def __init__(self, iniItems, landmask, Dir, cloneMap, tmpDir):
object.__init__(self)
# clone map file names, temporary directory and global/absolute path of input directory
self.cloneMap = cloneMap #iniItems.cloneMap
self.tmpDir = tmpDir #iniItems.tmpDir
self.inputDir = Dir #iniItems.globalOptions['inputDir']
self.landmask = landmask
# local drainage direction:
self.lddMap = vos.readPCRmapClone(
iniItems.get("routingOptions", "lddMap"), self.cloneMap,
self.tmpDir, self.inputDir, True)
self.lddMap = pcr.lddrepair(pcr.ldd(self.lddMap))
self.lddMap = pcr.lddrepair(self.lddMap)
# option to activate water balance check
self.debugWaterBalance = True
if configget(iniItems, "routingOptions", "debugWaterBalance",
"True") == "False":
self.debugWaterBalance = False
# option to perform a run with only natural lakes (without reservoirs)
self.onlyNaturalWaterBodies = False
if "onlyNaturalWaterBodies" in iniItems._sections[
'routingOptions'] and configget(iniItems, "routingOptions",
"onlyNaturalWaterBodies",
"False") == "True":
logger.info(
"Using only natural water bodies identified in the year 1900. All reservoirs in 1900 are assumed as lakes."
)
self.onlyNaturalWaterBodies = True
self.dateForNaturalCondition = "1900-01-01" # The run for a natural condition should access only this date.
# names of files containing water bodies parameters
if configget(iniItems, "routingOptions", "waterBodyInputNC",
"None") == str(None):
self.useNetCDF = False
self.fracWaterInp = iniItems.get("routingOptions", "fracWaterInp")
self.waterBodyIdsInp = iniItems.get("routingOptions",
"waterBodyIds")
self.waterBodyTypInp = iniItems.get("routingOptions",
"waterBodyTyp")
self.resMaxCapInp = iniItems.get("routingOptions", "resMaxCapInp")
self.resSfAreaInp = iniItems.get("routingOptions", "resSfAreaInp")
else:
self.useNetCDF = True
self.ncFileInp = vos.getFullPath(\
iniItems.get("routingOptions","waterBodyInputNC"),\
self.inputDir)
# minimum width (m) used in the weir formula # TODO: define minWeirWidth based on the GLWD, GRanD database and/or bankfull discharge formula
self.minWeirWidth = 10.
# lower and upper limits at which reservoir release is terminated and
# at which reservoir release is equal to long-term average outflow
self.minResvrFrac = 0.10
self.maxResvrFrac = 0.75
def __init__(self, configuration, currTimeStep, initialState = None):
self._configuration = configuration
self._modelTime = currTimeStep
pcr.setclone(configuration.cloneMap)
# Read the ldd map.
self.lddMap = vos.readPCRmapClone(\
configuration.routingOptions['lddMap'],
configuration.cloneMap,configuration.tmpDir,configuration.globalOptions['inputDir'],True)
#ensure ldd map is correct, and actually of type "ldd"
self.lddMap = pcr.lddrepair(pcr.ldd(self.lddMap))
if configuration.globalOptions['landmask'] != "None":
self.landmask = vos.readPCRmapClone(\
configuration.globalOptions['landmask'],
configuration.cloneMap,configuration.tmpDir,configuration.globalOptions['inputDir'])
else:
self.landmask = pcr.defined(self.lddMap)
# ADDED: variables necessary for 2-way coupling functions
# ----------------------------------------------------------------------------------------------------------------
# variable to control activation of 2-way coupling functions (can be changed through BMI)
self.ActivateCoupling = self._configuration.globalOptions['ActivateCoupling']
# ----------------------------------------------------------------------------------------------------------------
# defining catchment areas
self.catchment_class = 1.0
# number of upperSoilLayers:
self.numberOfSoilLayers = int(configuration.landSurfaceOptions['numberOfUpperSoilLayers'])
self.createSubmodels(initialState)
def __init__(self, configuration, currTimeStep, initialState = None):
self._configuration = configuration
self._modelTime = currTimeStep
pcr.setclone(configuration.cloneMap)
# Read the ldd map.
self.lddMap = vos.readPCRmapClone(\
configuration.routingOptions['lddMap'],
configuration.cloneMap,configuration.tmpDir,configuration.globalOptions['inputDir'],True)
#ensure ldd map is correct, and actually of type "ldd"
self.lddMap = pcr.lddrepair(pcr.ldd(self.lddMap))
if configuration.globalOptions['landmask'] != "None":
self.landmask = vos.readPCRmapClone(\
configuration.globalOptions['landmask'],
configuration.cloneMap,configuration.tmpDir,configuration.globalOptions['inputDir'])
else:
self.landmask = pcr.defined(self.lddMap)
# defining catchment areas
self.catchment_class = 1.0
# number of upperSoilLayers:
self.numberOfSoilLayers = int(configuration.landSurfaceOptions['numberOfUpperSoilLayers'])
self.createSubmodels(initialState)
def initial(self):
""" initial part of the evapo water module
"""
# ************************************************************
# ***** EVAPORATION
# ************************************************************
self.var.EvaCumM3 = MaskInfo.instance().in_zero()
# water use cumulated amount
# water use substep amount
settings = LisSettings.instance()
option = settings.options
binding = settings.binding
maskinfo = MaskInfo.instance()
if option['openwaterevapo']:
LakeMask = loadmap('LakeMask', pcr=True)
lmask = ifthenelse(LakeMask != 0, self.var.LddStructuresKinematic,
5)
LddEva = lddrepair(lmask)
lddC = compressArray(LddEva)
inAr = decompress(np.arange(maskinfo.info.mapC[0], dtype="int32"))
self.var.downEva = (compressArray(downstream(
LddEva, inAr))).astype("int32")
# each upstream pixel gets the id of the downstream pixel
self.var.downEva[lddC == 5] = maskinfo.info.mapC[0]
self.var.maxNoEva = int(loadmap('maxNoEva'))
# all pits gets a high number
# still to test if this works
# ldd only inside lakes for calculating evaporation
if option['varfractionwater']:
self.var.diffmaxwater = loadmap(
'FracMaxWater') - self.var.WaterFraction
# Fraction of maximum extend of water - fraction of water in lakes and rivers
varWNo = [
1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 370
]
self.var.varW = [] # variable fraction of water
self.var.varW1 = []
self.var.varW1.append(12)
j = 0
for i in range(1, 367):
if i >= varWNo[j + 1]: j += 1
self.var.varW1.append(j)
for i in range(12):
varWName = generateName(binding['WFractionMaps'],
varWNo[i])
self.var.varW.append(
loadLAI(binding['WFractionMaps'], varWName, i))
def initial(self):
""" initial part of the structures module
"""
self.var.LddStructuresKinematic = self.var.LddKinematic
settings = LisSettings.instance()
option = settings.options
if not option['InitLisflood']:
# not done in Init Lisflood
IsUpsOfStructureKinematic = downstream(
self.var.LddKinematic,
cover(boolean(decompress(self.var.IsStructureKinematic)), boolean(0))
)
# Get all pixels just upstream of kinematic structure locations
self.var.IsUpsOfStructureKinematicC = compressArray(IsUpsOfStructureKinematic)
# Unmodified version of LddKinematic is needed to connect inflow and outflow points
# of each structure (called LddStructuresKinematic now)
self.var.LddKinematic = lddrepair(ifthenelse(IsUpsOfStructureKinematic, 5, self.var.LddKinematic))
def __init__(self, configuration, currTimeStep, initialState=None):
self._configuration = configuration
self._modelTime = currTimeStep
pcr.setclone(configuration.cloneMap)
# Read the ldd map.
self.lddMap = vos.readPCRmapClone(\
configuration.routingOptions['lddMap'],
configuration.cloneMap,configuration.tmpDir,configuration.globalOptions['inputDir'],True)
#ensure ldd map is correct, and actually of type "ldd"
self.lddMap = pcr.lddrepair(pcr.ldd(self.lddMap))
if configuration.globalOptions['landmask'] != "None":
self.landmask = vos.readPCRmapClone(\
configuration.globalOptions['landmask'],
configuration.cloneMap,configuration.tmpDir,configuration.globalOptions['inputDir'])
else:
self.landmask = pcr.defined(self.lddMap)
# defining catchment areas
self.catchment_class = 1.0
# number of upperSoilLayers:
self.numberOfSoilLayers = int(
configuration.landSurfaceOptions['numberOfUpperSoilLayers'])
# preparing sub-modules
self.createSubmodels(initialState)
# option for debugging to PCR-GLOBWB version 1.0
self.debug_to_version_one = False
if configuration.debug_to_version_one: self.debug_to_version_one = True
if self.debug_to_version_one:
# preparing initial folder directory
self.directory_for_initial_maps = vos.getFullPath(
"initials/", self.configuration.mapsDir)
if os.path.exists(self.directory_for_initial_maps):
shutil.rmtree(self.directory_for_initial_maps)
os.makedirs(self.directory_for_initial_maps)
# dump the initial state
self.dumpState(self.directory_for_initial_maps, "initial")
def pcr_preprocess(dem_in, x, y, itile, tempdir, ldd_in=None,
test=False, create_ldd=True):
"""
function to set pcr clone and translate DEM (terrain) and ldd numpy 2d arrays to pcr maps
:param terrain: masked numpy 2d array with elevation data
:param x: numpy 1d array with x coordinates of elevation grid
:param y: numpy 1d array with Y coordinates of elevation grid
:param tempdir: string with directory to temporary save clone pcrmap
:param ldd: numpy 2d array with ldd grid, make sure it uses the pcrmap definition of ldd
:param test: if True do not remove clone maps
:return: pcr maps for dem and ldd
"""
# create clone in temp_dir
fn_clone = os.path.join(tempdir, '_{:03d}_dem.map'.format(itile))
cl.makeDir(tempdir) # make dir if not exist
# DEM
gdal_writemap(fn_clone, 'PCRaster', x, y, dem_in, -9999) # note: missing value needs conversion in python item
pcr.setclone(fn_clone)
pcr.setglobaloption("unitcell")
dem = pcr.readmap(fn_clone)
# cleanup
if not test:
os.unlink(fn_clone) # cleanup clone file
os.unlink(fn_clone+'.aux.xml')
# LDD
if create_ldd:
if ldd_in is None:
print('Calculating LDD')
ldd = pcr.lddcreate(dem, 1E31, 1E31, 1E31, 1E31)
else:
# TODO note that np.nan is default NoDataValue for ldd file. check this when reading data
# TODO: x and y axis got mixed up when translating with numpy2pcr. check if it works here!
# in process_tile function in coastal_inun.py
ldd = pcr.lddrepair(pcr.ldd(pcr.numpy2pcr(pcr.Ldd, ldd_in, np.nan)))
else:
ldd = None
return dem, ldd
def __init__(self, configuration, currTimeStep):
self._configuration = configuration
self._modelTime = currTimeStep
pcr.setclone(configuration.cloneMap)
# read the ldd map
self.lddMap = vos.netcdf2PCRobjCloneWithoutTime(configuration.modflowParameterOptions['channelNC'],'lddMap',\
configuration.cloneMap)
# ensure ldd map is correct, and actually of type "ldd"
self.lddMap = pcr.lddrepair(pcr.ldd(self.lddMap))
# defining the landmask map
if configuration.globalOptions['landmask'] != "None":
self.landmask = vos.readPCRmapClone(\
configuration.globalOptions['landmask'],
configuration.cloneMap,configuration.tmpDir,configuration.globalOptions['inputDir'])
else:
self.landmask = pcr.defined(self.lddMap)
# preparing the sub-model(s) - Currently, there is only one sub-model.
self.createSubmodels()
def __init__(self, iniItems, landmask):
object.__init__(self)
# cloneMap, temporary directory for the resample process, temporary directory for the modflow process, absolute path for input directory, landmask
self.cloneMap = iniItems.cloneMap
self.tmpDir = iniItems.tmpDir
self.tmp_modflow_dir = iniItems.tmp_modflow_dir
self.inputDir = iniItems.globalOptions['inputDir']
self.landmask = landmask
# configuration from the ini file
self.iniItems = iniItems
# topography properties: read several variables from the netcdf file
for var in ['dem_minimum','dem_maximum','dem_average','dem_standard_deviation',\
'slopeLength','orographyBeta','tanslope',\
'dzRel0000','dzRel0001','dzRel0005',\
'dzRel0010','dzRel0020','dzRel0030','dzRel0040','dzRel0050',\
'dzRel0060','dzRel0070','dzRel0080','dzRel0090','dzRel0100']:
vars(self)[var] = vos.netcdf2PCRobjCloneWithoutTime(self.iniItems.modflowParameterOptions['topographyNC'], \
var, self.cloneMap)
vars(self)[var] = pcr.cover(vars(self)[var], 0.0)
# channel properties: read several variables from the netcdf file
for var in ['lddMap','cellAreaMap','gradient','bankfull_width',
'bankfull_depth','dem_floodplain','dem_riverbed']:
vars(self)[var] = vos.netcdf2PCRobjCloneWithoutTime(self.iniItems.modflowParameterOptions['channelNC'], \
var, self.cloneMap)
vars(self)[var] = pcr.cover(vars(self)[var], 0.0)
# minimum channel width
minimum_channel_width = 0.5 # TODO: Define this one in the configuration file
self.bankfull_width = pcr.max(minimum_channel_width, self.bankfull_width)
#~ # cell fraction if channel water reaching the flood plan # NOT USED YET
#~ self.flood_plain_fraction = self.return_innundation_fraction(pcr.max(0.0, self.dem_floodplain - self.dem_minimum))
# coefficient of Manning
self.manningsN = vos.readPCRmapClone(self.iniItems.modflowParameterOptions['manningsN'],\
self.cloneMap,self.tmpDir,self.inputDir)
# minimum channel gradient
minGradient = 0.00005 # TODO: Define this one in the configuration file
self.gradient = pcr.max(minGradient, pcr.cover(self.gradient, minGradient))
# correcting lddMap
self.lddMap = pcr.ifthen(pcr.scalar(self.lddMap) > 0.0, self.lddMap)
self.lddMap = pcr.lddrepair(pcr.ldd(self.lddMap))
# channelLength = approximation of channel length (unit: m) # This is approximated by cell diagonal.
cellSizeInArcMin = np.round(pcr.clone().cellSize()*60.) # FIXME: This one will not work if you use the resolution: 0.5, 1.5, 2.5 arc-min
verticalSizeInMeter = cellSizeInArcMin*1852.
horizontalSizeInMeter = self.cellAreaMap/verticalSizeInMeter
self.channelLength = ((horizontalSizeInMeter)**(2)+\
(verticalSizeInMeter)**(2))**(0.5)
# option for lakes and reservoir
self.onlyNaturalWaterBodies = False
if self.iniItems.modflowParameterOptions['onlyNaturalWaterBodies'] == "True": self.onlyNaturalWaterBodies = True
# groundwater linear recession coefficient (day-1) ; the linear reservoir concept is still being used to represent fast response flow
# particularly from karstic aquifer in mountainous regions
self.recessionCoeff = vos.netcdf2PCRobjCloneWithoutTime(self.iniItems.modflowParameterOptions['groundwaterPropertiesNC'],\
'recessionCoeff', self.cloneMap)
self.recessionCoeff = pcr.cover(self.recessionCoeff,0.00)
self.recessionCoeff = pcr.min(1.0000,self.recessionCoeff)
#
if 'minRecessionCoeff' in iniItems.modflowParameterOptions.keys():
minRecessionCoeff = float(iniItems.modflowParameterOptions['minRecessionCoeff'])
else:
minRecessionCoeff = 1.0e-4 # This is the minimum value used in Van Beek et al. (2011).
self.recessionCoeff = pcr.max(minRecessionCoeff,self.recessionCoeff)
# aquifer saturated conductivity (m/day)
self.kSatAquifer = vos.netcdf2PCRobjCloneWithoutTime(self.iniItems.modflowParameterOptions['groundwaterPropertiesNC'],\
'kSatAquifer', self.cloneMap)
self.kSatAquifer = pcr.cover(self.kSatAquifer,pcr.mapmaximum(self.kSatAquifer))
self.kSatAquifer = pcr.max(0.001,self.kSatAquifer)
# TODO: Define the minimum value as part of the configuration file
# aquifer specific yield (dimensionless)
self.specificYield = vos.netcdf2PCRobjCloneWithoutTime(self.iniItems.modflowParameterOptions['groundwaterPropertiesNC'],\
'specificYield', self.cloneMap)
self.specificYield = pcr.cover(self.specificYield,pcr.mapmaximum(self.specificYield))
self.specificYield = pcr.max(0.010,self.specificYield) # TODO: TO BE CHECKED: The resample process of specificYield
self.specificYield = pcr.min(1.000,self.specificYield)
# TODO: Define the minimum value as part of the configuration file
# estimate of thickness (unit: m) of accesible groundwater
totalGroundwaterThickness = vos.netcdf2PCRobjCloneWithoutTime(self.iniItems.modflowParameterOptions['estimateOfTotalGroundwaterThicknessNC'],\
'thickness', self.cloneMap)
# extrapolation
totalGroundwaterThickness = pcr.cover(totalGroundwaterThickness,\
pcr.windowaverage(totalGroundwaterThickness, 1.0))
totalGroundwaterThickness = pcr.cover(totalGroundwaterThickness,\
pcr.windowaverage(totalGroundwaterThickness, 1.5))
totalGroundwaterThickness = pcr.cover(totalGroundwaterThickness, 0.0)
#
# set minimum thickness
minimumThickness = pcr.scalar(float(\
self.iniItems.modflowParameterOptions['minimumTotalGroundwaterThickness']))
totalGroundwaterThickness = pcr.max(minimumThickness, totalGroundwaterThickness)
#
# set maximum thickness: 250 m. # TODO: Define this one as part of the ini file
maximumThickness = 250.
self.totalGroundwaterThickness = pcr.min(maximumThickness, totalGroundwaterThickness)
# TODO: Define the maximum value as part of the configuration file
# surface water bed thickness (unit: m)
bed_thickness = 0.1 # TODO: Define this as part of the configuration file
# surface water bed resistance (unit: day)
bed_resistance = bed_thickness / (self.kSatAquifer)
minimum_bed_resistance = 1.0 # TODO: Define this as part of the configuration file
self.bed_resistance = pcr.max(minimum_bed_resistance,\
bed_resistance,)
# option to ignore capillary rise
self.ignoreCapRise = True
if self.iniItems.modflowParameterOptions['ignoreCapRise'] == "False": self.ignoreCapRise = False
# a variable to indicate if the modflow has been called or not
self.modflow_has_been_called = False
# list of the convergence criteria for HCLOSE (unit: m)
# - Deltares default's value is 0.001 m # check this value with Jarno
self.criteria_HCLOSE = [0.001, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
self.criteria_HCLOSE = sorted(self.criteria_HCLOSE)
# list of the convergence criteria for RCLOSE (unit: m3)
# - Deltares default's value for their 25 and 250 m resolution models is 10 m3 # check this value with Jarno
cell_area_assumption = verticalSizeInMeter * float(pcr.cellvalue(pcr.mapmaximum(horizontalSizeInMeter),1)[0])
self.criteria_RCLOSE = [10., 10.* cell_area_assumption/(250.*250.), 10.* cell_area_assumption/(25.*25.)]
self.criteria_RCLOSE = sorted(self.criteria_RCLOSE)
# initiate the index for HCLOSE and RCLOSE
self.iteration_HCLOSE = 0
self.iteration_RCLOSE = 0
# initiate old style reporting # TODO: remove this!
self.initiate_old_style_groundwater_reporting(iniItems)
def __init__(self,
iniItems,
landmask,
onlyNaturalWaterBodies=False,
lddMap=None):
object.__init__(self)
# clone map file names, temporary directory and global/absolute path of input directory
self.cloneMap = iniItems.cloneMap
self.tmpDir = iniItems.tmpDir
self.inputDir = iniItems.globalOptions['inputDir']
self.landmask = landmask
self.iniItems = iniItems
# local drainage direction:
if lddMap is None:
self.lddMap = vos.readPCRmapClone(
iniItems.routingOptions['lddMap'], self.cloneMap, self.tmpDir,
self.inputDir, True)
self.lddMap = pcr.lddrepair(pcr.ldd(self.lddMap))
self.lddMap = pcr.lddrepair(self.lddMap)
else:
self.lddMap = lddMap
# the following is needed for a modflowOfflineCoupling run
if 'modflowOfflineCoupling' in list(iniItems.globalOptions.keys(
)) and iniItems.globalOptions[
'modflowOfflineCoupling'] == "True" and 'routingOptions' not in iniItems.allSections:
logger.info(
"The 'routingOptions' are not defined in the configuration ini file. We will adopt them from the 'modflowParameterOptions'."
)
iniItems.routingOptions = iniItems.modflowParameterOptions
# option to activate water balance check
self.debugWaterBalance = True
if 'debugWaterBalance' in list(iniItems.routingOptions.keys(
)) and iniItems.routingOptions['debugWaterBalance'] == "False":
self.debugWaterBalance = False
# option to perform a run with only natural lakes (without reservoirs)
self.onlyNaturalWaterBodies = onlyNaturalWaterBodies
if "onlyNaturalWaterBodies" in list(iniItems.routingOptions.keys(
)) and iniItems.routingOptions['onlyNaturalWaterBodies'] == "True":
logger.info(
"Using only natural water bodies identified in the year 1900. All reservoirs in 1900 are assumed as lakes."
)
self.onlyNaturalWaterBodies = True
self.dateForNaturalCondition = "1900-01-01" # The run for a natural condition should access only this date.
# names of files containing water bodies parameters
if iniItems.routingOptions['waterBodyInputNC'] == str(None):
self.useNetCDF = False
self.fracWaterInp = iniItems.routingOptions['fracWaterInp']
self.waterBodyIdsInp = iniItems.routingOptions['waterBodyIds']
self.waterBodyTypInp = iniItems.routingOptions['waterBodyTyp']
self.resMaxCapInp = iniItems.routingOptions['resMaxCapInp']
self.resSfAreaInp = iniItems.routingOptions['resSfAreaInp']
else:
self.useNetCDF = True
self.ncFileInp = vos.getFullPath(\
iniItems.routingOptions['waterBodyInputNC'],\
self.inputDir)
# minimum width (m) used in the weir formula # TODO: define minWeirWidth based on the GLWD, GRanD database and/or bankfull discharge formula
self.minWeirWidth = 10.
# lower and upper limits at which reservoir release is terminated and
# at which reservoir release is equal to long-term average outflow
# - default values
self.minResvrFrac = 0.10
self.maxResvrFrac = 0.75
# - from the ini file
if "minResvrFrac" in list(iniItems.routingOptions.keys()):
minResvrFrac = iniItems.routingOptions['minResvrFrac']
self.minResvrFrac = vos.readPCRmapClone(minResvrFrac,
self.cloneMap, self.tmpDir,
self.inputDir)
if "maxResvrFrac" in list(iniItems.routingOptions.keys()):
maxResvrFrac = iniItems.routingOptions['maxResvrFrac']
self.maxResvrFrac = vos.readPCRmapClone(maxResvrFrac,
self.cloneMap, self.tmpDir,
self.inputDir)
def main():
### Read input arguments #####
parser = OptionParser()
usage = "usage: %prog [options]"
parser = OptionParser(usage=usage)
parser.add_option('-q',
'--quiet',
dest='verbose',
default=True,
action='store_false',
help='do not print status messages to stdout')
parser.add_option('-i',
'--ini',
dest='inifile',
default='hand_contour_inun.ini',
nargs=1,
help='ini configuration file')
parser.add_option('-f',
'--flood_map',
nargs=1,
dest='flood_map',
help='Flood map file (NetCDF point time series file')
parser.add_option('-v',
'--flood_variable',
nargs=1,
dest='flood_variable',
default='water_level',
help='variable name of flood water level')
parser.add_option(
'-b',
'--bankfull_map',
dest='bankfull_map',
default='',
help=
'Map containing bank full level (is subtracted from flood map, in NetCDF)'
)
parser.add_option(
'-c',
'--catchment',
dest='catchment_strahler',
default=7,
type='int',
help='Strahler order threshold >= are selected as catchment boundaries'
)
parser.add_option('-s',
'--hand_strahler',
dest='hand_strahler',
default=7,
type='int',
help='Strahler order threshold >= selected as riverine')
parser.add_option('-d',
'--destination',
dest='dest_path',
default='inun',
help='Destination path')
(options, args) = parser.parse_args()
if not os.path.exists(options.inifile):
print 'path to ini file cannot be found'
sys.exit(1)
options.dest_path = os.path.abspath(options.dest_path)
if not (os.path.isdir(options.dest_path)):
os.makedirs(options.dest_path)
# set up the logger
flood_name = os.path.split(options.flood_map)[1].split('.')[0]
case_name = 'inun_{:s}_hand_{:02d}_catch_{:02d}'.format(
flood_name, options.hand_strahler, options.catchment_strahler)
logfilename = os.path.join(options.dest_path, 'hand_contour_inun.log')
logger, ch = inun_lib.setlogger(logfilename, 'HAND_INUN', options.verbose)
logger.info('$Id: $')
logger.info('Flood map: {:s}'.format(options.flood_map))
logger.info('Bank full map: {:s}'.format(options.bankfull_map))
logger.info('Destination path: {:s}'.format(options.dest_path))
# read out ini file
### READ CONFIG FILE
# open config-file
config = inun_lib.open_conf(options.inifile)
# read settings
options.dem_file = inun_lib.configget(config, 'maps', 'dem_file', True)
options.ldd_file = inun_lib.configget(config, 'maps', 'ldd_file', True)
options.stream_file = inun_lib.configget(config, 'maps', 'stream_file',
True)
options.riv_length_file = inun_lib.configget(config, 'maps',
'riv_length_file', True)
options.riv_width_file = inun_lib.configget(config, 'maps',
'riv_width_file', True)
options.file_format = inun_lib.configget(config,
'maps',
'file_format',
0,
datatype='int')
options.x_tile = inun_lib.configget(config,
'tiling',
'x_tile',
10000,
datatype='int')
options.y_tile = inun_lib.configget(config,
'tiling',
'y_tile',
10000,
datatype='int')
options.x_overlap = inun_lib.configget(config,
'tiling',
'x_overlap',
1000,
datatype='int')
options.y_overlap = inun_lib.configget(config,
'tiling',
'y_overlap',
1000,
datatype='int')
options.iterations = inun_lib.configget(config,
'inundation',
'iterations',
20,
datatype='int')
options.initial_level = inun_lib.configget(config,
'inundation',
'initial_level',
32.,
datatype='float')
options.area_multiplier = inun_lib.configget(config,
'inundation',
'area_multiplier',
1.,
datatype='float')
logger.info('DEM file: {:s}'.format(options.dem_file))
logger.info('LDD file: {:s}'.format(options.ldd_file))
logger.info('Columns per tile: {:d}'.format(options.x_tile))
logger.info('Rows per tile: {:d}'.format(options.y_tile))
logger.info('Columns overlap: {:d}'.format(options.x_overlap))
logger.info('Rows overlap: {:d}'.format(options.y_overlap))
metadata_global = {}
# add metadata from the section [metadata]
meta_keys = config.options('metadata_global')
for key in meta_keys:
metadata_global[key] = config.get('metadata_global', key)
# add a number of metadata variables that are mandatory
metadata_global['config_file'] = os.path.abspath(options.inifile)
metadata_var = {}
metadata_var['units'] = 'm'
metadata_var[
'standard_name'] = 'water_surface_height_above_reference_datum'
metadata_var['long_name'] = 'Coastal flooding'
metadata_var[
'comment'] = 'water_surface_reference_datum_altitude is given in file {:s}'.format(
options.dem_file)
if not os.path.exists(options.dem_file):
logger.error('path to dem file {:s} cannot be found'.format(
options.dem_file))
sys.exit(1)
if not os.path.exists(options.ldd_file):
logger.error('path to ldd file {:s} cannot be found'.format(
options.ldd_file))
sys.exit(1)
# Read extent from a GDAL compatible file
try:
extent = inun_lib.get_gdal_extent(options.dem_file)
except:
msg = 'Input file {:s} not a gdal compatible file'.format(
options.dem_file)
inun_lib.close_with_error(logger, ch, msg)
sys.exit(1)
try:
x, y = inun_lib.get_gdal_axes(options.dem_file, logging=logger)
srs = inun_lib.get_gdal_projection(options.dem_file, logging=logger)
except:
msg = 'Input file {:s} not a gdal compatible file'.format(
options.dem_file)
inun_lib.close_with_error(logger, ch, msg)
sys.exit(1)
# read history from flood file
if options.file_format == 0:
a = nc.Dataset(options.flood_map, 'r')
metadata_global[
'history'] = 'Created by: $Id: $, boundary conditions from {:s},\nhistory: {:s}'.format(
os.path.abspath(options.flood_map), a.history)
a.close()
else:
metadata_global[
'history'] = 'Created by: $Id: $, boundary conditions from {:s},\nhistory: {:s}'.format(
os.path.abspath(options.flood_map),
'PCRaster file, no history')
# first write subcatch maps and hand maps
############### TODO ######
# setup a HAND file
dem_name = os.path.split(options.dem_file)[1].split('.')[0]
hand_file = os.path.join(
options.dest_path,
'{:s}_hand_strahler_{:02d}.tif'.format(dem_name,
options.hand_strahler))
if not (os.path.isfile(hand_file)):
# hand file does not exist yet! Generate it, otherwise skip!
logger.info(
'HAND file {:s} setting up...please wait...'.format(hand_file))
hand_file_tmp = os.path.join(
options.dest_path,
'{:s}_hand_strahler_{:02d}.tif.tmp'.format(dem_name,
options.hand_strahler))
ds_hand = inun_lib.prepare_gdal(hand_file_tmp,
x,
y,
logging=logger,
srs=srs)
band_hand = ds_hand.GetRasterBand(1)
# Open terrain data for reading
ds_dem, rasterband_dem = inun_lib.get_gdal_rasterband(options.dem_file)
ds_ldd, rasterband_ldd = inun_lib.get_gdal_rasterband(options.ldd_file)
ds_stream, rasterband_stream = inun_lib.get_gdal_rasterband(
options.stream_file)
n = 0
for x_loop in range(0, len(x), options.x_tile):
x_start = np.maximum(x_loop, 0)
x_end = np.minimum(x_loop + options.x_tile, len(x))
# determine actual overlap for cutting
for y_loop in range(0, len(y), options.y_tile):
x_overlap_min = x_start - np.maximum(
x_start - options.x_overlap, 0)
x_overlap_max = np.minimum(x_end + options.x_overlap,
len(x)) - x_end
n += 1
# print('tile {:001d}:'.format(n))
y_start = np.maximum(y_loop, 0)
y_end = np.minimum(y_loop + options.y_tile, len(y))
y_overlap_min = y_start - np.maximum(
y_start - options.y_overlap, 0)
y_overlap_max = np.minimum(y_end + options.y_overlap,
len(y)) - y_end
# cut out DEM
logger.debug(
'Computing HAND for xmin: {:d} xmax: {:d} ymin {:d} ymax {:d}'
.format(x_start, x_end, y_start, y_end))
terrain = rasterband_dem.ReadAsArray(
x_start - x_overlap_min, y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min))
drainage = rasterband_ldd.ReadAsArray(
x_start - x_overlap_min, y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min))
stream = rasterband_stream.ReadAsArray(
x_start - x_overlap_min, y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min))
# write to temporary file
terrain_temp_file = os.path.join(options.dest_path,
'terrain_temp.map')
drainage_temp_file = os.path.join(options.dest_path,
'drainage_temp.map')
stream_temp_file = os.path.join(options.dest_path,
'stream_temp.map')
if rasterband_dem.GetNoDataValue() is not None:
inun_lib.gdal_writemap(terrain_temp_file,
'PCRaster',
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
terrain,
rasterband_dem.GetNoDataValue(),
gdal_type=gdal.GDT_Float32,
logging=logger)
else:
# in case no nodata value is found
logger.warning(
'No nodata value found in {:s}. assuming -9999'.format(
options.dem_file))
inun_lib.gdal_writemap(terrain_temp_file,
'PCRaster',
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
terrain,
-9999.,
gdal_type=gdal.GDT_Float32,
logging=logger)
inun_lib.gdal_writemap(drainage_temp_file,
'PCRaster',
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
drainage,
rasterband_ldd.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger)
inun_lib.gdal_writemap(stream_temp_file,
'PCRaster',
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
stream,
rasterband_ldd.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger)
# read as pcr objects
pcr.setclone(terrain_temp_file)
terrain_pcr = pcr.readmap(terrain_temp_file)
drainage_pcr = pcr.lddrepair(
pcr.ldd(pcr.readmap(
drainage_temp_file))) # convert to ldd type map
stream_pcr = pcr.scalar(
pcr.readmap(stream_temp_file)) # convert to ldd type map
# compute streams
stream_ge, subcatch = inun_lib.subcatch_stream(
drainage_pcr, stream_pcr,
options.hand_strahler) # generate streams
basin = pcr.boolean(subcatch)
hand_pcr, dist_pcr = inun_lib.derive_HAND(
terrain_pcr,
drainage_pcr,
3000,
rivers=pcr.boolean(stream_ge),
basin=basin)
# convert to numpy
hand = pcr.pcr2numpy(hand_pcr, -9999.)
# cut relevant part
if y_overlap_max == 0:
y_overlap_max = -hand.shape[0]
if x_overlap_max == 0:
x_overlap_max = -hand.shape[1]
hand_cut = hand[0 + y_overlap_min:-y_overlap_max,
0 + x_overlap_min:-x_overlap_max]
band_hand.WriteArray(hand_cut, x_start, y_start)
os.unlink(terrain_temp_file)
os.unlink(drainage_temp_file)
band_hand.FlushCache()
ds_dem = None
ds_ldd = None
ds_stream = None
band_hand.SetNoDataValue(-9999.)
ds_hand = None
logger.info('Finalizing {:s}'.format(hand_file))
# rename temporary file to final hand file
os.rename(hand_file_tmp, hand_file)
else:
logger.info(
'HAND file {:s} already exists...skipping...'.format(hand_file))
#####################################################################################
# HAND file has now been prepared, moving to flood mapping part #
#####################################################################################
# load the staticmaps needed to estimate volumes across all
xax, yax, riv_length, fill_value = inun_lib.gdal_readmap(
options.riv_length_file, 'GTiff')
riv_length = np.ma.masked_where(riv_length == fill_value, riv_length)
xax, yax, riv_width, fill_value = inun_lib.gdal_readmap(
options.riv_width_file, 'GTiff')
riv_width[riv_width == fill_value] = 0
x_res = np.abs((xax[-1] - xax[0]) / (len(xax) - 1))
y_res = np.abs((yax[-1] - yax[0]) / (len(yax) - 1))
flood_folder = os.path.join(options.dest_path, case_name)
flood_vol_map = os.path.join(
flood_folder, '{:s}_vol.tif'.format(
os.path.split(options.flood_map)[1].split('.')[0]))
if not (os.path.isdir(flood_folder)):
os.makedirs(flood_folder)
inun_file_tmp = os.path.join(flood_folder,
'{:s}.tif.tmp'.format(case_name))
inun_file = os.path.join(flood_folder, '{:s}.tif'.format(case_name))
hand_temp_file = os.path.join(flood_folder, 'hand_temp.map')
drainage_temp_file = os.path.join(flood_folder, 'drainage_temp.map')
stream_temp_file = os.path.join(flood_folder, 'stream_temp.map')
flood_vol_temp_file = os.path.join(flood_folder, 'flood_warp_temp.tif')
# load the data with river levels and compute the volumes
if options.file_format == 0:
# assume we need the maximum value in a NetCDF time series grid
a = nc.Dataset(options.flood_map, 'r')
xax = a.variables['x'][:]
yax = a.variables['y'][:]
flood_series = a.variables[options.flood_variable][:]
flood_data = flood_series.max(axis=0)
if np.ma.is_masked(flood_data):
flood = flood_data.data
flood[flood_data.mask] = 0
if yax[-1] > yax[0]:
yax = np.flipud(yax)
flood = np.flipud(flood)
a.close()
elif options.file_format == 1:
xax, yax, flood, flood_fill_value = inun_lib.gdal_readmap(
options.flood_map, 'PCRaster')
flood[flood == flood_fill_value] = 0.
#res_x = x[1]-x[0]
#res_y = y[1]-y[0]
# load the bankfull depths
if options.bankfull_map == '':
bankfull = np.zeros(flood.shape)
else:
if options.file_format == 0:
a = nc.Dataset(options.bankfull_map, 'r')
xax = a.variables['x'][:]
yax = a.variables['y'][:]
bankfull = a.variables[options.flood_variable][0, :, :]
if yax[-1] > yax[0]:
yax = np.flipud(yax)
bankfull = np.flipud(bankful)
a.close()
elif options.file_format == 1:
xax, yax, bankfull, bankfull_fill_value = inun_lib.gdal_readmap(
options.bankfull_map, 'PCRaster')
# flood = bankfull*2
# res_x = 2000
# res_y = 2000
# subtract the bankfull water level to get flood levels (above bankfull)
flood_vol = np.maximum(flood - bankfull, 0)
flood_vol_m = riv_length * riv_width * flood_vol / (
x_res * y_res
) # volume expressed in meters water disc (1e6 is the surface area of one wflow grid cell)
flood_vol_m_data = flood_vol_m.data
flood_vol_m_data[flood_vol_m.mask] = -999.
print('Saving water layer map to {:s}'.format(flood_vol_map))
# write to a tiff file
inun_lib.gdal_writemap(flood_vol_map, 'GTiff', xax, yax,
np.maximum(flood_vol_m_data, 0), -999.)
ds_hand, rasterband_hand = inun_lib.get_gdal_rasterband(hand_file)
ds_ldd, rasterband_ldd = inun_lib.get_gdal_rasterband(options.ldd_file)
ds_stream, rasterband_stream = inun_lib.get_gdal_rasterband(
options.stream_file)
logger.info(
'Preparing flood map in {:s} ...please wait...'.format(inun_file))
ds_inun = inun_lib.prepare_gdal(inun_file_tmp,
x,
y,
logging=logger,
srs=srs)
band_inun = ds_inun.GetRasterBand(1)
# loop over all the tiles
n = 0
for x_loop in range(0, len(x), options.x_tile):
x_start = np.maximum(x_loop, 0)
x_end = np.minimum(x_loop + options.x_tile, len(x))
# determine actual overlap for cutting
for y_loop in range(0, len(y), options.y_tile):
x_overlap_min = x_start - np.maximum(x_start - options.x_overlap,
0)
x_overlap_max = np.minimum(x_end + options.x_overlap,
len(x)) - x_end
n += 1
# print('tile {:001d}:'.format(n))
y_start = np.maximum(y_loop, 0)
y_end = np.minimum(y_loop + options.y_tile, len(y))
y_overlap_min = y_start - np.maximum(y_start - options.y_overlap,
0)
y_overlap_max = np.minimum(y_end + options.y_overlap,
len(y)) - y_end
x_tile_ax = x[x_start - x_overlap_min:x_end + x_overlap_max]
y_tile_ax = y[y_start - y_overlap_min:y_end + y_overlap_max]
# cut out DEM
logger.debug(
'handling xmin: {:d} xmax: {:d} ymin {:d} ymax {:d}'.format(
x_start, x_end, y_start, y_end))
hand = rasterband_hand.ReadAsArray(
x_start - x_overlap_min, y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min))
drainage = rasterband_ldd.ReadAsArray(
x_start - x_overlap_min, y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min))
stream = rasterband_stream.ReadAsArray(
x_start - x_overlap_min, y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min))
print('len x-ax: {:d} len y-ax {:d} x-shape {:d} y-shape {:d}'.
format(len(x_tile_ax), len(y_tile_ax), hand.shape[1],
hand.shape[0]))
inun_lib.gdal_writemap(hand_temp_file,
'PCRaster',
x_tile_ax,
y_tile_ax,
hand,
rasterband_hand.GetNoDataValue(),
gdal_type=gdal.GDT_Float32,
logging=logger)
inun_lib.gdal_writemap(drainage_temp_file,
'PCRaster',
x_tile_ax,
y_tile_ax,
drainage,
rasterband_ldd.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger)
inun_lib.gdal_writemap(stream_temp_file,
'PCRaster',
x_tile_ax,
y_tile_ax,
stream,
rasterband_stream.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger)
# read as pcr objects
pcr.setclone(hand_temp_file)
hand_pcr = pcr.readmap(hand_temp_file)
drainage_pcr = pcr.lddrepair(
pcr.ldd(pcr.readmap(
drainage_temp_file))) # convert to ldd type map
stream_pcr = pcr.scalar(
pcr.readmap(drainage_temp_file)) # convert to ldd type map
# prepare a subcatchment map
stream_ge, subcatch = inun_lib.subcatch_stream(
drainage_pcr, stream_pcr,
options.catchment_strahler) # generate subcatchments
drainage_surf = pcr.ifthen(
stream_ge > 0, pcr.accuflux(
drainage_pcr,
1)) # proxy of drainage surface inaccurate at tile edges
# compute weights for spreadzone (1/drainage_surf)
subcatch = pcr.spreadzone(subcatch, 0, 0)
# TODO check weighting scheme, perhaps not necessary
# weight = 1./pcr.scalar(pcr.spreadzone(pcr.cover(pcr.ordinal(drainage_surf), 0), 0, 0))
# subcatch_fill = pcr.scalar(pcr.spreadzone(subcatch, 0, weight))
# # cover subcatch with subcatch_fill
# pcr.report(weight, 'weight_{:02d}.map'.format(n))
# pcr.report(subcatch, 'subcatch_{:02d}.map'.format(n))
# pcr.report(pcr.nominal(subcatch_fill), 'subcatch_fill_{:02d}.map'.format(n))
inun_lib.gdal_warp(flood_vol_map,
hand_temp_file,
flood_vol_temp_file,
gdal_interp=gdalconst.GRA_NearestNeighbour) # ,
x_tile_ax, y_tile_ax, flood_meter, fill_value = inun_lib.gdal_readmap(
flood_vol_temp_file, 'GTiff')
# convert meter depth to volume [m3]
flood_vol = pcr.numpy2pcr(pcr.Scalar, flood_meter, fill_value) * (
(x_tile_ax[1] - x_tile_ax[0]) * (y_tile_ax[0] - y_tile_ax[1])
) # resolution of SRTM *1166400000.
## now we have some nice volume. Now we need to redistribute!
inundation_pcr = inun_lib.volume_spread(
drainage_pcr,
hand_pcr,
subcatch,
flood_vol,
volume_thres=0.,
iterations=options.iterations,
area_multiplier=options.area_multiplier) # 1166400000.
inundation = pcr.pcr2numpy(inundation_pcr, -9999.)
# cut relevant part
if y_overlap_max == 0:
y_overlap_max = -inundation.shape[0]
if x_overlap_max == 0:
x_overlap_max = -inundation.shape[1]
inundation_cut = inundation[0 + y_overlap_min:-y_overlap_max,
0 + x_overlap_min:-x_overlap_max]
# inundation_cut
band_inun.WriteArray(inundation_cut, x_start, y_start)
band_inun.FlushCache()
# clean up
os.unlink(flood_vol_temp_file)
os.unlink(drainage_temp_file)
os.unlink(hand_temp_file)
# if n == 35:
# band_inun.SetNoDataValue(-9999.)
# ds_inun = None
# sys.exit(0)
os.unlink(flood_vol_map)
logger.info('Finalizing {:s}'.format(inun_file))
# add the metadata to the file and band
band_inun.SetNoDataValue(-9999.)
ds_inun.SetMetadata(metadata_global)
band_inun.SetMetadata(metadata_var)
ds_inun = None
ds_hand = None
ds_ldd = None
# rename temporary file to final hand file
if os.path.isfile(inun_file):
# remove an old result if available
os.unlink(inun_file)
os.rename(inun_file_tmp, inun_file)
logger.info('Done! Thank you for using hand_contour_inun.py')
logger, ch = inun_lib.closeLogger(logger, ch)
del logger, ch
sys.exit(0)
def __init__(self, iniItems, landmask):
object.__init__(self)
# cloneMap, temporary directory, absolute path for input directory, landmask
self.cloneMap = iniItems.cloneMap
self.tmpDir = iniItems.tmpDir
self.inputDir = iniItems.globalOptions['inputDir']
self.landmask = landmask
# configuration from the ini file
self.iniItems = iniItems
# topography properties: read several variables from the netcdf file
for var in ['dem_minimum','dem_maximum','dem_average','dem_standard_deviation',\
'slopeLength','orographyBeta','tanslope',\
'dzRel0000','dzRel0001','dzRel0005',\
'dzRel0010','dzRel0020','dzRel0030','dzRel0040','dzRel0050',\
'dzRel0060','dzRel0070','dzRel0080','dzRel0090','dzRel0100']:
vars(self)[var] = vos.netcdf2PCRobjCloneWithoutTime(self.iniItems.modflowParameterOptions['topographyNC'], \
var, self.cloneMap)
vars(self)[var] = pcr.cover(vars(self)[var], 0.0)
# channel properties: read several variables from the netcdf file
for var in [
'lddMap', 'cellAreaMap', 'gradient', 'bankfull_width',
'bankfull_depth', 'dem_floodplain', 'dem_riverbed'
]:
vars(self)[var] = vos.netcdf2PCRobjCloneWithoutTime(self.iniItems.modflowParameterOptions['channelNC'], \
var, self.cloneMap)
vars(self)[var] = pcr.cover(vars(self)[var], 0.0)
# minimum channel width
minimum_channel_width = 0.1
self.bankfull_width = pcr.max(minimum_channel_width,
self.bankfull_width)
#~ # cell fraction if channel water reaching the flood plan # NOT USED
#~ self.flood_plain_fraction = self.return_innundation_fraction(pcr.max(0.0, self.dem_floodplain - self.dem_minimum))
# coefficient of Manning
self.manningsN = vos.readPCRmapClone(self.iniItems.modflowParameterOptions['manningsN'],\
self.cloneMap,self.tmpDir,self.inputDir)
# minimum channel gradient
minGradient = 0.00005
self.gradient = pcr.max(minGradient,
pcr.cover(self.gradient, minGradient))
# correcting lddMap
self.lddMap = pcr.ifthen(pcr.scalar(self.lddMap) > 0.0, self.lddMap)
self.lddMap = pcr.lddrepair(pcr.ldd(self.lddMap))
# channelLength = approximation of channel length (unit: m) # This is approximated by cell diagonal.
cellSizeInArcMin = np.round(pcr.clone().cellSize() * 60.)
verticalSizeInMeter = cellSizeInArcMin * 1852.
self.channelLength = ((self.cellAreaMap/verticalSizeInMeter)**(2)+\
(verticalSizeInMeter)**(2))**(0.5)
# option for lakes and reservoir
self.onlyNaturalWaterBodies = False
if self.iniItems.modflowParameterOptions[
'onlyNaturalWaterBodies'] == "True":
self.onlyNaturalWaterBodies = True
# groundwater linear recession coefficient (day-1) ; the linear reservoir concept is still being used to represent fast response flow
# particularly from karstic aquifer in mountainous regions
self.recessionCoeff = vos.netcdf2PCRobjCloneWithoutTime(self.iniItems.modflowParameterOptions['groundwaterPropertiesNC'],\
'recessionCoeff', self.cloneMap)
self.recessionCoeff = pcr.cover(self.recessionCoeff, 0.00)
self.recessionCoeff = pcr.min(1.0000, self.recessionCoeff)
#
if 'minRecessionCoeff' in iniItems.modflowParameterOptions.keys():
minRecessionCoeff = float(
iniItems.modflowParameterOptions['minRecessionCoeff'])
else:
minRecessionCoeff = 1.0e-4 # This is the minimum value used in Van Beek et al. (2011).
self.recessionCoeff = pcr.max(minRecessionCoeff, self.recessionCoeff)
# aquifer saturated conductivity (m/day)
self.kSatAquifer = vos.netcdf2PCRobjCloneWithoutTime(self.iniItems.modflowParameterOptions['groundwaterPropertiesNC'],\
'kSatAquifer', self.cloneMap)
self.kSatAquifer = pcr.cover(self.kSatAquifer,
pcr.mapmaximum(self.kSatAquifer))
self.kSatAquifer = pcr.max(0.010, self.kSatAquifer)
self.kSatAquifer *= 0.001
# aquifer specific yield (dimensionless)
self.specificYield = vos.netcdf2PCRobjCloneWithoutTime(self.iniItems.modflowParameterOptions['groundwaterPropertiesNC'],\
'specificYield', self.cloneMap)
self.specificYield = pcr.cover(self.specificYield,
pcr.mapmaximum(self.specificYield))
self.specificYield = pcr.max(
0.010, self.specificYield
) # TODO: TO BE CHECKED: The resample process of specificYield
self.specificYield = pcr.min(1.000, self.specificYield)
# estimate of thickness (unit: m) of accesible groundwater
totalGroundwaterThickness = vos.netcdf2PCRobjCloneWithoutTime(self.iniItems.modflowParameterOptions['estimateOfTotalGroundwaterThicknessNC'],\
'thickness', self.cloneMap)
# extrapolation
totalGroundwaterThickness = pcr.cover(totalGroundwaterThickness,\
pcr.windowaverage(totalGroundwaterThickness, 1.0))
totalGroundwaterThickness = pcr.cover(totalGroundwaterThickness,\
pcr.windowaverage(totalGroundwaterThickness, 1.5))
totalGroundwaterThickness = pcr.cover(totalGroundwaterThickness, 0.0)
#
# set minimum thickness
minimumThickness = pcr.scalar(float(\
self.iniItems.modflowParameterOptions['minimumTotalGroundwaterThickness']))
totalGroundwaterThickness = pcr.max(minimumThickness,
totalGroundwaterThickness)
#
# set maximum thickness: 250 m.
maximumThickness = 250.
self.totalGroundwaterThickness = pcr.min(maximumThickness,
totalGroundwaterThickness)
# river bed resistance (unit: day)
self.bed_resistance = 1.0
# option to ignore capillary rise
self.ignoreCapRise = True
if self.iniItems.modflowParameterOptions['ignoreCapRise'] == "False":
self.ignoreCapRise = False
# initiate old style reporting # TODO: remove this!
self.initiate_old_style_groundwater_reporting(iniItems)
def subcatch_order_b(
ldd, oorder, sizelimit=0, fill=False, fillcomplete=False, stoporder=0
):
"""
Determines subcatchments using the catchment order
This version tries to keep the number op upstream/downstream catchment the
small by first dederivingatchment connected to the major river(the order) given, and fill
up from there.
Input:
- ldd
- oorder - order to use
- sizelimit - smallest catchments to include, default is all (sizelimit=0) in number of cells
- if fill is set to True the higer order catchment are filled also
- if fillcomplete is set to True the whole ldd is filled with catchments.
:returns sc, dif, nldd; Subcatchment, Points, subcatchldd
"""
# outl = find_outlet(ldd)
# large = pcr.subcatchment(ldd,pcr.boolean(outl))
if stoporder == 0:
stoporder = oorder
stt = pcr.streamorder(ldd)
sttd = pcr.downstream(ldd, stt)
pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(stt)) > 0.0, sttd)
maxorder = pcraster.framework.getCellValue(pcr.mapmaximum(stt), 1, 1)
dif = pcr.uniqueid(pcr.boolean(pcr.ifthen(stt == pcr.ordinal(oorder), pts)))
if fill:
for order in range(oorder, maxorder):
m_pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(order)) > 0.0, sttd)
m_dif = pcr.uniqueid(
pcr.boolean(pcr.ifthen(stt == pcr.ordinal(order), m_pts))
)
dif = pcr.uniqueid(pcr.boolean(pcr.cover(m_dif, dif)))
for myorder in range(oorder - 1, stoporder, -1):
sc = pcr.subcatchment(ldd, pcr.nominal(dif))
m_pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(stt)) > 0.0, sttd)
m_dif = pcr.uniqueid(
pcr.boolean(pcr.ifthen(stt == pcr.ordinal(myorder - 1), m_pts))
)
dif = pcr.uniqueid(
pcr.boolean(pcr.cover(pcr.ifthen(pcr.scalar(sc) == 0, m_dif), dif))
)
if fillcomplete:
sc = pcr.subcatchment(ldd, pcr.nominal(dif))
cs, m_dif, stt = subcatch_order_a(ldd, stoporder)
dif = pcr.uniqueid(
pcr.boolean(
pcr.cover(
pcr.ifthen(pcr.scalar(sc) == 0, pcr.ordinal(m_dif)),
pcr.ordinal(dif),
)
)
)
scsize = pcr.catchmenttotal(1, ldd)
dif = pcr.ordinal(pcr.uniqueid(pcr.boolean(pcr.ifthen(scsize >= sizelimit, dif))))
sc = pcr.subcatchment(ldd, dif)
# Make pit ldd
nldd = pcr.lddrepair(pcr.ifthenelse(pcr.cover(dif, 0) > 0, 5, ldd))
return sc, dif, nldd
def main():
# output folder
clean_out_folder = True
if os.path.exists(out_folder):
if clean_out_folder:
shutil.rmtree(out_folder)
os.makedirs(out_folder)
else:
os.makedirs(out_folder)
os.chdir(out_folder)
os.system("pwd")
# tmp folder
tmp_folder = out_folder + "/tmp/"
if os.path.exists(tmp_folder): shutil.rmtree(tmp_folder)
os.makedirs(tmp_folder)
# set the clone map
print("set the clone map")
pcr.setclone(global_clone_map_file)
# read ldd map
print("define the ldd")
# ~ ldd_map = pcr.readmap(global_ldd_inp_file)
ldd_map = pcr.lddrepair(pcr.lddrepair(pcr.ldd(vos.readPCRmapClone(v = global_ldd_inp_file, \
cloneMapFileName = global_clone_map_file, \
tmpDir = tmp_folder, \
absolutePath = None, \
isLddMap = True, \
cover = None, \
isNomMap = False))))
# define the landmask
if landmask_map_file == None:
print("define the landmask based on the ldd input")
# ~ landmask = pcr.defined(pcr.readmap(global_ldd_inp_file))
landmask = pcr.defined(ldd_map)
landmask = pcr.ifthen(landmask, landmask)
else:
print("define the landmask based on the input landmask_map_file")
landmask = pcr.readmap(landmask_map_file)
ldd_map = pcr.ifthen(landmask, pcr.cover(ldd_map, pcr.ldd(5)))
ldd_map = pcr.lddrepair(pcr.lddrepair(pcr.ldd(ldd_map)))
landmask = pcr.defined(ldd_map)
landmask = pcr.ifthen(landmask, landmask)
# save ldd files used
# - global ldd
cmd = "cp " + str(global_ldd_inp_file) + " ."
print(cmd)
os.system(cmd)
# - ldd map that is used
pcr.report(ldd_map, "lddmap_used.map")
# make catchment map
print("make catchment map")
catchment_map = pcr.catchment(ldd_map, pcr.pit(ldd_map))
# read global subdomain file
print("read global subdomain file")
global_subdomain_map = vos.readPCRmapClone(
v=global_subdomain_file,
cloneMapFileName=global_clone_map_file,
tmpDir=tmp_folder,
absolutePath=None,
isLddMap=False,
cover=None,
isNomMap=True)
# set initial subdomain
print("assign subdomains to all catchments")
subdomains_initial = pcr.areamajority(global_subdomain_map, catchment_map)
subdomains_initial = pcr.ifthen(landmask, subdomains_initial)
pcr.aguila(subdomains_initial)
pcr.report(subdomains_initial, "global_subdomains_initial.map")
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[0])))
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[1])))
print("Checking all subdomains, avoid too large subdomains")
num_of_masks = int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[1])
# clone code that will be assigned
assigned_number = 0
subdomains_final = pcr.ifthen(
pcr.scalar(subdomains_initial) < -7777, pcr.nominal(0))
for nr in range(1, num_of_masks + 1, 1):
msg = "Processing the landmask %s" % (str(nr))
print(msg)
mask_selected_boolean = pcr.ifthen(subdomains_initial == nr,
pcr.boolean(1.0))
process_this_clone = False
if pcr.cellvalue(pcr.mapmaximum(pcr.scalar(mask_selected_boolean)), 1,
1)[0] > 0:
process_this_clone = True
# ~ if nr == 1: pcr.aguila(mask_selected_boolean)
# - initial check value
check_ok = True
if process_this_clone:
xmin, ymin, xmax, ymax = boundingBox(mask_selected_boolean)
area_in_degree2 = (xmax - xmin) * (ymax - ymin)
# ~ print(str(area_in_degree2))
# check whether the size of bounding box is ok
reference_area_in_degree2 = 2500.
if area_in_degree2 > 1.50 * reference_area_in_degree2:
check_ok = False
if (xmax - xmin) > 10 * (ymax - ymin): check_ok = False
# ~ # ignore checking
# ~ check_ok = True
if check_ok == True and process_this_clone == True:
msg = "Clump is not needed."
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# assign the clone code
assigned_number = assigned_number + 1
# update global landmask for river and land
mask_selected_nominal = pcr.ifthen(mask_selected_boolean,
pcr.nominal(assigned_number))
subdomains_final = pcr.cover(subdomains_final,
mask_selected_nominal)
if check_ok == False and process_this_clone == True:
msg = "Clump is needed."
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# make clump
clump_ids = pcr.nominal(pcr.clump(mask_selected_boolean))
# merge clumps that are close together
clump_ids_window_majority = pcr.windowmajority(clump_ids, 10.0)
clump_ids = pcr.areamajority(clump_ids_window_majority, clump_ids)
# ~ pcr.aguila(clump_ids)
# minimimum and maximum values
min_clump_id = int(
pcr.cellvalue(pcr.mapminimum(pcr.scalar(clump_ids)), 1)[0])
max_clump_id = int(
pcr.cellvalue(pcr.mapmaximum(pcr.scalar(clump_ids)), 1)[0])
for clump_id in range(min_clump_id, max_clump_id + 1, 1):
msg = "Processing the clump %s of %s from the landmask %s" % (
str(clump_id), str(max_clump_id), str(nr))
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# identify mask based on the clump
mask_selected_boolean_from_clump = pcr.ifthen(
clump_ids == pcr.nominal(clump_id), mask_selected_boolean)
mask_selected_boolean_from_clump = pcr.ifthen(
mask_selected_boolean_from_clump,
mask_selected_boolean_from_clump)
# check whether the clump is empty
check_mask_selected_boolean_from_clump = pcr.ifthen(
mask_selected_boolean, mask_selected_boolean_from_clump)
check_if_empty = float(
pcr.cellvalue(
pcr.mapmaximum(
pcr.scalar(
pcr.defined(
check_mask_selected_boolean_from_clump))),
1)[0])
if check_if_empty == 0.0:
msg = "Map is empty !"
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
else:
msg = "Map is NOT empty !"
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# assign the clone code
assigned_number = assigned_number + 1
# update global landmask for river and land
mask_selected_nominal = pcr.ifthen(
mask_selected_boolean_from_clump,
pcr.nominal(assigned_number))
subdomains_final = pcr.cover(subdomains_final,
mask_selected_nominal)
# ~ # kill all aguila processes if exist
# ~ os.system('killall aguila')
pcr.aguila(subdomains_final)
print("")
print("")
print("")
print("The subdomain map is READY.")
pcr.report(subdomains_final, "global_subdomains_final.map")
num_of_masks = int(vos.getMinMaxMean(pcr.scalar(subdomains_final))[1])
print(num_of_masks)
print("")
print("")
print("")
print("Making the clone and landmask maps for all subdomains")
num_of_masks = int(vos.getMinMaxMean(pcr.scalar(subdomains_final))[1])
# clone and mask folders
clone_folder = out_folder + "/clone/"
if os.path.exists(clone_folder): shutil.rmtree(clone_folder)
os.makedirs(clone_folder)
mask_folder = out_folder + "/mask/"
if os.path.exists(mask_folder): shutil.rmtree(mask_folder)
os.makedirs(mask_folder)
print("")
print("")
for nr in range(1, num_of_masks + 1, 1):
msg = "Processing the subdomain %s" % (str(nr))
print(msg)
# set the global clone
pcr.setclone(global_clone_map_file)
mask_selected_boolean = pcr.ifthen(subdomains_final == nr,
pcr.boolean(1.0))
mask_selected_nominal = pcr.ifthen(subdomains_final == nr,
pcr.nominal(nr))
mask_file = "mask/mask_%s.map" % (str(nr))
pcr.report(mask_selected_nominal, mask_file)
xmin, ymin, xmax, ymax = boundingBox(mask_selected_boolean)
area_in_degree2 = (xmax - xmin) * (ymax - ymin)
print(
str(nr) + " ; " + str(area_in_degree2) + " ; " +
str((xmax - xmin)) + " ; " + str((ymax - ymin)))
# cellsize in arcdegree
cellsize = cellsize_in_arcmin / 60.
# number of rows and cols
num_rows = int(round(ymax - ymin) / cellsize)
num_cols = int(round(xmax - xmin) / cellsize)
# make the clone map using mapattr
clonemap_mask_file = "clone/clonemap_mask_%s.map" % (str(nr))
cmd = "mapattr -s -R %s -C %s -B -P yb2t -x %s -y %s -l %s %s" % (
str(num_rows), str(num_cols), str(xmin), str(ymax), str(cellsize),
clonemap_mask_file)
print(cmd)
os.system(cmd)
# set the local landmask for the clump
pcr.setclone(clonemap_mask_file)
local_mask = vos.readPCRmapClone(v = mask_file, \
cloneMapFileName = clonemap_mask_file,
tmpDir = tmp_folder, \
absolutePath = None, isLddMap = False, cover = None, isNomMap = True)
local_mask_boolean = pcr.defined(local_mask)
local_mask_boolean = pcr.ifthen(local_mask_boolean, local_mask_boolean)
pcr.report(local_mask_boolean, mask_file)
print("")
print("")
print("")
print(num_of_masks)
def evaluateAllModelResults(self,globalCloneMapFileName,\
catchmentClassFileName,\
lddMapFileName,\
cellAreaMapFileName,\
pcrglobwb_output,\
analysisOutputDir="",\
tmpDir = "/dev/shm/edwin_grdc_"):
# output directory for all analyses for all stations
analysisOutputDir = str(analysisOutputDir)
self.chartOutputDir = analysisOutputDir+"/chart/"
self.tableOutputDir = analysisOutputDir+"/table/"
#
if analysisOutputDir == "": self.chartOutputDir = "chart/"
if analysisOutputDir == "": self.tableOutputDir = "table/"
#
# make the chart and table directories:
os.system('rm -r '+self.chartOutputDir+"*")
os.system('rm -r '+self.tableOutputDir+"*")
os.makedirs(self.chartOutputDir)
os.makedirs(self.tableOutputDir)
# cloneMap for all pcraster operations
pcr.setclone(globalCloneMapFileName)
cloneMap = pcr.boolean(1)
self.cell_size_in_arc_degree = vos.getMapAttributesALL(globalCloneMapFileName)['cellsize']
lddMap = pcr.lddrepair(pcr.readmap(lddMapFileName))
cellArea = pcr.scalar(pcr.readmap(cellAreaMapFileName))
# The landMaskClass map contains the nominal classes for all landmask regions.
landMaskClass = pcr.nominal(cloneMap) # default: if catchmentClassFileName is not given
if catchmentClassFileName != None:
landMaskClass = pcr.nominal(pcr.readmap(catchmentClassFileName))
# model catchment areas and cordinates
catchmentAreaAll = pcr.catchmenttotal(cellArea, lddMap) / (1000*1000) # unit: km2
xCoordinate = pcr.xcoordinate(cloneMap)
yCoordinate = pcr.ycoordinate(cloneMap)
print "Jaaaaaa"
for id in self.list_of_grdc_ids:
logger.info("Evaluating simulated discharge to the grdc observation at "+str(self.attributeGRDC["id_from_grdc"][str(id)])+".")
# identify model pixel
self.identifyModelPixel(tmpDir,catchmentAreaAll,landMaskClass,xCoordinate,yCoordinate,str(id))
# evaluate model results to GRDC data
self.evaluateModelResultsToGRDC(str(id),pcrglobwb_output,catchmentClassFileName,tmpDir)
# write the summary to a table
summary_file = analysisOutputDir+"summary.txt"
#
logger.info("Writing the summary for all stations to the file: "+str(summary_file)+".")
#
# prepare the file:
summary_file_handle = open(summary_file,"w")
#
# write the header
summary_file_handle.write( ";".join(self.grdc_dict_keys)+"\n")
#
# write the content
for id in self.list_of_grdc_ids:
rowLine = ""
for key in self.grdc_dict_keys: rowLine += str(self.attributeGRDC[key][str(id)]) + ";"
rowLine = rowLine[0:-1] + "\n"
summary_file_handle.write(rowLine)
summary_file_handle.close()
def main():
### Read input arguments #####
parser = OptionParser()
usage = "usage: %prog [options]"
parser = OptionParser(usage=usage)
parser.add_option('-q', '--quiet',
dest='verbose', default=True, action='store_false',
help='do not print status messages to stdout')
parser.add_option('-i', '--ini', dest='inifile',
default='hand_contour_inun.ini', nargs=1,
help='ini configuration file')
parser.add_option('-f', '--flood_map',
nargs=1, dest='flood_map',
help='Flood map file (NetCDF point time series file')
parser.add_option('-v', '--flood_variable',
nargs=1, dest='flood_variable',
default='water_level',
help='variable name of flood water level')
parser.add_option('-b', '--bankfull_map',
dest='bankfull_map', default='',
help='Map containing bank full level (is subtracted from flood map, in NetCDF)')
parser.add_option('-c', '--catchment',
dest='catchment_strahler', default=7, type='int',
help='Strahler order threshold >= are selected as catchment boundaries')
parser.add_option('-s', '--hand_strahler',
dest='hand_strahler', default=7, type='int',
help='Strahler order threshold >= selected as riverine')
parser.add_option('-d', '--destination',
dest='dest_path', default='inun',
help='Destination path')
(options, args) = parser.parse_args()
if not os.path.exists(options.inifile):
print 'path to ini file cannot be found'
sys.exit(1)
options.dest_path = os.path.abspath(options.dest_path)
if not(os.path.isdir(options.dest_path)):
os.makedirs(options.dest_path)
# set up the logger
flood_name = os.path.split(options.flood_map)[1].split('.')[0]
case_name = 'inun_{:s}_hand_{:02d}_catch_{:02d}'.format(flood_name, options.hand_strahler, options.catchment_strahler)
logfilename = os.path.join(options.dest_path, 'hand_contour_inun.log')
logger, ch = inun_lib.setlogger(logfilename, 'HAND_INUN', options.verbose)
logger.info('$Id: $')
logger.info('Flood map: {:s}'.format(options.flood_map))
logger.info('Bank full map: {:s}'.format(options.bankfull_map))
logger.info('Destination path: {:s}'.format(options.dest_path))
# read out ini file
### READ CONFIG FILE
# open config-file
config = inun_lib.open_conf(options.inifile)
# read settings
options.dem_file = inun_lib.configget(config, 'maps',
'dem_file',
True)
options.ldd_file = inun_lib.configget(config, 'maps',
'ldd_file',
True)
options.stream_file = inun_lib.configget(config, 'maps',
'stream_file',
True)
options.riv_length_file = inun_lib.configget(config, 'maps',
'riv_length_file',
True)
options.riv_width_file = inun_lib.configget(config, 'maps',
'riv_width_file',
True)
options.file_format = inun_lib.configget(config, 'maps',
'file_format', 0, datatype='int')
options.x_tile = inun_lib.configget(config, 'tiling',
'x_tile', 10000, datatype='int')
options.y_tile = inun_lib.configget(config, 'tiling',
'y_tile', 10000, datatype='int')
options.x_overlap = inun_lib.configget(config, 'tiling',
'x_overlap', 1000, datatype='int')
options.y_overlap = inun_lib.configget(config, 'tiling',
'y_overlap', 1000, datatype='int')
options.iterations = inun_lib.configget(config, 'inundation',
'iterations', 20, datatype='int')
options.initial_level = inun_lib.configget(config, 'inundation',
'initial_level', 32., datatype='float')
options.area_multiplier = inun_lib.configget(config, 'inundation',
'area_multiplier', 1., datatype='float')
logger.info('DEM file: {:s}'.format(options.dem_file))
logger.info('LDD file: {:s}'.format(options.ldd_file))
logger.info('Columns per tile: {:d}'.format(options.x_tile))
logger.info('Rows per tile: {:d}'.format(options.y_tile))
logger.info('Columns overlap: {:d}'.format(options.x_overlap))
logger.info('Rows overlap: {:d}'.format(options.y_overlap))
metadata_global = {}
# add metadata from the section [metadata]
meta_keys = config.options('metadata_global')
for key in meta_keys:
metadata_global[key] = config.get('metadata_global', key)
# add a number of metadata variables that are mandatory
metadata_global['config_file'] = os.path.abspath(options.inifile)
metadata_var = {}
metadata_var['units'] = 'm'
metadata_var['standard_name'] = 'water_surface_height_above_reference_datum'
metadata_var['long_name'] = 'Coastal flooding'
metadata_var['comment'] = 'water_surface_reference_datum_altitude is given in file {:s}'.format(options.dem_file)
if not os.path.exists(options.dem_file):
logger.error('path to dem file {:s} cannot be found'.format(options.dem_file))
sys.exit(1)
if not os.path.exists(options.ldd_file):
logger.error('path to ldd file {:s} cannot be found'.format(options.ldd_file))
sys.exit(1)
# Read extent from a GDAL compatible file
try:
extent = inun_lib.get_gdal_extent(options.dem_file)
except:
msg = 'Input file {:s} not a gdal compatible file'.format(options.dem_file)
inun_lib.close_with_error(logger, ch, msg)
sys.exit(1)
try:
x, y = inun_lib.get_gdal_axes(options.dem_file, logging=logger)
srs = inun_lib.get_gdal_projection(options.dem_file, logging=logger)
except:
msg = 'Input file {:s} not a gdal compatible file'.format(options.dem_file)
inun_lib.close_with_error(logger, ch, msg)
sys.exit(1)
# read history from flood file
if options.file_format == 0:
a = nc.Dataset(options.flood_map, 'r')
metadata_global['history'] = 'Created by: $Id: $, boundary conditions from {:s},\nhistory: {:s}'.format(os.path.abspath(options.flood_map), a.history)
a.close()
else:
metadata_global['history'] = 'Created by: $Id: $, boundary conditions from {:s},\nhistory: {:s}'.format(os.path.abspath(options.flood_map), 'PCRaster file, no history')
# first write subcatch maps and hand maps
############### TODO ######
# setup a HAND file
dem_name = os.path.split(options.dem_file)[1].split('.')[0]
hand_file = os.path.join(options.dest_path, '{:s}_hand_strahler_{:02d}.tif'.format(dem_name, options.hand_strahler))
if not(os.path.isfile(hand_file)):
# hand file does not exist yet! Generate it, otherwise skip!
logger.info('HAND file {:s} setting up...please wait...'.format(hand_file))
hand_file_tmp = os.path.join(options.dest_path, '{:s}_hand_strahler_{:02d}.tif.tmp'.format(dem_name, options.hand_strahler))
ds_hand = inun_lib.prepare_gdal(hand_file_tmp, x, y, logging=logger, srs=srs)
band_hand = ds_hand.GetRasterBand(1)
# Open terrain data for reading
ds_dem, rasterband_dem = inun_lib.get_gdal_rasterband(options.dem_file)
ds_ldd, rasterband_ldd = inun_lib.get_gdal_rasterband(options.ldd_file)
ds_stream, rasterband_stream = inun_lib.get_gdal_rasterband(options.stream_file)
n = 0
for x_loop in range(0, len(x), options.x_tile):
x_start = np.maximum(x_loop, 0)
x_end = np.minimum(x_loop + options.x_tile, len(x))
# determine actual overlap for cutting
for y_loop in range(0, len(y), options.y_tile):
x_overlap_min = x_start - np.maximum(x_start - options.x_overlap, 0)
x_overlap_max = np.minimum(x_end + options.x_overlap, len(x)) - x_end
n += 1
# print('tile {:001d}:'.format(n))
y_start = np.maximum(y_loop, 0)
y_end = np.minimum(y_loop + options.y_tile, len(y))
y_overlap_min = y_start - np.maximum(y_start - options.y_overlap, 0)
y_overlap_max = np.minimum(y_end + options.y_overlap, len(y)) - y_end
# cut out DEM
logger.debug('Computing HAND for xmin: {:d} xmax: {:d} ymin {:d} ymax {:d}'.format(x_start, x_end,y_start, y_end))
terrain = rasterband_dem.ReadAsArray(x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min)
)
drainage = rasterband_ldd.ReadAsArray(x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min)
)
stream = rasterband_stream.ReadAsArray(x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min)
)
# write to temporary file
terrain_temp_file = os.path.join(options.dest_path, 'terrain_temp.map')
drainage_temp_file = os.path.join(options.dest_path, 'drainage_temp.map')
stream_temp_file = os.path.join(options.dest_path, 'stream_temp.map')
if rasterband_dem.GetNoDataValue() is not None:
inun_lib.gdal_writemap(terrain_temp_file, 'PCRaster',
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
terrain, rasterband_dem.GetNoDataValue(),
gdal_type=gdal.GDT_Float32,
logging=logger)
else:
# in case no nodata value is found
logger.warning('No nodata value found in {:s}. assuming -9999'.format(options.dem_file))
inun_lib.gdal_writemap(terrain_temp_file, 'PCRaster',
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
terrain, -9999.,
gdal_type=gdal.GDT_Float32,
logging=logger)
inun_lib.gdal_writemap(drainage_temp_file, 'PCRaster',
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
drainage, rasterband_ldd.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger)
inun_lib.gdal_writemap(stream_temp_file, 'PCRaster',
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
stream, rasterband_ldd.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger)
# read as pcr objects
pcr.setclone(terrain_temp_file)
terrain_pcr = pcr.readmap(terrain_temp_file)
drainage_pcr = pcr.lddrepair(pcr.ldd(pcr.readmap(drainage_temp_file))) # convert to ldd type map
stream_pcr = pcr.scalar(pcr.readmap(stream_temp_file)) # convert to ldd type map
# compute streams
stream_ge, subcatch = inun_lib.subcatch_stream(drainage_pcr, stream_pcr, options.hand_strahler) # generate streams
basin = pcr.boolean(subcatch)
hand_pcr, dist_pcr = inun_lib.derive_HAND(terrain_pcr, drainage_pcr, 3000, rivers=pcr.boolean(stream_ge), basin=basin)
# convert to numpy
hand = pcr.pcr2numpy(hand_pcr, -9999.)
# cut relevant part
if y_overlap_max == 0:
y_overlap_max = -hand.shape[0]
if x_overlap_max == 0:
x_overlap_max = -hand.shape[1]
hand_cut = hand[0+y_overlap_min:-y_overlap_max, 0+x_overlap_min:-x_overlap_max]
band_hand.WriteArray(hand_cut, x_start, y_start)
os.unlink(terrain_temp_file)
os.unlink(drainage_temp_file)
band_hand.FlushCache()
ds_dem = None
ds_ldd = None
ds_stream = None
band_hand.SetNoDataValue(-9999.)
ds_hand = None
logger.info('Finalizing {:s}'.format(hand_file))
# rename temporary file to final hand file
os.rename(hand_file_tmp, hand_file)
else:
logger.info('HAND file {:s} already exists...skipping...'.format(hand_file))
#####################################################################################
# HAND file has now been prepared, moving to flood mapping part #
#####################################################################################
# load the staticmaps needed to estimate volumes across all
xax, yax, riv_length, fill_value = inun_lib.gdal_readmap(options.riv_length_file, 'GTiff')
riv_length = np.ma.masked_where(riv_length==fill_value, riv_length)
xax, yax, riv_width, fill_value = inun_lib.gdal_readmap(options.riv_width_file, 'GTiff')
riv_width[riv_width == fill_value] = 0
x_res = np.abs((xax[-1]-xax[0])/(len(xax)-1))
y_res = np.abs((yax[-1]-yax[0])/(len(yax)-1))
flood_folder = os.path.join(options.dest_path, case_name)
flood_vol_map = os.path.join(flood_folder, '{:s}_vol.tif'.format(os.path.split(options.flood_map)[1].split('.')[0]))
if not(os.path.isdir(flood_folder)):
os.makedirs(flood_folder)
inun_file_tmp = os.path.join(flood_folder, '{:s}.tif.tmp'.format(case_name))
inun_file = os.path.join(flood_folder, '{:s}.tif'.format(case_name))
hand_temp_file = os.path.join(flood_folder, 'hand_temp.map')
drainage_temp_file = os.path.join(flood_folder, 'drainage_temp.map')
stream_temp_file = os.path.join(flood_folder, 'stream_temp.map')
flood_vol_temp_file = os.path.join(flood_folder, 'flood_warp_temp.tif')
# load the data with river levels and compute the volumes
if options.file_format == 0:
# assume we need the maximum value in a NetCDF time series grid
a = nc.Dataset(options.flood_map, 'r')
xax = a.variables['x'][:]
yax = a.variables['y'][:]
flood_series = a.variables[options.flood_variable][:]
flood_data = flood_series.max(axis=0)
if np.ma.is_masked(flood_data):
flood = flood_data.data
flood[flood_data.mask] = 0
if yax[-1] > yax[0]:
yax = np.flipud(yax)
flood = np.flipud(flood)
a.close()
elif options.file_format == 1:
xax, yax, flood, flood_fill_value = inun_lib.gdal_readmap(options.flood_map, 'PCRaster')
flood[flood==flood_fill_value] = 0.
#res_x = x[1]-x[0]
#res_y = y[1]-y[0]
# load the bankfull depths
if options.bankfull_map == '':
bankfull = np.zeros(flood.shape)
else:
if options.file_format == 0:
a = nc.Dataset(options.bankfull_map, 'r')
xax = a.variables['x'][:]
yax = a.variables['y'][:]
bankfull = a.variables[options.flood_variable][0, :, :]
if yax[-1] > yax[0]:
yax = np.flipud(yax)
bankfull = np.flipud(bankful)
a.close()
elif options.file_format == 1:
xax, yax, bankfull, bankfull_fill_value = inun_lib.gdal_readmap(options.bankfull_map, 'PCRaster')
# flood = bankfull*2
# res_x = 2000
# res_y = 2000
# subtract the bankfull water level to get flood levels (above bankfull)
flood_vol = np.maximum(flood-bankfull, 0)
flood_vol_m = riv_length*riv_width*flood_vol/(x_res * y_res) # volume expressed in meters water disc (1e6 is the surface area of one wflow grid cell)
flood_vol_m_data = flood_vol_m.data
flood_vol_m_data[flood_vol_m.mask] = -999.
print('Saving water layer map to {:s}'.format(flood_vol_map))
# write to a tiff file
inun_lib.gdal_writemap(flood_vol_map, 'GTiff', xax, yax, np.maximum(flood_vol_m_data, 0), -999.)
ds_hand, rasterband_hand = inun_lib.get_gdal_rasterband(hand_file)
ds_ldd, rasterband_ldd = inun_lib.get_gdal_rasterband(options.ldd_file)
ds_stream, rasterband_stream = inun_lib.get_gdal_rasterband(options.stream_file)
logger.info('Preparing flood map in {:s} ...please wait...'.format(inun_file))
ds_inun = inun_lib.prepare_gdal(inun_file_tmp, x, y, logging=logger, srs=srs)
band_inun = ds_inun.GetRasterBand(1)
# loop over all the tiles
n = 0
for x_loop in range(0, len(x), options.x_tile):
x_start = np.maximum(x_loop, 0)
x_end = np.minimum(x_loop + options.x_tile, len(x))
# determine actual overlap for cutting
for y_loop in range(0, len(y), options.y_tile):
x_overlap_min = x_start - np.maximum(x_start - options.x_overlap, 0)
x_overlap_max = np.minimum(x_end + options.x_overlap, len(x)) - x_end
n += 1
# print('tile {:001d}:'.format(n))
y_start = np.maximum(y_loop, 0)
y_end = np.minimum(y_loop + options.y_tile, len(y))
y_overlap_min = y_start - np.maximum(y_start - options.y_overlap, 0)
y_overlap_max = np.minimum(y_end + options.y_overlap, len(y)) - y_end
x_tile_ax = x[x_start - x_overlap_min:x_end + x_overlap_max]
y_tile_ax = y[y_start - y_overlap_min:y_end + y_overlap_max]
# cut out DEM
logger.debug('handling xmin: {:d} xmax: {:d} ymin {:d} ymax {:d}'.format(x_start, x_end, y_start, y_end))
hand = rasterband_hand.ReadAsArray(x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min)
)
drainage = rasterband_ldd.ReadAsArray(x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min)
)
stream = rasterband_stream.ReadAsArray(x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min)
)
print('len x-ax: {:d} len y-ax {:d} x-shape {:d} y-shape {:d}'.format(len(x_tile_ax), len(y_tile_ax), hand.shape[1], hand.shape[0]))
inun_lib.gdal_writemap(hand_temp_file, 'PCRaster',
x_tile_ax,
y_tile_ax,
hand, rasterband_hand.GetNoDataValue(),
gdal_type=gdal.GDT_Float32,
logging=logger)
inun_lib.gdal_writemap(drainage_temp_file, 'PCRaster',
x_tile_ax,
y_tile_ax,
drainage, rasterband_ldd.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger)
inun_lib.gdal_writemap(stream_temp_file, 'PCRaster',
x_tile_ax,
y_tile_ax,
stream, rasterband_stream.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger)
# read as pcr objects
pcr.setclone(hand_temp_file)
hand_pcr = pcr.readmap(hand_temp_file)
drainage_pcr = pcr.lddrepair(pcr.ldd(pcr.readmap(drainage_temp_file))) # convert to ldd type map
stream_pcr = pcr.scalar(pcr.readmap(drainage_temp_file)) # convert to ldd type map
# prepare a subcatchment map
stream_ge, subcatch = inun_lib.subcatch_stream(drainage_pcr, stream_pcr, options.catchment_strahler) # generate subcatchments
drainage_surf = pcr.ifthen(stream_ge > 0, pcr.accuflux(drainage_pcr, 1)) # proxy of drainage surface inaccurate at tile edges
# compute weights for spreadzone (1/drainage_surf)
subcatch = pcr.spreadzone(subcatch, 0, 0)
# TODO check weighting scheme, perhaps not necessary
# weight = 1./pcr.scalar(pcr.spreadzone(pcr.cover(pcr.ordinal(drainage_surf), 0), 0, 0))
# subcatch_fill = pcr.scalar(pcr.spreadzone(subcatch, 0, weight))
# # cover subcatch with subcatch_fill
# pcr.report(weight, 'weight_{:02d}.map'.format(n))
# pcr.report(subcatch, 'subcatch_{:02d}.map'.format(n))
# pcr.report(pcr.nominal(subcatch_fill), 'subcatch_fill_{:02d}.map'.format(n))
inun_lib.gdal_warp(flood_vol_map, hand_temp_file, flood_vol_temp_file, gdal_interp=gdalconst.GRA_NearestNeighbour) # ,
x_tile_ax, y_tile_ax, flood_meter, fill_value = inun_lib.gdal_readmap(flood_vol_temp_file, 'GTiff')
# convert meter depth to volume [m3]
flood_vol = pcr.numpy2pcr(pcr.Scalar, flood_meter, fill_value)*((x_tile_ax[1] - x_tile_ax[0]) * (y_tile_ax[0] - y_tile_ax[1])) # resolution of SRTM *1166400000.
## now we have some nice volume. Now we need to redistribute!
inundation_pcr = inun_lib.volume_spread(drainage_pcr, hand_pcr, subcatch, flood_vol,
volume_thres=0., iterations=options.iterations,
area_multiplier=options.area_multiplier) # 1166400000.
inundation = pcr.pcr2numpy(inundation_pcr, -9999.)
# cut relevant part
if y_overlap_max == 0:
y_overlap_max = -inundation.shape[0]
if x_overlap_max == 0:
x_overlap_max = -inundation.shape[1]
inundation_cut = inundation[0+y_overlap_min:-y_overlap_max, 0+x_overlap_min:-x_overlap_max]
# inundation_cut
band_inun.WriteArray(inundation_cut, x_start, y_start)
band_inun.FlushCache()
# clean up
os.unlink(flood_vol_temp_file)
os.unlink(drainage_temp_file)
os.unlink(hand_temp_file)
# if n == 35:
# band_inun.SetNoDataValue(-9999.)
# ds_inun = None
# sys.exit(0)
os.unlink(flood_vol_map)
logger.info('Finalizing {:s}'.format(inun_file))
# add the metadata to the file and band
band_inun.SetNoDataValue(-9999.)
ds_inun.SetMetadata(metadata_global)
band_inun.SetMetadata(metadata_var)
ds_inun = None
ds_hand = None
ds_ldd = None
# rename temporary file to final hand file
if os.path.isfile(inun_file):
# remove an old result if available
os.unlink(inun_file)
os.rename(inun_file_tmp, inun_file)
logger.info('Done! Thank you for using hand_contour_inun.py')
logger, ch = inun_lib.closeLogger(logger, ch)
del logger, ch
sys.exit(0)
def __init__(self, clone_map_file, \
dem_average_netcdf, dem_floodplain_netcdf, ldd_netcdf, \
lookup_table_average_thickness, lookup_table_zscore, \
number_of_samples, include_percentile = True,\
threshold_sedimentary_basin = 50.0, elevation_F_min = 0.0, elevation_F_max = 50.0): # values defined in de Graaf et al. (2014)
DynamicModel.__init__(self)
MonteCarloModel.__init__(self)
msg = "\n"
msg += "\n"
msg += 'For each step, please refer to de Graaf et al. (2014).'
msg += "\n"
logger.info(msg)
# set clone
self.clone_map_file = clone_map_file
pcr.setclone(clone_map_file)
# an option to include_percentile or not
self.include_percentile = include_percentile
# number of samples
self.number_of_samples = pcr.scalar(number_of_samples)
logger.info(
"Step 1: Identify the cells belonging to the sedimentary basin region."
)
dem_average = vos.netcdf2PCRobjCloneWithoutTime(dem_average_netcdf['file_name'],\
dem_average_netcdf['variable_name'],\
clone_map_file)
dem_average = pcr.max(0.0, dem_average)
self.dem_average = pcr.cover(dem_average, 0.0)
dem_floodplain = vos.netcdf2PCRobjCloneWithoutTime(
dem_floodplain_netcdf['file_name'],
dem_floodplain_netcdf['variable_name'], clone_map_file)
dem_floodplain = pcr.max(0.0, dem_floodplain)
lddMap = vos.netcdf2PCRobjCloneWithoutTime(ldd_netcdf['file_name'],
ldd_netcdf['variable_name'],
clone_map_file)
self.lddMap = pcr.lddrepair(pcr.lddrepair(pcr.ldd(lddMap)))
self.landmask = pcr.defined(self.lddMap)
elevation_F = dem_average - dem_floodplain
sedimentary_basin_extent = pcr.ifthen(
elevation_F < pcr.scalar(threshold_sedimentary_basin),
pcr.boolean(1))
# include the continuity along the river network
sedimentary_basin_extent = pcr.windowmajority(
sedimentary_basin_extent,
3.00 * vos.getMapAttributes(clone_map_file, "cellsize"))
sedimentary_basin_extent = pcr.cover(sedimentary_basin_extent, \
pcr.path(self.lddMap, pcr.defined(sedimentary_basin_extent)))
# TODO: We should also include the extent of major aquifer basins and unconsolidated sediments in the GLiM map.
elevation_F = pcr.ifthen(sedimentary_basin_extent, elevation_F)
elevation_F = pcr.max(0.0, elevation_F)
elevation_F = pcr.min(50., elevation_F)
logger.info(
"Step 2: Calculate relative difference and associate z_score.")
relative_elevation_F = pcr.scalar(
1.0) - (elevation_F - elevation_F_min) / (elevation_F_max -
elevation_F_min)
z_score_relat_elev_F = pcr.lookupscalar(lookup_table_zscore,
relative_elevation_F)
self.F = z_score_relat_elev_F # zscore (varying over the map)
# maximum and minimum z_score
self.F = pcr.min(3.75, self.F)
self.F = pcr.max(-10.00, self.F)
logger.info(
"Step 3: Assign average and variation of aquifer thickness.")
self.lookup_table_average_thickness = lookup_table_average_thickness
self.lnCV = pcr.scalar(
0.1
) # According to Inge, this lnCV value corresponds to the table "lookup_table_average_thickness".
# - set the landmask
landmask_map_file = "/projects/0/dfguu/data/hydroworld/others/05ArcMinCloneMaps/new_masks_from_top/mask_" + str(mask_code) + ".map"
msg = "Set the landmask to : " + str(landmask_map_file)
logger.info(msg)
landmask = pcr.readmap(landmask_map_file)
# resampling low resolution ldd map
msg = "Resample the low resolution ldd map."
logger.info(msg)
ldd_map_low_resolution_file_name = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/lddsound_05min.map"
ldd_map_low_resolution = vos.readPCRmapClone(ldd_map_low_resolution_file_name, \
clone_map_file, \
tmp_folder, \
None, True, None, False)
ldd_map_low_resolution = pcr.ifthen(landmask, ldd_map_low_resolution) # NOTE THAT YOU MAY NOT HAVE TO MASK-OUT THE LDD.
ldd_map_low_resolution = pcr.lddrepair(pcr.ldd(ldd_map_low_resolution))
ldd_map_low_resolution = pcr.lddrepair(ldd_map_low_resolution)
pcr.report(ldd_map_low_resolution, "resampled_low_resolution_ldd.map")
# permanent water bodies files (at 5 arc-minute resolution)
reservoir_capacity_file = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/reservoirs/waterBodiesFinal_version15Sept2013/maps/reservoircapacity_2010.map"
fracwat_file = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/reservoirs/waterBodiesFinal_version15Sept2013/maps/fracwat_2010.map"
water_body_id_file = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/reservoirs/waterBodiesFinal_version15Sept2013/maps/waterbodyid_2010.map"
# cell_area_file
cell_area_file = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/cellsize05min.correct.map"
# bankfull capacity (5 arcmin, volume: m3)
def main():
### Read input arguments #####
parser = OptionParser()
usage = "usage: %prog [options]"
parser = OptionParser(usage=usage)
parser.add_option('-q', '--quiet',
dest='verbose', default=True, action='store_false',
help='do not print status messages to stdout')
parser.add_option('-i', '--ini', dest='inifile',
default='hand_contour_inun.ini', nargs=1,
help='ini configuration file')
parser.add_option('-f', '--flood_map',
nargs=1, dest='flood_map',
help='Flood map file (NetCDF point time series file')
parser.add_option('-v', '--flood_variable',
nargs=1, dest='flood_variable',
default='water_level',
help='variable name of flood water level')
parser.add_option('-b', '--bankfull_map',
dest='bankfull_map', default='',
help='Map containing bank full level (is subtracted from flood map, in NetCDF)')
parser.add_option('-c', '--catchment',
dest='catchment_strahler', default=7, type='int',
help='Strahler order threshold >= are selected as catchment boundaries')
parser.add_option('-t', '--time',
dest='time', default='',
help='time in YYYYMMDDHHMMSS, overrides time in NetCDF input if set')
# parser.add_option('-s', '--hand_strahler',
# dest='hand_strahler', default=7, type='int',
# help='Strahler order threshold >= selected as riverine')
parser.add_option('-m', '--max_strahler',
dest = 'max_strahler', default=1000, type='int',
help='Maximum Strahler order to loop over')
parser.add_option('-d', '--destination',
dest='dest_path', default='inun',
help='Destination path')
parser.add_option('-H', '--hand_file_prefix',
dest='hand_file_prefix', default='',
help='optional HAND file prefix of already generated HAND files')
parser.add_option('-n', '--neg_HAND',
dest='neg_HAND', default=0, type='int',
help='if set to 1, allow for negative HAND values in HAND maps')
(options, args) = parser.parse_args()
if not os.path.exists(options.inifile):
print 'path to ini file cannot be found'
sys.exit(1)
options.dest_path = os.path.abspath(options.dest_path)
if not(os.path.isdir(options.dest_path)):
os.makedirs(options.dest_path)
# set up the logger
flood_name = os.path.split(options.flood_map)[1].split('.')[0]
# case_name = 'inun_{:s}_hand_{:02d}_catch_{:02d}'.format(flood_name, options.hand_strahler, options.catchment_strahler)
case_name = 'inun_{:s}_catch_{:02d}'.format(flood_name, options.catchment_strahler)
logfilename = os.path.join(options.dest_path, 'hand_contour_inun.log')
logger, ch = inun_lib.setlogger(logfilename, 'HAND_INUN', options.verbose)
logger.info('$Id: $')
logger.info('Flood map: {:s}'.format(options.flood_map))
logger.info('Bank full map: {:s}'.format(options.bankfull_map))
logger.info('Destination path: {:s}'.format(options.dest_path))
# read out ini file
### READ CONFIG FILE
# open config-file
config = inun_lib.open_conf(options.inifile)
# read settings
options.dem_file = inun_lib.configget(config, 'HighResMaps',
'dem_file',
True)
options.ldd_file = inun_lib.configget(config, 'HighResMaps',
'ldd_file',
True)
options.stream_file = inun_lib.configget(config, 'HighResMaps',
'stream_file',
True)
options.riv_length_fact_file = inun_lib.configget(config, 'wflowResMaps',
'riv_length_fact_file',
True)
options.ldd_wflow = inun_lib.configget(config, 'wflowResMaps',
'ldd_wflow',
True)
options.riv_width_file = inun_lib.configget(config, 'wflowResMaps',
'riv_width_file',
True)
options.file_format = inun_lib.configget(config, 'file_settings',
'file_format', 0, datatype='int')
options.out_format = inun_lib.configget(config, 'file_settings',
'out_format', 0, datatype='int')
options.latlon = inun_lib.configget(config, 'file_settings',
'latlon', 0, datatype='int')
options.x_tile = inun_lib.configget(config, 'tiling',
'x_tile', 10000, datatype='int')
options.y_tile = inun_lib.configget(config, 'tiling',
'y_tile', 10000, datatype='int')
options.x_overlap = inun_lib.configget(config, 'tiling',
'x_overlap', 1000, datatype='int')
options.y_overlap = inun_lib.configget(config, 'tiling',
'y_overlap', 1000, datatype='int')
options.iterations = inun_lib.configget(config, 'inundation',
'iterations', 20, datatype='int')
options.initial_level = inun_lib.configget(config, 'inundation',
'initial_level', 32., datatype='float')
options.flood_volume_type = inun_lib.configget(config, 'inundation',
'flood_volume_type', 0, datatype='int')
# options.area_multiplier = inun_lib.configget(config, 'inundation',
# 'area_multiplier', 1., datatype='float')
logger.info('DEM file: {:s}'.format(options.dem_file))
logger.info('LDD file: {:s}'.format(options.ldd_file))
logger.info('Columns per tile: {:d}'.format(options.x_tile))
logger.info('Rows per tile: {:d}'.format(options.y_tile))
logger.info('Columns overlap: {:d}'.format(options.x_overlap))
logger.info('Rows overlap: {:d}'.format(options.y_overlap))
metadata_global = {}
# add metadata from the section [metadata]
meta_keys = config.options('metadata_global')
for key in meta_keys:
metadata_global[key] = config.get('metadata_global', key)
# add a number of metadata variables that are mandatory
metadata_global['config_file'] = os.path.abspath(options.inifile)
metadata_var = {}
metadata_var['units'] = 'm'
metadata_var['standard_name'] = 'water_surface_height_above_reference_datum'
metadata_var['long_name'] = 'flooding'
metadata_var['comment'] = 'water_surface_reference_datum_altitude is given in file {:s}'.format(options.dem_file)
if not os.path.exists(options.dem_file):
logger.error('path to dem file {:s} cannot be found'.format(options.dem_file))
sys.exit(1)
if not os.path.exists(options.ldd_file):
logger.error('path to ldd file {:s} cannot be found'.format(options.ldd_file))
sys.exit(1)
# Read extent from a GDAL compatible file
try:
extent = inun_lib.get_gdal_extent(options.dem_file)
except:
msg = 'Input file {:s} not a gdal compatible file'.format(options.dem_file)
inun_lib.close_with_error(logger, ch, msg)
sys.exit(1)
try:
x, y = inun_lib.get_gdal_axes(options.dem_file, logging=logger)
srs = inun_lib.get_gdal_projection(options.dem_file, logging=logger)
except:
msg = 'Input file {:s} not a gdal compatible file'.format(options.dem_file)
inun_lib.close_with_error(logger, ch, msg)
sys.exit(1)
# read history from flood file
if options.file_format == 0:
a = nc.Dataset(options.flood_map, 'r')
metadata_global['history'] = 'Created by: $Id: $, boundary conditions from {:s},\nhistory: {:s}'.format(os.path.abspath(options.flood_map), a.history)
a.close()
else:
metadata_global['history'] = 'Created by: $Id: $, boundary conditions from {:s},\nhistory: {:s}'.format(os.path.abspath(options.flood_map), 'PCRaster file, no history')
# first write subcatch maps and hand maps
############### TODO ######
# setup a HAND file for each strahler order
max_s = inun_lib.define_max_strahler(options.stream_file, logging=logger)
stream_max = np.minimum(max_s, options.max_strahler)
for hand_strahler in range(options.catchment_strahler, stream_max + 1, 1):
dem_name = os.path.split(options.dem_file)[1].split('.')[0]
if os.path.isfile('{:s}_{:02d}.tif'.format(options.hand_file_prefix, hand_strahler)):
hand_file = '{:s}_{:02d}.tif'.format(options.hand_file_prefix, hand_strahler)
else:
logger.info('No HAND files with HAND prefix were found, checking {:s}_hand_strahler_{:02d}.tif'.format(dem_name, hand_strahler))
hand_file = os.path.join(options.dest_path, '{:s}_hand_strahler_{:02d}.tif'.format(dem_name, hand_strahler))
if not(os.path.isfile(hand_file)):
# hand file does not exist yet! Generate it, otherwise skip!
logger.info('HAND file {:s} not found, start setting up...please wait...'.format(hand_file))
hand_file_tmp = os.path.join(options.dest_path, '{:s}_hand_strahler_{:02d}.tif.tmp'.format(dem_name, hand_strahler))
ds_hand, band_hand = inun_lib.prepare_gdal(hand_file_tmp, x, y, logging=logger, srs=srs)
# band_hand = ds_hand.GetRasterBand(1)
# Open terrain data for reading
ds_dem, rasterband_dem = inun_lib.get_gdal_rasterband(options.dem_file)
ds_ldd, rasterband_ldd = inun_lib.get_gdal_rasterband(options.ldd_file)
ds_stream, rasterband_stream = inun_lib.get_gdal_rasterband(options.stream_file)
n = 0
for x_loop in range(0, len(x), options.x_tile):
x_start = np.maximum(x_loop, 0)
x_end = np.minimum(x_loop + options.x_tile, len(x))
# determine actual overlap for cutting
for y_loop in range(0, len(y), options.y_tile):
x_overlap_min = x_start - np.maximum(x_start - options.x_overlap, 0)
x_overlap_max = np.minimum(x_end + options.x_overlap, len(x)) - x_end
n += 1
# print('tile {:001d}:'.format(n))
y_start = np.maximum(y_loop, 0)
y_end = np.minimum(y_loop + options.y_tile, len(y))
y_overlap_min = y_start - np.maximum(y_start - options.y_overlap, 0)
y_overlap_max = np.minimum(y_end + options.y_overlap, len(y)) - y_end
# cut out DEM
logger.debug('Computing HAND for xmin: {:d} xmax: {:d} ymin {:d} ymax {:d}'.format(x_start, x_end,y_start, y_end))
terrain = rasterband_dem.ReadAsArray(x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min)
)
drainage = rasterband_ldd.ReadAsArray(x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min)
)
stream = rasterband_stream.ReadAsArray(x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min)
)
# write to temporary file
terrain_temp_file = os.path.join(options.dest_path, 'terrain_temp.map')
drainage_temp_file = os.path.join(options.dest_path, 'drainage_temp.map')
stream_temp_file = os.path.join(options.dest_path, 'stream_temp.map')
if rasterband_dem.GetNoDataValue() is not None:
inun_lib.gdal_writemap(terrain_temp_file, 'PCRaster',
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
terrain, rasterband_dem.GetNoDataValue(),
gdal_type=gdal.GDT_Float32,
logging=logger)
else:
# in case no nodata value is found
logger.warning('No nodata value found in {:s}. assuming -9999'.format(options.dem_file))
inun_lib.gdal_writemap(terrain_temp_file, 'PCRaster',
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
terrain, -9999.,
gdal_type=gdal.GDT_Float32,
logging=logger)
inun_lib.gdal_writemap(drainage_temp_file, 'PCRaster',
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
drainage, rasterband_ldd.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger)
inun_lib.gdal_writemap(stream_temp_file, 'PCRaster',
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
stream, rasterband_ldd.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger)
# read as pcr objects
pcr.setclone(terrain_temp_file)
terrain_pcr = pcr.readmap(terrain_temp_file)
drainage_pcr = pcr.lddrepair(pcr.ldd(pcr.readmap(drainage_temp_file))) # convert to ldd type map
stream_pcr = pcr.scalar(pcr.readmap(stream_temp_file)) # convert to ldd type map
#check if the highest stream order of the tile is below the hand_strahler
# if the highest stream order of the tile is smaller than hand_strahler, than DEM values are taken instead of HAND values.
max_stream_tile = inun_lib.define_max_strahler(stream_temp_file, logging=logger)
if max_stream_tile < hand_strahler:
hand_pcr = terrain_pcr
logger.info('For this tile, DEM values are used instead of HAND because there is no stream order larger than {:02d}'.format(hand_strahler))
else:
# compute streams
stream_ge, subcatch = inun_lib.subcatch_stream(drainage_pcr, hand_strahler, stream=stream_pcr) # generate streams
# compute basins
stream_ge_dummy, subcatch = inun_lib.subcatch_stream(drainage_pcr, options.catchment_strahler, stream=stream_pcr) # generate streams
basin = pcr.boolean(subcatch)
hand_pcr, dist_pcr = inun_lib.derive_HAND(terrain_pcr, drainage_pcr, 3000,
rivers=pcr.boolean(stream_ge), basin=basin, neg_HAND=options.neg_HAND)
# convert to numpy
hand = pcr.pcr2numpy(hand_pcr, -9999.)
# cut relevant part
if y_overlap_max == 0:
y_overlap_max = -hand.shape[0]
if x_overlap_max == 0:
x_overlap_max = -hand.shape[1]
hand_cut = hand[0+y_overlap_min:-y_overlap_max, 0+x_overlap_min:-x_overlap_max]
band_hand.WriteArray(hand_cut, x_start, y_start)
os.unlink(terrain_temp_file)
os.unlink(drainage_temp_file)
os.unlink(stream_temp_file)
band_hand.FlushCache()
ds_dem = None
ds_ldd = None
ds_stream = None
band_hand.SetNoDataValue(-9999.)
ds_hand = None
logger.info('Finalizing {:s}'.format(hand_file))
# rename temporary file to final hand file
os.rename(hand_file_tmp, hand_file)
else:
logger.info('HAND file {:s} already exists...skipping...'.format(hand_file))
#####################################################################################
# HAND file has now been prepared, moving to flood mapping part #
#####################################################################################
# set the clone
pcr.setclone(options.ldd_wflow)
# read wflow ldd as pcraster object
ldd_pcr = pcr.readmap(options.ldd_wflow)
xax, yax, riv_width, fill_value = inun_lib.gdal_readmap(options.riv_width_file, 'GTiff', logging=logger)
# determine cell length in meters using ldd_pcr as clone (if latlon=True, values are converted to m2
x_res, y_res, reallength_wflow = pcrut.detRealCellLength(pcr.scalar(ldd_pcr), not(bool(options.latlon)))
cell_surface_wflow = pcr.pcr2numpy(x_res * y_res, 0)
if options.flood_volume_type == 0:
# load the staticmaps needed to estimate volumes across all
# xax, yax, riv_length, fill_value = inun_lib.gdal_readmap(options.riv_length_file, 'GTiff', logging=logger)
# riv_length = np.ma.masked_where(riv_length==fill_value, riv_length)
xax, yax, riv_width, fill_value = inun_lib.gdal_readmap(options.riv_width_file, 'GTiff', logging=logger)
riv_width[riv_width == fill_value] = 0
# read river length factor file (multiplier)
xax, yax, riv_length_fact, fill_value = inun_lib.gdal_readmap(options.riv_length_fact_file, 'GTiff', logging=logger)
riv_length_fact = np.ma.masked_where(riv_length_fact==fill_value, riv_length_fact)
drain_length = wflow_lib.detdrainlength(ldd_pcr, x_res, y_res)
# compute river length in each cell
riv_length = pcr.pcr2numpy(drain_length, 0) * riv_length_fact
# riv_length_pcr = pcr.numpy2pcr(pcr.Scalar, riv_length, 0)
flood_folder = os.path.join(options.dest_path, case_name)
flood_vol_map = os.path.join(flood_folder, '{:s}_vol.tif'.format(os.path.split(options.flood_map)[1].split('.')[0]))
if not(os.path.isdir(flood_folder)):
os.makedirs(flood_folder)
if options.out_format == 0:
inun_file_tmp = os.path.join(flood_folder, '{:s}.tif.tmp'.format(case_name))
inun_file = os.path.join(flood_folder, '{:s}.tif'.format(case_name))
else:
inun_file_tmp = os.path.join(flood_folder, '{:s}.nc.tmp'.format(case_name))
inun_file = os.path.join(flood_folder, '{:s}.nc'.format(case_name))
hand_temp_file = os.path.join(flood_folder, 'hand_temp.map')
drainage_temp_file = os.path.join(flood_folder, 'drainage_temp.map')
stream_temp_file = os.path.join(flood_folder, 'stream_temp.map')
flood_vol_temp_file = os.path.join(flood_folder, 'flood_warp_temp.tif')
# load the data with river levels and compute the volumes
if options.file_format == 0:
# assume we need the maximum value in a NetCDF time series grid
logger.info('Reading flood from {:s} NetCDF file'.format(options.flood_map))
a = nc.Dataset(options.flood_map, 'r')
if options.latlon == 0:
xax = a.variables['x'][:]
yax = a.variables['y'][:]
else:
xax = a.variables['lon'][:]
yax = a.variables['lat'][:]
if options.time == '':
time_list = nc.num2date(a.variables['time'][:], units = a.variables['time'].units, calendar=a.variables['time'].calendar)
time = [time_list[len(time_list)/2]]
else:
time = [dt.datetime.strptime(options.time, '%Y%m%d%H%M%S')]
flood_series = a.variables[options.flood_variable][:]
flood_data = flood_series.max(axis=0)
if np.ma.is_masked(flood_data):
flood = flood_data.data
flood[flood_data.mask] = 0
if yax[-1] > yax[0]:
yax = np.flipud(yax)
flood = np.flipud(flood)
a.close()
elif options.file_format == 1:
logger.info('Reading flood from {:s} PCRaster file'.format(options.flood_map))
xax, yax, flood, flood_fill_value = inun_lib.gdal_readmap(options.flood_map, 'PCRaster', logging=logger)
flood = np.ma.masked_equal(flood, flood_fill_value)
if options.time == '':
options.time = '20000101000000'
time = [dt.datetime.strptime(options.time, '%Y%m%d%H%M%S')]
flood[flood==flood_fill_value] = 0.
# load the bankfull depths
if options.bankfull_map == '':
bankfull = np.zeros(flood.shape)
else:
if options.file_format == 0:
logger.info('Reading bankfull from {:s} NetCDF file'.format(options.bankfull_map))
a = nc.Dataset(options.bankfull_map, 'r')
xax = a.variables['x'][:]
yax = a.variables['y'][:]
bankfull_series = a.variables[options.flood_variable][:]
bankfull_data = bankfull_series.max(axis=0)
if np.ma.is_masked(bankfull_data):
bankfull = bankfull_data.data
bankfull[bankfull_data.mask] = 0
if yax[-1] > yax[0]:
yax = np.flipud(yax)
bankfull = np.flipud(bankfull)
a.close()
elif options.file_format == 1:
logger.info('Reading bankfull from {:s} PCRaster file'.format(options.bankfull_map))
xax, yax, bankfull, bankfull_fill_value = inun_lib.gdal_readmap(options.bankfull_map, 'PCRaster', logging=logger)
bankfull = np.ma.masked_equal(bankfull, bankfull_fill_value)
# flood = bankfull*2
# res_x = 2000
# res_y = 2000
# subtract the bankfull water level to get flood levels (above bankfull)
flood_vol = np.maximum(flood-bankfull, 0)
if options.flood_volume_type == 0:
flood_vol_m = riv_length*riv_width*flood_vol/cell_surface_wflow # volume expressed in meters water disc
flood_vol_m_pcr = pcr.numpy2pcr(pcr.Scalar, flood_vol_m, 0)
else:
flood_vol_m = flood_vol/cell_surface_wflow
flood_vol_m_data = flood_vol_m.data
flood_vol_m_data[flood_vol_m.mask] = -999.
logger.info('Saving water layer map to {:s}'.format(flood_vol_map))
# write to a tiff file
inun_lib.gdal_writemap(flood_vol_map, 'GTiff', xax, yax, np.maximum(flood_vol_m_data, 0), -999., logging=logger)
# this is placed later in the hand loop
# ds_hand, rasterband_hand = inun_lib.get_gdal_rasterband(hand_file)
ds_ldd, rasterband_ldd = inun_lib.get_gdal_rasterband(options.ldd_file)
ds_stream, rasterband_stream = inun_lib.get_gdal_rasterband(options.stream_file)
logger.info('Preparing flood map in {:s} ...please wait...'.format(inun_file))
if options.out_format == 0:
ds_inun, band_inun = inun_lib.prepare_gdal(inun_file_tmp, x, y, logging=logger, srs=srs)
# band_inun = ds_inun.GetRasterBand(1)
else:
ds_inun, band_inun = inun_lib.prepare_nc(inun_file_tmp, time, x, np.flipud(y), metadata=metadata_global,
metadata_var=metadata_var, logging=logger)
# loop over all the tiles
n = 0
for x_loop in range(0, len(x), options.x_tile):
x_start = np.maximum(x_loop, 0)
x_end = np.minimum(x_loop + options.x_tile, len(x))
# determine actual overlap for cutting
for y_loop in range(0, len(y), options.y_tile):
x_overlap_min = x_start - np.maximum(x_start - options.x_overlap, 0)
x_overlap_max = np.minimum(x_end + options.x_overlap, len(x)) - x_end
n += 1
# print('tile {:001d}:'.format(n))
y_start = np.maximum(y_loop, 0)
y_end = np.minimum(y_loop + options.y_tile, len(y))
y_overlap_min = y_start - np.maximum(y_start - options.y_overlap, 0)
y_overlap_max = np.minimum(y_end + options.y_overlap, len(y)) - y_end
x_tile_ax = x[x_start - x_overlap_min:x_end + x_overlap_max]
y_tile_ax = y[y_start - y_overlap_min:y_end + y_overlap_max]
# cut out DEM
logger.debug('handling xmin: {:d} xmax: {:d} ymin {:d} ymax {:d}'.format(x_start, x_end, y_start, y_end))
drainage = rasterband_ldd.ReadAsArray(x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min)
)
stream = rasterband_stream.ReadAsArray(x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min)
)
# stream_max = np.minimum(stream.max(), options.max_strahler)
inun_lib.gdal_writemap(drainage_temp_file, 'PCRaster',
x_tile_ax,
y_tile_ax,
drainage, rasterband_ldd.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger)
inun_lib.gdal_writemap(stream_temp_file, 'PCRaster',
x_tile_ax,
y_tile_ax,
stream, rasterband_stream.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger)
# read as pcr objects
pcr.setclone(stream_temp_file)
drainage_pcr = pcr.lddrepair(pcr.ldd(pcr.readmap(drainage_temp_file))) # convert to ldd type map
stream_pcr = pcr.scalar(pcr.readmap(stream_temp_file)) # convert to ldd type map
# warp of flood volume to inundation resolution
inun_lib.gdal_warp(flood_vol_map, stream_temp_file, flood_vol_temp_file, gdal_interp=gdalconst.GRA_NearestNeighbour) # ,
x_tile_ax, y_tile_ax, flood_meter, fill_value = inun_lib.gdal_readmap(flood_vol_temp_file, 'GTiff', logging=logger)
# make sure that the option unittrue is on !! (if unitcell was is used in another function)
x_res_tile, y_res_tile, reallength = pcrut.detRealCellLength(pcr.scalar(stream_pcr), not(bool(options.latlon)))
cell_surface_tile = pcr.pcr2numpy(x_res_tile * y_res_tile, 0)
# convert meter depth to volume [m3]
flood_vol = pcr.numpy2pcr(pcr.Scalar, flood_meter*cell_surface_tile, fill_value)
# first prepare a basin map, belonging to the lowest order we are looking at
inundation_pcr = pcr.scalar(stream_pcr) * 0
for hand_strahler in range(options.catchment_strahler, stream_max + 1, 1):
# hand_temp_file = os.path.join(flood_folder, 'hand_temp.map')
if os.path.isfile(os.path.join(options.dest_path, '{:s}_hand_strahler_{:02d}.tif'.format(dem_name, hand_strahler))):
hand_file = os.path.join(options.dest_path, '{:s}_hand_strahler_{:02d}.tif'.format(dem_name, hand_strahler))
else:
hand_file = '{:s}_{:02d}.tif'.format(options.hand_file_prefix, hand_strahler)
ds_hand, rasterband_hand = inun_lib.get_gdal_rasterband(hand_file)
hand = rasterband_hand.ReadAsArray(x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min)
)
print('len x-ax: {:d} len y-ax {:d} x-shape {:d} y-shape {:d}'.format(len(x_tile_ax), len(y_tile_ax), hand.shape[1], hand.shape[0]))
inun_lib.gdal_writemap(hand_temp_file, 'PCRaster',
x_tile_ax,
y_tile_ax,
hand, rasterband_hand.GetNoDataValue(),
gdal_type=gdal.GDT_Float32,
logging=logger)
hand_pcr = pcr.readmap(hand_temp_file)
stream_ge_hand, subcatch_hand = inun_lib.subcatch_stream(drainage_pcr, options.catchment_strahler, stream=stream_pcr)
# stream_ge_hand, subcatch_hand = inun_lib.subcatch_stream(drainage_pcr, hand_strahler, stream=stream_pcr)
stream_ge, subcatch = inun_lib.subcatch_stream(drainage_pcr,
options.catchment_strahler,
stream=stream_pcr,
basin=pcr.boolean(pcr.cover(subcatch_hand, 0)),
assign_existing=True,
min_strahler=hand_strahler,
max_strahler=hand_strahler) # generate subcatchments, only within basin for HAND
flood_vol_strahler = pcr.ifthenelse(pcr.boolean(pcr.cover(subcatch, 0)), flood_vol, 0) # mask the flood volume map with the created subcatch map for strahler order = hand_strahler
inundation_pcr_step = inun_lib.volume_spread(drainage_pcr, hand_pcr,
pcr.subcatchment(drainage_pcr, subcatch), # to make sure backwater effects can occur from higher order rivers to lower order rivers
flood_vol_strahler,
volume_thres=0.,
iterations=options.iterations,
cell_surface=pcr.numpy2pcr(pcr.Scalar, cell_surface_tile, -9999),
logging=logger,
order=hand_strahler,
neg_HAND=options.neg_HAND) # 1166400000.
# use maximum value of inundation_pcr_step and new inundation for higher strahler order
inundation_pcr = pcr.max(inundation_pcr, inundation_pcr_step)
inundation = pcr.pcr2numpy(inundation_pcr, -9999.)
# cut relevant part
if y_overlap_max == 0:
y_overlap_max = -inundation.shape[0]
if x_overlap_max == 0:
x_overlap_max = -inundation.shape[1]
inundation_cut = inundation[0+y_overlap_min:-y_overlap_max, 0+x_overlap_min:-x_overlap_max]
# inundation_cut
if options.out_format == 0:
band_inun.WriteArray(inundation_cut, x_start, y_start)
band_inun.FlushCache()
else:
# with netCDF, data is up-side-down.
inun_lib.write_tile_nc(band_inun, inundation_cut, x_start, y_start)
# clean up
os.unlink(flood_vol_temp_file)
os.unlink(drainage_temp_file)
os.unlink(hand_temp_file)
os.unlink(stream_temp_file) #also remove temp stream file from output folder
# if n == 35:
# band_inun.SetNoDataValue(-9999.)
# ds_inun = None
# sys.exit(0)
# os.unlink(flood_vol_map)
logger.info('Finalizing {:s}'.format(inun_file))
# add the metadata to the file and band
# band_inun.SetNoDataValue(-9999.)
# ds_inun.SetMetadata(metadata_global)
# band_inun.SetMetadata(metadata_var)
if options.out_format == 0:
ds_inun = None
ds_hand = None
else:
ds_inun.close()
ds_ldd = None
# rename temporary file to final hand file
if os.path.isfile(inun_file):
# remove an old result if available
os.unlink(inun_file)
os.rename(inun_file_tmp, inun_file)
logger.info('Done! Thank you for using hand_contour_inun.py')
logger, ch = inun_lib.closeLogger(logger, ch)
del logger, ch
sys.exit(0)
def __init__(self, modelTime, output_folder, totat_runoff_input_file):
DynamicModel.__init__(self)
self.modelTime = modelTime
# netcdf input files - based on PCR-GLOBWB output
# - total runoff (m/month)
self.totat_runoff_input_file = "/scratch-shared/edwin/05min_runs_for_gmd_paper_30_oct_2017/05min_runs_4LCs_accutraveltime_cru-forcing_1958-2015/non-natural_starting_from_1958/merged_1958_to_2015/totalRunoff_monthTot_output_1958-01-31_to_2015-12-31.nc"
self.totat_runoff_input_file = totat_runoff_input_file
#
#~ # - discharge (m3/s) - NOT USED anymore
#~ self.discharge_input_file = "/scratch-shared/edwin/05min_runs_for_gmd_paper_30_oct_2017/05min_runs_4LCs_accutraveltime_cru-forcing_1958-2015/non-natural_starting_from_1958/merged_1958_to_2015/discharge_monthAvg_output_1958-01-31_to_2015-12-31.nc"
# output files - in netcdf format
self.total_flow_output_file = output_folder + "/total_flow.nc"
self.internal_flow_output_file = output_folder + "/internal_flow.nc"
# - all will have unit m3/s
# preparing temporary directory
self.temporary_directory = output_folder + "/tmp/"
os.makedirs(self.temporary_directory)
# clone map
logger.info("Set the clone map")
self.clonemap_file_name = "/home/edwinvua/github/edwinkost/estimate_discharge_from_local_runoff/making_subcatchment_map/version_20180202/clone_version_20180202.map"
pcr.setclone(self.clonemap_file_name)
# pcraster input files
landmask_file_name = None
# - river network map and sub-catchment map
ldd_file_name = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/lddsound_05min.map"
sub_catchment_file_name = "/home/edwinvua/github/edwinkost/estimate_discharge_from_local_runoff/making_subcatchment_map/version_20180202/subcatchments_of_station_pcraster_ids.nom.bigger_than_zero.map"
# - cell area (unit: m2)
cell_area_file_name = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/cellsize05min.correct.map"
# loading pcraster input maps
self.sub_catchment = vos.readPCRmapClone(sub_catchment_file_name, \
self.clonemap_file_name, \
self.temporary_directory, \
None, False, None, \
True)
self.ldd_network = vos.readPCRmapClone(ldd_file_name, \
self.clonemap_file_name, \
self.temporary_directory, \
None, \
True)
self.ldd_network = pcr.lddrepair(pcr.ldd(self.ldd_network))
self.ldd_network = pcr.lddrepair(self.ldd_network)
self.cell_area = vos.readPCRmapClone(cell_area_file_name, \
self.clonemap_file_name, \
self.temporary_directory, \
None)
# define the landmask
self.landmask = pcr.defined(self.ldd_network)
if landmask_file_name != None:
self.landmask = vos.readPCRmapClone(landmask_file_name, \
self.clonemap_file_name, \
self.temporary_directory, \
None)
self.landmask = pcr.ifthen(pcr.defined(self.ldd_network), self.landmask)
self.landmask = pcr.ifthen(self.landmask, self.landmask)
# set/limit all input maps to the defined landmask
self.sub_catchment = pcr.ifthen(self.landmask, self.sub_catchment)
self.ldd_network = pcr.ifthen(self.landmask, self.ldd_network)
self.cell_area = pcr.ifthen(self.landmask, self.cell_area)
# preparing an object for reporting netcdf files:
self.netcdf_report = netcdf_writer.PCR2netCDF(self.clonemap_file_name)
# preparing netcdf output files:
# - for total inflow
self.netcdf_report.createNetCDF(self.total_flow_output_file,\
"total_flow",\
"m3/s")
self.netcdf_report.createNetCDF(self.internal_flow_output_file,\
"internal_flow",\
"m3/s")
def main(
source,
destination,
inifile,
dem_in,
rivshp,
catchshp,
gaugeshp=None,
landuse=None,
soil=None,
lai=None,
other_maps=None,
logfilename="wtools_static_maps.log",
verbose=True,
clean=True,
alltouch=False,
outlets=([], []),
):
# parse other maps into an array
if not other_maps == None:
if type(other_maps) == str:
print other_maps
other_maps = (other_maps.replace(" ", "").replace("[", "").replace(
"]", "").split(","))
source = os.path.abspath(source)
clone_map = os.path.join(source, "mask.map")
clone_shp = os.path.join(source, "mask.shp")
clone_prj = os.path.join(source, "mask.prj")
if None in (rivshp, catchshp, dem_in):
msg = """The following files are compulsory:
- DEM (raster)
- river (shape)
- catchment (shape)
"""
print(msg)
parser.print_help()
sys.exit(1)
if (inifile is not None) and (not os.path.exists(inifile)):
print "path to ini file cannot be found"
sys.exit(1)
if not os.path.exists(rivshp):
print "path to river shape cannot be found"
sys.exit(1)
if not os.path.exists(catchshp):
print "path to catchment shape cannot be found"
sys.exit(1)
if not os.path.exists(dem_in):
print "path to DEM cannot be found"
sys.exit(1)
# open a logger, dependent on verbose print to screen or not
logger, ch = wt.setlogger(logfilename, "WTOOLS", verbose)
# create directories # TODO: check if workdir is still necessary, try to
# keep in memory as much as possible
# delete old files (when the source and destination folder are different)
if np.logical_and(os.path.isdir(destination), destination is not source):
shutil.rmtree(destination)
if destination is not source:
os.makedirs(destination)
# Read mask
if not (os.path.exists(clone_map)):
logger.error(
"Clone file {:s} not found. Please run create_grid first.".format(
clone_map))
sys.exit(1)
else:
# set clone
pcr.setclone(clone_map)
# get the extent from clone.tif
xax, yax, clone, fill_value = wt.gdal_readmap(clone_map, "GTiff")
trans = wt.get_geotransform(clone_map)
extent = wt.get_extent(clone_map)
xmin, ymin, xmax, ymax = extent
zeros = np.zeros(clone.shape)
ones = pcr.numpy2pcr(pcr.Scalar, np.ones(clone.shape), -9999)
# get the projection from clone.tif
srs = wt.get_projection(clone_map)
unit_clone = srs.GetAttrValue("UNIT").lower()
# READ CONFIG FILE
# open config-file
if inifile is None:
config = ConfigParser.SafeConfigParser()
config.optionxform = str
else:
config = wt.OpenConf(inifile)
# read settings
snapgaugestoriver = wt.configget(config,
"settings",
"snapgaugestoriver",
True,
datatype="boolean")
burnalltouching = wt.configget(config,
"settings",
"burncatchalltouching",
True,
datatype="boolean")
burninorder = wt.configget(config,
"settings",
"burncatchalltouching",
False,
datatype="boolean")
verticetollerance = wt.configget(config,
"settings",
"vertice_tollerance",
0.0001,
datatype="float")
""" read parameters """
burn_outlets = wt.configget(config,
"parameters",
"burn_outlets",
10000,
datatype="int")
burn_rivers = wt.configget(config,
"parameters",
"burn_rivers",
200,
datatype="int")
burn_connections = wt.configget(config,
"parameters",
"burn_connections",
100,
datatype="int")
burn_gauges = wt.configget(config,
"parameters",
"burn_gauges",
100,
datatype="int")
minorder = wt.configget(config,
"parameters",
"riverorder_min",
3,
datatype="int")
try:
percentiles = np.array(
config.get("parameters", "statisticmaps",
"0, 100").replace(" ", "").split(","),
dtype="float",
)
except ConfigParser.NoOptionError:
percentiles = [0.0, 100.0]
# read the parameters for generating a temporary very high resolution grid
if unit_clone == "degree":
cellsize_hr = wt.configget(config,
"parameters",
"highres_degree",
0.0005,
datatype="float")
elif (unit_clone == "metre") or (unit_clone == "meter"):
cellsize_hr = wt.configget(config,
"parameters",
"highres_metre",
50,
datatype="float")
cols_hr = int((float(xmax) - float(xmin)) / cellsize_hr + 2)
rows_hr = int((float(ymax) - float(ymin)) / cellsize_hr + 2)
hr_trans = (float(xmin), cellsize_hr, float(0), float(ymax), 0,
-cellsize_hr)
clone_hr = os.path.join(destination, "clone_highres.tif")
# make a highres clone as well!
wt.CreateTif(clone_hr, rows_hr, cols_hr, hr_trans, srs, 0)
# read staticmap locations
catchment_map = wt.configget(config, "staticmaps", "catchment",
"wflow_catchment.map")
dem_map = wt.configget(config, "staticmaps", "dem", "wflow_dem.map")
demmax_map = wt.configget(config, "staticmaps", "demmax",
"wflow_demmax.map")
demmin_map = wt.configget(config, "staticmaps", "demmin",
"wflow_demmin.map")
gauges_map = wt.configget(config, "staticmaps", "gauges",
"wflow_gauges.map")
landuse_map = wt.configget(config, "staticmaps", "landuse",
"wflow_landuse.map")
ldd_map = wt.configget(config, "staticmaps", "ldd", "wflow_ldd.map")
river_map = wt.configget(config, "staticmaps", "river", "wflow_river.map")
outlet_map = wt.configget(config, "staticmaps", "outlet",
"wflow_outlet.map")
riverlength_fact_map = wt.configget(config, "staticmaps",
"riverlength_fact",
"wflow_riverlength_fact.map")
soil_map = wt.configget(config, "staticmaps", "soil", "wflow_soil.map")
streamorder_map = wt.configget(config, "staticmaps", "streamorder",
"wflow_streamorder.map")
subcatch_map = wt.configget(config, "staticmaps", "subcatch",
"wflow_subcatch.map")
# read mask location (optional)
masklayer = wt.configget(config, "mask", "masklayer", catchshp)
# ???? empty = pcr.ifthen(ones == 0, pcr.scalar(0))
# TODO: check if extents are correct this way
# TODO: check what the role of missing values is in zeros and ones (l. 123
# in old code)
# first add a missing value to dem_in
ds = gdal.Open(dem_in, gdal.GA_Update)
RasterBand = ds.GetRasterBand(1)
fill_val = RasterBand.GetNoDataValue()
if fill_val is None:
RasterBand.SetNoDataValue(-9999)
ds = None
# reproject to clone map: see http://stackoverflow.com/questions/10454316/how-to-project-and-resample-a-grid-to-match-another-grid-with-gdal-python
# resample DEM
logger.info("Resampling dem from {:s} to {:s}".format(
os.path.abspath(dem_in), os.path.join(destination, dem_map)))
wt.gdal_warp(
dem_in,
clone_map,
os.path.join(destination, dem_map),
format="PCRaster",
gdal_interp=gdalconst.GRA_Average,
)
# retrieve amount of rows and columns from clone
# TODO: make windowstats applicable to source/target with different projections. This does not work yet.
# retrieve srs from DEM
try:
srs_dem = wt.get_projection(dem_in)
except:
logger.warning(
"No projection found in DEM, assuming WGS 1984 lat long")
srs_dem = osr.SpatialReference()
srs_dem.ImportFromEPSG(4326)
clone2dem_transform = osr.CoordinateTransformation(srs, srs_dem)
# if srs.ExportToProj4() == srs_dem.ExportToProj4():
wt.windowstats(
dem_in,
len(yax),
len(xax),
trans,
srs,
destination,
percentiles,
transform=clone2dem_transform,
logger=logger,
)
## read catchment shape-file to create catchment map
src = rasterio.open(clone_map)
shapefile = fiona.open(catchshp, "r")
catchment_shapes = [feature["geometry"] for feature in shapefile]
image = features.rasterize(catchment_shapes,
out_shape=src.shape,
all_touched=True,
transform=src.transform)
catchment_domain = pcr.numpy2pcr(pcr.Ordinal, image.copy(), 0)
## read river shape-file and create burn layer
shapefile = fiona.open(rivshp, "r")
river_shapes = [feature["geometry"] for feature in shapefile]
image = features.rasterize(river_shapes,
out_shape=src.shape,
all_touched=False,
transform=src.transform)
rivers = pcr.numpy2pcr(pcr.Nominal, image.copy(), 0)
riverdem = pcr.scalar(rivers) * pcr.readmap(
os.path.join(destination, dem_map))
pcr.setglobaloption("lddin")
riverldd = pcr.lddcreate(riverdem, 1e35, 1e35, 1e35, 1e35)
riveroutlet = pcr.cover(
pcr.ifthen(pcr.scalar(riverldd) == 5, pcr.scalar(1000)), 0)
burn_layer = pcr.cover(
(pcr.scalar(
pcr.ifthen(
pcr.streamorder(riverldd) > 1, pcr.streamorder(riverldd))) - 1)
* 1000 + riveroutlet,
0,
)
outlets_x, outlets_y = outlets
n_outlets = len(outlets_x)
logger.info("Number of outlets: {}".format(n_outlets))
if n_outlets >= 1:
outlets_map_numbered = tr.points_to_map(pcr.scalar(0), outlets_x,
outlets_y, 0.5)
outlets_map = pcr.boolean(outlets_map_numbered)
# snap outlets to closest river (max 1 cell closer to river)
outlets_map = pcr.boolean(
pcr.cover(tr.snaptomap(pcr.ordinal(outlets_map), rivers), 0))
## create ldd per catchment
logger.info("Calculating ldd")
ldddem = pcr.scalar(clone_map)
# per subcatchment, burn dem, then create modified dem that fits the ldd of the subcatchment
# this ldd dem is merged over catchments, to create a global ldd that abides to the subcatchment boundaries
for idx, shape in enumerate(catchment_shapes):
logger.info("Computing ldd for catchment " + str(idx + 1) + "/" +
str(len(catchment_shapes)))
image = features.rasterize([shape],
out_shape=src.shape,
all_touched=True,
transform=src.transform)
catchment = pcr.numpy2pcr(pcr.Scalar, image.copy(), 0)
dem_burned_catchment = (
pcr.readmap(os.path.join(destination, dem_map)) *
pcr.scalar(catchment_domain) * catchment) - burn_layer
# ldddem_catchment = pcr.lddcreatedem(
# dem_burned_catchment, 1e35, 1e35, 1e35, 1e35)
ldddem = pcr.cover(ldddem, dem_burned_catchment)
pcr.report(ldddem, os.path.join(destination, "ldddem.map"))
wflow_ldd = pcr.lddcreate(ldddem, 1e35, 1e35, 1e35, 1e35)
if n_outlets >= 1:
# set outlets to pit
wflow_ldd = pcr.ifthenelse(outlets_map, pcr.ldd(5), wflow_ldd)
wflow_ldd = pcr.lddrepair(wflow_ldd)
pcr.report(wflow_ldd, os.path.join(destination, "wflow_ldd.map"))
# compute stream order, identify river cells
streamorder = pcr.ordinal(pcr.streamorder(wflow_ldd))
river = pcr.ifthen(streamorder >= pcr.ordinal(minorder), pcr.boolean(1))
# find the minimum value in the DEM and cover missing values with a river with this value. Effect is none!! so now left out!
# mindem = int(np.min(pcr.pcr2numpy(pcr.ordinal(os.path.join(destination, dem_map)),9999999)))
# dem_resample_map = pcr.cover(os.path.join(destination, dem_map), pcr.scalar(river)*0+mindem)
# pcr.report(dem_resample_map, os.path.join(destination, dem_map))
pcr.report(streamorder, os.path.join(destination, streamorder_map))
pcr.report(river, os.path.join(destination, river_map))
# deal with your catchments
if gaugeshp == None:
logger.info("No gauges defined, using outlets instead")
gauges = pcr.ordinal(
pcr.uniqueid(
pcr.boolean(
pcr.ifthen(pcr.scalar(wflow_ldd) == 5, pcr.boolean(1)))))
pcr.report(gauges, os.path.join(destination, gauges_map))
# TODO: Add the gauge shape code from StaticMaps.py (line 454-489)
# TODO: add river length map (see SticMaps.py, line 492-499)
# since the products here (river length fraction) are not yet used
# this is disabled for now, as it also takes a lot of computation time
if False:
# report river length
# make a high resolution empty map
dem_hr_file = os.path.join(destination, "dem_highres.tif")
burn_hr_file = os.path.join(destination, "burn_highres.tif")
demburn_hr_file = os.path.join(destination, "demburn_highres.map")
riv_hr_file = os.path.join(destination, "riv_highres.map")
wt.gdal_warp(dem_in, clone_hr, dem_hr_file)
# wt.CreateTif(riv_hr, rows_hr, cols_hr, hr_trans, srs, 0)
# open the shape layer
ds = ogr.Open(rivshp)
lyr = ds.GetLayer(0)
wt.ogr_burn(
lyr,
clone_hr,
-100,
file_out=burn_hr_file,
format="GTiff",
gdal_type=gdal.GDT_Float32,
fill_value=0,
)
# read dem and burn values and add
xax_hr, yax_hr, burn_hr, fill = wt.gdal_readmap(burn_hr_file, "GTiff")
burn_hr[burn_hr == fill] = 0
xax_hr, yax_hr, dem_hr, fill = wt.gdal_readmap(dem_hr_file, "GTiff")
dem_hr[dem_hr == fill] = np.nan
demburn_hr = dem_hr + burn_hr
demburn_hr[np.isnan(demburn_hr)] = -9999
wt.gdal_writemap(demburn_hr_file, "PCRaster", xax_hr, yax_hr,
demburn_hr, -9999.)
pcr.setclone(demburn_hr_file)
demburn_hr = pcr.readmap(demburn_hr_file)
logger.info("Calculating ldd to determine river length")
ldd_hr = pcr.lddcreate(demburn_hr, 1e35, 1e35, 1e35, 1e35)
pcr.report(ldd_hr, os.path.join(destination, "ldd_hr.map"))
pcr.setglobaloption("unitcell")
riv_hr = pcr.scalar(
pcr.streamorder(ldd_hr) >= minorder) * pcr.downstreamdist(ldd_hr)
pcr.report(riv_hr, riv_hr_file)
pcr.setglobaloption("unittrue")
pcr.setclone(clone_map)
logger.info("Computing river length")
wt.windowstats(
riv_hr_file,
len(yax),
len(xax),
trans,
srs,
destination,
stat="fact",
transform=False,
logger=logger,
)
# TODO: nothing happens with the river lengths yet. Need to decide how to use these
# report outlet map
pcr.report(
pcr.ifthen(pcr.ordinal(wflow_ldd) == 5, pcr.ordinal(1)),
os.path.join(destination, outlet_map),
)
# report subcatchment map
subcatchment = pcr.subcatchment(wflow_ldd, gauges)
pcr.report(pcr.ordinal(subcatchment),
os.path.join(destination, subcatch_map))
# Report land use map
if landuse == None:
logger.info(
"No land use map used. Preparing {:s} with only ones.".format(
os.path.join(destination, landuse_map)))
pcr.report(pcr.nominal(ones), os.path.join(destination, landuse_map))
else:
logger.info("Resampling land use from {:s} to {:s}".format(
os.path.abspath(landuse),
os.path.join(destination, os.path.abspath(landuse_map)),
))
wt.gdal_warp(
landuse,
clone_map,
os.path.join(destination, landuse_map),
format="PCRaster",
gdal_interp=gdalconst.GRA_Mode,
gdal_type=gdalconst.GDT_Int32,
)
# report soil map
if soil == None:
logger.info("No soil map used. Preparing {:s} with only ones.".format(
os.path.join(destination, soil_map)))
pcr.report(pcr.nominal(ones), os.path.join(destination, soil_map))
else:
logger.info("Resampling soil from {:s} to {:s}".format(
os.path.abspath(soil),
os.path.join(destination, os.path.abspath(soil_map)),
))
wt.gdal_warp(
soil,
clone_map,
os.path.join(destination, soil_map),
format="PCRaster",
gdal_interp=gdalconst.GRA_Mode,
gdal_type=gdalconst.GDT_Int32,
)
if lai == None:
logger.info(
"No vegetation LAI maps used. Preparing default maps {:s} with only ones."
.format(os.path.join(destination, soil_map)))
pcr.report(pcr.nominal(ones), os.path.join(destination, soil_map))
else:
dest_lai = os.path.join(destination, "clim")
os.makedirs(dest_lai)
for month in range(12):
lai_in = os.path.join(lai, "LAI00000.{:03d}".format(month + 1))
lai_out = os.path.join(dest_lai,
"LAI00000.{:03d}".format(month + 1))
logger.info("Resampling vegetation LAI from {:s} to {:s}".format(
os.path.abspath(lai_in), os.path.abspath(lai_out)))
wt.gdal_warp(
lai_in,
clone_map,
lai_out,
format="PCRaster",
gdal_interp=gdalconst.GRA_Bilinear,
gdal_type=gdalconst.GDT_Float32,
)
# report soil map
if other_maps == None:
logger.info("No other maps used. Skipping other maps.")
else:
logger.info("Resampling list of other maps...")
for map_file in other_maps:
map_name = os.path.split(map_file)[1]
logger.info("Resampling a map from {:s} to {:s}".format(
os.path.abspath(map_file),
os.path.join(
destination,
os.path.splitext(os.path.basename(map_file))[0] + ".map",
),
))
wt.gdal_warp(
map_file,
clone_map,
os.path.join(
destination,
os.path.splitext(os.path.basename(map_file))[0] + ".map",
),
format="PCRaster",
gdal_interp=gdalconst.GRA_Mode,
gdal_type=gdalconst.GDT_Float32,
)
if clean:
wt.DeleteList(glob.glob(os.path.join(destination, "*.xml")),
logger=logger)
wt.DeleteList(glob.glob(os.path.join(destination, "clim", "*.xml")),
logger=logger)
wt.DeleteList(glob.glob(os.path.join(destination, "*highres*")),
logger=logger)
def main():
### Read input arguments #####
parser = OptionParser()
usage = "usage: %prog [options]"
parser = OptionParser(usage=usage)
parser.add_option(
"-q",
"--quiet",
dest="verbose",
default=True,
action="store_false",
help="do not print status messages to stdout",
)
parser.add_option(
"-i",
"--ini",
dest="inifile",
default="hand_contour_inun.ini",
nargs=1,
help="ini configuration file",
)
parser.add_option(
"-f",
"--flood_map",
nargs=1,
dest="flood_map",
help="Flood map file (NetCDF point time series file",
)
parser.add_option(
"-v",
"--flood_variable",
nargs=1,
dest="flood_variable",
default="water_level",
help="variable name of flood water level",
)
parser.add_option(
"-b",
"--bankfull_map",
dest="bankfull_map",
default="",
help="Map containing bank full level (is subtracted from flood map, in NetCDF)",
)
parser.add_option(
"-c",
"--catchment",
dest="catchment_strahler",
default=7,
type="int",
help="Strahler order threshold >= are selected as catchment boundaries",
)
parser.add_option(
"-t",
"--time",
dest="time",
default="",
help="time in YYYYMMDDHHMMSS, overrides time in NetCDF input if set",
)
# parser.add_option('-s', '--hand_strahler',
# dest='hand_strahler', default=7, type='int',
# help='Strahler order threshold >= selected as riverine')
parser.add_option(
"-m",
"--max_strahler",
dest="max_strahler",
default=1000,
type="int",
help="Maximum Strahler order to loop over",
)
parser.add_option(
"-d", "--destination", dest="dest_path", default="inun", help="Destination path"
)
parser.add_option(
"-H",
"--hand_file_prefix",
dest="hand_file_prefix",
default="",
help="optional HAND file prefix of already generated HAND files",
)
parser.add_option(
"-n",
"--neg_HAND",
dest="neg_HAND",
default=0,
type="int",
help="if set to 1, allow for negative HAND values in HAND maps",
)
(options, args) = parser.parse_args()
if not os.path.exists(options.inifile):
print "path to ini file cannot be found"
sys.exit(1)
options.dest_path = os.path.abspath(options.dest_path)
if not (os.path.isdir(options.dest_path)):
os.makedirs(options.dest_path)
# set up the logger
flood_name = os.path.split(options.flood_map)[1].split(".")[0]
# case_name = 'inun_{:s}_hand_{:02d}_catch_{:02d}'.format(flood_name, options.hand_strahler, options.catchment_strahler)
case_name = "inun_{:s}_catch_{:02d}".format(flood_name, options.catchment_strahler)
logfilename = os.path.join(options.dest_path, "hand_contour_inun.log")
logger, ch = inun_lib.setlogger(logfilename, "HAND_INUN", options.verbose)
logger.info("$Id: $")
logger.info("Flood map: {:s}".format(options.flood_map))
logger.info("Bank full map: {:s}".format(options.bankfull_map))
logger.info("Destination path: {:s}".format(options.dest_path))
# read out ini file
### READ CONFIG FILE
# open config-file
config = inun_lib.open_conf(options.inifile)
# read settings
options.dem_file = inun_lib.configget(config, "HighResMaps", "dem_file", True)
options.ldd_file = inun_lib.configget(config, "HighResMaps", "ldd_file", True)
options.stream_file = inun_lib.configget(config, "HighResMaps", "stream_file", True)
options.riv_length_fact_file = inun_lib.configget(
config, "wflowResMaps", "riv_length_fact_file", True
)
options.ldd_wflow = inun_lib.configget(config, "wflowResMaps", "ldd_wflow", True)
options.riv_width_file = inun_lib.configget(
config, "wflowResMaps", "riv_width_file", True
)
options.file_format = inun_lib.configget(
config, "file_settings", "file_format", 0, datatype="int"
)
options.out_format = inun_lib.configget(
config, "file_settings", "out_format", 0, datatype="int"
)
options.latlon = inun_lib.configget(
config, "file_settings", "latlon", 0, datatype="int"
)
options.x_tile = inun_lib.configget(
config, "tiling", "x_tile", 10000, datatype="int"
)
options.y_tile = inun_lib.configget(
config, "tiling", "y_tile", 10000, datatype="int"
)
options.x_overlap = inun_lib.configget(
config, "tiling", "x_overlap", 1000, datatype="int"
)
options.y_overlap = inun_lib.configget(
config, "tiling", "y_overlap", 1000, datatype="int"
)
options.iterations = inun_lib.configget(
config, "inundation", "iterations", 20, datatype="int"
)
options.initial_level = inun_lib.configget(
config, "inundation", "initial_level", 32., datatype="float"
)
options.flood_volume_type = inun_lib.configget(
config, "inundation", "flood_volume_type", 0, datatype="int"
)
# options.area_multiplier = inun_lib.configget(config, 'inundation',
# 'area_multiplier', 1., datatype='float')
logger.info("DEM file: {:s}".format(options.dem_file))
logger.info("LDD file: {:s}".format(options.ldd_file))
logger.info("streamfile: {:s}".format(options.stream_file))
logger.info("Columns per tile: {:d}".format(options.x_tile))
logger.info("Rows per tile: {:d}".format(options.y_tile))
logger.info("Columns overlap: {:d}".format(options.x_overlap))
logger.info("Rows overlap: {:d}".format(options.y_overlap))
metadata_global = {}
# add metadata from the section [metadata]
meta_keys = config.options("metadata_global")
for key in meta_keys:
metadata_global[key] = config.get("metadata_global", key)
# add a number of metadata variables that are mandatory
metadata_global["config_file"] = os.path.abspath(options.inifile)
metadata_var = {}
metadata_var["units"] = "m"
metadata_var["standard_name"] = "water_surface_height_above_reference_datum"
metadata_var["long_name"] = "flooding"
metadata_var[
"comment"
] = "water_surface_reference_datum_altitude is given in file {:s}".format(
options.dem_file
)
if not os.path.exists(options.dem_file):
logger.error("path to dem file {:s} cannot be found".format(options.dem_file))
sys.exit(1)
if not os.path.exists(options.ldd_file):
logger.error("path to ldd file {:s} cannot be found".format(options.ldd_file))
sys.exit(1)
# Read extent from a GDAL compatible file
try:
extent = inun_lib.get_gdal_extent(options.dem_file)
except:
msg = "Input file {:s} not a gdal compatible file".format(options.dem_file)
inun_lib.close_with_error(logger, ch, msg)
sys.exit(1)
try:
x, y = inun_lib.get_gdal_axes(options.dem_file, logging=logger)
srs = inun_lib.get_gdal_projection(options.dem_file, logging=logger)
except:
msg = "Input file {:s} not a gdal compatible file".format(options.dem_file)
inun_lib.close_with_error(logger, ch, msg)
sys.exit(1)
# read history from flood file
if options.file_format == 0:
a = nc.Dataset(options.flood_map, "r")
metadata_global[
"history"
] = "Created by: $Id: $, boundary conditions from {:s},\nhistory: {:s}".format(
os.path.abspath(options.flood_map), a.history
)
a.close()
else:
metadata_global[
"history"
] = "Created by: $Id: $, boundary conditions from {:s},\nhistory: {:s}".format(
os.path.abspath(options.flood_map), "PCRaster file, no history"
)
# first write subcatch maps and hand maps
############### TODO ######
# setup a HAND file for each strahler order
max_s = inun_lib.define_max_strahler(options.stream_file, logging=logger)
stream_max = np.minimum(max_s, options.max_strahler)
for hand_strahler in range(options.catchment_strahler, stream_max + 1, 1):
dem_name = os.path.split(options.dem_file)[1].split(".")[0]
if os.path.isfile(
"{:s}_{:02d}.tif".format(options.hand_file_prefix, hand_strahler)
):
hand_file = "{:s}_{:02d}.tif".format(
options.hand_file_prefix, hand_strahler
)
else:
logger.info(
"No HAND files with HAND prefix were found, checking {:s}_hand_strahler_{:02d}.tif".format(
dem_name, hand_strahler
)
)
hand_file = os.path.join(
options.dest_path,
"{:s}_hand_strahler_{:02d}.tif".format(dem_name, hand_strahler),
)
if not (os.path.isfile(hand_file)):
# hand file does not exist yet! Generate it, otherwise skip!
logger.info(
"HAND file {:s} not found, start setting up...please wait...".format(
hand_file
)
)
hand_file_tmp = os.path.join(
options.dest_path,
"{:s}_hand_strahler_{:02d}.tif.tmp".format(dem_name, hand_strahler),
)
ds_hand, band_hand = inun_lib.prepare_gdal(
hand_file_tmp, x, y, logging=logger, srs=srs
)
# band_hand = ds_hand.GetRasterBand(1)
# Open terrain data for reading
ds_dem, rasterband_dem = inun_lib.get_gdal_rasterband(options.dem_file)
ds_ldd, rasterband_ldd = inun_lib.get_gdal_rasterband(options.ldd_file)
ds_stream, rasterband_stream = inun_lib.get_gdal_rasterband(
options.stream_file
)
n = 0
for x_loop in range(0, len(x), options.x_tile):
x_start = np.maximum(x_loop, 0)
x_end = np.minimum(x_loop + options.x_tile, len(x))
# determine actual overlap for cutting
for y_loop in range(0, len(y), options.y_tile):
x_overlap_min = x_start - np.maximum(x_start - options.x_overlap, 0)
x_overlap_max = (
np.minimum(x_end + options.x_overlap, len(x)) - x_end
)
n += 1
# print('tile {:001d}:'.format(n))
y_start = np.maximum(y_loop, 0)
y_end = np.minimum(y_loop + options.y_tile, len(y))
y_overlap_min = y_start - np.maximum(y_start - options.y_overlap, 0)
y_overlap_max = (
np.minimum(y_end + options.y_overlap, len(y)) - y_end
)
# cut out DEM
logger.debug(
"Computing HAND for xmin: {:d} xmax: {:d} ymin {:d} ymax {:d}".format(
x_start, x_end, y_start, y_end
)
)
terrain = rasterband_dem.ReadAsArray(
x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min),
)
drainage = rasterband_ldd.ReadAsArray(
x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min),
)
stream = rasterband_stream.ReadAsArray(
x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min),
)
# write to temporary file
terrain_temp_file = os.path.join(
options.dest_path, "terrain_temp.map"
)
drainage_temp_file = os.path.join(
options.dest_path, "drainage_temp.map"
)
stream_temp_file = os.path.join(
options.dest_path, "stream_temp.map"
)
if rasterband_dem.GetNoDataValue() is not None:
inun_lib.gdal_writemap(
terrain_temp_file,
"PCRaster",
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
terrain,
rasterband_dem.GetNoDataValue(),
gdal_type=gdal.GDT_Float32,
logging=logger,
)
else:
# in case no nodata value is found
logger.warning(
"No nodata value found in {:s}. assuming -9999".format(
options.dem_file
)
)
inun_lib.gdal_writemap(
terrain_temp_file,
"PCRaster",
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
terrain,
-9999.,
gdal_type=gdal.GDT_Float32,
logging=logger,
)
inun_lib.gdal_writemap(
drainage_temp_file,
"PCRaster",
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
drainage,
rasterband_ldd.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger,
)
inun_lib.gdal_writemap(
stream_temp_file,
"PCRaster",
np.arange(0, terrain.shape[1]),
np.arange(0, terrain.shape[0]),
stream,
rasterband_ldd.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger,
)
# read as pcr objects
pcr.setclone(terrain_temp_file)
terrain_pcr = pcr.readmap(terrain_temp_file)
drainage_pcr = pcr.lddrepair(
pcr.ldd(pcr.readmap(drainage_temp_file))
) # convert to ldd type map
stream_pcr = pcr.scalar(
pcr.readmap(stream_temp_file)
) # convert to ldd type map
# check if the highest stream order of the tile is below the hand_strahler
# if the highest stream order of the tile is smaller than hand_strahler, than DEM values are taken instead of HAND values.
max_stream_tile = inun_lib.define_max_strahler(
stream_temp_file, logging=logger
)
if max_stream_tile < hand_strahler:
hand_pcr = terrain_pcr
logger.info(
"For this tile, DEM values are used instead of HAND because there is no stream order larger than {:02d}".format(
hand_strahler
)
)
else:
# compute streams
stream_ge, subcatch = inun_lib.subcatch_stream(
drainage_pcr, hand_strahler, stream=stream_pcr
) # generate streams
# compute basins
stream_ge_dummy, subcatch = inun_lib.subcatch_stream(
drainage_pcr, options.catchment_strahler, stream=stream_pcr
) # generate streams
basin = pcr.boolean(subcatch)
hand_pcr, dist_pcr = inun_lib.derive_HAND(
terrain_pcr,
drainage_pcr,
3000,
rivers=pcr.boolean(stream_ge),
basin=basin,
neg_HAND=options.neg_HAND,
)
# convert to numpy
hand = pcr.pcr2numpy(hand_pcr, -9999.)
# cut relevant part
if y_overlap_max == 0:
y_overlap_max = -hand.shape[0]
if x_overlap_max == 0:
x_overlap_max = -hand.shape[1]
hand_cut = hand[
0 + y_overlap_min : -y_overlap_max,
0 + x_overlap_min : -x_overlap_max,
]
band_hand.WriteArray(hand_cut, x_start, y_start)
os.unlink(terrain_temp_file)
os.unlink(drainage_temp_file)
os.unlink(stream_temp_file)
band_hand.FlushCache()
ds_dem = None
ds_ldd = None
ds_stream = None
band_hand.SetNoDataValue(-9999.)
ds_hand = None
logger.info("Finalizing {:s}".format(hand_file))
# rename temporary file to final hand file
os.rename(hand_file_tmp, hand_file)
else:
logger.info("HAND file {:s} already exists...skipping...".format(hand_file))
#####################################################################################
# HAND file has now been prepared, moving to flood mapping part #
#####################################################################################
# set the clone
pcr.setclone(options.ldd_wflow)
# read wflow ldd as pcraster object
ldd_pcr = pcr.readmap(options.ldd_wflow)
xax, yax, riv_width, fill_value = inun_lib.gdal_readmap(
options.riv_width_file, "GTiff", logging=logger
)
# determine cell length in meters using ldd_pcr as clone (if latlon=True, values are converted to m2
x_res, y_res, reallength_wflow = pcrut.detRealCellLength(
pcr.scalar(ldd_pcr), not (bool(options.latlon))
)
cell_surface_wflow = pcr.pcr2numpy(x_res * y_res, 0)
if options.flood_volume_type == 0:
# load the staticmaps needed to estimate volumes across all
# xax, yax, riv_length, fill_value = inun_lib.gdal_readmap(options.riv_length_file, 'GTiff', logging=logger)
# riv_length = np.ma.masked_where(riv_length==fill_value, riv_length)
xax, yax, riv_width, fill_value = inun_lib.gdal_readmap(
options.riv_width_file, "GTiff", logging=logger
)
riv_width[riv_width == fill_value] = 0
# read river length factor file (multiplier)
xax, yax, riv_length_fact, fill_value = inun_lib.gdal_readmap(
options.riv_length_fact_file, "GTiff", logging=logger
)
riv_length_fact = np.ma.masked_where(
riv_length_fact == fill_value, riv_length_fact
)
drain_length = wflow_lib.detdrainlength(ldd_pcr, x_res, y_res)
# compute river length in each cell
riv_length = pcr.pcr2numpy(drain_length, 0) * riv_length_fact
# riv_length_pcr = pcr.numpy2pcr(pcr.Scalar, riv_length, 0)
flood_folder = os.path.join(options.dest_path, case_name)
flood_vol_map = os.path.join(
flood_folder,
"{:s}_vol.tif".format(os.path.split(options.flood_map)[1].split(".")[0]),
)
if not (os.path.isdir(flood_folder)):
os.makedirs(flood_folder)
if options.out_format == 0:
inun_file_tmp = os.path.join(flood_folder, "{:s}.tif.tmp".format(case_name))
inun_file = os.path.join(flood_folder, "{:s}.tif".format(case_name))
else:
inun_file_tmp = os.path.join(flood_folder, "{:s}.nc.tmp".format(case_name))
inun_file = os.path.join(flood_folder, "{:s}.nc".format(case_name))
hand_temp_file = os.path.join(flood_folder, "hand_temp.map")
drainage_temp_file = os.path.join(flood_folder, "drainage_temp.map")
stream_temp_file = os.path.join(flood_folder, "stream_temp.map")
flood_vol_temp_file = os.path.join(flood_folder, "flood_warp_temp.tif")
# load the data with river levels and compute the volumes
if options.file_format == 0:
# assume we need the maximum value in a NetCDF time series grid
logger.info("Reading flood from {:s} NetCDF file".format(options.flood_map))
a = nc.Dataset(options.flood_map, "r")
if options.latlon == 0:
xax = a.variables["x"][:]
yax = a.variables["y"][:]
else:
try:
xax = a.variables["lon"][:]
yax = a.variables["lat"][:]
except:
xax = a.variables["x"][:]
yax = a.variables["y"][:]
if options.time == "":
time_list = nc.num2date(
a.variables["time"][:],
units=a.variables["time"].units,
calendar=a.variables["time"].calendar,
)
time = [time_list[len(time_list) / 2]]
else:
time = [dt.datetime.strptime(options.time, "%Y%m%d%H%M%S")]
flood_series = a.variables[options.flood_variable][:]
flood_data = flood_series.max(axis=0)
if np.ma.is_masked(flood_data):
flood = flood_data.data
flood[flood_data.mask] = 0
if yax[-1] > yax[0]:
yax = np.flipud(yax)
flood = np.flipud(flood)
a.close()
elif options.file_format == 1:
logger.info("Reading flood from {:s} PCRaster file".format(options.flood_map))
xax, yax, flood, flood_fill_value = inun_lib.gdal_readmap(
options.flood_map, "PCRaster", logging=logger
)
flood = np.ma.masked_equal(flood, flood_fill_value)
if options.time == "":
options.time = "20000101000000"
time = [dt.datetime.strptime(options.time, "%Y%m%d%H%M%S")]
flood[flood == flood_fill_value] = 0.
# load the bankfull depths
if options.bankfull_map == "":
bankfull = np.zeros(flood.shape)
else:
if options.file_format == 0:
logger.info(
"Reading bankfull from {:s} NetCDF file".format(options.bankfull_map)
)
a = nc.Dataset(options.bankfull_map, "r")
xax = a.variables["x"][:]
yax = a.variables["y"][:]
# xax = a.variables['lon'][:]
# yax = a.variables['lat'][:]
bankfull_series = a.variables[options.flood_variable][:]
bankfull_data = bankfull_series.max(axis=0)
if np.ma.is_masked(bankfull_data):
bankfull = bankfull_data.data
bankfull[bankfull_data.mask] = 0
if yax[-1] > yax[0]:
yax = np.flipud(yax)
bankfull = np.flipud(bankfull)
a.close()
elif options.file_format == 1:
logger.info(
"Reading bankfull from {:s} PCRaster file".format(options.bankfull_map)
)
xax, yax, bankfull, bankfull_fill_value = inun_lib.gdal_readmap(
options.bankfull_map, "PCRaster", logging=logger
)
bankfull = np.ma.masked_equal(bankfull, bankfull_fill_value)
# flood = bankfull*2
# res_x = 2000
# res_y = 2000
# subtract the bankfull water level to get flood levels (above bankfull)
flood_vol = np.maximum(flood - bankfull, 0)
if options.flood_volume_type == 0:
flood_vol_m = (
riv_length * riv_width * flood_vol / cell_surface_wflow
) # volume expressed in meters water disc
flood_vol_m_pcr = pcr.numpy2pcr(pcr.Scalar, flood_vol_m, 0)
else:
flood_vol_m = flood_vol / cell_surface_wflow
flood_vol_m_data = flood_vol_m.data
flood_vol_m_data[flood_vol_m.mask] = -999.
logger.info("Saving water layer map to {:s}".format(flood_vol_map))
# write to a tiff file
inun_lib.gdal_writemap(
flood_vol_map,
"GTiff",
xax,
yax,
np.maximum(flood_vol_m_data, 0),
-999.,
logging=logger,
)
# this is placed later in the hand loop
# ds_hand, rasterband_hand = inun_lib.get_gdal_rasterband(hand_file)
ds_ldd, rasterband_ldd = inun_lib.get_gdal_rasterband(options.ldd_file)
ds_stream, rasterband_stream = inun_lib.get_gdal_rasterband(options.stream_file)
logger.info("Preparing flood map in {:s} ...please wait...".format(inun_file))
if options.out_format == 0:
ds_inun, band_inun = inun_lib.prepare_gdal(
inun_file_tmp, x, y, logging=logger, srs=srs
)
# band_inun = ds_inun.GetRasterBand(1)
else:
ds_inun, band_inun = inun_lib.prepare_nc(
inun_file_tmp,
time,
x,
np.flipud(y),
metadata=metadata_global,
metadata_var=metadata_var,
logging=logger,
)
# loop over all the tiles
n = 0
for x_loop in range(0, len(x), options.x_tile):
x_start = np.maximum(x_loop, 0)
x_end = np.minimum(x_loop + options.x_tile, len(x))
# determine actual overlap for cutting
for y_loop in range(0, len(y), options.y_tile):
x_overlap_min = x_start - np.maximum(x_start - options.x_overlap, 0)
x_overlap_max = np.minimum(x_end + options.x_overlap, len(x)) - x_end
n += 1
# print('tile {:001d}:'.format(n))
y_start = np.maximum(y_loop, 0)
y_end = np.minimum(y_loop + options.y_tile, len(y))
y_overlap_min = y_start - np.maximum(y_start - options.y_overlap, 0)
y_overlap_max = np.minimum(y_end + options.y_overlap, len(y)) - y_end
x_tile_ax = x[x_start - x_overlap_min : x_end + x_overlap_max]
y_tile_ax = y[y_start - y_overlap_min : y_end + y_overlap_max]
# cut out DEM
logger.debug(
"handling xmin: {:d} xmax: {:d} ymin {:d} ymax {:d}".format(
x_start, x_end, y_start, y_end
)
)
drainage = rasterband_ldd.ReadAsArray(
x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min),
)
stream = rasterband_stream.ReadAsArray(
x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min),
)
# stream_max = np.minimum(stream.max(), options.max_strahler)
inun_lib.gdal_writemap(
drainage_temp_file,
"PCRaster",
x_tile_ax,
y_tile_ax,
drainage,
rasterband_ldd.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger,
)
inun_lib.gdal_writemap(
stream_temp_file,
"PCRaster",
x_tile_ax,
y_tile_ax,
stream,
rasterband_stream.GetNoDataValue(),
gdal_type=gdal.GDT_Int32,
logging=logger,
)
# read as pcr objects
pcr.setclone(stream_temp_file)
drainage_pcr = pcr.lddrepair(
pcr.ldd(pcr.readmap(drainage_temp_file))
) # convert to ldd type map
stream_pcr = pcr.scalar(
pcr.readmap(stream_temp_file)
) # convert to ldd type map
# warp of flood volume to inundation resolution
inun_lib.gdal_warp(
flood_vol_map,
stream_temp_file,
flood_vol_temp_file,
gdal_interp=gdalconst.GRA_NearestNeighbour,
) # ,
x_tile_ax, y_tile_ax, flood_meter, fill_value = inun_lib.gdal_readmap(
flood_vol_temp_file, "GTiff", logging=logger
)
# make sure that the option unittrue is on !! (if unitcell was is used in another function)
x_res_tile, y_res_tile, reallength = pcrut.detRealCellLength(
pcr.scalar(stream_pcr), not (bool(options.latlon))
)
cell_surface_tile = pcr.pcr2numpy(x_res_tile * y_res_tile, 0)
# convert meter depth to volume [m3]
flood_vol = pcr.numpy2pcr(
pcr.Scalar, flood_meter * cell_surface_tile, fill_value
)
# first prepare a basin map, belonging to the lowest order we are looking at
inundation_pcr = pcr.scalar(stream_pcr) * 0
for hand_strahler in range(options.catchment_strahler, stream_max + 1, 1):
# hand_temp_file = os.path.join(flood_folder, 'hand_temp.map')
if os.path.isfile(
os.path.join(
options.dest_path,
"{:s}_hand_strahler_{:02d}.tif".format(dem_name, hand_strahler),
)
):
hand_file = os.path.join(
options.dest_path,
"{:s}_hand_strahler_{:02d}.tif".format(dem_name, hand_strahler),
)
else:
hand_file = "{:s}_{:02d}.tif".format(
options.hand_file_prefix, hand_strahler
)
ds_hand, rasterband_hand = inun_lib.get_gdal_rasterband(hand_file)
hand = rasterband_hand.ReadAsArray(
x_start - x_overlap_min,
y_start - y_overlap_min,
(x_end + x_overlap_max) - (x_start - x_overlap_min),
(y_end + y_overlap_max) - (y_start - y_overlap_min),
)
print (
"len x-ax: {:d} len y-ax {:d} x-shape {:d} y-shape {:d}".format(
len(x_tile_ax), len(y_tile_ax), hand.shape[1], hand.shape[0]
)
)
inun_lib.gdal_writemap(
hand_temp_file,
"PCRaster",
x_tile_ax,
y_tile_ax,
hand,
rasterband_hand.GetNoDataValue(),
gdal_type=gdal.GDT_Float32,
logging=logger,
)
hand_pcr = pcr.readmap(hand_temp_file)
stream_ge_hand, subcatch_hand = inun_lib.subcatch_stream(
drainage_pcr, options.catchment_strahler, stream=stream_pcr
)
# stream_ge_hand, subcatch_hand = inun_lib.subcatch_stream(drainage_pcr, hand_strahler, stream=stream_pcr)
stream_ge, subcatch = inun_lib.subcatch_stream(
drainage_pcr,
options.catchment_strahler,
stream=stream_pcr,
basin=pcr.boolean(pcr.cover(subcatch_hand, 0)),
assign_existing=True,
min_strahler=hand_strahler,
max_strahler=hand_strahler,
) # generate subcatchments, only within basin for HAND
flood_vol_strahler = pcr.ifthenelse(
pcr.boolean(pcr.cover(subcatch, 0)), flood_vol, 0
) # mask the flood volume map with the created subcatch map for strahler order = hand_strahler
inundation_pcr_step = inun_lib.volume_spread(
drainage_pcr,
hand_pcr,
pcr.subcatchment(
drainage_pcr, subcatch
), # to make sure backwater effects can occur from higher order rivers to lower order rivers
flood_vol_strahler,
volume_thres=0.,
iterations=options.iterations,
cell_surface=pcr.numpy2pcr(pcr.Scalar, cell_surface_tile, -9999),
logging=logger,
order=hand_strahler,
neg_HAND=options.neg_HAND,
) # 1166400000.
# use maximum value of inundation_pcr_step and new inundation for higher strahler order
inundation_pcr = pcr.max(inundation_pcr, inundation_pcr_step)
inundation = pcr.pcr2numpy(inundation_pcr, -9999.)
# cut relevant part
if y_overlap_max == 0:
y_overlap_max = -inundation.shape[0]
if x_overlap_max == 0:
x_overlap_max = -inundation.shape[1]
inundation_cut = inundation[
0 + y_overlap_min : -y_overlap_max, 0 + x_overlap_min : -x_overlap_max
]
# inundation_cut
if options.out_format == 0:
band_inun.WriteArray(inundation_cut, x_start, y_start)
band_inun.FlushCache()
else:
# with netCDF, data is up-side-down.
inun_lib.write_tile_nc(band_inun, inundation_cut, x_start, y_start)
# clean up
os.unlink(flood_vol_temp_file)
os.unlink(drainage_temp_file)
os.unlink(hand_temp_file)
os.unlink(
stream_temp_file
) # also remove temp stream file from output folder
# if n == 35:
# band_inun.SetNoDataValue(-9999.)
# ds_inun = None
# sys.exit(0)
# os.unlink(flood_vol_map)
logger.info("Finalizing {:s}".format(inun_file))
# add the metadata to the file and band
# band_inun.SetNoDataValue(-9999.)
# ds_inun.SetMetadata(metadata_global)
# band_inun.SetMetadata(metadata_var)
if options.out_format == 0:
ds_inun = None
ds_hand = None
else:
ds_inun.close()
ds_ldd = None
# rename temporary file to final hand file
if os.path.isfile(inun_file):
# remove an old result if available
os.unlink(inun_file)
os.rename(inun_file_tmp, inun_file)
logger.info("Done! Thank you for using hand_contour_inun.py")
logger, ch = inun_lib.closeLogger(logger, ch)
del logger, ch
sys.exit(0)
#-maps: clone map and cell area clone= pcr.readmap(os.path.join(mapsDir,'cloneAfrica.map')) #-set sample domain xMin= -25.5 xMax= 57.5 yMin= -35.0 yMax= 37.5 sampleResolution= 0.5 resampleRatio= 1. #-class to clip map during read clippedRead= pcrPartialRead(xMin,xMax,yMin,yMax,sampleResolution,resampleRatio,clone) cellAreaMap= os.path.join(mapsDir,'cellarea30.map') cellArea= clippedRead.get(cellAreaMap,'scalar') #-channel and floodplain characteristics LDDMap= os.path.join(mapsDir,'lddsound_30min.map') LDD= pcr.lddrepair(clippedRead.get(LDDMap,'ldd')) channelGradient= clippedRead.get(os.path.join(mapsDir,'globalgradchannel.map')) channelWidth= clippedRead.get(os.path.join(mapsDir,'channel_width.map')) channelLength= clippedRead.get(os.path.join(mapsDir,'channel_length.map')) channelDepth= clippedRead.get(os.path.join(mapsDir,'channel_depth.map')) floodplainMask= pcr.spatial(pcr.boolean(1)) # NOTE: set to zero for static, to one for dynamic floodplains channelManN= 0.04 floodplainManN= 0.10 #-flood plain parameterization #-root of file name with maps of relative elvation above floodplain # and associated fractions relZFileName= 'elev%04d.map' areaFractions=[0.0,0.01,0.05,0.10,0.20,0.30,0.40,\ 0.50,0.60,0.70,0.80,0.90,1.00] # reduction parameter of smoothing interval and error threshold reductionKK= 0.5
print("")
print(cmd)
os.system(cmd)
print("")
input_files['averageClimatologyDischargeMonthAvg'] = out_file
# set the pcraster clone, ldd, landmask, and cell area map
msg = "Setting the clone, ldd, landmask, and cell area maps" + ":"
logger.info(msg)
# - clone
clone_map_file = input_files['clone_map_05min']
pcr.setclone(clone_map_file)
# - ldd
ldd = vos.readPCRmapClone(input_files['ldd_map_05min'], clone_map_file,
output_files['tmp_folder'], None, True)
ldd = pcr.lddrepair(pcr.ldd(ldd))
ldd = pcr.lddrepair(ldd)
# - landmask
landmask = pcr.ifthen(pcr.defined(ldd), pcr.boolean(1.0))
# - cell area
cell_area = vos.readPCRmapClone(input_files['cell_area_05min'], clone_map_file,
output_files['tmp_folder'])
# set the basin map
msg = "Setting the basin map" + ":"
logger.info(msg)
basin_map = pcr.nominal(\
vos.readPCRmapClone(input_files['basin_map_05min'],
input_files['clone_map_05min'],
output_files['tmp_folder'],
None, False, None, True))
def subcatch_order_b(
ldd, oorder, sizelimit=0, fill=False, fillcomplete=False, stoporder=0
):
"""
Determines subcatchments using the catchment order
This version tries to keep the number op upstream/downstream catchment the
small by first dederivingatchment connected to the major river(the order) given, and fill
up from there.
Input:
- ldd
- oorder - order to use
- sizelimit - smallest catchments to include, default is all (sizelimit=0) in number of cells
- if fill is set to True the higer order catchment are filled also
- if fillcomplete is set to True the whole ldd is filled with catchments.
:returns sc, dif, nldd; Subcatchment, Points, subcatchldd
"""
# outl = find_outlet(ldd)
# large = pcr.subcatchment(ldd,pcr.boolean(outl))
if stoporder == 0:
stoporder = oorder
stt = pcr.streamorder(ldd)
sttd = pcr.downstream(ldd, stt)
pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(stt)) > 0.0, sttd)
maxorder = pcraster.framework.getCellValue(pcr.mapmaximum(stt), 1, 1)
dif = pcr.uniqueid(pcr.boolean(pcr.ifthen(stt == pcr.ordinal(oorder), pts)))
if fill:
for order in range(oorder, maxorder):
m_pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(order)) > 0.0, sttd)
m_dif = pcr.uniqueid(
pcr.boolean(pcr.ifthen(stt == pcr.ordinal(order), m_pts))
)
dif = pcr.uniqueid(pcr.boolean(pcr.cover(m_dif, dif)))
for myorder in range(oorder - 1, stoporder, -1):
sc = pcr.subcatchment(ldd, pcr.nominal(dif))
m_pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(stt)) > 0.0, sttd)
m_dif = pcr.uniqueid(
pcr.boolean(pcr.ifthen(stt == pcr.ordinal(myorder - 1), m_pts))
)
dif = pcr.uniqueid(
pcr.boolean(pcr.cover(pcr.ifthen(pcr.scalar(sc) == 0, m_dif), dif))
)
if fillcomplete:
sc = pcr.subcatchment(ldd, pcr.nominal(dif))
cs, m_dif, stt = subcatch_order_a(ldd, stoporder)
dif = pcr.uniqueid(
pcr.boolean(
pcr.cover(
pcr.ifthen(pcr.scalar(sc) == 0, pcr.ordinal(m_dif)),
pcr.ordinal(dif),
)
)
)
scsize = pcr.catchmenttotal(1, ldd)
dif = pcr.ordinal(pcr.uniqueid(pcr.boolean(pcr.ifthen(scsize >= sizelimit, dif))))
sc = pcr.subcatchment(ldd, dif)
# Make pit ldd
nldd = pcr.lddrepair(pcr.ifthenelse(pcr.cover(dif, 0) > 0, 5, ldd))
return sc, dif, nldd
pitsDict = {13 : [13,3153],
28 : [28,3515]}
lddOld = pcr.readmap('/home/straa005/LDD/lddOutput/ldd_HydroSHEDS_Hydro1k_5min.map')
lddDiff = pcr.scalar(ldd) - pcr.scalar(lddOld)
pcr.report(pcr.boolean(lddDiff), 'lddDiff.map')
p
############################### main ###################################
ldd = pcr.nominal(ldd)
ldd = fillMVBoundingBox(ldd, 1, 57, 76, 26, 47)
ldd = fillMVBoundingBox(ldd, 1, 58, 62, 51, 60)
ldd = fillMVBoundingBox(ldd, 1, -108, -101, 31, 40)
ldd = pcr.lddrepair(pcr.ldd(ldd))
pits = pcr.pit(ldd)
pcr.setglobaloption('unitcell')
continentMasks = pcr.cover(mask48, pcr.windowmajority(mask48,5))
#- adjust area masks with 5 min ldd
continentMasksNew = pcr.ifthen(pcr.boolean(pcr.readmap(cloneMap)) == 0, pcr.scalar(1))
for i in areas[0:]:
print 'area mask = ', i
mask = pcr.ifthen(continentMasks == i, pcr.boolean(1))
pitsContinent = pcr.pcrand(mask, pcr.boolean(pits))
pitsContinent = pcr.nominal(pcr.uniqueid(pcr.ifthen(pitsContinent == 1, pcr.boolean(1))))
catchments = pcr.catchment(ldd, pitsContinent)
newMask = pcr.ifthen(pcr.boolean(catchments) == 1, pcr.scalar(i))
msg = "Set the landmask to : " + str(landmask_map_file)
logger.info(msg)
landmask = pcr.readmap(landmask_map_file)
# resampling low resolution ldd map
msg = "Resample the low resolution ldd map."
logger.info(msg)
ldd_map_low_resolution_file_name = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/lddsound_05min.map"
ldd_map_low_resolution = vos.readPCRmapClone(ldd_map_low_resolution_file_name, \
clone_map_file, \
tmp_folder, \
None, True, None, False)
ldd_map_low_resolution = pcr.ifthen(
landmask,
ldd_map_low_resolution) # NOTE THAT YOU MAY NOT HAVE TO MASK-OUT THE LDD.
ldd_map_low_resolution = pcr.lddrepair(pcr.ldd(ldd_map_low_resolution))
ldd_map_low_resolution = pcr.lddrepair(ldd_map_low_resolution)
pcr.report(ldd_map_low_resolution, "resampled_low_resolution_ldd.map")
# permanent water bodies files (at 5 arc-minutes resolution)
reservoir_capacity_file = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/reservoirs/waterBodiesFinal_version15Sept2013/maps/reservoircapacity_2010.map"
fracwat_file = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/reservoirs/waterBodiesFinal_version15Sept2013/maps/fracwat_2010.map"
water_body_id_file = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/reservoirs/waterBodiesFinal_version15Sept2013/maps/waterbodyid_2010.map"
# cell_area_file
cell_area_file = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/cellsize05min.correct.map"
# read all extreme value maps (low resolution maps), resample them, and save them to the output folder
msg = "Resampling extreme value maps."
logger.info(msg)
file_names = [
def evaluateAllBaseflowResults(self,globalCloneMapFileName,\
catchmentClassFileName,\
lddMapFileName,\
cellAreaMapFileName,\
pcrglobwb_output,\
analysisOutputDir="",\
tmpDir = None):
# temporary directory
if tmpDir == None: tmpDir = self.tmpDir+"/edwin_iwmi_"
# output directory for all analyses for all stations
analysisOutputDir = str(analysisOutputDir)
self.chartOutputDir = analysisOutputDir+"/chart/"
self.tableOutputDir = analysisOutputDir+"/table/"
#
if analysisOutputDir == "": self.chartOutputDir = "chart/"
if analysisOutputDir == "": self.tableOutputDir = "table/"
#
# make the chart and table directories:
os.system('rm -r '+self.chartOutputDir+"*")
os.system('rm -r '+self.tableOutputDir+"*")
os.makedirs(self.chartOutputDir)
os.makedirs(self.tableOutputDir)
# cloneMap for all pcraster operations
pcr.setclone(globalCloneMapFileName)
cloneMap = pcr.boolean(1)
lddMap = pcr.lddrepair(pcr.readmap(lddMapFileName))
cellArea = pcr.scalar(pcr.readmap(cellAreaMapFileName))
# The landMaskClass map contains the nominal classes for all landmask regions.
landMaskClass = pcr.nominal(cloneMap) # default: if catchmentClassFileName is not given
if catchmentClassFileName != None:
landMaskClass = pcr.nominal(pcr.readmap(catchmentClassFileName))
for id in self.list_of_grdc_ids:
logger.info("Evaluating simulated annual baseflow time series to IWMI baseflow time series at "+str(self.attributeGRDC["id_from_grdc"][str(id)])+".")
# evaluate model results to GRDC data
self.evaluateBaseflowResult(str(id),pcrglobwb_output,catchmentClassFileName,tmpDir)
# write the summary to a table
summary_file = analysisOutputDir+"baseflow_summary.txt"
#
logger.info("Writing the summary for all stations to the file: "+str(summary_file)+".")
#
# prepare the file:
summary_file_handle = open(summary_file,"w")
#
# write the header
summary_file_handle.write( ";".join(self.grdc_dict_keys)+"\n")
#
# write the content
for id in self.list_of_grdc_ids:
rowLine = ""
for key in self.grdc_dict_keys: rowLine += str(self.attributeGRDC[key][str(id)]) + ";"
rowLine = rowLine[0:-1] + "\n"
summary_file_handle.write(rowLine)
summary_file_handle.close()
def initial(self):
""" initial part of the routing module
"""
maskinfo = MaskInfo.instance()
self.var.avgdis = maskinfo.in_zero()
self.var.Beta = loadmap('beta')
self.var.InvBeta = 1 / self.var.Beta
# Inverse of beta for kinematic wave
self.var.ChanLength = loadmap('ChanLength').astype(float)
self.var.InvChanLength = 1 / self.var.ChanLength
# Inverse of channel length [1/m]
self.var.NoRoutSteps = int(
np.maximum(1, round(self.var.DtSec / self.var.DtSecChannel, 0)))
# Number of sub-steps based on value of DtSecChannel,
# or 1 if DtSec is smaller than DtSecChannel
settings = LisSettings.instance()
option = settings.options
if option['InitLisflood']:
self.var.NoRoutSteps = 1
# InitLisflood is used!
# so channel routing step is the same as the general time step
self.var.DtRouting = self.var.DtSec / self.var.NoRoutSteps
# Corresponding sub-timestep (seconds)
self.var.InvDtRouting = 1 / self.var.DtRouting
self.var.InvNoRoutSteps = 1 / float(self.var.NoRoutSteps)
# inverse for faster calculation inside the dynamic section
# -------------------------- LDD
self.var.Ldd = lddmask(loadmap('Ldd', pcr=True, lddflag=True),
self.var.MaskMap)
# Cut ldd to size of MaskMap (NEW, 29/9/2004)
# Prevents 'unsound' ldd if MaskMap covers sub-area of ldd
# Count (inverse of) upstream area for each pixel
# Needed if we want to calculate average values of variables
# upstream of gauge locations
self.var.UpArea = accuflux(self.var.Ldd, self.var.PixelAreaPcr)
# Upstream contributing area for each pixel
# Note that you might expext that values of UpArea would be identical to
# those of variable CatchArea (see below) at the outflow points.
# This is NOT actually the case, because outflow points are shifted 1
# cell in upstream direction in the calculation of CatchArea!
self.var.InvUpArea = 1 / self.var.UpArea
# Calculate inverse, so we can multiply in dynamic (faster than divide)
self.var.IsChannelPcr = boolean(loadmap('Channels', pcr=True))
self.var.IsChannel = np.bool8(compressArray(self.var.IsChannelPcr))
# Identify channel pixels
self.var.IsChannelKinematic = self.var.IsChannel.copy()
# Identify kinematic wave channel pixels
# (identical to IsChannel, unless dynamic wave is used, see below)
#self.var.IsStructureKinematic = pcraster.boolean(0)
self.var.IsStructureKinematic = np.bool8(maskinfo.in_zero())
# Map that identifies special inflow/outflow structures (reservoirs, lakes) within the
# kinematic wave channel routing. Set to (dummy) value of zero modified in reservoir and lake
# routines (if those are used)
LddChan = lddmask(self.var.Ldd, self.var.IsChannelPcr)
# ldd for Channel network
self.var.MaskMap = boolean(self.var.Ldd)
# Use boolean version of Ldd as calculation mask
# (important for correct mass balance check
# any water generated outside of Ldd won't reach
# channel anyway)
self.var.LddToChan = lddrepair(
ifthenelse(self.var.IsChannelPcr, 5, self.var.Ldd))
# Routing of runoff (incl. ground water)en
AtOutflow = boolean(pit(self.var.Ldd))
# find outlet points...
if option['dynamicWave']:
IsChannelDynamic = boolean(loadmap('ChannelsDynamic', pcr=True))
# Identify channel pixels where dynamic wave is used
self.var.IsChannelKinematic = (self.var.IsChannelPcr
== 1) & (IsChannelDynamic == 0)
# Identify (update) channel pixels where kinematic wave is used
self.var.LddKinematic = lddmask(self.var.Ldd,
self.var.IsChannelKinematic)
# Ldd for kinematic wave: ends (pit) just before dynamic stretch
LddDynamic = lddmask(self.var.Ldd, IsChannelDynamic)
# Ldd for dynamic wave
# Following statements produce an ldd network that connects the pits in
# LddKinematic to the nearest downstream dynamic wave pixel
LddToDyn = lddrepair(ifthenelse(IsChannelDynamic, 5, self.var.Ldd))
# Temporary ldd: flow paths end in dynamic pixels
PitsKinematic = cover(boolean(pit(self.var.LddKinematic)), 0)
# Define start of each flow path at pit on LddKinematic
PathKinToDyn = path(LddToDyn, PitsKinematic)
# Identify paths that connect pits in LddKinematic to dynamic wave
# pixels
LddKinToDyn = lddmask(LddToDyn, PathKinToDyn)
# Create ldd
DynWaveBoundaryCondition = boolean(pit(LddDynamic))
# NEW 12-7-2005 (experimental)
# Location of boundary condition dynamic wave
self.var.AtLastPoint = (downstream(
self.var.Ldd,
AtOutflow) == 1) & (AtOutflow != 1) & self.var.IsChannelPcr
# NEW 23-6-2005
# Dynamic wave routine gives no outflow out of pits, so we calculate this
# one cell upstream (WvD)
# (implies that most downstream cell is not taken into account in mass balance
# calculations, even if dyn wave is not used)
# Only include points that are on a channel (otherwise some small 'micro-catchments'
# are included, for which the mass balance cannot be calculated
# properly)
else:
self.var.LddKinematic = LddChan
# No dynamic wave, so kinematic ldd equals channel ldd
self.var.AtLastPoint = AtOutflow
self.var.AtLastPointC = np.bool8(
compressArray(self.var.AtLastPoint))
# assign unique identifier to each of them
maskinfo = MaskInfo.instance()
lddC = compressArray(self.var.LddKinematic)
inAr = decompress(np.arange(maskinfo.info.mapC[0], dtype="int32"))
# giving a number to each non missing pixel as id
self.var.downstruct = (compressArray(
downstream(self.var.LddKinematic, inAr))).astype("int32")
# each upstream pixel gets the id of the downstream pixel
self.var.downstruct[lddC == 5] = maskinfo.info.mapC[0]
# all pits gets a high number
#d3=np.bincount(self.var.down, weights=loadmap('AvgDis'))[:-1]
# upstream function in numpy
OutflowPoints = nominal(uniqueid(self.var.AtLastPoint))
# and assign unique identifier to each of them
self.var.Catchments = (compressArray(
catchment(self.var.Ldd, OutflowPoints))).astype(np.int32)
CatchArea = np.bincount(
self.var.Catchments,
weights=self.var.PixelArea)[self.var.Catchments]
#CatchArea = CatchArea[self.var.Catchments]
# define catchment for each outflow point
#CatchArea = areatotal(self.var.PixelArea, self.var.Catchments)
# Compute area of each catchment [m2]
# Note: in earlier versions this was calculated using the "areaarea" function,
# changed to "areatotal" in order to enable handling of grids with spatially
# variable cell areas (e.g. lat/lon grids)
self.var.InvCatchArea = 1 / CatchArea
# inverse of catchment area [1/m2]
# ************************************************************
# ***** CHANNEL GEOMETRY ************************************
# ************************************************************
self.var.ChanGrad = np.maximum(loadmap('ChanGrad'),
loadmap('ChanGradMin'))
# avoid calculation of Alpha using ChanGrad=0: this creates MV!
self.var.CalChanMan = loadmap('CalChanMan')
self.var.ChanMan = self.var.CalChanMan * loadmap('ChanMan')
# Manning's n is multiplied by ChanManCal
# enables calibration for peak timing
self.var.ChanBottomWidth = loadmap('ChanBottomWidth')
ChanDepthThreshold = loadmap('ChanDepthThreshold')
ChanSdXdY = loadmap('ChanSdXdY')
self.var.ChanUpperWidth = self.var.ChanBottomWidth + 2 * ChanSdXdY * ChanDepthThreshold
# Channel upper width [m]
self.var.TotalCrossSectionAreaBankFull = 0.5 * \
ChanDepthThreshold * (self.var.ChanUpperWidth + self.var.ChanBottomWidth)
# Area (sq m) of bank full discharge cross section [m2]
# (trapezoid area equation)
TotalCrossSectionAreaHalfBankFull = 0.5 * self.var.TotalCrossSectionAreaBankFull
# Cross-sectional area at half bankfull [m2]
# This can be used to initialise channel flow (see below)
TotalCrossSectionAreaInitValue = loadmap(
'TotalCrossSectionAreaInitValue')
self.var.TotalCrossSectionArea = np.where(
TotalCrossSectionAreaInitValue == -9999,
TotalCrossSectionAreaHalfBankFull, TotalCrossSectionAreaInitValue)
# Total cross-sectional area [m2]: if initial value in binding equals -9999 the value at half bankfull is used,
# otherwise TotalCrossSectionAreaInitValue (typically end map from previous simulation)
if option['SplitRouting']:
# in_zero = maskinfo.in_zero()
CrossSection2AreaInitValue = loadmap('CrossSection2AreaInitValue')
self.var.CrossSection2Area = np.where(
CrossSection2AreaInitValue == -9999, maskinfo.in_zero(),
CrossSection2AreaInitValue)
# cross-sectional area [m2] for 2nd line of routing: if initial value in binding equals -9999 the value is set to 0
# otherwise CrossSection2AreaInitValue (typically end map from previous simulation)
PrevSideflowInitValue = loadmap('PrevSideflowInitValue')
self.var.Sideflow1Chan = np.where(PrevSideflowInitValue == -9999,
maskinfo.in_zero(),
PrevSideflowInitValue)
# sideflow from previous run for 1st line of routing: if initial value in binding equals -9999 the value is set to 0
# otherwise PrevSideflowInitValue (typically end map from previous simulation)
# ************************************************************
# ***** CHANNEL ALPHA (KIN. WAVE)*****************************
# ************************************************************
# Following calculations are needed to calculate Alpha parameter in kinematic
# wave. Alpha currently fixed at half of bankful depth (this may change in
# future versions!)
ChanWaterDepthAlpha = np.where(self.var.IsChannel,
0.5 * ChanDepthThreshold, 0.0)
# Reference water depth for calculation of Alpha: half of bankfull
self.var.ChanWettedPerimeterAlpha = self.var.ChanBottomWidth + 2 * \
np.sqrt(np.square(ChanWaterDepthAlpha) + np.square(ChanWaterDepthAlpha * ChanSdXdY))
# Channel wetted perimeter [m](Pythagoras)
AlpTermChan = (self.var.ChanMan /
(np.sqrt(self.var.ChanGrad)))**self.var.Beta
self.var.AlpPow = 2.0 / 3.0 * self.var.Beta
self.var.ChannelAlpha = (
AlpTermChan *
(self.var.ChanWettedPerimeterAlpha**self.var.AlpPow)).astype(float)
self.var.InvChannelAlpha = 1 / self.var.ChannelAlpha
# ChannelAlpha for kinematic wave
# ************************************************************
# ***** CHANNEL INITIAL DISCHARGE ****************************
# ************************************************************
self.var.ChanM3 = self.var.TotalCrossSectionArea * self.var.ChanLength
# channel water volume [m3]
self.var.ChanIniM3 = self.var.ChanM3.copy()
self.var.ChanM3Kin = self.var.ChanIniM3.copy().astype(float)
# Initialise water volume in kinematic wave channels [m3]
self.var.ChanQKin = np.where(self.var.ChannelAlpha > 0,
(self.var.TotalCrossSectionArea /
self.var.ChannelAlpha)**self.var.InvBeta,
0).astype(float)
# Initialise discharge at kinematic wave pixels (note that InvBeta is
# simply 1/beta, computational efficiency!)
self.var.CumQ = maskinfo.in_zero()
# ininialise sum of discharge to calculate average
# ************************************************************
# ***** CHANNEL INITIAL DYNAMIC WAVE *************************
# ************************************************************
if option['dynamicWave']:
pass
# TODO !!!!!!!!!!!!!!!!!!!!
# lookchan = lookupstate(TabCrossSections, ChanCrossSections, ChanBottomLevel, self.var.ChanLength,
# DynWaveConstantHeadBoundary + ChanBottomLevel)
# ChanIniM3 = ifthenelse(AtOutflow, lookchan, ChanIniM3)
# Correct ChanIniM3 for constant head boundary in pit (only if
# dynamic wave is used)
# ChanM3Dyn = ChanIniM3
# Set volume of water in dynamic wave channel to initial value
# (note that initial condition is expressed as a state in [m3] for the dynamic wave,
# and as a rate [m3/s] for the kinematic wave (a bit confusing)
# Estimate number of iterations needed in first time step (based on Courant criterium)
# TO DO !!!!!!!!!!!!!!!!!!!!
# Potential = lookuppotential(
# TabCrossSections, ChanCrossSections, ChanBottomLevel, self.var.ChanLength, ChanM3Dyn)
# Potential
# WaterLevelDyn = Potential - ChanBottomLevel
# Water level [m above bottom level)
# WaveCelerityDyn = pcraster.sqrt(9.81 * WaterLevelDyn)
# Dynamic wave celerity [m/s]
# CourantDynamic = self.var.DtSec * \
# (WaveCelerityDyn + 2) / self.var.ChanLength
# Courant number for dynamic wave
# We don't know the water velocity at this time so
# we just guess it's 2 m/s (Odra tests show that flow velocity
# is typically much lower than wave celerity, and 2 m/s is quite
# high already so this gives a pretty conservative/safe estimate
# for DynWaveIterations)
# DynWaveIterationsTemp = max(
# 1, roundup(CourantDynamic / CourantDynamicCrit))
# DynWaveIterations = ordinal(mapmaximum(DynWaveIterationsTemp))
# Number of sub-steps needed for required numerical
# accuracy. Always greater than or equal to 1
# (otherwise division by zero!)
# TEST
# If polder option is used, we need an estimate of the initial channel discharge, but we don't know this
# for the dynamic wave pixels (since only initial state is known)! Try if this works (dyn wave flux based on zero inflow 1 iteration)
# Note that resulting ChanQ is ONLY used in the polder routine!!!
# Since we need instantaneous estimate at start of time step, a
# ChanQM3Dyn is calculated for one single one-second time step!!!
# ChanQDyn = dynwaveflux(TabCrossSections,
# ChanCrossSections,
# LddDynamic,
# ChanIniM3,
# 0.0,
# ChanBottomLevel,
# self.var.ChanMan,
# self.var.ChanLength,
# 1,
# 1,
# DynWaveBoundaryCondition)
# Compute volume and discharge in channel after dynamic wave
# ChanM3Dyn in [cu m]
# ChanQDyn in [cu m / s]
# self.var.ChanQ = ifthenelse(
# IsChannelDynamic, ChanQDyn, self.var.ChanQKin)
# Channel discharge: combine results of kinematic and dynamic wave
else:
# ***** NO DYNAMIC WAVE *************************
# Dummy code if dynamic wave is not used, in which case ChanQ equals ChanQKin
# (needed only for polder routine)
PrevDischarge = loadmap('PrevDischarge')
self.var.ChanQ = np.where(PrevDischarge == -9999,
self.var.ChanQKin, PrevDischarge)
# initialise channel discharge: cold start: equal to ChanQKin
# [m3/s]
# Initialising cumulative output variables
# These are all needed to compute the cumulative mass balance error
self.var.DischargeM3Out = maskinfo.in_zero()
# cumulative discharge at outlet [m3]
self.var.TotalQInM3 = maskinfo.in_zero()
# cumulative inflow from inflow hydrographs [m3]
#self.var.sumDis = maskinfo.in_zero()
self.var.sumDis = maskinfo.in_zero()
self.var.sumIn = maskinfo.in_zero()
def __init__(self, modelTime, input_file, output_file, variable_name,
variable_unit):
DynamicModel.__init__(self)
self.modelTime = modelTime
# netcdf input file - based on PCR-GLOBWB output
self.input_file = "/projects/0/dfguu/users/edwin/pcr-globwb-aqueduct/historical/1951-2005/gfdl-esm2m/temperature_annuaAvg_output_%s-12-31_to_%s-12-31.nc"
self.input_file = input_file
# output file - in netcdf format
self.output_file = output_folder + "/mekong/basin_temperature_annuaAvg_output.nc"
self.output_file = output_file
# output variable name and unit
self.variable_name = variable_name
self.variable_unit = variable_unit
# preparing temporary directory
self.temporary_directory = output_folder + "/tmp/"
os.makedirs(self.temporary_directory)
# clone and landmask maps
logger.info("Set the clone and landmask maps.")
self.clonemap_file_name = "/projects/0/dfguu/users/edwinhs/data/mekong_etc_clone/version_2018-10-22/final/clone_mekong.map"
pcr.setclone(self.clonemap_file_name)
landmask_file_name = "/projects/0/dfguu/users/edwinhs/data/mekong_etc_clone/version_2018-10-22/final/mask_mekong.map"
self.landmask = vos.readPCRmapClone(landmask_file_name, \
self.clonemap_file_name, \
self.temporary_directory, \
None, False, None, True)
# pcraster input files
# - river network map and sub-catchment map
ldd_file_name = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/lddsound_05min.map"
# - cell area (unit: m2)
cell_area_file_name = "/projects/0/dfguu/data/hydroworld/PCRGLOBWB20/input5min/routing/cellsize05min.correct.map"
# loading pcraster input maps
self.ldd_network = vos.readPCRmapClone(ldd_file_name, \
self.clonemap_file_name, \
self.temporary_directory, \
None, \
True)
self.ldd_network = pcr.lddrepair(pcr.ldd(self.ldd_network))
self.ldd_network = pcr.lddrepair(self.ldd_network)
self.cell_area = vos.readPCRmapClone(cell_area_file_name, \
self.clonemap_file_name, \
self.temporary_directory, \
None)
# set/limit all input maps to the defined landmask
self.ldd_network = pcr.ifthen(self.landmask, self.ldd_network)
self.cell_area = pcr.ifthen(self.landmask, self.cell_area)
# calculate basin/catchment area
self.basin_area = pcr.catchmenttotal(self.cell_area, self.ldd_network)
self.basin_area = pcr.ifthen(self.landmask, self.basin_area)
# preparing an object for reporting netcdf files:
self.netcdf_report = netcdf_writer.PCR2netCDF(self.clonemap_file_name)
# preparing netcdf output files:
self.netcdf_report.createNetCDF(self.output_file, \
self.variable_name, \
self.variable_unit)
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()
logger.info(msg)
# set the pcraster clone, ldd, landmask, and cell area map
msg = "Setting the clone, ldd, landmask, and cell area maps" + ":"
logger.info(msg)
# - clone
clone_map_file = input_files['clone_map_05min']
pcr.setclone(clone_map_file)
# - ldd
ldd = vos.readPCRmapClone(input_files['ldd_map_05min'],
clone_map_file,
output_files['tmp_folder'],
None,
True)
ldd = pcr.lddrepair(pcr.ldd(ldd))
ldd = pcr.lddrepair(ldd)
# - landmask
landmask = pcr.ifthen(pcr.defined(ldd), pcr.boolean(1.0))
# - cell area
cell_area = vos.readPCRmapClone(input_files['cell_area_05min'],
clone_map_file,
output_files['tmp_folder'])
# read the hydrological year
msg = "Reading the hydrological year types" + ":"
logger.info(msg)
hydro_year_type = pcr.nominal(\
vos.readPCRmapClone(input_files['hydro_year_05min'],
input_files['clone_map_05min'],
def __init__(self, iniItems, landmask, Dir, cloneMap, tmpDir):
object.__init__(self)
# clone map file names, temporary directory and global/absolute path of input directory
self.cloneMap = cloneMap # iniItems.cloneMap
self.tmpDir = tmpDir # iniItems.tmpDir
self.inputDir = Dir # iniItems.globalOptions['inputDir']
self.landmask = landmask
# local drainage direction:
self.lddMap = vos.readPCRmapClone(
iniItems.get("routingOptions", "lddMap"),
self.cloneMap,
self.tmpDir,
self.inputDir,
True,
)
self.lddMap = pcr.lddrepair(pcr.ldd(self.lddMap))
self.lddMap = pcr.lddrepair(self.lddMap)
# option to activate water balance check
self.debugWaterBalance = True
if (
configget(iniItems, "routingOptions", "debugWaterBalance", "True")
== "False"
):
self.debugWaterBalance = False
# option to perform a run with only natural lakes (without reservoirs)
self.onlyNaturalWaterBodies = False
if (
"onlyNaturalWaterBodies" in iniItems._sections["routingOptions"]
and configget(iniItems, "routingOptions", "onlyNaturalWaterBodies", "False")
== "True"
):
logger.info(
"Using only natural water bodies identified in the year 1900. All reservoirs in 1900 are assumed as lakes."
)
self.onlyNaturalWaterBodies = True
self.dateForNaturalCondition = (
"1900-01-01"
) # The run for a natural condition should access only this date.
# names of files containing water bodies parameters
if configget(iniItems, "routingOptions", "waterBodyInputNC", "None") == str(
None
):
self.useNetCDF = False
self.fracWaterInp = iniItems.get("routingOptions", "fracWaterInp")
self.waterBodyIdsInp = iniItems.get("routingOptions", "waterBodyIds")
self.waterBodyTypInp = iniItems.get("routingOptions", "waterBodyTyp")
self.resMaxCapInp = iniItems.get("routingOptions", "resMaxCapInp")
self.resSfAreaInp = iniItems.get("routingOptions", "resSfAreaInp")
else:
self.useNetCDF = True
self.ncFileInp = vos.getFullPath(
iniItems.get("routingOptions", "waterBodyInputNC"), self.inputDir
)
# minimum width (m) used in the weir formula # TODO: define minWeirWidth based on the GLWD, GRanD database and/or bankfull discharge formula
self.minWeirWidth = 10.0
# lower and upper limits at which reservoir release is terminated and
# at which reservoir release is equal to long-term average outflow
self.minResvrFrac = 0.10
self.maxResvrFrac = 0.75
def evaluateAllModelResults(self,globalCloneMapFileName,\
catchmentClassFileName,\
lddMapFileName,\
cellAreaMapFileName,\
pcrglobwb_output,\
analysisOutputDir="",\
tmpDir = None):
# temporary directory
if tmpDir == None: tmpDir = self.tmpDir+"/edwin_grdc_"
# output directory for all analyses for all stations
analysisOutputDir = str(analysisOutputDir)
self.chartOutputDir = analysisOutputDir+"/chart/"
self.tableOutputDir = analysisOutputDir+"/table/"
#
if analysisOutputDir == "": self.chartOutputDir = "chart/"
if analysisOutputDir == "": self.tableOutputDir = "table/"
#
# make the chart and table directories:
os.system('rm -r '+self.chartOutputDir+"*")
os.system('rm -r '+self.tableOutputDir+"*")
os.makedirs(self.chartOutputDir)
os.makedirs(self.tableOutputDir)
# cloneMap for all pcraster operations
pcr.setclone(globalCloneMapFileName)
cloneMap = pcr.boolean(1)
self.cell_size_in_arc_degree = vos.getMapAttributesALL(globalCloneMapFileName)['cellsize']
lddMap = pcr.lddrepair(pcr.readmap(lddMapFileName))
cellArea = pcr.scalar(pcr.readmap(cellAreaMapFileName))
# The landMaskClass map contains the nominal classes for all landmask regions.
landMaskClass = pcr.nominal(cloneMap) # default: if catchmentClassFileName is not given
if catchmentClassFileName != None:
landMaskClass = pcr.nominal(pcr.readmap(catchmentClassFileName))
# model catchment areas and cordinates
catchmentAreaAll = pcr.catchmenttotal(cellArea, lddMap) / (1000*1000) # unit: km2
xCoordinate = pcr.xcoordinate(cloneMap)
yCoordinate = pcr.ycoordinate(cloneMap)
for id in self.list_of_grdc_ids:
logger.info("Evaluating simulated discharge to the grdc observation at "+str(self.attributeGRDC["id_from_grdc"][str(id)])+".")
# identify model pixel
self.identifyModelPixel(tmpDir,catchmentAreaAll,landMaskClass,xCoordinate,yCoordinate,str(id))
# evaluate model results to GRDC data
self.evaluateModelResultsToGRDC(str(id),pcrglobwb_output,catchmentClassFileName,tmpDir)
# write the summary to a table
summary_file = analysisOutputDir+"summary.txt"
#
logger.info("Writing the summary for all stations to the file: "+str(summary_file)+".")
#
# prepare the file:
summary_file_handle = open(summary_file,"w")
#
# write the header
summary_file_handle.write( ";".join(self.grdc_dict_keys)+"\n")
#
# write the content
for id in self.list_of_grdc_ids:
rowLine = ""
for key in self.grdc_dict_keys: rowLine += str(self.attributeGRDC[key][str(id)]) + ";"
rowLine = rowLine[0:-1] + "\n"
summary_file_handle.write(rowLine)
summary_file_handle.close()
def main():
try:
opts, args = getopt.getopt(sys.argv[1:], "fhC:N:I:s:M:", ['version'])
except getopt.error as msg:
usage(msg)
factor = 1
Verbose = 1
inmaps = True
force = False
caseName = "thecase"
caseNameNew = "thecase_resamp"
maxcpu = 4
for o, a in opts:
if o == "-C":
caseName = a
if o == "-N":
caseNameNew = a
if o == "-s":
subcatch = int(a)
if o == "-I":
inmaps = False
if o == "-h":
usage()
if o == "-f":
force = True
if o == "-M":
maxcpu = int(a)
if o == "--version":
import wflow
print("wflow version: ", wflow.__version__)
sys.exit(0)
dirs = [
"/intbl/",
"/staticmaps/",
"/intss/",
"/instate/",
"/outstate/",
"/inmaps/",
"/inmaps/clim/",
"/intbl/clim/",
]
ext_to_copy = ["*.tss", "*.tbl", "*.col", "*.xml"]
if os.path.isdir(caseNameNew) and not force:
print("Refusing to write into an existing directory:" + caseNameNew)
exit()
# ddir = []
dirs = []
for (path, thedirs, files) in os.walk(caseName):
print(path)
dirs.append(path)
if not os.path.isdir(caseNameNew):
for ddir in dirs:
os.makedirs(ddir.replace(caseName, caseNameNew))
for inifile in glob.glob(caseName + "/*.ini"):
shutil.copy(inifile, inifile.replace(caseName, caseNameNew))
# read subcatchment map
x, y, subcatchmap, FillVal = readMap(
os.path.join(caseName, "staticmaps", "wflow_subcatch.map"), "PCRaster")
for ddir in dirs:
print(ddir)
allcmd = []
for mfile in glob.glob(ddir + "/*.map"):
if not os.path.exists(mfile.replace(caseName, caseNameNew)):
x, y, data, FillVal = readMap(mfile, "PCRaster")
try:
good = 1
xn, yn, datan = cutMapById(data, subcatchmap, subcatch, x,
y, FillVal)
except Exception as e:
good = 0
print("Skipping: " + mfile + " exception: " + str(e))
if xn.size == 0:
good = 0
print("Skipping: " + mfile + " size does not match...")
if good:
ofile = mfile.replace(caseName, caseNameNew)
if data.dtype == np.int32 or data.dtype == np.uint8:
writeMap(ofile, "PCRaster", xn, yn,
datan.astype(np.int32), FillVal)
else:
writeMap(ofile, "PCRaster", xn, yn, datan, FillVal)
# Assume ldd and repair
if (data.dtype == np.uint8 and 'wflow_ldd.map' in mfile):
myldd = pcr.ldd(pcr.readmap(ofile))
myldd = pcr.lddrepair(myldd)
pcr.report(myldd, ofile)
for mfile in glob.glob(ddir + "/*.[0-9][0-9][0-9]"):
if not os.path.exists(mfile.replace(caseName, caseNameNew)):
x, y, data, FillVal = readMap(mfile, "PCRaster")
try:
good = 1
xn, yn, datan = cutMapById(data, subcatchmap, subcatch, x,
y, FillVal)
except Exception as e:
good = 0
print("Skipping: " + mfile + " exception: " + str(e))
if xn.size == 0:
good = 0
print("Skipping: " + mfile + " size does not match...")
if good:
ofile = mfile.replace(caseName, caseNameNew)
if data.dtype == np.int32 or data.dtype == np.uint8:
writeMap(ofile, "PCRaster", xn, yn,
datan.astype(np.int32), FillVal)
else:
writeMap(ofile, "PCRaster", xn, yn, datan, FillVal)
for ext in ext_to_copy:
for mfile in glob.glob(os.path.join(ddir, ext)):
shutil.copy(mfile, mfile.replace(caseName, caseNameNew))
# Copy ini files
for mfile in glob.glob(os.path.join(caseName, "*.ini")):
shutil.copy(mfile, mfile.replace(caseName, caseNameNew))
