def run(input_nc, Inflow_Text_Files):
'''
This functions add inflow to the runoff dataset before the channel routing.
The inflow must be a text file with a certain format. The first line of this format are the latitude and longitude.
Hereafter for each line the time (ordinal time) and the inflow (m3/month) seperated with one space is defined. See example below:
lat lon
733042 156225.12
733073 32511321.2
733102 212315.25
733133 2313266.554
'''
# General modules
import numpy as np
# Water Accounting modules
import watools.General.raster_conversions as RC
import watools.Functions.Start.Area_converter as Area
Runoff = RC.Open_nc_array(input_nc, Var='Runoff_M')
# Open information and open the Runoff array
geo_out, epsg, size_X, size_Y, size_Z, Time = RC.Open_nc_info(input_nc)
# Calculate the surface area of every pixel
dlat, dlon = Area.Calc_dlat_dlon(geo_out, size_X, size_Y)
area_in_m2 = dlat * dlon
for Inflow_Text_File in Inflow_Text_Files:
# Open the inlet text data
Inlet = np.genfromtxt(Inflow_Text_File, dtype=None, delimiter=" ")
# Read out the coordinates
Coord = Inlet[0, :]
Lon_coord = Coord[0]
Lat_coord = Coord[1]
# Search for the pixel
lon_pix = int(np.ceil((float(Lon_coord) - geo_out[0]) / geo_out[1]))
lat_pix = int(np.ceil((float(Lat_coord) - geo_out[3]) / geo_out[5]))
# Add the value on top of the Runoff array
for i in range(1, len(Inlet)):
time = float(Inlet[i, 0])
time_step = np.argwhere(np.logical_and(Time >= time, Time <= time))
if len(time_step) > 0:
time_step_array = int(time_step[0][0])
value_m3_month = float(Inlet[i, 1])
area_in_m2_pixel = area_in_m2[lat_pix, lon_pix]
value_mm = (value_m3_month / area_in_m2_pixel) * 1000
Runoff[time_step_array, lat_pix,
lon_pix] = Runoff[time_step_array, lat_pix,
lon_pix] + value_mm
return (Runoff)
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 Run(input_nc, output_nc):
# Extract flow direction data from NetCDF file
flow_directions = RC.Open_nc_array(input_nc, Var = 'demdir')
# Open River Array
Rivers = RC.Open_nc_array(output_nc, Var = 'rivers')
# Open Accumulated Pixel Array
Accumulated_Pixels = RC.Open_nc_array(output_nc, Var = 'accpix')
# Open Routed discharge Array
Routed_Array = RC.Open_nc_array(output_nc, Var = 'discharge_natural')
# Get the raster shape
geo_out_example, epsg_example, size_X_example, size_Y_example, size_Z_example, Time_example = RC.Open_nc_info(input_nc)
geo_out_example = np.array(geo_out_example)
# Create a river array with a boundary of 1 pixel
Rivers_bounds = np.zeros([size_Y_example+2, size_X_example+2])
Rivers_bounds[1:-1,1:-1] = Rivers
# Create a flow direction array with a boundary of 1 pixel
flow_directions[flow_directions==0]=-32768
flow_directions_bound = np.ones([size_Y_example+2, size_X_example+2]) * -32768
flow_directions_bound[1:-1,1:-1] = flow_directions
# Create ID Matrix
y,x = np.indices((size_Y_example, size_X_example))
ID_Matrix = np.int32(np.ravel_multi_index(np.vstack((y.ravel(),x.ravel())),(size_Y_example,size_X_example),mode='clip').reshape(x.shape))
ID_Matrix_bound = np.ones([size_Y_example+2, size_X_example+2]) * -32768
ID_Matrix_bound[1:-1,1:-1] = ID_Matrix + 1
ID_Matrix_bound[flow_directions_bound==-32768]=-32768
del x, y
# Empty total from and to arrays
ID_to_total=np.array([])
ID_from_total=np.array([])
# The flow directions parameters of HydroSHED
Directions = [1, 2, 4, 8, 16, 32, 64, 128]
# Loop over the directions
for Direction in Directions:
# empty from and to arrays for 1 direction
data_flow_to = np.zeros([size_Y_example + 2, size_X_example + 2])
data_flow_from = np.zeros([size_Y_example + 2, size_X_example + 2])
# Get the ID of only the rivers
data_flow_to_ID = np.zeros([size_Y_example + 2, size_X_example + 2])
data_flow_in = np.ones([size_Y_example + 2, size_X_example + 2]) * Rivers_bounds
# Mask only one direction
data_flow_from[flow_directions_bound == Direction] = data_flow_in[flow_directions_bound == Direction] * ID_Matrix_bound[flow_directions_bound == Direction]
# Add the data flow to ID
if Direction == 4:
data_flow_to[1:,:] = data_flow_from[:-1,:]
if Direction == 2:
data_flow_to[1:,1:] = data_flow_from[:-1,:-1]
if Direction == 1:
data_flow_to[:,1:] = data_flow_from[:,:-1]
if Direction == 128:
data_flow_to[:-1,1:] = data_flow_from[1:,:-1]
if Direction == 64:
data_flow_to[:-1,:] = data_flow_from[1:,:]
if Direction == 32:
data_flow_to[:-1,:-1] = data_flow_from[1:,1:]
if Direction == 16:
data_flow_to[:,:-1] = data_flow_from[:,1:]
if Direction == 8:
data_flow_to[1:,:-1] = data_flow_from[:-1,1:]
# mask out the no river pixels
data_flow_to_ID[data_flow_to>0] = ID_Matrix_bound[data_flow_to>0]
# Collect to and from arrays
ID_from_total = np.append(ID_from_total,data_flow_from[data_flow_from!=0].ravel())
ID_to_total = np.append(ID_to_total,data_flow_to_ID[data_flow_to_ID!=0].ravel())
######################## Define the starting point ############################
# Open Basin area
Basin = RC.Open_nc_array(input_nc, Var = 'basin')
Basin = -1 * (Basin - 1)
Basin_Buffer = RC.Create_Buffer(Basin, 8)
Possible_End_Points = np.zeros(Basin.shape)
Possible_End_Points[(Basin_Buffer + Rivers) == 2] = 1
End_Points = [[0,0]]
rows_col_possible_end_pixels = np.argwhere(Possible_End_Points == 1)
# Accumulated_Pixels_possible = ID_Matrix * Possible_End_Points
for PosPix in rows_col_possible_end_pixels:
Accumulated_Pixels_possible_Area = Accumulated_Pixels[PosPix[0]-1:PosPix[0]+2, PosPix[1]-1:PosPix[1]+2]
Max_acc_possible_area = np.max(Accumulated_Pixels_possible_Area)
middle_pixel = Accumulated_Pixels_possible_Area[1,1]
if Max_acc_possible_area == middle_pixel:
if flow_directions[PosPix[0],PosPix[1]] == -32768:
acc_aux = np.copy(Accumulated_Pixels_possible_Area)
acc_aux[1,1] = 0
off_y = np.where(acc_aux == np.max(acc_aux))[1][0] - 1
off_x = np.where(acc_aux == np.max(acc_aux))[0][0] - 1
PosPix[0] = PosPix[0] + off_x
PosPix[1] = PosPix[1] + off_y
if End_Points == []:
End_Points = PosPix
else:
End_Points = np.vstack([End_Points, PosPix])
# Create an empty dictionary for the rivers
River_dict = dict()
# Create empty array for the loop
ID_starts_next = []
i = 0
for End_Point in End_Points[1:]:
# Define starting point
# Max_Acc_Pix = np.nanmax(Accumulated_Pixels[ID_Matrix_bound[1:-1,1:-1]>0])
# ncol, nrow = np.argwhere(Accumulated_Pixels==Max_Acc_Pix)[0]
# Add Bounds
# col = ncol + 1
# row = nrow + 1
col = End_Point[0] + 1
row = End_Point[1] + 1
############################ Route the river ##################################
# Get the ID of the starting point
ID_starts = [ID_Matrix_bound[col,row]]
# Keep going on till all the branches are looped
while len(ID_starts) > 0:
for ID_start in ID_starts:
ID_start = int(ID_start)
# Empty parameters for new starting point
new = 0
IDs = []
# Add starting point
Arrays_from = np.argwhere(ID_from_total[:] == ID_start)
ID_from = ID_to_total[int(Arrays_from[0])]
IDs = np.array([ID_from, ID_start])
ID_start_now = ID_start
# Keep going till the branch ends
while new == 0:
Arrays_to = np.argwhere(ID_to_total[:] == ID_start)
# Add IDs to the river dictionary
if len(Arrays_to)>1 or len(Arrays_to) == 0:
River_dict[i] = IDs
i += 1
new = 1
# Define the next loop for the new branches
for j in range(0, len(Arrays_to)):
ID_starts_next = np.append(ID_starts_next,ID_from_total[int(Arrays_to[j])])
# If it was the last one then empty ID_start_next
if ID_start_now == ID_starts[-1]:
ID_starts = ID_starts_next
ID_starts_next = []
# Add pixel to tree for river dictionary
else:
ID_start = ID_from_total[Arrays_to[0]]
IDs = np.append(IDs, ID_start)
######################## Create dict distance and dict dem ####################
# Extract DEM data from NetCDF file
DEM = RC.Open_nc_array(input_nc, Var = 'dem')
# Get the distance of a horizontal and vertical flow pixel (assuming it flows in a straight line)
import watools.Functions.Start.Area_converter as AC
vertical, horizontal = AC.Calc_dlat_dlon(geo_out_example,size_X_example, size_Y_example)
# Calculate a diagonal flowing pixel (assuming it flos in a straight line)
diagonal = np.power((np.square(vertical) + np.square(horizontal)),0.5)
# Create empty distance array
Distance = np.zeros([size_Y_example, size_X_example])
# Fill in the distance array
Distance[np.logical_or(flow_directions == 1,flow_directions == 16)] = horizontal[np.logical_or(flow_directions == 1,flow_directions == 16)]
Distance[np.logical_or(flow_directions == 64,flow_directions == 4)] = vertical[np.logical_or(flow_directions == 64,flow_directions == 4)]
Distance[np.logical_or(np.logical_or(np.logical_or(flow_directions == 32,flow_directions == 8),flow_directions == 128),flow_directions == 2)] = diagonal[np.logical_or(np.logical_or(np.logical_or(flow_directions == 32,flow_directions == 8),flow_directions == 128),flow_directions == 2)]
# Create empty dicionaries for discharge, distance, and DEM
Discharge_dict = dict()
Distance_dict = dict()
DEM_dict = dict()
# Create empty arrays needed for the loop
River_end = []
River_ends = np.zeros([2,3])
# Loop over the branches
for River_number in range(0,len(River_dict)):
# Get the pixels associated with the river section
River = River_dict[River_number]
i=1
# Create empty arrays
Distances_river = np.zeros([len(River)])
DEM_river = np.zeros([len(River)])
Discharge_river = np.zeros([len(River)])
# for the first pixel get the previous pixel value from another branche
row_start = np.argwhere(River_ends[:,0] == River[0])
if len(row_start) < 1:
Distances_river[0] = 0
row, col = np.argwhere(ID_Matrix_bound == River[0])[0][:]
DEM_river[0] = DEM[row - 1, col - 1]
Discharge_river[0] = -9999
else:
Distances_river[0] = River_ends[row_start, 1]
DEM_river[0] = River_ends[row_start, 2]
row, col = np.argwhere(ID_Matrix_bound == River[0])[0][:]
#Discharge_river[0] = Routed_Discharge[timestep, row - 1, col - 1]
# For the other pixels get the value of the River ID pixel
for River_part in River[1:]:
row, col = np.argwhere(ID_Matrix_bound == River_part)[0][:]
Distances_river[i] = Distance[row - 1, col - 1]
DEM_river[i] = np.max([DEM_river[i-1],DEM[row - 1, col - 1]])
#Discharge_river[i] = Routed_Discharge[timestep, row - 1, col - 1]
if River_part == River[1] and Discharge_river[i-1] == -9999:
Discharge_river[i - 1] = Discharge_river[i]
i += 1
# Write array in dictionary
DEM_dict[River_number] = DEM_river
Discharge_dict[River_number] = Discharge_river
Distance_dict[River_number] = np.cumsum(Distances_river)
# Save the last pixel value
River_end[:] = [River_part , np.cumsum(Distances_river)[-1], DEM_river[-1]]
River_ends = np.vstack((River_ends, River_end))
########################## Discharge Dictionary ###############################
# Create ID Matrix
y,x = np.indices((size_Y_example, size_X_example))
ID_Matrix = np.int32(np.ravel_multi_index(np.vstack((y.ravel(),x.ravel())),(size_Y_example,size_X_example),mode='clip').reshape(x.shape))
ID_Matrix_bound = np.ones([size_Y_example+2, size_X_example+2]) * -32768
ID_Matrix_bound[1:-1,1:-1] = ID_Matrix + 1
del x, y
# Create empty dicionaries for discharge, distance, and DEM
Discharge_dict = dict()
Amount_months = len(RC.Open_nc_array(input_nc, Var = 'time'))
# Loop over the branches
for River_number in range(0,len(River_dict)):
# Get the pixels associated with the river section
River = River_dict[River_number]
i=0
# Create empty arrays
Discharge_river = np.zeros([Amount_months, len(River)])
# For the other pixels get the value of the River ID pixel
for River_part in River[:]:
row, col = np.argwhere(ID_Matrix_bound == River_part)[0][:]
Discharge_river[:,i] = Routed_Array[:, row - 1, col - 1]
i += 1
# Write array in dictionary
Discharge_dict[River_number] = Discharge_river
print(River_number)
return(DEM_dict, River_dict, Distance_dict, Discharge_dict)
def Find_Area_Volume_Relation(region, input_JRC, input_nc):
# Find relation between V and A
import numpy as np
import watools.General.raster_conversions as RC
import watools.General.data_conversions as DC
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
def func(x, a, b):
"""
This function is used for finding relation area and volume
"""
return (a * x**b)
def func3(x, a, b, c, d):
"""
This function is used for finding relation area and volume
"""
return (a * (x - c)**b + d)
#Array, Geo_out = RC.clip_data(input_JRC,latlim=[14.528,14.985],lonlim =[35.810,36.005])
Array, Geo_out = RC.clip_data(
input_JRC,
latlim=[region[2], region[3]],
lonlim=[region[0], region[1]
]) # This reservoir was not filled when SRTM was taken
size_Y = int(np.shape([Array])[-2])
size_X = int(np.shape([Array])[-1])
Water_array = np.zeros(np.shape(Array))
buffer_zone = 4
Array[Array > 0] = 1
for i in range(0, size_Y):
for j in range(0, size_X):
Water_array[i, j] = np.max(Array[
np.maximum(0, i -
buffer_zone):np.minimum(size_Y, i + buffer_zone +
1),
np.maximum(0, j -
buffer_zone):np.minimum(size_X, j + buffer_zone +
1)])
del Array
# Open DEM and reproject
DEM_Array = RC.Open_nc_array(input_nc, "dem")
Geo_out_dem, proj_dem, size_X_dem, size_Y_dem, size_Z_dem, time = RC.Open_nc_info(
input_nc)
# Save Example as memory file
dest_example = DC.Save_as_MEM(Water_array, Geo_out, projection='WGS84')
dest_dem = DC.Save_as_MEM(DEM_Array, Geo_out_dem, projection='WGS84')
# reproject DEM by using example
dest_out = RC.reproject_dataset_example(dest_dem, dest_example, method=2)
DEM = dest_out.GetRasterBand(1).ReadAsArray()
# find DEM water heights
DEM_water = np.zeros(np.shape(Water_array))
DEM_water[Water_array != 1] = np.nan
DEM_water[Water_array == 1.] = DEM[Water_array == 1.]
# Get array with areas
import watools.Functions.Start.Area_converter as Area
dlat, dlon = Area.Calc_dlat_dlon(Geo_out, size_X, size_Y)
area_in_m2 = dlat * dlon
# find volume and Area
min_DEM_water = int(np.round(np.nanmin(DEM_water)))
max_DEM_water = int(np.round(np.nanmax(DEM_water)))
Reservoir_characteristics = np.zeros([1, 5])
i = 0
for height in range(min_DEM_water + 1, max_DEM_water):
DEM_water_below_height = np.zeros(np.shape(DEM_water))
DEM_water[np.isnan(DEM_water)] = 1000000
DEM_water_below_height[DEM_water < height] = 1
pixels = np.sum(DEM_water_below_height)
area = np.sum(DEM_water_below_height * area_in_m2)
if height == min_DEM_water + 1:
volume = 0.5 * area
histogram = pixels
Reservoir_characteristics[:] = [
height, pixels, area, volume, histogram
]
else:
area_previous = Reservoir_characteristics[i, 2]
volume_previous = Reservoir_characteristics[i, 3]
volume = volume_previous + 0.5 * (
area - area_previous) + 1 * area_previous
histogram_previous = Reservoir_characteristics[i, 1]
histogram = pixels - histogram_previous
Reservoir_characteristics_one = [
height, pixels, area, volume, histogram
]
Reservoir_characteristics = np.append(
Reservoir_characteristics, Reservoir_characteristics_one)
i += 1
Reservoir_characteristics = np.resize(Reservoir_characteristics,
(i + 1, 5))
maxi = int(len(Reservoir_characteristics[:, 3]))
# find minimum value for reservoirs height (DEM is same value if reservoir was already filled whe SRTM was created)
Historgram = Reservoir_characteristics[:, 4]
hist_mean = np.mean(Historgram)
hist_std = np.std(Historgram)
mini_tresh = hist_std * 5 + hist_mean
Check_hist = np.zeros([len(Historgram)])
Check_hist[Historgram > mini_tresh] = Historgram[Historgram > mini_tresh]
if np.max(Check_hist) != 0.0:
col = np.argwhere(Historgram == np.max(Check_hist))[0][0]
mini = col + 1
else:
mini = 0
fitted = 0
# find starting point reservoirs
V0 = Reservoir_characteristics[mini, 3]
A0 = Reservoir_characteristics[mini, 2]
# Calculate the best maxi reservoir characteristics, based on the normal V = a*x**b relation
while fitted == 0:
try:
if mini == 0:
popt1, pcov1 = curve_fit(
func, Reservoir_characteristics[mini:maxi, 2],
Reservoir_characteristics[mini:maxi, 3])
else:
popt1, pcov1 = curve_fit(
func, Reservoir_characteristics[mini:maxi, 2] - A0,
Reservoir_characteristics[mini:maxi, 3] - V0)
fitted = 1
except:
maxi -= 1
if maxi < mini:
print('ERROR: was not able to find optimal fit')
fitted = 1
# Remove last couple of pixels of maxi
maxi_end = int(np.round(maxi - 0.2 * (maxi - mini)))
done = 0
times = 0
while done == 0 and times > 20 and maxi_end < mini:
try:
if mini == 0:
popt, pcov = curve_fit(
func, Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3])
else:
popt, pcov = curve_fit(
func3, Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3])
except:
maxi_end = int(maxi)
if mini == 0:
popt, pcov = curve_fit(
func, Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3])
else:
popt, pcov = curve_fit(
func3, Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3])
if mini == 0:
plt.plot(Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3], 'ro')
t = np.arange(0., np.max(Reservoir_characteristics[:, 2]), 1000)
plt.plot(t, popt[0] * (t)**popt[1], 'g--')
plt.axis([
0,
np.max(Reservoir_characteristics[mini:maxi_end, 2]), 0,
np.max(Reservoir_characteristics[mini:maxi_end, 3])
])
plt.show()
done = 1
else:
plt.plot(Reservoir_characteristics[mini:maxi_end, 2],
Reservoir_characteristics[mini:maxi_end, 3], 'ro')
t = np.arange(0., np.max(Reservoir_characteristics[:, 2]), 1000)
plt.plot(t, popt[0] * (t - popt[2])**popt[1] + popt[3], 'g--')
plt.axis([
0,
np.max(Reservoir_characteristics[mini:maxi_end, 2]), 0,
np.max(Reservoir_characteristics[mini:maxi_end, 3])
])
plt.show()
Volume_error = popt[3] / V0 * 100 - 100
print('error Volume = %s percent' % Volume_error)
print('error Area = %s percent' % (A0 / popt[2] * 100 - 100))
if Volume_error < 30 and Volume_error > -30:
done = 1
else:
times += 1
maxi_end -= 1
print('Another run is done in order to improve the result')
if done == 0:
popt = np.append(popt1, [A0, V0])
if len(popt) == 2:
popt = np.append(popt, [0, 0])
return (popt)