# -------------------------------------------------------------------------
# Name: Evaporation module
# Purpose: Actual evapotranspiration calculation module for different land cover types.
# Processes crop coefficients and calculates land cover specific evapotranspiration rates.
# Handles bare soil evaporation and vegetation-specific water consumption.
#
# Author: PB, MS, DF, JdB
# Created: 01/08/2016
# CWatM is licensed under GNU GENERAL PUBLIC LICENSE Version 3.
# -------------------------------------------------------------------------
from cwatm.management_modules.data_handling import *
import re
[docs]class evaporation(object):
"""
Evaporation module for hydrological modeling.
This class handles the calculation of potential evaporation and potential transpiration
for different land cover types. It processes crop coefficients, calculates bare soil
evaporation, and manages crop-specific evapotranspiration calculations.
**Global variables**
=================================== ========== ====================================================================== =====
Variable [self.var] Type Description Unit
=================================== ========== ====================================================================== =====
cropKCmonth Array Crop KC factor for different crops and different seasons --
snowEvap Array total evaporation from snow for a snow layers m
iceEvap Array Evaporation from ice (sublimation) m
Crops_names Array Internal: List of specific crops --
activatedCrops Array Fraction of area a specific crop is planted --
load_initial Flag Settings initLoad holds initial conditions for variables bool
monthCounter Array Month counter for each crop after crop has planted --
fracCrops_IrrLandDemand Array Month counter for each crop after crop has planted --
fracCrops_nonIrrLandDemand --
ratio_a_p_nonIrr Array Ratio actual to potential evapotranspiration, monthly, non-irrigated [ %
totalPotET_month Array Total potential evapotranspiration in a month m
ratio_a_p_Irr Array Ratio actual to potential evapotranspiration, monthly [crop specific] %
Yield_nonIrr Array Relative monthly non-irrigated yield [crop specific] %
currentKY Array Yield sensitivity coefficient [crop specific] --
Yield_Irr Array Relative monthly irrigated yield [crop specific] %
currentKC Array Current crop coefficient for specific crops --
generalIrrCrop_max Array --
generalnonIrrCrop_max Array --
weighted_KC_nonIrr Array --
weighted_KC_nonIrr_woFallow Array --
weighted_KC_Irr Array --
_weighted_KC_Irr Array --
weighted_KC_Irr_woFallow Array --
totalPotET_month_segment Array --
PotETaverage_crop_segments Array --
areaCrops_Irr_segment Array --
areaCrops_nonIrr_segment Array --
areaPaddy_Irr_segment Array --
Precipitation_segment Array --
availableArableLand_segment Array --
cropCorrect Array calibration factor of crop KC factor --
crop_correct_landCover Array --
includeCrops Flag 1 when includeCrops=True in Settings, 0 otherwise bool
Crops Array Internal: List of specific crops and Kc/Ky parameters --
daily_crop_KC Array --
interceptCap Array interception capacity of vegetation m
potTranspiration Array Potential transpiration (after removing of evaporation) m
cropKC Array crop coefficient for each of the 4 different land cover types (forest, --
minCropKC Array minimum crop factor (default 0.2) --
minInterceptCap Array Maximum interception read from file for forest and grassland land cove m
irrigatedArea_original Array --
frac_totalnonIrr Array Fraction sown with specific non-irrigated crops %
frac_totalIrr_max Array Fraction sown with specific irrigated crops, maximum throughout simula %
frac_totalnonIrr_max Array Fraction sown with specific non-irrigated crops, maximum throughout si %
GeneralCrop_Irr Array Fraction of irrigated land class sown with generally representative cr %
fallowIrr Array Fraction of fallowed irrigated land %
fallowIrr_max Array Fraction of fallowed irrigated land, maximum throughout simulation %
GeneralCrop_nonIrr Array Fraction of grasslands sown with generally representative crop %
fallownonIrr Array Fraction of fallowed non-irrigated land %
fallownonIrr_max Array Fraction of fallowed non-irrigated land, maximum throughout simulation %
availableArableLand Array Fraction of land not currently planted with specific crops %
ETRef Array potential evapotranspiration rate from reference crop m
Precipitation Array Precipitation (input for the model) m
coverTypes Array land cover types - forest - grassland - irrPaddy - irrNonPaddy - water --
SnowMelt Array total snow melt from all layers m
IceMelt Array Ice melt (not really ice but an additional snow melt in summer) m
potBareSoilEvap Array potential bare soil evaporation (calculated with minus snow evaporatio m
irr_Paddy_month Array --
ET_crop_Irr_paddy Array --
ET_crop_Irr_paddy_fraccrop Array --
fracCrops_Irr Array Fraction of cell currently planted with specific irrigated crops %
fracCrops_nonIrr Array Fraction of cell currently planted with specific non-irr crops %
actTransTotal_month_nonIrr Array Internal variable: Running total of transpiration for specific non-ir m
actTransTotal_month_Irr Array Internal variable: Running total of transpiration for specific irriga m
irr_crop_month Number --
frac_totalIrr Array Fraction sown with specific irrigated crops %
weighted_KC_Irr_woFallow_fullKc Array --
totalPotET Array Potential evaporation per land use class m
PotET_crop Array --
fracVegCover Array Fraction of specific land covers (0=forest, 1=grasslands, etc.) %
adminSegments Array Domestic agents Int
cellArea Array Area of cell m2
=================================== ========== ====================================================================== =====
Attributes
----------
var : object
Model variables container
model : object
CWatM model instance
"""
def __init__(self, model):
"""
Initialize the evaporation module.
Parameters
----------
model : object
CWatM model instance containing variables and configuration
"""
self.var = model.var
self.model = model
[docs] def initial(self):
"""
Initialize evaporation module arrays and parameters.
Sets up crop coefficient arrays, interception capacity arrays, and reads
initial data for different cover types including forest, grassland, and
irrigated crops. Initializes monthly crop coefficient data from NetCDF files.
"""
# no_types = len (self.var.coverTypes)
self.var.cropKCmonth = np.zeros((4, 13, len(globals.inZero)))
self.var.cropKC = np.zeros((4, len(globals.inZero)))
self.var.interceptCap = np.zeros((2, 13, len(globals.inZero)))
j = 0
for coverType in self.var.coverTypes:
if coverType in ['forest', 'grassland', 'irrPaddy', 'irrNonPaddy']:
for i in range(13):
self.var.cropKCmonth[j, i, :] = readnetcdf2(coverType + '_cropCoefficientNC', i * 3, "10day")
self.var.cropKCmonth[j, i, :] = np.maximum(self.var.cropKCmonth[j, i, :], self.var.minCropKC)
iii = 1
if coverType in ['forest', 'grassland']:
for i in range(13):
self.var.interceptCap[j, i, :] = readnetcdf2(coverType + '_interceptCapNC', i * 3, "10day")
self.var.interceptCap[j, i, :] = np.maximum(self.var.interceptCap[j, i, :], self.var.minInterceptCap[j])
j = j + 1
ii = 1
[docs] def dynamic(self, coverType, No):
"""
Calculate potential evapotranspiration for a specific land cover type.
This method computes potential evaporation and transpiration using crop coefficients,
handles crop dynamics when crops are enabled, and calculates bare soil evaporation.
It processes monthly crop coefficient data with daily interpolation.
Parameters
----------
coverType : str
Land cover type identifier (e.g., 'forest', 'grassland', 'irrPaddy')
No : int
Numerical identifier for land cover type (forest=0, grassland=1, etc.)
Returns
-------
tuple
Potential evaporation from bare soil and potential transpiration values
"""
# get crop coefficient
# to get ETc from ET0 x kc factor ((see http://www.fao.org/docrep/X0490E/x0490e04.htm#TopOfPage figure 4:)
# crop coefficient read for forest and grassland from file
# calculate potential bare soil evaporation - only once
if No == 0:
self.var.potBareSoilEvap = self.var.cropCorrect * self.var.minCropKC * self.var.ETRef
if self.var.usepySnowClim:
# if snow on ground no bare soil evap
self.var.potBareSoilEvap = np.where(self.var.ExistSnow == 1,0,self.var.potBareSoilEvap)
# snowEvap calcualted already in snow-frost
else:
# calculate snow and ice evaporation
self.var.snowEvap = np.minimum(self.var.SnowMelt, self.var.potBareSoilEvap)
self.var.potBareSoilEvap -= self.var.snowEvap
self.var.iceEvap = np.minimum(self.var.IceMelt, self.var.potBareSoilEvap)
self.var.potBareSoilEvap -= self.var.iceEvap
self.var.SnowMelt -= self.var.snowEvap
self.var.IceMelt -= self.var.iceEvap
#if dateVar['newStart'] or (dateVar['currDate'].day in [1,11,21]):
# self.var.cropKC[No] = readnetcdf2(coverType + '_cropCoefficientNC', dateVar['10day'], "10day")
# self.var.cropKC[No] = np.maximum(self.var.cropKC[No], self.var.minCropKC)
# self.var.cropKC_landCover[No] = self.var.cropKC[No].copy()
# interpolation for each day from monthly values
dplus = dateVar['30day'] + 1
dpart = dateVar['doy'] % 30
if dplus > 12:
dplus = 0
self.var.cropKC[No] = ((self.var.cropKCmonth[No, dplus, :] - self.var.cropKCmonth[No, dateVar['30day'], :]) / 30. *
dpart + self.var.cropKCmonth[No, dateVar['30day'], :])
cropKC_landCover = self.var.cropKC[No]
if self.var.includeCrops:
# includeCrops allows for crops and fallow land to makeup the landcovers grasslands and non-paddy, and
# maintains including a representative vegetation. It is developed to allow users to decide on the crops
# and parameters that are relevant for the study. The Excel cwatm_settings.xlsx is used to detail the crops
# and associated parameters. Crops have a unique planting month and four growth stages. Each stage is associated with a
# crop coefficient (Kc), yield response factor (Ky), and length.
if No == 1:
# Only go through this once:
# I. new start and II. beginning of the month
# I. new start
if dateVar['newStart']:
for z in ['irrM3_Paddy_month_segment', 'irr_Paddy_month', 'irr_crop', 'irr_crop_month',
'irrM3_crop_month_segment', 'ratio_a_p_nonIrr', 'ratio_a_p_Irr',
'fracCrops_IrrLandDemand', 'fracCrops_Irr', 'areaCrops_Irr_segment', 'areaCrops_nonIrr_segment',
'fracCrops_nonIrrLandDemand', 'fracCrops_nonIrr', 'activatedCrops', 'monthCounter',
'currentKC', 'totalPotET_month', 'PET_cropIrr_m3', 'actTransTotal_month_Irr',
'actTransTotal_month_nonIrr', 'currentKY', 'Yield_Irr', 'Yield_nonIrr',
'actTransTotal_crops_Irr', 'actTransTotal_crops_nonIrr', 'PotET_crop',
'PotETaverage_crop_segments', 'totalPotET_month_segment', 'ET_crop_nonIrr', 'ET_crop_Irr',
'ratio_a_p_nonIrr_daily', 'ratio_a_p_Irr_daily']:
vars(self.var)[z] = np.tile(globals.inZero, (len(self.var.Crops), 1))
self.var.irr_Paddy_month = globals.inZero
for z in [crop for crop in self.var.Crops_names]:
vars(self.var)[z + '_Irr'] = globals.inZero
vars(self.var)[z + '_nonIrr'] = globals.inZero
self.var.ET_crop_Irr_paddy = globals.inZero
self.var.ET_crop_Irr_paddy_fraccrop = globals.inZero
for c in range(len(self.var.Crops)):
# For creating annual and month total outputs, since such totals don't work with square bracket variables
vars(self.var)['ET_crop_Irr_'+str(c)] = globals.inZero
vars(self.var)['ET_crop_Irr_fraccrop_'+str(c)] = globals.inZero
vars(self.var)['ET_crop_nonIrr_'+str(c)] = globals.inZero
vars(self.var)['ET_crop_nonIrr_fraccrop_'+str(c)] = globals.inZero
vars(self.var)['irr_crop_'+str(c)] = globals.inZero
self.var.activatedCrops[c] = self.var.load_initial("activatedCrops_" + str(c))
self.var.fracCrops_Irr[c] = self.var.load_initial('fracCrops_Irr_' + str(c))
self.var.fracCrops_nonIrr[c] = self.var.load_initial('fracCrops_nonIrr_' + str(c))
self.var.monthCounter[c] = self.var.load_initial("monthCounter_" + str(c))
if dateVar['newStart'] or dateVar['newYear']:
crop_inflate_factor = 1
for i in range(len(self.var.Crops)):
try:
self.var.fracCrops_IrrLandDemand[i] = np.where(
loadmap(self.var.Crops_names[i] + '_Irr') * crop_inflate_factor <= 1,
loadmap(self.var.Crops_names[i] + '_Irr') * crop_inflate_factor, 1)
self.var.fracCrops_nonIrrLandDemand[i] = np.where(
loadmap(self.var.Crops_names[i] + '_nonIrr') * crop_inflate_factor <= 1,
loadmap(self.var.Crops_names[i] + '_nonIrr') * crop_inflate_factor,
1)
except:
self.var.fracCrops_IrrLandDemand[i] = readnetcdf2(
self.var.Crops_names[i] + '_Irr', dateVar['currDate'], 'yearly',
value=re.split(r'[^a-zA-Z0-9_[\]]', cbinding(self.var.Crops_names[i] + '_Irr'))[-2])
self.var.fracCrops_nonIrrLandDemand[i] = readnetcdf2(
self.var.Crops_names[i] + '_nonIrr', dateVar['currDate'], 'yearly',
value=re.split(r'[^a-zA-Z0-9_[\]]', cbinding(self.var.Crops_names[i] + '_nonIrr'))[-2])
# in two places
if 'crops_leftoverNotIrrigated' in binding:
if i <= int(cbinding('crops_leftoverNotIrrigated')):
#print('in evaporation: some crops not rainfed')
self.var.fracCrops_nonIrrLandDemand[i] = globals.inZero.copy()
# activatedCrops[c] = 1 where crop c is planned in at least 0.001% of the cell, and 0 otherwise.
self.var.activatedCrops[i] = np.minimum(
np.maximum((self.var.fracCrops_IrrLandDemand[i] + self.var.fracCrops_nonIrrLandDemand[i] +
0.99999) // 1, self.var.activatedCrops[i]), 1)
if dateVar['currDate'].day == 1 or self.var.daily_crop_KC:
if 'moveIrrFallowToNonIrr' in option:
if checkOption('moveIrrFallowToNonIrr'):
# The irrigated land class may have given up fallow land to the grasslands land class.
# If this is the case, these fallow lands are returned to the irrigated land class briefly to
# allow them to be planted on in the irrigated land class, and then returned to the
# grasslands land class.
self.var.fracVegCover[3] = self.var.irrigatedArea_original.copy()
remainderLand = np.maximum(
globals.inZero.copy() + 1 - self.var.fracVegCover[4] - self.var.fracVegCover[3] -
self.var.fracVegCover[5] - self.var.fracVegCover[2] - self.var.fracVegCover[0],
globals.inZero.copy())
self.var.fracVegCover[1] = remainderLand.copy()
for c in range(len(self.var.Crops)):
# Dawn of the next month
# We first harvest, and then we plant
# Add a month, if the crop has already been planted
self.var.monthCounter[c] += np.where(self.var.monthCounter[c] > 0, 1, 0)
# Calculate relative yield for the last month
self.var.ratio_a_p_nonIrr[c] = np.where(
self.var.totalPotET_month[c] * self.var.activatedCrops[c] > 0,
self.var.actTransTotal_month_nonIrr[c] / (
self.var.totalPotET_month[c] * self.var.fracCrops_nonIrr[c]),
0) # This should always be <= 1.
self.var.ratio_a_p_Irr[c] = np.where(
self.var.totalPotET_month[c] * self.var.activatedCrops[c] > 0,
self.var.actTransTotal_month_Irr[c] / (
self.var.totalPotET_month[c] * self.var.fracCrops_Irr[c]),
0) # This should always be <= 1.
self.var.Yield_nonIrr[c] = np.where(
self.var.monthCounter[c] > 0,
np.where(self.var.actTransTotal_month_nonIrr[c] > 0,
np.maximum(1 - self.var.currentKY[c] * (1 - self.var.ratio_a_p_nonIrr[c]), 0),
0), 0)
self.var.Yield_Irr[c] = np.where(
self.var.monthCounter[c] > 0,
np.where(self.var.actTransTotal_month_Irr[c] > 0,
np.maximum(1 - self.var.currentKY[c] * (1 - self.var.ratio_a_p_Irr[c]), 0),
0), 0)
# With the previous month's calculations of yields completed, on this first day of the month, we
# reset the running totals of potential transpiration and transpiration (m)
self.var.totalPotET_month[c] = globals.inZero.copy()
self.var.actTransTotal_month_nonIrr[c] = globals.inZero.copy()
self.var.actTransTotal_month_Irr[c] = globals.inZero.copy()
self.var.irr_crop_month[c] = globals.inZero.copy()
self.var.irr_Paddy_month = globals.inZero.copy()
# Harvest crops that are finished growing: reset month counter and KC. New seeds are sown after harvesting towards the end.
# todo experiment keeping flexible crop_counter, now monthCounter
if self.var.daily_crop_KC:
self.var.monthCounter[c] = np.where(
self.var.monthCounter[c] > len(self.var.Crops[c][-1]), 0, self.var.monthCounter[c])
else:
self.var.monthCounter[c] = np.where(self.var.monthCounter[c] > self.var.Crops[c][-1][0], 0,
self.var.monthCounter[c])
# Removing crops that been harvested
self.var.fracCrops_Irr[c] = np.where(self.var.monthCounter[c] > 0, self.var.fracCrops_Irr[c], 0)
self.var.fracCrops_nonIrr[c] = np.where(self.var.monthCounter[c] > 0,
self.var.fracCrops_nonIrr[c], 0)
if self.var.daily_crop_KC:
self.var.currentKC[c] = np.where(self.var.monthCounter[c] > 0,
self.var.Crops[c][-1][self.var.monthCounter[c]-1], 0)
for a in range(1, 4):
self.var.currentKY[c] = np.where(self.var.monthCounter[c] > self.var.Crops[c][a][0],
self.var.Crops[c][a + 1][2], self.var.currentKY[c])
else:
self.var.currentKC[c] = np.where(self.var.monthCounter[c] == 0, 0, self.var.currentKC[c])
for a in range(1, 4):
self.var.currentKC[c] = np.where(self.var.monthCounter[c] > self.var.Crops[c][a][0],
self.var.Crops[c][a + 1][1], self.var.currentKC[c])
self.var.currentKY[c] = np.where(self.var.monthCounter[c] > self.var.Crops[c][a][0],
self.var.Crops[c][a + 1][2], self.var.currentKY[c])
# This calculates the current land being used for irrigated and non-irrigated crops
frac_totalIrr, frac_totalnonIrr = globals.inZero.copy(), globals.inZero.copy()
for i in range(len(self.var.Crops)):
frac_totalIrr += self.var.fracCrops_Irr[i]
frac_totalnonIrr += self.var.fracCrops_nonIrr[i]
remainder_land_nonIrr = self.var.fracVegCover[1] - frac_totalnonIrr
remainder_land_Irr = self.var.fracVegCover[3] - frac_totalIrr
# Sowing seeds, if crop is not already growing, if there is sufficient space
# If it is the planting month of the crop,
# the crop is planted both irrigated and non-irrigated,
# in the remaining available land.
if self.var.daily_crop_KC:
self.var.fracCrops_Irr[c] = np.where(
self.var.Crops[c][0] == dateVar['doy'] and self.var.monthCounter[c] == 0,
np.where(remainder_land_Irr > 0,
np.minimum(remainder_land_Irr, self.var.fracCrops_IrrLandDemand[c]),
0),
self.var.fracCrops_Irr[c])
else:
self.var.fracCrops_Irr[c] = np.where(
self.var.Crops[c][0] == dateVar['currDate'].month and self.var.monthCounter[c] == 0,
np.where(remainder_land_Irr > 0,
np.minimum(remainder_land_Irr, self.var.fracCrops_IrrLandDemand[c]),
0),
self.var.fracCrops_Irr[c])
if 'leftoverIrrigatedCropIsRainfed' in option:
if checkOption('leftoverIrrigatedCropIsRainfed'):
self.var.fracCrops_nonIrrLandDemand[c] = self.var.fracCrops_IrrLandDemand[c] - \
self.var.fracCrops_Irr[c]
if 'crops_leftoverNotIrrigated' in binding:
if c <= int(cbinding('crops_leftoverNotIrrigated')):
self.var.fracCrops_nonIrrLandDemand[c] = globals.inZero.copy()
if self.var.daily_crop_KC:
self.var.fracCrops_nonIrr[c] = np.where(
self.var.Crops[c][0] == dateVar['doy'] and self.var.monthCounter[c] == 0,
np.where(remainder_land_nonIrr > 0,
np.minimum(remainder_land_nonIrr, self.var.fracCrops_nonIrrLandDemand[c]),
0),
self.var.fracCrops_nonIrr[c])
else:
self.var.fracCrops_nonIrr[c] = np.where(
self.var.Crops[c][0] == dateVar['currDate'].month and self.var.monthCounter[c] == 0,
np.where(remainder_land_nonIrr > 0,
np.minimum(remainder_land_nonIrr, self.var.fracCrops_nonIrrLandDemand[c]),
0),
self.var.fracCrops_nonIrr[c])
frac_totalIrr, frac_totalnonIrr = globals.inZero.copy(), globals.inZero.copy()
for i in range(len(self.var.Crops)):
frac_totalIrr += self.var.fracCrops_Irr[i]
frac_totalnonIrr += self.var.fracCrops_nonIrr[i]
# self.var.frac_totalIrr = frac_totalIrr.copy()
# self.var.frac_totalnonIrr = frac_totalnonIrr.copy()
remainder_land_nonIrr = self.var.fracVegCover[1] - frac_totalnonIrr
remainder_land_Irr = self.var.fracVegCover[3] - frac_totalIrr
# When it is the crop's planting month and it is not yet already planted (the month counter is zero).
# The counter only starts if there is some of the crop growing in the cell (it is activated).
# Otherwise, the month counter is kept constant
if self.var.daily_crop_KC:
self.var.monthCounter[c] = np.where(
self.var.Crops[c][0] == dateVar['doy'] and self.var.monthCounter[c] == 0,
self.var.activatedCrops[c], self.var.monthCounter[c])
self.var.currentKC[c] = np.where(self.var.monthCounter[c] > 0,
self.var.Crops[c][-1][self.var.monthCounter[c] - 1], 0)
self.var.currentKY[c] = np.where(
self.var.Crops[c][0] == dateVar['doy'] and self.var.monthCounter[c] == 1,
self.var.Crops[c][1][2],
self.var.currentKY[c])
else:
self.var.monthCounter[c] = np.where(
self.var.Crops[c][0] == dateVar['currDate'].month and self.var.monthCounter[c] == 0,
self.var.activatedCrops[c], self.var.monthCounter[c])
self.var.currentKC[c] = np.where(
self.var.Crops[c][0] == dateVar['currDate'].month and self.var.monthCounter[c] == 1,
self.var.Crops[c][1][1],
self.var.currentKC[c])
self.var.currentKY[c] = np.where(
self.var.Crops[c][0] == dateVar['currDate'].month and self.var.monthCounter[c] == 1,
self.var.Crops[c][1][2],
self.var.currentKY[c])
#if No == 3 and (dateVar['newStart'] or dateVar['currDate'].day == 1):
if dateVar['newStart'] or dateVar['currDate'].day == 1:
frac_totalIrr, frac_totalnonIrr = globals.inZero.copy(), globals.inZero.copy()
for i in range(len(self.var.Crops)):
frac_totalIrr += self.var.fracCrops_Irr[i]
frac_totalnonIrr += self.var.fracCrops_nonIrr[i]
self.var.frac_totalIrr = frac_totalIrr.copy()
self.var.frac_totalnonIrr = frac_totalnonIrr.copy()
self.var.frac_totalIrr_max = np.maximum(frac_totalIrr, self.var.frac_totalIrr_max)
self.var.frac_totalnonIrr_max = np.maximum(frac_totalnonIrr, self.var.frac_totalnonIrr_max)
# UNDER CONSTRUCTION: Automatic fallowing for irrigated land
self.var.generalIrrCrop_max = np.maximum(self.var.fracVegCover[3] - self.var.frac_totalIrr_max, globals.inZero.copy())
self.var.generalnonIrrCrop_max = np.maximum(self.var.fracVegCover[1] - self.var.frac_totalnonIrr_max, globals.inZero.copy())
# The representative vegetation is determined from a specific user-input map, as compared to being
# determined automatically otherwise.
if 'GeneralCrop_Irr' in binding and checkOption('use_GeneralCropIrr') is True:
self.var.GeneralCrop_Irr = loadmap('GeneralCrop_Irr')
self.var.GeneralCrop_Irr = np.minimum(self.var.fracVegCover[3] - frac_totalIrr,
self.var.GeneralCrop_Irr)
# Fallowing and general crop are determined automatically, and are not specific input maps.
elif checkOption('use_GeneralCropIrr') is False:
# Fallow land exists alongside general land as non-specific crop options.
if checkOption('activate_fallow') is True:
# Crop land that has been previously planted by a specific-crop is fallowed between plantings.
if checkOption('automaticFallowingIrr') is True:
self.var.GeneralCrop_Irr = self.var.generalIrrCrop_max.copy()
# With the interest in fallowing without automatic fallowing nor a specific input map implies
# the scenario without general lands -- only specific planted crops and fallow land.
else:
self.var.GeneralCrop_Irr = globals.inZero.copy()
else:
# activate_fallow = False implies that all non-planted grassland and non-paddy land is made
# to be representative vegetation.
self.var.GeneralCrop_Irr = self.var.fracVegCover[3] - self.var.frac_totalIrr
self.var.fallowIrr = self.var.fracVegCover[3] - (self.var.frac_totalIrr + self.var.GeneralCrop_Irr)
self.var.fallowIrr_max = np.maximum(self.var.fallowIrr, self.var.fallowIrr_max)
# Updating irrigated land to not include fallow
# Irrigated fallow land is moved to non-irrigated fallow land. Irrigated fallow land is
#UNDER CONSTRUCTION
if 'moveIrrFallowToNonIrr' in option:
if checkOption('moveIrrFallowToNonIrr'):
self.var.fracVegCover[3] = self.var.frac_totalIrr + self.var.GeneralCrop_Irr
remainderLand = np.maximum(
globals.inZero.copy() + 1 - self.var.fracVegCover[4] - self.var.fracVegCover[3] -
self.var.fracVegCover[5] - self.var.fracVegCover[2] - self.var.fracVegCover[0],
globals.inZero.copy())
self.var.fracVegCover[1] = remainderLand.copy()
if 'GeneralCrop_nonIrr' in binding and checkOption('use_GeneralCropnonIrr') is True:
self.var.GeneralCrop_nonIrr = loadmap('GeneralCrop_nonIrr')
self.var.GeneralCrop_nonIrr = np.minimum(self.var.fracVegCover[1] - frac_totalnonIrr,
self.var.GeneralCrop_nonIrr)
elif checkOption('use_GeneralCropnonIrr') is False:
if checkOption('activate_fallow') is True:
self.var.GeneralCrop_nonIrr = self.var.generalnonIrrCrop_max.copy()
else:
self.var.GeneralCrop_nonIrr = self.var.fracVegCover[1] - self.var.frac_totalnonIrr
self.var.fallownonIrr = self.var.fracVegCover[1] - (
self.var.frac_totalnonIrr + self.var.GeneralCrop_nonIrr)
self.var.fallownonIrr_max = np.maximum(self.var.fallownonIrr, self.var.fallownonIrr_max)
self.var.availableArableLand = self.var.fallowIrr + self.var.fracVegCover[1] - frac_totalnonIrr
if No == 1:
self.var.weighted_KC_nonIrr = self.var.GeneralCrop_nonIrr * cropKC_landCover
for c in range(len(self.var.Crops)):
self.var.weighted_KC_nonIrr += self.var.fracCrops_nonIrr[c] * self.var.currentKC[c]
self.var.weighted_KC_nonIrr_woFallow = self.var.weighted_KC_nonIrr.copy()
self.var.weighted_KC_nonIrr += self.var.fallownonIrr * self.var.minCropKC
self.var.weighted_KC_nonIrr = np.where(self.var.fracVegCover[1] > 0,
self.var.weighted_KC_nonIrr / self.var.fracVegCover[1], 0)
self.var.cropKC[1] = self.var.weighted_KC_nonIrr.copy()
if No == 3:
self.var.weighted_KC_Irr = self.var.GeneralCrop_Irr * cropKC_landCover
for c in range(len(self.var.Crops)):
self.var.weighted_KC_Irr += self.var.fracCrops_Irr[c] * self.var.currentKC[c]
self.var.weighted_KC_Irr_woFallow_fullKc = self.var.weighted_KC_Irr.copy()
self.var.weighted_KC_Irr += self.var.fallowIrr * self.var.minCropKC
self.var.weighted_KC_Irr = np.where(self.var.fracVegCover[3] > 0,
self.var.weighted_KC_Irr / self.var.fracVegCover[3], 0)
self.var.cropKC[3] = self.var.weighted_KC_Irr.copy()
self.var._weighted_KC_Irr = self.var.GeneralCrop_Irr * (cropKC_landCover - self.var.minCropKC)
for c in range(len(self.var.Crops)):
self.var._weighted_KC_Irr += self.var.fracCrops_Irr[c] * (self.var.currentKC[c]-self.var.minCropKC)
self.var.weighted_KC_Irr_woFallow = self.var._weighted_KC_Irr.copy()
# without crops
# calculate potential ET
## self.var.totalPotET total potential evapotranspiration for a reference crop for a land cover class [m]
self.var.totalPotET[No] = self.var.cropCorrect * self.var.crop_correct_landCover[No] * self.var.cropKC[No] * self.var.ETRef
# calculate transpiration
# potTranspiration: Transpiration for each land cover class
self.var.potTranspiration[No] = np.maximum(0., self.var.totalPotET[No] - self.var.potBareSoilEvap)
# checkOption('includeCrops') and checkOption('includeCropSpecificWaterUse')
if self.var.includeCrops:
# only goes through ones
if No == 3:
for c in range(len(self.var.Crops)):
self.var.PotET_crop[c] = (self.var.cropCorrect * self.var.crop_correct_landCover[No] *
self.var.currentKC[c] * self.var.ETRef)
self.var.totalPotET_month[c] += self.var.PotET_crop[c]
# For creating named crop maps
vars(self.var)[self.var.Crops_names[c] + '_Irr'] = self.var.fracCrops_Irr[c].copy()
vars(self.var)[self.var.Crops_names[c] + '_nonIrr'] = self.var.fracCrops_nonIrr[c].copy()
if 'adminSegments' in binding:
self.var.totalPotET_month_segment[c] = npareaaverage(self.var.totalPotET_month[c], self.var.adminSegments)
self.var.PotETaverage_crop_segments[c] = npareaaverage(self.var.PotET_crop[c], self.var.adminSegments)
self.var.areaCrops_Irr_segment[c] = npareatotal(self.var.fracCrops_Irr[c] * self.var.cellArea,
self.var.adminSegments)
self.var.areaCrops_nonIrr_segment[c] = npareatotal(
self.var.fracCrops_nonIrr[c] * self.var.cellArea,
self.var.adminSegments)
if 'adminSegments' in binding:
self.var.areaPaddy_Irr_segment = npareatotal(self.var.fracVegCover[2] * self.var.cellArea,
self.var.adminSegments)
self.var.Precipitation_segment = npareatotal(self.var.Precipitation * self.var.cellArea,
self.var.adminSegments)
self.var.availableArableLand_segment = npareatotal(self.var.availableArableLand * self.var.cellArea,
self.var.adminSegments)