def waterBalanceCheck(fluxesIn,fluxesOut,preStorages,endStorages,processName,PrintOnlyErrors,dateStr,threshold=1e-5,landmask=None):
""" Returns the water balance for a list of input, output, and storage map files """
# modified by Edwin (22 Apr 2013)
inMap = pcr.spatial(pcr.scalar(0.0))
outMap = pcr.spatial(pcr.scalar(0.0))
dsMap = pcr.spatial(pcr.scalar(0.0))
for fluxIn in fluxesIn:
inMap += fluxIn
for fluxOut in fluxesOut:
outMap += fluxOut
for preStorage in preStorages:
dsMap += preStorage
for endStorage in endStorages:
dsMap -= endStorage
a,b,c = getMinMaxMean(inMap + dsMap- outMap)
if abs(a) > threshold or abs(b) > threshold:
if PrintOnlyErrors:
msg = "\n"
msg += "\n"
msg = "\n"
msg += "\n"
msg += "##############################################################################################################################################\n"
msg += "WARNING !!!!!!!! Water Balance Error %s Min %f Max %f Mean %f" %(processName,a,b,c)
msg += "\n"
msg += "##############################################################################################################################################\n"
msg += "\n"
msg += "\n"
msg += "\n"
logger.error(msg)
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 find_outlet(ldd):
"""
Tries to find the outlet of the largest catchment in the Ldd
Input:
- Ldd
Output:
- outlet map (single point in the map)
"""
largest = pcr.mapmaximum(pcr.catchmenttotal(pcr.spatial(pcr.scalar(1.0)), ldd))
outlet = pcr.ifthen(
pcr.catchmenttotal(1.0, ldd) == largest, pcr.spatial(pcr.scalar(1.0))
)
return outlet
def sample(self, expression):
"""
Sampling the current values of 'expression' at the given locations for the current timestep
"""
arrayRowPos = self._userModel.currentTimeStep() - self._userModel.firstTimeStep()
#if isinstance(expression, float):
# expression = pcraster.scalar(expression)
try:
# store the data type for tss file header
if self._spatialDatatype == None:
self._spatialDatatype = str(expression.dataType())
except AttributeError as e:
datatype, sep, tail = str(e).partition(" ")
msg = "Argument must be a PCRaster map, type %s given. If necessary use data conversion functions like scalar()" % (datatype)
raise AttributeError(msg)
if self._spatialIdGiven:
if expression.dataType() == pcraster.Scalar or expression.dataType() == pcraster.Directional:
tmp = pcraster.areaaverage(pcraster.spatial(expression), pcraster.spatial(self._spatialId))
else:
tmp = pcraster.areamajority(pcraster.spatial(expression), pcraster.spatial(self._spatialId))
col = 0
for cellIndex in self._sampleAddresses:
value, valid = pcraster.cellvalue(tmp, cellIndex)
if not valid:
value = Decimal("NaN")
self._sampleValues[arrayRowPos][col] = value
col += 1
else:
if expression.dataType() == pcraster.Scalar or expression.dataType() == pcraster.Directional:
tmp = pcraster.maptotal(pcraster.spatial(expression))\
/ pcraster.maptotal(pcraster.scalar(pcraster.defined(pcraster.spatial(expression))))
else:
tmp = pcraster.mapmaximum(pcraster.maptotal(pcraster.areamajority(pcraster.spatial(expression),\
pcraster.spatial(pcraster.nominal(1)))))
value, valid = pcraster.cellvalue(tmp, 1)
if not valid:
value = Decimal("NaN")
self._sampleValues[arrayRowPos] = value
if self._userModel.currentTimeStep() == self._userModel.nrTimeSteps():
self._writeTssFile()
def waterBalanceCheck(fluxesIn,fluxesOut,preStorages,endStorages,processName,PrintOnlyErrors,dateStr,threshold=1e-5,landmask=None):
""" Returns the water balance for a list of input, output, and storage map files """
# modified by Edwin (22 Apr 2013)
inMap = pcr.spatial(pcr.scalar(0.0))
outMap = pcr.spatial(pcr.scalar(0.0))
dsMap = pcr.spatial(pcr.scalar(0.0))
for fluxIn in fluxesIn:
inMap += fluxIn
for fluxOut in fluxesOut:
outMap += fluxOut
for preStorage in preStorages:
dsMap += preStorage
for endStorage in endStorages:
dsMap -= endStorage
a,b,c = getMinMaxMean(inMap + dsMap- outMap)
if abs(a) > threshold or abs(b) > threshold:
if PrintOnlyErrors:
print "WBError %s Min %f Max %f Mean %f" %(processName,a,b,c)
print ""
wb = inMap + dsMap - outMap
maxWBError = pcr.cellvalue(pcr.mapmaximum(pcr.abs(wb)), 1, 1)[0]
def arbitraryMapEquals(self, firstMap, secondMap):
import math
def floatEquals(float1, float2):
threshold = 1e-6
return math.fabs(float1 - float2) <= threshold
if((firstMap.dataType() == _pcraster.Scalar) | \
(firstMap.dataType() == _pcraster.Directional)):
result = _pcraster._closeAtTolerance(firstMap, secondMap)
else:
result = pcraster.pcreq(firstMap, secondMap)
minMap = pcraster.mapminimum(pcraster.spatial(pcraster.scalar(result)))
value, isValid = pcraster.cellvalue(minMap, 1)
assert isValid
return floatEquals(value, 1.0)
def test_003(self):
""" pcr2numpy should not run out of memory """
nrRows, nrCols, cellSize = 200, 200, 1.0
west, north = 0.0, 0.0
pcraster.setclone(nrRows, nrCols, cellSize, west, north)
raster = pcraster.uniform(1)
process = psutil.Process(os.getpid())
mem = process.memory_info()
init_mem = mem.rss / 2**10
nr_iterations = 50
mem_increase = False
# small memory increase can occur at runtime
# allow for, but less than iterations * size(raster)
max_diff = 400
for it in range(0, nr_iterations):
pcraster.pcr2numpy(raster, numpy.nan)
mem = process.memory_info()
curr_mem = mem.rss / 2**10
if curr_mem - init_mem > max_diff:
mem_increase = True
raster = pcraster.spatial(pcraster.boolean(1))
for it in range(0, nr_iterations):
pcraster.pcr2numpy(raster, numpy.nan)
mem = process.memory_info()
curr_mem = mem.rss / 2**10
if curr_mem - init_mem > max_diff:
mem_increase = True
raster = pcraster.nominal(pcraster.uniform(1) * 10)
for it in range(0, nr_iterations):
pcraster.pcr2numpy(raster, numpy.nan)
mem = process.memory_info()
curr_mem = mem.rss / 2**10
if curr_mem - init_mem > max_diff:
mem_increase = True
self.assertEqual(mem_increase, False)
def dayLength(doy, lat):
""" daylength fraction of day """
lat = lat * pcr.scalar(math.pi) / 180.0
M_PI_2 = pcr.spatial(pcr.scalar(math.pi / 2.0))
dec = pcr.sin((6.224111 + 0.017202 * doy) * 180 / math.pi)
dec = pcr.scalar(0.39785 * pcr.sin(
(4.868961 + .017203 * doy + 0.033446 * pcr.sin(dec * 180 / math.pi)) *
180 / math.pi))
dec = pcr.scalar(pcr.asin(dec))
lat = pcr.ifthenelse(
pcr.abs(lat) > M_PI_2, (M_PI_2 - pcr.scalar(0.01)) *
pcr.ifthenelse(lat > 0, pcr.scalar(1.0), pcr.scalar(-1.0)), lat)
arg = pcr.tan(dec) * pcr.tan(lat * 180.0 / math.pi) * -1
h = pcr.scalar(pcr.acos(arg))
h = h / 180 * math.pi
h = pcr.ifthenelse(arg > 1.0, 0.0, h) # /* sun stays below horizon */
h = pcr.ifthenelse(arg < -1.0, math.pi, h) # /* sun stays above horizon */
return (h / math.pi)
def areastat(Var, Area):
"""
Calculate several statistics of *Var* for each unique id in *Area*
Input:
- Var
- Area
Output:
- Standard_Deviation,Average,Max,Min
"""
Avg = pcr.areaaverage(Var, Area)
Sq = (Var - Avg) ** 2
N = pcr.areatotal(pcr.spatial(pcr.cellarea()), Area) / pcr.cellarea()
Sd = (pcr.areatotal(Sq, Area) / N) ** 0.5
Max = pcr.areamaximum(Var, Area)
Min = pcr.areaminimum(Var, Area)
return Sd, Avg, Max, Min
def areastat(Var, Area):
"""
Calculate several statistics of *Var* for each unique id in *Area*
Input:
- Var
- Area
Output:
- Standard_Deviation,Average,Max,Min
"""
Avg = pcr.areaaverage(Var, Area)
Sq = (Var - Avg) ** 2
N = pcr.areatotal(pcr.spatial(pcr.cellarea()), Area) / pcr.cellarea()
Sd = (pcr.areatotal(Sq, Area) / N) ** 0.5
Max = pcr.areamaximum(Var, Area)
Min = pcr.areaminimum(Var, Area)
return Sd, Avg, Max, Min
def main():
# output folder (and tmp folder)
clean_out_folder = True
if os.path.exists(out_folder):
if clean_out_folder:
shutil.rmtree(out_folder)
os.makedirs(out_folder)
else:
os.makedirs(out_folder)
os.chdir(out_folder)
os.system("pwd")
# set the clone map
print("set the clone")
pcr.setclone(global_ldd_30min_inp_file)
# define the landmask
print("define the landmask")
# - based on the 30min input
landmask_30min = define_landmask(input_file = global_landmask_30min_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_30min_only.map")
# - based on the 05min input
landmask_05min = define_landmask(input_file = global_landmask_05min_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_05min_only.map")
# - based on the 06min input
landmask_06min = define_landmask(input_file = global_landmask_06min_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_06min_only.map")
# - based on the 30sec input
landmask_30sec = define_landmask(input_file = global_landmask_30sec_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_30sec_only.map")
# - based on the 30sec input
landmask_03sec = define_landmask(input_file = global_landmask_03sec_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_03sec_only.map")
#
# - merge all landmasks
landmask = pcr.cover(landmask_30min, landmask_05min, landmask_06min,
landmask_30sec, landmask_03sec)
pcr.report(landmask, "global_landmask_extended_30min.map")
# ~ pcr.aguila(landmask)
# extend ldd
print("extend/define the ldd")
ldd_map = pcr.readmap(global_ldd_30min_inp_file)
ldd_map = pcr.ifthen(landmask, pcr.cover(ldd_map, pcr.ldd(5)))
pcr.report(ldd_map, "global_ldd_extended_30min.map")
# ~ pcr.aguila(ldd_map)
# catchment map and size
catchment_map = pcr.catchment(ldd_map, pcr.pit(ldd_map))
catchment_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), catchment_map)
# ~ pcr.aguila(catchment_size)
# identify small islands
print("identify small islands")
# - maps of islands smaller than 15000 cells (at half arc degree resolution)
island_map = pcr.ifthen(landmask, pcr.clump(pcr.defined(ldd_map)))
island_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), island_map)
island_map = pcr.ifthen(island_size < 15000., island_map)
# ~ # - use catchments (instead of islands)
# ~ island_map = catchment_map
# ~ island_size = catchment_size
# ~ island_map = pcr.ifthen(island_size < 10000., island_map)
# - sort from the largest island
# -- take one cell per island as a representative
island_map_rep_size = pcr.ifthen(
pcr.areaorder(island_size, island_map) == 1.0, island_size)
# -- sort from the largest island
island_map_rep_ids = pcr.areaorder(
island_map_rep_size * -1.00,
pcr.ifthen(pcr.defined(island_map_rep_size), pcr.nominal(1.0)))
# -- map of smaller islands, sorted from the largest one
island_map = pcr.areamajority(pcr.nominal(island_map_rep_ids), island_map)
# identify the biggest island for every group of small islands within a certain window (arcdeg cells)
print("the biggest island for every group of small islands")
large_island_map = pcr.ifthen(
pcr.scalar(island_map) == pcr.windowminimum(pcr.scalar(island_map),
15.), island_map)
# ~ pcr.aguila(large_island_map)
# identify big catchments
print("identify large catchments")
catchment_map = pcr.catchment(ldd_map, pcr.pit(ldd_map))
catchment_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), catchment_map)
# ~ # - identify all large catchments with size >= 50 cells (at the resolution of 30 arcmin) = 50 x (50^2) km2 = 125000 km2
# ~ large_catchment_map = pcr.ifthen(catchment_size >= 50, catchment_map)
# ~ # - identify all large catchments with size >= 10 cells (at the resolution of 30 arcmin)
# ~ large_catchment_map = pcr.ifthen(catchment_size >= 10, catchment_map)
# ~ # - identify all large catchments with size >= 5 cells (at the resolution of 30 arcmin)
# ~ large_catchment_map = pcr.ifthen(catchment_size >= 5, catchment_map)
# ~ # - identify all large catchments with size >= 20 cells (at the resolution of 30 arcmin)
# ~ large_catchment_map = pcr.ifthen(catchment_size >= 20, catchment_map)
# - identify all large catchments with size >= 25 cells (at the resolution of 30 arcmin)
large_catchment_map = pcr.ifthen(catchment_size >= 25, catchment_map)
# - give the codes that are different than islands
large_catchment_map = pcr.nominal(
pcr.scalar(large_catchment_map) +
10. * vos.getMinMaxMean(pcr.scalar(large_island_map))[1])
# merge biggest islands and big catchments
print("merge large catchments and islands")
large_catchment_and_island_map = pcr.cover(large_catchment_map,
large_island_map)
# ~ large_catchment_and_island_map = pcr.cover(large_island_map, large_catchment_map)
large_catchment_and_island_map_size = pcr.areatotal(
pcr.spatial(pcr.scalar(1.0)), large_catchment_and_island_map)
# - sort from the largest one
# -- take one cell per island as a representative
large_catchment_and_island_map_rep_size = pcr.ifthen(
pcr.areaorder(large_catchment_and_island_map_size,
large_catchment_and_island_map) == 1.0,
large_catchment_and_island_map_size)
# -- sort from the largest
large_catchment_and_island_map_rep_ids = pcr.areaorder(
large_catchment_and_island_map_rep_size * -1.00,
pcr.ifthen(pcr.defined(large_catchment_and_island_map_rep_size),
pcr.nominal(1.0)))
# -- map of largest catchments and islands, sorted from the largest one
large_catchment_and_island_map = pcr.areamajority(
pcr.nominal(large_catchment_and_island_map_rep_ids),
large_catchment_and_island_map)
# ~ pcr.report(large_catchment_and_island_map, "large_catchments_and_islands.map")
# ~ # perform cdo fillmiss2 in order to merge the small catchments to the nearest large catchments
# ~ print("spatial interpolation/extrapolation using cdo fillmiss2 to get initial subdomains")
# ~ cmd = "gdal_translate -of NETCDF large_catchments_and_islands.map large_catchments_and_islands.nc"
# ~ print(cmd); os.system(cmd)
# ~ cmd = "cdo fillmiss2 large_catchments_and_islands.nc large_catchments_and_islands_filled.nc"
# ~ print(cmd); os.system(cmd)
# ~ cmd = "gdal_translate -of PCRaster large_catchments_and_islands_filled.nc large_catchments_and_islands_filled.map"
# ~ print(cmd); os.system(cmd)
# ~ cmd = "mapattr -c " + global_ldd_30min_inp_file + " " + "large_catchments_and_islands_filled.map"
# ~ print(cmd); os.system(cmd)
# ~ # - initial subdomains
# ~ subdomains_initial = pcr.nominal(pcr.readmap("large_catchments_and_islands_filled.map"))
# ~ subdomains_initial = pcr.areamajority(subdomains_initial, catchment_map)
# ~ pcr.aguila(subdomains_initial)
# spatial interpolation/extrapolation in order to merge the small catchments to the nearest large catchments
print("spatial interpolation/extrapolation to get initial subdomains")
field = large_catchment_and_island_map
cellID = pcr.nominal(pcr.uniqueid(pcr.defined(field)))
zoneID = pcr.spreadzone(cellID, 0, 1)
field = pcr.areamajority(field, zoneID)
subdomains_initial = field
subdomains_initial = pcr.areamajority(subdomains_initial, catchment_map)
pcr.aguila(subdomains_initial)
pcr.report(subdomains_initial, "global_subdomains_30min_initial.map")
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[0])))
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[1])))
# ~ print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial_clump))[0])))
# ~ print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial_clump))[1])))
print("Checking all subdomains, avoid too large subdomains")
num_of_masks = int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[1])
# clone code that will be assigned
assigned_number = 0
subdomains_final = pcr.ifthen(
pcr.scalar(subdomains_initial) < -7777, pcr.nominal(0))
for nr in range(1, num_of_masks + 1, 1):
msg = "Processing the landmask %s" % (str(nr))
print(msg)
mask_selected_boolean = pcr.ifthen(subdomains_initial == nr,
pcr.boolean(1.0))
# ~ if nr == 1: pcr.aguila(mask_selected_boolean)
xmin, ymin, xmax, ymax = boundingBox(mask_selected_boolean)
area_in_degree2 = (xmax - xmin) * (ymax - ymin)
# ~ print(str(area_in_degree2))
# check whether the size of bounding box is ok
# - initial check value
check_ok = True
reference_area_in_degree2 = 2500.
if area_in_degree2 > 1.50 * reference_area_in_degree2: check_ok = False
if (xmax - xmin) > 10 * (ymax - ymin): check_ok = False
if check_ok == True:
msg = "Clump is not needed."
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# assign the clone code
assigned_number = assigned_number + 1
# update global landmask for river and land
mask_selected_nominal = pcr.ifthen(mask_selected_boolean,
pcr.nominal(assigned_number))
subdomains_final = pcr.cover(subdomains_final,
mask_selected_nominal)
if check_ok == False:
msg = "Clump is needed."
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# make clump
clump_ids = pcr.nominal(pcr.clump(mask_selected_boolean))
# merge clumps that are close together
clump_ids_window_majority = pcr.windowmajority(clump_ids, 10.0)
clump_ids = pcr.areamajority(clump_ids_window_majority, clump_ids)
# ~ pcr.aguila(clump_ids)
# minimimum and maximum values
min_clump_id = int(
pcr.cellvalue(pcr.mapminimum(pcr.scalar(clump_ids)), 1)[0])
max_clump_id = int(
pcr.cellvalue(pcr.mapmaximum(pcr.scalar(clump_ids)), 1)[0])
for clump_id in range(min_clump_id, max_clump_id + 1, 1):
msg = "Processing the clump %s of %s from the landmask %s" % (
str(clump_id), str(max_clump_id), str(nr))
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# identify mask based on the clump
mask_selected_boolean_from_clump = pcr.ifthen(
clump_ids == pcr.nominal(clump_id), mask_selected_boolean)
mask_selected_boolean_from_clump = pcr.ifthen(
mask_selected_boolean_from_clump,
mask_selected_boolean_from_clump)
# check whether the clump is empty
check_mask_selected_boolean_from_clump = pcr.ifthen(
mask_selected_boolean, mask_selected_boolean_from_clump)
check_if_empty = float(
pcr.cellvalue(
pcr.mapmaximum(
pcr.scalar(
pcr.defined(
check_mask_selected_boolean_from_clump))),
1)[0])
if check_if_empty == 0.0:
msg = "Map is empty !"
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
else:
msg = "Map is NOT empty !"
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# assign the clone code
assigned_number = assigned_number + 1
# update global landmask for river and land
mask_selected_nominal = pcr.ifthen(
mask_selected_boolean_from_clump,
pcr.nominal(assigned_number))
subdomains_final = pcr.cover(subdomains_final,
mask_selected_nominal)
# ~ # kill all aguila processes if exist
# ~ os.system('killall aguila')
pcr.aguila(subdomains_final)
print("")
print("")
print("")
print("The subdomain map is READY.")
pcr.report(subdomains_final, "global_subdomains_30min_final.map")
num_of_masks = int(vos.getMinMaxMean(pcr.scalar(subdomains_final))[1])
print(num_of_masks)
print("")
print("")
print("")
for nr in range(1, num_of_masks + 1, 1):
mask_selected_boolean = pcr.ifthen(subdomains_final == nr,
pcr.boolean(1.0))
xmin, ymin, xmax, ymax = boundingBox(mask_selected_boolean)
area_in_degree2 = (xmax - xmin) * (ymax - ymin)
print(
str(nr) + " ; " + str(area_in_degree2) + " ; " +
str((xmax - xmin)) + " ; " + str((ymax - ymin)))
print("")
print("")
print("")
print("Number of subdomains: " + str(num_of_masks))
print("")
print("")
print("")
# spatial extrapolation in order to cover the entire map
print("spatial interpolation/extrapolation to cover the entire map")
field = subdomains_final
cellID = pcr.nominal(pcr.uniqueid(pcr.defined(field)))
zoneID = pcr.spreadzone(cellID, 0, 1)
field = pcr.areamajority(field, zoneID)
subdomains_final_filled = field
pcr.aguila(subdomains_final_filled)
pcr.report(subdomains_final_filled,
"global_subdomains_30min_final_filled.map")
edwin_code_pcrglobwb_catchment_area_km2)
# make correction if required
abs_diff_value = pcr.cellvalue(pcr.mapmaximum(abs_diff), 1)[0]
usgs_drain_area_km2 = pcr.cellvalue(pcr.mapmaximum(usgs_drain_area_km2),
1)[0]
if (usgs_drain_area_km2 > 1000.0) and \
(abs_diff_value > 0.10 * usgs_drain_area_km2):
# class within 0.1 arc degree windows
edwin_code = pcr.windowmajority(edwin_code, 0.1)
# find the most accurate cell:
areaorder = pcr.areaorder(
pcr.windowmaximum(pcr.spatial(pcr.scalar(usgs_drain_area_km2)),
0.1) - pcrglobwb_catchment_area_km2, edwin_code)
# select pixel
edwin_code = pcr.ifthen(areaorder == 1., edwin_code)
# pcrglobwb catchment area
edwin_code_pcrglobwb_catchment_area_km2 = pcr.ifthen(
areaorder == 1., pcrglobwb_catchment_area_km2)
# save using map2col
pcr.report(edwin_code, "edwin_code.map")
pcr.report(edwin_code_pcrglobwb_catchment_area_km2,
"edwin_code_pcrglobwb_catchment_area_km2.map")
cmd = "map2col edwin_code.map edwin_code_pcrglobwb_catchment_area_km2.map edwin_code_pcrglobwb_catchment_area_km2.tmp"
print(cmd)
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"
)
yMin= -35.0 yMax= 37.5 sampleResolution= 0.5 resampleRatio= 1. #-class to clip map during read clippedRead= pcrPartialRead(xMin,xMax,yMin,yMax,sampleResolution,resampleRatio,clone) cellAreaMap= os.path.join(mapsDir,'cellarea30.map') cellArea= clippedRead.get(cellAreaMap,'scalar') #-channel and floodplain characteristics LDDMap= os.path.join(mapsDir,'lddsound_30min.map') LDD= pcr.lddrepair(clippedRead.get(LDDMap,'ldd')) channelGradient= clippedRead.get(os.path.join(mapsDir,'globalgradchannel.map')) channelWidth= clippedRead.get(os.path.join(mapsDir,'channel_width.map')) channelLength= clippedRead.get(os.path.join(mapsDir,'channel_length.map')) channelDepth= clippedRead.get(os.path.join(mapsDir,'channel_depth.map')) floodplainMask= pcr.spatial(pcr.boolean(1)) # NOTE: set to zero for static, to one for dynamic floodplains channelManN= 0.04 floodplainManN= 0.10 #-flood plain parameterization #-root of file name with maps of relative elvation above floodplain # and associated fractions relZFileName= 'elev%04d.map' areaFractions=[0.0,0.01,0.05,0.10,0.20,0.30,0.40,\ 0.50,0.60,0.70,0.80,0.90,1.00] # reduction parameter of smoothing interval and error threshold reductionKK= 0.5 criterionKK= 40. #-modelSignature if pcr.cellvalue(pcr.mapmaximum(pcr.scalar(floodplainMask)),1)[0] == 1: modelSignature= forcingDataSet+'_dynamic-routing' else:
# finding the month that give the maximum discharge (from the climatology time series)
msg = "Identifying the month with peak discharge (from climatology time series):"
logger.info(msg)
# - read the maximum monthly discharge for every basin
maximum_discharge = vos.netcdf2PCRobjClone(input_files['maximumClimatologyDischargeMonthAvg'], \
"discharge", 1,\
useDoy = "Yes",
cloneMapFileName = clone_map_file,\
LatitudeLongitude = True,\
specificFillValue = None)
maximum_discharge = pcr.areamaximum(\
pcr.cover(maximum_discharge, 0.0), basin_map)
# - find the month with maximum discharge
maximum_month = pcr.spatial(pcr.scalar(1.0))
for i_month in range(1, 12 + 1):
# read the climatology discharge time series
discharge_for_this_month = vos.netcdf2PCRobjClone(input_files['climatologyDischargeMonthAvg'], \
"discharge", i_month,\
useDoy = "Yes",
cloneMapFileName = clone_map_file,\
LatitudeLongitude = True,\
specificFillValue = None)
# upscale it to the basin scale
discharge_for_this_month = pcr.areamaximum(discharge_for_this_month, basin_map)
maximum_month = pcr.ifthenelse(discharge_for_this_month == maximum_discharge, pcr.scalar(i_month), maximum_month)
pcr.report(maximum_month, "maximum_month.map")
# defining the hydrological year type
msg = "Defining the type of hydrological year:"
def report_summary(self, landWaterStoresAtBeginning, landWaterStoresAtEnd,\
surfaceWaterStoresAtBeginning, surfaceWaterStoresAtEnd):
# set total to 0 on first day of the year
if self._modelTime.doy == 1 or self._modelTime.isFirstTimestep():
# set all accumulated variables to zero
self.precipitationAcc = pcr.ifthen(self.landmask, pcr.spatial(pcr.scalar(0.0)))
for var in self.landSurface.fluxVars: vars(self)[var+'Acc'] = pcr.ifthen(self.landmask, pcr.spatial(pcr.scalar(0.0)))
self.baseflowAcc = pcr.ifthen(self.landmask, pcr.spatial(pcr.scalar(0.0)))
self.surfaceWaterInfAcc = pcr.ifthen(self.landmask, pcr.spatial(pcr.scalar(0.0)))
self.runoffAcc = pcr.ifthen(self.landmask, pcr.spatial(pcr.scalar(0.0)))
self.unmetDemandAcc = pcr.ifthen(self.landmask, pcr.spatial(pcr.scalar(0.0)))
self.waterBalanceAcc = pcr.ifthen(self.landmask, pcr.spatial(pcr.scalar(0.0)))
self.absWaterBalanceAcc = pcr.ifthen(self.landmask, pcr.spatial(pcr.scalar(0.0)))
# non irrigation water use (unit: m)
self.nonIrrigationWaterUseAcc = pcr.ifthen(self.landmask, pcr.spatial(pcr.scalar(0.0)))
# non irrigation return flow to water body and water body evaporation (unit: m)
self.nonIrrReturnFlowAcc = pcr.ifthen(self.landmask, pcr.spatial(pcr.scalar(0.0)))
self.waterBodyEvaporationAcc = pcr.ifthen(self.landmask, pcr.spatial(pcr.scalar(0.0)))
# surface water input/loss volume (m3) and outgoing volume (m3) at pits
self.surfaceWaterInputAcc = pcr.ifthen(self.landmask, pcr.spatial(pcr.scalar(0.0)))
self.dischargeAtPitAcc = pcr.ifthen(self.landmask, pcr.spatial(pcr.scalar(0.0)))
# also save the storages at the first day of the year (or the first time step)
# - land surface storage (unit: m)
self.storageAtFirstDay = pcr.ifthen(self.landmask, landWaterStoresAtBeginning)
# - channel storages (unit: m3)
self.channelVolumeAtFirstDay = pcr.ifthen(self.landmask, surfaceWaterStoresAtBeginning)
# accumulating until the last day of the year:
self.precipitationAcc += self.meteo.precipitation
for var in self.landSurface.fluxVars: vars(self)[var+'Acc'] += vars(self.landSurface)[var]
self.baseflowAcc += self.groundwater.baseflow
self.surfaceWaterInfAcc += self.groundwater.surfaceWaterInf
self.runoffAcc += self.routing.runoff
self.unmetDemandAcc += self.groundwater.unmetDemand
self.waterBalance = \
(landWaterStoresAtBeginning - landWaterStoresAtEnd +\
self.meteo.precipitation + self.landSurface.irrGrossDemand + self.groundwater.surfaceWaterInf -\
self.landSurface.actualET - self.routing.runoff - self.groundwater.nonFossilGroundwaterAbs)
self.waterBalanceAcc += self.waterBalance
self.absWaterBalanceAcc += pcr.abs(self.waterBalance)
# consumptive water use for non irrigation demand (m)
self.nonIrrigationWaterUseAcc += self.routing.nonIrrWaterConsumption
self.waterBodyEvaporationAcc += self.routing.waterBodyEvaporation
self.surfaceWaterInputAcc += self.routing.local_input_to_surface_water # unit: m3
self.dischargeAtPitAcc += self.routing.outgoing_volume_at_pits # unit: m3
if self._modelTime.isLastDayOfYear() or self._modelTime.isLastTimeStep():
logger.info("")
msg = 'The following summary values do not include storages in surface water bodies (lake, reservoir and channel storages).'
logger.info(msg) # TODO: Improve these water balance checks.
totalCellArea = vos.getMapTotal(pcr.ifthen(self.landmask, self.routing.cellArea))
msg = 'Total area = %e km2'\
% (totalCellArea/1e6)
logger.info(msg)
deltaStorageOneYear = vos.getMapVolume( \
pcr.ifthen(self.landmask,landWaterStoresAtBeginning) - \
pcr.ifthen(self.landmask,self.storageAtFirstDay),
self.routing.cellArea)
msg = 'Delta total storage days 1 to %i in %i = %e km3 = %e mm'\
% ( int(self._modelTime.doy),\
int(self._modelTime.year),\
deltaStorageOneYear/1e9,\
deltaStorageOneYear*1000/totalCellArea)
logger.info(msg)
variableList = ['precipitation',
'baseflow',
'surfaceWaterInf',
'runoff',
'unmetDemand']
variableList += self.landSurface.fluxVars
variableList += ['waterBalance','absWaterBalance','irrigationEvaporationWaterUse','nonIrrigationWaterUse']
# consumptive water use for irrigation (unit: m)
self.irrigationEvaporationWaterUseAcc = vos.getValDivZero(self.irrGrossDemandAcc,\
self.precipitationAcc + self.irrGrossDemandAcc) * self.actualETAcc
for var in variableList:
volume = vos.getMapVolume(\
self.__getattribute__(var + 'Acc'),\
self.routing.cellArea)
#~ # an eperiment: To test an improved version of map total - still need to be tested
#~ if var == "actSurfaceWaterAbstract" or var == "allocSurfaceWaterAbstract":
#~ volume = vos.getMapTotalHighPrecisionButOnlyForPositiveValues(self.__getattribute__(var + 'Acc') * self.routing.cellArea)
msg = 'Accumulated %s days 1 to %i in %i = %e km3 = %e mm'\
% (var,int(self._modelTime.doy),\
int(self._modelTime.year),volume/1e9,volume*1000/totalCellArea) # TODO: Calculation does not always start from day 1.
logger.info(msg)
logger.info("")
msg = 'The following summary is for surface water bodies.'
logger.info(msg)
deltaChannelStorageOneYear = vos.getMapTotal( \
pcr.ifthen(self.landmask,surfaceWaterStoresAtEnd) - \
pcr.ifthen(self.landmask,self.channelVolumeAtFirstDay))
msg = 'Delta surface water storage days 1 to %i in %i = %e km3 = %e mm'\
% ( int(self._modelTime.doy),\
int(self._modelTime.year),\
deltaChannelStorageOneYear/1e9,\
deltaChannelStorageOneYear*1000/totalCellArea)
logger.info(msg)
variableList = ['waterBodyEvaporation']
for var in variableList:
volume = vos.getMapVolume(\
self.__getattribute__(var + 'Acc'),\
self.routing.cellArea)
msg = 'Accumulated %s days 1 to %i in %i = %e km3 = %e mm'\
% (var,int(self._modelTime.doy),\
int(self._modelTime.year),volume/1e9,volume*1000/totalCellArea)
logger.info(msg)
# surface water balance check
surfaceWaterInputTotal = vos.getMapTotal(self.surfaceWaterInputAcc)
msg = 'Accumulated %s days 1 to %i in %i = %e km3 = %e mm'\
% ("surfaceWaterInput",int(self._modelTime.doy),\
int(self._modelTime.year),surfaceWaterInputTotal/1e9,surfaceWaterInputTotal*1000/totalCellArea)
logger.info(msg)
dischargeAtPitTotal = vos.getMapTotal(self.dischargeAtPitAcc)
msg = 'Accumulated %s days 1 to %i in %i = %e km3 = %e mm'\
% ("dischargeAtPitTotal",int(self._modelTime.doy),\
int(self._modelTime.year),dischargeAtPitTotal/1e9, dischargeAtPitTotal*1000/totalCellArea)
logger.info(msg)
surfaceWaterBalance = deltaChannelStorageOneYear - surfaceWaterInputTotal + dischargeAtPitTotal
msg = 'Accumulated %s days 1 to %i in %i = %e km3 = %e mm'\
% ("surfaceWaterBalance",int(self._modelTime.doy),\
int(self._modelTime.year),surfaceWaterBalance/1e9, surfaceWaterBalance*1000/totalCellArea)
logger.info(msg)
def waterBalance( fluxesIn, fluxesOut, deltaStorages, processName, PrintOnlyErrors, dateStr,threshold=1e-5):
""" Returns the water balance for a list of input, output, and storage map files and """
inMap = pcr.spatial(pcr.scalar(0.0))
dsMap = pcr.spatial(pcr.scalar(0.0))
outMap = pcr.spatial(pcr.scalar(0.0))
inflow = 0
outflow = 0
deltaS = 0
for fluxIn in fluxesIn:
inflow += getMapTotal(fluxIn)
inMap += fluxIn
for fluxOut in fluxesOut:
outflow += getMapTotal(fluxOut)
outMap += fluxOut
for deltaStorage in deltaStorages:
deltaS += getMapTotal(deltaStorage)
dsMap += deltaStorage
#if PrintOnlyErrors:
a,b,c = getMinMaxMean(inMap + dsMap- outMap)
# if abs(a) > 1e-5 or abs(b) > 1e-5:
# if abs(a) > 1e-4 or abs(b) > 1e-4:
if abs(a) > threshold or abs(b) > threshold:
print "WBError %s Min %f Max %f Mean %f" %(processName,a,b,c)
# if abs(inflow + deltaS - outflow) > 1e-5:
# print "Water balance Error for %s on %s: in = %f\tout=%f\tdeltaS=%f\tBalance=%f" \
# %(processName,dateStr,inflow,outflow,deltaS,inflow + deltaS - outflow)
#else:
# print "Water balance for %s: on %s in = %f\tout=%f\tdeltaS=%f\tBalance=%f" \
# %(processName,dateStr,inflow,outflow,deltaS,inflow + deltaS - outflow)
wb = inMap + dsMap - outMap
maxWBError = pcr.cellvalue(pcr.mapmaximum(pcr.abs(wb)), 1, 1)[0]
#if maxWBError > 0.001 / 1000:
#row = 0
#col = 0
#cellID = 1
#troubleCell = 0
#print "Water balance for %s on %s: %f mm !!! " %(processName,dateStr,maxWBError * 1000)
#pcr.report(wb,"%s-WaterBalanceError-%s" %(processName,dateStr))
#npWBMError = pcr2numpy(wb, -9999)
#(nr, nc) = np.shape(npWBMError)
#for r in range(0, nr):
#for c in range(0, nc):
## print r,c
#if npWBMError[r, c] != -9999.0:
#val = npWBMError[r, c]
#if math.fabs(val) > 0.0001 / 1000:
## print npWBMError[r,c]
#row = r
#col = c
#troubleCell = cellID
#cellID += 1
#print 'Water balance for %s on %s: %f mm row %i col %i cellID %i!!! ' % (
#processName,
#dateStr,
#maxWBError * 1000,
#row,
#col,
#troubleCell,
#)
return inMap + dsMap - outMap
def modflow_simulation(self,\
simulation_type,\
initial_head,\
currTimeStep = None,\
PERLEN = 1.0,
NSTP = 1, \
HCLOSE = 1.0,\
RCLOSE = 10.* 400.*400.,\
MXITER = 50,\
ITERI = 30,\
NPCOND = 1,\
RELAX = 1.00,\
NBPOL = 2,\
DAMP = 1,\
ITMUNI = 4, LENUNI = 2, TSMULT = 1.0):
# initiate pcraster modflow object if modflow is not called yet:
if self.modflow_has_been_called == False or self.modflow_converged == False:
self.initiate_modflow()
self.modflow_has_been_called = True
if simulation_type == "transient":
logger.info("Preparing MODFLOW input for a transient simulation.")
SSTR = 0
if simulation_type == "steady-state":
logger.info("Preparing MODFLOW input for a steady-state simulation.")
SSTR = 1
# waterBody class to define the extent of lakes and reservoirs
#
if simulation_type == "steady-state":
self.WaterBodies = waterBodies.WaterBodies(self.iniItems,\
self.landmask,\
self.onlyNaturalWaterBodies)
self.WaterBodies.getParameterFiles(date_given = self.iniItems.globalOptions['startTime'],\
cellArea = self.cellAreaMap, \
ldd = self.lddMap)
#
if simulation_type == "transient":
if currTimeStep.timeStepPCR == 1:
self.WaterBodies = waterBodies.WaterBodies(self.iniItems,\
self.landmask,\
self.onlyNaturalWaterBodies)
if currTimeStep.timeStepPCR == 1 or currTimeStep.doy == 1:
self.WaterBodies.getParameterFiles(date_given = str(currTimeStep.fulldate),\
cellArea = self.cellAreaMap, \
ldd = self.lddMap)
print "here"
# using dem_average as the initial groundwater head value
self.pcr_modflow.setInitialHead(initial_head, 1)
# set parameter values for the DIS package and PCG solver
self.pcr_modflow.setDISParameter(ITMUNI, LENUNI, PERLEN, NSTP, TSMULT, SSTR)
self.pcr_modflow.setPCG(MXITER, ITERI, NPCOND, HCLOSE, RCLOSE, RELAX, NBPOL, DAMP)
#
# Some notes about the values
#
# ITMUNI = 4 # indicates the time unit (0: undefined, 1: seconds, 2: minutes, 3: hours, 4: days, 5: years)
# LENUNI = 2 # indicates the length unit (0: undefined, 1: feet, 2: meters, 3: centimeters)
# PERLEN = 1.0 # duration of a stress period
# NSTP = 1 # number of time steps in a stress period
# TSMULT = 1.0 # multiplier for the length of the successive iterations
# SSTR = 1 # 0 - transient, 1 - steady state
#
# MXITER = 50 # maximum number of outer iterations # Deltares use 50
# ITERI = 30 # number of inner iterations # Deltares use 30
# NPCOND = 1 # 1 - Modified Incomplete Cholesky, 2 - Polynomial matrix conditioning method;
# HCLOSE = 0.01 # HCLOSE (unit: m)
# RCLOSE = 10.* 400.*400. # RCLOSE (unit: m3)
# RELAX = 1.00 # relaxation parameter used with NPCOND = 1
# NBPOL = 2 # indicates whether the estimate of the upper bound on the maximum eigenvalue is 2.0 (but we don ot use it, since NPCOND = 1)
# DAMP = 1 # no damping (DAMP introduced in MODFLOW 2000)
# read input files (for the steady-state condition, we use pcraster maps):
if simulation_type == "steady-state":
# - discharge (m3/s) from PCR-GLOBWB
discharge = vos.readPCRmapClone(self.iniItems.modflowSteadyStateInputOptions['avgDischargeInputMap'],\
self.cloneMap, self.tmpDir, self.inputDir)
# - recharge/capillary rise (unit: m/day) from PCR-GLOBWB
gwRecharge = vos.readPCRmapClone(self.iniItems.modflowSteadyStateInputOptions['avgGroundwaterRechargeInputMap'],\
self.cloneMap, self.tmpDir, self.inputDir)
if self.ignoreCapRise: gwRecharge = pcr.max(0.0, gwRecharge)
gwAbstraction = pcr.spatial(pcr.scalar(0.0))
# read input files (for the transient, input files are given in netcdf files):
if simulation_type == "transient":
# - discharge (m3/s) from PCR-GLOBWB
discharge = vos.netcdf2PCRobjClone(self.iniItems.modflowTransientInputOptions['dischargeInputNC'],
"discharge",str(currTimeStep.fulldate),None,self.cloneMap)
# - recharge/capillary rise (unit: m/day) from PCR-GLOBWB
gwRecharge = vos.netcdf2PCRobjClone(self.iniItems.modflowTransientInputOptions['groundwaterRechargeInputNC'],\
"groundwater_recharge",str(currTimeStep.fulldate),None,self.cloneMap)
if self.ignoreCapRise: gwRecharge = pcr.max(0.0, gwRecharge)
# - groundwater abstraction (unit: m/day) from PCR-GLOBWB
gwAbstraction = vos.netcdf2PCRobjClone(self.iniItems.modflowTransientInputOptions['groundwaterAbstractionInputNC'],\
"total_groundwater_abstraction",str(currTimeStep.fulldate),None,self.cloneMap)
# set recharge, river, well and drain packages
self.set_river_package(discharge, currTimeStep)
self.set_recharge_package(gwRecharge)
self.set_well_package(gwAbstraction)
self.set_drain_package()
# execute MODFLOW
logger.info("Executing MODFLOW.")
self.pcr_modflow.run()
logger.info("Check if the model whether a run has converged or not")
self.modflow_converged = self.check_modflow_convergence()
if self.modflow_converged == False:
msg = "MODFLOW FAILED TO CONVERGE with HCLOSE = "+str(HCLOSE)+" and RCLOSE = "+str(RCLOSE)
logger.info(msg)
# iteration index for the RCLOSE
self.iteration_RCLOSE += 1
# reset if the index has reached the length of available criteria
if self.iteration_RCLOSE > (len(self.criteria_RCLOSE)-1): self.iteration_RCLOSE = 0
# iteration index for the HCLOSE
if self.iteration_RCLOSE == 0: self.iteration_HCLOSE += 1
# we have to reset modflow as we want to change the PCG setup
self.modflow_has_been_called = False
# for the steady state simulation, we still save the calculated head as the initial estimate for the next iteration
if simulation_type == "steady-state": self.groundwaterHead = self.pcr_modflow.getHeads(1)
# NOTE: We cannot implement this principle for transient simulation
else:
msg = "HURRAY!!! MODFLOW CONVERGED with HCLOSE = "+str(HCLOSE)+" and RCLOSE = "+str(RCLOSE)
logger.info(msg)
# reset the iteration because modflow has converged
self.iteration_HCLOSE = 0
self.iteration_RCLOSE = 0
self.modflow_has_been_called = True
# obtaining the results from modflow simulation
self.groundwaterHead = None
self.groundwaterHead = self.pcr_modflow.getHeads(1)
# calculate groundwater depth only in the landmask region
self.groundwaterDepth = pcr.ifthen(self.landmask, self.dem_average - self.groundwaterHead)
def main():
# output folder (and tmp folder)
clean_out_folder = True
if os.path.exists(out_folder):
if clean_out_folder:
shutil.rmtree(out_folder)
os.makedirs(out_folder)
else:
os.makedirs(out_folder)
os.chdir(out_folder)
os.system("pwd")
# set the clone map
print("set the clone")
pcr.setclone(global_ldd_30min_inp_file)
# define the landmask
print("define the landmask")
# - based on the 30min input
landmask_30min = define_landmask(input_file = global_landmask_30min_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_30min_only")
# - based on the 05min input
landmask_05min = define_landmask(input_file = global_landmask_05min_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_05min_only")
# - based on the 30sec input
landmask_30sec = define_landmask(input_file = global_landmask_30sec_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_30sec_only")
# - based on the 30sec input
landmask_03sec = define_landmask(input_file = global_landmask_03sec_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_03sec_only")
#
# - merge all landmasks
landmask = pcr.cover(landmask_30min, landmask_05min, landmask_30sec,
landmask_03sec)
pcr.report(landmask, "global_landmask_30min_final.map")
# ~ pcr.aguila(landmask)
# extend ldd
print("extend/define the ldd")
ldd_map = pcr.readmap(global_ldd_30min_inp_file)
ldd_map = pcr.ifthen(landmask, pcr.cover(ldd_map, pcr.ldd(5)))
pcr.report(ldd_map, "global_ldd_final.map")
# ~ pcr.aguila(ldd_map)
# identify small islands
print("identify small islands")
# - maps of islands smaller than 10000 cells (at half arc degree resolution)
island_map = pcr.ifthen(landmask, pcr.clump(pcr.defined(ldd_map)))
island_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), island_map)
island_map = pcr.ifthen(island_size < 10000., island_map)
UNTIL_THIS
# identify the biggest island for every group of small islands within the windows 10x10 arcdeg cells
# - sort from the largest catchment
catchment_pits_boolean = pcr.ifthen(
pcr.scalar(pcr.pit(ldd_map)) > 0.0, pcr.boolean(1.0))
catchment_pits_boolean = pcr.ifthen(catchment_pits_boolean,
catchment_pits_boolean)
pcr.aguila(catchment_pits_boolean)
catchment_map = pcr.nominal(
pcr.areaorder(catchment_size * -1.0,
pcr.nominal(catchment_pits_boolean)))
island_map = pcr.ifthen(island_size == pcr.windowmaximum(island_size, 10.),
island_map)
pcr.aguila(island_map)
# identify big catchments
# merge biggest islands and big catchments
# make catchment and island map
print("make catchment and island map")
# - catchment map
catchment_map = pcr.catchment(ldd_map, pcr.pit(ldd_map))
pcr.report(catchment_map, "global_catchment_not_sorted.map")
os.system("mapattr -p global_catchment_not_sorted.map")
num_of_catchments = int(vos.getMinMaxMean(pcr.scalar(catchment_map))[1])
# - maps of islands smaller than 10000 cells
island_map = pcr.ifthen(landmask, pcr.clump(pcr.defined(ldd_map)))
island_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), island_map)
island_map = pcr.ifthen(island_size < 10000., island_map)
island_map = pcr.nominal(
pcr.scalar(pcr.ifthen(landmask, pcr.clump(island_map))) +
pcr.scalar(num_of_catchments) * 100.)
pcr.aguila(island_map)
island_size = pcr.ifthen(pcr.defined(island_map), island_size)
island_map = pcr.ifthen(island_size == pcr.windowmaximum(island_size, 10.),
island_map)
pcr.aguila(island_map)
catchment_map = pcr.cover(island_map, catchment_map)
catchment_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), catchment_map)
# - calculate the size
catchment_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), catchment_map)
# - sort from the largest catchment
catchment_pits_boolean = pcr.ifthen(
pcr.scalar(pcr.pit(ldd_map)) > 0.0, pcr.boolean(1.0))
catchment_pits_boolean = pcr.ifthen(catchment_pits_boolean,
catchment_pits_boolean)
pcr.aguila(catchment_pits_boolean)
catchment_map = pcr.nominal(
pcr.areaorder(catchment_size * -1.0,
pcr.nominal(catchment_pits_boolean)))
catchment_map = pcr.catchment(ldd_map, catchment_map)
pcr.report(catchment_map, "global_catchment_final.map")
os.system("mapattr -p global_catchment_final.map")
# - calculate the size
catchment_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), catchment_map)
pcr.report(catchment_size, "global_catchment_size_in_number_of_cells.map")
# number of catchments
num_of_catchments = int(vos.getMinMaxMean(pcr.scalar(catchment_map))[1])
# size of the largest catchment
catchment_size_max = vos.getMinMaxMean(catchment_size)[1]
print("")
print(str(float(catchment_size_max)))
print("")
# identify all large catchments with size >= 50 cells (at the resolution of 30 arcmin) = 50 x (50^2) km2 = 125000 km2
print("identify catchments with the minimum size of 50 cells")
catchment_map_ge_50 = pcr.ifthen(catchment_size >= 50, catchment_map)
pcr.report(catchment_map_ge_50, "global_catchment_ge_50_cells.map")
# include the island
catchment_map_ge_50 = pcr.cover(island_map, catchment_map_ge_50)
# perform cdo fillmiss2 in order to merge the small catchments to the nearest large catchments
cmd = "gdal_translate -of NETCDF global_catchment_ge_50_cells.map global_catchment_ge_50_cells.nc"
print(cmd)
os.system(cmd)
cmd = "cdo fillmiss2 global_catchment_ge_50_cells.nc global_catchment_ge_50_cells_filled.nc"
print(cmd)
os.system(cmd)
cmd = "cdo fillmiss2 global_catchment_ge_50_cells_filled.nc global_catchment_ge_50_cells_filled.nc"
print(cmd)
os.system(cmd)
cmd = "gdal_translate -of PCRaster global_catchment_ge_50_cells_filled.nc global_catchment_ge_50_cells_filled.map"
print(cmd)
os.system(cmd)
cmd = "mapattr -c " + global_ldd_30min_inp_file + " " + "global_catchment_ge_50_cells_filled.map"
print(cmd)
os.system(cmd)
# - initial subdomains
subdomains_initial = pcr.nominal(
pcr.readmap("global_catchment_ge_50_cells_filled.map"))
subdomains_initial = pcr.areamajority(subdomains_initial, catchment_map)
pcr.aguila(subdomains_initial)
# - initial subdomains clump
subdomains_initial_clump = pcr.clump(subdomains_initial)
pcr.aguila(subdomains_initial_clump)
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[0])))
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[1])))
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial_clump))[0])))
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial_clump))[1])))
# - remove temporay files (not used)
cmd = "rm global_catchment_ge_50_cells_filled*"
print(cmd)
os.system(cmd)
# clone code that will be assigned
assigned_number = 0
def dynamic(self): ##################### # * dynamic section # ##################### #-evaluation of the current date: return current month and the time step used #-reading in fluxes over land and water area for current time step [m/d] # and read in reservoir demand and surface water extraction [m3] try: self.landSurfaceQ= clippedRead.get(pcrm.generateNameT(landSurfaceQFileName,self.currentTimeStep())) except: pass try: self.potWaterSurfaceQ= clippedRead.get(pcrm.generateNameT(waterSurfaceQFileName,self.currentTimeStep())) except: pass #-surface water extraction and reservoir demand currently set to zero, should # be computed automatically and updated to reservoirs self.potSurfaceWaterExtraction= pcr.spatial(pcr.scalar(0.)) #self.waterBodies.demand= #self.reservoirDemandTSS.assignID(self.waterBodies.ID,self.currentTimeStep(),0.)*self.timeSec #-initialization of cumulative values of actual water extractions self.actWaterSurfaceQ= pcr.spatial(pcr.scalar(0.)) self.actSurfaceWaterExtraction= pcr.spatial(pcr.scalar(0.)) #-definition of sub-loop for routing scheme - explicit scheme has to satisfy Courant condition timeLimit= pcr.cellvalue(pcr.mapminimum((pcr.cover(pcr.ifthen(self.waterBodies.distribution == 0,\ self.channelLength/self.flowVelocity),\ self.timeSec/self.nrIterDefault)*self.timeSec/self.nrIterDefault)**0.5),1)[0] nrIter= int(self.timeSec/timeLimit) nrIter= min(nrIter,int(self.timeSec/300.)) while float(self.timeSec/nrIter) % 1 <> 0: nrIter+= 1 deltaTime= self.timeSec/nrIter #-sub-loop for current time step if self.currentDate.day == 1 or nrIter >= 24: print '\n*\tprocessing %s, currently using %d substeps of %d seconds\n' % \ (self.currentDate.date(),nrIter,deltaTime) #-update discharge and storage for nrICur in range(nrIter): #-initializing discharge for the current sub-timestep and fill in values # for channels and at outlets of waterbodies # * channels * estQ= pcr.ifthenelse((self.actualStorage > 0.) & (self.waterBodies.distribution == 0) ,\ (self.wettedArea/self.alphaQ)**(1./self.betaQ),0.) #estQ= pcr.ifthenelse((self.actualStorage > 0.) & (self.waterBodies.distribution == 0) ,\ #0.5*(self.Q+(self.wettedArea/self.alphaQ)**(1./self.betaQ)),0.) #estQ= pcr.min(estQ,self.actualStorage/deltaTime) self.report(estQ,'results/qest') self.Q= pcr.spatial(pcr.scalar(0.)) self.Q= pcr.ifthenelse(self.waterBodies.distribution == 0,\ pcr.kinematic(self.channelLDD,estQ,0.,self.alphaQ,\ self.betaQ,1,deltaTime,self.channelLength),self.Q) # * water bodies * self.waterBodies.dischargeUpdate() self.Q= self.waterBodies.returnMapValue(self.Q,self.waterBodies.actualQ) #-fluxes and resulting change in storage: first the local fluxes are evaluated # and aggregated over the water bodies where applicable; this includes the specific runoff [m/day/m2] # from input and the estimated extraction from surface water as volume per day [m3/day]; # specific runoff from the land surface is always positive whereas the fluxes over the water surface # are potential, including discharge, and are adjusted to match the availabe storage; to this end, # surface water storage and fluxes over water bodies are totalized and assigned to the outlet; # discharge is updated in a separate step, after vertical fluxes are compared to the actual storage deltaActualStorage= ((self.landFraction*self.landSurfaceQ+\ self.waterFraction*self.potWaterSurfaceQ)*self.cellArea-\ self.potSurfaceWaterExtraction)*float(self.duration)/nrIter deltaActualStorage= pcr.ifthenelse(self.waterBodies.distribution != 0,\ pcr.ifthenelse(self.waterBodies.location != 0,\ pcr.areatotal(deltaActualStorage,self.waterBodies.distribution),0),\ deltaActualStorage) adjustmentRatio= pcr.ifthenelse(deltaActualStorage < 0.,\ pcr.min(1.,-self.actualStorage/deltaActualStorage),1.) self.actWaterSurfaceQ+= adjustmentRatio*self.potWaterSurfaceQ self.actSurfaceWaterExtraction+= adjustmentRatio*self.actSurfaceWaterExtraction deltaActualStorage*= adjustmentRatio #-local water balance check if testLocalWaterBalance: differenceActualStorage= self.actualStorage differenceActualStorage+= deltaActualStorage #-overall water balance check: net input self.cumulativeDeltaStorage+= pcr.catchmenttotal(deltaActualStorage,self.LDD) #-update storage first with local changes, then balance discharge with storage and update storage # with lateral flow and return value to water bodies self.actualStorage+= deltaActualStorage self.actualStorage= pcr.max(0.,self.actualStorage) self.Q= pcr.min(self.Q,self.actualStorage/deltaTime) deltaActualStorage= (-self.Q+pcr.upstream(self.LDD,self.Q))*deltaTime deltaActualStorage= pcr.ifthenelse(self.waterBodies.distribution != 0,\ pcr.ifthenelse(self.waterBodies.location != 0,\ pcr.areatotal(deltaActualStorage,self.waterBodies.distribution),0),\ deltaActualStorage) self.actualStorage+= deltaActualStorage self.actualStorage= pcr.max(0.,self.actualStorage) self.waterBodies.actualStorage= self.waterBodies.retrieveMapValue(self.actualStorage) #-flooded fraction returned floodedFraction,floodedDepth,\ self.wettedArea,self.alphaQ= self.kinAlphaComposite(self.actualStorage,self.floodplainMask) self.wettedArea= self.waterBodies.returnMapValue(self.wettedArea,\ self.waterBodies.channelWidth+2.*self.waterBodies.updateWaterHeight()) self.waterFraction= pcr.ifthenelse(self.waterBodies.distribution == 0,\ pcr.max(self.waterFractionMask,floodedFraction),self.waterFractionMask) self.landFraction= pcr.max(0.,1.-self.waterFraction) self.flowVelocity= pcr.ifthenelse(self.wettedArea > 0,self.Q/self.wettedArea,0.) #-local water balance check if testLocalWaterBalance: differenceActualStorage+= deltaActualStorage differenceActualStorage-= self.actualStorage totalDifference= pcr.cellvalue(pcr.maptotal(differenceActualStorage),1)[0] minimumDifference= pcr.cellvalue(pcr.mapminimum(differenceActualStorage),1)[0] maximumDifference= pcr.cellvalue(pcr.mapmaximum(differenceActualStorage),1)[0] if abs(totalDifference) > 1.e-3: print 'water balance error: total %e; min %e; max %e' %\ (totalDifference,minimumDifference,maximumDifference) if reportLocalWaterBalance: pcr.report(differenceActualStorage,'mbe_%s.map' % self.currentDate.date()) #-overall water balance check: updating cumulative discharge and total storage [m3] self.totalDischarge+= self.Q*deltaTime self.totalStorage= pcr.catchmenttotal(self.actualStorage,self.LDD) #-check on occurrence of last day and report mass balance if self.currentDate == self.endDate: #-report initial maps pcr.report(self.Q,self.QIniMap) pcr.report(self.actualStorage,self.actualStorageIniMap) #-return relative and absolute water balance error per cell and # as total at basin outlets self.totalDischarge= pcr.ifthen((self.waterBodies.distribution == 0) | \ (self.waterBodies.location != 0),self.totalDischarge) self.cumulativeDeltaStorage= pcr.ifthen((self.waterBodies.distribution == 0) | \ (self.waterBodies.location != 0),self.cumulativeDeltaStorage) massBalanceError= self.totalStorage+self.totalDischarge-\ self.cumulativeDeltaStorage relMassBalanceError= 1.+pcr.ifthenelse(self.cumulativeDeltaStorage <> 0., massBalanceError/self.cumulativeDeltaStorage,0.) totalMassBalanceError= pcr.cellvalue(pcr.maptotal(pcr.ifthen(self.basinOutlet,\ massBalanceError)),1)[0] totalCumulativeDeltaStorage= pcr.cellvalue(pcr.maptotal(pcr.ifthen(self.basinOutlet,\ self.cumulativeDeltaStorage)),1)[0] if totalCumulativeDeltaStorage > 0: totalRelativeMassBalanceError= 1.+totalMassBalanceError/totalCumulativeDeltaStorage else: totalRelativeMassBalanceError= 1. #-report maps and echo value pcr.report(massBalanceError,mbeFileName) pcr.report(relMassBalanceError,mbrFileName) print '\n*\ttotal global mass balance error [m3]: %8.3g' % totalMassBalanceError print '\n*\trelative global mass balance error [-]: %5.3f' % totalRelativeMassBalanceError #-echo to screen: total mass balance error and completion of run print '\trun completed' #-end of day: return states and fluxes #-get surface water attributes? if getSurfaceWaterAttributes: #-compute the following secondary variables: # surface water area [m2]: area given dynamic surface water fraction # residence time [days]: volume over discharge, assigned -1 in case discharge is zero # surface water depth [m], weighed by channel and floodplain volume surfaceWaterArea= self.waterFraction*self.cellArea surfaceWaterArea= pcr.ifthenelse(self.waterBodies.distribution != 0,\ pcr.ifthenelse(self.waterBodies.location != 0,\ pcr.areatotal(surfaceWaterArea,self.waterBodies.distribution),0),\ surfaceWaterArea) surfaceWaterResidenceTime= pcr.ifthenelse(self.Q > 0.,self.actualStorage/(self.Q*self.timeSec),-1) surfaceWaterDepth= pcr.ifthenelse(self.actualStorage > 0.,\ pcr.max(0.,self.actualStorage-self.channelStorageCapacity)**2/\ (self.actualStorage*surfaceWaterArea),0.) surfaceWaterDepth+= pcr.ifthenelse(self.actualStorage > 0.,\ pcr.min(self.channelStorageCapacity,self.actualStorage)**2/(self.waterFractionMask*\ self.cellArea*self.actualStorage),0.) #-reports: values at outlet of lakes or reservoirs are assigned to their full extent self.report(pcr.ifthenelse(self.waterBodies.distribution != 0,\ pcr.areamaximum(surfaceWaterArea,self.waterBodies.distribution),surfaceWaterArea),\ surfaceWaterAreaFileName) self.report(pcr.ifthenelse(self.waterBodies.distribution != 0,\ pcr.areamaximum(surfaceWaterResidenceTime,self.waterBodies.distribution),surfaceWaterResidenceTime),\ surfaceWaterResidenceTimeFileName) self.report(pcr.ifthenelse(self.waterBodies.distribution != 0,\ pcr.areamaximum(surfaceWaterDepth,self.waterBodies.distribution),surfaceWaterDepth),\ surfaceWaterDepthFileName) #-reports on standard output: values at outlet of lakes or reservoirs are assigned to their full extent self.report(pcr.ifthenelse(self.waterBodies.distribution != 0, pcr.areamaximum(self.flowVelocity,self.waterBodies.distribution),self.flowVelocity),flowVelocityFileName) self.report(pcr.ifthenelse(self.waterBodies.distribution != 0, pcr.areamaximum(self.Q,self.waterBodies.distribution),self.Q),QFileName) self.report(pcr.ifthenelse(self.waterBodies.distribution == 0,\ floodedFraction,0.),floodedFractionFileName) self.report(pcr.ifthenelse(self.waterBodies.distribution == 0,\ floodedDepth,0.),floodedDepthFileName) self.report(self.actualStorage,actualStorageFileName) #-update date for time step and report relevant daily output self.currentDate= self.currentDate+datetime.timedelta(self.duration)
mldd = pcraster.mldd.initialise(pcraster.clone())
mldd.setStream(
# TODO Create overload which takes strings, or handle internally.
pcraster.readmap(os.path.join(dataPath, "ELddF000.out")),
pcraster.readmap(os.path.join(dataPath, "NELddF00.out")),
pcraster.readmap(os.path.join(dataPath, "NLddF000.out")),
pcraster.readmap(os.path.join(dataPath, "NWLddF00.out")),
pcraster.readmap(os.path.join(dataPath, "SELddF00.out")),
pcraster.readmap(os.path.join(dataPath, "SLddF000.out")),
pcraster.readmap(os.path.join(dataPath, "SWLddF00.out")),
pcraster.readmap(os.path.join(dataPath, "WLddF000.out")))
mldd.addStream(
pcraster.readmap(os.path.join(dataPath, "ELddF000.out")))
mldd.setDem(pcraster.spatial(pcraster.scalar(1)))
upstream = mldd.upstream(pcraster.spatial(pcraster.scalar(1)))
pcraster.report(upstream, "upstream.map")
accuflux = mldd.accuflux(pcraster.ifthen(pcraster.defined(upstream),
pcraster.spatial(pcraster.scalar(1))))
pcraster.report(accuflux, "accuflux.map")
dem = mldd.getDem()
pcraster.report(dem, "dem.map")
streamN, streamNE, streamE, streamSE, streamS, streamSW, streamW, streamNW = \
mldd.getStream()
def read_forcings(self, currTimeStep):
#-----------------------------------------------------------------------
# NOTE: RvB 13/07/2016 hard-coded reference to the variable names
# preciptiation, temperature and evapotranspiration have been replaced
# by the variable names used in the netCDF and passed from the ini file
#-----------------------------------------------------------------------
# method for finding time indexes in the precipitation netdf file:
# - the default one
method_for_time_index = None
# - based on the ini/configuration file (if given)
if 'time_index_method_for_precipitation_netcdf' in list(self.iniItems.meteoOptions.keys()) and\
self.iniItems.meteoOptions['time_index_method_for_precipitation_netcdf'] != "None":
method_for_time_index = self.iniItems.meteoOptions[
'time_index_method_for_precipitation_netcdf']
# reading precipitation:
if self.precipitation_set_per_year:
#~ print currTimeStep.year
nc_file_per_year = self.preFileNC % (float(
currTimeStep.year), float(currTimeStep.year))
self.precipitation = vos.netcdf2PCRobjClone(\
nc_file_per_year, self.preVarName,\
str(currTimeStep.fulldate),
useDoy = method_for_time_index,
cloneMapFileName = self.cloneMap,\
LatitudeLongitude = True)
else:
self.precipitation = vos.netcdf2PCRobjClone(\
self.preFileNC, self.preVarName,\
str(currTimeStep.fulldate),
useDoy = method_for_time_index,
cloneMapFileName = self.cloneMap,\
LatitudeLongitude = True)
#-----------------------------------------------------------------------
# NOTE: RvB 13/07/2016 added to automatically update precipitation
self.precipitation = self.preConst + self.preFactor * pcr.ifthen(
self.landmask, self.precipitation)
#-----------------------------------------------------------------------
# make sure that precipitation is always positive
self.precipitation = pcr.max(0., self.precipitation)
self.precipitation = pcr.cover(self.precipitation, 0.0)
# ignore very small values of precipitation (less than 0.00001 m/day or less than 0.01 kg.m-2.day-1 )
if self.usingDailyTimeStepForcingData:
self.precipitation = pcr.rounddown(
self.precipitation * 100000.) / 100000.
# method for finding time index in the temperature netdf file:
# - the default one
method_for_time_index = None
# - based on the ini/configuration file (if given)
if 'time_index_method_for_temperature_netcdf' in list(self.iniItems.meteoOptions.keys()) and\
self.iniItems.meteoOptions['time_index_method_for_temperature_netcdf'] != "None":
method_for_time_index = self.iniItems.meteoOptions[
'time_index_method_for_temperature_netcdf']
# reading temperature
if self.temperature_set_per_year:
nc_file_per_year = self.tmpFileNC % (int(
currTimeStep.year), int(currTimeStep.year))
self.temperature = vos.netcdf2PCRobjClone(\
nc_file_per_year, self.tmpVarName,\
str(currTimeStep.fulldate),
useDoy = method_for_time_index,
cloneMapFileName = self.cloneMap,\
LatitudeLongitude = True)
else:
self.temperature = vos.netcdf2PCRobjClone(\
self.tmpFileNC,self.tmpVarName,\
str(currTimeStep.fulldate),
useDoy = method_for_time_index,
cloneMapFileName=self.cloneMap,\
LatitudeLongitude = True)
#-----------------------------------------------------------------------
# NOTE: RvB 13/07/2016 added to automatically update temperature
self.temperature = self.tmpConst + self.tmpFactor * pcr.ifthen(
self.landmask, self.temperature)
#-----------------------------------------------------------------------
# Downscaling precipitation and temperature
if self.downscalePrecipitationOption:
self.downscalePrecipitation(currTimeStep)
if self.downscaleTemperatureOption:
self.downscaleTemperature(currTimeStep)
# calculate or obtain referencePotET
if self.refETPotMethod == 'Hamon': self.referencePotET = \
refPotET.HamonPotET(self.temperature,\
currTimeStep.doy,\
self.latitudes)
if self.refETPotMethod == 'Input':
# method for finding time indexes in the precipitation netdf file:
# - the default one
method_for_time_index = None
# - based on the ini/configuration file (if given)
if 'time_index_method_for_ref_pot_et_netcdf' in list(self.iniItems.meteoOptions.keys()) and\
self.iniItems.meteoOptions['time_index_method_for_ref_pot_et_netcdf'] != "None":
method_for_time_index = self.iniItems.meteoOptions[
'time_index_method_for_ref_pot_et_netcdf']
if self.refETPotFileNC_set_per_year:
nc_file_per_year = self.etpFileNC % (int(
currTimeStep.year), int(currTimeStep.year))
self.referencePotET = vos.netcdf2PCRobjClone(\
nc_file_per_year, self.refETPotVarName,\
str(currTimeStep.fulldate),
useDoy = method_for_time_index,
cloneMapFileName = self.cloneMap,\
LatitudeLongitude = True)
else:
self.referencePotET = vos.netcdf2PCRobjClone(\
self.etpFileNC,self.refETPotVarName,\
str(currTimeStep.fulldate),
useDoy = method_for_time_index,
cloneMapFileName=self.cloneMap,\
LatitudeLongitude = True)
#-----------------------------------------------------------------------
# NOTE: RvB 13/07/2016 added to automatically update reference potential evapotranspiration
self.referencePotET = self.refETPotConst + self.refETPotFactor * pcr.ifthen(
self.landmask, self.referencePotET)
#-----------------------------------------------------------------------
# Downscaling referenceETPot (based on temperature)
if self.downscaleReferenceETPotOption: self.downscaleReferenceETPot()
# smoothing:
if self.forcingSmoothing == True:
logger.debug("Forcing data are smoothed.")
self.precipitation = pcr.windowaverage(self.precipitation,
self.smoothingWindowsLength)
self.temperature = pcr.windowaverage(self.temperature,
self.smoothingWindowsLength)
self.referencePotET = pcr.windowaverage(
self.referencePotET, self.smoothingWindowsLength)
# rounding temperature values to minimize numerical errors (note only to minimize, not remove)
self.temperature = pcr.roundoff(self.temperature * 1000.) / 1000.
# ignore snow by setting temperature to 25 deg C
if self.ignore_snow: self.temperature = pcr.spatial(pcr.scalar(25.))
# define precipitation, temperature and referencePotET ONLY at landmask area (for reporting):
self.precipitation = pcr.ifthen(self.landmask, self.precipitation)
self.temperature = pcr.ifthen(self.landmask, self.temperature)
self.referencePotET = pcr.ifthen(self.landmask, self.referencePotET)
# make sure precipitation and referencePotET are always positive:
self.precipitation = pcr.max(0.0, self.precipitation)
self.referencePotET = pcr.max(0.0, self.referencePotET)
if self.report == True:
timeStamp = datetime.datetime(currTimeStep.year,\
currTimeStep.month,\
currTimeStep.day,\
0)
# writing daily output to netcdf files
timestepPCR = currTimeStep.timeStepPCR
if self.outDailyTotNC[0] != "None":
for var in self.outDailyTotNC:
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_dailyTot.nc",\
var,\
pcr2numpy(self.__getattribute__(var),vos.MV),\
timeStamp,timestepPCR-1)
# writing monthly output to netcdf files
# -cummulative
if self.outMonthTotNC[0] != "None":
for var in self.outMonthTotNC:
# introduce variables at the beginning of simulation or
# reset variables at the beginning of the month
if currTimeStep.timeStepPCR == 1 or \
currTimeStep.day == 1: \
vars(self)[var+'MonthTot'] = pcr.scalar(0.0)
# accumulating
vars(self)[var + 'MonthTot'] += vars(self)[var]
# reporting at the end of the month:
if currTimeStep.endMonth == True:
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_monthTot.nc",\
var,\
pcr2numpy(self.__getattribute__(var+'MonthTot'),\
vos.MV),timeStamp,currTimeStep.monthIdx-1)
# -average
if self.outMonthAvgNC[0] != "None":
for var in self.outMonthAvgNC:
# only if a accumulator variable has not been defined:
if var not in self.outMonthTotNC:
# introduce accumulator at the beginning of simulation or
# reset accumulator at the beginning of the month
if currTimeStep.timeStepPCR == 1 or \
currTimeStep.day == 1: \
vars(self)[var+'MonthTot'] = pcr.scalar(0.0)
# accumulating
vars(self)[var + 'MonthTot'] += vars(self)[var]
# calculating average & reporting at the end of the month:
if currTimeStep.endMonth == True:
vars(self)[var+'MonthAvg'] = vars(self)[var+'MonthTot']/\
currTimeStep.day
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_monthAvg.nc",\
var,\
pcr2numpy(self.__getattribute__(var+'MonthAvg'),\
vos.MV),timeStamp,currTimeStep.monthIdx-1)
#
# -last day of the month
if self.outMonthEndNC[0] != "None":
for var in self.outMonthEndNC:
# reporting at the end of the month:
if currTimeStep.endMonth == True:
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_monthEnd.nc",\
var,\
pcr2numpy(self.__getattribute__(var),vos.MV),\
timeStamp,currTimeStep.monthIdx-1)
# writing yearly output to netcdf files
# -cummulative
if self.outAnnuaTotNC[0] != "None":
for var in self.outAnnuaTotNC:
# introduce variables at the beginning of simulation or
# reset variables at the beginning of the month
if currTimeStep.timeStepPCR == 1 or \
currTimeStep.doy == 1: \
vars(self)[var+'AnnuaTot'] = pcr.scalar(0.0)
# accumulating
vars(self)[var + 'AnnuaTot'] += vars(self)[var]
# reporting at the end of the year:
if currTimeStep.endYear == True:
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_annuaTot.nc",\
var,\
pcr2numpy(self.__getattribute__(var+'AnnuaTot'),\
vos.MV),timeStamp,currTimeStep.annuaIdx-1)
# -average
if self.outAnnuaAvgNC[0] != "None":
for var in self.outAnnuaAvgNC:
# only if a accumulator variable has not been defined:
if var not in self.outAnnuaTotNC:
# introduce accumulator at the beginning of simulation or
# reset accumulator at the beginning of the year
if currTimeStep.timeStepPCR == 1 or \
currTimeStep.doy == 1: \
vars(self)[var+'AnnuaTot'] = pcr.scalar(0.0)
# accumulating
vars(self)[var + 'AnnuaTot'] += vars(self)[var]
#
# calculating average & reporting at the end of the year:
if currTimeStep.endYear == True:
vars(self)[var+'AnnuaAvg'] = vars(self)[var+'AnnuaTot']/\
currTimeStep.doy
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_annuaAvg.nc",\
var,\
pcr2numpy(self.__getattribute__(var+'AnnuaAvg'),\
vos.MV),timeStamp,currTimeStep.annuaIdx-1)
#
# -last day of the year
if self.outAnnuaEndNC[0] != "None":
for var in self.outAnnuaEndNC:
# reporting at the end of the year:
if currTimeStep.endYear == True:
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_annuaEnd.nc",\
var,\
pcr2numpy(self.__getattribute__(var),vos.MV),\
timeStamp,currTimeStep.annuaIdx-1)
def getParameterFiles(self,currTimeStep,cellArea,ldd,\
initial_condition_dictionary = None,\
currTimeStepInDateTimeFormat = False):
# parameters for Water Bodies: fracWat
# waterBodyIds
# waterBodyOut
# waterBodyArea
# waterBodyTyp
# waterBodyCap
# cell surface area (m2) and ldd
self.cellArea = cellArea
ldd = pcr.ifthen(self.landmask, ldd)
# date used for accessing/extracting water body information
if currTimeStepInDateTimeFormat:
date_used = currTimeStep
year_used = currTimeStep.year
else:
date_used = currTimeStep.fulldate
year_used = currTimeStep.year
if self.onlyNaturalWaterBodies == True:
date_used = self.dateForNaturalCondition
year_used = self.dateForNaturalCondition[0:4]
# fracWat = fraction of surface water bodies (dimensionless)
self.fracWat = pcr.spatial(pcr.scalar(0.0))
if self.useNetCDF:
self.fracWat = vos.netcdf2PCRobjClone(self.ncFileInp,'fracWaterInp', \
date_used, useDoy = 'yearly',\
cloneMapFileName = self.cloneMap)
else:
self.fracWat = vos.readPCRmapClone(\
self.fracWaterInp+str(year_used)+".map",
self.cloneMap,self.tmpDir,self.inputDir)
self.fracWat = pcr.cover(self.fracWat, pcr.spatial(pcr.scalar(0.0)))
self.fracWat = pcr.max(0.0, self.fracWat)
self.fracWat = pcr.min(1.0, self.fracWat)
self.waterBodyIds = pcr.spatial(pcr.nominal(0)) # waterBody ids
self.waterBodyOut = pcr.spatial(pcr.boolean(0)) # waterBody outlets
self.waterBodyArea = pcr.spatial(
pcr.scalar(0.)) # waterBody surface areas
# water body ids
if self.useNetCDF:
self.waterBodyIds = vos.netcdf2PCRobjClone(self.ncFileInp,'waterBodyIds', \
date_used, useDoy = 'yearly',\
cloneMapFileName = self.cloneMap)
else:
self.waterBodyIds = vos.readPCRmapClone(\
self.waterBodyIdsInp+str(year_used)+".map",\
self.cloneMap,self.tmpDir,self.inputDir,False,None,True)
#
self.waterBodyIds = pcr.ifthen(\
pcr.scalar(self.waterBodyIds) > 0.,\
pcr.nominal(self.waterBodyIds))
# water body outlets (correcting outlet positions)
wbCatchment = pcr.catchmenttotal(pcr.scalar(1), ldd)
self.waterBodyOut = pcr.ifthen(wbCatchment ==\
pcr.areamaximum(wbCatchment, \
self.waterBodyIds),\
self.waterBodyIds) # = outlet ids # This may give more than two outlets, particularly if there are more than one cells that have largest upstream areas
# - make sure that there is only one outlet for each water body
self.waterBodyOut = pcr.ifthen(\
pcr.areaorder(pcr.scalar(self.waterBodyOut), \
self.waterBodyOut) == 1., self.waterBodyOut)
self.waterBodyOut = pcr.ifthen(\
pcr.scalar(self.waterBodyIds) > 0.,\
self.waterBodyOut)
# TODO: Please also consider endorheic lakes!
# correcting water body ids
self.waterBodyIds = pcr.ifthen(\
pcr.scalar(self.waterBodyIds) > 0.,\
pcr.subcatchment(ldd,self.waterBodyOut))
# boolean map for water body outlets:
self.waterBodyOut = pcr.ifthen(\
pcr.scalar(self.waterBodyOut) > 0.,\
pcr.spatial(pcr.boolean(1)))
# reservoir surface area (m2):
if self.useNetCDF:
resSfArea = 1000. * 1000. * \
vos.netcdf2PCRobjClone(self.ncFileInp,'resSfAreaInp', \
date_used, useDoy = 'yearly',\
cloneMapFileName = self.cloneMap)
else:
resSfArea = 1000. * 1000. * vos.readPCRmapClone(
self.resSfAreaInp+str(year_used)+".map",\
self.cloneMap,self.tmpDir,self.inputDir)
resSfArea = pcr.areaaverage(resSfArea, self.waterBodyIds)
resSfArea = pcr.cover(resSfArea, 0.)
# water body surface area (m2): (lakes and reservoirs)
self.waterBodyArea = pcr.max(pcr.areatotal(\
pcr.cover(\
self.fracWat*self.cellArea, 0.0), self.waterBodyIds),
pcr.areaaverage(\
pcr.cover(resSfArea, 0.0) , self.waterBodyIds))
self.waterBodyArea = pcr.ifthen(self.waterBodyArea > 0.,\
self.waterBodyArea)
# correcting water body ids and outlets (exclude all water bodies with surfaceArea = 0)
self.waterBodyIds = pcr.ifthen(self.waterBodyArea > 0.,
self.waterBodyIds)
self.waterBodyOut = pcr.ifthen(pcr.boolean(self.waterBodyIds),
self.waterBodyOut)
# water body types:
# - 2 = reservoirs (regulated discharge)
# - 1 = lakes (weirFormula)
# - 0 = non lakes or reservoirs (e.g. wetland)
self.waterBodyTyp = pcr.nominal(0)
if self.useNetCDF:
self.waterBodyTyp = vos.netcdf2PCRobjClone(self.ncFileInp,'waterBodyTyp', \
date_used, useDoy = 'yearly',\
cloneMapFileName = self.cloneMap)
else:
self.waterBodyTyp = vos.readPCRmapClone(
self.waterBodyTypInp+str(year_used)+".map",\
self.cloneMap,self.tmpDir,self.inputDir,False,None,True)
# excluding wetlands (waterBodyTyp = 0) in all functions related to lakes/reservoirs
#
self.waterBodyTyp = pcr.ifthen(\
pcr.scalar(self.waterBodyTyp) > 0,\
pcr.nominal(self.waterBodyTyp))
self.waterBodyTyp = pcr.ifthen(\
pcr.scalar(self.waterBodyIds) > 0,\
pcr.nominal(self.waterBodyTyp))
self.waterBodyTyp = pcr.areamajority(self.waterBodyTyp,\
self.waterBodyIds) # choose only one type: either lake or reservoir
self.waterBodyTyp = pcr.ifthen(\
pcr.scalar(self.waterBodyTyp) > 0,\
pcr.nominal(self.waterBodyTyp))
self.waterBodyTyp = pcr.ifthen(pcr.boolean(self.waterBodyIds),
self.waterBodyTyp)
# correcting lakes and reservoirs ids and outlets
self.waterBodyIds = pcr.ifthen(
pcr.scalar(self.waterBodyTyp) > 0, self.waterBodyIds)
self.waterBodyOut = pcr.ifthen(
pcr.scalar(self.waterBodyIds) > 0, self.waterBodyOut)
# reservoir maximum capacity (m3):
self.resMaxCap = pcr.scalar(0.0)
self.waterBodyCap = pcr.scalar(0.0)
if self.useNetCDF:
self.resMaxCap = 1000. * 1000. * \
vos.netcdf2PCRobjClone(self.ncFileInp,'resMaxCapInp', \
date_used, useDoy = 'yearly',\
cloneMapFileName = self.cloneMap)
else:
self.resMaxCap = 1000. * 1000. * vos.readPCRmapClone(\
self.resMaxCapInp+str(year_used)+".map", \
self.cloneMap,self.tmpDir,self.inputDir)
self.resMaxCap = pcr.ifthen(self.resMaxCap > 0,\
self.resMaxCap)
self.resMaxCap = pcr.areaaverage(self.resMaxCap,\
self.waterBodyIds)
# water body capacity (m3): (lakes and reservoirs)
self.waterBodyCap = pcr.cover(
self.resMaxCap, 0.0) # Note: Most of lakes have capacities > 0.
self.waterBodyCap = pcr.ifthen(pcr.boolean(self.waterBodyIds),
self.waterBodyCap)
# correcting water body types: # Reservoirs that have zero capacities will be assumed as lakes.
self.waterBodyTyp = \
pcr.ifthen(pcr.scalar(self.waterBodyTyp) > 0.,\
self.waterBodyTyp)
self.waterBodyTyp = pcr.ifthenelse(self.waterBodyCap > 0.,\
self.waterBodyTyp,\
pcr.ifthenelse(pcr.scalar(self.waterBodyTyp) == 2,\
pcr.nominal(1),\
self.waterBodyTyp))
# final corrections:
self.waterBodyTyp = pcr.ifthen(self.waterBodyArea > 0.,\
self.waterBodyTyp) # make sure that all lakes and/or reservoirs have surface areas
self.waterBodyTyp = \
pcr.ifthen(pcr.scalar(self.waterBodyTyp) > 0.,\
self.waterBodyTyp) # make sure that only types 1 and 2 will be considered in lake/reservoir functions
self.waterBodyIds = pcr.ifthen(pcr.scalar(self.waterBodyTyp) > 0.,\
self.waterBodyIds) # make sure that all lakes and/or reservoirs have ids
self.waterBodyOut = pcr.ifthen(pcr.scalar(self.waterBodyIds) > 0.,\
self.waterBodyOut) # make sure that all lakes and/or reservoirs have outlets
# for a natural run (self.onlyNaturalWaterBodies == True)
# which uses only the year 1900, assume all reservoirs are lakes
if self.onlyNaturalWaterBodies == True and date_used == self.dateForNaturalCondition:
logger.info(
"Using only natural water bodies identified in the year 1900. All reservoirs in 1900 are assumed as lakes."
)
self.waterBodyTyp = \
pcr.ifthen(pcr.scalar(self.waterBodyTyp) > 0.,\
pcr.nominal(1))
# check that all lakes and/or reservoirs have types, ids, surface areas and outlets:
test = pcr.defined(self.waterBodyTyp) & pcr.defined(self.waterBodyArea) &\
pcr.defined(self.waterBodyIds) & pcr.boolean(pcr.areamaximum(pcr.scalar(self.waterBodyOut), self.waterBodyIds))
a, b, c = vos.getMinMaxMean(
pcr.cover(pcr.scalar(test), 1.0) - pcr.scalar(1.0))
threshold = 1e-3
if abs(a) > threshold or abs(b) > threshold:
logger.warning(
"Missing information in some lakes and/or reservoirs.")
# at the beginning of simulation period (timeStepPCR = 1)
# - we have to define/get the initial conditions
#
if initial_condition_dictionary != None and currTimeStep.timeStepPCR == 1:
self.getICs(initial_condition_dictionary)
# For each new reservoir (introduced at the beginning of the year)
# initiating storage, average inflow and outflow
# PS: THIS IS NOT NEEDED FOR OFFLINE MODFLOW RUN!
#
try:
self.waterBodyStorage = pcr.cover(self.waterBodyStorage, 0.0)
self.avgInflow = pcr.cover(self.avgInflow, 0.0)
self.avgOutflow = pcr.cover(self.avgOutflow, 0.0)
self.waterBodyStorage = pcr.ifthen(self.landmask,
self.waterBodyStorage)
self.avgInflow = pcr.ifthen(self.landmask, self.avgInflow)
self.avgOutflow = pcr.ifthen(self.landmask, self.avgOutflow)
except:
# PS: FOR OFFLINE MODFLOW RUN!
pass
# TODO: Remove try and except
# cropping only in the landmask region:
self.fracWat = pcr.ifthen(self.landmask, self.fracWat)
self.waterBodyIds = pcr.ifthen(self.landmask, self.waterBodyIds)
self.waterBodyOut = pcr.ifthen(self.landmask, self.waterBodyOut)
self.waterBodyArea = pcr.ifthen(self.landmask, self.waterBodyArea)
self.waterBodyTyp = pcr.ifthen(self.landmask, self.waterBodyTyp)
self.waterBodyCap = pcr.ifthen(self.landmask, self.waterBodyCap)
#~ pcr.aguila(basin_area)
# finding the month that give the maximum discharge (from the climatology time series)
msg = "Identifying the month with peak discharge (from climatology time series):"
logger.info(msg)
# - read the maximum monthly discharge for every basin
maximum_discharge = vos.netcdf2PCRobjClone(input_files['maximumClimatologyDischargeMonthAvg'], \
"discharge", 1,\
useDoy = "Yes",
cloneMapFileName = clone_map_file,\
LatitudeLongitude = True,\
specificFillValue = None)
maximum_discharge = pcr.areamaximum(\
pcr.cover(maximum_discharge, 0.0), basin_map)
# - find the month with maximum discharge
maximum_month = pcr.spatial(pcr.scalar(1.0))
for i_month in range(1, 12 + 1):
# read the climatology discharge time series
discharge_for_this_month = vos.netcdf2PCRobjClone(input_files['climatologyDischargeMonthAvg'], \
"discharge", i_month,\
useDoy = "Yes",
cloneMapFileName = clone_map_file,\
LatitudeLongitude = True,\
specificFillValue = None)
# upscale it to the basin scale
discharge_for_this_month = pcr.areamaximum(discharge_for_this_month,
basin_map)
maximum_month = pcr.ifthenelse(
discharge_for_this_month == maximum_discharge, pcr.scalar(i_month),
maximum_month)
pcr.report(maximum_month, "maximum_month.map")
pcraster.setclone(os.path.join(dataPath, "ELddF000.out"))
mldd = pcraster.mldd.initialise(pcraster.clone())
mldd.setStream(
# TODO Create overload which takes strings, or handle internally.
pcraster.readmap(os.path.join(dataPath, "ELddF000.out")),
pcraster.readmap(os.path.join(dataPath, "NELddF00.out")),
pcraster.readmap(os.path.join(dataPath, "NLddF000.out")),
pcraster.readmap(os.path.join(dataPath, "NWLddF00.out")),
pcraster.readmap(os.path.join(dataPath, "SELddF00.out")),
pcraster.readmap(os.path.join(dataPath, "SLddF000.out")),
pcraster.readmap(os.path.join(dataPath, "SWLddF00.out")),
pcraster.readmap(os.path.join(dataPath, "WLddF000.out")))
mldd.addStream(pcraster.readmap(os.path.join(dataPath, "ELddF000.out")))
mldd.setDem(pcraster.spatial(pcraster.scalar(1)))
upstream = mldd.upstream(pcraster.spatial(pcraster.scalar(1)))
pcraster.report(upstream, "upstream.map")
accuflux = mldd.accuflux(
pcraster.ifthen(pcraster.defined(upstream),
pcraster.spatial(pcraster.scalar(1))))
pcraster.report(accuflux, "accuflux.map")
dem = mldd.getDem()
pcraster.report(dem, "dem.map")
streamN, streamNE, streamE, streamSE, streamS, streamSW, streamW, streamNW = \
mldd.getStream()
def dynamic(self):
#####################
# * dynamic section #
#####################
#-evaluation of the current date: return current month and the time step used
#-reading in fluxes over land and water area for current time step [m/d]
# and read in reservoir demand and surface water extraction [m3]
try:
self.landSurfaceQ = clippedRead.get(
pcrm.generateNameT(landSurfaceQFileName,
self.currentTimeStep()))
except:
pass
try:
self.potWaterSurfaceQ = clippedRead.get(
pcrm.generateNameT(waterSurfaceQFileName,
self.currentTimeStep()))
except:
pass
#-surface water extraction and reservoir demand currently set to zero, should
# be computed automatically and updated to reservoirs
self.potSurfaceWaterExtraction = pcr.spatial(pcr.scalar(0.))
#self.waterBodies.demand= #self.reservoirDemandTSS.assignID(self.waterBodies.ID,self.currentTimeStep(),0.)*self.timeSec
#-initialization of cumulative values of actual water extractions
self.actWaterSurfaceQ = pcr.spatial(pcr.scalar(0.))
self.actSurfaceWaterExtraction = pcr.spatial(pcr.scalar(0.))
#-definition of sub-loop for routing scheme - explicit scheme has to satisfy Courant condition
timeLimit= pcr.cellvalue(pcr.mapminimum((pcr.cover(pcr.ifthen(self.waterBodies.distribution == 0,\
self.channelLength/self.flowVelocity),\
self.timeSec/self.nrIterDefault)*self.timeSec/self.nrIterDefault)**0.5),1)[0]
nrIter = int(self.timeSec / timeLimit)
nrIter = min(nrIter, int(self.timeSec / 300.))
while float(self.timeSec / nrIter) % 1 <> 0:
nrIter += 1
deltaTime = self.timeSec / nrIter
#-sub-loop for current time step
if self.currentDate.day == 1 or nrIter >= 24:
print '\n*\tprocessing %s, currently using %d substeps of %d seconds\n' % \
(self.currentDate.date(),nrIter,deltaTime)
#-update discharge and storage
for nrICur in range(nrIter):
#-initializing discharge for the current sub-timestep and fill in values
# for channels and at outlets of waterbodies
# * channels *
estQ= pcr.ifthenelse((self.actualStorage > 0.) & (self.waterBodies.distribution == 0) ,\
(self.wettedArea/self.alphaQ)**(1./self.betaQ),0.)
#estQ= pcr.ifthenelse((self.actualStorage > 0.) & (self.waterBodies.distribution == 0) ,\
#0.5*(self.Q+(self.wettedArea/self.alphaQ)**(1./self.betaQ)),0.)
#estQ= pcr.min(estQ,self.actualStorage/deltaTime)
self.report(estQ, 'results/qest')
self.Q = pcr.spatial(pcr.scalar(0.))
self.Q= pcr.ifthenelse(self.waterBodies.distribution == 0,\
pcr.kinematic(self.channelLDD,estQ,0.,self.alphaQ,\
self.betaQ,1,deltaTime,self.channelLength),self.Q)
# * water bodies *
self.waterBodies.dischargeUpdate()
self.Q = self.waterBodies.returnMapValue(self.Q,
self.waterBodies.actualQ)
#-fluxes and resulting change in storage: first the local fluxes are evaluated
# and aggregated over the water bodies where applicable; this includes the specific runoff [m/day/m2]
# from input and the estimated extraction from surface water as volume per day [m3/day];
# specific runoff from the land surface is always positive whereas the fluxes over the water surface
# are potential, including discharge, and are adjusted to match the availabe storage; to this end,
# surface water storage and fluxes over water bodies are totalized and assigned to the outlet;
# discharge is updated in a separate step, after vertical fluxes are compared to the actual storage
deltaActualStorage= ((self.landFraction*self.landSurfaceQ+\
self.waterFraction*self.potWaterSurfaceQ)*self.cellArea-\
self.potSurfaceWaterExtraction)*float(self.duration)/nrIter
deltaActualStorage= pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.ifthenelse(self.waterBodies.location != 0,\
pcr.areatotal(deltaActualStorage,self.waterBodies.distribution),0),\
deltaActualStorage)
adjustmentRatio= pcr.ifthenelse(deltaActualStorage < 0.,\
pcr.min(1.,-self.actualStorage/deltaActualStorage),1.)
self.actWaterSurfaceQ += adjustmentRatio * self.potWaterSurfaceQ
self.actSurfaceWaterExtraction += adjustmentRatio * self.actSurfaceWaterExtraction
deltaActualStorage *= adjustmentRatio
#-local water balance check
if testLocalWaterBalance:
differenceActualStorage = self.actualStorage
differenceActualStorage += deltaActualStorage
#-overall water balance check: net input
self.cumulativeDeltaStorage += pcr.catchmenttotal(
deltaActualStorage, self.LDD)
#-update storage first with local changes, then balance discharge with storage and update storage
# with lateral flow and return value to water bodies
self.actualStorage += deltaActualStorage
self.actualStorage = pcr.max(0., self.actualStorage)
self.Q = pcr.min(self.Q, self.actualStorage / deltaTime)
deltaActualStorage = (-self.Q +
pcr.upstream(self.LDD, self.Q)) * deltaTime
deltaActualStorage= pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.ifthenelse(self.waterBodies.location != 0,\
pcr.areatotal(deltaActualStorage,self.waterBodies.distribution),0),\
deltaActualStorage)
self.actualStorage += deltaActualStorage
self.actualStorage = pcr.max(0., self.actualStorage)
self.waterBodies.actualStorage = self.waterBodies.retrieveMapValue(
self.actualStorage)
#-flooded fraction returned
floodedFraction,floodedDepth,\
self.wettedArea,self.alphaQ= self.kinAlphaComposite(self.actualStorage,self.floodplainMask)
self.wettedArea= self.waterBodies.returnMapValue(self.wettedArea,\
self.waterBodies.channelWidth+2.*self.waterBodies.updateWaterHeight())
self.waterFraction= pcr.ifthenelse(self.waterBodies.distribution == 0,\
pcr.max(self.waterFractionMask,floodedFraction),self.waterFractionMask)
self.landFraction = pcr.max(0., 1. - self.waterFraction)
self.flowVelocity = pcr.ifthenelse(self.wettedArea > 0,
self.Q / self.wettedArea, 0.)
#-local water balance check
if testLocalWaterBalance:
differenceActualStorage += deltaActualStorage
differenceActualStorage -= self.actualStorage
totalDifference = pcr.cellvalue(
pcr.maptotal(differenceActualStorage), 1)[0]
minimumDifference = pcr.cellvalue(
pcr.mapminimum(differenceActualStorage), 1)[0]
maximumDifference = pcr.cellvalue(
pcr.mapmaximum(differenceActualStorage), 1)[0]
if abs(totalDifference) > 1.e-3:
print 'water balance error: total %e; min %e; max %e' %\
(totalDifference,minimumDifference,maximumDifference)
if reportLocalWaterBalance:
pcr.report(differenceActualStorage,
'mbe_%s.map' % self.currentDate.date())
#-overall water balance check: updating cumulative discharge and total storage [m3]
self.totalDischarge += self.Q * deltaTime
self.totalStorage = pcr.catchmenttotal(self.actualStorage,
self.LDD)
#-check on occurrence of last day and report mass balance
if self.currentDate == self.endDate:
#-report initial maps
pcr.report(self.Q, self.QIniMap)
pcr.report(self.actualStorage, self.actualStorageIniMap)
#-return relative and absolute water balance error per cell and
# as total at basin outlets
self.totalDischarge= pcr.ifthen((self.waterBodies.distribution == 0) | \
(self.waterBodies.location != 0),self.totalDischarge)
self.cumulativeDeltaStorage= pcr.ifthen((self.waterBodies.distribution == 0) | \
(self.waterBodies.location != 0),self.cumulativeDeltaStorage)
massBalanceError= self.totalStorage+self.totalDischarge-\
self.cumulativeDeltaStorage
relMassBalanceError = 1. + pcr.ifthenelse(
self.cumulativeDeltaStorage <> 0.,
massBalanceError / self.cumulativeDeltaStorage, 0.)
totalMassBalanceError= pcr.cellvalue(pcr.maptotal(pcr.ifthen(self.basinOutlet,\
massBalanceError)),1)[0]
totalCumulativeDeltaStorage= pcr.cellvalue(pcr.maptotal(pcr.ifthen(self.basinOutlet,\
self.cumulativeDeltaStorage)),1)[0]
if totalCumulativeDeltaStorage > 0:
totalRelativeMassBalanceError = 1. + totalMassBalanceError / totalCumulativeDeltaStorage
else:
totalRelativeMassBalanceError = 1.
#-report maps and echo value
pcr.report(massBalanceError, mbeFileName)
pcr.report(relMassBalanceError, mbrFileName)
print '\n*\ttotal global mass balance error [m3]: %8.3g' % totalMassBalanceError
print '\n*\trelative global mass balance error [-]: %5.3f' % totalRelativeMassBalanceError
#-echo to screen: total mass balance error and completion of run
print '\trun completed'
#-end of day: return states and fluxes
#-get surface water attributes?
if getSurfaceWaterAttributes:
#-compute the following secondary variables:
# surface water area [m2]: area given dynamic surface water fraction
# residence time [days]: volume over discharge, assigned -1 in case discharge is zero
# surface water depth [m], weighed by channel and floodplain volume
surfaceWaterArea = self.waterFraction * self.cellArea
surfaceWaterArea= pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.ifthenelse(self.waterBodies.location != 0,\
pcr.areatotal(surfaceWaterArea,self.waterBodies.distribution),0),\
surfaceWaterArea)
surfaceWaterResidenceTime = pcr.ifthenelse(
self.Q > 0., self.actualStorage / (self.Q * self.timeSec), -1)
surfaceWaterDepth= pcr.ifthenelse(self.actualStorage > 0.,\
pcr.max(0.,self.actualStorage-self.channelStorageCapacity)**2/\
(self.actualStorage*surfaceWaterArea),0.)
surfaceWaterDepth+= pcr.ifthenelse(self.actualStorage > 0.,\
pcr.min(self.channelStorageCapacity,self.actualStorage)**2/(self.waterFractionMask*\
self.cellArea*self.actualStorage),0.)
#-reports: values at outlet of lakes or reservoirs are assigned to their full extent
self.report(pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.areamaximum(surfaceWaterArea,self.waterBodies.distribution),surfaceWaterArea),\
surfaceWaterAreaFileName)
self.report(pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.areamaximum(surfaceWaterResidenceTime,self.waterBodies.distribution),surfaceWaterResidenceTime),\
surfaceWaterResidenceTimeFileName)
self.report(pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.areamaximum(surfaceWaterDepth,self.waterBodies.distribution),surfaceWaterDepth),\
surfaceWaterDepthFileName)
#-reports on standard output: values at outlet of lakes or reservoirs are assigned to their full extent
self.report(
pcr.ifthenelse(
self.waterBodies.distribution != 0,
pcr.areamaximum(self.flowVelocity,
self.waterBodies.distribution),
self.flowVelocity), flowVelocityFileName)
self.report(
pcr.ifthenelse(
self.waterBodies.distribution != 0,
pcr.areamaximum(self.Q, self.waterBodies.distribution),
self.Q), QFileName)
self.report(pcr.ifthenelse(self.waterBodies.distribution == 0,\
floodedFraction,0.),floodedFractionFileName)
self.report(pcr.ifthenelse(self.waterBodies.distribution == 0,\
floodedDepth,0.),floodedDepthFileName)
self.report(self.actualStorage, actualStorageFileName)
#-update date for time step and report relevant daily output
self.currentDate = self.currentDate + datetime.timedelta(self.duration)
class TimeoutputTimeseries(object):
"""
Class to create pcrcalc timeoutput style timeseries
"""
def __init__(self, tssFilename, model, idMap=None, noHeader=False):
"""
"""
if not isinstance(tssFilename, str):
raise Exception(
"timeseries output filename must be of type string")
self._outputFilename = tssFilename
self._maxId = 1
self._spatialId = None
self._spatialDatatype = None
self._spatialIdGiven = False
self._userModel = model
self._writeHeader = not noHeader
# array to store the timestep values
self._sampleValues = None
_idMap = False
if isinstance(idMap, str) or isinstance(idMap,
pcraster._pcraster.Field):
_idMap = True
nrRows = self._userModel.nrTimeSteps() - self._userModel.firstTimeStep(
) + 1
if _idMap:
self._spatialId = idMap
if isinstance(idMap, str):
self._spatialId = pcraster.readmap(idMap)
_allowdDataTypes = [
pcraster.Nominal, pcraster.Ordinal, pcraster.Boolean
]
if self._spatialId.dataType() not in _allowdDataTypes:
raise Exception(
"idMap must be of type Nominal, Ordinal or Boolean")
if self._spatialId.isSpatial():
self._maxId, valid = pcraster.cellvalue(
pcraster.mapmaximum(pcraster.ordinal(self._spatialId)), 1)
else:
self._maxId = 1
# cell indices of the sample locations
self._sampleAddresses = []
for cellId in range(1, self._maxId + 1):
self._sampleAddresses.append(self._getIndex(cellId))
self._spatialIdGiven = True
nrCols = self._maxId
self._sampleValues = [[Decimal("NaN")] * nrCols
for _ in [0] * nrRows]
else:
self._sampleValues = [[Decimal("NaN")] * 1 for _ in [0] * nrRows]
def _getIndex(self, cellId):
"""
returns the cell index of a sample location
"""
nrCells = pcraster.clone().nrRows() * pcraster.clone().nrCols()
found = False
cell = 1
index = 0
while found == False:
if pcraster.cellvalue(self._spatialId,
cell)[1] == True and pcraster.cellvalue(
self._spatialId, cell)[0] == cellId:
index = cell
found = True
cell += 1
if cell > nrCells:
raise RuntimeError(
"could not find a cell with the index number %d" %
(cellId))
return index
def sample(self, expression):
"""
Sampling the current values of 'expression' at the given locations for the current timestep
"""
arrayRowPos = self._userModel.currentTimeStep(
) - self._userModel.firstTimeStep()
#if isinstance(expression, float):
# expression = pcraster.scalar(expression)
try:
# store the data type for tss file header
if self._spatialDatatype == None:
self._spatialDatatype = str(expression.dataType())
except AttributeError, e:
datatype, sep, tail = str(e).partition(" ")
msg = "Argument must be a PCRaster map, type %s given. If necessary use data conversion functions like scalar()" % (
datatype)
raise AttributeError(msg)
if self._spatialIdGiven:
if expression.dataType() == pcraster.Scalar or expression.dataType(
) == pcraster.Directional:
tmp = pcraster.areaaverage(pcraster.spatial(expression),
pcraster.spatial(self._spatialId))
else:
tmp = pcraster.areamajority(pcraster.spatial(expression),
pcraster.spatial(self._spatialId))
col = 0
for cellIndex in self._sampleAddresses:
value, valid = pcraster.cellvalue(tmp, cellIndex)
if not valid:
value = Decimal("NaN")
self._sampleValues[arrayRowPos][col] = value
col += 1
else:
if expression.dataType() == pcraster.Scalar or expression.dataType(
) == pcraster.Directional:
tmp = pcraster.maptotal(pcraster.spatial(expression))\
/ pcraster.maptotal(pcraster.scalar(pcraster.defined(pcraster.spatial(expression))))
else:
tmp = pcraster.mapmaximum(pcraster.maptotal(pcraster.areamajority(pcraster.spatial(expression),\
pcraster.spatial(pcraster.nominal(1)))))
value, valid = pcraster.cellvalue(tmp, 1)
if not valid:
value = Decimal("NaN")
self._sampleValues[arrayRowPos] = value
if self._userModel.currentTimeStep() == self._userModel.nrTimeSteps():
self._writeTssFile()
def waterBalance( fluxesIn, fluxesOut, deltaStorages, processName, PrintOnlyErrors, dateStr,threshold=1e-5):
""" Returns the water balance for a list of input, output, and storage map files and """
inMap = pcr.spatial(pcr.scalar(0.0))
dsMap = pcr.spatial(pcr.scalar(0.0))
outMap = pcr.spatial(pcr.scalar(0.0))
inflow = 0
outflow = 0
deltaS = 0
for fluxIn in fluxesIn:
inflow += getMapTotal(fluxIn)
inMap += fluxIn
for fluxOut in fluxesOut:
outflow += getMapTotal(fluxOut)
outMap += fluxOut
for deltaStorage in deltaStorages:
deltaS += getMapTotal(deltaStorage)
dsMap += deltaStorage
#if PrintOnlyErrors:
a,b,c = getMinMaxMean(inMap + dsMap- outMap)
# if abs(a) > 1e-5 or abs(b) > 1e-5:
# if abs(a) > 1e-4 or abs(b) > 1e-4:
if abs(a) > threshold or abs(b) > threshold:
print "WBError %s Min %f Max %f Mean %f" %(processName,a,b,c)
# if abs(inflow + deltaS - outflow) > 1e-5:
# print "Water balance Error for %s on %s: in = %f\tout=%f\tdeltaS=%f\tBalance=%f" \
# %(processName,dateStr,inflow,outflow,deltaS,inflow + deltaS - outflow)
#else:
# print "Water balance for %s: on %s in = %f\tout=%f\tdeltaS=%f\tBalance=%f" \
# %(processName,dateStr,inflow,outflow,deltaS,inflow + deltaS - outflow)
wb = inMap + dsMap - outMap
maxWBError = pcr.cellvalue(pcr.mapmaximum(pcr.abs(wb)), 1, 1)[0]
#if maxWBError > 0.001 / 1000:
#row = 0
#col = 0
#cellID = 1
#troubleCell = 0
#print "Water balance for %s on %s: %f mm !!! " %(processName,dateStr,maxWBError * 1000)
#pcr.report(wb,"%s-WaterBalanceError-%s" %(processName,dateStr))
#npWBMError = pcr2numpy(wb, -9999)
#(nr, nc) = np.shape(npWBMError)
#for r in range(0, nr):
#for c in range(0, nc):
## print r,c
#if npWBMError[r, c] != -9999.0:
#val = npWBMError[r, c]
#if math.fabs(val) > 0.0001 / 1000:
## print npWBMError[r,c]
#row = r
#col = c
#troubleCell = cellID
#cellID += 1
#print 'Water balance for %s on %s: %f mm row %i col %i cellID %i!!! ' % (
#processName,
#dateStr,
#maxWBError * 1000,
#row,
#col,
#troubleCell,
#)
return inMap + dsMap - outMap
mldd = pcraster.mldd.initialise(pcraster.clone())
mldd.setStream(
# TODO Create overload which takes strings, or handle internally.
pcraster.readmap(os.path.join(dataPath, "ELddF000.out")),
pcraster.readmap(os.path.join(dataPath, "NELddF00.out")),
pcraster.readmap(os.path.join(dataPath, "NLddF000.out")),
pcraster.readmap(os.path.join(dataPath, "NWLddF00.out")),
pcraster.readmap(os.path.join(dataPath, "SELddF00.out")),
pcraster.readmap(os.path.join(dataPath, "SLddF000.out")),
pcraster.readmap(os.path.join(dataPath, "SWLddF00.out")),
pcraster.readmap(os.path.join(dataPath, "WLddF000.out")),
)
mldd.addStream(pcraster.readmap(os.path.join(dataPath, "ELddF000.out")))
mldd.setDem(pcraster.spatial(pcraster.scalar(1)))
upstream = mldd.upstream(pcraster.spatial(pcraster.scalar(1)))
pcraster.report(upstream, "upstream.map")
accuflux = mldd.accuflux(pcraster.ifthen(pcraster.defined(upstream), pcraster.spatial(pcraster.scalar(1))))
pcraster.report(accuflux, "accuflux.map")
dem = mldd.getDem()
pcraster.report(dem, "dem.map")
streamN, streamNE, streamE, streamSE, streamS, streamSW, streamW, streamNW = mldd.getStream()
pcraster.report(streamN, "streamN.map")
pcraster.report(streamNE, "streamNE.map")
def modflow_simulation(self,\
simulation_type,\
initial_head_layer_1, initial_head_layer_2,\
currTimeStep = None,\
NSTP = 1, \
HCLOSE = 0.5,\
RCLOSE = 100.* 400.*400.,\
MXITER = 300,\
ITERI = 100,\
NPCOND = 1,\
RELAX = 1.00,\
NBPOL = 2,\
DAMP = 1,\
ITMUNI = 4, LENUNI = 2, PERLEN = 1.0, TSMULT = 1.0):
# initiate pcraster modflow object
self.initiate_modflow()
if simulation_type == "transient":
logger.info("Preparing MODFLOW input for a transient simulation.")
SSTR = 0
if simulation_type == "steady-state":
logger.info(
"Preparing MODFLOW input for a steady-state simulation.")
SSTR = 1
# waterBody class to define the extent of lakes and reservoirs
#
if simulation_type == "steady-state":
self.WaterBodies = waterBodies.WaterBodies(self.iniItems,\
self.landmask,\
self.onlyNaturalWaterBodies)
self.WaterBodies.getParameterFiles(date_given = self.iniItems.globalOptions['startTime'],\
cellArea = self.cellAreaMap, \
ldd = self.lddMap)
#
if simulation_type == "transient":
if currTimeStep.timeStepPCR == 1:
self.WaterBodies = waterBodies.WaterBodies(self.iniItems,\
self.landmask,\
self.onlyNaturalWaterBodies)
if currTimeStep.timeStepPCR == 1 or currTimeStep.doy == 1:
self.WaterBodies.getParameterFiles(date_given = str(currTimeStep.fulldate),\
cellArea = self.cellAreaMap, \
ldd = self.lddMap)
# using dem_average as the initial groundwater head value
self.pcr_modflow.setInitialHead(initial_head_layer_1, 1)
self.pcr_modflow.setInitialHead(initial_head_layer_2, 2)
# set parameter values for the DIS package and PCG solver
self.pcr_modflow.setDISParameter(ITMUNI, LENUNI, PERLEN, NSTP, TSMULT,
SSTR)
self.pcr_modflow.setPCG(MXITER, ITERI, NPCOND, HCLOSE, RCLOSE, RELAX,
NBPOL, DAMP)
#
# Some notes about the values
#
# ITMUNI = 4 # indicates the time unit (0: undefined, 1: seconds, 2: minutes, 3: hours, 4: days, 5: years)
# LENUNI = 2 # indicates the length unit (0: undefined, 1: feet, 2: meters, 3: centimeters)
# PERLEN = 1.0 # duration of a stress period
# NSTP = 1 # number of time steps in a stress period
# TSMULT = 1.0 # multiplier for the length of the successive iterations
# SSTR = 1 # 0 - transient, 1 - steady state
#
# MXITER = 100 # maximum number of outer iterations
# ITERI = 30 # number of inner iterations
# NPCOND = 1 # 1 - Modified Incomplete Cholesky, 2 - Polynomial matrix conditioning method;
# HCLOSE = 0.01 # HCLOSE (unit: m) # 0.05 is working
# RCLOSE = 10.* 400.*400. # RCLOSE (unit: m3) ; Deltares people uses 100 m3 for their 25 m resolution modflow model
# RELAX = 1.00 # relaxation parameter used with NPCOND = 1
# NBPOL = 2 # indicates whether the estimate of the upper bound on the maximum eigenvalue is 2.0 (but we don ot use it, since NPCOND = 1)
# DAMP = 1 # no damping (DAMP introduced in MODFLOW 2000)
# read input files (for the steady-state condition, we use pcraster maps):
if simulation_type == "steady-state":
# - discharge (m3/s) from PCR-GLOBWB
discharge = vos.readPCRmapClone(self.iniItems.modflowSteadyStateInputOptions['avgDischargeInputMap'],\
self.cloneMap, self.tmpDir, self.inputDir)
# - recharge/capillary rise (unit: m/day) from PCR-GLOBWB
gwRecharge = vos.readPCRmapClone(self.iniItems.modflowSteadyStateInputOptions['avgGroundwaterRechargeInputMap'],\
self.cloneMap, self.tmpDir, self.inputDir)
if self.ignoreCapRise: gwRecharge = pcr.max(0.0, gwRecharge)
gwAbstraction = pcr.spatial(pcr.scalar(0.0))
# read input files (for the transient, input files are given in netcdf files):
if simulation_type == "transient":
# - discharge (m3/s) from PCR-GLOBWB
discharge = vos.netcdf2PCRobjClone(
self.iniItems.modflowTransientInputOptions['dischargeInputNC'],
"discharge", str(currTimeStep.fulldate), None, self.cloneMap)
# - recharge/capillary rise (unit: m/day) from PCR-GLOBWB
gwRecharge = vos.netcdf2PCRobjClone(self.iniItems.modflowTransientInputOptions['groundwaterRechargeInputNC'],\
"groundwater_recharge",str(currTimeStep.fulldate),None,self.cloneMap)
if self.ignoreCapRise: gwRecharge = pcr.max(0.0, gwRecharge)
# - groundwater abstraction (unit: m/day) from PCR-GLOBWB
gwAbstraction = vos.netcdf2PCRobjClone(self.iniItems.modflowTransientInputOptions['groundwaterAbstractionInputNC'],\
"total_groundwater_abstraction",str(currTimeStep.fulldate),None,self.cloneMap)
# set recharge and river packages
self.set_river_package(discharge)
self.set_recharge_package(gwRecharge)
self.set_well_package(gwAbstraction)
# execute MODFLOW
logger.info("Executing MODFLOW.")
self.pcr_modflow.run()
# TODO: Add the mechanism to check whether a run has converged or not.
# obtaining the results from modflow simulation
self.groundwaterHeadLayer2 = None
self.groundwaterHeadLayer2 = self.pcr_modflow.getHeads(2)
self.groundwaterHeadLayer1 = None
self.groundwaterHeadLayer1 = self.pcr_modflow.getHeads(1)
# calculate groundwater depth only in the landmask region
self.groundwaterDepth = pcr.ifthen(
self.landmask, self.dem_average - self.groundwaterHeadLayer2)
def read_forcings(self, currTimeStep):
# reading precipitation:
if self.precipitation_set_per_year:
#~ print currTimeStep.year
nc_file_per_year = self.preFileNC % (float(
currTimeStep.year), float(currTimeStep.year))
self.precipitation = vos.netcdf2PCRobjClone(\
nc_file_per_year, 'precipitation',\
str(currTimeStep.fulldate),
useDoy = None,
cloneMapFileName = self.cloneMap,\
LatitudeLongitude = True)
else:
#self.precipitation = vos.netcdf2PCRobjClone(\
# self.preFileNC, 'precipitation',\
# str(currTimeStep.fulldate),
# useDoy = None,
# cloneMapFileName = self.cloneMap,\
# LatitudeLongitude = True)
# reading precip when ISI-MIP is used
precipitation = vos.netcdf2PCRobjClone(\
self.preFileNC, 'pr',\
str(currTimeStep.fulldate),
useDoy = None,
cloneMapFileName = self.cloneMap,\
LatitudeLongitude = True)
# unit original km m-2 s-1 to m d-1
# soortelijk gewicht water = 998 km/m3
self.precipitation = (precipitation / 998.0) * (60. * 60. * 24)
precipitationCorrectionFactor = pcr.scalar(
1.0
) # Since 19 Feb 2014, Edwin removed the support for correcting precipitation.
self.precipitation = pcr.max(0.,self.precipitation*\
precipitationCorrectionFactor)
self.precipitation = pcr.cover(self.precipitation, 0.0)
# ignore very small values of precipitation (less than 0.00001 m/day or less than 0.01 kg.m-2.day-1 )
if self.usingDailyTimeStepForcingData:
self.precipitation = pcr.rounddown(
self.precipitation * 100000.) / 100000.
# reading temperature
if self.temperature_set_per_year:
nc_file_per_year = self.tmpFileNC % (int(
currTimeStep.year), int(currTimeStep.year))
self.temperature = vos.netcdf2PCRobjClone(\
nc_file_per_year, 'temperature',\
str(currTimeStep.fulldate),
useDoy = None,
cloneMapFileName = self.cloneMap,\
LatitudeLongitude = True)
else:
# updated to temp when using ISI-MIP projection
temperature = vos.netcdf2PCRobjClone(\
self.tmpFileNC,'tas',\
str(currTimeStep.fulldate),
useDoy = None,
cloneMapFileName=self.cloneMap,\
LatitudeLongitude = True)
# unit K to C
self.temperature = temperature - 273.15
# Downscaling precipitation and temperature
if self.downscalePrecipitationOption:
self.downscalePrecipitation(currTimeStep)
if self.downscaleTemperatureOption:
self.downscaleTemperature(currTimeStep)
# calculate or obtain referencePotET
if self.refETPotMethod == 'Hamon': self.referencePotET = \
refPotET.HamonPotET(self.temperature,\
currTimeStep.doy,\
self.latitudes)
if self.refETPotMethod == 'Input':
if self.refETPotFileNC_set_per_year:
nc_file_per_year = self.etpFileNC % (int(
currTimeStep.year), int(currTimeStep.year))
self.referencePotET = vos.netcdf2PCRobjClone(\
nc_file_per_year, 'evapotranspiration',\
str(currTimeStep.fulldate),
useDoy = None,
cloneMapFileName = self.cloneMap,\
LatitudeLongitude = True)
else:
self.referencePotET = vos.netcdf2PCRobjClone(\
self.etpFileNC,'evapotranspiration',\
str(currTimeStep.fulldate),
useDoy = None,
cloneMapFileName=self.cloneMap,\
LatitudeLongitude = True)
# Downscaling referenceETPot (based on temperature)
if self.downscaleReferenceETPotOption: self.downscaleReferenceETPot()
# smoothing:
if self.forcingSmoothing == True:
logger.debug("Forcing data are smoothed.")
self.precipitation = pcr.windowaverage(self.precipitation,
self.smoothingWindowsLength)
self.temperature = pcr.windowaverage(self.temperature,
self.smoothingWindowsLength)
self.referencePotET = pcr.windowaverage(
self.referencePotET, self.smoothingWindowsLength)
# make sure precipitation is always positive:
self.precipitation = pcr.max(0.0, self.precipitation)
# rounding temperature values to minimize numerical errors (note only to minimize, not remove)
self.temperature = pcr.roundoff(self.temperature * 1000.) / 1000.
# ignore snow by setting temperature to 25 deg C
if self.ignore_snow: self.temperature = pcr.spatial(pcr.scalar(25.))
# define precipitation, temperature and referencePotET ONLY at landmask area (for reporting):
self.precipitation = pcr.ifthen(self.landmask, self.precipitation)
self.temperature = pcr.ifthen(self.landmask, self.temperature)
self.referencePotET = pcr.ifthen(self.landmask, self.referencePotET)
if self.report == True:
timeStamp = datetime.datetime(currTimeStep.year,\
currTimeStep.month,\
currTimeStep.day,\
0)
# writing daily output to netcdf files
timestepPCR = currTimeStep.timeStepPCR
if self.outDailyTotNC[0] != "None":
for var in self.outDailyTotNC:
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_dailyTot.nc",\
var,\
pcr2numpy(self.__getattribute__(var),vos.MV),\
timeStamp,timestepPCR-1)
# writing monthly output to netcdf files
# -cummulative
if self.outMonthTotNC[0] != "None":
for var in self.outMonthTotNC:
# introduce variables at the beginning of simulation or
# reset variables at the beginning of the month
if currTimeStep.timeStepPCR == 1 or \
currTimeStep.day == 1: \
vars(self)[var+'MonthTot'] = pcr.scalar(0.0)
# accumulating
vars(self)[var + 'MonthTot'] += vars(self)[var]
# reporting at the end of the month:
if currTimeStep.endMonth == True:
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_monthTot.nc",\
var,\
pcr2numpy(self.__getattribute__(var+'MonthTot'),\
vos.MV),timeStamp,currTimeStep.monthIdx-1)
# -average
if self.outMonthAvgNC[0] != "None":
for var in self.outMonthAvgNC:
# only if a accumulator variable has not been defined:
if var not in self.outMonthTotNC:
# introduce accumulator at the beginning of simulation or
# reset accumulator at the beginning of the month
if currTimeStep.timeStepPCR == 1 or \
currTimeStep.day == 1: \
vars(self)[var+'MonthTot'] = pcr.scalar(0.0)
# accumulating
vars(self)[var + 'MonthTot'] += vars(self)[var]
# calculating average & reporting at the end of the month:
if currTimeStep.endMonth == True:
vars(self)[var+'MonthAvg'] = vars(self)[var+'MonthTot']/\
currTimeStep.day
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_monthAvg.nc",\
var,\
pcr2numpy(self.__getattribute__(var+'MonthAvg'),\
vos.MV),timeStamp,currTimeStep.monthIdx-1)
#
# -last day of the month
if self.outMonthEndNC[0] != "None":
for var in self.outMonthEndNC:
# reporting at the end of the month:
if currTimeStep.endMonth == True:
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_monthEnd.nc",\
var,\
pcr2numpy(self.__getattribute__(var),vos.MV),\
timeStamp,currTimeStep.monthIdx-1)
# writing yearly output to netcdf files
# -cummulative
if self.outAnnuaTotNC[0] != "None":
for var in self.outAnnuaTotNC:
# introduce variables at the beginning of simulation or
# reset variables at the beginning of the month
if currTimeStep.timeStepPCR == 1 or \
currTimeStep.doy == 1: \
vars(self)[var+'AnnuaTot'] = pcr.scalar(0.0)
# accumulating
vars(self)[var + 'AnnuaTot'] += vars(self)[var]
# reporting at the end of the year:
if currTimeStep.endYear == True:
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_annuaTot.nc",\
var,\
pcr2numpy(self.__getattribute__(var+'AnnuaTot'),\
vos.MV),timeStamp,currTimeStep.annuaIdx-1)
# -average
if self.outAnnuaAvgNC[0] != "None":
for var in self.outAnnuaAvgNC:
# only if a accumulator variable has not been defined:
if var not in self.outAnnuaTotNC:
# introduce accumulator at the beginning of simulation or
# reset accumulator at the beginning of the year
if currTimeStep.timeStepPCR == 1 or \
currTimeStep.doy == 1: \
vars(self)[var+'AnnuaTot'] = pcr.scalar(0.0)
# accumulating
vars(self)[var + 'AnnuaTot'] += vars(self)[var]
#
# calculating average & reporting at the end of the year:
if currTimeStep.endYear == True:
vars(self)[var+'AnnuaAvg'] = vars(self)[var+'AnnuaTot']/\
currTimeStep.doy
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_annuaAvg.nc",\
var,\
pcr2numpy(self.__getattribute__(var+'AnnuaAvg'),\
vos.MV),timeStamp,currTimeStep.annuaIdx-1)
#
# -last day of the year
if self.outAnnuaEndNC[0] != "None":
for var in self.outAnnuaEndNC:
# reporting at the end of the year:
if currTimeStep.endYear == True:
self.netcdfObj.data2NetCDF(str(self.outNCDir)+"/"+ \
str(var)+"_annuaEnd.nc",\
var,\
pcr2numpy(self.__getattribute__(var),vos.MV),\
timeStamp,currTimeStep.annuaIdx-1)
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 initial(self):
# make sure that you start from the script directory
os.chdir(self.script_directory)
# In this part (premcloop), we initiate parameters/variables/objects that are changing throughout all monte carlo samples.
msg = "\n"
msg += "Sample number: " + str(self.currentSampleNumber())
msg += "\n"
print(msg)
# conductivities for the BCF package, see: http://pcraster.geo.uu.nl/pcraster/4.1.0/doc/modflow/bcf.html
inp_soil_conductivity = self.model_setup['soil_conductivity'][
int(str(self.currentSampleNumber())) - 1]
self.soil_conductivity = pcr.spatial(pcr.scalar(inp_soil_conductivity))
#
# - horizontal and vertical conductivity
self.hConductivity = self.soil_conductivity
self.vConductivity = self.hConductivity
# - for one layer model, vConductivity is just dummy and never used
# - layer type, we use LAYTYPE = 0 (harmonic mean) and LAYCON = 0 (confined, constant transmissivities and storage coefficients)
# storage coefficients for the BCF package, see: http://pcraster.geo.uu.nl/pcraster/4.1.0/doc/modflow/bcf.html
# - sand porosity (m3.m-3) # TODO: Find the value from Sebastian paper.
self.sand_porosity = pcr.spatial(pcr.scalar(0.25))
#
# - primary and secondary storage coefficients
self.primary_storage_coefficient = self.sand_porosity
self.secondary_storage_coefficient = self.primary_storage_coefficient
# - for LAYCON = 0 (and 1), secondary_storage_coefficient is just dummy and never used
# The following are CURRENTLY just the same for all samples.
############################################################################
# defining the layer (one layer model), thickness (m), top and bottom elevations
self.thickness = 15.0
# - thickness value is suggested by Kim Cohen (put reference here)
self.top_elevation = self.input_dem
self.bottom_elevation = self.top_elevation - self.thickness
# DIS parameters, see http://pcraster.geo.uu.nl/pcraster/4.1.0/doc/modflow/dis.html
# - time and spatial units
self.ITMUNI = 4 # indicating that the time unit is "days"
self.LENUNI = 2 # indicating that the spatial unit is "meters"
# - PERLEN: duration of stress period (days)
# -- 10 minute stress period = 600 seconds stress period
self.length_of_stress_period = 600. / (24. * 60. * 60.)
self.PERLEN = self.length_of_stress_period
# - NSTP: number of sub time steps within the PERLEN
self.NSTP = 1
# - TSMULT # always 1 by default
self.TSMULT = 1
# - SSTR: transient (0) or steady state (1)
self.SSTR = 0
# values for the IBOND of the BAS package, see: http://pcraster.geo.uu.nl/pcraster/4.1.0/doc/modflow/bas.html
# - Alternative 1: all cells are active
self.ibound = pcr.spatial(pcr.nominal(1.))
#~ # - Alternative 2: in the ocean region (x < -75 m), assume the heads will follow the tides
#~ self.ibound = pcr.ifthenelse(pcr.xcoordinate(clone_map) < -75., pcr.nominal(-1.), pcr.nominal(1.))
#~ pcr.aguila(self.ibound)
# ibound with regional groundwater head values
if self.model_setup['regional_groundwater_head']['activation']:
self.ibound = pcr.ifthenelse(
pcr.xcoordinate(pcr.boolean(1.0)) >=
self.model_setup['regional_groundwater_head']['starting_x'],
pcr.nominal(-1.), self.ibound)
# parameter values for the SOLVER package
self.MXITER = 50 # maximum number of outer iterations # Deltares use 50
self.ITERI = 30 # number of inner iterations # Deltares use 30
self.NPCOND = 1 # 1 - Modified Incomplete Cholesky, 2 - Polynomial matrix conditioning method
self.HCLOSE = 0.001 # HCLOSE (unit: m)
self.RCLOSE = 0.001 # RCLOSE (unit: m3)
self.RELAX = 1.00 # relaxation parameter used with NPCOND = 1
self.NBPOL = 2 # indicates whether the estimate of the upper bound on the maximum eigenvalue is 2.0 (but we don ot use it, since NPCOND = 1)
self.DAMP = 1 # no damping (DAMP introduced in MODFLOW 2000)
# the initial head for the BAS package, see: http://pcraster.geo.uu.nl/pcraster/4.1.0/doc/modflow/bas.html
# - Gerben recommends to start using 0.8 m
self.initial_head = pcr.spatial(pcr.scalar(0.8))
# regional groundwater head
if self.model_setup['regional_groundwater_head']['activation']:
self.initial_head = pcr.ifthenelse(
pcr.xcoordinate(pcr.boolean(1.0)) >=
self.model_setup['regional_groundwater_head']['starting_x'],
self.model_setup['regional_groundwater_head']['value'],
self.initial_head)
# initialise timeoutput object for reporting time series in txt files
# - groundwater head
self.head_obs_point = pcr.readmap(
"input_files/groundwater_well_coordinates.map")
self.reportGwHeadTss = TimeoutputTimeseries("groundwater_head",
self,
self.head_obs_point,
noHeader=False)
# Save model parameter values (and other information) to a txt file.
# - output directory (that will contain result)
output_directory = self.output_folder + "/" + str(
self.currentSampleNumber())
try:
os.makedirs(output_directory)
except:
pass
# - file name for this information
information_file = output_directory + "/" + "info.txt"
file_info = open(information_file, 'w')
write_line = ""
# - DEM
write_line += "DEM (m): " + str(self.model_setup['dem_file_name'])
write_line += "\n"
# - tide
write_line += "Tide input file name: " + str(
self.model_setup['tide_file_name'])
write_line += "\n"
# - starting date
write_line += "Starting date and time: " + str(
self.model_setup['start_datetime'])
write_line += "\n"
# - soil conductivity
write_line += "Soil conductivity (m.day-1): " + str(
inp_soil_conductivity)
write_line += "\n"
# - regional groundwater head (optional):
if self.model_setup['regional_groundwater_head']['activation']:
write_line += "Regional groundwater head is fixed to : " + str(
self.model_setup['regional_groundwater_head']['value'])
write_line += "\n"
write_line += "for all cells in with x coordinate >=: " + str(
self.model_setup['regional_groundwater_head']['starting_x'])
write_line += "\n"
else:
write_line += "NO regional groundwater head. "
write_line += "\n"
# - sample number
write_line += "PCRaster sample number: " + str(
self.currentSampleNumber())
write_line += "\n"
file_info.write(write_line)
# - close the file
file_info.close()
# initiate netcdf files for groundwater head
netcdf_variable_name = "groundwater_head"
self.netcdf_file_name = self.output_folder + "/" + str(
self.currentSampleNumber()) + "/" + netcdf_variable_name + ".nc"
self.model_setup['netcdf_attributes']['notes'] = write_line
self.netcdf_writer.create_netcdf_file(
self.netcdf_file_name, self.model_setup['netcdf_attributes'])
# - create variable
netcdf_variable_unit = "m"
self.netcdf_writer.create_variable(self.netcdf_file_name,
netcdf_variable_name,
netcdf_variable_unit)
