class DeterministicRunner(DynamicModel):
def __init__(self, configuration, modelTime, initialState = None):
DynamicModel.__init__(self)
self.modelTime = modelTime
self.model = PCRGlobWB(configuration, modelTime, initialState)
self.reporting = Reporting(configuration, self.model, modelTime)
def initial(self):
pass
def dynamic(self):
# re-calculate current model time using current pcraster timestep value
self.modelTime.update(self.currentTimeStep())
# update model (will pick up current model time from model time object)
self.model.read_forcings()
self.model.update(report_water_balance=True)
#do any needed reporting for this time step
self.reporting.report()
class DeterministicRunner(DynamicModel):
def __init__(self, configuration, modelTime, initialState = None):
DynamicModel.__init__(self)
self.modelTime = modelTime
self.model = PCRGlobWB(configuration, modelTime, initialState)
self.reporting = Reporting(configuration, self.model, modelTime)
def initial(self):
pass
def dynamic(self):
# re-calculate current model time using current pcraster timestep value
self.modelTime.update(self.currentTimeStep())
self.model.read_forcings()
self.model.update(report_water_balance=True)
# get observation data
sattelite_satDegUpp000005 = self.get_satDegUpp000005_from_observation()
# set upper soil moisture state based on observation data
self.set_satDegUpp000005(sattelite_satDegUpp000005)
# do any needed reporting for this time step
self.reporting.report()
def get_satDegUpp000005_from_observation(self):
# assumption for observation values
# - this should be replaced by values from the ECV soil moisture value (sattelite data)
# - uncertainty should be included here
# - note that the value should be between 0.0 and 1.0
observed_satDegUpp000005 = pcr.min(1.0,\
pcr.max(0.0,\
pcr.normal(pcr.boolean(1)) + 1.0))
return observed_satDegUpp000005
def set_satDegUpp000005(self, observed_satDegUpp000005):
# ratio between observation and model
ratio_between_observation_and_model = pcr.ifthenelse(self.model.landSurface.satDegUpp000005> 0.0,
observed_satDegUpp000005 / \
self.model.landSurface.satDegUpp000005, 0.0)
# updating upper soil states for all lad cover types
for coverType in self.model.landSurface.coverTypes:
# correcting upper soil state (storUpp000005)
self.model.landSurface.landCoverObj[coverType].storUpp000005 *= ratio_between_observation_and_model
# if model value = 0.0, storUpp000005 is calculated based on storage capacity (model parameter) and observed saturation degree
self.model.landSurface.landCoverObj[coverType].storUpp000005 = pcr.ifthenelse(self.model.landSurface.satDegUpp000005 > 0.0,\
self.model.landSurface.landCoverObj[coverType].storUpp000005,\
observed_satDegUpp000005 * self.model.landSurface.parameters.storCapUpp000005)
def __init__(self,
configuration,
modelTime,
initialState=None,
system_argument=None):
DynamicModel.__init__(self)
self.modelTime = modelTime
self.model = PCRGlobWB(configuration, modelTime, initialState)
self.reporting = Reporting(configuration, self.model, modelTime)
# the model will set paramaters based on global pre-multipliers given in the argument:
self.adusting_parameters(configuration, system_argument)
# make the configuration available for the other method/function
self.configuration = configuration
def __init__(self, configuration, modelTime, initialState = None, system_argument = None):
DynamicModel.__init__(self)
self.modelTime = modelTime
self.model = PCRGlobWB(configuration, modelTime, initialState)
self.reporting = Reporting(configuration, self.model, modelTime)
# the model will set paramaters based on global pre-multipliers given in the argument:
if system_argument != None: self.adusting_parameters(configuration, system_argument)
# option to include merging processes for pcraster maps and netcdf files:
self.with_merging = True
if ('with_merging' in configuration.globalOptions.keys()) and (configuration.globalOptions['with_merging'] == "False"):
self.with_merging = False
# make the configuration available for the other method/function
self.configuration = configuration
def initialize_model(self):
if self.model is not None:
#already initialized
return
try:
logger.info("PCRGlobWB: initialize_model")
initial_state = None
self.model = PCRGlobWB(self.configuration, self.model_time,
initial_state)
self.reporting = Reporting(self.configuration, self.model,
self.model_time)
logger.info("Shape of maps is %s", str(self.shape))
logger.info("PCRGlobWB Initialized")
except:
import traceback
traceback.print_exc()
raise
class DeterministicRunner(DynamicModel):
def __init__(self,
configuration,
modelTime,
initialState=None,
system_argument=None):
DynamicModel.__init__(self)
self.modelTime = modelTime
self.model = PCRGlobWB(configuration, modelTime, initialState)
self.reporting = Reporting(configuration, self.model, modelTime)
# the model will set paramaters based on global pre-multipliers given in the argument:
if system_argument != None:
self.adusting_parameters(configuration, system_argument)
# option to include merging processes for pcraster maps and netcdf files:
self.with_merging = True
if ('with_merging' in configuration.globalOptions.keys()) and (
configuration.globalOptions['with_merging'] == "False"):
self.with_merging = False
# make the configuration available for the other method/function
self.configuration = configuration
def adusting_parameters(self, configuration, system_argument):
# global pre-multipliers given in the argument:
if len(system_argument) > 4:
logger.info(
"Adjusting some model parameters based on given values in the system argument."
)
# pre-multipliers for minSoilDepthFrac, kSat, recessionCoeff, storCap and degreeDayFactor
multiplier_for_minSoilDepthFrac = float(
system_argument[4]
) # linear scale # Note that this one does NOT work for the changing WMIN or Joyce land cover options.
multiplier_for_kSat = float(system_argument[5]) # log scale
multiplier_for_recessionCoeff = float(
system_argument[6]) # log scale
multiplier_for_storCap = float(system_argument[7]) # linear scale
multiplier_for_degreeDayFactor = float(
system_argument[8]) # linear scale
# pre-multiplier for the reference potential ET
self.multiplier_for_refPotET = float(
system_argument[9]) # linear scale
# it is also possible to define prefactors via the ini/configuration file:
# - this will be overwrite any previous given pre-multipliers
if 'prefactorOptions' in configuration.allSections:
logger.info(
"Adjusting some model parameters based on given values in the ini/configuration file."
)
self.multiplier_for_refPotET = float(
configuration.
prefactorOptions['linear_multiplier_for_refPotET']
) # linear scale # Note that this one does NOT work for the changing WMIN or Joyce land cover options.
multiplier_for_degreeDayFactor = float(
configuration.prefactorOptions[
'linear_multiplier_for_degreeDayFactor']) # linear scale
multiplier_for_minSoilDepthFrac = float(
configuration.prefactorOptions[
'linear_multiplier_for_minSoilDepthFrac']) # linear scale
multiplier_for_kSat = float(
configuration.prefactorOptions['log_10_multiplier_for_kSat']
) # log scale
multiplier_for_storCap = float(
configuration.prefactorOptions['linear_multiplier_for_storCap']
) # linear scale
multiplier_for_recessionCoeff = float(
configuration.prefactorOptions[
'log_10_multiplier_for_recessionCoeff']) # log scale
# saving global pre-multipliers to the log file:
msg = "\n"
msg += "\n"
msg += "Multiplier values used: " + "\n"
msg += "For minSoilDepthFrac : " + str(
multiplier_for_minSoilDepthFrac) + "\n"
msg += "For kSat (log-scale) : " + str(
multiplier_for_kSat) + "\n"
msg += "For recessionCoeff (log-scale) : " + str(
multiplier_for_recessionCoeff) + "\n"
msg += "For storCap : " + str(
multiplier_for_storCap) + "\n"
msg += "For degreeDayFactor : " + str(
multiplier_for_degreeDayFactor) + "\n"
msg += "For refPotET : " + str(
self.multiplier_for_refPotET) + "\n"
logger.info(msg)
# - also to a txt file
f = open(
"multiplier.txt", "w"
) # this will be stored in the "map" folder of the 'outputDir' (as we set the current working directory to this "map" folder, see configuration.py)
f.write(msg)
f.close()
# set parameter "recessionCoeff" based on the given pre-multiplier
# - also saving the adjusted parameter maps to pcraster files
# - these will be stored in the "map" folder of the 'outputDir' (as we set the current working directory to this "map" folder, see configuration.py)
# "recessionCoeff"
# minimum value is zero and using log-scale
self.model.groundwater.recessionCoeff = pcr.max(
0.0, (10**(multiplier_for_recessionCoeff)) *
self.model.groundwater.recessionCoeff)
self.model.groundwater.recessionCoeff = pcr.min(
1.0, self.model.groundwater.recessionCoeff)
# report the map
pcr.report(self.model.groundwater.recessionCoeff, "recessionCoeff.map")
# set parameters "kSat", "storCap", "minSoilDepthFrac", and "degreeDayFactor" based on the given pre-multipliers
for coverType in self.model.landSurface.coverTypes:
# "degreeDayFactor"
self.model.landSurface.landCoverObj[coverType].degreeDayFactor = pcr.max(0.0, multiplier_for_degreeDayFactor *\
self.model.landSurface.landCoverObj[coverType].degreeDayFactor)
# report the map
pcraster_filename = "degreeDayFactor" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].degreeDayFactor,
pcraster_filename)
# "kSat" and "storCap" for 2 layer model
if self.model.landSurface.numberOfSoilLayers == 2:
# "kSat"
# minimum value is zero and using-log-scale
self.model.landSurface.landCoverObj[coverType].parameters.kSatUpp = \
pcr.max(0.0, (10**(multiplier_for_kSat)) * self.model.landSurface.landCoverObj[coverType].parameters.kSatUpp)
self.model.landSurface.landCoverObj[coverType].parameters.kSatLow = \
pcr.max(0.0, (10**(multiplier_for_kSat)) * self.model.landSurface.landCoverObj[coverType].parameters.kSatLow)
# report the maps
pcraster_filename = "kSatUpp" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
kSatUpp, pcraster_filename)
pcraster_filename = "kSatLow" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
kSatLow, pcraster_filename)
# "storCap"
# minimum value is zero
self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp = pcr.max(0.0, multiplier_for_storCap*\
self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp)
self.model.landSurface.landCoverObj[coverType].parameters.storCapLow = pcr.max(0.0, multiplier_for_storCap*\
self.model.landSurface.landCoverObj[coverType].parameters.storCapLow)
# report the maps
pcraster_filename = "storCapUpp" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
storCapUpp, pcraster_filename)
pcraster_filename = "storCapLow" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
storCapLow, pcraster_filename)
# "kSat" and "storCap" for 3 layer model
if self.model.landSurface.numberOfSoilLayers == 3:
# "kSat"
# minimum value is zero and using-log-scale
self.model.landSurface.landCoverObj[coverType].parameters.kSatUpp000005 = \
pcr.max(0.0, (10**(multiplier_for_kSat)) * self.model.landSurface.landCoverObj[coverType].parameters.kSatUpp000005)
self.model.landSurface.landCoverObj[coverType].parameters.kSatUpp005030 = \
pcr.max(0.0, (10**(multiplier_for_kSat)) * self.model.landSurface.landCoverObj[coverType].parameters.kSatUpp005030)
self.model.landSurface.landCoverObj[coverType].parameters.kSatLow030150 = \
pcr.max(0.0, (10**(multiplier_for_kSat)) * self.model.landSurface.landCoverObj[coverType].parameters.kSatLow030150)
# report the maps
pcraster_filename = "kSatUpp000005" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
kSatUpp000005, pcraster_filename)
pcraster_filename = "kSatUpp005030" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
kSatUpp005030, pcraster_filename)
pcraster_filename = "kSatLow030150" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
kSatLow030150, pcraster_filename)
# "storCap"
# minimum value is zero
self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp000005 = pcr.max(0.0, multiplier_for_storCap*\
self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp000005)
self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp005030 = pcr.max(0.0, multiplier_for_storCap*\
self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp005030)
self.model.landSurface.landCoverObj[coverType].parameters.storCapLow030150 = pcr.max(0.0, multiplier_for_storCap*\
self.model.landSurface.landCoverObj[coverType].parameters.storCapLow030150)
# report the maps
pcraster_filename = "storCapUpp000005" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
storCapUpp000005, pcraster_filename)
pcraster_filename = "storCapUpp005030" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
storCapUpp005030, pcraster_filename)
pcraster_filename = "storCapLow030150" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
storCapLow030150, pcraster_filename)
# re-calculate rootZoneWaterStorageCap as the consequence of the modification of "storCap"
# This is WMAX in the oldcalc script.
if self.model.landSurface.numberOfSoilLayers == 2:
self.model.landSurface.landCoverObj[coverType].parameters.rootZoneWaterStorageCap = self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp +\
self.model.landSurface.landCoverObj[coverType].parameters.storCapLow
if self.model.landSurface.numberOfSoilLayers == 3:
self.model.landSurface.landCoverObj[coverType].parameters.rootZoneWaterStorageCap = self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp000005 +\
self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp005030 +\
self.model.landSurface.landCoverObj[coverType].parameters.storCapLow030150
# report the map
pcraster_filename = "rootZoneWaterStorageCap" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
rootZoneWaterStorageCap, pcraster_filename)
# "minSoilDepthFrac"
if multiplier_for_minSoilDepthFrac != 1.0:
# minimum value is zero
self.model.landSurface.landCoverObj[coverType].minSoilDepthFrac = pcr.max(0.0, multiplier_for_minSoilDepthFrac*\
self.model.landSurface.landCoverObj[coverType].minSoilDepthFrac)
# for minSoilDepthFrac - values will be limited by maxSoilDepthFrac
self.model.landSurface.landCoverObj[coverType].minSoilDepthFrac = pcr.min(\
self.model.landSurface.landCoverObj[coverType].minSoilDepthFrac,\
self.model.landSurface.landCoverObj[coverType].maxSoilDepthFrac)
# maximum value is 1.0
self.model.landSurface.landCoverObj[
coverType].minSoilDepthFrac = pcr.min(
1.0, self.model.landSurface.landCoverObj[coverType].
minSoilDepthFrac)
# report the map
pcraster_filename = "minSoilDepthFrac" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].
minSoilDepthFrac, pcraster_filename)
# re-calculate arnoBeta (as the consequence of the modification of minSoilDepthFrac)
self.model.landSurface.landCoverObj[coverType].arnoBeta = pcr.max(0.001,\
(self.model.landSurface.landCoverObj[coverType].maxSoilDepthFrac-1.)/(1.-self.model.landSurface.landCoverObj[coverType].minSoilDepthFrac)+\
self.model.landSurface.landCoverObj[coverType].parameters.orographyBeta-0.01)
self.model.landSurface.landCoverObj[coverType].arnoBeta = pcr.cover(pcr.max(0.001,\
self.model.landSurface.landCoverObj[coverType].arnoBeta), 0.001)
# report the map
pcraster_filename = "arnoBeta" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].arnoBeta,
pcraster_filename)
# re-calculate rootZoneWaterStorageMin (as the consequence of the modification of minSoilDepthFrac)
# This is WMIN in the oldcalc script.
# WMIN (unit: m): minimum local soil water capacity within the grid-cell
self.model.landSurface.landCoverObj[coverType].rootZoneWaterStorageMin = self.model.landSurface.landCoverObj[coverType].minSoilDepthFrac *\
self.model.landSurface.landCoverObj[coverType].parameters.rootZoneWaterStorageCap
# report the map
pcraster_filename = "rootZoneWaterStorageMin" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].
rootZoneWaterStorageMin, pcraster_filename)
# re-calculate rootZoneWaterStorageRange (as the consequence of the modification of rootZoneWaterStorageRange and minSoilDepthFrac)
# WMAX - WMIN (unit: m)
self.model.landSurface.landCoverObj[coverType].rootZoneWaterStorageRange = self.model.landSurface.landCoverObj[coverType].parameters.rootZoneWaterStorageCap -\
self.model.landSurface.landCoverObj[coverType].rootZoneWaterStorageMin
# report the map
pcraster_filename = "rootZoneWaterStorageRange" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].
rootZoneWaterStorageRange, pcraster_filename)
def initial(self):
pass
def dynamic(self):
# re-calculate current model time using current pcraster timestep value
self.modelTime.update(self.currentTimeStep())
# read model forcing (will pick up current model time from model time object)
self.model.read_forcings()
# adjust the reference potential ET according to the given pre-multiplier
self.model.meteo.referencePotET = self.model.meteo.referencePotET * self.multiplier_for_refPotET
# update model (will pick up current model time from model time object)
# - for a run coupled to MODFLOW, water balance checks are not valid due to lateral flow.
if self.configuration.online_coupling_between_pcrglobwb_and_modflow:
self.model.update(report_water_balance=False)
else:
self.model.update(report_water_balance=True)
# do any needed reporting for this time step
self.reporting.report()
# at the last day of the month, stop calculation until modflow and related merging process are ready (only for a run with modflow)
if self.modelTime.isLastDayOfMonth() and (self.configuration.online_coupling_between_pcrglobwb_and_modflow or\
self.with_merging):
# wait until modflow files are ready
if self.configuration.online_coupling_between_pcrglobwb_and_modflow:
modflow_is_ready = False
self.count_check = 0
while modflow_is_ready == False:
if datetime.datetime.now().second == 14 or\
datetime.datetime.now().second == 29 or\
datetime.datetime.now().second == 34 or\
datetime.datetime.now().second == 59: \
modflow_is_ready = self.check_modflow_status()
# wait until merged files are ready
merged_files_are_ready = False
while merged_files_are_ready == False:
self.count_check = 0
if datetime.datetime.now().second == 14 or\
datetime.datetime.now().second == 29 or\
datetime.datetime.now().second == 34 or\
datetime.datetime.now().second == 59: \
merged_files_are_ready = self.check_merging_status()
def check_modflow_status(self):
status_file = str(
self.configuration.main_output_directory
) + "/modflow/transient/maps/modflow_files_for_" + str(
self.modelTime.fulldate) + "_are_ready.txt"
msg = 'Waiting for the file: ' + status_file
if self.count_check == 1: logger.info(msg)
if self.count_check < 7:
#~ logger.debug(msg) # INACTIVATE THIS AS THIS MAKE A HUGE DEBUG (dbg) FILE
self.count_check += 1
status = os.path.exists(status_file)
if status == False: return status
if status: self.count_check = 0
return status
def check_merging_status(self):
status_file = str(self.configuration.main_output_directory
) + "/global/maps/merged_files_for_" + str(
self.modelTime.fulldate) + "_are_ready.txt"
msg = 'Waiting for the file: ' + status_file
if self.count_check == 1: logger.info(msg)
if self.count_check < 7:
#~ logger.debug(msg) # INACTIVATE THIS AS THIS MAKE A HUGE DEBUG (dbg) FILE
self.count_check += 1
status = os.path.exists(status_file)
if status == False: return status
if status: self.count_check = 0
return status
def __init__(self, configuration, modelTime, initialState = None):
DynamicModel.__init__(self)
self.modelTime = modelTime
self.model = PCRGlobWB(configuration, modelTime, initialState)
self.reporting = Reporting(configuration, self.model, modelTime)
class DeterministicRunner(DynamicModel):
def __init__(self,
configuration,
modelTime,
initialState=None,
system_argument=None):
DynamicModel.__init__(self)
self.modelTime = modelTime
self.model = PCRGlobWB(configuration, modelTime, initialState)
self.reporting = Reporting(configuration, self.model, modelTime)
# the model will set paramaters based on global pre-multipliers given in the argument:
self.adusting_parameters(configuration, system_argument)
# make the configuration available for the other method/function
self.configuration = configuration
def adusting_parameters(self, configuration, system_argument):
# it is also possible to define prefactors via the ini/configuration file:
# - this will be overwrite any previous given pre-multipliers
if 'prefactorOptions' in configuration.allSections:
logger.info(
"Adjusting some model parameters based on given values in the ini/configuration file."
)
self.multiplier_for_refPotET = float(
configuration.
prefactorOptions['linear_multiplier_for_refPotET']
) # linear scale # Note that this one does NOT work for the changing WMIN or Joyce land cover options.
multiplier_for_degreeDayFactor = float(
configuration.prefactorOptions[
'linear_multiplier_for_degreeDayFactor']) # linear scale
multiplier_for_minSoilDepthFrac = float(
configuration.prefactorOptions[
'linear_multiplier_for_minSoilDepthFrac']) # linear scale
multiplier_for_kSat = float(
configuration.prefactorOptions['log_10_multiplier_for_kSat']
) # log scale
multiplier_for_storCap = float(
configuration.prefactorOptions['linear_multiplier_for_storCap']
) # linear scale
multiplier_for_recessionCoeff = float(
configuration.prefactorOptions[
'log_10_multiplier_for_recessionCoeff']) # log scale
# saving global pre-multipliers to the log file:
msg = "\n"
msg += "\n"
msg += "Multiplier values used: " + "\n"
msg += "For minSoilDepthFrac : " + str(
multiplier_for_minSoilDepthFrac) + "\n"
msg += "For kSat (log-scale) : " + str(
multiplier_for_kSat) + "\n"
msg += "For recessionCoeff (log-scale) : " + str(
multiplier_for_recessionCoeff) + "\n"
msg += "For storCap : " + str(
multiplier_for_storCap) + "\n"
msg += "For degreeDayFactor : " + str(
multiplier_for_degreeDayFactor) + "\n"
msg += "For refPotET : " + str(
self.multiplier_for_refPotET) + "\n"
logger.info(msg)
# - also to a txt file
f = open(
"multiplier.txt", "w"
) # this will be stored in the "map" folder of the 'outputDir' (as we set the current working directory to this "map" folder, see configuration.py)
f.write(msg)
f.close()
# set parameter "recessionCoeff" based on the given pre-multiplier
# - also saving the adjusted parameter maps to pcraster files
# - these will be stored in the "map" folder of the 'outputDir' (as we set the current working directory to this "map" folder, see configuration.py)
# "recessionCoeff"
# minimum value is zero and using log-scale
self.model.groundwater.recessionCoeff = pcr.max(
0.0, (10**(multiplier_for_recessionCoeff)) *
self.model.groundwater.recessionCoeff)
self.model.groundwater.recessionCoeff = pcr.min(
1.0, self.model.groundwater.recessionCoeff)
# report the map
pcr.report(self.model.groundwater.recessionCoeff, "recessionCoeff.map")
# set parameters "kSat", "storCap", "minSoilDepthFrac", and "degreeDayFactor" based on the given pre-multipliers
for coverType in self.model.landSurface.coverTypes:
# "degreeDayFactor"
self.model.landSurface.landCoverObj[coverType].degreeDayFactor = pcr.max(0.0, multiplier_for_degreeDayFactor *\
self.model.landSurface.landCoverObj[coverType].degreeDayFactor)
# report the map
pcraster_filename = "degreeDayFactor" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].degreeDayFactor,
pcraster_filename)
# "kSat" and "storCap" for 2 layer model
if self.model.landSurface.numberOfSoilLayers == 2:
# "kSat"
# minimum value is zero and using-log-scale
self.model.landSurface.parameters.kSatUpp = \
pcr.max(0.0, (10**(multiplier_for_kSat)) * self.model.landSurface.parameters.kSatUpp)
self.model.landSurface.parameters.kSatLow = \
pcr.max(0.0, (10**(multiplier_for_kSat)) * self.model.landSurface.parameters.kSatLow)
# report the maps (for debugging)
#pcraster_filename = "kSatUpp"+ "_" + coverType + ".map"
#pcr.report(self.model.landSurface.parameters.kSatUpp, pcraster_filename)
#pcraster_filename = "kSatLow"+ "_" + coverType + ".map"
#pcr.report(self.model.landSurface.parameters.kSatLow, pcraster_filename)
# "storCap"
# minimum value is zero
self.model.landSurface.parameters.storCapUpp = pcr.max(0.0, multiplier_for_storCap*\
self.model.landSurface.parameters.storCapUpp)
self.model.landSurface.parameters.storCapLow = pcr.max(0.0, multiplier_for_storCap*\
self.model.landSurface.parameters.storCapLow)
# report the maps (for debugging)
#pcraster_filename = "storCapUpp"+ "_" + coverType + ".map"
#pcr.report(self.model.landSurface.parameters.storCapUpp, pcraster_filename)
#pcraster_filename = "storCapLow"+ "_" + coverType + ".map"
#pcr.report(self.model.landSurface.parameters.storCapLow, pcraster_filename)
# "kSat" and "storCap" for 3 layer model
if self.model.landSurface.numberOfSoilLayers == 3:
# "kSat"
# minimum value is zero and using-log-scale
self.model.landSurface.landCoverObj[coverType].parameters.kSatUpp000005 = \
pcr.max(0.0, (10**(multiplier_for_kSat)) * self.model.landSurface.landCoverObj[coverType].parameters.kSatUpp000005)
self.model.landSurface.landCoverObj[coverType].parameters.kSatUpp005030 = \
pcr.max(0.0, (10**(multiplier_for_kSat)) * self.model.landSurface.landCoverObj[coverType].parameters.kSatUpp005030)
self.model.landSurface.landCoverObj[coverType].parameters.kSatLow030150 = \
pcr.max(0.0, (10**(multiplier_for_kSat)) * self.model.landSurface.landCoverObj[coverType].parameters.kSatLow030150)
# report the maps
pcraster_filename = "kSatUpp000005" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
kSatUpp000005, pcraster_filename)
pcraster_filename = "kSatUpp005030" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
kSatUpp005030, pcraster_filename)
pcraster_filename = "kSatLow030150" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
kSatLow030150, pcraster_filename)
# "storCap"
# minimum value is zero
self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp000005 = pcr.max(0.0, multiplier_for_storCap*\
self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp000005)
self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp005030 = pcr.max(0.0, multiplier_for_storCap*\
self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp005030)
self.model.landSurface.landCoverObj[coverType].parameters.storCapLow030150 = pcr.max(0.0, multiplier_for_storCap*\
self.model.landSurface.landCoverObj[coverType].parameters.storCapLow030150)
# report the maps
pcraster_filename = "storCapUpp000005" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
storCapUpp000005, pcraster_filename)
pcraster_filename = "storCapUpp005030" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
storCapUpp005030, pcraster_filename)
pcraster_filename = "storCapLow030150" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].parameters.
storCapLow030150, pcraster_filename)
# re-calculate rootZoneWaterStorageCap as the consequence of the modification of "storCap"
# This is WMAX in the oldcalc script.
if self.model.landSurface.numberOfSoilLayers == 2:
self.model.landSurface.parameters.rootZoneWaterStorageCap = self.model.landSurface.parameters.storCapUpp +\
self.model.landSurface.parameters.storCapLow
if self.model.landSurface.numberOfSoilLayers == 3:
self.model.landSurface.landCoverObj[coverType].parameters.rootZoneWaterStorageCap = self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp000005 +\
self.model.landSurface.landCoverObj[coverType].parameters.storCapUpp005030 +\
self.model.landSurface.landCoverObj[coverType].parameters.storCapLow030150
# report the map
#pcraster_filename = "rootZoneWaterStorageCap"+ "_" + coverType + ".map"
#pcr.report(self.model.landSurface.parameters.rootZoneWaterStorageCap, pcraster_filename)
# "minSoilDepthFrac"
if multiplier_for_minSoilDepthFrac != 1.0:
for coverType in self.model.landSurface.coverTypes:
# minimum value is zero
self.model.landSurface.landCoverObj[coverType].minSoilDepthFrac = pcr.max(0.0, multiplier_for_minSoilDepthFrac*\
self.model.landSurface.landCoverObj[coverType].minSoilDepthFrac)
# for minSoilDepthFrac - values will be limited by maxSoilDepthFrac
self.model.landSurface.landCoverObj[coverType].minSoilDepthFrac = pcr.min(\
self.model.landSurface.landCoverObj[coverType].minSoilDepthFrac,\
self.model.landSurface.landCoverObj[coverType].maxSoilDepthFrac)
# maximum value is 1.0
self.model.landSurface.landCoverObj[
coverType].minSoilDepthFrac = pcr.min(
1.0, self.model.landSurface.landCoverObj[coverType].
minSoilDepthFrac)
# report the map
pcraster_filename = "minSoilDepthFrac" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].
minSoilDepthFrac, pcraster_filename)
# re-calculate arnoBeta (as the consequence of the modification of minSoilDepthFrac)
self.model.landSurface.landCoverObj[coverType].arnoBeta = pcr.max(0.001,\
(self.model.landSurface.landCoverObj[coverType].maxSoilDepthFrac-1.)/(1.-self.model.landSurface.landCoverObj[coverType].minSoilDepthFrac)+\
self.model.landSurface.parameters.orographyBeta-0.01)
self.model.landSurface.landCoverObj[coverType].arnoBeta = pcr.cover(pcr.max(0.001,\
self.model.landSurface.landCoverObj[coverType].arnoBeta), 0.001)
# report the map
pcraster_filename = "arnoBeta" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].arnoBeta,
pcraster_filename)
# re-calculate rootZoneWaterStorageMin (as the consequence of the modification of minSoilDepthFrac)
# This is WMIN in the oldcalc script.
# WMIN (unit: m): minimum local soil water capacity within the grid-cell
self.model.landSurface.landCoverObj[coverType].rootZoneWaterStorageMin = self.model.landSurface.landCoverObj[coverType].minSoilDepthFrac *\
self.model.landSurface.parameters.rootZoneWaterStorageCap
# report the map
pcraster_filename = "rootZoneWaterStorageMin" + "_" + coverType + ".map"
pcr.report(
self.model.landSurface.landCoverObj[coverType].
rootZoneWaterStorageMin, pcraster_filename)
# re-calculate rootZoneWaterStorageRange (as the consequence of the modification of rootZoneWaterStorageRange and minSoilDepthFrac)
# WMAX - WMIN (unit: m)
self.model.landSurface.landCoverObj[coverType].rootZoneWaterStorageRange = self.model.landSurface.parameters.rootZoneWaterStorageCap -\
self.model.landSurface.landCoverObj[coverType].rootZoneWaterStorageMin
# report the map
#pcraster_filename = "rootZoneWaterStorageRange"+ "_" + coverType + ".map"
#pcr.report(self.model.landSurface.landCoverObj[coverType].rootZoneWaterStorageRange, pcraster_filename)
def initial(self):
pass
def dynamic(self):
# re-calculate current model time using current pcraster timestep value
self.modelTime.update(self.currentTimeStep())
# update model (will pick up current model time from model time object)
self.model.read_forcings()
self.model.update(report_water_balance=True)
#do any needed reporting for this time step
self.reporting.report()
class BmiPCRGlobWB(EBmi):
#we use the same epoch as pcrglobwb netcdf reporting
def days_since_industry_epoch(self, modeltime):
return (modeltime - datetime.date(1901, 1, 1)).days
def in_modeltime(self, days_since_industry_epoch):
return (datetime.datetime(1901, 1, 1) +
datetime.timedelta(days=days_since_industry_epoch)).date()
def calculate_shape(self):
# return pcr.pcr2numpy(self.model.landmask, 1e20).shape
return (pcr.clone().nrRows(), pcr.clone().nrCols())
#BMI initialize (as a single step)
def initialize(self, fileName):
self.initialize_config(fileName)
self.initialize_model()
#EBMI initialize (first step of two)
def initialize_config(self, fileName):
logger.info("PCRGlobWB: initialize_config")
try:
self.configuration = Configuration(fileName,
relative_ini_meteo_paths=True)
pcr.setclone(self.configuration.cloneMap)
# set start and end time based on configuration
self.model_time = ModelTime()
self.model_time.getStartEndTimeSteps(
self.configuration.globalOptions['startTime'],
self.configuration.globalOptions['endTime'])
self.model_time.update(0)
self.shape = self.calculate_shape()
logger.info("Shape of maps is %s", str(self.shape))
self.model = None
except:
import traceback
traceback.print_exc()
raise
#EBMI initialize (second step of two)
def initialize_model(self):
if self.model is not None:
#already initialized
return
try:
logger.info("PCRGlobWB: initialize_model")
initial_state = None
self.model = PCRGlobWB(self.configuration, self.model_time,
initial_state)
self.reporting = Reporting(self.configuration, self.model,
self.model_time)
logger.info("Shape of maps is %s", str(self.shape))
logger.info("PCRGlobWB Initialized")
except:
import traceback
traceback.print_exc()
raise
def update(self):
timestep = self.model_time.timeStepPCR
self.model_time.update(timestep + 1)
self.model.read_forcings()
self.model.update(report_water_balance=True)
self.reporting.report()
# #numpy = pcr.pcr2numpy(self.model.landSurface.satDegUpp000005, 1e20)
# numpy = pcr.pcr2numpy(self.model.landSurface.satDegUpp000005, np.NaN)
# print numpy.shape
# print numpy
def update_until(self, time):
while self.get_current_time() + 0.001 < time:
self.update()
def update_frac(self, time_frac):
raise NotImplementedError
def finalize(self):
pass
def get_component_name(self):
return "pcrglobwb"
def get_input_var_names(self):
return ["top_layer_soil_saturation"]
def get_output_var_names(self):
return ["top_layer_soil_saturation"]
def get_var_type(self, long_var_name):
return 'float64'
def get_var_units(self, long_var_name):
#TODO: this is not a proper unit
return '1'
def get_var_rank(self, long_var_name):
return 0
def get_var_size(self, long_var_name):
return np.prod(self.get_grid_shape(long_var_name))
def get_var_nbytes(self, long_var_name):
return self.get_var_size(long_var_name) * np.float64.itemsize
def get_start_time(self):
return self.days_since_industry_epoch(self.model_time.startTime)
def get_current_time(self):
return self.days_since_industry_epoch(self.model_time.currTime)
def get_end_time(self):
return self.days_since_industry_epoch(self.model_time.endTime)
def get_time_step(self):
return 1
def get_time_units(self):
return "Days since 1901-01-01"
def get_value(self, long_var_name):
logger.info("getting value for var %s", long_var_name)
if (long_var_name == "top_layer_soil_saturation"):
if self.model is not None and hasattr(self.model.landSurface,
'satDegUpp000005'):
#first make all NanS into 0.0 with cover, then cut out the model using the landmask.
# This should not actually make a difference.
remasked = pcr.ifthen(
self.model.landmask,
pcr.cover(self.model.landSurface.satDegUpp000005, 0.0))
pcr.report(self.model.landSurface.satDegUpp000005, "value.map")
pcr.report(remasked, "remasked.map")
value = pcr.pcr2numpy(remasked, np.NaN)
else:
logger.info(
"model has not run yet, returning empty state for top_layer_soil_saturation"
)
value = pcr.pcr2numpy(pcr.scalar(0.0), np.NaN)
# print "getting var", value
# sys.stdout.flush()
doubles = value.astype(np.float64)
# print "getting var as doubles!!!!", doubles
result = np.flipud(doubles)
# print "getting var as doubles flipped!!!!", result
# sys.stdout.flush()
return result
else:
raise Exception("unknown var name" + long_var_name)
def get_value_at_indices(self, long_var_name, inds):
raise NotImplementedError
# def get_satDegUpp000005_from_observation(self):
#
# # assumption for observation values
# # - this should be replaced by values from the ECV soil moisture value (sattelite data)
# # - uncertainty should be included here
# # - note that the value should be between 0.0 and 1.0
# observed_satDegUpp000005 = pcr.min(1.0,\
# pcr.max(0.0,\
# pcr.normal(pcr.boolean(1)) + 1.0))
# return observed_satDegUpp000005
def set_satDegUpp000005(self, src):
mask = np.isnan(src)
src[mask] = 1e20
observed_satDegUpp000005 = pcr.numpy2pcr(pcr.Scalar, src, 1e20)
pcr.report(observed_satDegUpp000005, "observed.map")
constrained_satDegUpp000005 = pcr.min(
1.0, pcr.max(0.0, observed_satDegUpp000005))
pcr.report(constrained_satDegUpp000005, "constrained.map")
pcr.report(self.model.landSurface.satDegUpp000005, "origmap.map")
diffmap = constrained_satDegUpp000005 - self.model.landSurface.satDegUpp000005
pcr.report(diffmap, "diffmap.map")
# ratio between observation and model
ratio_between_observation_and_model = pcr.ifthenelse(self.model.landSurface.satDegUpp000005 > 0.0,
constrained_satDegUpp000005 / \
self.model.landSurface.satDegUpp000005, 0.0)
# updating upper soil states for all lad cover types
for coverType in self.model.landSurface.coverTypes:
# correcting upper soil state (storUpp000005)
self.model.landSurface.landCoverObj[
coverType].storUpp000005 *= ratio_between_observation_and_model
# if model value = 0.0, storUpp000005 is calculated based on storage capacity (model parameter) and observed saturation degree
self.model.landSurface.landCoverObj[coverType].storUpp000005 = pcr.ifthenelse(
self.model.landSurface.satDegUpp000005 > 0.0, \
self.model.landSurface.landCoverObj[coverType].storUpp000005, \
constrained_satDegUpp000005 * self.model.landSurface.parameters.storCapUpp000005)
# correct for any scaling issues (value < 0 or > 1 do not make sense
self.model.landSurface.landCoverObj[
coverType].storUpp000005 = pcr.min(
1.0,
pcr.max(
0.0, self.model.landSurface.landCoverObj[coverType].
storUpp000005))
def set_value(self, long_var_name, src):
if self.model is None or not hasattr(self.model.landSurface,
'satDegUpp000005'):
logger.info("cannot set value for %s, as model has not run yet.",
long_var_name)
return
logger.info("setting value for %s", long_var_name)
# logger.info("dumping state to %s", self.configuration.endStateDir)
# self.model.dumpStateDir(self.configuration.endStateDir + "/pre/")
# print "got value to set", src
# make sure the raster is the right side up
src = np.flipud(src)
# print "flipped", src
# cast to pcraster precision
src = src.astype(np.float32)
# print "as float 32", src
sys.stdout.flush()
logger.info("setting value shape %s", src.shape)
if (long_var_name == "top_layer_soil_saturation"):
self.set_satDegUpp000005(src)
else:
raise Exception("unknown var name" + long_var_name)
# write state here to facilitate restarting tomorrow
# logger.info("dumping state to %s", self.configuration.endStateDir)
# self.model.dumpStateDir(self.configuration.endStateDir + "/post/")
def set_value_at_indices(self, long_var_name, inds, src):
raise NotImplementedError
def get_grid_type(self, long_var_name):
return BmiGridType.UNIFORM
def get_grid_shape(self, long_var_name):
return
def get_grid_shape(self, long_var_name):
return self.shape
def get_grid_spacing(self, long_var_name):
cellsize = pcr.clone().cellSize()
return np.array([cellsize, cellsize])
def get_grid_origin(self, long_var_name):
north = pcr.clone().north()
cellSize = pcr.clone().cellSize()
nrRows = pcr.clone().nrRows()
south = north - (cellSize * nrRows)
west = pcr.clone().west()
return np.array([south, west])
def get_grid_x(self, long_var_name):
raise ValueError
def get_grid_y(self, long_var_name):
raise ValueError
def get_grid_z(self, long_var_name):
raise ValueError
def get_grid_connectivity(self, long_var_name):
raise ValueError
def get_grid_offset(self, long_var_name):
raise ValueError
#EBMI functions
def set_start_time(self, start_time):
self.model_time.setStartTime(self.in_modeltime(start_time))
def set_end_time(self, end_time):
self.model_time.setEndTime(self.in_modeltime(end_time))
def get_attribute_names(self):
raise NotImplementedError
def get_attribute_value(self, attribute_name):
raise NotImplementedError
def set_attribute_value(self, attribute_name, attribute_value):
raise NotImplementedError
def save_state(self, destination_directory):
logger.info("saving state to %s", destination_directory)
self.model.dumpStateDir(destination_directory)
def load_state(self, source_directory):
raise NotImplementedError