def returnFloodedFraction(self, volume):
#-returns the flooded fraction given the flood volume and the associated water height
# using a logistic smoother near intersections (K&K, 2007)
#-find the match on the basis of the shortest distance to the available intersections or steps
deltaXMin = self.floodVolume[self.nrEntries - 1]
y_i = pcr.scalar(1.)
k = [pcr.scalar(0.)] * 2
mInt = pcr.scalar(0.)
for iCnt in range(self.nrEntries - 1, 0, -1):
#-find x_i for current volume and update match if applicable
# also update slope and intercept
deltaX = volume - self.floodVolume[iCnt]
mask = pcr.abs(deltaX) < pcr.abs(deltaXMin)
deltaXMin = pcr.ifthenelse(mask, deltaX, deltaXMin)
y_i = pcr.ifthenelse(mask, self.areaFractions[iCnt], y_i)
k[0] = pcr.ifthenelse(mask, self.kSlope[iCnt - 1], k[0])
k[1] = pcr.ifthenelse(mask, self.kSlope[iCnt], k[1])
mInt = pcr.ifthenelse(mask, self.mInterval[iCnt], mInt)
#-all values returned, process data: calculate scaled deltaX and smoothed function
# on the basis of the integrated logistic functions PHI(x) and 1-PHI(x)
deltaX = deltaXMin
deltaXScaled= pcr.ifthenelse(deltaX < 0.,pcr.scalar(-1.),1.)*\
pcr.min(criterionKK,pcr.abs(deltaX/pcr.max(1.,mInt)))
logInt = self.integralLogisticFunction(deltaXScaled)
#-compute fractional flooded area and flooded depth
floodedFraction= pcr.ifthenelse(volume > 0.,\
pcr.ifthenelse(pcr.abs(deltaXScaled) < criterionKK,\
y_i-k[0]*mInt*logInt[0]+k[1]*mInt*logInt[1],\
y_i+pcr.ifthenelse(deltaX < 0.,k[0],k[1])*deltaX),0.)
floodedFraction = pcr.max(0., pcr.min(1., floodedFraction))
floodDepth = pcr.ifthenelse(floodedFraction > 0.,
volume / (floodedFraction * self.cellArea),
0.)
return floodedFraction, floodDepth
def mapEllipse(r_a, r_b, azimuth, latC, lonC, lat, lon, numberStepsArc= 360): '''Maps an ellipse for the given spatial attributes using a predefined number \ of steps along the arc: r_a: length of semi_major axis r_b: length of semi_major axis azimuth: angle of semi_major axis with the centroid to north latC: latitude of centroid lonC: longitude of centroid lat: map of latitude lon: map of longitude numberStepsArc= 360 (default) where r= r_a*r_b/(r_a**2*sin(theta)**2+r_b**2*cos(theta)**2)**0.5 \n''' #-compute theta as the azimuth and rotated towards the semi-major axis theta= np.linspace(0.,360.-360./numberStepsArc,numberStepsArc) radius= (r_a*r_b)/(r_a**2*(np.sin(theta*deg2Rad))**2+r_b**2*(np.cos(theta*deg2Rad))**2)**0.5 theta= (theta+azimuth) % 360. lat1, lon1= getDestinationPoint(latC, lonC, radius, theta) ellipse= pcr.boolean(0) for iCnt in xrange(lat1.size): ellipse= ellipse | \ (pcr.abs(lat-lat1[iCnt]) == pcr.mapminimum(pcr.abs(lat-lat1[iCnt]))) & \ (pcr.abs(lon-lon1[iCnt]) == pcr.mapminimum(pcr.abs(lon-lon1[iCnt]))) ellipse= pcr.ifthen(ellipse, ellipse) #-return ellipse return ellipse
def returnFloodedFraction(self,volume): #-returns the flooded fraction given the flood volume and the associated water height # using a logistic smoother near intersections (K&K, 2007) #-find the match on the basis of the shortest distance to the available intersections or steps deltaXMin= self.floodVolume[self.nrEntries-1] y_i= pcr.scalar(1.) k= [pcr.scalar(0.)]*2 mInt= pcr.scalar(0.) for iCnt in range(self.nrEntries-1,0,-1): #-find x_i for current volume and update match if applicable # also update slope and intercept deltaX= volume-self.floodVolume[iCnt] mask= pcr.abs(deltaX) < pcr.abs(deltaXMin) deltaXMin= pcr.ifthenelse(mask,deltaX,deltaXMin) y_i= pcr.ifthenelse(mask,self.areaFractions[iCnt],y_i) k[0]= pcr.ifthenelse(mask,self.kSlope[iCnt-1],k[0]) k[1]= pcr.ifthenelse(mask,self.kSlope[iCnt],k[1]) mInt= pcr.ifthenelse(mask,self.mInterval[iCnt],mInt) #-all values returned, process data: calculate scaled deltaX and smoothed function # on the basis of the integrated logistic functions PHI(x) and 1-PHI(x) deltaX= deltaXMin deltaXScaled= pcr.ifthenelse(deltaX < 0.,pcr.scalar(-1.),1.)*\ pcr.min(criterionKK,pcr.abs(deltaX/pcr.max(1.,mInt))) logInt= self.integralLogisticFunction(deltaXScaled) #-compute fractional flooded area and flooded depth floodedFraction= pcr.ifthenelse(volume > 0.,\ pcr.ifthenelse(pcr.abs(deltaXScaled) < criterionKK,\ y_i-k[0]*mInt*logInt[0]+k[1]*mInt*logInt[1],\ y_i+pcr.ifthenelse(deltaX < 0.,k[0],k[1])*deltaX),0.) floodedFraction= pcr.max(0.,pcr.min(1.,floodedFraction)) floodDepth= pcr.ifthenelse(floodedFraction > 0.,volume/(floodedFraction*self.cellArea),0.) return floodedFraction, floodDepth
def fractionXY(surface): #-returns the fraction of transport in the x- and y-directions # given a gradient in a surface aspect= pcr.aspect(surface) noAspect= pcr.nodirection(pcr.directional(aspect)) sinAspect= pcr.sin(aspect) cosAspect= pcr.cos(aspect) fracX= pcr.ifthenelse(noAspect,0.,sinAspect/(pcr.abs(sinAspect)+pcr.abs(cosAspect))) fracY= pcr.ifthenelse(noAspect,0.,cosAspect/(pcr.abs(sinAspect)+pcr.abs(cosAspect))) return fracX,fracY
def dayLength(doy,lat):
""" daylength fraction of day """
lat = lat * pcr.scalar(math.pi) / 180.0
M_PI_2 = pcr.spatial(pcr.scalar(math.pi / 2.0))
dec = pcr.sin( (6.224111 + 0.017202 * doy) * 180. / math.pi)
dec = pcr.scalar(0.39785 * pcr.sin ((4.868961 + .017203 * doy + 0.033446 * pcr.sin (dec* 180 / math.pi)) * 180 / math.pi))
dec = pcr.scalar(pcr.asin(dec))
lat = pcr.ifthenelse(pcr.abs(lat) > M_PI_2, (M_PI_2 - pcr.scalar(0.01)) * pcr.ifthenelse(lat > 0, pcr.scalar(1.0), pcr.scalar(-1.0)) ,lat )
arg = pcr.tan(dec ) * pcr.tan(lat * 180.0 / math.pi ) * -1.0
h = pcr.scalar( pcr.acos(arg ) )
h = h / 180. * math.pi
h = pcr.ifthenelse(arg > 1.0, 0.0,h) # /* sun stays below horizon */
h = pcr.ifthenelse(arg < -1.0 ,math.pi,h) # /* sun stays above horizon */
return (h / math.pi)
def set_recharge_package(self, \
gwRecharge, gwAbstraction,
gwAbstractionReturnFlow = 0.0): # Note: We ignored the latter as MODFLOW should capture this part as well.
logger.info("Set the recharge package based on the given recharge, abstraction and abstraction return flow fields.")
# specify the recharge package
# + recharge/capillary rise (unit: m/day) from PCR-GLOBWB
# - groundwater abstraction (unit: m/day) from PCR-GLOBWB
# + return flow of groundwater abstraction (unit: m/day) from PCR-GLOBWB
net_recharge = gwRecharge - gwAbstraction + \
gwAbstractionReturnFlow
# - correcting values (considering MODFLOW lat/lon cell properties)
# and pass them to the RCH package
net_RCH = pcr.cover(net_recharge * self.cellAreaMap/(pcr.clone().cellSize()*pcr.clone().cellSize()), 0.0)
net_RCH = pcr.cover(pcr.ifthenelse(pcr.abs(net_RCH) < 1e-20, 0.0, net_RCH), 0.0)
self.pcr_modflow.setRecharge(net_RCH, 1)
def waterBalanceCheck(fluxesIn,fluxesOut,preStorages,endStorages,processName,PrintOnlyErrors,dateStr,threshold=1e-5,landmask=None):
""" Returns the water balance for a list of input, output, and storage map files """
# modified by Edwin (22 Apr 2013)
inMap = pcr.spatial(pcr.scalar(0.0))
outMap = pcr.spatial(pcr.scalar(0.0))
dsMap = pcr.spatial(pcr.scalar(0.0))
for fluxIn in fluxesIn:
inMap += fluxIn
for fluxOut in fluxesOut:
outMap += fluxOut
for preStorage in preStorages:
dsMap += preStorage
for endStorage in endStorages:
dsMap -= endStorage
a,b,c = getMinMaxMean(inMap + dsMap- outMap)
if abs(a) > threshold or abs(b) > threshold:
if PrintOnlyErrors:
print "WBError %s Min %f Max %f Mean %f" %(processName,a,b,c)
print ""
wb = inMap + dsMap - outMap
maxWBError = pcr.cellvalue(pcr.mapmaximum(pcr.abs(wb)), 1, 1)[0]
def identifyModelPixel(self,tmpDir,\
catchmentAreaAll,\
landMaskClass,\
xCoordinate,yCoordinate,id):
# TODO: Include an option to consider average discharge.
logger.info("Identify model pixel for the grdc station "+str(id)+".")
# make a temporary directory:
randomDir = self.makeRandomDir(tmpDir)
# coordinate of grdc station
xCoord = float(self.attributeGRDC["grdc_longitude_in_arc_degree"][str(id)])
yCoord = float(self.attributeGRDC["grdc_latitude_in_arc_degree"][str(id)])
# identify the point at pcraster model
point = pcr.ifthen((pcr.abs(xCoordinate - xCoord) == pcr.mapminimum(pcr.abs(xCoordinate - xCoord))) &\
(pcr.abs(yCoordinate - yCoord) == pcr.mapminimum(pcr.abs(yCoordinate - yCoord))), \
pcr.boolean(1))
# expanding the point
point = pcr.windowmajority(point, self.cell_size_in_arc_degree * 5.0)
point = pcr.ifthen(catchmentAreaAll > 0, point)
point = pcr.boolean(point)
# values based on the model;
modelCatchmentArea = pcr.ifthen(point, catchmentAreaAll) # unit: km2
model_x_ccordinate = pcr.ifthen(point, xCoordinate) # unit: arc degree
model_y_ccordinate = pcr.ifthen(point, yCoordinate) # unit: arc degree
# calculate (absolute) difference with GRDC data
# - initiating all of them with the values of MV
diffCatchArea = pcr.abs(pcr.scalar(vos.MV)) # difference between the model and grdc catchment area (unit: km2)
diffDistance = pcr.abs(pcr.scalar(vos.MV)) # distance between the model pixel and grdc catchment station (unit: arc degree)
diffLongitude = pcr.abs(pcr.scalar(vos.MV)) # longitude difference (unit: arc degree)
diffLatitude = pcr.abs(pcr.scalar(vos.MV)) # latitude difference (unit: arc degree)
#
# - calculate (absolute) difference with GRDC data
try:
diffCatchArea = pcr.abs(modelCatchmentArea-\
float(self.attributeGRDC["grdc_catchment_area_in_km2"][str(id)]))
except:
logger.info("The difference in the model and grdc catchment area cannot be calculated.")
try:
diffLongitude = pcr.abs(model_x_ccordinate - xCoord)
except:
logger.info("The difference in longitude cannot be calculated.")
try:
diffLatitude = pcr.abs(model_y_ccordinate - yCoord)
except:
logger.info("The difference in latitude cannot be calculated.")
try:
diffDistance = (diffLongitude**(2) + \
diffLatitude**(2))**(0.5) # TODO: calculate distance in meter
except:
logger.info("Distance cannot be calculated.")
# identify masks
masks = pcr.ifthen(pcr.boolean(point), landMaskClass)
# export the difference to temporary files: maps and txt
catchmentAreaMap = randomDir+"/"+vos.get_random_word()+".area.map"
diffCatchAreaMap = randomDir+"/"+vos.get_random_word()+".dare.map"
diffDistanceMap = randomDir+"/"+vos.get_random_word()+".dist.map"
diffLatitudeMap = randomDir+"/"+vos.get_random_word()+".dlat.map"
diffLongitudeMap = randomDir+"/"+vos.get_random_word()+".dlon.map"
diffLatitudeMap = randomDir+"/"+vos.get_random_word()+".dlat.map"
#
maskMap = randomDir+"/"+vos.get_random_word()+".mask.map"
diffColumnFile = randomDir+"/"+vos.get_random_word()+".cols.txt" # output
#
pcr.report(pcr.ifthen(point,modelCatchmentArea), catchmentAreaMap)
pcr.report(pcr.ifthen(point,diffCatchArea ), diffCatchAreaMap)
pcr.report(pcr.ifthen(point,diffDistance ), diffDistanceMap )
pcr.report(pcr.ifthen(point,diffLatitude ), diffLongitudeMap)
pcr.report(pcr.ifthen(point,diffLongitude ), diffLatitudeMap )
pcr.report(pcr.ifthen(point,masks ), maskMap)
#
cmd = 'map2col '+catchmentAreaMap +' '+\
diffCatchAreaMap +' '+\
diffDistanceMap +' '+\
diffLongitudeMap +' '+\
diffLatitudeMap +' '+\
maskMap+' '+diffColumnFile
print(cmd); os.system(cmd)
# use R to sort the file
cmd = 'R -f saveIdentifiedPixels.R '+diffColumnFile
print(cmd); os.system(cmd)
try:
# read the output file (from R)
f = open(diffColumnFile+".sel") ; allLines = f.read() ; f.close()
# split the content of the file into several lines
allLines = allLines.replace("\r",""); allLines = allLines.split("\n")
selectedPixel = allLines[0].split(";")
model_longitude_in_arc_degree = float(selectedPixel[0])
model_latitude_in_arc_degree = float(selectedPixel[1])
model_catchment_area_in_km2 = float(selectedPixel[2])
model_landmask = str(selectedPixel[7])
log_message = "Model pixel for grdc station "+str(id)+" is identified (lat/lon in arc degree): "
log_message += str(model_latitude_in_arc_degree) + " ; " + str(model_longitude_in_arc_degree)
logger.info(log_message)
self.attributeGRDC["model_longitude_in_arc_degree"][str(id)] = model_longitude_in_arc_degree
self.attributeGRDC["model_latitude_in_arc_degree"][str(id)] = model_latitude_in_arc_degree
self.attributeGRDC["model_catchment_area_in_km2"][str(id)] = model_catchment_area_in_km2
self.attributeGRDC["model_landmask"][str(id)] = model_landmask
except:
logger.info("Model pixel for grdc station "+str(id)+" can NOT be identified.")
self.cleanRandomDir(randomDir)
def waterBalance( fluxesIn, fluxesOut, deltaStorages, processName, PrintOnlyErrors, dateStr,threshold=1e-5):
""" Returns the water balance for a list of input, output, and storage map files and """
inMap = pcr.spatial(pcr.scalar(0.0))
dsMap = pcr.spatial(pcr.scalar(0.0))
outMap = pcr.spatial(pcr.scalar(0.0))
inflow = 0
outflow = 0
deltaS = 0
for fluxIn in fluxesIn:
inflow += getMapTotal(fluxIn)
inMap += fluxIn
for fluxOut in fluxesOut:
outflow += getMapTotal(fluxOut)
outMap += fluxOut
for deltaStorage in deltaStorages:
deltaS += getMapTotal(deltaStorage)
dsMap += deltaStorage
#if PrintOnlyErrors:
a,b,c = getMinMaxMean(inMap + dsMap- outMap)
# if abs(a) > 1e-5 or abs(b) > 1e-5:
# if abs(a) > 1e-4 or abs(b) > 1e-4:
if abs(a) > threshold or abs(b) > threshold:
print "WBError %s Min %f Max %f Mean %f" %(processName,a,b,c)
# if abs(inflow + deltaS - outflow) > 1e-5:
# print "Water balance Error for %s on %s: in = %f\tout=%f\tdeltaS=%f\tBalance=%f" \
# %(processName,dateStr,inflow,outflow,deltaS,inflow + deltaS - outflow)
#else:
# print "Water balance for %s: on %s in = %f\tout=%f\tdeltaS=%f\tBalance=%f" \
# %(processName,dateStr,inflow,outflow,deltaS,inflow + deltaS - outflow)
wb = inMap + dsMap - outMap
maxWBError = pcr.cellvalue(pcr.mapmaximum(pcr.abs(wb)), 1, 1)[0]
#if maxWBError > 0.001 / 1000:
#row = 0
#col = 0
#cellID = 1
#troubleCell = 0
#print "Water balance for %s on %s: %f mm !!! " %(processName,dateStr,maxWBError * 1000)
#pcr.report(wb,"%s-WaterBalanceError-%s" %(processName,dateStr))
#npWBMError = pcr2numpy(wb, -9999)
#(nr, nc) = np.shape(npWBMError)
#for r in range(0, nr):
#for c in range(0, nc):
## print r,c
#if npWBMError[r, c] != -9999.0:
#val = npWBMError[r, c]
#if math.fabs(val) > 0.0001 / 1000:
## print npWBMError[r,c]
#row = r
#col = c
#troubleCell = cellID
#cellID += 1
#print 'Water balance for %s on %s: %f mm row %i col %i cellID %i!!! ' % (
#processName,
#dateStr,
#maxWBError * 1000,
#row,
#col,
#troubleCell,
#)
return inMap + dsMap - outMap
def report_summary(self, landWaterStoresAtBeginning, landWaterStoresAtEnd,\
surfaceWaterStoresAtBeginning, surfaceWaterStoresAtEnd):
# set total to 0 on first day of the year
if self._modelTime.doy == 1 or self._modelTime.isFirstTimestep():
# set all accumulated variables to zero
self.precipitationAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
for var in self.landSurface.fluxVars: vars(self)[var+'Acc'] = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.baseflowAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.surfaceWaterInfAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.runoffAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.unmetDemandAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.waterBalanceAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.absWaterBalanceAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
# non irrigation water use (unit: m)
self.nonIrrigationWaterUseAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
# non irrigation return flow to water body and water body evaporation (unit: m)
self.nonIrrReturnFlowAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.waterBodyEvaporationAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
# surface water input/loss volume (m3) and outgoing volume (m3) at pits
self.surfaceWaterInputAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.dischargeAtPitAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
# also save the storages at the first day of the year (or the first time step)
# - land surface storage (unit: m)
self.storageAtFirstDay = pcr.ifthen(self.landmask, landWaterStoresAtBeginning)
# - channel storages (unit: m3)
self.channelVolumeAtFirstDay = pcr.ifthen(self.landmask, surfaceWaterStoresAtBeginning)
# accumulating until the last day of the year:
self.precipitationAcc += self.meteo.precipitation
for var in self.landSurface.fluxVars: vars(self)[var+'Acc'] += vars(self.landSurface)[var]
self.baseflowAcc += self.groundwater.baseflow
self.surfaceWaterInfAcc += self.groundwater.surfaceWaterInf
self.runoffAcc += self.routing.runoff
self.unmetDemandAcc += self.groundwater.unmetDemand
self.waterBalance = \
(landWaterStoresAtBeginning - landWaterStoresAtEnd +\
self.meteo.precipitation + self.landSurface.irrGrossDemand + self.groundwater.surfaceWaterInf -\
self.landSurface.actualET - self.routing.runoff - self.groundwater.nonFossilGroundwaterAbs)
self.waterBalanceAcc += self.waterBalance
self.absWaterBalanceAcc += pcr.abs(self.waterBalance)
# consumptive water use for non irrigation demand (m)
self.nonIrrigationWaterUseAcc += self.routing.nonIrrWaterConsumption
self.nonIrrReturnFlowAcc += self.routing.nonIrrReturnFlow
self.waterBodyEvaporationAcc += self.routing.waterBodyEvaporation
self.surfaceWaterInputAcc += self.routing.local_input_to_surface_water # unit: m3
self.dischargeAtPitAcc += self.routing.outgoing_volume_at_pits # unit: m3
#TODO: hack for eWatercycle operational spinup
#if self._modelTime.isLastDayOfMonth() or self._modelTime.isLastTimestep():
#TODO: extra hack! dump every state
self.dumpStateDir(self._configuration.endStateDir)
if self._modelTime.isLastDayOfYear():
self.dumpState(self._configuration.endStateDir)
logger.info("")
msg = 'The following summary values do not include storages in surface water bodies (lake, reservoir and channel storages).'
logger.info(msg) # TODO: Improve these water balance checks.
totalCellArea = vos.getMapTotal(pcr.ifthen(self.landmask,self.routing.cellArea))
msg = 'Total area = %e km2'\
% (totalCellArea/1e6)
logger.info(msg)
deltaStorageOneYear = vos.getMapVolume( \
pcr.ifthen(self.landmask,landWaterStoresAtBeginning) - \
pcr.ifthen(self.landmask,self.storageAtFirstDay),
self.routing.cellArea)
msg = 'Delta total storage days 1 to %i in %i = %e km3 = %e mm'\
% ( int(self._modelTime.doy),\
int(self._modelTime.year),\
deltaStorageOneYear/1e9,\
deltaStorageOneYear*1000/totalCellArea)
logger.info(msg)
variableList = ['precipitation',
'baseflow',
'surfaceWaterInf',
'runoff',
'unmetDemand']
variableList += self.landSurface.fluxVars
variableList += ['waterBalance','absWaterBalance','irrigationWaterUse','nonIrrigationWaterUse']
# consumptive water use for irrigation (unit: m)
self.irrigationWaterUseAcc = vos.getValDivZero(self.irrGrossDemandAcc,\
self.precipitationAcc + self.irrGrossDemandAcc) * self.actualETAcc
for var in variableList:
volume = vos.getMapVolume(\
self.__getattribute__(var + 'Acc'),\
self.routing.cellArea)
msg = 'Accumulated %s days 1 to %i in %i = %e km3 = %e mm'\
% (var,int(self._modelTime.doy),\
int(self._modelTime.year),volume/1e9,volume*1000/totalCellArea)
logger.info(msg)
logger.info("")
msg = 'The following summary is for surface water bodies.'
logger.info(msg)
deltaChannelStorageOneYear = vos.getMapTotal( \
pcr.ifthen(self.landmask,surfaceWaterStoresAtEnd) - \
pcr.ifthen(self.landmask,self.channelVolumeAtFirstDay))
msg = 'Delta surface water storage days 1 to %i in %i = %e km3 = %e mm'\
% ( int(self._modelTime.doy),\
int(self._modelTime.year),\
deltaChannelStorageOneYear/1e9,\
deltaChannelStorageOneYear*1000/totalCellArea)
logger.info(msg)
variableList = ['nonIrrReturnFlow','waterBodyEvaporation']
for var in variableList:
volume = vos.getMapVolume(\
self.__getattribute__(var + 'Acc'),\
self.routing.cellArea)
msg = 'Accumulated %s days 1 to %i in %i = %e km3 = %e mm'\
% (var,int(self._modelTime.doy),\
int(self._modelTime.year),volume/1e9,volume*1000/totalCellArea)
logger.info(msg)
# surface water balance check
surfaceWaterInputTotal = vos.getMapTotal(self.surfaceWaterInputAcc)
msg = 'Accumulated %s days 1 to %i in %i = %e km3 = %e mm'\
% ("surfaceWaterInput",int(self._modelTime.doy),\
int(self._modelTime.year),surfaceWaterInputTotal/1e9,surfaceWaterInputTotal*1000/totalCellArea)
logger.info(msg)
dischargeAtPitTotal = vos.getMapTotal(self.dischargeAtPitAcc)
msg = 'Accumulated %s days 1 to %i in %i = %e km3 = %e mm'\
% ("dischargeAtPitTotal",int(self._modelTime.doy),\
int(self._modelTime.year),dischargeAtPitTotal/1e9, dischargeAtPitTotal*1000/totalCellArea)
logger.info(msg)
surfaceWaterBalance = surfaceWaterInputTotal - dischargeAtPitTotal + deltaChannelStorageOneYear
msg = 'Accumulated %s days 1 to %i in %i = %e km3 = %e mm'\
% ("surfaceWaterBalance",int(self._modelTime.doy),\
int(self._modelTime.year),surfaceWaterBalance/1e9, surfaceWaterBalance*1000/totalCellArea)
logger.info(msg)
def report(self, storesAtBeginning, storesAtEnd):
#report the state. which states are written when is based on the configuration
#set total to 0 on first day of the year
if self._modelTime.doy == 1 or self._modelTime.isFirstTimestep():
# set all accumulated variables to zero
self.precipitationAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
for var in self.landSurface.fluxVars: vars(self)[var+'Acc'] = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.nonFossilGroundwaterAbsAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.allocNonFossilGroundwaterAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.baseflowAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.surfaceWaterInfAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.runoffAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.unmetDemandAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.waterBalanceAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
self.absWaterBalanceAcc = pcr.ifthen(self.landmask, pcr.scalar(0.0))
# also save the storage at the first day of the year (or the first time step)
self.storageAtFirstDay = pcr.ifthen(self.landmask, storesAtBeginning)
# accumulating until the last day of the year:
self.precipitationAcc += self.meteo.precipitation
for var in self.landSurface.fluxVars: vars(self)[var+'Acc'] += vars(self.landSurface)[var]
self.nonFossilGroundwaterAbsAcc += self.groundwater.nonFossilGroundwaterAbs
self.allocNonFossilGroundwaterAcc += self.groundwater.allocNonFossilGroundwater
self.baseflowAcc += self.groundwater.baseflow
self.surfaceWaterInfAcc += self.groundwater.surfaceWaterInf
self.runoffAcc += self.routing.runoff
self.unmetDemandAcc += self.groundwater.unmetDemand
self.waterBalance = \
(storesAtBeginning - storesAtEnd +\
self.meteo.precipitation + self.landSurface.irrGrossDemand + self.groundwater.surfaceWaterInf -\
self.landSurface.actualET - self.routing.runoff - self.groundwater.nonFossilGroundwaterAbs)
self.waterBalanceAcc = self.waterBalanceAcc + self.waterBalance
self.absWaterBalanceAcc = self.absWaterBalanceAcc + pcr.abs(self.waterBalance)
if self._modelTime.isLastDayOfYear():
self.dumpState(self._configuration.endStateDir)
msg = 'The following waterBalance checks assume fracWat = 0 for all cells (not including surface water bodies).'
logging.getLogger("model").info(msg) # TODO: Improve these water balance checks.
totalCellArea = vos.getMapTotal(pcr.ifthen(self.landmask,self.routing.cellArea))
msg = 'Total area = %e km2'\
% (totalCellArea/1e6)
logging.getLogger("model").info(msg)
deltaStorageOneYear = vos.getMapVolume( \
pcr.ifthen(self.landmask,storesAtEnd) - \
pcr.ifthen(self.landmask,self.storageAtFirstDay),
self.routing.cellArea)
msg = 'Delta total storage days 1 to %i in %i = %e km3 = %e mm'\
% ( int(self._modelTime.doy),\
int(self._modelTime.year),\
deltaStorageOneYear/1e9,\
deltaStorageOneYear*1000/totalCellArea)
logging.getLogger("model").info(msg)
# reporting the endStates at the end of the Year:
variableList = ['precipitation',
'nonFossilGroundwaterAbs',
'allocNonFossilGroundwater',
'baseflow',
'surfaceWaterInf',
'runoff',
'unmetDemand']
variableList += self.landSurface.fluxVars
variableList += ['waterBalance','absWaterBalance']
for var in variableList:
volume = vos.getMapVolume(\
self.__getattribute__(var + 'Acc'),\
self.routing.cellArea)
msg = 'Accumulated %s days 1 to %i in %i = %e km3 = %e mm'\
% (var,int(self._modelTime.doy),\
int(self._modelTime.year),volume/1e9,volume*1000/totalCellArea)
logging.getLogger("model").info(msg)
def waterBalance( fluxesIn, fluxesOut, deltaStorages, processName, PrintOnlyErrors, dateStr,threshold=1e-5):
""" Returns the water balance for a list of input, output, and storage map files and """
inMap = pcr.spatial(pcr.scalar(0.0))
dsMap = pcr.spatial(pcr.scalar(0.0))
outMap = pcr.spatial(pcr.scalar(0.0))
inflow = 0
outflow = 0
deltaS = 0
for fluxIn in fluxesIn:
inflow += getMapTotal(fluxIn)
inMap += fluxIn
for fluxOut in fluxesOut:
outflow += getMapTotal(fluxOut)
outMap += fluxOut
for deltaStorage in deltaStorages:
deltaS += getMapTotal(deltaStorage)
dsMap += deltaStorage
#if PrintOnlyErrors:
a,b,c = getMinMaxMean(inMap + dsMap- outMap)
# if abs(a) > 1e-5 or abs(b) > 1e-5:
# if abs(a) > 1e-4 or abs(b) > 1e-4:
if abs(a) > threshold or abs(b) > threshold:
print "WBError %s Min %f Max %f Mean %f" %(processName,a,b,c)
# if abs(inflow + deltaS - outflow) > 1e-5:
# print "Water balance Error for %s on %s: in = %f\tout=%f\tdeltaS=%f\tBalance=%f" \
# %(processName,dateStr,inflow,outflow,deltaS,inflow + deltaS - outflow)
#else:
# print "Water balance for %s: on %s in = %f\tout=%f\tdeltaS=%f\tBalance=%f" \
# %(processName,dateStr,inflow,outflow,deltaS,inflow + deltaS - outflow)
wb = inMap + dsMap - outMap
maxWBError = pcr.cellvalue(pcr.mapmaximum(pcr.abs(wb)), 1, 1)[0]
#if maxWBError > 0.001 / 1000:
#row = 0
#col = 0
#cellID = 1
#troubleCell = 0
#print "Water balance for %s on %s: %f mm !!! " %(processName,dateStr,maxWBError * 1000)
#pcr.report(wb,"%s-WaterBalanceError-%s" %(processName,dateStr))
#npWBMError = pcr2numpy(wb, -9999)
#(nr, nc) = np.shape(npWBMError)
#for r in range(0, nr):
#for c in range(0, nc):
## print r,c
#if npWBMError[r, c] != -9999.0:
#val = npWBMError[r, c]
#if math.fabs(val) > 0.0001 / 1000:
## print npWBMError[r,c]
#row = r
#col = c
#troubleCell = cellID
#cellID += 1
#print 'Water balance for %s on %s: %f mm row %i col %i cellID %i!!! ' % (
#processName,
#dateStr,
#maxWBError * 1000,
#row,
#col,
#troubleCell,
#)
return inMap + dsMap - outMap
os.system(cmd)
edwin_code = pcr.readmap("edwin_code.map")
# usgs_drain_area.map: col2map --clone lddsound_05min.map -S -x 3 -y 2 -v 4 one_line_from_edwin_code_and_usgs_drain_area_km2.txt usgs_drain_area.map
cmd = "col2map --clone " + ldd_file_name + \
" -S -x 3 -y 2 -v 4 " + "one_line.tmp" + " usgs_drain_area.map"
print(cmd)
os.system(cmd)
usgs_drain_area_km2 = pcr.readmap("usgs_drain_area.map")
# pcrglobwb catchment area
edwin_code_pcrglobwb_catchment_area_km2 = pcr.ifthen(
pcr.defined(edwin_code), pcrglobwb_catchment_area_km2)
# calculate the absolute difference
abs_diff = pcr.abs(usgs_drain_area_km2 -
edwin_code_pcrglobwb_catchment_area_km2)
# make correction if required
abs_diff_value = pcr.cellvalue(pcr.mapmaximum(abs_diff), 1)[0]
usgs_drain_area_km2 = pcr.cellvalue(pcr.mapmaximum(usgs_drain_area_km2),
1)[0]
if (usgs_drain_area_km2 > 1000.0) and \
(abs_diff_value > 0.10 * usgs_drain_area_km2):
# class within 0.1 arc degree windows
edwin_code = pcr.windowmajority(edwin_code, 0.1)
# find the most accurate cell:
areaorder = pcr.areaorder(
pcr.windowmaximum(pcr.spatial(pcr.scalar(usgs_drain_area_km2)),
def identifyModelPixel(self,tmpDir,\
catchmentAreaAll,\
landMaskClass,\
xCoordinate,yCoordinate,id):
# TODO: Include an option to consider average discharge.
logger.info("Identify model pixel for the grdc station "+str(id)+".")
# make a temporary directory:
randomDir = self.makeRandomDir(tmpDir)
# coordinate of grdc station
xCoord = float(self.attributeGRDC["grdc_longitude_in_arc_degree"][str(id)])
yCoord = float(self.attributeGRDC["grdc_latitude_in_arc_degree"][str(id)])
# identify the point at pcraster model
point = pcr.ifthen((pcr.abs(xCoordinate - xCoord) == pcr.mapminimum(pcr.abs(xCoordinate - xCoord))) &\
(pcr.abs(yCoordinate - yCoord) == pcr.mapminimum(pcr.abs(yCoordinate - yCoord))), \
pcr.boolean(1))
# expanding the point
point = pcr.windowmajority(point, self.cell_size_in_arc_degree * 5.0)
point = pcr.ifthen(catchmentAreaAll > 0, point)
point = pcr.boolean(point)
# values based on the model;
modelCatchmentArea = pcr.ifthen(point, catchmentAreaAll) # unit: km2
model_x_ccordinate = pcr.ifthen(point, xCoordinate) # unit: arc degree
model_y_ccordinate = pcr.ifthen(point, yCoordinate) # unit: arc degree
# calculate (absolute) difference with GRDC data
# - initiating all of them with the values of MV
diffCatchArea = pcr.abs(pcr.scalar(vos.MV)) # difference between the model and grdc catchment area (unit: km2)
diffDistance = pcr.abs(pcr.scalar(vos.MV)) # distance between the model pixel and grdc catchment station (unit: arc degree)
diffLongitude = pcr.abs(pcr.scalar(vos.MV)) # longitude difference (unit: arc degree)
diffLatitude = pcr.abs(pcr.scalar(vos.MV)) # latitude difference (unit: arc degree)
#
# - calculate (absolute) difference with GRDC data
try:
diffCatchArea = pcr.abs(modelCatchmentArea-\
float(self.attributeGRDC["grdc_catchment_area_in_km2"][str(id)]))
except:
logger.info("The difference in the model and grdc catchment area cannot be calculated.")
try:
diffLongitude = pcr.abs(model_x_ccordinate - xCoord)
except:
logger.info("The difference in longitude cannot be calculated.")
try:
diffLatitude = pcr.abs(model_y_ccordinate - yCoord)
except:
logger.info("The difference in latitude cannot be calculated.")
try:
diffDistance = (diffLongitude**(2) + \
diffLatitude**(2))**(0.5) # TODO: calculate distance in meter
except:
logger.info("Distance cannot be calculated.")
# identify masks
masks = pcr.ifthen(pcr.boolean(point), landMaskClass)
# export the difference to temporary files: maps and txt
catchmentAreaMap = randomDir+"/"+vos.get_random_word()+".area.map"
diffCatchAreaMap = randomDir+"/"+vos.get_random_word()+".dare.map"
diffDistanceMap = randomDir+"/"+vos.get_random_word()+".dist.map"
diffLatitudeMap = randomDir+"/"+vos.get_random_word()+".dlat.map"
diffLongitudeMap = randomDir+"/"+vos.get_random_word()+".dlon.map"
diffLatitudeMap = randomDir+"/"+vos.get_random_word()+".dlat.map"
#
maskMap = randomDir+"/"+vos.get_random_word()+".mask.map"
diffColumnFile = randomDir+"/"+vos.get_random_word()+".cols.txt" # output
#
pcr.report(pcr.ifthen(point,modelCatchmentArea), catchmentAreaMap)
pcr.report(pcr.ifthen(point,diffCatchArea ), diffCatchAreaMap)
pcr.report(pcr.ifthen(point,diffDistance ), diffDistanceMap )
pcr.report(pcr.ifthen(point,diffLatitude ), diffLongitudeMap)
pcr.report(pcr.ifthen(point,diffLongitude ), diffLatitudeMap )
pcr.report(pcr.ifthen(point,masks ), maskMap)
#
cmd = 'map2col '+catchmentAreaMap +' '+\
diffCatchAreaMap +' '+\
diffDistanceMap +' '+\
diffLongitudeMap +' '+\
diffLatitudeMap +' '+\
maskMap+' '+diffColumnFile
print(cmd); os.system(cmd)
# use R to sort the file
cmd = 'R -f saveIdentifiedPixels.R '+diffColumnFile
print(cmd); os.system(cmd)
try:
# read the output file (from R)
f = open(diffColumnFile+".sel") ; allLines = f.read() ; f.close()
# split the content of the file into several lines
allLines = allLines.replace("\r",""); allLines = allLines.split("\n")
selectedPixel = allLines[0].split(";")
model_longitude_in_arc_degree = float(selectedPixel[0])
model_latitude_in_arc_degree = float(selectedPixel[1])
model_catchment_area_in_km2 = float(selectedPixel[2])
model_landmask = str(selectedPixel[7])
log_message = "Model pixel for grdc station "+str(id)+" is identified (lat/lon in arc degree): "
log_message += str(model_latitude_in_arc_degree) + " ; " + str(model_longitude_in_arc_degree)
logger.info(log_message)
self.attributeGRDC["model_longitude_in_arc_degree"][str(id)] = model_longitude_in_arc_degree
self.attributeGRDC["model_latitude_in_arc_degree"][str(id)] = model_latitude_in_arc_degree
self.attributeGRDC["model_catchment_area_in_km2"][str(id)] = model_catchment_area_in_km2
self.attributeGRDC["model_landmask"][str(id)] = model_landmask
except:
logger.info("Model pixel for grdc station "+str(id)+" can NOT be identified.")
self.cleanRandomDir(randomDir)