def subcatch_order_a(ldd, oorder):
"""
Determines subcatchments using the catchment order
This version uses the last cell BELOW order to derive the
catchments. In general you want the _b version
Input:
- ldd
- order - order to use
Output:
- map with catchment for the given streamorder
"""
outl = find_outlet(ldd)
large = pcr.subcatchment(ldd, pcr.boolean(outl))
stt = pcr.streamorder(ldd)
sttd = pcr.downstream(ldd, stt)
pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(stt)) > 0.0, sttd)
dif = pcr.upstream(
ldd,
pcr.cover(
pcr.ifthen(
large,
pcr.uniqueid(pcr.boolean(pcr.ifthen(stt == pcr.ordinal(oorder), pts))),
),
0,
),
)
dif = pcr.cover(pcr.scalar(outl), dif) # Add catchment outlet
dif = pcr.ordinal(pcr.uniqueid(pcr.boolean(dif)))
sc = pcr.subcatchment(ldd, dif)
return sc, dif, stt
def subcatch_stream(ldd, stream, threshold):
"""
Derive catchments based upon strahler threshold
Input:
ldd -- pcraster object direction, local drain directions
stream -- pcraster object direction, streamorder
threshold -- integer, strahler threshold, subcatchments ge threshold are
derived
output:
stream_ge -- pcraster object, streams of strahler order ge threshold
subcatch -- pcraster object, subcatchments of strahler order ge threshold
"""
# derive stream order
# stream = pcr.streamorder(ldd)
stream_ge = pcr.ifthen(stream >= threshold, stream)
stream_up_sum = pcr.ordinal(pcr.upstream(ldd, pcr.cover(pcr.scalar(stream_ge), 0)))
# detect any transfer of strahler order, to a higher strahler order.
transition_strahler = pcr.ifthenelse(pcr.downstream(ldd, stream_ge) != stream_ge, pcr.boolean(1),
pcr.ifthenelse(pcr.nominal(ldd) == 5, pcr.boolean(1), pcr.ifthenelse(pcr.downstream(ldd, pcr.scalar(stream_up_sum)) > pcr.scalar(stream_ge), pcr.boolean(1),
pcr.boolean(0))))
# make unique ids (write to file)
transition_unique = pcr.ordinal(pcr.uniqueid(transition_strahler))
# derive upstream catchment areas (write to file)
subcatch = pcr.nominal(pcr.subcatchment(ldd, transition_unique))
return stream_ge, subcatch
def subcatch_stream(ldd, stream, threshold):
"""
Derive catchments based upon strahler threshold
Input:
ldd -- pcraster object direction, local drain directions
stream -- pcraster object direction, streamorder
threshold -- integer, strahler threshold, subcatchments ge threshold are
derived
output:
stream_ge -- pcraster object, streams of strahler order ge threshold
subcatch -- pcraster object, subcatchments of strahler order ge threshold
"""
# derive stream order
# stream = pcr.streamorder(ldd)
stream_ge = pcr.ifthen(stream >= threshold, stream)
stream_up_sum = pcr.ordinal(
pcr.upstream(ldd, pcr.cover(pcr.scalar(stream_ge), 0)))
# detect any transfer of strahler order, to a higher strahler order.
transition_strahler = pcr.ifthenelse(
pcr.downstream(ldd, stream_ge) != stream_ge, pcr.boolean(1),
pcr.ifthenelse(
pcr.nominal(ldd) == 5, pcr.boolean(1),
pcr.ifthenelse(
pcr.downstream(ldd, pcr.scalar(stream_up_sum)) >
pcr.scalar(stream_ge), pcr.boolean(1), pcr.boolean(0))))
# make unique ids (write to file)
transition_unique = pcr.ordinal(pcr.uniqueid(transition_strahler))
# derive upstream catchment areas (write to file)
subcatch = pcr.nominal(pcr.subcatchment(ldd, transition_unique))
return stream_ge, subcatch
def subcatch_order_a(ldd, oorder):
"""
Determines subcatchments using the catchment order
This version uses the last cell BELOW order to derive the
catchments. In general you want the _b version
Input:
- ldd
- order - order to use
Output:
- map with catchment for the given streamorder
"""
outl = find_outlet(ldd)
large = pcr.subcatchment(ldd, pcr.boolean(outl))
stt = pcr.streamorder(ldd)
sttd = pcr.downstream(ldd, stt)
pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(stt)) > 0.0, sttd)
dif = pcr.upstream(
ldd,
pcr.cover(
pcr.ifthen(
large,
pcr.uniqueid(pcr.boolean(pcr.ifthen(stt == pcr.ordinal(oorder), pts))),
),
0,
),
)
dif = pcr.cover(pcr.scalar(outl), dif) # Add catchment outlet
dif = pcr.ordinal(pcr.uniqueid(pcr.boolean(dif)))
sc = pcr.subcatchment(ldd, dif)
return sc, dif, stt
def initialSecond(self):
""" initial part of the second channel routing module
"""
settings = LisSettings.instance()
option = settings.options
binding = settings.binding
self.var.ChannelAlpha2 = None # default value, if split-routing is not active and only water is routed only in the main channel
# ************************************************************
# ***** CHANNEL INITIAL SPLIT UP IN SECOND CHANNEL************
# ************************************************************
if option['SplitRouting']:
ChanMan2 = (self.var.ChanMan / self.var.CalChanMan) * loadmap('CalChanMan2')
AlpTermChan2 = (ChanMan2 / (np.sqrt(self.var.ChanGrad))) ** self.var.Beta
self.var.ChannelAlpha2 = (AlpTermChan2 * (self.var.ChanWettedPerimeterAlpha ** self.var.AlpPow)).astype(float)
self.var.InvChannelAlpha2 = 1 / self.var.ChannelAlpha2
# calculating second Alpha for second (virtual) channel
if not(option['InitLisflood']):
self.var.QLimit = loadmap('AvgDis') * loadmap('QSplitMult')
# Over bankful discharge starts at QLimit
# lower discharge limit for second line of routing
# set to mutiple of average discharge (map from prerun)
# QSplitMult =2 is around 90 to 95% of Q
self.var.M3Limit = self.var.ChannelAlpha * self.var.ChanLength * (self.var.QLimit ** self.var.Beta)
# Water volume in bankful when over bankful discharge starts
###############################################
# CM mod
# TEMPORARY WORKAROUND FOR EFAS XDOM!!!!!!!!!!
# This must be removed
# QLimit should NOT be dependent on the NoRoutSteps (number of routing steps)
# self.var.QLimit = self.var.QLimit / self.var.NoRoutSteps #original
# TEMPORARY WORKAROUND FOR EFAS XDOM!!!!!!!!!!
# This must be removed
self.var.QLimit = self.var.QLimit / 24.0
###############################################
self.var.Chan2M3Start = self.var.ChannelAlpha2 * self.var.ChanLength * (self.var.QLimit ** self.var.Beta)
# virtual amount of water in the channel through second line
self.var.Chan2QStart = self.var.QLimit - compressArray(upstream(self.var.LddKinematic, decompress(self.var.QLimit)))
# because kinematic routing with a low amount of discharge leads to long travel time:
# Starting Q for second line is set to a higher value
self.var.Chan2M3Kin = self.var.CrossSection2Area * self.var.ChanLength + self.var.Chan2M3Start
self.var.ChanM3Kin = self.var.ChanM3 - self.var.Chan2M3Kin + self.var.Chan2M3Start
self.var.Chan2QKin = (self.var.Chan2M3Kin*self.var.InvChanLength*self.var.InvChannelAlpha2)**(self.var.InvBeta)
self.var.ChanQKin = (self.var.ChanM3Kin*self.var.InvChanLength*self.var.InvChannelAlpha)**(self.var.InvBeta)
# Initialise parallel kinematic wave router: main channel-only routing if self.var.ChannelAlpha2 is None; else split-routing(main channel + floodplains)
maskinfo = MaskInfo.instance()
self.river_router = kinematicWave(compressArray(self.var.LddKinematic), ~maskinfo.info.mask, self.var.ChannelAlpha,
self.var.Beta, self.var.ChanLength, self.var.DtRouting,
int(binding["numCPUs_parallelKinematicWave"]), alpha_floodplains=self.var.ChannelAlpha2)
def lateralFlow(self):
self.calculateLateralFlow()
self.soilMoistureThick = self.soilMoistureThick - self.lateralFlowFluxAmount + pcr.upstream(
self.ldd, self.lateralFlowFluxAmount)
self.upwardSeepageAmount = pcr.max(
pcr.scalar(0.0), self.soilMoistureThick - self.soilPorosityThick)
self.soilMoistureThick = pcr.min(self.soilMoistureThick,
self.soilPorosityThick)
self.upwardSeepageFlux = self.amountToFlux(self.upwardSeepageAmount)
self.totalUpwardSeepageInUpstreamArea()
self.totalSoilMoistureThickInUpstreamArea()
self.totalSoilMoistureFractionInUpstreamArea()
self.totalSaturatedThickInUpstreamArea()
return self.upwardSeepageFlux
os.path.join(mapsDir,'channel_width.map'),os.path.join(pathIniSource,'qavg_longterm.map'),\
os.path.join(pathIniSource,'qbank_longterm.map'),LDDMap,os.path.join(tempDir,'reservoirparameters_%d_30min_.tbl' % initYear),timeSec,clippedRead)
#-create initial files if the routing model is initialized
if initializeRoutingModel:
for variable in resStackList[iniOutVarStart:addOutVarStart]:
targetFileName= os.path.join(pathIniSource,pcrm.generateNameT(variable,0).replace('.000','.ini'))
if 'qc' in variable:
pcr.report(pcr.scalar(0),targetFileName)
elif 'wst'in variable:
waterBodiesType= pcr.nominal(waterBodies.returnMapValue(pcr.spatial(pcr.scalar(0.)),waterBodies.type))
endorheicLakes= pcr.ifthenelse((pcr.areatotal(pcr.scalar(waterBodies.outlet != 0),waterBodies.distribution) == 0) & \
(waterBodies.distribution != 0),waterBodies.distribution,0)
#-storage of water bodies is assigned at outlets
#-storage over endorheic lakes
actualStorage= pcr.ifthen(endorheicLakes != 0,\
pcr.upstream(LDD,pcr.ifthenelse((endorheicLakes == 0) & ((waterBodies.distribution == 0) | (waterBodies.outlet != 0)),\
averageQ,0)))
actualStorage= pcr.ifthen((waterBodies.location != 0) & (endorheicLakes != 0),\
pcr.max(0.,pcr.areatotal(pcr.cover(actualStorage*timeSec*365.25+averageQWat,0),endorheicLakes)))
#-storage over other water bodies
actualStorage= pcr.cover(actualStorage,\
pcr.ifthen((waterBodiesType == 2) & (waterBodies.location != 0),\
waterBodies.returnMapValue(pcr.spatial(pcr.scalar(0.)),waterBodies.capacity*waterBodies.maxLimit)),\
pcr.ifthen((waterBodiesType == 1) & (waterBodies.location != 0),\
pcr.areatotal(fractionWater*cellArea,waterBodies.distribution)*(averageQ/(waterBodies.cLake*channelWidth))**(2./3.)))
actualStorage= pcr.ifthen(waterBodies.distribution != 0,pcr.cover(actualStorage,0))
#-storage over rivers
actualStorage= pcr.cover(actualStorage,channelLength* (channelManN*averageQ*channelWidth**(2./3.)*channelGradient**-0.5 )**0.6)
pcr.report(actualStorage,targetFileName)
#-initial conditions created, execute the followingl until exitCode becomes true
# set run counter and completion identifier
runCnt= 0
def subcatch_stream(
ldd,
threshold,
min_strahler=-999,
max_strahler=999,
assign_edge=False,
assign_existing=False,
up_area=None,
):
"""
(From Deltares Hydrotools)
Derive catchments based upon strahler threshold
Input:
ldd -- pcraster object direction, local drain directions
threshold -- integer, strahler threshold, subcatchments ge threshold
are derived
min_strahler -- integer, minimum strahler threshold of river catchments
to return
max_strahler -- integer, maximum strahler threshold of river catchments
to return
assign_unique=False -- if set to True, unassigned connected areas at
the edges of the domain are assigned a unique id as well. If set
to False, edges are not assigned
assign_existing=False == if set to True, unassigned edges are assigned
to existing basins with an upstream weighting. If set to False,
edges are assigned to unique IDs, or not assigned
output:
stream_ge -- pcraster object, streams of strahler order ge threshold
subcatch -- pcraster object, subcatchments of strahler order ge threshold
"""
# derive stream order
stream = pcr.streamorder(ldd)
stream_ge = pcr.ifthen(stream >= threshold, stream)
stream_up_sum = pcr.ordinal(pcr.upstream(ldd, pcr.cover(pcr.scalar(stream_ge), 0)))
# detect any transfer of strahler order, to a higher strahler order.
transition_strahler = pcr.ifthenelse(
pcr.downstream(ldd, stream_ge) != stream_ge,
pcr.boolean(1),
pcr.ifthenelse(
pcr.nominal(ldd) == 5,
pcr.boolean(1),
pcr.ifthenelse(
pcr.downstream(ldd, pcr.scalar(stream_up_sum)) > pcr.scalar(stream_ge),
pcr.boolean(1),
pcr.boolean(0),
),
),
)
# make unique ids (write to file)
transition_unique = pcr.ordinal(pcr.uniqueid(transition_strahler))
# derive upstream catchment areas (write to file)
subcatch = pcr.nominal(pcr.subcatchment(ldd, transition_unique))
if assign_edge:
# fill unclassified areas (in pcraster equal to zero) with a unique id, above the maximum id assigned so far
unique_edge = pcr.clump(pcr.ifthen(subcatch == 0, pcr.ordinal(0)))
subcatch = pcr.ifthenelse(
subcatch == 0,
pcr.nominal(pcr.mapmaximum(pcr.scalar(subcatch)) + pcr.scalar(unique_edge)),
pcr.nominal(subcatch),
)
elif assign_existing:
# unaccounted areas are added to largest nearest draining basin
if up_area is None:
up_area = pcr.ifthen(
pcr.boolean(pcr.cover(stream_ge, 0)), pcr.accuflux(ldd, 1)
)
riverid = pcr.ifthen(pcr.boolean(pcr.cover(stream_ge, 0)), subcatch)
friction = 1.0 / pcr.scalar(
pcr.spreadzone(pcr.cover(pcr.ordinal(up_area), 0), 0, 0)
) # *(pcr.scalar(ldd)*0+1)
delta = pcr.ifthen(
pcr.scalar(ldd) >= 0,
pcr.ifthen(
pcr.cover(subcatch, 0) == 0,
pcr.spreadzone(pcr.cover(riverid, 0), 0, friction),
),
)
subcatch = pcr.ifthenelse(pcr.boolean(pcr.cover(subcatch, 0)), subcatch, delta)
# finally, only keep basins with minimum and maximum river order flowing through them
strahler_subcatch = pcr.areamaximum(stream, subcatch)
subcatch = pcr.ifthen(
pcr.ordinal(strahler_subcatch) >= min_strahler,
pcr.ifthen(pcr.ordinal(strahler_subcatch) <= max_strahler, subcatch),
)
return stream_ge, pcr.ordinal(subcatch)
if not keepall:
catchments = pcr.nominal(pcr.ifthen(pcr.mapmaximum(pcr.areatotal(pcr.scalar(catchments)*0+1,pcr.nominal(catchments))) == pcr.areatotal(pcr.scalar(catchments)*0+1,pcr.nominal(catchments)),catchments))
pcr.report(ldd,ldd_map)
pcr.report(streamorder,streamorder_map)
pcr.report(river,river_map)
pcr.report(catchments,catchments_map)
if not EPSG == None:
call(('gdal_translate','-of','GTiff','-stats','-a_srs',EPSG,'-ot','Float32',catchments_map,catchments_tif))
else: call(('gdal_translate','-of','GTiff','-stats','-ot','Float32',catchments_map,catchments_tif))
wt.Raster2Pol(catchments_tif,catchshp,srs)
riversid_map = workdir + 'riverid.map'
drain_map = workdir + 'drain.map'
ldd_mask = pcr.ifthen(river, ldd)
upstream = pcr.upstream(ldd_mask, pcr.scalar(river))
downstream = pcr.downstream(ldd_mask, upstream)
#pcr.report(downstream,'downstream.map')
confluences = pcr.boolean(pcr.ifthen(downstream >= 2, pcr.boolean(1)))
#pcr.report(confluences,'confluences.map')
boundaries = pcr.boolean(pcr.ifthen(pcr.scalar(ldd_mask) == 5, pcr.boolean(1)))
catch_points = pcr.nominal(pcr.uniqueid(pcr.cover(confluences,boundaries)))
catchmentsid = pcr.nominal(pcr.subcatchment(ldd, catch_points))
drain = pcr.accuflux(ldd_mask, 1)
riversid = pcr.ifthen(river, catchmentsid)
if not keepall:
riversid = pcr.nominal(pcr.ifthen(pcr.mapmaximum(pcr.areatotal(pcr.scalar(catchments)*0+1,pcr.nominal(catchments))) == pcr.areatotal(pcr.scalar(catchments)*0+1,pcr.nominal(catchments)),riversid))
pcr.report(riversid,riversid_map)
pcr.report(drain,drain_map)
def initial(self):
""" initial part of the water abstraction module
"""
# self.testmap=windowaverage(self.var.Elevation,5)
# self.report(self.testmap,"test.map")
# ************************************************************
# ***** WATER USE
# ************************************************************
settings = LisSettings.instance()
option = settings.options
binding = settings.binding
maskinfo = MaskInfo.instance()
if option['wateruse']:
self.var.WUsePercRemain = loadmap('WUsePercRemain')
self.var.NoWaterUseSteps = int(loadmap('maxNoWateruse'))
self.var.GroundwaterBodies = loadmap('GroundwaterBodies')
self.var.FractionGroundwaterUsed = np.minimum(
np.maximum(loadmap('FractionGroundwaterUsed'),
maskinfo.in_zero()), 1.0)
self.var.FractionNonConventionalWaterUsed = loadmap(
'FractionNonConventionalWaterUsed')
self.var.FractionLakeReservoirWaterUsed = loadmap(
'FractionLakeReservoirWaterUsed')
self.var.EFlowThreshold = loadmap('EFlowThreshold')
# EFlowThreshold is map with m3/s discharge, e.g. the 10th percentile discharge of the baseline run
self.var.WUseRegionC = loadmap('WUseRegion').astype(int)
self.var.IrrigationMult = loadmap('IrrigationMult')
# ************************************************************
# ***** water use constant maps ******************************
# ************************************************************
self.var.IndustryConsumptiveUseFraction = loadmap(
'IndustryConsumptiveUseFraction')
# fraction (0-1)
self.var.WaterReUseFraction = loadmap('WaterReUseFraction')
# fraction of water re-used (0-1)
self.var.EnergyConsumptiveUseFraction = loadmap(
'EnergyConsumptiveUseFraction')
# fraction (0-1), value depends on cooling technology of power plants
self.var.LivestockConsumptiveUseFraction = loadmap(
'LivestockConsumptiveUseFraction')
# fraction (0-1)
self.var.LeakageFraction = np.minimum(
np.maximum(
loadmap('LeakageFraction') *
(1 - loadmap('LeakageReductionFraction')),
maskinfo.in_zero()), 1.0)
self.var.DomesticLeakageConstant = np.minimum(
np.maximum(1 / (1 - self.var.LeakageFraction),
maskinfo.in_zero()), 1.0)
# Domestic Water Abstraction becomes larger in case of leakage
# LeakageFraction is LeakageFraction (0-1) multiplied by reduction scenario (10% reduction is 0.1 in map)
# 0.65 leakage and 0.1 reduction leads to 0.585 effective leakage, resulting in 2.41 times more water abstraction
self.var.DomesticWaterSavingConstant = np.minimum(
np.maximum(1 - loadmap('WaterSavingFraction'),
maskinfo.in_zero()), 1.0)
# Domestic water saving if in place, changes this value from 1 to a value between 0 and 1, and will reduce demand and abstraction
# so value = 0.9 if WaterSavingFraction equals 0.1 (10%)
self.var.DomesticConsumptiveUseFraction = loadmap(
'DomesticConsumptiveUseFraction')
# fraction (0-1), typically rather low ~ 0.10
self.var.LeakageWaterLossFraction = loadmap('LeakageWaterLoss')
# fraction (0-1), 0=no leakage
# Initialize water demand. Read from a static map either value or pcraster map or netcdf (single or stack).
# If reading from NetCDF stack, get time step corresponding to model step.
# Added management for sub-daily modelling time steps
# Added possibility to use one single average year to be repeated during the simulation
if option['useWaterDemandAveYear']:
# CM: using one water demand average year throughout the model simulation
self.var.DomesticDemandMM = loadmap(
'DomesticDemandMaps',
timestampflag='closest',
averageyearflag=True) * self.var.DtDay
self.var.IndustrialDemandMM = loadmap(
'IndustrialDemandMaps',
timestampflag='closest',
averageyearflag=True) * self.var.DtDay
self.var.LivestockDemandMM = loadmap(
'LivestockDemandMaps',
timestampflag='closest',
averageyearflag=True) * self.var.DtDay
self.var.EnergyDemandMM = loadmap(
'EnergyDemandMaps',
timestampflag='closest',
averageyearflag=True) * self.var.DtDay
else:
# CM: using information on water demand from NetCDF files
self.var.DomesticDemandMM = loadmap(
'DomesticDemandMaps',
timestampflag='closest') * self.var.DtDay
self.var.IndustrialDemandMM = loadmap(
'IndustrialDemandMaps',
timestampflag='closest') * self.var.DtDay
self.var.LivestockDemandMM = loadmap(
'LivestockDemandMaps',
timestampflag='closest') * self.var.DtDay
self.var.EnergyDemandMM = loadmap(
'EnergyDemandMaps',
timestampflag='closest') * self.var.DtDay
# Check consistency with the reference calendar that is read from the precipitation forcing file (global_modules.zusatz.optionBinding)
if option['TransientWaterDemandChange'] and option[
'readNetcdfStack']:
for k in ('DomesticDemandMaps', 'IndustrialDemandMaps',
'LivestockDemandMaps', 'EnergyDemandMaps'):
with Dataset(binding[k] + '.nc') as nc:
cal_type = get_calendar_type(nc)
if cal_type != binding['calendar_type']:
warnings.warn(
calendar_inconsistency_warning(
binding[k], cal_type,
binding['calendar_type']))
if option['groundwaterSmooth']:
self.var.GroundwaterBodiesPcr = decompress(
self.var.GroundwaterBodies)
self.var.groundwaterCatch = boolean(
decompress((self.var.GroundwaterBodies *
self.var.Catchments).astype(int)))
# nominal(scalar(GroundwaterBodies)*scalar(self.var.Catchments));
# smoothing for groundwater to correct error by using windowtotal, based on groundwater bodies and catchments
self.var.LZSmoothRange = loadmap('LZSmoothRange')
if option['wateruseRegion']:
WUseRegion = nominal(loadmap('WUseRegion', pcr=True))
pitWuse1 = ifthen(self.var.AtLastPoint != 0, boolean(1))
pitWuse1b = ifthen(defined(pitWuse1), WUseRegion)
# use every existing pit in the Ldd and number them by the water regions
# coastal water regions can have more than one pit per water region
pitWuseMax = areamaximum(self.var.UpArea, WUseRegion)
pitWuse2 = ifthen(pitWuseMax == self.var.UpArea, WUseRegion)
# search outlets in the inland water regions by using the maximum upstream area as criterium
pitWuse3 = downstream(self.var.LddStructuresKinematic,
WUseRegion)
pitWuse3b = ifthen(pitWuse3 != WUseRegion, WUseRegion)
# search point where ldd leaves a water region
pitWuse = cover(pitWuse1b, pitWuse2, pitWuse3b, nominal(0))
# join all sources of pits
LddWaterRegion = lddrepair(
ifthenelse(pitWuse == 0, self.var.LddStructuresKinematic,
5))
# create a Ldd with pits at every water region outlet
# this results in a interrupted ldd, so water cannot be transfered to the next water region
lddC = compressArray(LddWaterRegion)
inAr = decompress(
np.arange(maskinfo.info.mapC[0], dtype="int32"))
# giving a number to each non missing pixel as id
self.var.downWRegion = (compressArray(
downstream(LddWaterRegion, inAr))).astype(np.int32)
# each upstream pixel gets the id of the downstream pixel
self.var.downWRegion[lddC == 5] = maskinfo.info.mapC[0]
# all pits gets a high number
# ************************************************************
# ***** OUTFLOW AND INFLOW POINTS FOR WATER REGIONS **********
# ************************************************************
self.var.WaterRegionOutflowPoints = ifthen(
pitWuse != 0, boolean(1))
# outflowpoints to calculate upstream inflow for balances and Water Exploitation Index
# both inland outflowpoints to downstream subbasin, and coastal outlets
WaterRegionInflow1 = boolean(
upstream(
self.var.LddStructuresKinematic,
cover(scalar(self.var.WaterRegionOutflowPoints), 0)))
self.var.WaterRegionInflowPoints = ifthen(
WaterRegionInflow1, boolean(1))
# inflowpoints to calculate upstream inflow for balances and Water Exploitation Index
else:
self.var.downWRegion = self.var.downstruct.copy()
self.var.downWRegion = self.var.downWRegion.astype(np.int32)
# ************************************************************
# ***** Initialising cumulative output variables *************
# ************************************************************
# These are all needed to compute the cumulative mass balance error
self.var.wateruseCum = maskinfo.in_zero()
# water use cumulated amount
self.var.WUseAddM3Dt = maskinfo.in_zero()
self.var.WUseAddM3 = maskinfo.in_zero()
self.var.IrriLossCUM = maskinfo.in_zero()
# Cumulative irrigation loss [mm]
# Cumulative abstraction from surface water [mm]
self.var.TotalAbstractionFromSurfaceWaterM3 = maskinfo.in_zero()
self.var.TotalAbstractionFromGroundwaterM3 = maskinfo.in_zero()
self.var.TotalIrrigationAbstractionM3 = maskinfo.in_zero()
self.var.TotalPaddyRiceIrrigationAbstractionM3 = maskinfo.in_zero()
self.var.TotalLivestockAbstractionM3 = maskinfo.in_zero()
self.var.IrrigationType = loadmap('IrrigationType')
self.var.IrrigationEfficiency = loadmap('IrrigationEfficiency')
self.var.ConveyanceEfficiency = loadmap('ConveyanceEfficiency')
self.var.GroundwaterRegionPixels = np.take(
np.bincount(self.var.WUseRegionC,
weights=self.var.GroundwaterBodies),
self.var.WUseRegionC)
self.var.AllRegionPixels = np.take(
np.bincount(self.var.WUseRegionC,
weights=self.var.GroundwaterBodies * 0.0 + 1.0),
self.var.WUseRegionC)
self.var.RatioGroundWaterUse = self.var.AllRegionPixels / (
self.var.GroundwaterRegionPixels + 0.01)
self.var.FractionGroundwaterUsed = np.minimum(
self.var.FractionGroundwaterUsed *
self.var.RatioGroundWaterUse,
1 - self.var.FractionNonConventionalWaterUsed)
# FractionGroundwaterUsed is a percentage given at national scale
# since the water needs to come from the GroundwaterBodies pixels,
# the fraction needs correction for the non-Groundwaterbodies; this is done here
self.var.EFlowIndicator = maskinfo.in_zero()
self.var.ReservoirAbstractionM3 = maskinfo.in_zero()
self.var.PotentialSurfaceWaterAvailabilityForIrrigationM3 = maskinfo.in_zero(
)
self.var.LakeAbstractionM3 = maskinfo.in_zero()
self.var.FractionAbstractedFromChannels = maskinfo.in_zero()
self.var.AreatotalIrrigationUseM3 = maskinfo.in_zero()
self.var.totalAddM3 = maskinfo.in_zero()
self.var.TotalDemandM3 = maskinfo.in_zero()
water_body_outlet = pcr.ifthen(\
pcr.areaorder(pcr.scalar( water_body_outlet), \
water_body_outlet) == 1., water_body_outlet)
water_body_outlet = pcr.ifthen(\
pcr.scalar(water_body_outlet) > 0., water_body_outlet)
# calculate overbank volume from reservoirs (and lakes)
lake_reservoir_volume = pcr.areatotal(extreme_value_map, water_body_id)
lake_reservoir_overbank_volume = pcr.cover(
pcr.max(0.0, lake_reservoir_volume - reservoir_capacity), 0.0)
#~ pcr.aguila(lake_reservoir_overbank_volume)
#
# transfer 75% of overbank volume to the downstream (several cells downstream)
transfer_to_downstream = pcr.cover(\
pcr.ifthen(pcr.scalar(water_body_outlet) > 0., lake_reservoir_overbank_volume * 0.50), 0.0)
transfer_to_downstream = pcr.upstream(ldd_map_low_resolution,
transfer_to_downstream)
transfer_to_downstream = pcr.upstream(ldd_map_low_resolution,
transfer_to_downstream)
transfer_to_downstream = pcr.upstream(ldd_map_low_resolution,
transfer_to_downstream)
extreme_value_map = transfer_to_downstream + \
pcr.ifthenelse(pcr.cover(pcr.scalar(water_body_id), 0.0) > 0.00, 0.00, extreme_value_map)
#
# the remaining overbank volume (50%) will be distributed to the shores
lake_reservoir_overbank_volume = lake_reservoir_overbank_volume * 0.50
land_area = cell_area * pcr.max(0.0, 1.0 - fracwat)
land_area_average = pcr.areaaverage(land_area, water_body_id)
land_area_weight = pcr.ifthenelse(land_area < land_area_average, 0.0,
land_area_average)
distributed_lake_reservoir_overbank_volume = pcr.cover(\
lake_reservoir_overbank_volume * land_area_weight / pcr.max(0.00, pcr.areatotal(land_area_weight, water_body_id)), 0.0)
def subcatch_stream(ldd, threshold, stream=None, min_strahler=-999, max_strahler=999, assign_edge=False, assign_existing=False, up_area=None, basin=None):
"""
Derive catchments based upon strahler threshold
Input:
ldd -- pcraster object direction, local drain directions
threshold -- integer, strahler threshold, subcatchments ge threshold
are derived
stream=None -- pcraster object ordinal, stream order map (made with pcr.streamorder), if provided, stream order
map is not generated on the fly but used from this map. Useful when a subdomain within a catchment is
provided, which would cause edge effects in the stream order map
min_strahler=-999 -- integer, minimum strahler threshold of river catchments
to return
max_strahler=999 -- integer, maximum strahler threshold of river catchments
to return
assign_unique=False -- if set to True, unassigned connected areas at
the edges of the domain are assigned a unique id as well. If set
to False, edges are not assigned
assign_existing=False == if set to True, unassigned edges are assigned
to existing basins with an upstream weighting. If set to False,
edges are assigned to unique IDs, or not assigned
output:
stream_ge -- pcraster object, streams of strahler order ge threshold
subcatch -- pcraster object, subcatchments of strahler order ge threshold
"""
# derive stream order
if stream is None:
stream = pcr.streamorder(ldd)
stream_ge = pcr.ifthen(stream >= threshold, stream)
stream_up_sum = pcr.ordinal(pcr.upstream(ldd, pcr.cover(pcr.scalar(stream_ge), 0)))
# detect any transfer of strahler order, to a higher strahler order.
transition_strahler = pcr.ifthenelse(pcr.downstream(ldd, stream_ge) != stream_ge, pcr.boolean(1),
pcr.ifthenelse(pcr.nominal(ldd) == 5, pcr.boolean(1), pcr.ifthenelse(pcr.downstream(ldd, pcr.scalar(stream_up_sum)) > pcr.scalar(stream_ge), pcr.boolean(1),
pcr.boolean(0))))
# make unique ids (write to file)
transition_unique = pcr.ordinal(pcr.uniqueid(transition_strahler))
# derive upstream catchment areas (write to file)
subcatch = pcr.nominal(pcr.subcatchment(ldd, transition_unique))
# mask out areas outside basin
if basin is not None:
subcatch = pcr.ifthen(basin, subcatch)
if assign_edge:
# fill unclassified areas (in pcraster equal to zero) with a unique id, above the maximum id assigned so far
unique_edge = pcr.clump(pcr.ifthen(subcatch==0, pcr.ordinal(0)))
subcatch = pcr.ifthenelse(subcatch==0, pcr.nominal(pcr.mapmaximum(pcr.scalar(subcatch)) + pcr.scalar(unique_edge)), pcr.nominal(subcatch))
elif assign_existing:
# unaccounted areas are added to largest nearest draining basin
if up_area is None:
up_area = pcr.ifthen(pcr.boolean(pcr.cover(stream_ge, 0)), pcr.accuflux(ldd, 1))
riverid = pcr.ifthen(pcr.boolean(pcr.cover(stream_ge, 0)), subcatch)
friction = 1./pcr.scalar(pcr.spreadzone(pcr.cover(pcr.ordinal(up_area), 0), 0, 0)) # *(pcr.scalar(ldd)*0+1)
delta = pcr.ifthen(pcr.scalar(ldd)>=0, pcr.ifthen(pcr.cover(subcatch, 0)==0, pcr.spreadzone(pcr.cover(riverid, 0), 0, friction)))
subcatch = pcr.ifthenelse(pcr.boolean(pcr.cover(subcatch, 0)),
subcatch,
delta)
# finally, only keep basins with minimum and maximum river order flowing through them
strahler_subcatch = pcr.areamaximum(stream, subcatch)
subcatch = pcr.ifthen(pcr.ordinal(strahler_subcatch) >= min_strahler, pcr.ifthen(pcr.ordinal(strahler_subcatch) <= max_strahler, subcatch))
return stream_ge, pcr.ordinal(subcatch)
def dynamic(self):
""" dynamic part of the indicator calculation module
"""
settings = LisSettings.instance()
binding = settings.binding
option = settings.options
maskinfo = MaskInfo.instance()
if option['TransientLandUseChange']:
self.var.Population = readnetcdf(binding['PopulationMaps'],
self.var.currentTimeStep())
self.var.RegionPopulation = np.take(
np.bincount(self.var.WUseRegionC, weights=self.var.Population),
self.var.WUseRegionC)
# population sum in Regions
if option['wateruse'] and option['indicator']:
# check if it is the last monthly or annual time step
next_date_time = self.var.CalendarDate + datetime.timedelta(
seconds=int(binding["DtSec"]))
self.var.monthend = next_date_time.month != self.var.CalendarDate.month
self.var.yearend = next_date_time.year != self.var.CalendarDate.year
# sum up every day
self.var.DayCounter = self.var.DayCounter + 1.0
self.var.MonthETpotMM = self.var.MonthETpotMM + self.var.ETRef
self.var.MonthETactMM = self.var.MonthETactMM + self.var.deffraction(
self.var.TaInterception
) + self.var.TaPixel + self.var.ESActPixel
if option['openwaterevapo']:
self.var.MonthETactMM += self.var.EvaAddM3 * self.var.M3toMM
self.var.MonthETdifMM = np.maximum(
(self.var.MonthETpotMM - self.var.MonthETactMM) *
self.var.LandUseMask, maskinfo.in_zero())
# ; land use mask can be used to mask out deserts and high mountains, where no agriculture is possible
self.var.MonthWDemandM3 = self.var.MonthWDemandM3 + self.var.TotalDemandM3
self.var.MonthWAbstractionM3 = self.var.MonthWAbstractionM3 + self.var.TotalAbstractionFromSurfaceWaterM3 + self.var.ReservoirAbstractionM3 + self.var.LakeAbstractionM3 + self.var.TotalAbstractionFromGroundwaterM3
self.var.MonthWConsumptionM3 = self.var.MonthWConsumptionM3 + self.var.WUseAddM3 + self.var.ReservoirAbstractionM3 + self.var.LakeAbstractionM3 + self.var.TotalAbstractionFromGroundwaterM3
self.var.MonthDisM3 = self.var.MonthDisM3 + self.var.ChanQAvg * self.var.DtSec
self.var.MonthWaterAbstractedfromLakesReservoirsM3 = self.var.MonthWaterAbstractedfromLakesReservoirsM3 + self.var.ReservoirAbstractionM3 + self.var.LakeAbstractionM3
self.var.RegionMonthIrrigationShortageM3 = self.var.RegionMonthIrrigationShortageM3 + self.var.AreatotalIrrigationShortageM3
# INTERNAL FLOW
self.var.MonthInternalFlowM3 = self.var.MonthInternalFlowM3 + self.var.ToChanM3Runoff
# --------------------------------------------------------------------------
if self.var.monthend:
# INTERNAL FLOW
if option['simulateReservoirs'] or option['simulateLakes']:
# available LakeStorageM3 and ReservoirStorageM3 for potential abstraction at end of month in region
self.var.RegionMonthReservoirAndLakeStorageM3 = np.take(
np.bincount(self.var.WUseRegionC,
weights=(self.var.ReservoirStorageM3 +
self.var.LakeStorageM3)),
self.var.WUseRegionC)
# monthly abstraction from lakes and reservoirs
self.var.RegionMonthWaterAbstractedfromLakesReservoirsM3 = np.take(
np.bincount(self.var.WUseRegionC,
weights=self.var.
MonthWaterAbstractedfromLakesReservoirsM3),
self.var.WUseRegionC)
#PerMonthInternalFlowM3 = areatotal(cover(decompress(self.var.MonthInternalFlow),0.0),wreg)
self.var.RegionMonthInternalFlowM3 = np.take(
np.bincount(self.var.WUseRegionC,
weights=self.var.MonthInternalFlowM3),
self.var.WUseRegionC)
# note Reservoir and Lake storage need to be taken into account seperately
# EXTERNAL FLOW
wreg = decompress(self.var.WUseRegionC)
# to pcraster map because of the following expression!
self.var.RegionMonthExternalInflowM3 = compressArray(
areatotal(
cover(
ifthen(
self.var.WaterRegionInflowPoints != 0,
upstream(self.var.LddStructuresKinematic,
decompress(self.var.MonthDisM3))), 0),
wreg))
self.var.RegionMonthWAbstractionM3 = np.take(
np.bincount(self.var.WUseRegionC,
weights=self.var.MonthWAbstractionM3),
self.var.WUseRegionC)
self.var.RegionMonthWConsumptionM3 = np.take(
np.bincount(self.var.WUseRegionC,
weights=self.var.MonthWConsumptionM3),
self.var.WUseRegionC)
self.var.RegionMonthWDemandM3 = np.take(
np.bincount(self.var.WUseRegionC,
weights=self.var.MonthWDemandM3),
self.var.WUseRegionC)
# Calculation of WEI: everything in m3, totalled over the water region
UpstreamInflowM3 = self.var.RegionMonthExternalInflowM3
LocalFreshwaterM3 = self.var.RegionMonthInternalFlowM3
# still to be decided if reservoir and lake water availability is added here
LocalTotalWaterDemandM3 = self.var.RegionMonthWDemandM3
RemainingDemandM3 = np.maximum(
LocalTotalWaterDemandM3 - LocalFreshwaterM3, 0.0)
# this is the demand that cannot be met by local water supply
UpstreamInflowUsedM3 = np.minimum(RemainingDemandM3,
UpstreamInflowM3)
# the amount of upstream water an area really depends on
FossilGroundwaterUsedM3 = np.maximum(
RemainingDemandM3 - UpstreamInflowUsedM3, 0.0)
# the amount of water that cannot be met locally nor with international water
# likely this is the amount of water drawn from deep groundwater
self.var.WEI_Cns = np.where(
(UpstreamInflowM3 + LocalFreshwaterM3) > 0.0,
self.var.RegionMonthWConsumptionM3 /
(UpstreamInflowM3 + LocalFreshwaterM3), 0.0)
self.var.WEI_Abs = np.where(
(UpstreamInflowM3 + LocalFreshwaterM3) > 0.0,
self.var.RegionMonthWAbstractionM3 /
(UpstreamInflowM3 + LocalFreshwaterM3), 0.0)
self.var.WEI_Dem = np.where(
(UpstreamInflowM3 + LocalFreshwaterM3) > 0.0,
self.var.RegionMonthWDemandM3 /
(UpstreamInflowM3 + LocalFreshwaterM3), 0.0)
self.var.WaterSustainabilityIndex = np.where(
LocalTotalWaterDemandM3 > 0.0,
FossilGroundwaterUsedM3 / (LocalTotalWaterDemandM3 + 1),
0.0)
# De Roo 2015: WTI, if above zero, indicates that situation is unsustainable
# if index is 0 means sustainable situtation: no groundwater or desalination water used
# if index is 1 means area relies completely on groundwater or desalination water
# the '+1' is to prevent division by small values, leading to very large and misleading indicator values
self.var.WaterDependencyIndex = np.where(
LocalTotalWaterDemandM3 > 0.0,
UpstreamInflowUsedM3 / (LocalTotalWaterDemandM3 + 1), 0.0)
# De Roo 2015: WDI, dependency on upstreamwater, as a fraction of the total local demand
# the '+1' is to prevent division by small values, leading to very large and misleading indicator values
self.var.WaterSecurityIndex = np.where(
UpstreamInflowM3 > 0.0,
UpstreamInflowUsedM3 / (UpstreamInflowM3 + 1), 0.0)
# De Roo 2015: WSI, indicates the vulnerability to the available upstream water;
# if only 10% of upstream inflow is use, WSI would be 0.1 indicating low vulnerability
# if WSI is close to 1, situation is very vulnerable
# the '+1' is to prevent division by small values, leading to very large and misleading indicator values
self.var.FalkenmarkM3Capita1 = np.where(
self.var.RegionPopulation > 0.0,
self.var.RegionMonthInternalFlowM3 * 12 /
self.var.RegionPopulation, maskinfo.in_zero())
self.var.FalkenmarkM3Capita2 = np.where(
self.var.RegionPopulation > 0.0,
LocalFreshwaterM3 * 12 / self.var.RegionPopulation,
maskinfo.in_zero())
self.var.FalkenmarkM3Capita3 = np.where(
self.var.RegionPopulation > 0.0,
(UpstreamInflowM3 + LocalFreshwaterM3) * 12 /
self.var.RegionPopulation, maskinfo.in_zero())
water_body_outlet = pcr.ifthen(\
pcr.areaorder(pcr.scalar( water_body_outlet), \
water_body_outlet) == 1., water_body_outlet)
water_body_outlet = pcr.ifthen(\
pcr.scalar(water_body_outlet) > 0., water_body_outlet)
# calculate overbank volume from reservoirs (and lakes)
lake_reservoir_volume = pcr.areatotal(extreme_value_map, water_body_id)
lake_reservoir_overbank_volume = pcr.cover(
pcr.max(0.0, lake_reservoir_volume - reservoir_capacity), 0.0)
#~ pcr.aguila(lake_reservoir_overbank_volume)
#
# transfer 75% of overbank volume to the downstream (several cells downstream)
transfer_to_downstream = pcr.cover(\
pcr.ifthen(pcr.scalar(water_body_outlet) > 0., lake_reservoir_overbank_volume * 0.50), 0.0)
transfer_to_downstream = pcr.upstream(ldd_map_low_resolution, transfer_to_downstream)
transfer_to_downstream = pcr.upstream(ldd_map_low_resolution, transfer_to_downstream)
transfer_to_downstream = pcr.upstream(ldd_map_low_resolution, transfer_to_downstream)
extreme_value_map = transfer_to_downstream + \
pcr.ifthenelse(pcr.cover(pcr.scalar(water_body_id), 0.0) > 0.00, 0.00, extreme_value_map)
#
# the remaining overbank volume (50%) will be distributed to the shores
lake_reservoir_overbank_volume = lake_reservoir_overbank_volume * 0.50
land_area = cell_area * pcr.max(0.0, 1.0 - fracwat)
land_area_average = pcr.areaaverage(land_area, water_body_id)
land_area_weight = pcr.ifthenelse( land_area < land_area_average, 0.0, land_area_average)
distributed_lake_reservoir_overbank_volume = pcr.cover(\
lake_reservoir_overbank_volume * land_area_weight / pcr.max(0.00, pcr.areatotal(land_area_weight, water_body_id)), 0.0)
extreme_value_map = pcr.ifthenelse(pcr.cover(pcr.scalar(water_body_id), 0.0) > 0.00, distributed_lake_reservoir_overbank_volume, extreme_value_map)
#
# - cover the rests to zero (so they will not contribute to any flood/inundation)
def dynamic(self):
#####################
# * dynamic section #
#####################
#-evaluation of the current date: return current month and the time step used
#-reading in fluxes over land and water area for current time step [m/d]
# and read in reservoir demand and surface water extraction [m3]
try:
self.landSurfaceQ = clippedRead.get(
pcrm.generateNameT(landSurfaceQFileName,
self.currentTimeStep()))
except:
pass
try:
self.potWaterSurfaceQ = clippedRead.get(
pcrm.generateNameT(waterSurfaceQFileName,
self.currentTimeStep()))
except:
pass
#-surface water extraction and reservoir demand currently set to zero, should
# be computed automatically and updated to reservoirs
self.potSurfaceWaterExtraction = pcr.spatial(pcr.scalar(0.))
#self.waterBodies.demand= #self.reservoirDemandTSS.assignID(self.waterBodies.ID,self.currentTimeStep(),0.)*self.timeSec
#-initialization of cumulative values of actual water extractions
self.actWaterSurfaceQ = pcr.spatial(pcr.scalar(0.))
self.actSurfaceWaterExtraction = pcr.spatial(pcr.scalar(0.))
#-definition of sub-loop for routing scheme - explicit scheme has to satisfy Courant condition
timeLimit= pcr.cellvalue(pcr.mapminimum((pcr.cover(pcr.ifthen(self.waterBodies.distribution == 0,\
self.channelLength/self.flowVelocity),\
self.timeSec/self.nrIterDefault)*self.timeSec/self.nrIterDefault)**0.5),1)[0]
nrIter = int(self.timeSec / timeLimit)
nrIter = min(nrIter, int(self.timeSec / 300.))
while float(self.timeSec / nrIter) % 1 <> 0:
nrIter += 1
deltaTime = self.timeSec / nrIter
#-sub-loop for current time step
if self.currentDate.day == 1 or nrIter >= 24:
print '\n*\tprocessing %s, currently using %d substeps of %d seconds\n' % \
(self.currentDate.date(),nrIter,deltaTime)
#-update discharge and storage
for nrICur in range(nrIter):
#-initializing discharge for the current sub-timestep and fill in values
# for channels and at outlets of waterbodies
# * channels *
estQ= pcr.ifthenelse((self.actualStorage > 0.) & (self.waterBodies.distribution == 0) ,\
(self.wettedArea/self.alphaQ)**(1./self.betaQ),0.)
#estQ= pcr.ifthenelse((self.actualStorage > 0.) & (self.waterBodies.distribution == 0) ,\
#0.5*(self.Q+(self.wettedArea/self.alphaQ)**(1./self.betaQ)),0.)
#estQ= pcr.min(estQ,self.actualStorage/deltaTime)
self.report(estQ, 'results/qest')
self.Q = pcr.spatial(pcr.scalar(0.))
self.Q= pcr.ifthenelse(self.waterBodies.distribution == 0,\
pcr.kinematic(self.channelLDD,estQ,0.,self.alphaQ,\
self.betaQ,1,deltaTime,self.channelLength),self.Q)
# * water bodies *
self.waterBodies.dischargeUpdate()
self.Q = self.waterBodies.returnMapValue(self.Q,
self.waterBodies.actualQ)
#-fluxes and resulting change in storage: first the local fluxes are evaluated
# and aggregated over the water bodies where applicable; this includes the specific runoff [m/day/m2]
# from input and the estimated extraction from surface water as volume per day [m3/day];
# specific runoff from the land surface is always positive whereas the fluxes over the water surface
# are potential, including discharge, and are adjusted to match the availabe storage; to this end,
# surface water storage and fluxes over water bodies are totalized and assigned to the outlet;
# discharge is updated in a separate step, after vertical fluxes are compared to the actual storage
deltaActualStorage= ((self.landFraction*self.landSurfaceQ+\
self.waterFraction*self.potWaterSurfaceQ)*self.cellArea-\
self.potSurfaceWaterExtraction)*float(self.duration)/nrIter
deltaActualStorage= pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.ifthenelse(self.waterBodies.location != 0,\
pcr.areatotal(deltaActualStorage,self.waterBodies.distribution),0),\
deltaActualStorage)
adjustmentRatio= pcr.ifthenelse(deltaActualStorage < 0.,\
pcr.min(1.,-self.actualStorage/deltaActualStorage),1.)
self.actWaterSurfaceQ += adjustmentRatio * self.potWaterSurfaceQ
self.actSurfaceWaterExtraction += adjustmentRatio * self.actSurfaceWaterExtraction
deltaActualStorage *= adjustmentRatio
#-local water balance check
if testLocalWaterBalance:
differenceActualStorage = self.actualStorage
differenceActualStorage += deltaActualStorage
#-overall water balance check: net input
self.cumulativeDeltaStorage += pcr.catchmenttotal(
deltaActualStorage, self.LDD)
#-update storage first with local changes, then balance discharge with storage and update storage
# with lateral flow and return value to water bodies
self.actualStorage += deltaActualStorage
self.actualStorage = pcr.max(0., self.actualStorage)
self.Q = pcr.min(self.Q, self.actualStorage / deltaTime)
deltaActualStorage = (-self.Q +
pcr.upstream(self.LDD, self.Q)) * deltaTime
deltaActualStorage= pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.ifthenelse(self.waterBodies.location != 0,\
pcr.areatotal(deltaActualStorage,self.waterBodies.distribution),0),\
deltaActualStorage)
self.actualStorage += deltaActualStorage
self.actualStorage = pcr.max(0., self.actualStorage)
self.waterBodies.actualStorage = self.waterBodies.retrieveMapValue(
self.actualStorage)
#-flooded fraction returned
floodedFraction,floodedDepth,\
self.wettedArea,self.alphaQ= self.kinAlphaComposite(self.actualStorage,self.floodplainMask)
self.wettedArea= self.waterBodies.returnMapValue(self.wettedArea,\
self.waterBodies.channelWidth+2.*self.waterBodies.updateWaterHeight())
self.waterFraction= pcr.ifthenelse(self.waterBodies.distribution == 0,\
pcr.max(self.waterFractionMask,floodedFraction),self.waterFractionMask)
self.landFraction = pcr.max(0., 1. - self.waterFraction)
self.flowVelocity = pcr.ifthenelse(self.wettedArea > 0,
self.Q / self.wettedArea, 0.)
#-local water balance check
if testLocalWaterBalance:
differenceActualStorage += deltaActualStorage
differenceActualStorage -= self.actualStorage
totalDifference = pcr.cellvalue(
pcr.maptotal(differenceActualStorage), 1)[0]
minimumDifference = pcr.cellvalue(
pcr.mapminimum(differenceActualStorage), 1)[0]
maximumDifference = pcr.cellvalue(
pcr.mapmaximum(differenceActualStorage), 1)[0]
if abs(totalDifference) > 1.e-3:
print 'water balance error: total %e; min %e; max %e' %\
(totalDifference,minimumDifference,maximumDifference)
if reportLocalWaterBalance:
pcr.report(differenceActualStorage,
'mbe_%s.map' % self.currentDate.date())
#-overall water balance check: updating cumulative discharge and total storage [m3]
self.totalDischarge += self.Q * deltaTime
self.totalStorage = pcr.catchmenttotal(self.actualStorage,
self.LDD)
#-check on occurrence of last day and report mass balance
if self.currentDate == self.endDate:
#-report initial maps
pcr.report(self.Q, self.QIniMap)
pcr.report(self.actualStorage, self.actualStorageIniMap)
#-return relative and absolute water balance error per cell and
# as total at basin outlets
self.totalDischarge= pcr.ifthen((self.waterBodies.distribution == 0) | \
(self.waterBodies.location != 0),self.totalDischarge)
self.cumulativeDeltaStorage= pcr.ifthen((self.waterBodies.distribution == 0) | \
(self.waterBodies.location != 0),self.cumulativeDeltaStorage)
massBalanceError= self.totalStorage+self.totalDischarge-\
self.cumulativeDeltaStorage
relMassBalanceError = 1. + pcr.ifthenelse(
self.cumulativeDeltaStorage <> 0.,
massBalanceError / self.cumulativeDeltaStorage, 0.)
totalMassBalanceError= pcr.cellvalue(pcr.maptotal(pcr.ifthen(self.basinOutlet,\
massBalanceError)),1)[0]
totalCumulativeDeltaStorage= pcr.cellvalue(pcr.maptotal(pcr.ifthen(self.basinOutlet,\
self.cumulativeDeltaStorage)),1)[0]
if totalCumulativeDeltaStorage > 0:
totalRelativeMassBalanceError = 1. + totalMassBalanceError / totalCumulativeDeltaStorage
else:
totalRelativeMassBalanceError = 1.
#-report maps and echo value
pcr.report(massBalanceError, mbeFileName)
pcr.report(relMassBalanceError, mbrFileName)
print '\n*\ttotal global mass balance error [m3]: %8.3g' % totalMassBalanceError
print '\n*\trelative global mass balance error [-]: %5.3f' % totalRelativeMassBalanceError
#-echo to screen: total mass balance error and completion of run
print '\trun completed'
#-end of day: return states and fluxes
#-get surface water attributes?
if getSurfaceWaterAttributes:
#-compute the following secondary variables:
# surface water area [m2]: area given dynamic surface water fraction
# residence time [days]: volume over discharge, assigned -1 in case discharge is zero
# surface water depth [m], weighed by channel and floodplain volume
surfaceWaterArea = self.waterFraction * self.cellArea
surfaceWaterArea= pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.ifthenelse(self.waterBodies.location != 0,\
pcr.areatotal(surfaceWaterArea,self.waterBodies.distribution),0),\
surfaceWaterArea)
surfaceWaterResidenceTime = pcr.ifthenelse(
self.Q > 0., self.actualStorage / (self.Q * self.timeSec), -1)
surfaceWaterDepth= pcr.ifthenelse(self.actualStorage > 0.,\
pcr.max(0.,self.actualStorage-self.channelStorageCapacity)**2/\
(self.actualStorage*surfaceWaterArea),0.)
surfaceWaterDepth+= pcr.ifthenelse(self.actualStorage > 0.,\
pcr.min(self.channelStorageCapacity,self.actualStorage)**2/(self.waterFractionMask*\
self.cellArea*self.actualStorage),0.)
#-reports: values at outlet of lakes or reservoirs are assigned to their full extent
self.report(pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.areamaximum(surfaceWaterArea,self.waterBodies.distribution),surfaceWaterArea),\
surfaceWaterAreaFileName)
self.report(pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.areamaximum(surfaceWaterResidenceTime,self.waterBodies.distribution),surfaceWaterResidenceTime),\
surfaceWaterResidenceTimeFileName)
self.report(pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.areamaximum(surfaceWaterDepth,self.waterBodies.distribution),surfaceWaterDepth),\
surfaceWaterDepthFileName)
#-reports on standard output: values at outlet of lakes or reservoirs are assigned to their full extent
self.report(
pcr.ifthenelse(
self.waterBodies.distribution != 0,
pcr.areamaximum(self.flowVelocity,
self.waterBodies.distribution),
self.flowVelocity), flowVelocityFileName)
self.report(
pcr.ifthenelse(
self.waterBodies.distribution != 0,
pcr.areamaximum(self.Q, self.waterBodies.distribution),
self.Q), QFileName)
self.report(pcr.ifthenelse(self.waterBodies.distribution == 0,\
floodedFraction,0.),floodedFractionFileName)
self.report(pcr.ifthenelse(self.waterBodies.distribution == 0,\
floodedDepth,0.),floodedDepthFileName)
self.report(self.actualStorage, actualStorageFileName)
#-update date for time step and report relevant daily output
self.currentDate = self.currentDate + datetime.timedelta(self.duration)
def dynamic(self): ##################### # * dynamic section # ##################### #-evaluation of the current date: return current month and the time step used #-reading in fluxes over land and water area for current time step [m/d] # and read in reservoir demand and surface water extraction [m3] try: self.landSurfaceQ= clippedRead.get(pcrm.generateNameT(landSurfaceQFileName,self.currentTimeStep())) except: pass try: self.potWaterSurfaceQ= clippedRead.get(pcrm.generateNameT(waterSurfaceQFileName,self.currentTimeStep())) except: pass #-surface water extraction and reservoir demand currently set to zero, should # be computed automatically and updated to reservoirs self.potSurfaceWaterExtraction= pcr.spatial(pcr.scalar(0.)) #self.waterBodies.demand= #self.reservoirDemandTSS.assignID(self.waterBodies.ID,self.currentTimeStep(),0.)*self.timeSec #-initialization of cumulative values of actual water extractions self.actWaterSurfaceQ= pcr.spatial(pcr.scalar(0.)) self.actSurfaceWaterExtraction= pcr.spatial(pcr.scalar(0.)) #-definition of sub-loop for routing scheme - explicit scheme has to satisfy Courant condition timeLimit= pcr.cellvalue(pcr.mapminimum((pcr.cover(pcr.ifthen(self.waterBodies.distribution == 0,\ self.channelLength/self.flowVelocity),\ self.timeSec/self.nrIterDefault)*self.timeSec/self.nrIterDefault)**0.5),1)[0] nrIter= int(self.timeSec/timeLimit) nrIter= min(nrIter,int(self.timeSec/300.)) while float(self.timeSec/nrIter) % 1 <> 0: nrIter+= 1 deltaTime= self.timeSec/nrIter #-sub-loop for current time step if self.currentDate.day == 1 or nrIter >= 24: print '\n*\tprocessing %s, currently using %d substeps of %d seconds\n' % \ (self.currentDate.date(),nrIter,deltaTime) #-update discharge and storage for nrICur in range(nrIter): #-initializing discharge for the current sub-timestep and fill in values # for channels and at outlets of waterbodies # * channels * estQ= pcr.ifthenelse((self.actualStorage > 0.) & (self.waterBodies.distribution == 0) ,\ (self.wettedArea/self.alphaQ)**(1./self.betaQ),0.) #estQ= pcr.ifthenelse((self.actualStorage > 0.) & (self.waterBodies.distribution == 0) ,\ #0.5*(self.Q+(self.wettedArea/self.alphaQ)**(1./self.betaQ)),0.) #estQ= pcr.min(estQ,self.actualStorage/deltaTime) self.report(estQ,'results/qest') self.Q= pcr.spatial(pcr.scalar(0.)) self.Q= pcr.ifthenelse(self.waterBodies.distribution == 0,\ pcr.kinematic(self.channelLDD,estQ,0.,self.alphaQ,\ self.betaQ,1,deltaTime,self.channelLength),self.Q) # * water bodies * self.waterBodies.dischargeUpdate() self.Q= self.waterBodies.returnMapValue(self.Q,self.waterBodies.actualQ) #-fluxes and resulting change in storage: first the local fluxes are evaluated # and aggregated over the water bodies where applicable; this includes the specific runoff [m/day/m2] # from input and the estimated extraction from surface water as volume per day [m3/day]; # specific runoff from the land surface is always positive whereas the fluxes over the water surface # are potential, including discharge, and are adjusted to match the availabe storage; to this end, # surface water storage and fluxes over water bodies are totalized and assigned to the outlet; # discharge is updated in a separate step, after vertical fluxes are compared to the actual storage deltaActualStorage= ((self.landFraction*self.landSurfaceQ+\ self.waterFraction*self.potWaterSurfaceQ)*self.cellArea-\ self.potSurfaceWaterExtraction)*float(self.duration)/nrIter deltaActualStorage= pcr.ifthenelse(self.waterBodies.distribution != 0,\ pcr.ifthenelse(self.waterBodies.location != 0,\ pcr.areatotal(deltaActualStorage,self.waterBodies.distribution),0),\ deltaActualStorage) adjustmentRatio= pcr.ifthenelse(deltaActualStorage < 0.,\ pcr.min(1.,-self.actualStorage/deltaActualStorage),1.) self.actWaterSurfaceQ+= adjustmentRatio*self.potWaterSurfaceQ self.actSurfaceWaterExtraction+= adjustmentRatio*self.actSurfaceWaterExtraction deltaActualStorage*= adjustmentRatio #-local water balance check if testLocalWaterBalance: differenceActualStorage= self.actualStorage differenceActualStorage+= deltaActualStorage #-overall water balance check: net input self.cumulativeDeltaStorage+= pcr.catchmenttotal(deltaActualStorage,self.LDD) #-update storage first with local changes, then balance discharge with storage and update storage # with lateral flow and return value to water bodies self.actualStorage+= deltaActualStorage self.actualStorage= pcr.max(0.,self.actualStorage) self.Q= pcr.min(self.Q,self.actualStorage/deltaTime) deltaActualStorage= (-self.Q+pcr.upstream(self.LDD,self.Q))*deltaTime deltaActualStorage= pcr.ifthenelse(self.waterBodies.distribution != 0,\ pcr.ifthenelse(self.waterBodies.location != 0,\ pcr.areatotal(deltaActualStorage,self.waterBodies.distribution),0),\ deltaActualStorage) self.actualStorage+= deltaActualStorage self.actualStorage= pcr.max(0.,self.actualStorage) self.waterBodies.actualStorage= self.waterBodies.retrieveMapValue(self.actualStorage) #-flooded fraction returned floodedFraction,floodedDepth,\ self.wettedArea,self.alphaQ= self.kinAlphaComposite(self.actualStorage,self.floodplainMask) self.wettedArea= self.waterBodies.returnMapValue(self.wettedArea,\ self.waterBodies.channelWidth+2.*self.waterBodies.updateWaterHeight()) self.waterFraction= pcr.ifthenelse(self.waterBodies.distribution == 0,\ pcr.max(self.waterFractionMask,floodedFraction),self.waterFractionMask) self.landFraction= pcr.max(0.,1.-self.waterFraction) self.flowVelocity= pcr.ifthenelse(self.wettedArea > 0,self.Q/self.wettedArea,0.) #-local water balance check if testLocalWaterBalance: differenceActualStorage+= deltaActualStorage differenceActualStorage-= self.actualStorage totalDifference= pcr.cellvalue(pcr.maptotal(differenceActualStorage),1)[0] minimumDifference= pcr.cellvalue(pcr.mapminimum(differenceActualStorage),1)[0] maximumDifference= pcr.cellvalue(pcr.mapmaximum(differenceActualStorage),1)[0] if abs(totalDifference) > 1.e-3: print 'water balance error: total %e; min %e; max %e' %\ (totalDifference,minimumDifference,maximumDifference) if reportLocalWaterBalance: pcr.report(differenceActualStorage,'mbe_%s.map' % self.currentDate.date()) #-overall water balance check: updating cumulative discharge and total storage [m3] self.totalDischarge+= self.Q*deltaTime self.totalStorage= pcr.catchmenttotal(self.actualStorage,self.LDD) #-check on occurrence of last day and report mass balance if self.currentDate == self.endDate: #-report initial maps pcr.report(self.Q,self.QIniMap) pcr.report(self.actualStorage,self.actualStorageIniMap) #-return relative and absolute water balance error per cell and # as total at basin outlets self.totalDischarge= pcr.ifthen((self.waterBodies.distribution == 0) | \ (self.waterBodies.location != 0),self.totalDischarge) self.cumulativeDeltaStorage= pcr.ifthen((self.waterBodies.distribution == 0) | \ (self.waterBodies.location != 0),self.cumulativeDeltaStorage) massBalanceError= self.totalStorage+self.totalDischarge-\ self.cumulativeDeltaStorage relMassBalanceError= 1.+pcr.ifthenelse(self.cumulativeDeltaStorage <> 0., massBalanceError/self.cumulativeDeltaStorage,0.) totalMassBalanceError= pcr.cellvalue(pcr.maptotal(pcr.ifthen(self.basinOutlet,\ massBalanceError)),1)[0] totalCumulativeDeltaStorage= pcr.cellvalue(pcr.maptotal(pcr.ifthen(self.basinOutlet,\ self.cumulativeDeltaStorage)),1)[0] if totalCumulativeDeltaStorage > 0: totalRelativeMassBalanceError= 1.+totalMassBalanceError/totalCumulativeDeltaStorage else: totalRelativeMassBalanceError= 1. #-report maps and echo value pcr.report(massBalanceError,mbeFileName) pcr.report(relMassBalanceError,mbrFileName) print '\n*\ttotal global mass balance error [m3]: %8.3g' % totalMassBalanceError print '\n*\trelative global mass balance error [-]: %5.3f' % totalRelativeMassBalanceError #-echo to screen: total mass balance error and completion of run print '\trun completed' #-end of day: return states and fluxes #-get surface water attributes? if getSurfaceWaterAttributes: #-compute the following secondary variables: # surface water area [m2]: area given dynamic surface water fraction # residence time [days]: volume over discharge, assigned -1 in case discharge is zero # surface water depth [m], weighed by channel and floodplain volume surfaceWaterArea= self.waterFraction*self.cellArea surfaceWaterArea= pcr.ifthenelse(self.waterBodies.distribution != 0,\ pcr.ifthenelse(self.waterBodies.location != 0,\ pcr.areatotal(surfaceWaterArea,self.waterBodies.distribution),0),\ surfaceWaterArea) surfaceWaterResidenceTime= pcr.ifthenelse(self.Q > 0.,self.actualStorage/(self.Q*self.timeSec),-1) surfaceWaterDepth= pcr.ifthenelse(self.actualStorage > 0.,\ pcr.max(0.,self.actualStorage-self.channelStorageCapacity)**2/\ (self.actualStorage*surfaceWaterArea),0.) surfaceWaterDepth+= pcr.ifthenelse(self.actualStorage > 0.,\ pcr.min(self.channelStorageCapacity,self.actualStorage)**2/(self.waterFractionMask*\ self.cellArea*self.actualStorage),0.) #-reports: values at outlet of lakes or reservoirs are assigned to their full extent self.report(pcr.ifthenelse(self.waterBodies.distribution != 0,\ pcr.areamaximum(surfaceWaterArea,self.waterBodies.distribution),surfaceWaterArea),\ surfaceWaterAreaFileName) self.report(pcr.ifthenelse(self.waterBodies.distribution != 0,\ pcr.areamaximum(surfaceWaterResidenceTime,self.waterBodies.distribution),surfaceWaterResidenceTime),\ surfaceWaterResidenceTimeFileName) self.report(pcr.ifthenelse(self.waterBodies.distribution != 0,\ pcr.areamaximum(surfaceWaterDepth,self.waterBodies.distribution),surfaceWaterDepth),\ surfaceWaterDepthFileName) #-reports on standard output: values at outlet of lakes or reservoirs are assigned to their full extent self.report(pcr.ifthenelse(self.waterBodies.distribution != 0, pcr.areamaximum(self.flowVelocity,self.waterBodies.distribution),self.flowVelocity),flowVelocityFileName) self.report(pcr.ifthenelse(self.waterBodies.distribution != 0, pcr.areamaximum(self.Q,self.waterBodies.distribution),self.Q),QFileName) self.report(pcr.ifthenelse(self.waterBodies.distribution == 0,\ floodedFraction,0.),floodedFractionFileName) self.report(pcr.ifthenelse(self.waterBodies.distribution == 0,\ floodedDepth,0.),floodedDepthFileName) self.report(self.actualStorage,actualStorageFileName) #-update date for time step and report relevant daily output self.currentDate= self.currentDate+datetime.timedelta(self.duration)
