def spatial(self):
"""Computes requruired biosafe output for a spatial domain"""
#-determine a representative points for each floodplain section
points = pcrr.representativePoint(self.sections)
clone = pcr.defined(self.sections)
pcr.setglobaloption('unittrue')
xcoor = pcr.xcoordinate(clone)
ycoor = pcr.ycoordinate(clone)
geoDf = pcrr.getCellValues(points, \
mapList = [points, xcoor, ycoor],\
columns = ['ID', 'xcoor', 'ycoor'])
geoDf.set_index('ID', inplace=True, drop=False)
geoDf.drop(['rowIdx', 'colIdx', 'ID'], axis=1, inplace=True)
#-compupte the required biosafe parameters for all sections
sectionIDs = np.unique(pcr.pcr2numpy(self.sections,-9999))[1:]
ll = []
for sectionID in sectionIDs:
ll.append(self.sectionScores(sectionID))
paramLL = zip(*ll)
dfParamLL = []
for ii in range(len(self.params)):
bsScores = pd.concat(paramLL[ii], axis=1).T
bsScores = bsScores.join(geoDf)
bsScores.index.name = 'ID'
bsScores.columns.name = self.params[ii]
dfParamLL.append(bsScores)
return dfParamLL
def initial(self):
"""
*Required*
Initial part of the model, executed only once. It reads all static model
information (parameters) and sets-up the variables used in modelling.
This function is required. The contents is free. However, in order to
easily connect to other models it is advised to adhere to the directory
structure used in the other models.
"""
#: pcraster option to calculate with units or cells. Not really an issue
#: in this model but always good to keep in mind.
pcr.setglobaloption("unittrue")
self.timestepsecs = int(
configget(self.config, "model", "timestepsecs", "86400")
)
self.basetimestep = 86400
# Reads all parameter from disk
self.wf_updateparameters()
self.wf_multparameters() # needed so parameters can be altered in the inifile
self.logger.info("Starting Dynamic run...")
def lattometres(lat):
""""
Determines the length of one degree lat/long at a given latitude (in meter).
Code taken from http:www.nga.mil/MSISiteContent/StaticFiles/Calculators/degree.html
Input: map with lattitude values for each cell
Returns: length of a cell lat, length of a cell long
"""
# radlat = pcr.spatial(lat * ((2.0 * math.pi)/360.0))
# radlat = lat * (2.0 * math.pi)/360.0
pcr.setglobaloption("degrees")
radlat = pcr.spatial(lat) # pcraster cos/sin work in degrees!
m1 = 111132.92 # latitude calculation term 1
m2 = -559.82 # latitude calculation term 2
m3 = 1.175 # latitude calculation term 3
m4 = -0.0023 # latitude calculation term 4
p1 = 111412.84 # longitude calculation term 1
p2 = -93.5 # longitude calculation term 2
p3 = 0.118 # longitude calculation term 3
# # Calculate the length of a degree of latitude and longitude in meters
latlen = (m1 + (m2 * pcr.cos(2.0 * radlat)) +
(m3 * pcr.cos(4.0 * radlat)) + (m4 * pcr.cos(6.0 * radlat)))
longlen = ((p1 * pcr.cos(radlat)) + (p2 * pcr.cos(3.0 * radlat)) +
(p3 * pcr.cos(5.0 * radlat)))
return latlen, longlen
def lattometres(lat):
""""
Determines the length of one degree lat/long at a given latitude (in meter).
Code taken from http:www.nga.mil/MSISiteContent/StaticFiles/Calculators/degree.html
Input: map with lattitude values for each cell
Returns: length of a cell lat, length of a cell long
"""
# radlat = pcr.spatial(lat * ((2.0 * math.pi)/360.0))
# radlat = lat * (2.0 * math.pi)/360.0
pcr.setglobaloption("degrees")
radlat = pcr.spatial(lat) # pcraster cos/sin work in degrees!
m1 = 111132.92 # latitude calculation term 1
m2 = -559.82 # latitude calculation term 2
m3 = 1.175 # latitude calculation term 3
m4 = -0.0023 # latitude calculation term 4
p1 = 111412.84 # longitude calculation term 1
p2 = -93.5 # longitude calculation term 2
p3 = 0.118 # longitude calculation term 3
# # Calculate the length of a degree of latitude and longitude in meters
latlen = (
m1
+ (m2 * pcr.cos(2.0 * radlat))
+ (m3 * pcr.cos(4.0 * radlat))
+ (m4 * pcr.cos(6.0 * radlat))
)
longlen = (
(p1 * pcr.cos(radlat))
+ (p2 * pcr.cos(3.0 * radlat))
+ (p3 * pcr.cos(5.0 * radlat))
)
return latlen, longlen
def getCellValues(pointMap, mapList=[], columns=[]):
""" Get the cell values of the maps in mapList at the locations of pointMap
"""
#-determine where the indices are True
arr = pcr.pcr2numpy(pcr.boolean(pointMap), 0).astype('bool')
indices = np.where(arr == True)
#-loop over the points in pointMap
pcr.setglobaloption('unitcell')
ll = []
for rowIdx, colIdx in zip(indices[0], indices[1]):
line = []
line.append(rowIdx)
line.append(colIdx)
for pcrMap in mapList:
line.append(
pcr.cellvalue(pcrMap, np.int(rowIdx + 1),
np.int(colIdx + 1))[0])
ll.append(line)
#-optionally add column names
if len(columns) == len(mapList):
columnNames = ['rowIdx', 'colIdx'] + columns
else:
columnNames = ['rowIdx', 'colIdx'] + \
['map' + str(ii) for ii in range(1, 1 + len(mapList), 1)]
#-return as Pandas DataFrame
return pd.DataFrame(np.array(ll), columns=columnNames)
def getgridparams():
""" return grid parameters in a python friendly way
Output:
[ Xul, Yul, xsize, ysize, rows, cols]
- xul - x upper left centre
- yul - y upper left centre
- xsize - size of a cell in x direction
- ysize - size of a cell in y direction
- cols - number of columns
- rows - number of rows
- xlr - x lower right centre
- ylr - y lower right centre
"""
# This is the default, but add for safety...
pcr.setglobaloption("coorcentre")
# x and Y are the same for now
xy = pcr.pcr2numpy(pcr.celllength(), np.nan)[0, 0]
xu = pcr.pcr2numpy(pcr.xcoordinate(1), np.nan)[0, 0]
yu = pcr.pcr2numpy(pcr.ycoordinate(1), np.nan)[0, 0]
ylr = pcr.pcr2numpy(pcr.ycoordinate(1), np.nan)[getrows() - 1, getcols() - 1]
xlr = pcr.pcr2numpy(pcr.xcoordinate(1), np.nan)[getrows() - 1, getcols() - 1]
return [xu, yu, xy, xy, getrows(), getcols(), xlr, ylr]
def zonalSumArea(nominalMap, areaClass):
"""Memory efficient method to sum up the surface area of the different
classes in the nominal map. Separate by the regions in areaClass
input:
nominalMap: nominal map, e.g. ecotope map
areaClass: regions to compute surface areas over
"""
#-create a pointMap of the output locations, one for each areaClass
outputPointMap = pcrr.pointPerClass(areaClass)
#-iniate output DataFrame
dfInit = pcrr.getCellValues(outputPointMap, mapList = [areaClass], columns = ['areaClass'])
#-loop over the classes in nominalMap and compute the summed area per areaClass
IDs = np.unique(pcr.pcr2numpy(nominalMap, -9999))[1:]
dfList = []
for ID in IDs[:]:
pcrID = pcr.nominal(ID)
pcr.setglobaloption('unittrue')
IDArea = pcr.ifthen(nominalMap == pcrID, pcr.cellarea())
sectionSum = pcr.areatotal(IDArea, areaClass)
df = pcrr.getCellValues(outputPointMap, [sectionSum], [ID])
# df columns = rowIdx, colIdx, ID
df = df.drop(['rowIdx', 'colIdx'], axis=1)
dfList.append(df)
dfOut = dfInit.join(dfList)
#return dfInit, df, dfOut, dfList
return dfOut
def getgridparams():
""" return grid parameters in a python friendly way
Output:
[ Xul, Yul, xsize, ysize, rows, cols]
- xul - x upper left centre
- yul - y upper left centre
- xsize - size of a cell in x direction
- ysize - size of a cell in y direction
- cols - number of columns
- rows - number of rows
- xlr - x lower right centre
- ylr - y lower right centre
"""
# This is the default, but add for safety...
pcr.setglobaloption("coorcentre")
# x and Y are the same for now
xy = pcr.pcr2numpy(pcr.celllength(), np.nan)[0, 0]
xu = pcr.pcr2numpy(pcr.xcoordinate(1), np.nan)[0, 0]
yu = pcr.pcr2numpy(pcr.ycoordinate(1), np.nan)[0, 0]
ylr = pcr.pcr2numpy(pcr.ycoordinate(1), np.nan)[getrows() - 1, getcols() - 1]
xlr = pcr.pcr2numpy(pcr.xcoordinate(1), np.nan)[getrows() - 1, getcols() - 1]
return [xu, yu, xy, xy, getrows(), getcols(), xlr, ylr]
def volume_spread(ldd,
hand,
subcatch,
volume,
volume_thres=0.,
area_multiplier=1.,
iterations=15):
"""
Estimate 2D flooding from a 1D simulation per subcatchment reach
Input:
ldd -- pcraster object direction, local drain directions
hand -- pcraster object float32, elevation data normalised to nearest drain
subcatch -- pcraster object ordinal, subcatchments with IDs
volume -- pcraster object float32, scalar flood volume (i.e. m3 volume outside the river bank within subcatchment)
volume_thres=0. -- scalar threshold, at least this amount of m3 of volume should be present in a catchment
area_multiplier=1. -- in case the maps are not in m2, set a multiplier other than 1. to convert
iterations=15 -- number of iterations to use
Output:
inundation -- pcraster object float32, scalar inundation estimate
"""
#initial values
pcr.setglobaloption("unittrue")
dem_min = pcr.areaminimum(hand,
subcatch) # minimum elevation in subcatchments
# pcr.report(dem_min, 'dem_min.map')
dem_norm = hand - dem_min
# pcr.report(dem_norm, 'dem_norm.map')
# surface of each subcatchment
surface = pcr.areaarea(subcatch) * area_multiplier
pcr.report(surface, 'surface.map')
error_abs = pcr.scalar(1e10) # initial error (very high)
volume_catch = pcr.areatotal(volume, subcatch)
# pcr.report(volume_catch, 'volume_catch.map')
depth_catch = volume_catch / surface
pcr.report(depth_catch, 'depth_catch.map')
dem_max = pcr.ifthenelse(volume_catch > volume_thres, pcr.scalar(32.),
pcr.scalar(0)) # bizarre high inundation depth
dem_min = pcr.scalar(0.)
for n in range(iterations):
print('Iteration: {:02d}'.format(n + 1))
#####while np.logical_and(error_abs > error_thres, dem_min < dem_max):
dem_av = (dem_min + dem_max) / 2
# pcr.report(dem_av, 'dem_av00.{:03d}'.format(n + 1))
# compute value at dem_av
average_depth_catch = pcr.areaaverage(pcr.max(dem_av - dem_norm, 0),
subcatch)
# pcr.report(average_depth_catch, 'depth_c0.{:03d}'.format(n + 1))
error = pcr.cover((depth_catch - average_depth_catch) / depth_catch,
depth_catch * 0)
# pcr.report(error, 'error000.{:03d}'.format(n + 1))
dem_min = pcr.ifthenelse(error > 0, dem_av, dem_min)
dem_max = pcr.ifthenelse(error <= 0, dem_av, dem_max)
# error_abs = np.abs(error) # TODO: not needed probably, remove
inundation = pcr.max(dem_av - dem_norm, 0)
return inundation
def initial(self):
"""
*Required*
Initial part of the model, executed only once. It reads all static model
information (parameters) and sets-up the variables used in modelling.
This function is required. The contents is free. However, in order to
easily connect to other models it is advised to adhere to the directory
structure used in the other models.
"""
#: pcraster option to calculate with units or cells. Not really an issue
#: in this model but always good to keep in mind.
pcr.setglobaloption("unittrue")
self.timestepsecs = int(
configget(self.config, "model", "timestepsecs", "86400"))
self.basetimestep = 86400
# Reads all parameter from disk
self.wf_updateparameters()
self.logger.info("Starting LINTUL Dynamic Crop Growth Simulation...")
# Read a static map of the rice area. To be replaced with real-time radar images of rice area in the future? Todo
# Simulation is mostly restricted to the rice area (to be checked), which saves calculation time. Todo
wflow_ricemask = configget(self.config, "model", "wflow_ricemask",
"staticmaps/wflow_ricemask.map")
self.ricemask = self.wf_readmap(os.path.join(self.Dir, wflow_ricemask),
0.0,
fail=True)
# Create a PCRaster boolean map too:
self.ricemask_BOOL = pcr.boolean(self.ricemask)
self.Pausedays = self.Pause + 1
# Calculate initial development stage (at the time of transplanting)
self.DVSI = self.TSUMI / self.TSUMAN
# Turn all interpolation tables (model parameters) into instances of the Interpol_Obj class
self.RDRTB = Interpol_Obj(self.RDRTB)
self.PHOTTB = Interpol_Obj(self.PHOTTB)
self.SLACF = Interpol_Obj(self.SLACF)
self.FRTTB = Interpol_Obj(self.FRTTB)
self.FLVTB = Interpol_Obj(self.FLVTB)
self.FSTTB = Interpol_Obj(self.FSTTB)
self.FSOTB = Interpol_Obj(self.FSOTB)
# Calculate the initial leaf area correction function as a function of development stage, DVS.
SLACFI = self.SLACF.lookup_linear(self.DVSI)
# Multiply with specific leaf area constant => initial specific leaf area
ISLA = self.SLAC * SLACFI
# Multiply with weight of green leaves to obtain initial LAI
self.LAII = self.WLVGI * ISLA
# Calculate total temperature sum from transplanting to crop maturity:
self.TTSUM = self.TSUMAN + self.TSUMMT
def testDirectionalArray2Raster(self):
pcraster.setclone("boolean_Expr.map")
pcraster.setglobaloption("degrees")
try:
a = numpy.array([ [math.radians(350),math.radians(0),math.radians(0.01)],\
[20,math.radians(350),math.radians(21)],\
[math.radians(359),math.radians(40),math.radians(0)] ])
result = pcraster.numpy2pcr(pcraster.Directional, a, 20)
self.failUnless(self.mapEqualsValidated(result, "directional_Result2.map"), "test1: %s" % ("Result and validated result are not the same"))
except Exception as exception:
self.failUnless(False, "test1: %s" % (str(exception)))
def representativePoint(nominalMap):
"""Select a representative point for a nominal map
"""
pcr.setglobaloption('unitcell')
filled = pcr.cover(nominalMap, 0)
edges = pcr.windowdiversity(filled, 3) > 1
edges = pcr.ifthen(pcr.defined(nominalMap), edges)
edges = map_edges(nominalMap) | edges
dist = pcr.spread(edges, 0, 1)
dist = dist + pcr.uniform(pcr.defined(nominalMap))
points = dist == pcr.areamaximum(dist, nominalMap)
return pcr.ifthen(points, nominalMap)
def volume_spread(ldd,
hand,
subcatch,
volume,
volume_thres=0.,
cell_surface=1.,
iterations=15,
logging=logging,
order=0):
"""
Estimate 2D flooding from a 1D simulation per subcatchment reach
Input:
ldd -- pcraster object direction, local drain directions
hand -- pcraster object float32, elevation data normalised to nearest drain
subcatch -- pcraster object ordinal, subcatchments with IDs
volume -- pcraster object float32, scalar flood volume (i.e. m3 volume outside the river bank within subcatchment)
volume_thres=0. -- scalar threshold, at least this amount of m3 of volume should be present in a catchment
area_multiplier=1. -- in case the maps are not in m2, set a multiplier other than 1. to convert
iterations=15 -- number of iterations to use
Output:
inundation -- pcraster object float32, scalar inundation estimate
"""
#initial values
pcr.setglobaloption("unitcell")
dem_min = pcr.areaminimum(hand,
subcatch) # minimum elevation in subcatchments
dem_norm = hand - dem_min
# surface of each subcatchment
surface = pcr.areaarea(subcatch) * pcr.areaaverage(
cell_surface, subcatch) # area_multiplier
error_abs = pcr.scalar(1e10) # initial error (very high)
volume_catch = pcr.areatotal(volume, subcatch)
depth_catch = volume_catch / surface # meters water disc averaged over subcatchment
# ilt(depth_catch, 'depth_catch_{:02d}.map'.format(order))
# pcr.report(volume, 'volume_{:02d}.map'.format(order))
dem_max = pcr.ifthenelse(volume_catch > volume_thres, pcr.scalar(32.),
pcr.scalar(0)) # bizarre high inundation depth
dem_min = pcr.scalar(0.)
for n in range(iterations):
logging.debug('Iteration: {:02d}'.format(n + 1))
#####while np.logical_and(error_abs > error_thres, dem_min < dem_max):
dem_av = (dem_min + dem_max) / 2
# compute value at dem_av
average_depth_catch = pcr.areaaverage(pcr.max(dem_av - dem_norm, 0),
subcatch)
error = pcr.cover((depth_catch - average_depth_catch) / depth_catch,
depth_catch * 0)
dem_min = pcr.ifthenelse(error > 0, dem_av, dem_min)
dem_max = pcr.ifthenelse(error <= 0, dem_av, dem_max)
inundation = pcr.max(dem_av - dem_norm, 0)
pcr.setglobaloption('unittrue')
return inundation
def map_edges(clone):
"""Boolean map true map edges, false elsewhere"""
pcr.setglobaloption('unittrue')
xmin, xmax, ymin, ymax, nr_rows, nr_cols, cell_size = clone_attributes()
clone = pcr.ifthenelse(pcr.defined(clone), pcr.boolean(1), pcr.boolean(1))
x_coor = pcr.xcoordinate(clone)
y_coor = pcr.ycoordinate(clone)
north = y_coor > (ymax - cell_size)
south = y_coor < (ymin + cell_size)
west = x_coor < (xmin + cell_size)
east = x_coor > (xmax - cell_size)
edges = north | south | west | east
return edges
def volume_spread(ldd, hand, subcatch, volume, volume_thres=0., area_multiplier=1., iterations=15):
"""
Estimate 2D flooding from a 1D simulation per subcatchment reach
Input:
ldd -- pcraster object direction, local drain directions
hand -- pcraster object float32, elevation data normalised to nearest drain
subcatch -- pcraster object ordinal, subcatchments with IDs
volume -- pcraster object float32, scalar flood volume (i.e. m3 volume outside the river bank within subcatchment)
volume_thres=0. -- scalar threshold, at least this amount of m3 of volume should be present in a catchment
area_multiplier=1. -- in case the maps are not in m2, set a multiplier other than 1. to convert
iterations=15 -- number of iterations to use
Output:
inundation -- pcraster object float32, scalar inundation estimate
"""
#initial values
pcr.setglobaloption("unittrue")
dem_min = pcr.areaminimum(hand, subcatch) # minimum elevation in subcatchments
# pcr.report(dem_min, 'dem_min.map')
dem_norm = hand - dem_min
# pcr.report(dem_norm, 'dem_norm.map')
# surface of each subcatchment
surface = pcr.areaarea(subcatch)*area_multiplier
pcr.report(surface, 'surface.map')
error_abs = pcr.scalar(1e10) # initial error (very high)
volume_catch = pcr.areatotal(volume, subcatch)
# pcr.report(volume_catch, 'volume_catch.map')
depth_catch = volume_catch/surface
pcr.report(depth_catch, 'depth_catch.map')
dem_max = pcr.ifthenelse(volume_catch > volume_thres, pcr.scalar(32.),
pcr.scalar(0)) # bizarre high inundation depth
dem_min = pcr.scalar(0.)
for n in range(iterations):
print('Iteration: {:02d}'.format(n + 1))
#####while np.logical_and(error_abs > error_thres, dem_min < dem_max):
dem_av = (dem_min + dem_max)/2
# pcr.report(dem_av, 'dem_av00.{:03d}'.format(n + 1))
# compute value at dem_av
average_depth_catch = pcr.areaaverage(pcr.max(dem_av - dem_norm, 0), subcatch)
# pcr.report(average_depth_catch, 'depth_c0.{:03d}'.format(n + 1))
error = pcr.cover((depth_catch-average_depth_catch)/depth_catch, depth_catch*0)
# pcr.report(error, 'error000.{:03d}'.format(n + 1))
dem_min = pcr.ifthenelse(error > 0, dem_av, dem_min)
dem_max = pcr.ifthenelse(error <= 0, dem_av, dem_max)
# error_abs = np.abs(error) # TODO: not needed probably, remove
inundation = pcr.max(dem_av - dem_norm, 0)
return inundation
def pcr2col(listOfMaps, MV, selection='ONE_TRUE'):
"""converts a set of maps to a column array: X, Y, map values
selection can be set to ALL, ALL_TRUE, ONE_TRUE"""
#-intersect all maps and get X and Y coordinates
intersection = pcr.boolean(pcr.cover(listOfMaps[0], 0))
for mapX in listOfMaps[1:]:
intersection = intersection | pcr.boolean(pcr.cover(mapX, 0))
pcr.setglobaloption("unittrue")
xCoor = pcr.ifthen(intersection, pcr.xcoordinate(intersection))
yCoor = pcr.ifthen(intersection, pcr.ycoordinate(intersection))
pcr.setglobaloption("unitcell")
#-initiate outArray with xCoor and yCoor
xCoorArr = pcr.pcr2numpy(xCoor, MV)
yCoorArr = pcr.pcr2numpy(yCoor, MV)
nRows, nCols = xCoorArr.shape
nrCells = nRows * nCols
outArray = np.hstack((xCoorArr.reshape(nrCells,
1), yCoorArr.reshape(nrCells, 1)))
#-add subsequent maps
for mapX in listOfMaps:
arr = pcr.pcr2numpy(mapX, MV).reshape(nrCells, 1)
outArray = np.hstack((outArray, arr))
#-subset output based on selection criterium
ll = []
nrMaps = len(listOfMaps)
if selection == 'ONE_TRUE':
for line in outArray:
nrMV = len(line[line == MV])
if nrMV < nrMaps:
ll.append(line)
else:
pass
outArray = np.array(ll)
elif selection == 'ALL_TRUE':
for line in outArray:
if MV not in line:
ll.append(line)
else:
pass
outArray = np.array(ll)
elif selection == 'ALL':
pass
return outArray
def volume_spread(ldd, hand, subcatch, volume, volume_thres=0., cell_surface=1., iterations=15, logging=logging, order=0, neg_HAND=None):
"""
Estimate 2D flooding from a 1D simulation per subcatchment reach
Input:
ldd -- pcraster object direction, local drain directions
hand -- pcraster object float32, elevation data normalised to nearest drain
subcatch -- pcraster object ordinal, subcatchments with IDs
volume -- pcraster object float32, scalar flood volume (i.e. m3 volume outside the river bank within subcatchment)
volume_thres=0. -- scalar threshold, at least this amount of m3 of volume should be present in a catchment
area_multiplier=1. -- in case the maps are not in m2, set a multiplier other than 1. to convert
iterations=15 -- number of iterations to use
neg_HAND -- if set to 1, HAND maps can have negative values when elevation outside of stream is lower than
stream (for example when there are natural embankments)
Output:
inundation -- pcraster object float32, scalar inundation estimate
"""
#initial values
pcr.setglobaloption("unitcell")
dem_min = pcr.areaminimum(hand, subcatch) # minimum elevation in subcatchments
dem_norm = hand - dem_min
# surface of each subcatchment
surface = pcr.areaarea(subcatch)*pcr.areaaverage(cell_surface, subcatch) # area_multiplier
error_abs = pcr.scalar(1e10) # initial error (very high)
volume_catch = pcr.areatotal(volume, subcatch)
depth_catch = volume_catch/surface # meters water disc averaged over subcatchment
# ilt(depth_catch, 'depth_catch_{:02d}.map'.format(order))
# pcr.report(volume, 'volume_{:02d}.map'.format(order))
if neg_HAND == 1:
dem_max = pcr.ifthenelse(volume_catch > volume_thres, pcr.scalar(32.),
pcr.scalar(-32.)) # bizarre high inundation depth☻
dem_min = pcr.scalar(-32.)
else:
dem_max = pcr.ifthenelse(volume_catch > volume_thres, pcr.scalar(32.),
pcr.scalar(0.)) # bizarre high inundation depth☻
dem_min = pcr.scalar(0.)
for n in range(iterations):
logging.debug('Iteration: {:02d}'.format(n + 1))
#####while np.logical_and(error_abs > error_thres, dem_min < dem_max):
dem_av = (dem_min + dem_max)/2
# compute value at dem_av
average_depth_catch = pcr.areaaverage(pcr.max(dem_av - dem_norm, 0), subcatch)
error = pcr.cover((depth_catch-average_depth_catch)/depth_catch, depth_catch*0)
dem_min = pcr.ifthenelse(error > 0, dem_av, dem_min)
dem_max = pcr.ifthenelse(error <= 0, dem_av, dem_max)
inundation = pcr.max(dem_av - dem_norm, 0)
pcr.setglobaloption('unittrue')
return inundation
def test_5(self):
""" test windowaverage and kernel size larger than raster """
filename = "windowaverage_Expr.map"
pcraster.setclone(filename)
raster = pcraster.readmap(filename)
result1 = windowaverage(raster, 18)
result2 = mapmaximum(result1)
value, isValid = pcraster.cellvalue(result2, 1)
self.assertEqual(isValid, True)
self.assertAlmostEqual(value, 1.708333, places=6)
pcraster.setglobaloption("unitcell")
result1 = windowaverage(raster, 9)
result2 = mapmaximum(result1)
value, isValid = pcraster.cellvalue(result2, 1)
self.assertEqual(isValid, True)
self.assertAlmostEqual(value, 1.708333, places=6)
def test_5(self):
""" test windowaverage and kernel size larger than raster """
filename = "windowaverage_Expr.map"
pcraster.setclone(filename)
raster = pcraster.readmap(filename)
result1 = windowaverage(raster, 18)
result2 = mapmaximum(result1)
value, isValid = pcraster.cellvalue(result2, 1)
self.assertEqual(isValid, True)
self.assertAlmostEqual(value, 1.708333, places=6)
pcraster.setglobaloption("unitcell")
result1 = windowaverage(raster, 9)
result2 = mapmaximum(result1)
value, isValid = pcraster.cellvalue(result2, 1)
self.assertEqual(isValid, True)
self.assertAlmostEqual(value, 1.708333, places=6)
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 pcr_coast(dem, points):
""" project points to coast with nearest neighbourhood
finds coastal cells based on dem with NoDataValues and the locations of boundary conditions at sea
using pcr spread the a nearest neighbor interpolation of the point ids is done for coastal cells
:param dem: pcr dem
:param points: pcrmap with ids in cells
:returns: pcr map with location ids projected to coastline
"""
# clump areas based on NoDataValues in dem
dem_NoDataValues = pcr.cover(pcr.ifthenelse(dem > -9999, pcr.boolean(0), pcr.boolean(1)), pcr.boolean(1))
# find number of boundary conditions in area where dem_novalue
pcr.setglobaloption("nondiagonal") # only top, bottom, left, right
area_nbounds = pcr.areatotal(pcr.scalar(points), pcr.clump(dem_NoDataValues)) * pcr.scalar(dem_NoDataValues)
pcr.setglobaloption("diagonal") # diagonal again
# make sea (True) and land (False) mask
if np.any(pcr.pcr2numpy(area_nbounds,-9999) > 0):
sea = pcr.ifthenelse(area_nbounds > 0, pcr.boolean(1), pcr.boolean(0))
else:
sea = dem_NoDataValues
# find coast based on sea in neighboring cells and at land (sea = 0)
coast = pcr.ifthenelse((pcr.window4total(pcr.scalar(sea)) > pcr.scalar(0)) & (sea == pcr.boolean(0)),
pcr.boolean(1), pcr.boolean(0))
# move points to nearest sea cell(s)
point_dist = pcr.ifthenelse(sea, pcr.spread(points, 0, 1), 1E31) # distance from each point for sea cells
nnpoints = pcr.ifthenelse(sea, pcr.spreadzone(points, 0, 1), 0) # closest point for sea cells
dist2sea = pcr.areaminimum(point_dist, nnpoints) # shortest distance to each point to sea
points_in_sea = pcr.nominal(pcr.ifthenelse(dist2sea == point_dist, nnpoints, 0)) # map points to nearest sea cell
# map point at sea to coastline according to shortest distance over sea
res = pcr.ifthenelse((pcr.scalar(sea) + pcr.scalar(coast)) >= 1, pcr.scalar(1), 1E31) # mask out non sea or coast cells
ids_coastline = pcr.scalar(pcr.spreadzone(points_in_sea, 0, res)) * pcr.scalar(coast)
return ids_coastline, points_in_sea
def main():
"""
"""
workdir = "."
inifile = "wflow_prepare.ini"
try:
opts, args = getopt.getopt(sys.argv[1:], "W:hI:f", ['version'])
except getopt.error as msg:
usage(msg)
for o, a in opts:
if o == "-W":
workdir = a
if o == "-I":
inifile = a
if o == "-h":
usage()
if o == "-f":
recreate = True
if o == "--version":
import wflow
print("wflow version: ", wflow.__version__)
sys.exit(0)
os.chdir(workdir)
config = OpenConf(workdir + "/" + inifile)
step1dir = configget(config, "directories", "step1dir", "step1")
step2dir = configget(config, "directories", "step2dir", "step2")
snapgaugestoriver = bool(
int(configget(config, "settings", "snapgaugestoriver", "1"))
)
# make the directories to save results in
if not os.path.isdir(step1dir + "/"):
os.makedirs(step1dir)
if not os.path.isdir(step2dir):
os.makedirs(step2dir)
##first make the clone map
try:
Xul = float(config.get("settings", "Xul"))
Yul = float(config.get("settings", "Yul"))
Xlr = float(config.get("settings", "Xlr"))
Ylr = float(config.get("settings", "Ylr"))
except:
print("Xul, Xul, Xlr and Ylr are required entries in the ini file")
sys.exit(1)
csize = float(configget(config, "settings", "cellsize", "1"))
try:
gauges_x = config.get("settings", "gauges_x")
gauges_y = config.get("settings", "gauges_y")
except:
print("gauges_x and gauges_y are required entries in the ini file")
sys.exit(1)
strRiver = int(configget(config, "settings", "riverorder_step2", "4"))
corevolume = float(configget(config, "settings", "corevolume", "1E35"))
catchmentprecipitation = float(
configget(config, "settings", "catchmentprecipitation", "1E35")
)
corearea = float(configget(config, "settings", "corearea", "1E35"))
outflowdepth = float(configget(config, "settings", "lddoutflowdepth", "1E35"))
lddmethod = configget(config, "settings", "lddmethod", "dem")
lddglobaloption = configget(config, "settings", "lddglobaloption", "lddout")
pcr.setglobaloption(lddglobaloption)
nrrow = round(abs(Yul - Ylr) / csize)
nrcol = round(abs(Xlr - Xul) / csize)
mapstr = (
"mapattr -s -S -R "
+ str(nrrow)
+ " -C "
+ str(nrcol)
+ " -l "
+ str(csize)
+ " -x "
+ str(Xul)
+ " -y "
+ str(Yul)
+ " -P yb2t "
+ step2dir
+ "/cutout.map"
)
os.system(mapstr)
pcr.setclone(step2dir + "/cutout.map")
lu_water = configget(config, "files", "lu_water", "")
lu_paved = configget(config, "files", "lu_paved", "")
if lu_water:
os.system(
"resample --clone "
+ step2dir
+ "/cutout.map "
+ lu_water
+ " "
+ step2dir
+ "/wflow_waterfrac.map"
)
if lu_paved:
os.system(
"resample --clone "
+ step2dir
+ "/cutout.map "
+ lu_paved
+ " "
+ step2dir
+ "/PathFrac.map"
)
#
try:
lumap = config.get("files", "landuse")
except:
print("no landuse map...creating uniform map")
clone = pcr.readmap(step2dir + "/cutout.map")
pcr.report(pcr.nominal(clone), step2dir + "/wflow_landuse.map")
else:
os.system(
"resample --clone "
+ step2dir
+ "/cutout.map "
+ lumap
+ " "
+ step2dir
+ "/wflow_landuse.map"
)
try:
soilmap = config.get("files", "soil")
except:
print("no soil map..., creating uniform map")
clone = pcr.readmap(step2dir + "/cutout.map")
pcr.report(pcr.nominal(clone), step2dir + "/wflow_soil.map")
else:
os.system(
"resample --clone "
+ step2dir
+ "/cutout.map "
+ soilmap
+ " "
+ step2dir
+ "/wflow_soil.map"
)
resamplemaps(step1dir, step2dir)
dem = pcr.readmap(step2dir + "/wflow_dem.map")
demmin = pcr.readmap(step2dir + "/wflow_demmin.map")
demmax = pcr.readmap(step2dir + "/wflow_demmax.map")
# catchcut = pcr.readmap(step2dir + "/catchment_cut.map")
catchcut = pcr.readmap(step2dir + "/cutout.map")
# now apply the area of interest (catchcut) to the DEM
# dem=pcr.ifthen(catchcut >=1 , dem)
#
# See if there is a shape file of the river to burn in
try:
rivshp = config.get("files", "river")
except:
print("no river file specified")
riverburn = pcr.readmap(step2dir + "/wflow_riverburnin.map")
else:
print("river file speficied.....")
# rivshpattr = config.get("files","riverattr")
pcr.report(dem * 0.0, step2dir + "/nilmap.map")
thestr = (
"gdal_translate -of GTiff "
+ step2dir
+ "/nilmap.map "
+ step2dir
+ "/wflow_riverburnin.tif"
)
os.system(thestr)
rivshpattr = os.path.splitext(os.path.basename(rivshp))[0]
os.system(
"gdal_rasterize -burn 1 -l "
+ rivshpattr
+ " "
+ rivshp
+ " "
+ step2dir
+ "/wflow_riverburnin.tif"
)
thestr = (
"gdal_translate -of PCRaster "
+ step2dir
+ "/wflow_riverburnin.tif "
+ step2dir
+ "/wflow_riverburnin.map"
)
os.system(thestr)
riverburn = pcr.readmap(step2dir + "/wflow_riverburnin.map")
# ldddem = pcr.ifthenelse(riverburn >= 1.0, dem -1000 , dem)
# Only burn within the original catchment
riverburn = pcr.ifthen(pcr.scalar(catchcut) >= 1, riverburn)
# Now setup a very high wall around the catchment that is scale
# based on the distance to the catchment so that it slopes away from the
# catchment
if lddmethod != "river":
print("Burning in highres-river ...")
disttocatch = pcr.spread(pcr.nominal(catchcut), 0.0, 1.0)
demmax = pcr.ifthenelse(
pcr.scalar(catchcut) >= 1.0,
demmax,
demmax + (pcr.celllength() * 100.0) / disttocatch,
)
pcr.setglobaloption("unitcell")
# demregional=pcr.windowaverage(demmin,100)
demburn = pcr.cover(pcr.ifthen(pcr.boolean(riverburn), demmin - 100.0), demmax)
else:
print("using average dem..")
demburn = dem
ldd = tr.lddcreate_save(
step2dir + "/wflow_ldd.map",
demburn,
True,
outflowdepth=outflowdepth,
corevolume=corevolume,
catchmentprecipitation=catchmentprecipitation,
corearea=corearea,
)
# Find catchment (overall)
outlet = tr.find_outlet(ldd)
sub = tr.subcatch(ldd, outlet)
pcr.report(sub, step2dir + "/wflow_catchment.map")
pcr.report(outlet, step2dir + "/wflow_outlet.map")
# make river map
strorder = pcr.streamorder(ldd)
pcr.report(strorder, step2dir + "/wflow_streamorder.map")
river = pcr.ifthen(pcr.boolean(strorder >= strRiver), strorder)
pcr.report(river, step2dir + "/wflow_river.map")
# make subcatchments
# os.system("col2map --clone " + step2dir + "/cutout.map gauges.col " + step2dir + "/wflow_gauges.map")
X = np.fromstring(gauges_x, sep=',')
Y = np.fromstring(gauges_y, sep=',')
pcr.setglobaloption("unittrue")
outlmap = tr.points_to_map(dem, X, Y, 0.5)
pcr.report(outlmap, step2dir + "/wflow_gauges_.map")
if snapgaugestoriver:
print("Snapping gauges to river")
pcr.report(outlmap, step2dir + "/wflow_orggauges.map")
outlmap = tr.snaptomap(outlmap, river)
outlmap = pcr.ifthen(outlmap > 0, outlmap)
pcr.report(outlmap, step2dir + "/wflow_gauges.map")
scatch = tr.subcatch(ldd, outlmap)
pcr.report(scatch, step2dir + "/wflow_subcatch.map")
def dynamic(self):
"""
*Required*
This is where all the time dependent functions are executed. Time dependent
output should also be saved here.
"""
# print 'useETPdata' , self.UseETPdata
# Put the W3RA here. Stuff from W3RA_timestep_model.m
# read meteo from file
self.logger.debug("Running for: " + str(self.currentdatetime))
self.PRECIP = pcr.cover(
self.wf_readmap(self.PRECIP_mapstack, 0.0), pcr.scalar(0.0)
) # mm
if self.UseETPdata == 1:
self.TDAY = pcr.cover(
self.wf_readmap(self.TDAY_mapstack, 10.0), pcr.scalar(10.0)
) # T in degC
self.EPOT = pcr.cover(
self.wf_readmap(self.EPOT_mapstack, 0.0), pcr.scalar(0.0)
) # mm
self.WINDSPEED = pcr.cover(
self.wf_readmap(self.WINDSPEED_mapstack, default=1.0), pcr.scalar(1.0)
)
self.AIRPRESS = pcr.cover(
self.wf_readmap(self.AIRPRESS_mapstack, default=980.0),
pcr.scalar(980.0),
)
# print "Using climatology for wind, air pressure and albedo."
elif self.UseETPdata == 0:
self.TMIN = pcr.cover(
self.wf_readmap(self.TMIN_mapstack, 10.0), pcr.scalar(10.0)
) # T in degC
self.TMAX = pcr.cover(
self.wf_readmap(self.TMAX_mapstack, 10.0), pcr.scalar(10.0)
) # T in degC
self.RAD = pcr.cover(
self.wf_readmap(self.RAD_mapstack, 10.0), pcr.scalar(10.0)
) # W m-2 s-1
self.WINDSPEED = pcr.cover(
self.wf_readmap(self.WINDSPEED_mapstack, 10.0), pcr.scalar(10.0)
) # ms-1
self.AIRPRESS = pcr.cover(
self.wf_readmap(self.AIRPRESS_mapstack, 10.0), pcr.scalar(10.0)
) # Pa
self.ALBEDO = pcr.cover(
self.wf_readmapClimatology(self.ALBEDO_mapstack, default=0.1),
pcr.scalar(0.1),
)
self.wf_multparameters()
doy = self.currentdatetime.timetuple().tm_yday
# conversion daylength
pcr.setglobaloption("radians")
m = pcr.scalar(1) - pcr.tan(
(self.latitude * pcr.scalar(math.pi) / pcr.scalar(180))
) * pcr.tan(
(
(pcr.scalar(23.439) * pcr.scalar(math.pi) / pcr.scalar(180))
* pcr.cos(
pcr.scalar(2)
* pcr.scalar(math.pi)
* (doy + pcr.scalar(9))
/ pcr.scalar(365.25)
)
)
)
self.fday = pcr.min(
pcr.max(
pcr.scalar(0.02),
pcr.scalar(
pcr.acos(
pcr.scalar(1)
- pcr.min(pcr.max(pcr.scalar(0), m), pcr.scalar(2))
)
)
/ pcr.scalar(math.pi),
),
pcr.scalar(1),
) # fraction daylength
# Assign forcing and estimate effective meteorological variables
Pg = self.PRECIP # mm
if self.UseETPdata == 1:
Ta = self.TDAY # T in degC
T24 = self.TDAY # T in degC
elif self.UseETPdata == 0:
Rg = pcr.max(
self.RAD, pcr.scalar(0.0001)
) # already in W m-2 s-1; set minimum of 0.01 to avoid numerical problems
Ta = self.TMIN + pcr.scalar(0.75) * (self.TMAX - self.TMIN) # T in degC
T24 = self.TMIN + pcr.scalar(0.5) * (self.TMAX - self.TMIN) # T in degC
pex = pcr.min(
pcr.scalar(17.27) * (self.TMIN) / (pcr.scalar(237.3) + self.TMIN),
pcr.scalar(10),
) # T in degC
pe = pcr.min(
pcr.scalar(610.8) * (pcr.exp(pex)), pcr.scalar(10000.0)
) # Mean actual vapour pressure, from dewpoint temperature
# rescale factor because windspeed climatology is at 2m
WindFactor = 1.0
# u2 = pcr.scalar(WindFactor)*self.WINDSPEED*(pcr.scalar(1)-(pcr.scalar(1)-self.fday)*scalar(0.25))/self.fday
self.u2 = (
pcr.scalar(WindFactor)
* self.WINDSPEED
* (pcr.scalar(1) - (pcr.scalar(1) - self.fday) * pcr.scalar(0.25))
/ self.fday
)
pair = self.AIRPRESS # already in Pa
# diagnostic equations
self.LAI1 = self.SLA1 * self.Mleaf1 # (5.3)
self.LAI2 = self.SLA2 * self.Mleaf2 # (5.3)
fveg1 = pcr.max(1 - pcr.exp(-self.LAI1 / self.LAIref1), 0.000001) # (5.3)
fveg2 = pcr.max(1 - pcr.exp(-self.LAI2 / self.LAIref2), 0.000001)
# Vc = pcr.max(0,EVI-0.07)/fveg
fsoil1 = 1 - fveg1
fsoil2 = 1 - fveg2
w01 = self.S01 / self.S0FC1 # (2.1)
w02 = self.S02 / self.S0FC2
ws1 = self.Ss1 / self.SsFC1 # (2.1)
ws2 = self.Ss2 / self.SsFC2
wd1 = self.Sd1 / self.SdFC1 # (2.1)
wd2 = self.Sd2 / self.SdFC2 # (2.1)
TotSnow1 = self.FreeWater1 + self.DrySnow1
TotSnow2 = self.FreeWater2 + self.DrySnow2
wSnow1 = self.FreeWater1 / (TotSnow1 + 1e-5)
wSnow2 = self.FreeWater2 / (TotSnow2 + 1e-5)
# Spatialise catchment fractions
Sgfree = pcr.max(self.Sg, 0.0)
# JS: Not sure if this is translated properly....
# for i=1:par.Nhru
fwater1 = pcr.min(0.005, (0.007 * self.Sr ** 0.75))
fwater2 = pcr.min(0.005, (0.007 * self.Sr ** 0.75))
fsat1 = pcr.min(
1.0, pcr.max(pcr.min(0.005, 0.007 * self.Sr ** 0.75), Sgfree / self.Sgref)
)
fsat2 = pcr.min(
1.0, pcr.max(pcr.min(0.005, 0.007 * self.Sr ** 0.75), Sgfree / self.Sgref)
)
Sghru1 = self.Sg
Sghru2 = self.Sg
# CALCULATION OF PET
# Conversions and coefficients (3.1)
pesx = pcr.min(
(pcr.scalar(17.27) * Ta / (pcr.scalar(237.3) + Ta)), pcr.scalar(10)
)
pes = pcr.min(
pcr.scalar((pcr.scalar(610.8)) * pcr.exp(pesx)), pcr.scalar(10000)
) # saturated vapour pressure
# fRH = pe/pes # relative air humidity -------------- check
cRE = 0.03449 + 4.27e-5 * Ta
# Caero = self.fday*0.176*(1+Ta/209.1)*(pair-0.417*pe)*(1-fRH) -------------- check
# keps = 1.4e-3*((Ta/187)**2+Ta/107+1)*(6.36*pair+pe)/pes
ga1 = self.ku2_1 * self.u2
ga2 = self.ku2_2 * self.u2
if self.UseETPdata == 1:
self.E01 = pcr.max(self.EPOT, 0)
self.E02 = pcr.max(self.EPOT, 0)
keps = (
0.655e-3 * pair / pes
) # See Appendix A3 (http://www.clw.csiro.au/publications/waterforahealthycountry/2010/wfhc-aus-water-resources-assessment-system.pdf) -------------------------------- check!
elif self.UseETPdata == 0:
# Aerodynamic conductance (3.7)
ns_alb = self.ALBEDO
Rgeff = Rg / self.fday
# shortwave radiation balance (3.2)
# alb_veg = 0.452*Vc
# alb_soil = alb_wet+(alb_dry-alb_wet)*exp(-w0/w0ref_alb)
# new equations for snow albedo
alb_snow1 = 0.65 - 0.2 * wSnow1 # assumed; ideally some lit research needed
alb_snow2 = 0.65 - 0.2 * wSnow2
fsnow1 = pcr.min(
1.0, 0.05 * TotSnow1
) # assumed; ideally some lit research needed
fsnow2 = pcr.min(1.0, 0.05 * TotSnow2)
# alb = fveg*alb_veg+(fsoil-fsnow)*alb_soil +fsnow*alb_snow
# alb = albedo
alb1 = (1 - fsnow1) * ns_alb + fsnow1 * alb_snow1
alb2 = (1 - fsnow2) * ns_alb + fsnow2 * alb_snow2
RSn1 = (1 - alb1) * Rgeff
RSn2 = (1 - alb2) * Rgeff
# long wave radiation balance (3.3 to 3.5)
StefBolz = 5.67e-8
Tkelv = Ta + 273.16
self.RLin = (0.65 * (pe / Tkelv) ** 0.14) * StefBolz * Tkelv ** 4 # (3.3)
RLout = StefBolz * Tkelv ** 4.0 # (3.4)
self.RLn = self.RLin - RLout
self.fGR1 = self.Gfrac_max1 * (1 - pcr.exp(-fsoil1 / self.fvegref_G1))
self.fGR2 = self.Gfrac_max2 * (
1 - pcr.exp(-fsoil2 / self.fvegref_G2)
) # (3.5)
self.Rneff1 = (RSn1 + self.RLn) * (1 - self.fGR1)
self.Rneff2 = (RSn2 + self.RLn) * (1 - self.fGR2)
fRH = pe / pes # relative air humidity
Caero = (
self.fday * 0.176 * (1 + Ta / 209.1) * (pair - 0.417 * pe) * (1 - fRH)
) # -------------- check
keps = 1.4e-3 * ((Ta / 187) ** 2 + Ta / 107 + 1) * (6.36 * pair + pe) / pes
# Potential evaporation
kalpha1 = 1 + Caero * ga1 / self.Rneff1
kalpha2 = 1 + Caero * ga2 / self.Rneff2
self.E01 = cRE * (1 / (1 + keps)) * kalpha1 * self.Rneff1 * self.fday
self.E02 = cRE * (1 / (1 + keps)) * kalpha2 * self.Rneff2 * self.fday
self.E01 = pcr.max(self.E01, 0)
self.E02 = pcr.max(self.E02, 0)
# CALCULATION OF ET FLUXES AND ROOT WATER UPTAKE
# Root water uptake constraint (4.4)
Usmax1 = pcr.max(
0, self.Us01 * pcr.min(1, ws1 / self.wslimU1)
) ##0-waarden omdat ws1 bevat 0-waarden (zie regel 116)
Usmax2 = pcr.max(
0, self.Us02 * pcr.min(1, ws2 / self.wslimU2)
) ##0-waarden omdat ws2 bevat 0-waarden (zie regel 117)
Udmax1 = pcr.max(
0, self.Ud01 * pcr.min(1, wd1 / self.wdlimU1)
) ##0-waarden omdat wd1 bevat 0-waarden (zie regel 118)
Udmax2 = pcr.max(
0, self.Ud02 * pcr.min(1, wd2 / self.wdlimU2)
) ##0-waarden omdat wd2 bevat 0-waarden (zie regel 119)
# U0max = pcr.max(0, Us0*min(1,w0/wslimU))
U0max1 = pcr.scalar(0)
U0max2 = pcr.scalar(0)
Utot1 = pcr.max(Usmax1, pcr.max(Udmax1, U0max1))
Utot2 = pcr.max(Usmax2, pcr.max(Udmax2, U0max2))
# Maximum transpiration (4.3)
Gsmax1 = self.cGsmax1 * self.Vc1
gs1 = fveg1 * Gsmax1
ft1 = 1 / (1 + (keps / (1 + keps)) * ga1 / gs1)
Etmax1 = ft1 * self.E01
Gsmax2 = self.cGsmax2 * self.Vc2
gs2 = fveg2 * Gsmax2
ft2 = 1 / (1 + (keps / (1 + keps)) * ga2 / gs2)
Etmax2 = ft2 * self.E02
# Actual transpiration (4.1)
Et1 = pcr.min(Utot1, Etmax1)
Et2 = pcr.min(Utot2, Etmax2)
# # Root water uptake distribution (2.3)
U01 = pcr.max(
pcr.min((U0max1 / (U0max1 + Usmax1 + Udmax1)) * Et1, self.S01 - 1e-2), 0
)
Us1 = pcr.max(
pcr.min((Usmax1 / (U0max1 + Usmax1 + Udmax1)) * Et1, self.Ss1 - 1e-2), 0
)
Ud1 = pcr.max(
pcr.min((Udmax1 / (U0max1 + Usmax1 + Udmax1)) * Et1, self.Sd1 - 1e-2), 0
)
Et1 = U01 + Us1 + Ud1 # to ensure mass balance
U02 = pcr.max(
pcr.min((U0max2 / (U0max2 + Usmax2 + Udmax2)) * Et2, self.S02 - 1e-2), 0
)
Us2 = pcr.max(
pcr.min((Usmax2 / (U0max2 + Usmax2 + Udmax2)) * Et2, self.Ss2 - 1e-2), 0
)
Ud2 = pcr.max(
pcr.min((Udmax2 / (U0max2 + Usmax2 + Udmax2)) * Et2, self.Sd2 - 1e-2), 0
)
Et2 = U02 + Us2 + Ud2
# Soil evaporation (4.5)
self.S01 = pcr.max(0, self.S01 - U01)
self.S02 = pcr.max(0, self.S02 - U02)
w01 = self.S01 / self.S0FC1 # (2.1)
w02 = self.S02 / self.S0FC2 # (2.1)
fsoilE1 = self.FsoilEmax1 * pcr.min(1, w01 / self.w0limE1)
fsoilE2 = self.FsoilEmax2 * pcr.min(1, w02 / self.w0limE2)
Es1 = pcr.max(
0, pcr.min(((1 - fsat1) * fsoilE1 * (self.E01 - Et1)), self.S01 - 1e-2)
)
Es2 = pcr.max(
0, pcr.min(((1 - fsat2) * fsoilE2 * (self.E02 - Et2)), self.S02 - 1e-2)
)
# Groundwater evaporation (4.6)
Eg1 = pcr.min((fsat1 - fwater1) * self.FsoilEmax1 * (self.E01 - Et1), Sghru1)
Eg2 = pcr.min((fsat2 - fwater2) * self.FsoilEmax2 * (self.E02 - Et2), Sghru2)
# Open water evaporation (4.7)
Er1 = pcr.min(fwater1 * self.FwaterE1 * pcr.max(0, self.E01 - Et1), self.Sr)
Er2 = pcr.min(fwater2 * self.FwaterE2 * pcr.max(0, self.E02 - Et2), self.Sr)
# Rainfall interception evaporation (4.2)
Sveg1 = self.S_sls1 * self.LAI1
fER1 = self.ER_frac_ref1 * fveg1
Pwet1 = -pcr.ln(1 - fER1 / fveg1) * Sveg1 / fER1
Ei1 = pcr.scalar(Pg < Pwet1) * fveg1 * Pg + pcr.scalar(Pg >= Pwet1) * (
fveg1 * Pwet1 + fER1 * (Pg - Pwet1)
)
Sveg2 = self.S_sls2 * self.LAI2
fER2 = self.ER_frac_ref2 * fveg2
Pwet2 = -pcr.ln(1 - fER2 / fveg2) * Sveg2 / fER2
Ei2 = pcr.scalar(Pg < Pwet2) * fveg2 * Pg + pcr.scalar(Pg >= Pwet2) * (
fveg2 * Pwet2 + fER2 * (Pg - Pwet2)
)
self.EACT1 = (Et1 + Es1 + Eg1 + Er1 + Ei1) * self.Fhru1
self.EACT2 = (Et2 + Es2 + Eg2 + Er2 + Ei2) * self.Fhru2
self.EACT = self.EACT1 + self.EACT2
# HBV snow routine
# Matlab: function [FreeWater,DrySnow,InSoil]=snow_submodel(Precipitation,Temperature,FreeWater,DrySnow)
# derived from HBV-96 shared by Jaap Schellekens (Deltares) in May 2011
# original in PCraster, adapted to Matlab by Albert van Dijk
# HBV snow routine
Pn1 = Pg - Ei1
Pn2 = Pg - Ei2
Precipitation1 = Pn1
Precipitation2 = Pn2
# Snow routine parameters
# parameters
# TODO: Check this, not sure if this works.......
x = pcr.scalar(Pg)
Cfmax1 = 0.6 * 3.75653 * pcr.scalar(x >= 0)
Cfmax2 = 3.75653 * pcr.scalar(x >= 0)
TT1 = -1.41934 * pcr.scalar(
x >= 0
) # critical temperature for snowmelt and refreezing
TT2 = -1.41934 * pcr.scalar(x >= 0)
TTI1 = 1.00000 * pcr.scalar(
x >= 0
) # defines interval in which precipitation falls as rainfall and snowfall
TTI2 = 1.00000 * pcr.scalar(x >= 0)
CFR1 = 0.05000 * pcr.scalar(
x >= 0
) # refreezing efficiency constant in refreezing of freewater in snow
CFR2 = 0.05000 * pcr.scalar(x >= 0)
WHC1 = 0.10000 * pcr.scalar(x >= 0)
WHC2 = 0.10000 * pcr.scalar(x >= 0)
# Partitioning into fractions rain and snow
Temperature = T24 # Dimmie, let op: tijdelijke regel!!
RainFrac1 = pcr.max(0, pcr.min((Temperature - (TT1 - TTI1 / 2)) / TTI1, 1))
RainFrac2 = pcr.max(0, pcr.min((Temperature - (TT2 - TTI2 / 2)) / TTI2, 1))
SnowFrac1 = 1 - RainFrac1 # fraction of precipitation which falls as snow
SnowFrac2 = 1 - RainFrac2
# Snowfall/melt calculations
SnowFall1 = SnowFrac1 * Precipitation1 # snowfall depth
SnowFall2 = SnowFrac2 * Precipitation2
RainFall1 = RainFrac1 * Precipitation1 # rainfall depth
RainFall2 = RainFrac2 * Precipitation2
PotSnowMelt1 = Cfmax1 * pcr.max(
0, Temperature - TT1
) # Potential snow melt, based on temperature
PotSnowMelt2 = Cfmax2 * pcr.max(0, Temperature - TT2)
PotRefreezing1 = (
Cfmax1 * CFR1 * pcr.max(TT1 - Temperature, 0)
) # Potential refreezing, based on temperature
PotRefreezing2 = Cfmax2 * CFR2 * pcr.max(TT2 - Temperature, 0)
Refreezing1 = pcr.min(PotRefreezing1, self.FreeWater1) # actual refreezing
Refreezing2 = pcr.min(PotRefreezing2, self.FreeWater2)
SnowMelt1 = pcr.min(PotSnowMelt1, self.DrySnow1) # actual snow melt
SnowMelt2 = pcr.min(PotSnowMelt2, self.DrySnow2)
self.DrySnow1 = (
self.DrySnow1 + SnowFall1 + Refreezing1 - SnowMelt1
) # dry snow content
self.DrySnow2 = self.DrySnow2 + SnowFall2 + Refreezing2 - SnowMelt2
self.FreeWater1 = self.FreeWater1 - Refreezing1 # free water content in snow
self.FreeWater2 = self.FreeWater2 - Refreezing2
MaxFreeWater1 = self.DrySnow1 * WHC1
MaxFreeWater2 = self.DrySnow2 * WHC2
self.FreeWater1 = self.FreeWater1 + SnowMelt1 + RainFall1
self.FreeWater2 = self.FreeWater2 + SnowMelt2 + RainFall2
InSoil1 = pcr.max(
self.FreeWater1 - MaxFreeWater1, 0
) # abundant water in snow pack which goes into soil
InSoil2 = pcr.max(self.FreeWater2 - MaxFreeWater2, 0)
self.FreeWater1 = self.FreeWater1 - InSoil1
self.FreeWater2 = self.FreeWater2 - InSoil2
# End of Snow Module
# CALCULATION OF WATER BALANCES
# surface water fluxes (2.2)
NetInSoil1 = pcr.max(0, (InSoil1 - self.InitLoss1))
NetInSoil2 = pcr.max(0, (InSoil2 - self.InitLoss2))
Rhof1 = (1 - fsat1) * (NetInSoil1 / (NetInSoil1 + self.PrefR1)) * NetInSoil1
Rhof2 = (1 - fsat2) * (NetInSoil2 / (NetInSoil2 + self.PrefR2)) * NetInSoil2
Rsof1 = fsat1 * NetInSoil1
Rsof2 = fsat2 * NetInSoil2
QR1 = Rhof1 + Rsof1
QR2 = Rhof2 + Rsof2
I1 = InSoil1 - QR1
I2 = InSoil2 - QR2
# SOIL WATER BALANCES (2.1 & 2.4)
# Topsoil water balance (S0)
self.S01 = self.S01 + I1 - Es1 - U01
self.S02 = self.S02 + I2 - Es2 - U02
SzFC1 = self.S0FC1
SzFC2 = self.S0FC2
Sz1 = self.S01
Sz2 = self.S02
wz1 = pcr.max(1e-2, Sz1) / SzFC1
wz2 = pcr.max(1e-2, Sz2) / SzFC2
self.TMP = SzFC1
# TODO: Check if this works
fD1 = pcr.scalar(wz1 > 1) * pcr.max(self.FdrainFC1, 1 - 1 / wz1) + pcr.scalar(
wz1 <= 1
) * self.FdrainFC1 * pcr.exp(self.beta1 * pcr.scalar(wz1 - 1))
fD2 = pcr.scalar(wz2 > 1) * pcr.max(self.FdrainFC2, 1 - 1 / wz2) + pcr.scalar(
wz2 <= 1
) * self.FdrainFC2 * pcr.exp(self.beta2 * pcr.scalar(wz2 - 1))
Dz1 = pcr.max(0, pcr.min(fD1 * Sz1, Sz1 - 1e-2))
Dz2 = pcr.max(0, pcr.min(fD2 * Sz2, Sz2 - 1e-2))
D01 = Dz1
D02 = Dz2
self.S01 = self.S01 - D01
self.S02 = self.S02 - D02
# Shallow root zone water balance (Ss)
self.Ss1 = self.Ss1 + D01 - Us1
self.Ss2 = self.Ss2 + D02 - Us2
SzFC1 = self.SsFC1
SzFC2 = self.SsFC2
Sz1 = self.Ss1
Sz2 = self.Ss2
wz1 = pcr.max(1e-2, Sz1) / SzFC1
wz2 = pcr.max(1e-2, Sz2) / SzFC2
fD1 = pcr.scalar(wz1 > 1) * pcr.max(self.FdrainFC1, 1 - 1 / wz1) + pcr.scalar(
wz1 <= 1
) * self.FdrainFC1 * pcr.exp(self.beta1 * pcr.scalar(wz1 - 1))
fD2 = pcr.scalar(wz2 > 1) * pcr.max(self.FdrainFC2, 1 - 1 / wz2) + pcr.scalar(
wz2 <= 1
) * self.FdrainFC2 * pcr.exp(self.beta2 * pcr.scalar(wz2 - 1))
Dz1 = pcr.max(0, pcr.min(fD1 * Sz1, Sz1 - 1e-2))
Dz2 = pcr.max(0, pcr.min(fD2 * Sz2, Sz2 - 1e-2))
Ds1 = Dz1
Ds2 = Dz2
self.Ss1 = self.Ss1 - Ds1
self.Ss2 = self.Ss2 - Ds2
# Deep root zone water balance (Sd) (2.6)
self.Sd1 = self.Sd1 + Ds1 - Ud1
self.Sd2 = self.Sd2 + Ds2 - Ud2
SzFC1 = self.SdFC1
SzFC2 = self.SdFC2
Sz1 = self.Sd1
Sz2 = self.Sd2
wz1 = pcr.max(1e-2, Sz1) / SzFC1
wz2 = pcr.max(1e-2, Sz2) / SzFC2
fD1 = pcr.scalar(wz1 > 1) * pcr.max(self.FdrainFC1, 1 - 1 / wz1) + pcr.scalar(
wz1 <= 1
) * self.FdrainFC1 * pcr.exp(self.beta1 * pcr.scalar(wz1 - 1))
fD2 = pcr.scalar(wz2 > 1) * pcr.max(self.FdrainFC2, 1 - 1 / wz2) + pcr.scalar(
wz2 <= 1
) * self.FdrainFC2 * pcr.exp(self.beta2 * pcr.scalar(wz2 - 1))
Dz1 = pcr.max(0, pcr.min(fD1 * Sz1, Sz1 - 1e-2))
Dz2 = pcr.max(0, pcr.min(fD2 * Sz2, Sz2 - 1e-2))
Dd1 = Dz1
Dd2 = Dz2
self.Sd1 = self.Sd1 - Dd1
self.Sd2 = self.Sd2 - Dd2
Y1 = pcr.min(
self.Fgw_conn1 * pcr.max(0, self.wdlimU1 * self.SdFC1 - self.Sd1),
Sghru1 - Eg1,
)
Y2 = pcr.min(
self.Fgw_conn2 * pcr.max(0, self.wdlimU2 * self.SdFC2 - self.Sd2),
Sghru2 - Eg2,
)
# Y = Fgw_conn.*max(0,wdlimU.*SdFC-Sd); #nog matlab script
self.Sd1 = self.Sd1 + Y1
self.Sd2 = self.Sd2 + Y2
# CATCHMENT WATER BALANCE
# Groundwater store water balance (Sg) (2.5)
NetGf = (self.Fhru1 * (Dd1 - Eg1 - Y1)) + (self.Fhru2 * (Dd2 - Eg2 - Y2))
self.Sg = self.Sg + NetGf
Sgfree = pcr.max(self.Sg, 0)
Qg = pcr.min(Sgfree, (1 - pcr.exp(-self.K_gw)) * Sgfree)
self.Sg = self.Sg - Qg
# Surface water store water balance (Sr) (2.7)
self.Sr = self.Sr + (self.Fhru1 * (QR1 - Er1)) + (self.Fhru2 * (QR2 - Er2)) + Qg
self.Qtot = pcr.min(self.Sr, (1 - pcr.exp(-self.K_rout)) * self.Sr)
self.Sr = self.Sr - self.Qtot
# VEGETATION ADJUSTMENT (5)
fveq1 = (
(1 / pcr.max((self.E01 / Utot1) - 1, 1e-3))
* (keps / (1 + keps))
* (ga1 / Gsmax1)
)
fveq2 = (
(1 / pcr.max((self.E02 / Utot2) - 1, 1e-3))
* (keps / (1 + keps))
* (ga2 / Gsmax2)
)
fvmax1 = 1 - pcr.exp(-self.LAImax1 / self.LAIref1)
fvmax2 = 1 - pcr.exp(-self.LAImax2 / self.LAIref2)
fveq1 = pcr.min(fveq1, fvmax1)
fveq2 = pcr.min(fveq2, fvmax2)
dMleaf1 = -pcr.ln(1 - fveq1) * self.LAIref1 / self.SLA1 - self.Mleaf1
dMleaf2 = -pcr.ln(1 - fveq2) * self.LAIref2 / self.SLA2 - self.Mleaf2
# Mleafnet1 = dMleaf1 * (dMleaf1/self.Tgrow1) + dMleaf1 * dMleaf1/self.Tsenc1
# Mleafnet2 = dMleaf2 * (dMleaf1/self.Tgrow2) + dMleaf2 * dMleaf2/self.Tsenc2
Mleafnet1 = (
pcr.scalar(dMleaf1 > 0) * (dMleaf1 / self.Tgrow1)
+ pcr.scalar(dMleaf1 < 0) * dMleaf1 / self.Tsenc1
)
Mleafnet2 = (
pcr.scalar(dMleaf2 > 0) * (dMleaf2 / self.Tgrow2)
+ pcr.scalar(dMleaf2 < 0) * dMleaf2 / self.Tsenc2
)
self.Mleaf1 = self.Mleaf1 + Mleafnet1
self.Mleaf2 = self.Mleaf2 + Mleafnet2
self.LAI1 = self.SLA1 * self.Mleaf1 # (5.3)
self.LAI2 = self.SLA2 * self.Mleaf2
# Updating diagnostics
self.LAI1 = self.SLA1 * self.Mleaf1 # (5.3)
self.LAI2 = self.SLA2 * self.Mleaf2
fveg1 = 1 - pcr.exp(-self.LAI1 / self.LAIref1) # (5.3)
fveg2 = 1 - pcr.exp(-self.LAI2 / self.LAIref2)
fsoil1 = 1 - fveg1
fsoil2 = 1 - fveg2
w01 = self.S01 / self.S0FC1 # (2.1)
w02 = self.S02 / self.S0FC2
ws1 = self.Ss1 / self.SsFC1 # (2.1)
ws2 = self.Ss2 / self.SsFC2
wd1 = self.Sd1 / self.SdFC1 # (2.1)
wd2 = self.Sd2 / self.SdFC2
def initial(self):
"""
*Required*
Initial part of the model, executed only once. It reads all static model
information (parameters) and sets-up the variables used in modelling.
This function is required. The contents is free. However, in order to
easily connect to other models it is advised to adhere to the directory
structure used in the other models.
"""
#: pcraster option to calculate with units or cells. Not really an issue
#: in this model but always good to keep in mind.
pcr.setglobaloption("unittrue")
pcr.setglobaloption(
"radians"
) # Needed as W3RA was originally written in matlab
# SET GLBOAL PARAMETER VALUES (however not used in original script)
# Nhru=2
# K_gw_scale=0.0146
# K_gw_shape=0.0709
# K_rout_scale=0.1943
# K_rout_int=0.0589
# FdrainFC_scale=0.2909
# FdrainFC_shape=0.5154
# Sgref_scale=3.2220
# Sgref_shape=3.2860
# fday=0.5000
self.timestepsecs = int(
configget(self.config, "model", "timestepsecs", "86400")
)
self.reinit = int(configget(self.config, "run", "reinit", "0"))
self.OverWriteInit = int(configget(self.config, "model", "OverWriteInit", "0"))
self.UseETPdata = int(
configget(self.config, "model", "UseETPdata", "1")
) # 1: Use ETP data, 0: Compute ETP from meteorological variables
self.logger.debug("use DATA: " + str(self.UseETPdata))
self.basetimestep = 86400
self.SaveMapDir = self.Dir + "/" + self.runId + "/outmaps"
# Define here the W3RA mapstacks (best to read these via netcdf)
self.TMAX_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "TMAX", "/inmaps/TMAX"
)
self.TMIN_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "TMIN", "/inmaps/TMIN"
)
self.TDAY_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "TDAY", "/inmaps/TDAY"
)
self.EPOT_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "EPOT", "/inmaps/EPOT"
)
self.PRECIP_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "PRECIP", "/inmaps/PRECIP"
)
self.RAD_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "RAD", "/inmaps/RAD"
)
# self.WINDSPEED_mapstack=self.Dir + configget(self.config,"inputmapstacks","WINDSPEED","/inmaps/ClimatologyMapFiles/WINDS/WNDSPEED")
# self.AIRPRESS_mapstack=self.Dir + configget(self.config,"inputmapstacks","AIRPRESS","/inmaps/ClimatologyMapFiles/AIRPRESS/AIRPRESS")
self.ALBEDO_mapstack = self.Dir + configget(
self.config,
"inputmapstacks",
"ALBEDO",
"/inmaps/ClimatologyMapFiles/ALBEDO/ALBEDO",
)
self.WINDSPEED_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "WINDSPEED", "/inmaps/WIND"
)
self.AIRPRESS_mapstack = self.Dir + configget(
self.config, "inputmapstacks", "AIRPRESS", "/inmaps/PRES"
)
self.Altitude = pcr.readmap(self.Dir + "/staticmaps/wflow_dem")
self.latitude = pcr.ycoordinate(pcr.boolean(self.Altitude))
# Add reading of parameters here
self.K_gw = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/k_gw.map"), 0.0, fail=True
)
self.K_rout = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/k_rout.map"), 0.0, fail=True
)
self.Sgref = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/sgref.map"), 0.0, fail=True
)
self.alb_dry1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/alb_dry.map"), 0.0, fail=True
)
self.alb_wet1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/alb_wet.map"), 0.0, fail=True
)
self.beta1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/beta.map"), 0.0, fail=True
)
self.cGsmax1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/cgsmax.map"), 0.0, fail=True
)
self.ER_frac_ref1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/er_frac_ref.map"), 0.0, fail=True
)
self.FdrainFC1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fdrainfc.map"), 0.0, fail=True
)
self.Fgw_conn1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fgw_conn.map"), 0.0, fail=True
)
self.Fhru1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fhru.map"), 0.0, fail=True
)
self.SLA1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/sla.map"), 0.0, fail=True
)
self.LAIref1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/lairef.map"), 0.0, fail=True
)
self.FsoilEmax1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fsoilemax.map"), 0.0, fail=True
)
self.fvegref_G1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fvegref_g.map"), 0.0, fail=True
)
self.FwaterE1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fwatere.map"), 0.0, fail=True
)
self.Gfrac_max1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/gfrac_max.map"), 0.0, fail=True
)
self.hveg1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/hveg.map"), 0.0, fail=True
)
self.InitLoss1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/initloss.map"), 0.0, fail=True
)
self.LAImax1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/laimax.map"), 0.0, fail=True
)
self.PrefR1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/prefr.map"), 0.0, fail=True
)
self.S_sls1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/s_sls.map"), 0.0, fail=True
)
self.S0FC1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/s0fc.map"), 0.0, fail=True
)
self.SsFC1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/ssfc.map"), 0.0, fail=True
)
self.SdFC1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/sdfc.map"), 0.0, fail=True
)
self.Vc1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/vc.map"), 0.0, fail=True
)
self.w0ref_alb1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/w0ref_alb.map"), 0.0, fail=True
)
self.Us01 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/us0.map"), 0.0, fail=True
)
self.Ud01 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/ud0.map"), 0.0, fail=True
)
self.wslimU1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/wslimu.map"), 0.0, fail=True
)
self.wdlimU1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/wdlimu.map"), 0.0, fail=True
)
self.w0limE1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/w0lime.map"), 0.0, fail=True
)
self.Tgrow1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/tgrow.map"), 0.0, fail=True
)
self.Tsenc1 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/tsenc.map"), 0.0, fail=True
)
self.alb_dry2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/alb_dry2.map"), 0.0, fail=True
)
self.alb_wet2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/alb_wet2.map"), 0.0, fail=True
)
self.beta2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/beta2.map"), 0.0, fail=True
)
self.cGsmax2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/cgsmax2.map"), 0.0, fail=True
)
self.ER_frac_ref2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/er_frac_ref2.map"), 0.0, fail=True
)
self.FdrainFC2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fdrainfc2.map"), 0.0, fail=True
)
self.Fgw_conn2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fgw_conn2.map"), 0.0, fail=True
)
self.Fhru2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fhru2.map"), 0.0, fail=True
)
self.SLA2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/sla2.map"), 0.0, fail=True
)
self.LAIref2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/lairef2.map"), 0.0, fail=True
)
self.FsoilEmax2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fsoilemax2.map"), 0.0, fail=True
)
self.fvegref_G2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fvegref_g2.map"), 0.0, fail=True
)
self.FwaterE2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/fwatere2.map"), 0.0, fail=True
)
self.Gfrac_max2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/gfrac_max2.map"), 0.0, fail=True
)
self.hveg2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/hveg2.map"), 0.0, fail=True
)
self.InitLoss2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/initloss2.map"), 0.0, fail=True
)
self.LAImax2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/laimax2.map"), 0.0, fail=True
)
self.PrefR2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/prefr2.map"), 0.0, fail=True
)
self.S_sls2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/s_sls2.map"), 0.0, fail=True
)
self.S0FC2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/s0fc2.map"), 0.0, fail=True
)
self.SsFC2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/ssfc2.map"), 0.0, fail=True
)
self.SdFC2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/sdfc2.map"), 0.0, fail=True
)
self.Vc2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/vc2.map"), 0.0, fail=True
)
self.w0ref_alb2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/w0ref_alb2.map"), 0.0, fail=True
)
self.Us02 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/us02.map"), 0.0, fail=True
)
self.Ud02 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/ud02.map"), 0.0, fail=True
)
self.wslimU2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/wslimu2.map"), 0.0, fail=True
)
self.wdlimU2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/wdlimu2.map"), 0.0, fail=True
)
self.w0limE2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/w0lime2.map"), 0.0, fail=True
)
self.Tgrow2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/tgrow2.map"), 0.0, fail=True
)
self.Tsenc2 = self.wf_readmap(
os.path.join(self.Dir, "staticmaps/tsenc2.map"), 0.0, fail=True
)
self.wf_multparameters()
# Static, for the computation of Aerodynamic conductance (3.7)
self.fh1 = pcr.ln(813.0 / self.hveg1 - 5.45)
self.fh2 = pcr.ln(813.0 / self.hveg2 - 5.45)
self.ku2_1 = 0.305 / (self.fh1 * (self.fh1 + 2.3))
self.ku2_2 = 0.305 / (self.fh2 * (self.fh2 + 2.3))
self.logger.info("Starting Dynamic run...")
point_map = workdir + 'point.map'
call(('gdal_translate','-of','GTiff','-a_srs',EPSG,'-ot','Float32',clone_map,point_tif))
call(('gdal_rasterize','-burn','1','-l',file_att,pointshp,point_tif))
call(('gdal_translate','-of','PCRaster','-a_srs',EPSG,'-ot','Float32',point_tif,point_map))
points = pcr.scalar(pcr.readmap(point_map))
if snapgaugestoriver:
print "Snapping points to line"
points= wt.snaptomap(pcr.ordinal(points),pcr.boolean(lines))
points= pcr.cover(pcr.scalar(points),pcr.scalar(0))
points = pcr.cover(points, pcr.scalar(0))
#pcr.report(points,'points.map')
burn = burn - (points * pcr.scalar(burnvalue)*2)
#pcr.report(burn,'burn3.map')
''' create ldd '''
pcr.setglobaloption("lddout")
if lddin:
pcr.setglobaloption("lddin")
ldd_map = workdir + 'ldd.map'
streamorder_map = workdir + 'streamorder.map'
river_map = workdir + 'river.map'
catchments_map = workdir + 'catchments.map'
catchments_tif = workdir + 'catchments.tif'
#catchments_shp = resultdir + 'catchments.shp'
generateldd = True
if skipldd:
print 'Option -S is set'
print 'ldd will be read from ' + ldd_map
if os.path.exists(ldd_map):
root_dir = os.path.dirname(os.getcwd())
input_dir = os.path.join(root_dir, 'input')
ref_map_dir = os.path.join(input_dir, 'reference_maps')
bio_dir = os.path.join(input_dir, 'bio')
cost_dir = os.path.join(input_dir, 'cost')
output_dir = os.path.join(root_dir, 'output/waal_XL')
ens_dir = os.path.join(output_dir, 'measures_ensemble02')
ens_map_dir = os.path.join(ens_dir, 'maps')
ens_FM_dir = os.path.join(ens_dir, 'hydro')
ens_overview_dir = os.path.join(ens_dir, 'overview')
scratch_dir = os.path.join(root_dir, 'scratch')
clone_file = os.path.join(ref_map_dir, 'clone.map')
pcr.setclone(clone_file)
pcr.setglobaloption('unittrue')
os.chdir(scratch_dir)
ppp
#%% Initialize BIOSAFE
ndff_species = pd.read_pickle(os.path.join(bio_dir, 'ndff_sub_BS_13.pkl'))
flpl_sections = pcr.readmap(os.path.join(bio_dir, 'flpl_sections.map'))
ecotopes = measures.read_map_with_legend(os.path.join(bio_dir, 'ecotopes.map'))
legalWeights, linksLaw, linksEco = bsIO.from_csv(bio_dir)
speciesPresence = pd.DataFrame(np.random.randint(2, size=len(linksLaw)),\
columns=['speciesPresence'], \
index=linksLaw.index)
ecotopeArea = pd.DataFrame(np.ones(82) * 1e5,\
columns = ['area_m2'],\
index = linksEco.columns.values[0:-1])
bs = biosafe.biosafe(legalWeights, linksLaw, linksEco, speciesPresence,
def main():
### Read input arguments #####
logfilename = 'wtools_static_maps.log'
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=None,
help='ini file with settings for static_maps.exe')
parser.add_option('-s',
'--source',
dest='source',
default='wflow',
help='Source folder containing clone (default=./wflow)')
parser.add_option('-d',
'--destination',
dest='destination',
default='staticmaps',
help='Destination folder (default=./staticmaps)')
parser.add_option('-r',
'--river',
dest='rivshp',
default=None,
help='river network polyline layer (ESRI Shapefile)')
parser.add_option('-c',
'--catchment',
dest='catchshp',
default=None,
help='catchment polygon layer (ESRI Shapefile)')
parser.add_option('-g',
'--gauges',
dest='gaugeshp',
default=None,
help='gauge point layer (ESRI Shapefile)')
parser.add_option('-D',
'--dem',
dest='dem_in',
default=None,
help='digital elevation model (GeoTiff)')
parser.add_option('-L',
'--landuse',
dest='landuse',
default=None,
help='land use / land cover layer (GeoTiff)')
parser.add_option('-S',
'--soiltype',
dest='soil',
default=None,
help='soil type layer (GeoTiff)')
parser.add_option(
'-V',
'--vegetation',
dest='lai',
default=None,
help=
'vegetation LAI layer location (containing 12 GeoTiffs <LAI00000.XXX.tif>)'
)
parser.add_option(
'-O',
'--other_maps',
dest='other_maps',
default=None,
help=
'bracketed [] comma-separated list of paths to other maps that should be reprojected'
)
parser.add_option(
'-C',
'--clean',
dest='clean',
default=False,
action='store_true',
help='Clean the .xml files from static maps folder when finished')
parser.add_option(
'-A',
'--alltouch',
dest='alltouch',
default=False,
action='store_true',
help=
'option to burn catchments "all touching".\nUseful when catchment-size is small compared to cellsize'
)
(options, args) = parser.parse_args()
# parse other maps into an array
options.other_maps = options.other_maps.replace(' ', '').replace(
'[', '').replace(']', '').split(',')
options.source = os.path.abspath(options.source)
clone_map = os.path.join(options.source, 'mask.map')
clone_shp = os.path.join(options.source, 'mask.shp')
clone_prj = os.path.join(options.source, 'mask.prj')
if None in (options.inifile, options.rivshp, options.catchshp,
options.dem_in):
msg = """The following files are compulsory:
- ini file
- DEM (raster)
- river (shape)
- catchment (shape)
"""
print(msg)
parser.print_help()
sys.exit(1)
if not os.path.exists(options.inifile):
print 'path to ini file cannot be found'
sys.exit(1)
if not os.path.exists(options.rivshp):
print 'path to river shape cannot be found'
sys.exit(1)
if not os.path.exists(options.catchshp):
print 'path to catchment shape cannot be found'
sys.exit(1)
if not os.path.exists(options.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 = wtools_lib.setlogger(logfilename, 'WTOOLS', options.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(options.destination), options.destination
is not options.source):
shutil.rmtree(options.destination)
if options.destination is not options.source:
os.makedirs(options.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 = gis.gdal_readmap(clone_map, 'GTiff')
trans = wtools_lib.get_geotransform(clone_map)
extent = wtools_lib.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 = wtools_lib.get_projection(clone_map)
unit_clone = srs.GetAttrValue('UNIT').lower()
### READ CONFIG FILE
# open config-file
config = wtools_lib.OpenConf(options.inifile)
# read settings
snapgaugestoriver = wtools_lib.configget(config,
'settings',
'snapgaugestoriver',
True,
datatype='boolean')
burnalltouching = wtools_lib.configget(config,
'settings',
'burncatchalltouching',
True,
datatype='boolean')
burninorder = wtools_lib.configget(config,
'settings',
'burncatchalltouching',
False,
datatype='boolean')
verticetollerance = wtools_lib.configget(config,
'settings',
'vertice_tollerance',
0.0001,
datatype='float')
''' read parameters '''
burn_outlets = wtools_lib.configget(config,
'parameters',
'burn_outlets',
10000,
datatype='int')
burn_rivers = wtools_lib.configget(config,
'parameters',
'burn_rivers',
200,
datatype='int')
burn_connections = wtools_lib.configget(config,
'parameters',
'burn_connections',
100,
datatype='int')
burn_gauges = wtools_lib.configget(config,
'parameters',
'burn_gauges',
100,
datatype='int')
minorder = wtools_lib.configget(config,
'parameters',
'riverorder_min',
3,
datatype='int')
percentiles = np.array(config.get('parameters', 'statisticmaps',
'0, 100').replace(' ', '').split(','),
dtype='float')
# read the parameters for generating a temporary very high resolution grid
if unit_clone == 'degree':
cellsize_hr = wtools_lib.configget(config,
'parameters',
'highres_degree',
0.0005,
datatype='float')
elif (unit_clone == 'metre') or (unit_clone == 'meter'):
cellsize_hr = wtools_lib.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(options.destination, 'clone_highres.tif')
# make a highres clone as well!
wtools_lib.CreateTif(clone_hr, rows_hr, cols_hr, hr_trans, srs, 0)
# read staticmap locations
catchment_map = wtools_lib.configget(config, 'staticmaps', 'catchment',
'wflow_catchment.map')
dem_map = wtools_lib.configget(config, 'staticmaps', 'dem',
'wflow_dem.map')
demmax_map = wtools_lib.configget(config, 'staticmaps', 'demmax',
'wflow_demmax.map')
demmin_map = wtools_lib.configget(config, 'staticmaps', 'demmin',
'wflow_demmin.map')
gauges_map = wtools_lib.configget(config, 'staticmaps', 'gauges',
'wflow_gauges.map')
landuse_map = wtools_lib.configget(config, 'staticmaps', 'landuse',
'wflow_landuse.map')
ldd_map = wtools_lib.configget(config, 'staticmaps', 'ldd',
'wflow_ldd.map')
river_map = wtools_lib.configget(config, 'staticmaps', 'river',
'wflow_river.map')
outlet_map = wtools_lib.configget(config, 'staticmaps', 'outlet',
'wflow_outlet.map')
riverlength_fact_map = wtools_lib.configget(config, 'staticmaps',
'riverlength_fact',
'wflow_riverlength_fact.map')
soil_map = wtools_lib.configget(config, 'staticmaps', 'soil',
'wflow_soil.map')
streamorder_map = wtools_lib.configget(config, 'staticmaps', 'streamorder',
'wflow_streamorder.map')
subcatch_map = wtools_lib.configget(config, 'staticmaps', 'subcatch',
'wflow_subcatch.map')
# read mask location (optional)
masklayer = wtools_lib.configget(config, 'mask', 'masklayer',
options.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(options.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(options.dem_in),
os.path.join(options.destination, dem_map)))
gis.gdal_warp(options.dem_in,
clone_map,
os.path.join(options.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 = wtools_lib.get_projection(options.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():
for percentile in percentiles:
if percentile >= 100:
logger.info('computing window maximum')
percentile_dem = os.path.join(options.destination,
'wflow_dem_max.map')
elif percentile <= 0:
logger.info('computing window minimum')
percentile_dem = os.path.join(options.destination,
'wflow_dem_min.map')
else:
logger.info('computing window {:d} percentile'.format(
int(percentile)))
percentile_dem = os.path.join(
options.destination,
'wflow_dem_{:03d}.map'.format(int(percentile)))
percentile_dem = os.path.join(
options.destination,
'wflow_dem_{:03d}.map'.format(int(percentile)))
stats = wtools_lib.windowstats(options.dem_in,
len(yax),
len(xax),
trans,
srs,
percentile_dem,
percentile,
transform=clone2dem_transform,
logger=logger)
# else:
# logger.warning('Projections of DEM and clone are different. DEM statistics for different projections is not yet implemented')
"""
# burn in rivers
# first convert and clip the river shapefile
# retrieve river shape projection, if not available assume EPSG:4326
file_att = os.path.splitext(os.path.basename(options.rivshp))[0]
ds = ogr.Open(options.rivshp)
lyr = ds.GetLayerByName(file_att)
extent = lyr.GetExtent()
extent_in = [extent[0], extent[2], extent[1], extent[3]]
try:
# get spatial reference from shapefile
srs_rivshp = lyr.GetSpatialRef()
logger.info('Projection in river shapefile is {:s}'.format(srs_rivshp.ExportToProj4()))
except:
logger.warning('No projection found in {:s}, assuming WGS 1984 lat-lon'.format(options.rivshp))
srs_rivshp = osr.SpatialReference()
srs_rivshp.ImportFromEPSG(4326)
rivprojshp = os.path.join(options.destination, 'rivshp_proj.shp')
logger.info('Projecting and clipping {:s} to {:s}'.format(options.rivshp, rivprojshp))
# TODO: Line below takes a very long time to process, the bigger the shapefile, the more time. How do we deal with this?
call(('ogr2ogr','-s_srs', srs_rivshp.ExportToProj4(),'-t_srs', srs.ExportToProj4(), '-clipsrc', '{:f}'.format(xmin), '{:f}'.format(ymin), '{:f}'.format(xmax), '{:f}'.format(ymax), rivprojshp, options.rivshp))
"""
# TODO: BURNING!!
# project catchment layer to projection of clone
file_att = os.path.splitext(os.path.basename(options.catchshp))[0]
print options.catchshp
ds = ogr.Open(options.catchshp)
lyr = ds.GetLayerByName(file_att)
extent = lyr.GetExtent()
extent_in = [extent[0], extent[2], extent[1], extent[3]]
try:
# get spatial reference from shapefile
srs_catchshp = lyr.GetSpatialRef()
logger.info('Projection in catchment shapefile is {:s}'.format(
srs_catchshp.ExportToProj4()))
except:
logger.warning(
'No projection found in {:s}, assuming WGS 1984 lat-lon'.format(
options.catchshp))
srs_catchshp = osr.SpatialReference()
srs_catchshp.ImportFromEPSG(4326)
catchprojshp = os.path.join(options.destination, 'catchshp_proj.shp')
logger.info('Projecting {:s} to {:s}'.format(options.catchshp,
catchprojshp))
call(('ogr2ogr', '-s_srs', srs_catchshp.ExportToProj4(), '-t_srs',
srs.ExportToProj4(), '-clipsrc', '{:f}'.format(xmin),
'{:f}'.format(ymin), '{:f}'.format(xmax), '{:f}'.format(ymax),
catchprojshp, options.catchshp))
#
logger.info('Calculating ldd')
ldddem = pcr.readmap(os.path.join(options.destination, dem_map))
ldd_select = pcr.lddcreate(ldddem, 1e35, 1e35, 1e35, 1e35)
pcr.report(ldd_select, os.path.join(options.destination, 'wflow_ldd.map'))
# compute stream order, identify river cells
streamorder = pcr.ordinal(pcr.streamorder(ldd_select))
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(options.destination, dem_map)),9999999)))
# dem_resample_map = pcr.cover(os.path.join(options.destination, dem_map), pcr.scalar(river)*0+mindem)
# pcr.report(dem_resample_map, os.path.join(options.destination, dem_map))
pcr.report(streamorder, os.path.join(options.destination, streamorder_map))
pcr.report(river, os.path.join(options.destination, river_map))
# deal with your catchments
if options.gaugeshp == None:
logger.info('No gauges defined, using outlets instead')
gauges = pcr.ordinal(
pcr.uniqueid(
pcr.boolean(
pcr.ifthen(pcr.scalar(ldd_select) == 5, pcr.boolean(1)))))
pcr.report(gauges, os.path.join(options.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)
# report river length
# make a high resolution empty map
dem_hr_file = os.path.join(options.destination, 'dem_highres.tif')
burn_hr_file = os.path.join(options.destination, 'burn_highres.tif')
demburn_hr_file = os.path.join(options.destination, 'demburn_highres.map')
riv_hr_file = os.path.join(options.destination, 'riv_highres.map')
gis.gdal_warp(options.dem_in, clone_hr, dem_hr_file)
# wtools_lib.CreateTif(riv_hr, rows_hr, cols_hr, hr_trans, srs, 0)
file_att = os.path.splitext(os.path.basename(options.rivshp))[0]
# open the shape layer
ds = ogr.Open(options.rivshp)
lyr = ds.GetLayerByName(file_att)
gis.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 = gis.gdal_readmap(burn_hr_file, 'GTiff')
burn_hr[burn_hr == fill] = 0
xax_hr, yax_hr, dem_hr, fill = gis.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
gis.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)
ldd_hr = pcr.lddcreate(demburn_hr, 1e35, 1e35, 1e35, 1e35)
pcr.report(ldd_hr, os.path.join(options.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')
#riverlength = wt.windowstats(riv_hr,clone_rows,clone_columns,clone_trans,srs_clone,resultdir,'frac',clone2dem_transform)
riverlength = wtools_lib.windowstats(riv_hr_file,
len(yax),
len(xax),
trans,
srs,
os.path.join(options.destination,
riverlength_fact_map),
stat='fact',
logger=logger)
# TODO: nothing happends with the river lengths yet. Need to decide how to use these
# report outlet map
pcr.report(pcr.ifthen(pcr.ordinal(ldd_select) == 5, pcr.ordinal(1)),
os.path.join(options.destination, outlet_map))
# report subcatchment map
subcatchment = pcr.subcatchment(ldd_select, gauges)
pcr.report(pcr.ordinal(subcatchment),
os.path.join(options.destination, subcatch_map))
# Report land use map
if options.landuse == None:
logger.info(
'No land use map used. Preparing {:s} with only ones.'.format(
os.path.join(options.destination, landuse_map)))
pcr.report(pcr.nominal(ones),
os.path.join(options.destination, landuse_map))
else:
logger.info('Resampling land use from {:s} to {:s}'.format(
os.path.abspath(options.landuse),
os.path.join(options.destination, os.path.abspath(landuse_map))))
gis.gdal_warp(options.landuse,
clone_map,
os.path.join(options.destination, landuse_map),
format='PCRaster',
gdal_interp=gdalconst.GRA_Mode,
gdal_type=gdalconst.GDT_Int32)
# report soil map
if options.soil == None:
logger.info('No soil map used. Preparing {:s} with only ones.'.format(
os.path.join(options.destination, soil_map)))
pcr.report(pcr.nominal(ones),
os.path.join(options.destination, soil_map))
else:
logger.info('Resampling soil from {:s} to {:s}'.format(
os.path.abspath(options.soil),
os.path.join(options.destination, os.path.abspath(soil_map))))
gis.gdal_warp(options.soil,
clone_map,
os.path.join(options.destination, soil_map),
format='PCRaster',
gdal_interp=gdalconst.GRA_Mode,
gdal_type=gdalconst.GDT_Int32)
if options.lai == None:
logger.info(
'No vegetation LAI maps used. Preparing default maps {:s} with only ones.'
.format(os.path.join(options.destination, soil_map)))
pcr.report(pcr.nominal(ones),
os.path.join(options.destination, soil_map))
else:
dest_lai = os.path.join(options.destination, 'clim')
os.makedirs(dest_lai)
for month in range(12):
lai_in = os.path.join(options.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)))
gis.gdal_warp(lai_in,
clone_map,
lai_out,
format='PCRaster',
gdal_interp=gdalconst.GRA_Bilinear,
gdal_type=gdalconst.GDT_Float32)
# report soil map
if options.other_maps == None:
logger.info('No other maps used. Skipping other maps.')
else:
logger.info('Resampling list of other maps...')
for map_file in options.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(options.destination, map_name)))
gis.gdal_warp(map_file,
clone_map,
os.path.join(options.destination, map_name),
format='PCRaster',
gdal_interp=gdalconst.GRA_Mode,
gdal_type=gdalconst.GDT_Float32)
if options.clean:
wtools_lib.DeleteList(glob.glob(
os.path.join(options.destination, '*.xml')),
logger=logger)
wtools_lib.DeleteList(glob.glob(
os.path.join(options.destination, 'clim', '*.xml')),
logger=logger)
wtools_lib.DeleteList(glob.glob(
os.path.join(options.destination, '*highres*')),
logger=logger)
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():
clone_map = "mask\mask.map"
clone_shp = "mask\mask.shp"
clone_prj = "mask\mask.prj"
workdir = "work\\"
resultdir = "staticmaps\\"
''' read commandline arguments '''
argv = sys.argv
clone_EPSG = False
try:
opts, args = getopt.getopt(argv[1:], 'i:g:p:r:c:d:l:s:CA')
except getopt.error:
print 'fout'
Usage()
sys.exit(1)
inifile = None
rivshp = None
catchshp = None
dem_in = None
landuse = None
soiltype = None
clean = False
gaugeshp = None
alltouching = False
for o, a in opts:
if o == '-i': inifile = a
if o == '-p': clone_EPSG = 'EPSG:' + a
if o == '-r': rivshp = a
if o == '-c': catchshp = a
if o == '-d': dem_in = a
if o == '-l': landuse = a
if o == '-s': soiltype = a
if o == '-C': clean = True
if o == '-g': gaugeshp = a
if o == '-A': alltouching = True
if inifile == None or rivshp == None or catchshp == None or dem_in == None:
print 'the following files are compulsory:'
print ' - ini-file'
print ' - DEM (raster)'
print ' - river (shape)'
print ' - catchment (shape)'
Usage()
sys.exit(1)
if landuse == None:
print 'no raster with landuse classifications is specified. 1 class will be applied for the entire domain'
if soiltype == None:
print 'no raster with soil classifications is specified. 1 class will be applied for the entire domain'
''' read mask '''
if not os.path.exists(clone_map):
print 'Mask not found. Make sure the file mask\mask.map exists'
print 'This file is usually created with the CreateGrid script'
sys.exit(1)
else:
pcr.setclone(clone_map)
ds = gdal.Open(clone_map, GA_ReadOnly)
clone_trans = ds.GetGeoTransform()
cellsize = clone_trans[1]
clone_rows = ds.RasterYSize
clone_columns = ds.RasterXSize
extent_mask = [
clone_trans[0], clone_trans[3] - ds.RasterYSize * cellsize,
clone_trans[0] + ds.RasterXSize * cellsize, clone_trans[3]
]
xmin, ymin, xmax, ymax = map(str, extent_mask)
ds = None
ones = pcr.scalar(pcr.readmap(clone_map))
zeros = ones * 0
empty = pcr.ifthen(ones == 0, pcr.scalar(0))
''' read projection from mask.shp '''
# TODO: check how to deal with projections (add .prj to mask.shp in creategrid)
if not os.path.exists(clone_prj):
print 'please add prj-file to mask.shp'
sys.exit(1)
if os.path.exists(clone_shp):
ds = ogr.Open(clone_shp)
file_att = os.path.splitext(os.path.basename(clone_shp))[0]
lyr = ds.GetLayerByName(file_att)
spatialref = lyr.GetSpatialRef()
if not spatialref == None:
srs_clone = osr.SpatialReference()
srs_clone.ImportFromWkt(spatialref.ExportToWkt())
srs_clone.AutoIdentifyEPSG()
unit_clone = False
unit_clone = srs_clone.GetAttrValue('UNIT').lower()
#clone_EPSG = 'EPSG:'+srs_clone.GetAttrValue("AUTHORITY",1)
# TODO: fix hard EPSG code below
clone_EPSG = 'EPSG:' + '4167'
print 'EPSG-code is read from mask.shp: ' + clone_EPSG
spatialref == None
if not clone_EPSG:
print 'EPSG-code cannot be read from mask.shp'
print 'please add prj-file to mask.shp or specify on command line'
print 'e.g. -p EPSG:4326 (for WGS84 lat lon projection)'
ds = None
clone_EPSG_int = int(clone_EPSG[5:len(clone_EPSG)])
''' open config-file '''
config = wt.OpenConf(inifile)
''' read settings '''
snapgaugestoriver = bool(
int(wt.configget(config, "settings", "snapgaugestoriver", "1")))
burnalltouching = bool(
int(wt.configget(config, "settings", "burncatchalltouching", "1")))
burninorder = bool(
int(wt.configget(config, "settings", "burncatchalltouching", "0")))
verticetollerance = float(
wt.configget(config, "settings", "vertice_tollerance", "0.0001"))
''' read parameters '''
burn_outlets = int(
wt.configget(config, "parameters", "burn_outlets", 10000))
burn_rivers = int(wt.configget(config, "parameters", "burn_rivers", 200))
burn_connections = int(
wt.configget(config, "parameters", "burn_connections", 100))
burn_gauges = int(wt.configget(config, "parameters", "burn_gauges", 100))
minorder = int(wt.configget(config, "parameters", "riverorder_min", 3))
exec "percentile=tr.array(" + wt.configget(config, "parameters",
"statisticmaps", [0, 100]) + ")"
if not unit_clone:
print 'failed to read unit (meter or degree) from mask projection'
unit_clone = str(wt.configget(config, "settings", "unit", 'meter'))
print 'unit read from settings: ' + unit_clone
if unit_clone == 'degree':
cellsize_hr = float(
wt.configget(config, "parameters", "highres_degree", 0.0005))
elif (unit_clone == 'metre') or (unit_clone == 'meter'):
cellsize_hr = float(
wt.configget(config, "parameters", "highres_metre", 50))
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)
''' 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)
''' create directories '''
if os.path.isdir(workdir):
shutil.rmtree(workdir)
os.makedirs(workdir)
if os.path.isdir(resultdir):
shutil.rmtree(resultdir)
os.makedirs(resultdir)
''' Preperation steps '''
zero_map = workdir + "zero.map"
zero_tif = workdir + "zero.tif"
pcr.report(zeros, zero_map)
# TODO: replace gdal_translate call
call(('gdal_translate', '-of', 'GTiff', '-a_srs', clone_EPSG, '-ot',
'Float32', zero_map, zero_tif))
pcr.setglobaloption("lddin")
''' resample DEM '''
dem_resample = workdir + "dem_resampled.tif"
ds = gdal.Open(dem_in, GA_ReadOnly)
band = ds.GetRasterBand(1)
nodata = band.GetNoDataValue()
proj = ds.GetGeoTransform()
cellsize_dem = proj[1]
''' read DEM projection '''
spatialref == None
spatialref = ds.GetProjection()
if not spatialref == None:
srs = osr.SpatialReference()
srs.ImportFromWkt(spatialref)
srs.AutoIdentifyEPSG()
dem_EPSG = 'EPSG:' + srs.GetAttrValue("AUTHORITY", 1)
print 'EPSG-code is read from ' + os.path.basename(
dem_in) + ': ' + dem_EPSG
spatialref == None
dem_EPSG_int = int(dem_EPSG[5:len(dem_EPSG)])
srs_DEM = osr.SpatialReference()
srs_DEM.ImportFromEPSG(dem_EPSG_int)
clone2dem_transform = osr.CoordinateTransformation(srs_clone, srs_DEM)
else:
dem_EPSG = clone_EPSG
print 'No projection defined for ' + os.path.basename(dem_in)
print 'Assumed to be the same as model projection (' + clone_EPSG + ')'
ds = None
print 'Resampling DEM...'
if nodata == None:
call(('gdalwarp', '-overwrite', '-t_srs', clone_prj, '-te', xmin, ymin,
xmax, ymax, '-tr', str(cellsize), str(-cellsize), '-dstnodata',
str(-9999), '-r', 'cubic', dem_in, dem_resample))
else:
call(('gdalwarp', '-overwrite', '-t_srs', clone_prj,
'-te', xmin, ymin, xmax, ymax, '-tr', str(cellsize),
str(-cellsize), '-srcnodata', str(nodata), '-dstnodata',
str(nodata), '-r', 'cubic', dem_in, dem_resample))
''' create dem.map and statistic maps '''
dem_resample_map = resultdir + dem_map
call(('gdal_translate', '-of', 'PCRaster', '-a_srs', clone_EPSG, '-ot',
'Float32', dem_resample, dem_resample_map))
print 'Computing DEM statistics ....'
stats = wt.windowstats(dem_in, clone_rows, clone_columns, clone_trans,
srs_clone, resultdir, percentile)
''' burn DEM '''
ds = ogr.Open(rivshp)
file_att = os.path.splitext(os.path.basename(rivshp))[0]
lyr = ds.GetLayerByName(file_att)
spatialref = lyr.GetSpatialRef()
# if not spatialref == None:
# srs = osr.SpatialReference()
# srs.ImportFromWkt(spatialref.ExportToWkt())
# srs.AutoIdentifyEPSG()
# rivshp_EPSG = 'EPSG:'+srs.GetAttrValue("AUTHORITY",1)
# spatialref == None
# else:
rivshp_EPSG = clone_EPSG
print 'No projection defined for ' + file_att + '.shp'
print 'Assumed to be the same as model projection (' + clone_EPSG + ')'
# strip rivers to nodes
xminc = str(float(xmin) + 0.5 * cellsize)
yminc = str(float(ymin) + 0.5 * cellsize)
xmaxc = str(float(xmax) - 0.5 * cellsize)
ymaxc = str(float(ymax) - 0.5 * cellsize)
if rivshp_EPSG == clone_EPSG:
rivclipshp = workdir + 'rivshape_clip.shp'
call(('ogr2ogr', '-s_srs', clone_EPSG, '-t_srs', clone_EPSG, '-spat',
xmin, ymin, xmax, ymax, '-clipsrc', xminc, yminc, xmaxc, ymaxc,
rivclipshp, rivshp))
else:
rivprojshp = workdir + 'rivshape_proj.shp'
rivclipshp = workdir + 'rivshape_clip.shp'
call(('ogr2ogr', '-s_srs', rivshp_EPSG, '-t_srs', clone_EPSG, '-spat',
xmin, ymin, xmax, ymax, rivprojshp, rivshp))
call(('ogr2ogr', '-s_srs', clone_EPSG, '-t_srs', clone_EPSG, '-spat',
xmin, ymin, xmax, ymax, '-clipsrc', xminc, yminc, xmaxc, ymaxc,
rivclipshp, rivprojshp))
rivshp = rivclipshp
#### BURNING BELOW ####
# TODO: check if extraction can be done within memory and retun a burn layer
shapes = wt.Reach2Nodes(rivclipshp, clone_EPSG_int,
cellsize * verticetollerance, workdir)
outlets = shapes[1]
connections = shapes[2]
outlets_att = os.path.splitext(os.path.basename(outlets))[0]
connections_att = os.path.splitext(os.path.basename(connections))[0]
dem_resample_att = os.path.splitext(os.path.basename(dem_resample))[0]
connections_tif = workdir + connections_att + ".tif"
outlets_tif = workdir + outlets_att + ".tif"
# TODO: make the burning in memory
call(('gdal_translate', '-of', 'GTiff', '-a_srs', clone_EPSG, '-ot',
'Float32', zero_map, connections_tif))
call(('gdal_translate', '-of', 'GTiff', '-a_srs', clone_EPSG, '-ot',
'Float32', zero_map, outlets_tif))
call(('gdal_rasterize', '-burn', '1', '-l', outlets_att, outlets,
outlets_tif))
call(('gdal_rasterize', '-burn', '1', '-l', connections_att, connections,
connections_tif))
# convert rivers to order
rivshp_att = os.path.splitext(os.path.basename(rivshp))[0]
rivers_tif = workdir + rivshp_att + ".tif"
call(('gdal_translate', '-of', 'GTiff', '-a_srs', clone_EPSG, '-ot',
'Float32', zero_map, rivers_tif))
if burninorder: # make river shape with an order attribute
OrderSHPs = wt.ReachOrder(rivshp, clone_EPSG_int,
cellsize * verticetollerance, workdir)
wt.Burn2Tif(OrderSHPs, 'order', rivers_tif)
else:
call(('gdal_rasterize', '-burn', '1', '-l', rivshp_att, rivshp,
rivers_tif))
# convert 2 maps
connections_map = workdir + connections_att + ".map"
rivers_map = workdir + rivshp_att + ".map"
outlets_map = workdir + outlets_att + ".map"
call(('gdal_translate', '-of', 'PCRaster', '-a_srs', clone_EPSG, '-ot',
'Float32', connections_tif, connections_map))
call(('gdal_translate', '-of', 'PCRaster', '-a_srs', clone_EPSG, '-ot',
'Float32', rivers_tif, rivers_map))
call(('gdal_translate', '-of', 'PCRaster', '-a_srs', clone_EPSG, '-ot',
'Float32', outlets_tif, outlets_map))
# burn the layers in DEM
outletsburn = pcr.scalar(
pcr.readmap(outlets_map)) * pcr.scalar(burn_outlets)
connectionsburn = pcr.scalar(
pcr.readmap(connections_map)) * pcr.scalar(burn_connections)
riverburn = pcr.scalar(pcr.readmap(rivers_map)) * pcr.scalar(burn_rivers)
ldddem = pcr.cover(dem_resample_map,
pcr.ifthen(riverburn > 0, pcr.scalar(0)))
ldddem = ldddem - outletsburn - connectionsburn - riverburn
ldddem = pcr.cover(ldddem, pcr.scalar(0))
pcr.report(ldddem, workdir + "dem_burn.map")
''' create ldd for multi-catchments '''
ldd = pcr.ldd(empty)
# reproject catchment shape-file
ds = ogr.Open(catchshp)
file_att = os.path.splitext(os.path.basename(catchshp))[0]
lyr = ds.GetLayerByName(file_att)
spatialref = lyr.GetSpatialRef()
# if not spatialref == None:
# srs = osr.SpatialReference()
# srs.ImportFromWkt(spatialref.ExportToWkt())
# srs.AutoIdentifyEPSG()
# catchshp_EPSG = 'EPSG:'+srs.GetAttrValue("AUTHORITY",1)
# spatialref == None
# else:
catchshp_EPSG = clone_EPSG
print 'No projection defined for ' + file_att + '.shp'
print 'Assumed to be the same as model projection (' + clone_EPSG + ')'
if not rivshp_EPSG == clone_EPSG:
catchprojshp = workdir + 'catchshape_proj.shp'
call(('ogr2ogr', '-s_srs', catchshp_EPSG, '-t_srs', clone_ESPG,
catchprojshp, catchshp))
catchshp = catchprojshp
ds.Destroy()
ds = ogr.Open(catchshp)
file_att = os.path.splitext(os.path.basename(catchshp))[0]
lyr = ds.GetLayerByName(file_att)
fieldDef = ogr.FieldDefn("ID", ogr.OFTString)
fieldDef.SetWidth(12)
TEMP_out = Driver.CreateDataSource(workdir + "temp.shp")
if not srs == None:
TEMP_LYR = TEMP_out.CreateLayer("temp",
srs,
geom_type=ogr.wkbMultiPolygon)
else:
TEMP_LYR = TEMP_out.CreateLayer("temp", geom_type=ogr.wkbMultiPolygon)
TEMP_LYR.CreateField(fieldDef)
for i in range(lyr.GetFeatureCount()):
orgfeature = lyr.GetFeature(i)
geometry = orgfeature.geometry()
feature = ogr.Feature(TEMP_LYR.GetLayerDefn())
feature.SetGeometry(geometry)
feature.SetField("ID", str(i + 1))
TEMP_LYR.CreateFeature(feature)
TEMP_out.Destroy()
ds.Destroy
# rasterize catchment map
catchments_tif = workdir + "catchments.tif"
catchments_map = workdir + "catchments.map"
call(('gdal_translate', '-of', 'GTiff', '-a_srs', clone_EPSG, zero_map,
catchments_tif))
if alltouching:
call(('gdal_rasterize', '-at', '-a', 'ID', '-l', "temp",
workdir + 'temp.shp', catchments_tif))
else:
call(('gdal_rasterize', '-a', 'ID', '-l', "temp", workdir + 'temp.shp',
catchments_tif))
call(('gdal_translate', '-of', 'PCRaster', '-a_srs', clone_EPSG,
catchments_tif, catchments_map))
catchments = pcr.readmap(catchments_map)
riverunique = pcr.clump(pcr.nominal(pcr.ifthen(riverburn > 0, riverburn)))
rivercatch = pcr.areamajority(pcr.ordinal(catchments), riverunique)
#catchments = pcr.cover(pcr.ordinal(rivercatch),pcr.ordinal(pcr.ifthen(catchments > 0, catchments)),pcr.ordinal(0))
catchments = pcr.cover(
pcr.ifthen(catchments > 0, pcr.ordinal(catchments)),
pcr.ifthen(
riverburn > 0,
pcr.ordinal(
pcr.spreadzone(pcr.nominal(catchments),
pcr.ifthen(riverburn > 0, pcr.scalar(1)), 1))))
rivercatch_map = workdir + "catchments_river.map"
catchclip_map = workdir + "catchments_clip.map"
pcr.report(rivercatch, rivercatch_map)
pcr.report(catchments, catchclip_map)
ds = ogr.Open(workdir + "temp.shp")
lyr = ds.GetLayerByName("temp")
print 'calculating ldd'
for i in range(lyr.GetFeatureCount()):
feature = lyr.GetFeature(i)
catch = int(feature.GetField("ID"))
print "calculating ldd for catchment: " + str(i + 1) + "/" + str(
lyr.GetFeatureCount()) + "...."
ldddem_select = pcr.scalar(pcr.ifthen(catchments == catch,
catchments)) * 0 + 1 * ldddem
ldd_select = pcr.lddcreate(ldddem_select, float("1E35"), float("1E35"),
float("1E35"), float("1E35"))
ldd = pcr.cover(ldd, ldd_select)
pcr.report(ldd, resultdir + ldd_map)
ds.Destroy()
''' report stream order, river and dem '''
streamorder = pcr.ordinal(pcr.streamorder(ldd))
river = pcr.ifthen(streamorder >= pcr.ordinal(minorder), pcr.boolean(1))
mindem = int(np.min(pcr.pcr2numpy(pcr.ordinal(dem_resample_map), 9999999)))
dem_resample_map = pcr.cover(dem_resample_map,
pcr.scalar(river) * 0 + mindem)
pcr.report(dem_resample_map, resultdir + dem_map)
pcr.report(streamorder, resultdir + streamorder_map)
pcr.report(river, resultdir + river_map)
''' deal with your catchments '''
if gaugeshp == None:
print 'No gauges defined, using outlets instead'
gauges = pcr.ordinal(
pcr.uniqueid(
pcr.boolean(pcr.ifthen(pcr.scalar(ldd) == 5, pcr.boolean(1)))))
pcr.report(gauges, resultdir + gauges_map)
# ds = ogr.Open(gaugeshp)
# file_att = os.path.splitext(os.path.basename(gaugeshp))[0]
# lyr = ds.GetLayerByName(file_att)
# spatialref = lyr.GetSpatialRef()
## if not spatialref == None:
## srs = osr.SpatialReference()
## srs.ImportFromWkt(spatialref.ExportToWkt())
## srs.AutoIdentifyEPSG()
## gaugeshp_EPSG = 'EPSG:'+srs.GetAttrValue("AUTHORITY",1)
## spatialref == None
# #else:
# gaugeshp_EPSG = clone_EPSG
# print 'No projection defined for ' + file_att + '.shp'
# print 'Assumed to be the same as model projection (' + clone_EPSG + ')'
#
# # reproject gauge shape if necesarry
# if not gaugeshp_EPSG == clone_EPSG:
# gaugeprojshp = workdir + 'gaugeshape_proj.shp'
# call(('ogr2ogr','-s_srs',rivshp_EPSG,'-t_srs',clone_ESPG,gaugeprojshp,gaugeshp))
# gaugeshp = gaugeprojshp
#
# file_att = os.path.splitext(os.path.basename(gaugeshp))[0]
# gaugestif = workdir + file_att + '.tif'
# gaugesmap = workdir + file_att + '.map'
# call(('gdal_translate','-of','GTiff','-a_srs',clone_EPSG,zero_map,gaugestif))
# call(('gdal_rasterize','-burn','1','-l',file_att,gaugeshp,gaugestif))
# call(('gdal_translate','-of','PCRaster','-a_srs',clone_EPSG,gaugestif,gaugesmap))
# gaugelocs = pcr.readmap(gaugesmap)
# snapgaugestoriver = True
#
# if snapgaugestoriver:
# print "Snapping gauges to river"
# gauges = pcr.uniqueid(pcr.boolean(gaugelocs))
# gauges= wt.snaptomap(pcr.ordinal(gauges),river)
#
# gaugesmap = pcr.ifthen(gauges > 0, gauges)
''' report riverlengthfrac '''
riv_hr = workdir + 'river_highres.tif'
wt.CreateTif(riv_hr, rows_hr, cols_hr, hr_trans, srs_clone, 0)
file_att = os.path.splitext(os.path.basename(rivshp))[0]
call(('gdal_rasterize', '-burn', '1', '-l', file_att, rivshp, riv_hr))
print 'Computing river length...'
#riverlength = wt.windowstats(riv_hr,clone_rows,clone_columns,clone_trans,srs_clone,resultdir,'frac',clone2dem_transform)
riverlength = wt.windowstats(riv_hr, clone_rows, clone_columns,
clone_trans, srs_clone, resultdir, 'frac')
''' report outlet map '''
pcr.report(pcr.ifthen(pcr.ordinal(ldd) == 5, pcr.ordinal(1)),
resultdir + outlet_map)
''' report map '''
catchment = pcr.ifthen(catchments > 0, pcr.ordinal(1))
pcr.report(catchment, resultdir + catchment_map)
''' report subcatchment map '''
subcatchment = pcr.subcatchment(ldd, gauges)
pcr.report(pcr.ordinal(subcatchment), resultdir + subcatch_map)
''' report landuse map '''
if landuse == None:
pcr.report(pcr.nominal(ones), resultdir + landuse_map)
else:
landuse_resample = workdir + 'landuse.tif'
landuse_map = resultdir + landuse_map
transform = wt.GetRasterTranform(landuse, srs_clone)
if not transform[0]:
call(('gdalwarp', '-overwrite', '-s_srs', clone_EPSG, '-t_srs',
clone_EPSG, '-te', xmin, ymin, xmax, ymax, '-tr',
str(cellsize), str(-cellsize), '-r', 'mode', landuse,
landuse_resample))
else:
call(('gdalwarp', '-overwrite', '-s_srs', transform[1], '-t_srs',
clone_EPSG, '-te', xmin, ymin, xmax, ymax, '-tr',
str(cellsize), str(-cellsize), '-r', 'mode', landuse,
landuse_resample))
call(('gdal_translate', '-of', 'PCRaster', '-ot', 'Float32',
landuse_resample, landuse_map))
landuse_work = pcr.readmap(landuse_map)
pcr.report(pcr.nominal(landuse_work), landuse_map)
''' report soil map '''
if soiltype == None:
pcr.report(pcr.nominal(ones), resultdir + soil_map)
else:
soiltype_resample = workdir + 'soiltype.tif'
soil_map = resultdir + soil_map
#transform = wt.GetRasterTranform(soiltype,srs_clone)
# if not transform[0]:
call(('gdalwarp', '-overwrite', '-s_srs', clone_EPSG, '-t_srs',
clone_EPSG, '-te', xmin, ymin, xmax, ymax, '-tr', str(cellsize),
str(-cellsize), '-r', 'mode', soiltype, soiltype_resample))
# else:
# call(('gdalwarp','-overwrite','-s_srs',transform[1],'-t_srs',clone_EPSG,'-te', xmin, ymin, xmax, ymax,'-tr',str(cellsize),str(-cellsize),'-r','mode',soiltype, soiltype_resample))
call(('gdal_translate', '-of', 'PCRaster', '-ot', 'Float32',
soiltype_resample, soil_map))
soiltype_work = pcr.readmap(soil_map)
pcr.report(pcr.nominal(soiltype_work), soil_map)
if clean:
wt.DeleteList(glob.glob(os.getcwd() + '\\' + resultdir + '/*.xml'))
def main():
"""
:ivar masterdem: digital elevation model
:ivar dem: digital elevation model
:ivar river: optional river map
"""
# Default values
strRiver = 8
masterdem = "dem.map"
step1dir = "step1"
step2dir = "step2"
workdir = "."
inifile = "wflow_prepare.ini"
recreate = False
snapgaugestoriver = False
try:
opts, args = getopt.getopt(sys.argv[1:], "W:hI:f",['version'])
except getopt.error as msg:
usage(msg)
for o, a in opts:
if o == "-W":
workdir = a
if o == "-I":
inifile = a
if o == "-h":
usage()
if o == "-f":
recreate = True
if o == "--version":
import wflow
print("wflow version: ", wflow.__version__)
sys.exit(0)
pcr.setglobaloption("unitcell")
os.chdir(workdir)
config = OpenConf(workdir + "/" + inifile)
masterdem = configget(config, "files", "masterdem", "dem.map")
pcr.setclone(masterdem)
strRiver = int(configget(config, "settings", "riverorder", "4"))
try:
gauges_x = config.get("settings", "gauges_x")
gauges_y = config.get("settings", "gauges_y")
except:
print("gauges_x and gauges_y are required entries in the ini file")
sys.exit(1)
step1dir = configget(config, "directories", "step1dir", "step1")
step2dir = configget(config, "directories", "step2dir", "step2")
# upscalefactor = float(config.get("settings","upscalefactor"))
corevolume = float(configget(config, "settings", "corevolume", "1E35"))
catchmentprecipitation = float(
configget(config, "settings", "catchmentprecipitation", "1E35")
)
corearea = float(configget(config, "settings", "corearea", "1E35"))
outflowdepth = float(configget(config, "settings", "lddoutflowdepth", "1E35"))
initialscale = int(configget(config, "settings", "initialscale", "1"))
csize = float(configget(config, "settings", "cellsize", "1"))
snapgaugestoriver = bool(
int(configget(config, "settings", "snapgaugestoriver", "1"))
)
lddglobaloption = configget(config, "settings", "lddglobaloption", "lddout")
pcr.setglobaloption(lddglobaloption)
lu_water = configget(config, "files", "lu_water", "")
lu_paved = configget(config, "files", "lu_paved", "")
# X/Y coordinates of the gauges the system
X = np.fromstring(gauges_x, sep=',')
Y = np.fromstring(gauges_y, sep=',')
tr.Verbose = 1
# make the directories to save results in
if not os.path.isdir(step1dir + "/"):
os.makedirs(step1dir)
if not os.path.isdir(step2dir):
os.makedirs(step2dir)
if initialscale > 1:
print("Initial scaling of DEM...")
os.system(
"resample -r "
+ str(initialscale)
+ " "
+ masterdem
+ " "
+ step1dir
+ "/dem_scaled.map"
)
print("Reading dem...")
dem = pcr.readmap(step1dir + "/dem_scaled.map")
ldddem = dem
else:
print ("Reading dem...")
dem = pcr.readmap(masterdem)
ldddem = dem
try:
catchmask = config.get("files", "catchment_mask")
except:
print("No catchment mask...")
else:
print("clipping DEM with mask.....")
mask = pcr.readmap(catchmask)
ldddem = pcr.ifthen(pcr.boolean(mask), ldddem)
dem = pcr.ifthen(pcr.boolean(mask), dem)
# See if there is a shape file of the river to burn in
try:
rivshp = config.get("files", "river")
except:
print("no river file specified")
outletpointX = float(configget(config, "settings", "outflowpointX", "0.0"))
outletpointY = float(configget(config, "settings", "outflowpointY", "0.0"))
else:
print("river file specified.....")
try:
outletpointX = float(configget(config, "settings", "outflowpointX", "0.0"))
outletpointY = float(configget(config, "settings", "outflowpointY", "0.0"))
except:
print(
"Need to specify the river outletpoint (a point at the end of the river within the current map)"
)
exit(1)
outletpointmap = tr.points_to_map(dem, outletpointX, outletpointY, 0.5)
pcr.report(outletpointmap, step1dir + "/outletpoint.map")
# rivshpattr = config.get("files","riverattr")
pcr.report(dem * 0.0, step1dir + "/nilmap.map")
thestr = (
"gdal_translate -of GTiff "
+ step1dir
+ "/nilmap.map "
+ step1dir
+ "/riverburn.tif"
)
os.system(thestr)
rivshpattr = os.path.splitext(os.path.basename(rivshp))[0]
os.system(
"gdal_rasterize -burn 1 -l "
+ rivshpattr
+ " "
+ rivshp
+ " "
+ step1dir
+ "/riverburn.tif"
)
thestr = (
"gdal_translate -of PCRaster "
+ step1dir
+ "/riverburn.tif "
+ step1dir
+ "/riverburn.map"
)
os.system(thestr)
riverburn = pcr.readmap(step1dir + "/riverburn.map")
# Determine regional slope assuming that is the way the river should run
# Determine regional slope assuming that is the way the river should run
# pcr.setglobaloption("unitcell")
# demregional=pcr.windowaverage(dem,100)
ldddem = pcr.ifthenelse(riverburn >= 1.0, dem - 1000, dem)
pcr.setglobaloption("unittrue")
upscalefactor = int(csize / pcr.celllength())
print("Creating ldd...")
ldd = tr.lddcreate_save(
step1dir + "/ldd.map",
ldddem,
recreate,
outflowdepth=outflowdepth,
corevolume=corevolume,
catchmentprecipitation=catchmentprecipitation,
corearea=corearea,
)
print("Determining streamorder...")
stro = pcr.streamorder(ldd)
pcr.report(stro, step1dir + "/streamorder.map")
strdir = pcr.ifthen(stro >= strRiver, stro)
pcr.report(strdir, step1dir + "/streamorderrive.map")
pcr.report(pcr.boolean(pcr.ifthen(stro >= strRiver, stro)), step1dir + "/rivers.map")
pcr.setglobaloption("unittrue")
# outlet (and other gauges if given)
# TODO: check is x/y set if not skip this
print("Outlet...")
outlmap = tr.points_to_map(dem, X, Y, 0.5)
if snapgaugestoriver:
print("Snapping gauges to nearest river cells...")
pcr.report(outlmap, step1dir + "/orggauges.map")
outlmap = tr.snaptomap(outlmap, strdir)
# noutletmap = tr.points_to_map(dem,XX,YY,0.5)
# pcr.report(noutletmap,'noutlet.map')
pcr.report(outlmap, step1dir + "/gauges.map")
# check if there is a pre-define catchment map
try:
catchmask = config.get("files", "catchment_mask")
except:
print("No catchment mask, finding outlet")
# Find catchment (overall)
outlet = tr.find_outlet(ldd)
sub = tr.subcatch(ldd, outlet)
pcr.report(sub, step1dir + "/catchment_overall.map")
else:
print("reading and converting catchment mask.....")
os.system(
"resample -r "
+ str(initialscale)
+ " "
+ catchmask
+ " "
+ step1dir
+ "/catchment_overall.map"
)
sub = pcr.readmap(step1dir + "/catchment_overall.map")
print("Scatch...")
sd = tr.subcatch(ldd, pcr.ifthen(outlmap > 0, outlmap))
pcr.report(sd, step1dir + "/scatch.map")
pcr.setglobaloption("unitcell")
print("Upscalefactor: " + str(upscalefactor))
if upscalefactor > 1:
gc.collect()
print("upscale river length1 (checkerboard map)...")
ck = tr.checkerboard(dem, upscalefactor)
pcr.report(ck, step1dir + "/ck.map")
pcr.report(dem, step1dir + "/demck.map")
print("upscale river length2...")
fact = tr.area_riverlength_factor(ldd, ck, upscalefactor)
pcr.report(fact, step1dir + "/riverlength_fact.map")
# print("make dem statistics...")
dem_ = pcr.areaaverage(dem, ck)
pcr.report(dem_, step1dir + "/demavg.map")
print("Create DEM statistics...")
dem_ = pcr.areaminimum(dem, ck)
pcr.report(dem_, step1dir + "/demmin.map")
dem_ = pcr.areamaximum(dem, ck)
pcr.report(dem_, step1dir + "/demmax.map")
# calculate percentiles
order = pcr.areaorder(dem, ck)
n = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), ck)
#: calculate 25 percentile
perc = tr.area_percentile(dem, ck, n, order, 25.0)
pcr.report(perc, step1dir + "/dem25.map")
perc = tr.area_percentile(dem, ck, n, order, 10.0)
pcr.report(perc, step1dir + "/dem10.map")
perc = tr.area_percentile(dem, ck, n, order, 50.0)
pcr.report(perc, step1dir + "/dem50.map")
perc = tr.area_percentile(dem, ck, n, order, 33.0)
pcr.report(perc, step1dir + "/dem33.map")
perc = tr.area_percentile(dem, ck, n, order, 66.0)
pcr.report(perc, step1dir + "/dem66.map")
perc = tr.area_percentile(dem, ck, n, order, 75.0)
pcr.report(perc, step1dir + "/dem75.map")
perc = tr.area_percentile(dem, ck, n, order, 90.0)
pcr.report(perc, step1dir + "/dem90.map")
else:
print("No fancy scaling done. Going strait to step2....")
pcr.report(dem, step1dir + "/demavg.map")
Xul = float(config.get("settings", "Xul"))
Yul = float(config.get("settings", "Yul"))
Xlr = float(config.get("settings", "Xlr"))
Ylr = float(config.get("settings", "Ylr"))
gdalstr = (
"gdal_translate -projwin "
+ str(Xul)
+ " "
+ str(Yul)
+ " "
+ str(Xlr)
+ " "
+ str(Ylr)
+ " -of PCRaster "
)
# gdalstr = "gdal_translate -a_ullr " + str(Xul) + " " + str(Yul) + " " +str(Xlr) + " " +str(Ylr) + " -of PCRaster "
print(gdalstr)
pcr.report(pcr.cover(1.0), step1dir + "/wflow_riverlength_fact.map")
# Now us gdat tp convert the maps
os.system(
gdalstr
+ step1dir
+ "/wflow_riverlength_fact.map"
+ " "
+ step2dir
+ "/wflow_riverlength_fact.map"
)
os.system(
gdalstr + step1dir + "/demavg.map" + " " + step2dir + "/wflow_dem.map"
)
os.system(
gdalstr + step1dir + "/demavg.map" + " " + step2dir + "/wflow_demmin.map"
)
os.system(
gdalstr + step1dir + "/demavg.map" + " " + step2dir + "/wflow_demmax.map"
)
os.system(
gdalstr + step1dir + "/gauges.map" + " " + step2dir + "/wflow_gauges.map"
)
os.system(
gdalstr + step1dir + "/rivers.map" + " " + step2dir + "/wflow_river.map"
)
os.system(
gdalstr
+ step1dir
+ "/streamorder.map"
+ " "
+ step2dir
+ "/wflow_streamorder.map"
)
os.system(
gdalstr + step1dir + "/gauges.map" + " " + step2dir + "/wflow_outlet.map"
)
os.system(
gdalstr + step1dir + "/scatch.map" + " " + step2dir + "/wflow_catchment.map"
)
os.system(gdalstr + step1dir + "/ldd.map" + " " + step2dir + "/wflow_ldd.map")
os.system(
gdalstr + step1dir + "/scatch.map" + " " + step2dir + "/wflow_subcatch.map"
)
if lu_water:
os.system(gdalstr + lu_water + " " + step2dir + "/WaterFrac.map")
if lu_paved:
os.system(gdalstr + lu_paved + " " + step2dir + "/PathFrac.map")
try:
lumap = config.get("files", "landuse")
except:
print("no landuse map...creating uniform map")
# clone=pcr.readmap(step2dir + "/wflow_dem.map")
pcr.setclone(step2dir + "/wflow_dem.map")
pcr.report(pcr.nominal(1), step2dir + "/wflow_landuse.map")
else:
os.system(
"resample --clone "
+ step2dir
+ "/wflow_dem.map "
+ lumap
+ " "
+ step2dir
+ "/wflow_landuse.map"
)
try:
soilmap = config.get("files", "soil")
except:
print("no soil map..., creating uniform map")
pcr.setclone(step2dir + "/wflow_dem.map")
pcr.report(pcr.nominal(1), step2dir + "/wflow_soil.map")
else:
os.system(
"resample --clone "
+ step2dir
+ "/wflow_dem.map "
+ soilmap
+ " "
+ step2dir
+ "/wflow_soil.map"
)
def pcr_inun(dem, ids, h_bounds, ids_coastline,
resistance=0., water_perc=None, zero_resistance_waterp=1.0,
cellres=1, dist_method='eucledian', ldd=None):
""" planar inundation routine per segment
:param dem: pcr dem
:param ids: local ids of boundary conditions, starting a 1 (not zero!)
:param h_bounds: water level boundary at diva segment
:param ids_coastline: pcraster map with coastal segments ids
:param resistance: constant or pcrmap unit km-1; (default 0: no resistance is calculated)
:param cellres: cell resolution in km, varies with latitude degrees
:param ldd: pcraster map with local drainage direction to calculate resistance along ldd;
if None (default) resistance is calculated using 'plain' nearest neighbour
:return: pcrmap with flood depth
"""
pcr.setglobaloption("unitcell")
if resistance > 0:
coastline = pcr.cover(pcr.ifthenelse(pcr.scalar(ids_coastline) > 0, pcr.boolean(1), 0), pcr.boolean(0))
mask = pcr.ifthen(dem > -9999, pcr.scalar(1))
if dist_method == 'ldd':
# Distance to coast along ldd
dist2coast0 = pcr.ldddist(ldd, coastline, cellres)
# find edge of area with distances -> water divide
dist2coast_mask = pcr.cover(pcr.ifthenelse(dist2coast0 > 0, pcr.boolean(0), pcr.boolean(1)),
pcr.boolean(1))
start = pcr.ifthenelse(
((pcr.window4total(pcr.scalar(dist2coast_mask)) > 0) & (dist2coast_mask == pcr.boolean(0))) |
coastline,
pcr.boolean(1), pcr.boolean(0))
# continue distance beyond water divide with eucledian dist
dist2coast1 = pcr.spread(start, dist2coast0, cellres*mask)
dist2coast = pcr.ifthenelse(dist2coast_mask, dist2coast1, dist2coast0)
elif dist_method == 'eucledian':
# dist to coast using nearest neighbor
if water_perc is None:
dist2coast = pcr.spread(coastline, 0, cellres*mask)
else:
# zero resistance for cells with water_perc >= zero_resistance_waterp
zrw = float(zero_resistance_waterp)
water_perc = pcr.ifthenelse(water_perc >= zrw,
pcr.scalar(1),
water_perc / zrw)
dist2coast = pcr.spread(coastline, 0, cellres*mask*(1 - water_perc))
dem_adjust = dem + pcr.cover(dist2coast, 0) * pcr.scalar(resistance) # raise the elevation using a damping factor
else:
dem_adjust = dem
dist2coast = pcr.scalar(1)
fld_depth = pcr.ifthen(dem > -9999, pcr.scalar(0))
for i, h in zip(ids, h_bounds):
coast_segment = pcr.ifthenelse(ids_coastline == i, pcr.boolean(1), pcr.boolean(0))
# find area below flood_level
fld_prone = pcr.ifthenelse(dem_adjust <= pcr.scalar(float(h)), pcr.boolean(1), pcr.boolean(0))
# make contiguous groups of cells which are below flood level
fld_clump = pcr.clump(fld_prone)
# find flooded area connected to diva segment
fld_coast = pcr.ifthenelse(pcr.areamaximum(pcr.scalar(fld_clump) * pcr.scalar(coast_segment), fld_clump) > 0,
pcr.boolean(1), pcr.boolean(0))
# get max fld depth map
fld_depth = pcr.max(fld_depth, pcr.ifthenelse(fld_coast, pcr.scalar(pcr.scalar(float(h)) - dem_adjust), 0))
return fld_depth, dist2coast, dem_adjust
def __init__(self):
# Print model info
print('The Spatial Processes in HYdrology (SPHY) model is')
print(
'developed and owned by FutureWater, Wageningen, The Netherlands')
print('Version 3.0, released June 2019')
print(' ')
#-Missing value definition
self.MV = -9999
# Read the modules to be used
self.GlacFLAG = config.getint('MODULES', 'GlacFLAG')
self.SnowFLAG = config.getint('MODULES', 'SnowFLAG')
self.RoutFLAG = config.getint('MODULES', 'RoutFLAG')
self.ResFLAG = config.getint('MODULES', 'ResFLAG')
self.LakeFLAG = config.getint('MODULES', 'LakeFLAG')
self.DynVegFLAG = config.getint('MODULES', 'DynVegFLAG')
self.GroundFLAG = config.getint('MODULES', 'GroundFLAG')
self.SedFLAG = config.getint('MODULES', 'SedFLAG')
self.SedTransFLAG = config.getint('MODULES', 'SedTransFLAG')
# import the required modules
import datetime, calendar, ET, rootzone, subzone
import utilities.reporting as reporting
import utilities.timecalc as timecalc
import utilities.netcdf2PCraster as netcdf2PCraster
from math import pi
#-standard python modules
self.datetime = datetime
self.calendar = calendar
self.pi = pi
#-FW defined modules
self.reporting = reporting
self.timecalc = timecalc
self.netcdf2PCraster = netcdf2PCraster
self.ET = ET
self.rootzone = rootzone
self.subzone = subzone
del datetime, calendar, pi, reporting, timecalc, ET, rootzone, subzone
#-import additional modules if required
if self.GlacFLAG == 1:
self.SnowFLAG = 1
self.GroundFLAG = 1
#-read the input and output directories from the configuration file
self.inpath = config.get('DIRS', 'inputdir')
self.outpath = config.get('DIRS', 'outputdir')
#-set the timing criteria
sy = config.getint('TIMING', 'startyear')
sm = config.getint('TIMING', 'startmonth')
sd = config.getint('TIMING', 'startday')
ey = config.getint('TIMING', 'endyear')
em = config.getint('TIMING', 'endmonth')
ed = config.getint('TIMING', 'endday')
self.startdate = self.datetime.datetime(sy, sm, sd)
self.enddate = self.datetime.datetime(ey, em, ed)
self.dateAfterUpdate = self.startdate - self.datetime.timedelta(
days=1
) #-only required for glacier retreat (create dummy value here to introduce the variable)
#-set date input for reporting
self.startYear = sy
self.endYear = ey
self.spinUpYears = config.getint('TIMING', 'spinupyears')
self.simYears = self.endYear - self.startYear - self.spinUpYears + 1
#-set the 2000 julian date number
self.julian_date_2000 = 2451545
#-read name of reporting table
self.RepTab = config.get('REPORTING', 'RepTab')
#-set the option to calculate the fluxes in mm for the upstream area
self.mm_rep_FLAG = config.getint('REPORTING', 'mm_rep_FLAG')
#-set the option to calculate the fluxes per component in mm for the upstream area
pars = [
'Prec', 'ETa', 'GMelt', 'QSNOW', 'QROOTR', 'QROOTD', 'QRAIN',
'QGLAC', 'QBASE', 'QTOT', 'Seep'
]
for i in pars:
var = i + '_mm_FLAG'
setattr(self, var, config.getint('REPORTING', var))
#-set the option to calculate the timeseries of the water balance
self.wbal_TSS_FLAG = config.getint('REPORTING', 'wbal_TSS_FLAG')
#-setting clone map
self.clonefile = self.inpath + config.get('GENERAL', 'mask')
pcr.setclone(self.clonefile)
self.clone = pcr.ifthen(pcr.readmap(self.clonefile), pcr.boolean(1))
self.cellArea = pcr.cellvalue(pcr.cellarea(), 1)[0]
#-read general maps
self.DEM = pcr.readmap(self.inpath + config.get('GENERAL', 'dem'))
self.Slope = pcr.readmap(self.inpath + config.get('GENERAL', 'Slope'))
self.Locations = pcr.readmap(self.inpath +
config.get('GENERAL', 'locations'))
#-read soil calibration fractions
self.RootFieldFrac = config.getfloat('SOIL_CAL', 'RootFieldFrac')
self.RootSatFrac = config.getfloat('SOIL_CAL', 'RootSatFrac')
self.RootDryFrac = config.getfloat('SOIL_CAL', 'RootDryFrac')
self.RootWiltFrac = config.getfloat('SOIL_CAL', 'RootWiltFrac')
self.RootKsatFrac = config.getfloat('SOIL_CAL', 'RootKsatFrac')
#-read soil maps
#-check for PedotransferFLAG
self.PedotransferFLAG = config.getint('PEDOTRANSFER',
'PedotransferFLAG')
#-if pedotransfer functions are used read the sand, clay, organic matter and bulk density maps, otherwise read the soil hydraulic properties
if self.PedotransferFLAG == 1:
import utilities.pedotransfer
self.pedotransfer = utilities.pedotransfer
del utilities.pedotransfer
#-read init processes pedotransfer
self.pedotransfer.init(self, pcr, config, np)
else:
#self.Soil = pcr.readmap(self.inpath + config.get('SOIL','Soil'))
self.RootFieldMap = pcr.readmap(self.inpath + config.get(
'SOIL', 'RootFieldMap')) * self.RootFieldFrac
self.RootSatMap = pcr.readmap(self.inpath + config.get(
'SOIL', 'RootSatMap')) * self.RootSatFrac
self.RootDryMap = pcr.readmap(self.inpath + config.get(
'SOIL', 'RootDryMap')) * self.RootDryFrac
self.RootWiltMap = pcr.readmap(self.inpath + config.get(
'SOIL', 'RootWiltMap')) * self.RootWiltFrac
self.RootKsat = pcr.readmap(self.inpath + config.get(
'SOIL', 'RootKsat')) * self.RootKsatFrac
self.SubSatMap = pcr.readmap(self.inpath +
config.get('SOIL', 'SubSatMap'))
self.SubFieldMap = pcr.readmap(self.inpath +
config.get('SOIL', 'SubFieldMap'))
self.SubKsat = pcr.readmap(self.inpath +
config.get('SOIL', 'SubKsat'))
self.RootDrainVel = self.RootKsat * self.Slope
#-Read and set the soil parameters
pars = ['CapRiseMax', 'RootDepthFlat', 'SubDepthFlat']
for i in pars:
try:
setattr(self, i,
pcr.readmap(self.inpath + config.get('SOILPARS', i)))
except:
setattr(self, i, config.getfloat('SOILPARS', i))
# groundwater storage as third storage layer. This is used instead of a fixed bottomflux
if self.GroundFLAG == 1:
import modules.groundwater
self.groundwater = modules.groundwater
del modules.groundwater
#-read init processes groundwater
self.groundwater.init(self, pcr, config)
else:
# if groundwater module is not used, read seepage and gwl_base
self.SeepStatFLAG = config.getint('SOILPARS', 'SeepStatic')
if self.SeepStatFLAG == 0: # set the seepage map series
self.Seepmaps = self.inpath + config.get('SOILPARS', 'SeePage')
else: #-set a static map or value for seepage
try:
self.SeePage = pcr.readmap(
self.inpath + config.get('SOILPARS', 'SeePage'))
except:
self.SeePage = config.getfloat('SOILPARS', 'SeePage')
try:
self.GWL_base = pcr.readmap(self.inpath +
config.get('SOILPARS', 'GWL_base'))
except:
self.GWL_base = config.getfloat('SOILPARS', 'GWL_base')
self.SubDrainVel = self.SubKsat * self.Slope
#-calculate soil properties
self.RootField = self.RootFieldMap * self.RootDepthFlat
self.RootSat = self.RootSatMap * self.RootDepthFlat
self.RootDry = self.RootDryMap * self.RootDepthFlat
self.RootWilt = self.RootWiltMap * self.RootDepthFlat
self.SubSat = self.SubSatMap * self.SubDepthFlat
self.SubField = self.SubFieldMap * self.SubDepthFlat
self.RootTT = pcr.max((self.RootSat - self.RootField) / self.RootKsat,
0.0001)
self.SubTT = pcr.max((self.SubSat - self.SubField) / self.SubKsat,
0.0001)
# soil max and soil min for scaling of gwl if groundwater module is not used
if self.GroundFLAG == 0:
self.SoilMax = self.RootSat + self.SubSat
self.SoilMin = self.RootDry + self.SubField
#-read land use map
self.LandUse = pcr.readmap(self.inpath +
config.get('LANDUSE', 'LandUse'))
#-Use the dynamic vegetation module
if self.DynVegFLAG == 1:
#-import dynamic vegetation module
import modules.dynamic_veg
self.dynamic_veg = modules.dynamic_veg
del modules.dynamic_veg
#-read init processes dynamic vegetation
self.dynamic_veg.init(self, pcr, config)
#-read the crop coefficient table if the dynamic vegetation module is not used
else:
self.KcStatFLAG = config.getint('LANDUSE', 'KCstatic')
if self.KcStatFLAG == 1:
#-read land use map and kc table
self.kc_table = self.inpath + config.get('LANDUSE', 'CropFac')
self.Kc = pcr.lookupscalar(self.kc_table, self.LandUse)
else:
#-set the kc map series
self.Kcmaps = self.inpath + config.get('LANDUSE', 'KC')
#-read the p factor table if the plant water stress module is used
self.PlantWaterStressFLAG = config.getint('PWS', 'PWS_FLAG')
if self.PlantWaterStressFLAG == 1:
PFactor = self.inpath + config.get('PWS', 'PFactor')
self.PMap = pcr.lookupscalar(PFactor, self.LandUse)
#-read and set glacier maps and parameters if glacier module is used
if self.GlacFLAG:
#-import glacier module
import modules.glacier
self.glacier = modules.glacier
del modules.glacier
#-read init processes glacier module
self.glacier.init(self, pcr, config, pd, np, os)
#-read and set snow maps and parameters if snow modules are used
if self.SnowFLAG == 1:
#-import snow module
import modules.snow
self.snow = modules.snow
del modules.snow
#-read init processes glacier module
self.snow.init(self, pcr, config)
#-read and set climate forcing and the calculation of etref
#-read precipitation data
#-read flag for precipitation forcing by netcdf
self.precNetcdfFLAG = config.getint('CLIMATE', 'precNetcdfFLAG')
if self.precNetcdfFLAG == 1:
#-read configuration for forcing by netcdf
self.netcdf2PCraster.getConfigNetcdf(self, config, 'Prec',
'CLIMATE')
#-determine x,y-coordinates of netcdf file and model domain and indices of netcdf corresponding to model domain
self.netcdf2PCraster.netcdf2pcrInit(self, pcr, 'Prec')
else:
#-read precipitation forcing folder
self.Prec = self.inpath + config.get('CLIMATE', 'Prec')
#-read precipitation data
#-read flag for temperature forcing by netcdf
self.tempNetcdfFLAG = config.getint('CLIMATE', 'tempNetcdfFLAG')
if self.tempNetcdfFLAG == 1:
#-read configuration for forcing by netcdf
self.netcdf2PCraster.getConfigNetcdf(self, config, 'Temp',
'CLIMATE')
#-determine x,y-coordinates of netcdf file and model domain and indices of netcdf corresponding to model domain
self.netcdf2PCraster.netcdf2pcrInit(self, pcr, 'Temp')
else:
#-read temperature forcing folder
self.Tair = self.inpath + config.get('CLIMATE', 'Tair')
#-read flag for etref time series input
self.ETREF_FLAG = config.getint('ETREF', 'ETREF_FLAG')
#-determine the use of a given etref time-series or calculate etref using Hargreaves
if self.ETREF_FLAG == 1:
self.ETref = self.inpath + config.get('ETREF', 'ETref')
else:
self.Lat = pcr.readmap(self.inpath + config.get('ETREF', 'Lat'))
#-read flag for minimum temperature forcing by netcdf
self.TminNetcdfFLAG = config.getint('ETREF', 'TminNetcdfFLAG')
if self.TminNetcdfFLAG == 1:
#-read configuration for forcing by netcdf
self.netcdf2PCraster.getConfigNetcdf(self, config, 'Tmin',
'ETREF')
#-determine x,y-coordinates of netcdf file and model domain and indices of netcdf corresponding to model domain
self.netcdf2PCraster.netcdf2pcrInit(self, pcr, 'Tmin')
else:
self.Tmin = self.inpath + config.get('ETREF', 'Tmin')
#-read flag for maximum temperature forcing by netcdf
self.TmaxNetcdfFLAG = config.getint('ETREF', 'TmaxNetcdfFLAG')
if self.TmaxNetcdfFLAG == 1:
#-read configuration for forcing by netcdf
self.netcdf2PCraster.getConfigNetcdf(self, config, 'Tmax',
'ETREF')
#-determine x,y-coordinates of netcdf file and model domain and indices of netcdf corresponding to model domain
self.netcdf2PCraster.netcdf2pcrInit(self, pcr, 'Tmax')
else:
self.Tmax = self.inpath + config.get('ETREF', 'Tmax')
self.Gsc = config.getfloat('ETREF', 'Gsc')
import hargreaves
self.Hargreaves = hargreaves
del hargreaves
#-read and set routing maps and parameters
if self.RoutFLAG == 1:
import modules.routing
self.routing = modules.routing
del modules.routing
#-read init processes routing
self.routing.init(self, pcr, config)
#-read and set routing maps and parameters
if self.ResFLAG == 1 or self.LakeFLAG == 1:
#-import advanced routing module
import modules.advanced_routing
self.advanced_routing = modules.advanced_routing
del modules.advanced_routing
#-read init processes advanced routing
self.advanced_routing.init(self, pcr, config)
#-read lake maps and parameters if lake module is used
if self.LakeFLAG == 1:
#-import lakes module
import modules.lakes
self.lakes = modules.lakes
del modules.lakes
#-read init processes lakes
self.lakes.init(self, pcr, config)
#-read reservior maps and parameters if reservoir module is used
if self.ResFLAG == 1:
#-import reservoirs module
import modules.reservoirs
self.reservoirs = modules.reservoirs
del modules.reservoirs
#-read init processes reservoirs
self.reservoirs.init(self, pcr, config)
#-read flag for calculation of ET in reservoirs
self.ETOpenWaterFLAG = config.getint('OPENWATER', 'ETOpenWaterFLAG')
if self.ETOpenWaterFLAG == 1:
#-read kc value for open water
self.kcOpenWater = config.getfloat('OPENWATER', 'kcOpenWater')
#-read openwater fraction map
self.openWaterFrac = pcr.readmap(
self.inpath + config.get('OPENWATER', 'openWaterFrac'))
#-determine openwater map with values of each reservoir/lake in the extent of the openwater
self.openWater = pcr.ifthenelse(self.openWaterFrac > 0,
pcr.scalar(1), pcr.scalar(0))
self.openWaterNominal = pcr.clump(pcr.nominal(self.openWater))
self.openWaterNominal = pcr.nominal(
pcr.areamaximum(pcr.scalar(self.ResID), self.openWaterNominal))
else:
#-set all cells to 0 for openwater fraction map
self.openWaterFrac = self.DEM * 0
self.openWater = 0
self.ETOpenWater = 0
#-read maps and parameters for infiltration excess
self.InfilFLAG = config.getfloat('INFILTRATION', 'Infil_excess')
if self.InfilFLAG == 1:
self.K_eff = config.getfloat('INFILTRATION', 'K_eff')
try:
self.Alpha = config.getfloat('INFILTRATION', 'Alpha')
except:
self.Alpha = pcr.readmap(self.inpath +
config.get('INFILTRATION', 'Alpha'))
try:
self.Labda_Infil = config.getfloat('INFILTRATION',
'Labda_infil')
except:
self.Labda_Infil = pcr.readmap(
self.inpath + config.get('INFILTRATION', 'Labda_infil'))
try:
self.paved_table = self.inpath + config.get(
'INFILTRATION', 'PavedFrac')
self.pavedFrac = pcr.lookupscalar(self.paved_table,
self.LandUse)
except:
self.pavedFrac = 0
#-read maps and parameters for soil erosion
if self.SedFLAG == 1:
#-read soil erosion model selector (1 for MUSLE, 2 for MMF)
self.SedModel = config.getfloat('SEDIMENT', 'SedModel')
#-read rock fraction map
self.RockFrac = pcr.readmap(self.inpath +
config.get('SEDIMENT', 'RockFrac'))
#-read MUSLE input parameters
if self.SedModel == 1:
#-import musle module
import modules.musle
self.musle = modules.musle
del modules.musle
#-read init processes musle
self.musle.init(self, pcr, config)
#-read MMF input parameters
if self.SedModel == 2:
#-import mmf module
import modules.mmf
self.mmf = modules.mmf
del modules.mmf
#-read init processes mmf
self.mmf.init(self, pcr, config)
#-read input parameters for sediment transport
if self.SedTransFLAG == 1:
#-import sediment transport module
import modules.sediment_transport
self.sediment_transport = modules.sediment_transport
del modules.sediment_transport
#-read init processes sediment transport
self.sediment_transport.init(self, pcr, config, csv, np)
#-set the global option for radians
pcr.setglobaloption('radians')
def getPCRcoords(PCRmap, missing_value_pcr=-999):
"""
Get all vertices coordinates of a PCRaster map.
Input:
-----
pcraster map (preferrably landmask)
value for MV (optional, default at -999)
Output:
------
list of (x,y) coordinates of each polygon
"""
# Get coordinates as numpy array:
# upper left coordinates
pcr.setglobaloption("coorul")
xcoord_pcr_ul_map = pcr.xcoordinate(PCRmap)
xcoord_pcr_ul_np = pcr.pcr2numpy(xcoord_pcr_ul_map, missing_value_pcr)
ycoord_pcr_ul_map = pcr.ycoordinate(PCRmap)
ycoord_pcr_ul_np = pcr.pcr2numpy(ycoord_pcr_ul_map, missing_value_pcr)
# lower right coordinates
pcr.setglobaloption("coorlr")
xcoord_pcr_lr_map = pcr.xcoordinate(PCRmap)
xcoord_pcr_lr_np = pcr.pcr2numpy(xcoord_pcr_lr_map, missing_value_pcr)
ycoord_pcr_lr_map = pcr.ycoordinate(PCRmap)
ycoord_pcr_lr_np = pcr.pcr2numpy(ycoord_pcr_lr_map, missing_value_pcr)
# centroid coordinates
pcr.setglobaloption("coorcentre")
xcoord_pcr_centr_map = pcr.xcoordinate(PCRmap)
xcoord_pcr_centr_np = pcr.pcr2numpy(xcoord_pcr_centr_map,
missing_value_pcr)
ycoord_pcr_centr_map = pcr.ycoordinate(PCRmap)
ycoord_pcr_centr_np = pcr.pcr2numpy(ycoord_pcr_centr_map,
missing_value_pcr)
# Construct collection of polygon vertices:
# number of arrays/elements to loop over and/or construct new arrays
array_count_pcr = len(ycoord_pcr_lr_np)
elements_per_array_pcr = np.size(ycoord_pcr_lr_np) / array_count_pcr
nonmiss_val_per_array_pcr = np.sum(ycoord_pcr_lr_np != missing_value_pcr)
# filling empty arrays while looping over data
i, j = np.where(xcoord_pcr_lr_np != missing_value_pcr)
xcoord_pcr_lr_np_nonmiss = xcoord_pcr_lr_np[i, j]
xcoord_pcr_ul_np_nonmiss = xcoord_pcr_ul_np[i, j]
xcoord_pcr_ll_np_nonmiss = xcoord_pcr_ul_np[i, j]
xcoord_pcr_ur_np_nonmiss = xcoord_pcr_lr_np[i, j]
ycoord_pcr_lr_np_nonmiss = ycoord_pcr_lr_np[i, j]
ycoord_pcr_ul_np_nonmiss = ycoord_pcr_ul_np[i, j]
ycoord_pcr_ll_np_nonmiss = ycoord_pcr_lr_np[i, j]
ycoord_pcr_ur_np_nonmiss = ycoord_pcr_ul_np[i, j]
xcoord_pcr_centr_np_nonmiss = xcoord_pcr_centr_np[i, j]
ycoord_pcr_centr_np_nonmiss = ycoord_pcr_centr_np[i, j]
# empty collection for polygons
ll = zip(xcoord_pcr_ll_np_nonmiss, ycoord_pcr_ll_np_nonmiss)
lr = zip(xcoord_pcr_lr_np_nonmiss, ycoord_pcr_lr_np_nonmiss)
ur = zip(xcoord_pcr_ur_np_nonmiss, ycoord_pcr_ur_np_nonmiss)
ul = zip(xcoord_pcr_ul_np_nonmiss, ycoord_pcr_ul_np_nonmiss)
# wrap all cell coordinates into a list of lists (one list per cell, with multiple tuples per cell corner)
all_cell_coords_pcr = [[ll[i], lr[i], ur[i], ul[i]]
for i in range(len(ll))]
return all_cell_coords_pcr
import os, sys
import numpy as np
import pcraster as pcr
from math import pi as m_pi
from types import NoneType
pcr.setglobaloption('radians')
deg2Rad= m_pi/180.
def getArcDistance(latA, lonA, latB, lonB, radius= 6371221.3, testVerbose= False):
'''Computes the distance between two points, positioned by \
their geographic coordinates along the surface of a perfect sphere \
in units given by the radius used. Input variables include:\n
- latA, latB: the latitude of the points considered in decimal degrees,\n
- lonA, lonB: the longitude of the points considered in decimal degrees,\n
- radius: the radius of the sphere in metres, set by default to \
that of Earth (6371221.3 m).'''
#-make arrays if needed
if isinstance(latA,float):
latA= np.array(latA)
if isinstance(lonA,float):
lonA= np.array(lonA)
if isinstance(latB,float):
latB= np.array(latB)
if isinstance(lonB,float):
lonB= np.array(lonB)
#-pad latitudes, longitudes
if latA.size <> latB.size:
latA= np.ones(latB.shape)*latA
if lonA.size <> lonB.size:
lonA= np.ones(lonB.shape)*lonA
def main():
"""
:ivar masterdem: digital elevation model
:ivar dem: digital elevation model
:ivar river: optional river map
"""
# Default values
strRiver = 8
masterdem = "dem.map"
step1dir = "step1"
step2dir = "step2"
workdir = "."
inifile = "wflow_prepare.ini"
recreate = False
snapgaugestoriver = False
try:
opts, args = getopt.getopt(sys.argv[1:], "W:hI:f")
except getopt.error as msg:
usage(msg)
for o, a in opts:
if o == "-W":
workdir = a
if o == "-I":
inifile = a
if o == "-h":
usage()
if o == "-f":
recreate = True
pcr.setglobaloption("unitcell")
os.chdir(workdir)
config = OpenConf(workdir + "/" + inifile)
masterdem = configget(config, "files", "masterdem", "dem.map")
pcr.setclone(masterdem)
strRiver = int(configget(config, "settings", "riverorder", "4"))
try:
gauges_x = config.get("settings", "gauges_x")
gauges_y = config.get("settings", "gauges_y")
except:
print("gauges_x and gauges_y are required entries in the ini file")
sys.exit(1)
step1dir = configget(config, "directories", "step1dir", "step1")
step2dir = configget(config, "directories", "step2dir", "step2")
# upscalefactor = float(config.get("settings","upscalefactor"))
corevolume = float(configget(config, "settings", "corevolume", "1E35"))
catchmentprecipitation = float(
configget(config, "settings", "catchmentprecipitation", "1E35"))
corearea = float(configget(config, "settings", "corearea", "1E35"))
outflowdepth = float(
configget(config, "settings", "lddoutflowdepth", "1E35"))
initialscale = int(configget(config, "settings", "initialscale", "1"))
csize = float(configget(config, "settings", "cellsize", "1"))
snapgaugestoriver = bool(
int(configget(config, "settings", "snapgaugestoriver", "1")))
lddglobaloption = configget(config, "settings", "lddglobaloption",
"lddout")
pcr.setglobaloption(lddglobaloption)
lu_water = configget(config, "files", "lu_water", "")
lu_paved = configget(config, "files", "lu_paved", "")
# X/Y coordinates of the gauges the system
exec("X=tr.array(" + gauges_x + ")")
exec("Y=tr.array(" + gauges_y + ")")
tr.Verbose = 1
# make the directories to save results in
mkoutputdirs(step1dir, step2dir)
ldddem = readdem(initialscale, masterdem, step1dir)
dem = ldddem
try:
catchmask = config.get("files", "catchment_mask")
except:
print("No catchment mask...")
else:
print("clipping DEM with mask.....")
mask = pcr.readmap(catchmask)
ldddem = pcr.ifthen(pcr.boolean(mask), ldddem)
dem = pcr.ifthen(pcr.boolean(mask), dem)
# See if there is a shape file of the river to burn in
try:
rivshp = config.get("files", "river")
except:
print("no river file specified")
outletpointX = float(
configget(config, "settings", "outflowpointX", "0.0"))
outletpointY = float(
configget(config, "settings", "outflowpointY", "0.0"))
else:
print("river file specified.....")
try:
outletpointX = float(
configget(config, "settings", "outflowpointX", "0.0"))
outletpointY = float(
configget(config, "settings", "outflowpointY", "0.0"))
except:
print(
"Need to specify the river outletpoint (a point at the end of the river within the current map)"
)
exit(1)
outletpointmap = tr.points_to_map(dem, outletpointX, outletpointY, 0.5)
pcr.report(outletpointmap, step1dir + "/outletpoint.map")
rivshpattr = config.get("files", "riverattr")
pcr.report(dem * 0.0, step1dir + "/nilmap.map")
thestr = ("gdal_translate -of GTiff " + step1dir + "/nilmap.map " +
step1dir + "/riverburn.tif")
os.system(thestr)
os.system("gdal_rasterize -burn 1 -l " + rivshpattr + " " + rivshp +
" " + step1dir + "/riverburn.tif")
thestr = ("gdal_translate -of PCRaster " + step1dir +
"/riverburn.tif " + step1dir + "/riverburn.map")
os.system(thestr)
riverburn = pcr.readmap(step1dir + "/riverburn.map")
# Determine regional slope assuming that is the way the river should run
pcr.setglobaloption("unitcell")
demregional = pcr.windowaverage(dem, 100)
ldddem = pcr.ifthenelse(riverburn >= 1.0, demregional - 1000, dem)
pcr.setglobaloption("unittrue")
upscalefactor = int(csize / pcr.celllength())
print("Creating ldd...")
ldd = tr.lddcreate_save(
step1dir + "/ldd.map",
ldddem,
recreate,
outflowdepth=outflowdepth,
corevolume=corevolume,
catchmentprecipitation=catchmentprecipitation,
corearea=corearea,
)
print("Determining streamorder...")
stro = pcr.streamorder(ldd)
pcr.report(stro, step1dir + "/streamorder.map")
strdir = pcr.ifthen(stro >= strRiver, stro)
pcr.report(strdir, step1dir + "/streamorderrive.map")
pcr.report(pcr.boolean(pcr.ifthen(stro >= strRiver, stro)),
step1dir + "/rivers.map")
pcr.setglobaloption("unittrue")
# outlet (and other gauges if given)
# TODO: check is x/y set if not skip this
print("Outlet...")
outlmap = tr.points_to_map(dem, X, Y, 0.5)
if snapgaugestoriver:
print("Snapping gauges to nearest river cells...")
pcr.report(outlmap, step1dir + "/orggauges.map")
outlmap = tr.snaptomap(outlmap, strdir)
# noutletmap = tr.points_to_map(dem,XX,YY,0.5)
# pcr.report(noutletmap,'noutlet.map')
pcr.report(outlmap, step1dir + "/gauges.map")
# check if there is a pre-define catchment map
try:
catchmask = config.get("files", "catchment_mask")
except:
print("No catchment mask, finding outlet")
# Find catchment (overall)
outlet = tr.find_outlet(ldd)
sub = pcr.subcatch(ldd, outlet)
pcr.report(sub, step1dir + "/catchment_overall.map")
else:
print("reading and converting catchment mask.....")
os.system("resample -r " + str(initialscale) + " " + catchmask + " " +
step1dir + "/catchment_overall.map")
sub = pcr.readmap(step1dir + "/catchment_overall.map")
print("Scatch...")
sd = pcr.subcatch(ldd, pcr.ifthen(outlmap > 0, outlmap))
pcr.report(sd, step1dir + "/scatch.map")
pcr.setglobaloption("unitcell")
print("Upscalefactor: " + str(upscalefactor))
if upscalefactor > 1:
gc.collect()
print("upscale river length1 (checkerboard map)...")
ck = tr.checkerboard(dem, upscalefactor)
pcr.report(ck, step1dir + "/ck.map")
pcr.report(dem, step1dir + "/demck.map")
print("upscale river length2...")
fact = tr.area_riverlength_factor(ldd, ck, upscalefactor)
pcr.report(fact, step1dir + "/riverlength_fact.map")
# print("make dem statistics...")
dem_ = pcr.areaaverage(dem, ck)
pcr.report(dem_, step1dir + "/demavg.map")
print("Create DEM statistics...")
dem_ = pcr.areaminimum(dem, ck)
pcr.report(dem_, step1dir + "/demmin.map")
dem_ = pcr.areamaximum(dem, ck)
pcr.report(dem_, step1dir + "/demmax.map")
# calculate percentiles
order = pcr.areaorder(dem, ck)
n = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), ck)
#: calculate 25 percentile
perc = tr.area_percentile(dem, ck, n, order, 25.0)
pcr.report(perc, step1dir + "/dem25.map")
perc = tr.area_percentile(dem, ck, n, order, 10.0)
pcr.report(perc, step1dir + "/dem10.map")
perc = tr.area_percentile(dem, ck, n, order, 50.0)
pcr.report(perc, step1dir + "/dem50.map")
perc = tr.area_percentile(dem, ck, n, order, 33.0)
pcr.report(perc, step1dir + "/dem33.map")
perc = tr.area_percentile(dem, ck, n, order, 66.0)
pcr.report(perc, step1dir + "/dem66.map")
perc = tr.area_percentile(dem, ck, n, order, 75.0)
pcr.report(perc, step1dir + "/dem75.map")
perc = tr.area_percentile(dem, ck, n, order, 90.0)
pcr.report(perc, step1dir + "/dem90.map")
else:
print("No fancy scaling done. Going strait to step2....")
pcr.report(dem, step1dir + "/demavg.map")
Xul = float(config.get("settings", "Xul"))
Yul = float(config.get("settings", "Yul"))
Xlr = float(config.get("settings", "Xlr"))
Ylr = float(config.get("settings", "Ylr"))
gdalstr = ("gdal_translate -projwin " + str(Xul) + " " + str(Yul) +
" " + str(Xlr) + " " + str(Ylr) + " -of PCRaster ")
# gdalstr = "gdal_translate -a_ullr " + str(Xul) + " " + str(Yul) + " " +str(Xlr) + " " +str(Ylr) + " -of PCRaster "
print(gdalstr)
pcr.report(pcr.cover(1.0), step1dir + "/wflow_riverlength_fact.map")
# Now us gdat tp convert the maps
os.system(gdalstr + step1dir + "/wflow_riverlength_fact.map" + " " +
step2dir + "/wflow_riverlength_fact.map")
os.system(gdalstr + step1dir + "/demavg.map" + " " + step2dir +
"/wflow_dem.map")
os.system(gdalstr + step1dir + "/demavg.map" + " " + step2dir +
"/wflow_demmin.map")
os.system(gdalstr + step1dir + "/demavg.map" + " " + step2dir +
"/wflow_demmax.map")
os.system(gdalstr + step1dir + "/gauges.map" + " " + step2dir +
"/wflow_gauges.map")
os.system(gdalstr + step1dir + "/rivers.map" + " " + step2dir +
"/wflow_river.map")
os.system(gdalstr + step1dir + "/streamorder.map" + " " + step2dir +
"/wflow_streamorder.map")
os.system(gdalstr + step1dir + "/gauges.map" + " " + step2dir +
"/wflow_outlet.map")
os.system(gdalstr + step1dir + "/scatch.map" + " " + step2dir +
"/wflow_catchment.map")
os.system(gdalstr + step1dir + "/ldd.map" + " " + step2dir +
"/wflow_ldd.map")
os.system(gdalstr + step1dir + "/scatch.map" + " " + step2dir +
"/wflow_subcatch.map")
if lu_water:
os.system(gdalstr + lu_water + " " + step2dir + "/WaterFrac.map")
if lu_paved:
os.system(gdalstr + lu_paved + " " + step2dir + "/PathFrac.map")
try:
lumap = config.get("files", "landuse")
except:
print("no landuse map...creating uniform map")
# clone=pcr.readmap(step2dir + "/wflow_dem.map")
pcr.setclone(step2dir + "/wflow_dem.map")
pcr.report(pcr.nominal(1), step2dir + "/wflow_landuse.map")
else:
os.system("resample --clone " + step2dir + "/wflow_dem.map " +
lumap + " " + step2dir + "/wflow_landuse.map")
try:
soilmap = config.get("files", "soil")
except:
print("no soil map..., creating uniform map")
pcr.setclone(step2dir + "/wflow_dem.map")
pcr.report(pcr.nominal(1), step2dir + "/wflow_soil.map")
else:
os.system("resample --clone " + step2dir + "/wflow_dem.map " +
soilmap + " " + step2dir + "/wflow_soil.map")
##################################
# Step 2 starts here
##################################
pcr.setclone(step2dir + "/cutout.map")
strRiver = int(configget(config, "settings", "riverorder_step2", "4"))
corevolume = float(configget(config, "settings", "corevolume", "1E35"))
catchmentprecipitation = float(
configget(config, "settings", "catchmentprecipitation", "1E35"))
corearea = float(configget(config, "settings", "corearea", "1E35"))
outflowdepth = float(
configget(config, "settings", "lddoutflowdepth", "1E35"))
lddmethod = configget(config, "settings", "lddmethod", "dem")
lddglobaloption = configget(config, "settings", "lddglobaloption",
"lddout")
pcr.setglobaloption(lddglobaloption)
nrrow = round(abs(Yul - Ylr) / csize)
nrcol = round(abs(Xlr - Xul) / csize)
mapstr = ("mapattr -s -S -R " + str(nrrow) + " -C " + str(nrcol) + " -l " +
str(csize) + " -x " + str(Xul) + " -y " + str(Yul) +
" -P yb2t " + step2dir + "/cutout.map")
os.system(mapstr)
pcr.setclone(step2dir + "/cutout.map")
lu_water = configget(config, "files", "lu_water", "")
lu_paved = configget(config, "files", "lu_paved", "")
if lu_water:
os.system("resample --clone " + step2dir + "/cutout.map " + lu_water +
" " + step2dir + "/wflow_waterfrac.map")
if lu_paved:
os.system("resample --clone " + step2dir + "/cutout.map " + lu_paved +
" " + step2dir + "/PathFrac.map")
#
try:
lumap = config.get("files", "landuse")
except:
print("no landuse map...creating uniform map")
clone = pcr.readmap(step2dir + "/cutout.map")
pcr.report(pcr.nominal(clone), step2dir + "/wflow_landuse.map")
else:
os.system("resample --clone " + step2dir + "/cutout.map " + lumap +
" " + step2dir + "/wflow_landuse.map")
try:
soilmap = config.get("files", "soil")
except:
print("no soil map..., creating uniform map")
clone = pcr.readmap(step2dir + "/cutout.map")
pcr.report(pcr.nominal(clone), step2dir + "/wflow_soil.map")
else:
os.system("resample --clone " + step2dir + "/cutout.map " + soilmap +
" " + step2dir + "/wflow_soil.map")
resamplemaps(step1dir, step2dir)
dem = pcr.readmap(step2dir + "/wflow_dem.map")
demmin = pcr.readmap(step2dir + "/wflow_demmin.map")
demmax = pcr.readmap(step2dir + "/wflow_demmax.map")
catchcut = pcr.readmap(step2dir + "/catchment_cut.map")
# now apply the area of interest (catchcut) to the DEM
# dem=pcr.ifthen(catchcut >=1 , dem)
#
# See if there is a shape file of the river to burn in
try:
rivshp = config.get("files", "river")
except:
print("no river file specified")
riverburn = pcr.readmap(step2dir + "/wflow_riverburnin.map")
else:
print("river file speficied.....")
rivshpattr = config.get("files", "riverattr")
pcr.report(dem * 0.0, step2dir + "/nilmap.map")
thestr = ("gdal_translate -of GTiff " + step2dir + "/nilmap.map " +
step2dir + "/wflow_riverburnin.tif")
os.system(thestr)
os.system("gdal_rasterize -burn 1 -l " + rivshpattr + " " + rivshp +
" " + step2dir + "/wflow_riverburnin.tif")
thestr = ("gdal_translate -of PCRaster " + step2dir +
"/wflow_riverburnin.tif " + step2dir +
"/wflow_riverburnin.map")
os.system(thestr)
riverburn = pcr.readmap(step2dir + "/wflow_riverburnin.map")
# ldddem = pcr.ifthenelse(riverburn >= 1.0, dem -1000 , dem)
# Only burn within the original catchment
riverburn = pcr.ifthen(pcr.scalar(catchcut) >= 1, riverburn)
# Now setup a very high wall around the catchment that is scale
# based on the distance to the catchment so that it slopes away from the
# catchment
if lddmethod != "river":
print("Burning in highres-river ...")
disttocatch = pcr.spread(pcr.nominal(catchcut), 0.0, 1.0)
demmax = pcr.ifthenelse(
pcr.scalar(catchcut) >= 1.0,
demmax,
demmax + (pcr.celllength() * 100.0) / disttocatch,
)
pcr.setglobaloption("unitcell")
demregional = pcr.windowaverage(demmin, 100)
demburn = pcr.cover(
pcr.ifthen(pcr.boolean(riverburn), demregional - 100.0), demmax)
else:
print("using average dem..")
demburn = dem
ldd = tr.lddcreate_save(
step2dir + "/ldd.map",
demburn,
True,
outflowdepth=outflowdepth,
corevolume=corevolume,
catchmentprecipitation=catchmentprecipitation,
corearea=corearea,
)
# Find catchment (overall)
outlet = tr.find_outlet(ldd)
sub = pcr.subcatch(ldd, outlet)
pcr.report(sub, step2dir + "/wflow_catchment.map")
pcr.report(outlet, step2dir + "/wflow_outlet.map")
# make river map
strorder = pcr.streamorder(ldd)
pcr.report(strorder, step2dir + "/wflow_streamorder.map")
river = pcr.ifthen(pcr.boolean(strorder >= strRiver), strorder)
pcr.report(river, step2dir + "/wflow_river.map")
# make subcatchments
# os.system("col2map --clone " + step2dir + "/cutout.map gauges.col " + step2dir + "/wflow_gauges.map")
exec("X=tr.array(" + gauges_x + ")")
exec("Y=tr.array(" + gauges_y + ")")
pcr.setglobaloption("unittrue")
outlmap = tr.points_to_map(dem, X, Y, 0.5)
pcr.report(outlmap, step2dir + "/wflow_gauges_.map")
if snapgaugestoriver:
print("Snapping gauges to river")
pcr.report(outlmap, step2dir + "/wflow_orggauges.map")
outlmap = tr.snaptomap(outlmap, river)
outlmap = pcr.ifthen(outlmap > 0, outlmap)
pcr.report(outlmap, step2dir + "/wflow_gauges.map")
scatch = pcr.subcatch(ldd, outlmap)
pcr.report(scatch, step2dir + "/wflow_subcatch.map")
def main():
#-initialization
# MVs
MV= -999.
# minimum catchment size to process
catchmentSizeLimit= 0.0
# period of interest, start and end year
startYear= 1961
endYear= 2010
# maps
cloneMapFileName= '/data/hydroworld/PCRGLOBWB20/input30min/global/Global_CloneMap_30min.map'
lddFileName= '/data/hydroworld/PCRGLOBWB20/input30min/routing/lddsound_30min.map'
cellAreaFileName= '/data/hydroworld/PCRGLOBWB20/input30min/routing/cellarea30min.map'
# set clone
pcr.setclone(cloneMapFileName)
# output
outputPath= '/scratch/rens/reservedrecharge'
percentileMapFileName= os.path.join(outputPath,'q%03d_cumsec.map')
textFileName= os.path.join(outputPath,'groundwater_environmentalflow_%d.txt')
fractionReservedRechargeMapFileName= os.path.join(outputPath,'fraction_reserved_recharge%d.map')
fractionMinimumReservedRechargeMapFileName= os.path.join(outputPath,'minimum_fraction_reserved_recharge%d.map')
# input
inputPath= '/nfsarchive/edwin-emergency-backup-DO-NOT-DELETE/rapid/edwin/05min_runs_results/2015_04_27/non_natural_2015_04_27/global/netcdf/'
# define data to be read from netCDF files
ncData= {}
variableName= 'totalRunoff'
ncData[variableName]= {}
ncData[variableName]['fileName']= os.path.join(inputPath,'totalRunoff_monthTot_output.nc')
ncData[variableName]['fileRoot']= os.path.join(outputPath,'qloc')
ncData[variableName]['annualAverage']= pcr.scalar(0)
variableName= 'gwRecharge'
ncData[variableName]= {}
ncData[variableName]['fileName']= os.path.join(inputPath,'gwRecharge_monthTot_output.nc')
ncData[variableName]['fileRoot']= os.path.join(outputPath,'gwrec')
ncData[variableName]['annualAverage']= pcr.scalar(0)
variableName= 'discharge'
ncData[variableName]= {}
ncData[variableName]['fileName']= os.path.join(inputPath,'totalRunoff_monthTot_output.nc')
ncData[variableName]['fileRoot']= os.path.join(outputPath,'qc')
ncData[variableName]['annualAverage']= pcr.scalar(0)
ncData[variableName]['mapStack']= np.array([])
# percents and environmental flow condition set as percentile
percents= range(10,110,10)
environmentalFlowPercent= 10
if environmentalFlowPercent not in percents:
percents.append(environmentalFlowPercent)
percents.sort()
#-start
# obtain attributes
pcr.setclone(cloneMapFileName)
cloneSpatialAttributes= spatialAttributes(cloneMapFileName)
years= range(startYear,endYear+1)
# output path
if not os.path.isdir(outputPath):
os.makedirs(outputPath)
os.chdir(outputPath)
# compute catchments
ldd= pcr.readmap(lddFileName)
cellArea= pcr.readmap(cellAreaFileName)
catchments= pcr.catchment(ldd,pcr.pit(ldd))
fractionWater= pcr.scalar(0.0) # temporary!
lakeMask= pcr.boolean(0) # temporary!
pcr.report(catchments,os.path.join(outputPath,'catchments.map'))
maximumCatchmentID= int(pcr.cellvalue(pcr.mapmaximum(pcr.scalar(catchments)),1)[0])
# iterate over years
weight= float(len(years))**-1
for year in years:
#-echo year
print ' - processing year %d' % year
#-process data
startDate= datetime.datetime(year,1,1)
endDate= datetime.datetime(year,12,31)
timeSteps= endDate.toordinal()-startDate.toordinal()+1
dynamicIncrement= 1
for variableName in ncData.keys():
print ' extracting %s' % variableName,
ncFileIn= ncData[variableName]['fileName']
#-process data
pcrDataSet= pcrObject(variableName, ncData[variableName]['fileRoot'],\
ncFileIn,cloneSpatialAttributes, pcrVALUESCALE= pcr.Scalar, resamplingAllowed= True,\
dynamic= True, dynamicStart= startDate, dynamicEnd= endDate, dynamicIncrement= dynamicIncrement, ncDynamicDimension= 'time')
pcrDataSet.initializeFileInfo()
pcrDataSet.processFileInfo()
for fileInfo in pcrDataSet.fileProcessInfo.values()[0]:
tempFileName= fileInfo[1]
variableField= pcr.readmap(tempFileName)
variableField= pcr.ifthen(pcr.defined(ldd),pcr.cover(variableField,0))
if variableName == 'discharge':
dayNumber= int(os.path.splitext(tempFileName)[1].strip('.'))
date= datetime.date(year,1,1)+datetime.timedelta(dayNumber-1)
numberDays= calendar.monthrange(year,date.month)[1]
variableField= pcr.max(0,pcr.catchmenttotal(variableField*cellArea,ldd)/(numberDays*24*3600))
ncData[variableName]['annualAverage']+= weight*variableField
if 'mapStack' in ncData[variableName].keys():
tempArray= pcr2numpy(variableField,MV)
mask= tempArray != MV
if ncData[variableName]['mapStack'].size != 0:
ncData[variableName]['mapStack']= np.vstack((ncData[variableName]['mapStack'],tempArray[mask]))
else:
ncData[variableName]['mapStack']= tempArray[mask]
coordinates= np.zeros((ncData[variableName]['mapStack'].size,2))
pcr.setglobaloption('unitcell')
tempArray= pcr2numpy(pcr.ycoordinate(pcr.boolean(1))+0.5,MV)
coordinates[:,0]= tempArray[mask]
tempArray= pcr2numpy(pcr.xcoordinate(pcr.boolean(1))+0.5,MV)
coordinates[:,1]= tempArray[mask]
os.remove(tempFileName)
# delete object
pcrDataSet= None
del pcrDataSet
# close line on screen
print
# report annual averages
key= 'annualAverage'
ncData['discharge'][key]/= 12
for variableName in ncData.keys():
ncData[variableName][key]= pcr.max(0,ncData[variableName][key])
pcr.report(ncData[variableName][key],\
os.path.join(outputPath,'%s_%s.map' % (variableName,key)))
# remove aux.xml
for tempFileName in os.listdir(outputPath):
if 'aux.xml' in tempFileName:
os.remove(tempFileName)
# sort data
print 'sorting discharge data'
variableName= 'discharge'
key= 'mapStack'
indices= np.zeros((ncData[variableName][key].shape),np.uint)
for iCnt in xrange(ncData[variableName][key].shape[1]):
indices[:,iCnt]= ncData[variableName][key][:,iCnt].argsort(kind= 'mergesort')
ncData[variableName][key][:,iCnt]= ncData[variableName][key][:,iCnt][indices[:,iCnt]]
# extract values for percentiles
print 'returning maps'
for percent in percents:
percentile= 0.01*percent
index0= min(ncData[variableName][key].shape[0]-1,int(percentile*ncData[variableName][key].shape[0]))
index1= min(ncData[variableName][key].shape[0]-1,int(percentile*ncData[variableName][key].shape[0])+1)
x0= float(index0)/ncData[variableName][key].shape[0]
x1= float(index1)/ncData[variableName][key].shape[0]
if x0 <> x1:
y= ncData[variableName][key][index0,:]+(percentile-x0)*\
(ncData[variableName][key][index1,:]-ncData[variableName][key][index0,:])/(x1-x0)
else:
y= ncData[variableName][key][index0,:]
# convert a slice of the stack into an array
tempArray= np.ones((cloneSpatialAttributes.numberRows,cloneSpatialAttributes.numberCols))*MV
for iCnt in xrange(coordinates.shape[0]):
row= coordinates[iCnt,0]-1
col= coordinates[iCnt,1]-1
tempArray[row,col]= y[iCnt]
variableField= numpy2pcr(pcr.Scalar,tempArray,MV)
pcr.report(variableField,percentileMapFileName % percent)
if percent == environmentalFlowPercent:
ncData[variableName]['environmentalFlow']= variableField
tempArray= None; variableField= None
del tempArray, variableField
# process environmental flow
# initialize map of reserved recharge fraction
fractionReservedRechargeMap= pcr.ifthen(ncData[variableName]['environmentalFlow'] < 0,pcr.scalar(0))
fractionMinimumReservedRechargeMap= pcr.ifthen(ncData[variableName]['environmentalFlow'] < 0,pcr.scalar(0))
textFile= open(textFileName % environmentalFlowPercent,'w')
hStr= 'Environmental flow analysis per basin, resulting in a map of renewable, exploitable recharge, for the %d%s quantile of discharge\n' % (environmentalFlowPercent,'%')
hStr+= 'Returns Q_%d/R, the fraction of reserved recharge needed to sustain fully the environental flow requirement defined as the %d percentile,\n' % (environmentalFlowPercent, environmentalFlowPercent)
hStr+= 'and Q*_%d/R, a reduced fraction that takes the availability of surface water into account\n' % environmentalFlowPercent
textFile.write(hStr)
print hStr
# create header to display on screen and write to file
# reported are: 1: ID, 2: Area, 3: average discharge, 4: environmental flow, 5: average recharge,
# 6: Q_%d/Q, 7: Q_%d/R_Avg, 8: R_Avg/Q_Avg, 9: Q*_%d/R_Avg
hStr= '%6s,%15s,%15s,%15s,%15s,%15s,%15s,%15s,%15s\n' % \
('ID','Area [km2]','Q_Avg [m3]','Q_%d [m3]' % environmentalFlowPercent ,'R_Avg [m3]','Q_%d/Q_Avg [-]' % environmentalFlowPercent,\
'Q_%d/Q_Avg [-]' % environmentalFlowPercent,'R_Avg/Q_Avg [-]','Q*_%d/Q_Avg [-]' % environmentalFlowPercent)
textFile.write(hStr)
print hStr
for catchment in xrange(1,maximumCatchmentID+1):
# create catchment mask and check whether it does not coincide with a lake
catchmentMask= catchments == catchment
catchmentSize= pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,cellArea*1.e-6)),1)[0]
#~ ##~ if pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,pcr.scalar(lakeMask))),1) <> \
#~ ##~ pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,pcr.scalar(catchmentMask))),1)[0] and \
#~ ##~ catchmentSize > catchmentSizeLimit:
key= 'annualAverage'
variableName= 'discharge'
if bool(pcr.cellvalue(pcr.maptotal(pcr.ifthen((ldd == 5) & catchmentMask,\
pcr.scalar(ncData[variableName][key] > 0))),1)[0]) and catchmentSize >= catchmentSizeLimit:
# valid catchment, process
# all volumes are in m3 per year
key= 'annualAverage'
catchmentAverageDischarge= pcr.cellvalue(pcr.mapmaximum(pcr.ifthen(catchmentMask & (ldd == 5),\
ncData[variableName][key])),1)[0]*365.25*3600*24
variableName= 'gwRecharge'
catchmentRecharge= pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,ncData[variableName][key]*\
(1.-fractionWater)*cellArea)),1)[0]
variableName= 'totalRunoff'
catchmentRunoff= pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,ncData[variableName][key]*\
cellArea)),1)[0]
key= 'environmentalFlow'
variableName= 'discharge'
catchmentEnvironmentalFlow= pcr.cellvalue(pcr.mapmaximum(pcr.ifthen(catchmentMask & (ldd == 5),\
ncData[variableName][key])),1)[0]*365.25*3600*24
catchmentRunoff= max(catchmentRunoff,catchmentEnvironmentalFlow)
if catchmentAverageDischarge > 0.:
fractionEnvironmentalFlow= catchmentEnvironmentalFlow/catchmentAverageDischarge
fractionGroundWaterContribution= catchmentRecharge/catchmentAverageDischarge
else:
fractionEnvironmentalFlow= 0.
fractionGroundWaterContribution= 0.
if catchmentRecharge > 0:
fractionReservedRecharge= min(1,catchmentEnvironmentalFlow/catchmentRecharge)
else:
fractionReservedRecharge= 1.0
fractionMinimumReservedRecharge= (fractionReservedRecharge+fractionGroundWaterContribution-\
fractionReservedRecharge*fractionGroundWaterContribution)*fractionReservedRecharge
#~ # echo to screen, and write to file and map
wStr= '%6s,%15.1f,%15.6g,%15.6g,%15.6g,%15.6f,%15.6f,%15.6f,%15.6f\n' % \
(catchment,catchmentSize,catchmentAverageDischarge,catchmentEnvironmentalFlow,catchmentRecharge,\
fractionEnvironmentalFlow,fractionReservedRecharge,fractionGroundWaterContribution,fractionMinimumReservedRecharge)
print wStr
textFile.write(wStr)
# update maps
fractionReservedRechargeMap= pcr.ifthenelse(catchmentMask,\
pcr.scalar(fractionReservedRecharge),fractionReservedRechargeMap)
fractionMinimumReservedRechargeMap= pcr.ifthenelse(catchmentMask,\
pcr.scalar(fractionMinimumReservedRecharge),fractionMinimumReservedRechargeMap)
#-report map and close text file
pcr.report(fractionReservedRechargeMap,fractionReservedRechargeMapFileName % environmentalFlowPercent)
pcr.report(fractionMinimumReservedRechargeMap,fractionMinimumReservedRechargeMapFileName % environmentalFlowPercent)
# close text file
textFile.close()
# finished
print 'all done!'