sinb_hor, fi, slope, ID = SlopeInfluence(
demmap, lat, lon, day)
SIDname = os.path.join(
SIDdir, 'SAF_SID_Daily_W-m2_' + date.strftime('%Y-%m-%d') + '.tif')
SISname = os.path.join(
SISdir, 'SAF_SIS_Daily_W-m2_' + date.strftime('%Y-%m-%d') + '.tif')
#PREPARE SID MAPS
SIDdest = RC.reproject_dataset_example(SIDname, DEMmap_str, method=3)
SIDdata = SIDdest.GetRasterBand(1).ReadAsArray()
#PREPARE SIS MAPS
SISdest = RC.reproject_dataset_example(SISname, DEMmap_str, method=3)
SISdata = SISdest.GetRasterBand(1).ReadAsArray()
# Calculate ShortWave net
Short_Wave_Net = SIDdata * (Sloping /
Horizontal) + SISdata * 86400 / 1e6
# Calculate ShortWave Clear
Short_Wave = Sloping
Short_Wave_Clear = Short_Wave * (0.75 + demmap * 2 * 10**-5)
# make directories
PathClear = os.path.join(Dir, 'Landsaf_Clipped', 'Shortwave_Clear_Sky')
if os.path.exists(PathClear) is False:
os.makedirs(PathClear)
PathNet = os.path.join(Dir, 'Landsaf_Clipped', 'Shortwave_Net')
if os.path.exists(PathNet) is False:
os.makedirs(PathNet)
# name Shortwave Clear and Net
nameFileNet = 'ShortWave_Net_Daily_W-m2_' + date.strftime(
'%Y-%m-%d') + '.tif'
nameNet = os.path.join(PathNet, nameFileNet)
nameFileClear = 'ShortWave_Clear_Daily_W-m2_' + date.strftime(
'%Y-%m-%d') + '.tif'
nameClear = os.path.join(PathClear, nameFileClear)
# Save net and clear short wave radiation
DC.Save_as_tiff(nameNet, Short_Wave_Net, geo_out, proj)
DC.Save_as_tiff(nameClear, Short_Wave_Clear, geo_out, proj)
return
def Calc_Rainy_Days(Dir_Basin, Data_Path_P, Startdate, Enddate):
"""
This functions calculates the amount of rainy days based on daily precipitation data.
Parameters
----------
Dir_Basin : str
Path to all the output data of the Basin
Data_Path_P : str
Path to the daily rainfall data
Startdate : str
Contains the start date of the model 'yyyy-mm-dd'
Enddate : str
Contains the end date of the model 'yyyy-mm-dd'
Returns
-------
Data_Path_RD : str
Path from the Dir_Basin to the rainy days data
"""
# import WA+ modules
import watools.General.data_conversions as DC
import watools.General.raster_conversions as RC
# Create an output directory to store the rainy days tiffs
Data_Path_RD = os.path.join(Dir_Basin, 'Rainy_Days')
if not os.path.exists(Data_Path_RD):
os.mkdir(Data_Path_RD)
# Define the dates that must be created
Dates = pd.date_range(Startdate, Enddate, freq ='MS')
# Set working directory to the rainfall folder
os.chdir(Data_Path_P)
# Open all the daily data and store the data in a 3D array
for Date in Dates:
# Define the year and month and amount of days in month
year = Date.year
month = Date.month
daysinmonth = calendar.monthrange(year, month)[1]
# Set the third (time) dimension of array starting at 0
i = 0
# Find all files of that month
files = glob.glob('*daily_%d.%02d.*.tif' %(year, month))
# Check if the amount of files corresponds with the amount of days in month
if len(files) is not daysinmonth:
print('ERROR: Not all Rainfall days for month %d and year %d are downloaded' %(month, year))
# Loop over the days and store data in raster
for File in files:
dir_file = os.path.join(Data_Path_P, File)
# Get array information and create empty numpy array for daily rainfall when looping the first file
if File == files[0]:
# Open geolocation info and define projection
geo_out, proj, size_X, size_Y = RC.Open_array_info(dir_file)
if int(proj.split('"')[-2]) == 4326:
proj = "WGS84"
# Create empty array for the whole month
P_Daily = np.zeros([daysinmonth,size_Y, size_X])
# Open data and put the data in 3D array
Data = RC.Open_tiff_array(dir_file)
# Remove the weird numbers
Data[Data<0] = 0
# Add the precipitation to the monthly cube
P_Daily[i, :, :] = Data
i += 1
# Define a rainy day
P_Daily[P_Daily > 0.201] = 1
P_Daily[P_Daily != 1] = 0
# Sum the amount of rainy days
RD_one_month = np.nansum(P_Daily,0)
# Define output name
Outname = os.path.join(Data_Path_RD, 'Rainy_Days_NumOfDays_monthly_%d.%02d.01.tif' %(year, month))
# Save tiff file
DC.Save_as_tiff(Outname, RD_one_month, geo_out, proj)
return(Data_Path_RD)
def calc_ETref(Dir, tmin_str, tmax_str, humid_str, press_str, wind_str,
down_short_str, down_long_str, up_long_str, DEMmap_str, DOY):
"""
This function calculates the ETref by using all the input parameters (path)
according to FAO standards
see: http://www.fao.org/docrep/x0490e/x0490e08.htm#TopOfPage
Keyword arguments:
tmin_str -- 'C:/' path to the minimal temperature tiff file [degrees Celcius], e.g. from GLDAS
tmax_str -- 'C:/' path to the maximal temperature tiff file [degrees Celcius], e.g. from GLDAS
humid_str -- 'C:/' path to the humidity tiff file [kg/kg], e.g. from GLDAS
press_str -- 'C:/' path to the air pressure tiff file [kPa], e.g. from GLDAS
wind_str -- 'C:/' path to the wind velocity tiff file [m/s], e.g. from GLDAS
down_short_str -- 'C:/' path to the downward shortwave radiation tiff file [W*m-2], e.g. from CFSR/LANDSAF
down_long_str -- 'C:/' path to the downward longwave radiation tiff file [W*m-2], e.g. from CFSR/LANDSAF
up_long_str -- 'C:/' path to the upward longwave radiation tiff file [W*m-2], e.g. from CFSR/LANDSAF
DEMmap_str -- 'C:/' path to the DEM tiff file [m] e.g. from HydroSHED
DOY -- Day of the year
"""
# Get some geo-data to save results
GeoT, Projection, xsize, ysize = RC.Open_array_info(DEMmap_str)
#NDV, xsize, ysize, GeoT, Projection, DataType = GetGeoInfo(DEMmap_str)
raster_shape = [xsize, ysize]
# Create array to store results
ETref = np.zeros(raster_shape)
# gap fill
tmin_str_GF = RC.gap_filling(tmin_str, -9999)
tmax_str_GF = RC.gap_filling(tmax_str, -9999)
humid_str_GF = RC.gap_filling(humid_str, -9999)
press_str_GF = RC.gap_filling(press_str, -9999)
wind_str_GF = RC.gap_filling(wind_str, -9999)
down_short_str_GF = RC.gap_filling(down_short_str, np.nan)
down_long_str_GF = RC.gap_filling(down_long_str, np.nan)
if up_long_str is not 'not':
up_long_str_GF = RC.gap_filling(up_long_str, np.nan)
else:
up_long_str_GF = 'nan'
#dictionary containing all tthe paths to the input-maps
inputs = dict({
'tmin': tmin_str_GF,
'tmax': tmax_str_GF,
'humid': humid_str_GF,
'press': press_str_GF,
'wind': wind_str_GF,
'down_short': down_short_str_GF,
'down_long': down_long_str_GF,
'up_long': up_long_str_GF
})
#dictionary containing numpy arrays of al initial and intermediate variables
input_array = dict({
'tmin': None,
'tmax': None,
'humid': None,
'press': None,
'wind': None,
'albedo': None,
'down_short': None,
'down_long': None,
'up_short': None,
'up_long': None,
'net_radiation': None,
'ea': None,
'es': None,
'delta': None
})
#APPLY LAPSE RATE CORRECTION ON TEMPERATURE
tmin = lapse_rate(Dir, inputs['tmin'], DEMmap_str)
tmax = lapse_rate(Dir, inputs['tmax'], DEMmap_str)
#PROCESS PRESSURE MAPS
press = adjust_P(Dir, inputs['press'], DEMmap_str)
#PREPARE HUMIDITY MAPS
dest = RC.reproject_dataset_example(inputs['humid'], DEMmap_str, method=2)
humid = dest.GetRasterBand(1).ReadAsArray()
dest = None
#CORRECT WIND MAPS
dest = RC.reproject_dataset_example(inputs['wind'], DEMmap_str, method=2)
wind = dest.GetRasterBand(1).ReadAsArray() * 0.75
dest = None
#PROCESS GLDAS DATA
input_array['ea'], input_array['es'], input_array['delta'] = process_GLDAS(
tmax, tmin, humid, press)
ea = input_array['ea']
es = input_array['es']
delta = input_array['delta']
if up_long_str == 'not':
#CORRECT WIND MAPS
dest = RC.reproject_dataset_example(down_short_str,
DEMmap_str,
method=2)
Short_Net_data = dest.GetRasterBand(1).ReadAsArray() * 0.75
dest = None
dest = RC.reproject_dataset_example(down_long_str,
DEMmap_str,
method=2)
Short_Clear_data = dest.GetRasterBand(1).ReadAsArray() * 0.75
dest = None
# Calculate Long wave Net radiation
Rnl = 4.903e-9 * (
((tmin + 273.16)**4 +
(tmax + 273.16)**4) / 2) * (0.34 - 0.14 * np.sqrt(ea)) * (
1.35 * Short_Net_data / Short_Clear_data - 0.35)
# Calulate Net Radiation and converted to MJ*d-1*m-2
net_radiation = (Short_Net_data * 0.77 + Rnl) * 86400 / 10**6
else:
#OPEN DOWNWARD SHORTWAVE RADIATION
dest = RC.reproject_dataset_example(inputs['down_short'],
DEMmap_str,
method=2)
down_short = dest.GetRasterBand(1).ReadAsArray()
dest = None
down_short, tau, bias = slope_correct(down_short, press, ea,
DEMmap_str, DOY)
#OPEN OTHER RADS
up_short = down_short * 0.23
dest = RC.reproject_dataset_example(inputs['down_long'],
DEMmap_str,
method=2)
down_long = dest.GetRasterBand(1).ReadAsArray()
dest = None
dest = RC.reproject_dataset_example(inputs['up_long'],
DEMmap_str,
method=2)
up_long = dest.GetRasterBand(1).ReadAsArray()
dest = None
#OPEN NET RADIATION AND CONVERT W*m-2 TO MJ*d-1*m-2
net_radiation = ((down_short - up_short) +
(down_long - up_long)) * 86400 / 10**6
#CALCULATE ETref
ETref = (0.408 * delta * net_radiation + 0.665 * 10**-3 * press *
(900 / ((tmax + tmin) / 2 + 273)) * wind *
(es - ea)) / (delta + 0.665 * 10**-3 * press * (1 + 0.34 * wind))
# Set limits ETref
ETref[ETref < 0] = 0
ETref[ETref > 400] = np.nan
#return a reference ET map (numpy array), a dictionary containing all intermediate information and a bias of the slope correction on down_short
return ETref
def Calc_Property(Dir, latlim, lonlim, SL):
import watools.Collect.SoilGrids as SG
# Download needed layers
SG.Clay_Content(Dir, latlim, lonlim, level=SL)
#SG.Organic_Carbon_Content(Dir, latlim, lonlim, level=SL)
SG.Bulk_Density(Dir, latlim, lonlim, level=SL)
# Define path to layers
filename_clay = os.path.join(
Dir, 'SoilGrids', 'Clay_Content',
'ClayContentMassFraction_%s_SoilGrids_percentage.tif' % SL)
#filename_om = os.path.join(Dir, 'SoilGrids', 'Soil_Organic_Carbon_Content' ,'SoilOrganicCarbonContent_%s_SoilGrids_g_kg.tif' %SL)
filename_bulkdensity = os.path.join(
Dir, 'SoilGrids', 'Bulk_density',
'BulkDensity_%s_SoilGrids_kg-m-3.tif' % SL)
# Define path for output
if SL == "sl3":
level = "Topsoil"
elif SL == "sl6":
level = "Subsoil"
filedir_out_densbulk = os.path.join(Dir, 'SoilGrids', 'Bulk_density')
if not os.path.exists(filedir_out_densbulk):
os.makedirs(filedir_out_densbulk)
filedir_out_thetasat = os.path.join(Dir, 'SoilGrids', 'Theta_Sat')
if not os.path.exists(filedir_out_thetasat):
os.makedirs(filedir_out_thetasat)
#filename_out_densbulk = os.path.join(filedir_out_densbulk ,'Bulk_Density_%s_SoilGrids_g-cm-3.tif' %level)
filename_out_thetasat = os.path.join(
filedir_out_thetasat, 'Theta_Sat2_%s_SoilGrids_kg-kg.tif' % level)
#if not (os.path.exists(filename_out_densbulk) and os.path.exists(filename_out_thetasat)):
if not os.path.exists(filename_out_thetasat):
# Open datasets
dest_clay = gdal.Open(filename_clay)
#dest_om = gdal.Open(filename_om)
dest_bulk = gdal.Open(filename_bulkdensity)
# Open Array info
geo_out, proj, size_X, size_Y = RC.Open_array_info(filename_clay)
# Open Arrays
Clay = dest_clay.GetRasterBand(1).ReadAsArray()
#OM = dest_om.GetRasterBand(1).ReadAsArray()
Clay = np.float_(Clay)
Clay[Clay > 100] = np.nan
#OM = np.float_(OM)
#OM[OM<0]=np.nan
#OM = OM/1000
# Calculate bulk density
#bulk_dens = 1/(0.6117 + 0.3601 * Clay/100 + 0.002172 * np.power(OM * 100, 2)+ 0.01715 * np.log(OM * 100))
bulk_dens = dest_bulk.GetRasterBand(1).ReadAsArray()
bulk_dens = bulk_dens / 1000
# Calculate theta saturated
theta_sat = 0.85 * (1 - (bulk_dens / 2.65)) + 0.13 * Clay / 100
# Save data
#DC.Save_as_tiff(filename_out_densbulk, bulk_dens, geo_out, "WGS84")
DC.Save_as_tiff(filename_out_thetasat, theta_sat, geo_out, "WGS84")
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 slope_correct(down_short_hor, pressure, ea, DEMmap, DOY):
"""
This function downscales the CFSR solar radiation by using the DEM map
The Slope correction is based on Allen et al. (2006)
'Analytical integrated functions for daily solar radiation on slope'
Keyword arguments:
down_short_hor -- numpy array with the horizontal downwards shortwave radiation
pressure -- numpy array with the air pressure
ea -- numpy array with the actual vapour pressure
DEMmap -- 'C:/' path to the DEM map
DOY -- day of the year
"""
# Get Geo Info
GeoT, Projection, xsize, ysize = RC.Open_array_info(DEMmap)
minx = GeoT[0]
miny = GeoT[3] + xsize * GeoT[4] + ysize * GeoT[5]
x = np.flipud(np.arange(xsize) * GeoT[1] + minx + GeoT[1] / 2)
y = np.flipud(np.arange(ysize) * -GeoT[5] + miny + -GeoT[5] / 2)
# Calculate Extraterrestrial Solar Radiation [W m-2]
demmap = RC.Open_tiff_array(DEMmap)
demmap[demmap < 0] = 0
# apply the slope correction
Ra_hor, Ra_slp, sinb, sinb_hor, fi, slope, ID = SlopeInfluence(
demmap, y, x, DOY)
# Calculate atmospheric transmissivity
Rs_hor = down_short_hor
# EQ 39
tau = Rs_hor / Ra_hor
#EQ 41
KB_hor = np.zeros(tau.shape) * np.nan
indice = np.where(tau.flat >= 0.42)
KB_hor.flat[indice] = 1.56 * tau.flat[indice] - 0.55
indice = np.logical_and(tau.flat > 0.175, tau.flat < 0.42)
KB_hor.flat[indice] = 0.022 - 0.280 * tau.flat[indice] + 0.828 * tau.flat[
indice]**2 + 0.765 * tau.flat[indice]**3
indice = np.where(tau.flat <= 0.175)
KB_hor.flat[indice] = 0.016 * tau.flat[indice]
# EQ 42
KD_hor = tau - KB_hor
Kt = 0.7
#EQ 18
W = 0.14 * ea * pressure + 2.1
KB0 = 0.98 * np.exp((-0.00146 * pressure / Kt / sinb) - 0.075 *
(W / sinb)**0.4)
KB0_hor = 0.98 * np.exp((-0.00146 * pressure / Kt / sinb_hor) - 0.075 *
(W / sinb_hor)**0.4)
#EQ 34
fB = KB0 / KB0_hor * Ra_slp / Ra_hor
fia = (1 - KB_hor) * (
1 + (KB_hor /
(KB_hor + KD_hor))**0.5 * np.sin(slope / 2)**3) * fi + fB * KB_hor
Rs = Rs_hor * (fB * (KB_hor / tau) + fia * (KD_hor / tau) + 0.23 *
(1 - fi))
Rs[np.isnan(Rs)] = Rs_hor[np.isnan(Rs)]
Rs_equiv = Rs / np.cos(slope)
bias = np.nansum(Rs_hor) / np.nansum(Rs_equiv)
return Rs_equiv, tau, bias
def RetrieveData(args):
"""
This function retrieves JRC data for a given date from the
http://storage.googleapis.com/global-surface-water/downloads/ server.
Keyword arguments:
args -- A list of parameters defined in the DownloadData function.
"""
# Argument
[output_folder, Names_to_download, lonlim, latlim] = args
# Collect the data from the JRC webpage and returns the data and lat and long in meters of those tiles
try:
Collect_data(Names_to_download, output_folder)
except:
print("Was not able to download the file")
# Clip the data to the users extend
if len(Names_to_download) == 1:
trash_folder = os.path.join(output_folder, "Trash")
data_in = os.path.join(trash_folder, Names_to_download[0])
data_end, geo_end = RC.clip_data(data_in, latlim, lonlim)
else:
data_end = np.zeros([
int((latlim[1] - latlim[0]) / 0.00025),
int((lonlim[1] - lonlim[0]) / 0.00025)
])
for Name_to_merge in Names_to_download:
trash_folder = os.path.join(output_folder, "Trash")
data_in = os.path.join(trash_folder, Name_to_merge)
geo_out, proj, size_X, size_Y = RC.Open_array_info(data_in)
lat_min_merge = np.maximum(latlim[0],
geo_out[3] + size_Y * geo_out[5])
lat_max_merge = np.minimum(latlim[1], geo_out[3])
lon_min_merge = np.maximum(lonlim[0], geo_out[0])
lon_max_merge = np.minimum(lonlim[1],
geo_out[0] + size_X * geo_out[1])
lonmerge = [lon_min_merge, lon_max_merge]
latmerge = [lat_min_merge, lat_max_merge]
data_one, geo_one = RC.clip_data(data_in, latmerge, lonmerge)
Ystart = int((geo_one[3] - latlim[1]) / geo_one[5])
Yend = int(Ystart + np.shape(data_one)[0])
Xstart = int((geo_one[0] - lonlim[0]) / geo_one[1])
Xend = int(Xstart + np.shape(data_one)[1])
data_end[Ystart:Yend, Xstart:Xend] = data_one
geo_end = tuple([lonlim[0], geo_one[1], 0, latlim[1], 0, geo_one[5]])
# Save results as Gtiff
fileName_out = os.path.join(output_folder, 'JRC_Occurrence_percent.tif')
DC.Save_as_tiff(name=fileName_out,
data=data_end,
geo=geo_end,
projection='WGS84')
shutil.rmtree(trash_folder)
return True
def NPP_GPP_Based(Dir_Basin, Data_Path_GPP, Data_Path_NPP, Startdate, Enddate):
"""
This functions calculated monthly NDM based on the yearly NPP and monthly GPP.
Parameters
----------
Dir_Basin : str
Path to all the output data of the Basin
Data_Path_GPP : str
Path from the Dir_Basin to the GPP data
Data_Path_NPP : str
Path from the Dir_Basin to the NPP data
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
Returns
-------
Data_Path_NDM : str
Path from the Dir_Basin to the normalized dry matter data
"""
# import WA+ modules
import watools.General.data_conversions as DC
import watools.General.raster_conversions as RC
# Define output folder for Normalized Dry Matter
Data_Path_NDM = os.path.join(Dir_Basin, "NDM")
if not os.path.exists(Data_Path_NDM):
os.mkdir(Data_Path_NDM)
# Define monthly time steps that will be created
Dates = pd.date_range(Startdate, Enddate, freq='MS')
# Define the years that will be calculated
Year_Start = int(Startdate[0:4])
Year_End = int(Enddate[0:4])
Years = list(range(Year_Start, Year_End + 1))
# Loop over the years
for year in Years:
# Change working directory to the NPP folder
os.chdir(Data_Path_NPP)
# Open yearly NPP data
yearly_NPP_File = glob.glob('*yearly*%d.01.01.tif' % int(year))[0]
Yearly_NPP = RC.Open_tiff_array(yearly_NPP_File)
# Get the No Data Value of the NPP file
dest = gdal.Open(yearly_NPP_File)
NDV = dest.GetRasterBand(1).GetNoDataValue()
# Set the No Data Value to Nan
Yearly_NPP[Yearly_NPP == NDV] = np.nan
# Change working directory to the GPP folder
os.chdir(Data_Path_GPP)
# Find all the monthly files of that year
monthly_GPP_Files = glob.glob('*monthly*%d.*.01.tif' % int(year))
# Check if it are 12 files otherwise something is wrong and send the ERROR
if not len(monthly_GPP_Files) == 12:
print('ERROR: Some monthly GPP Files are missing')
# Get the projection information of the GPP inputs
geo_out, proj, size_X, size_Y = RC.Open_array_info(
monthly_GPP_Files[0])
geo_out_NPP, proj_NPP, size_X_NPP, size_Y_NPP = RC.Open_array_info(
os.path.join(Data_Path_NPP, yearly_NPP_File))
if int(proj.split('"')[-2]) == 4326:
proj = "WGS84"
# Get the No Data Value of the GPP files
dest = gdal.Open(monthly_GPP_Files[0])
NDV = dest.GetRasterBand(1).GetNoDataValue()
# Create a empty numpy array
Yearly_GPP = np.zeros([size_Y, size_X])
# Calculte the total yearly GPP
for monthly_GPP_File in monthly_GPP_Files:
# Open array
Data = RC.Open_tiff_array(monthly_GPP_File)
# Remove nan values
Data[Data == NDV] = np.nan
Data[np.isnan(Data)] = 0
# Add data to yearly sum
Yearly_GPP += Data
# Check if size is the same of NPP and GPP otherwise resize
if not (size_X_NPP == size_X and size_Y_NPP == size_Y):
Yearly_NPP = RC.resize_array_example(Yearly_NPP, Yearly_GPP)
# Loop over the monthly dates
for Date in Dates:
# If the Date is in the same year as the yearly NPP and GPP
if Date.year == year:
# Create empty GPP array
monthly_GPP = np.ones([size_Y, size_X]) * np.nan
# Get current month
month = Date.month
# Get the GPP file of the current year and month
monthly_GPP_File = glob.glob('*monthly_%d.%02d.01.tif' %
(int(year), int(month)))[0]
monthly_GPP = RC.Open_tiff_array(monthly_GPP_File)
monthly_GPP[monthly_GPP == NDV] = np.nan
# Calculate the NDM based on the monthly and yearly NPP and GPP (fraction of GPP)
Monthly_NDM = Yearly_NPP * monthly_GPP / Yearly_GPP * (
30. / 12.) * 10000 # kg/ha
# Define output name
output_name = os.path.join(
Data_Path_NDM, 'NDM_MOD17_kg_ha-1_monthly_%d.%02d.01.tif' %
(int(year), int(month)))
# Save the NDM as tiff file
DC.Save_as_tiff(output_name, Monthly_NDM, geo_out, proj)
return (Data_Path_NDM)
