# - Using the top layer of the local model
local_model_folder = "/scratch-shared/edwinhs/colombia_model_results/head/l1_top/"
i_month = 0
cum_map = pcr.scalar(0.0)
for year in range(str_year, end_year + 1, 1):
for month in range(1, 12 + 1, 1):
# file name, e.g. "/scratch-shared/edwinhs/colombia_model_results/head/l1_top/head_20000201_l1.idf.map"
file_name = "head_%04d%02d" % (year, month)
file_name = local_model_folder + "/" + file_name + "01_l1.idf.map"
print(file_name)
# cummulative values
cum_map = cum_map + pcr.readmap(file_name)
i_month = i_month + 1
# calculating average and saving it to a pcraster map
average_local = cum_map / i_month
pcr.aguila(average_local)
output_filename = output_folder + "/average_local.map"
pcr.report(average_local, output_filename)
# calculate the average value/map of the global model
msg = "Calculate the average value/map of the global model!"
print(msg)
# - Using the top layer of the global model
global_model_folder = "/scratch-shared/edwinhs/modflow_results_in_pcraster/upper_layer/regional/"
i_month = 0
cum_map = pcr.scalar(0.0)
for year in range(str_year, end_year + 1, 1):
for month in range(1, 12 + 1, 1):
# file name, e.g. /scratch-shared/edwinhs/modflow_results_in_pcraster/upper_layer/regional/htop_2000_01.map
file_name = "htop_%04d_%02d" % (year, month)
file_name = global_model_folder + "/" + file_name + ".map"
def main():
# output folder
clean_out_folder = True
if os.path.exists(out_folder):
if clean_out_folder:
shutil.rmtree(out_folder)
os.makedirs(out_folder)
else:
os.makedirs(out_folder)
os.chdir(out_folder)
os.system("pwd")
# tmp folder
tmp_folder = out_folder + "/tmp/"
if os.path.exists(tmp_folder): shutil.rmtree(tmp_folder)
os.makedirs(tmp_folder)
# set the clone map
print("set the clone map")
pcr.setclone(global_clone_map_file)
# read ldd map
print("define the ldd")
# ~ ldd_map = pcr.readmap(global_ldd_inp_file)
ldd_map = pcr.lddrepair(pcr.lddrepair(pcr.ldd(vos.readPCRmapClone(v = global_ldd_inp_file, \
cloneMapFileName = global_clone_map_file, \
tmpDir = tmp_folder, \
absolutePath = None, \
isLddMap = True, \
cover = None, \
isNomMap = False))))
# define the landmask
if landmask_map_file == None:
print("define the landmask based on the ldd input")
# ~ landmask = pcr.defined(pcr.readmap(global_ldd_inp_file))
landmask = pcr.defined(ldd_map)
landmask = pcr.ifthen(landmask, landmask)
else:
print("define the landmask based on the input landmask_map_file")
landmask = pcr.readmap(landmask_map_file)
ldd_map = pcr.ifthen(landmask, pcr.cover(ldd_map, pcr.ldd(5)))
ldd_map = pcr.lddrepair(pcr.lddrepair(pcr.ldd(ldd_map)))
landmask = pcr.defined(ldd_map)
landmask = pcr.ifthen(landmask, landmask)
# save ldd files used
# - global ldd
cmd = "cp " + str(global_ldd_inp_file) + " ."
print(cmd)
os.system(cmd)
# - ldd map that is used
pcr.report(ldd_map, "lddmap_used.map")
# make catchment map
print("make catchment map")
catchment_map = pcr.catchment(ldd_map, pcr.pit(ldd_map))
# read global subdomain file
print("read global subdomain file")
global_subdomain_map = vos.readPCRmapClone(
v=global_subdomain_file,
cloneMapFileName=global_clone_map_file,
tmpDir=tmp_folder,
absolutePath=None,
isLddMap=False,
cover=None,
isNomMap=True)
# set initial subdomain
print("assign subdomains to all catchments")
subdomains_initial = pcr.areamajority(global_subdomain_map, catchment_map)
subdomains_initial = pcr.ifthen(landmask, subdomains_initial)
pcr.aguila(subdomains_initial)
pcr.report(subdomains_initial, "global_subdomains_initial.map")
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[0])))
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[1])))
print("Checking all subdomains, avoid too large subdomains")
num_of_masks = int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[1])
# clone code that will be assigned
assigned_number = 0
subdomains_final = pcr.ifthen(
pcr.scalar(subdomains_initial) < -7777, pcr.nominal(0))
for nr in range(1, num_of_masks + 1, 1):
msg = "Processing the landmask %s" % (str(nr))
print(msg)
mask_selected_boolean = pcr.ifthen(subdomains_initial == nr,
pcr.boolean(1.0))
process_this_clone = False
if pcr.cellvalue(pcr.mapmaximum(pcr.scalar(mask_selected_boolean)), 1,
1)[0] > 0:
process_this_clone = True
# ~ if nr == 1: pcr.aguila(mask_selected_boolean)
# - initial check value
check_ok = True
if process_this_clone:
xmin, ymin, xmax, ymax = boundingBox(mask_selected_boolean)
area_in_degree2 = (xmax - xmin) * (ymax - ymin)
# ~ print(str(area_in_degree2))
# check whether the size of bounding box is ok
reference_area_in_degree2 = 2500.
if area_in_degree2 > 1.50 * reference_area_in_degree2:
check_ok = False
if (xmax - xmin) > 10 * (ymax - ymin): check_ok = False
# ~ # ignore checking
# ~ check_ok = True
if check_ok == True and process_this_clone == True:
msg = "Clump is not needed."
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# assign the clone code
assigned_number = assigned_number + 1
# update global landmask for river and land
mask_selected_nominal = pcr.ifthen(mask_selected_boolean,
pcr.nominal(assigned_number))
subdomains_final = pcr.cover(subdomains_final,
mask_selected_nominal)
if check_ok == False and process_this_clone == True:
msg = "Clump is needed."
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# make clump
clump_ids = pcr.nominal(pcr.clump(mask_selected_boolean))
# merge clumps that are close together
clump_ids_window_majority = pcr.windowmajority(clump_ids, 10.0)
clump_ids = pcr.areamajority(clump_ids_window_majority, clump_ids)
# ~ pcr.aguila(clump_ids)
# minimimum and maximum values
min_clump_id = int(
pcr.cellvalue(pcr.mapminimum(pcr.scalar(clump_ids)), 1)[0])
max_clump_id = int(
pcr.cellvalue(pcr.mapmaximum(pcr.scalar(clump_ids)), 1)[0])
for clump_id in range(min_clump_id, max_clump_id + 1, 1):
msg = "Processing the clump %s of %s from the landmask %s" % (
str(clump_id), str(max_clump_id), str(nr))
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# identify mask based on the clump
mask_selected_boolean_from_clump = pcr.ifthen(
clump_ids == pcr.nominal(clump_id), mask_selected_boolean)
mask_selected_boolean_from_clump = pcr.ifthen(
mask_selected_boolean_from_clump,
mask_selected_boolean_from_clump)
# check whether the clump is empty
check_mask_selected_boolean_from_clump = pcr.ifthen(
mask_selected_boolean, mask_selected_boolean_from_clump)
check_if_empty = float(
pcr.cellvalue(
pcr.mapmaximum(
pcr.scalar(
pcr.defined(
check_mask_selected_boolean_from_clump))),
1)[0])
if check_if_empty == 0.0:
msg = "Map is empty !"
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
else:
msg = "Map is NOT empty !"
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# assign the clone code
assigned_number = assigned_number + 1
# update global landmask for river and land
mask_selected_nominal = pcr.ifthen(
mask_selected_boolean_from_clump,
pcr.nominal(assigned_number))
subdomains_final = pcr.cover(subdomains_final,
mask_selected_nominal)
# ~ # kill all aguila processes if exist
# ~ os.system('killall aguila')
pcr.aguila(subdomains_final)
print("")
print("")
print("")
print("The subdomain map is READY.")
pcr.report(subdomains_final, "global_subdomains_final.map")
num_of_masks = int(vos.getMinMaxMean(pcr.scalar(subdomains_final))[1])
print(num_of_masks)
print("")
print("")
print("")
print("Making the clone and landmask maps for all subdomains")
num_of_masks = int(vos.getMinMaxMean(pcr.scalar(subdomains_final))[1])
# clone and mask folders
clone_folder = out_folder + "/clone/"
if os.path.exists(clone_folder): shutil.rmtree(clone_folder)
os.makedirs(clone_folder)
mask_folder = out_folder + "/mask/"
if os.path.exists(mask_folder): shutil.rmtree(mask_folder)
os.makedirs(mask_folder)
print("")
print("")
for nr in range(1, num_of_masks + 1, 1):
msg = "Processing the subdomain %s" % (str(nr))
print(msg)
# set the global clone
pcr.setclone(global_clone_map_file)
mask_selected_boolean = pcr.ifthen(subdomains_final == nr,
pcr.boolean(1.0))
mask_selected_nominal = pcr.ifthen(subdomains_final == nr,
pcr.nominal(nr))
mask_file = "mask/mask_%s.map" % (str(nr))
pcr.report(mask_selected_nominal, mask_file)
xmin, ymin, xmax, ymax = boundingBox(mask_selected_boolean)
area_in_degree2 = (xmax - xmin) * (ymax - ymin)
print(
str(nr) + " ; " + str(area_in_degree2) + " ; " +
str((xmax - xmin)) + " ; " + str((ymax - ymin)))
# cellsize in arcdegree
cellsize = cellsize_in_arcmin / 60.
# number of rows and cols
num_rows = int(round(ymax - ymin) / cellsize)
num_cols = int(round(xmax - xmin) / cellsize)
# make the clone map using mapattr
clonemap_mask_file = "clone/clonemap_mask_%s.map" % (str(nr))
cmd = "mapattr -s -R %s -C %s -B -P yb2t -x %s -y %s -l %s %s" % (
str(num_rows), str(num_cols), str(xmin), str(ymax), str(cellsize),
clonemap_mask_file)
print(cmd)
os.system(cmd)
# set the local landmask for the clump
pcr.setclone(clonemap_mask_file)
local_mask = vos.readPCRmapClone(v = mask_file, \
cloneMapFileName = clonemap_mask_file,
tmpDir = tmp_folder, \
absolutePath = None, isLddMap = False, cover = None, isNomMap = True)
local_mask_boolean = pcr.defined(local_mask)
local_mask_boolean = pcr.ifthen(local_mask_boolean, local_mask_boolean)
pcr.report(local_mask_boolean, mask_file)
print("")
print("")
print("")
print(num_of_masks)
def main():
# global map of subdomain masks
global_clone_map = "/scratch/mo/nest/ulysses/data/edwin/clone_maps/version_2020-08-04/clone_all_maps/clone_ulysses_06min_global.map"
# number of subdomain masks
num_of_masks = 54
# list of subdomain mask files for land (all in nc files)
subdomain_land_nc = "/scratch/mo/nest/ulysses/data/subdomain_masks/subdomain_land_%s.nc"
# lisk of subdomain maks files for touring (all in nc files)
subdomain_river_nc = "/scratch/mo/nest/ulysses/data/subdomain_river_masks/subdomain_river_%s.nc"
# output folder (and tmp folder)
out_folder = "/scratch/mo/nest/ulysses/data/edwin/clone_maps/version_2020-08-04/pcraster_maps/"
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")
# make tmp folder
tmp_folder = os.path.join(out_folder, "tmp_clone_uly") + "/"
if os.path.exists(tmp_folder): shutil.rmtree(tmp_folder)
os.makedirs(tmp_folder)
# make initial landmask maps at the global extent
# - set to the global clone map
pcr.setclone(global_clone_map)
# - for land
landmask_land_all = pcr.ifthen(
pcr.scalar(global_clone_map) > 10, pcr.nominal(0))
# - for river
landmask_river_and_land_all = pcr.ifthen(
pcr.scalar(global_clone_map) > 10, pcr.nominal(0))
for nr in range(1, num_of_masks + 1, 1):
msg = "Processing the landmask %s" % (str(nr))
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# set to the global clone map
pcr.setclone(global_clone_map)
# read land nc file (and convert it to pcraster)
subdomain_land_nc_file = subdomain_land_nc % (str(nr))
mask_land_selected = vos.netcdf2PCRobjCloneWithoutTime(ncFile = subdomain_land_nc_file, \
varName = "mask",\
cloneMapFileName = global_clone_map,\
LatitudeLongitude = True,\
specificFillValue = "NaN",\
absolutePath = None)
mask_land_selected_boolean = pcr.ifthen(
pcr.scalar(mask_land_selected) > 0.0, pcr.boolean(1.0))
mask_land_selected_boolean = pcr.ifthen(mask_land_selected_boolean,
mask_land_selected_boolean)
# update global landmask for land
mask_land_selected_nominal = pcr.ifthen(mask_land_selected_boolean,
pcr.nominal(nr))
landmask_land_all = pcr.cover(landmask_land_all,
mask_land_selected_nominal)
# ~ pcr.aguila(landmask_land_all)
# read river nc file (and convert it to pcraster)
subdomain_river_nc_file = subdomain_river_nc % (str(nr))
mask_river_selected = vos.netcdf2PCRobjCloneWithoutTime(ncFile = subdomain_river_nc_file, \
varName = "mask",\
cloneMapFileName = global_clone_map,\
LatitudeLongitude = True,\
specificFillValue = "NaN",\
absolutePath = None)
mask_river_selected_boolean = pcr.ifthen(
pcr.scalar(mask_river_selected) > 0.0, pcr.boolean(1.0))
mask_river_selected_boolean = pcr.ifthen(mask_river_selected_boolean,
mask_river_selected_boolean)
# merge land and river landmask
mask_selected_boolean = pcr.cover(mask_land_selected_boolean,
mask_river_selected_boolean)
mask_selected_nominal = pcr.ifthen(mask_selected_boolean,
pcr.nominal(nr))
# ~ pcr.aguila(mask_selected_nominal)
filename_for_land_river_mask_at_global_extent = "global_landmask_river_and_land_mask_%s.map" % (
str(nr))
filename_for_land_river_mask_at_global_extent = os.path.join(
out_folder, filename_for_land_river_mask_at_global_extent)
pcr.report(mask_selected_nominal,
filename_for_land_river_mask_at_global_extent)
# update global landmask for land and river
landmask_river_and_land_all = pcr.cover(landmask_river_and_land_all,
mask_selected_nominal)
# ~ pcr.aguila(landmask_river_and_land_all)
# get the bounding box based on the landmask file
xmin, ymin, xmax, ymax = boundingBox(mask_selected_boolean)
# cellsize
cellsize = vos.getMapAttributes(global_clone_map, "cellsize")
num_rows = int(round(ymax - ymin) / cellsize)
num_cols = int(round(xmax - xmin) / cellsize)
# make the clone map using mapattr
clonemap_mask_file = "clonemap_mask_%s.map" % (str(nr))
# - example: mapattr -s -R 19 -C 68 -B -P yb2t -x 12 -y -14.02 -l 0.8 mask2.map
cmd = "mapattr -s -R %s -C %s -B -P yb2t -x %s -y %s -l %s %s" % (
str(num_rows), str(num_cols), str(xmin), str(ymax), str(cellsize),
clonemap_mask_file)
print(cmd)
os.system(cmd)
# set the landmask for land
pcr.setclone(clonemap_mask_file)
landmask_land = vos.netcdf2PCRobjCloneWithoutTime(ncFile = subdomain_land_nc_file, \
varName = "mask",\
cloneMapFileName = clonemap_mask_file,\
LatitudeLongitude = True,\
specificFillValue = "NaN",\
absolutePath = None)
landmask_land_boolean = pcr.ifthen(
pcr.scalar(landmask_land) > 0.0, pcr.boolean(1.0))
landmask_land_boolean = pcr.ifthen(landmask_land_boolean,
landmask_land_boolean)
# - save the landmask for land (used for PCR-GLOBWB reporting)
landmask_land_file = "landmask_land_mask_%s.map" % (str(nr))
pcr.report(landmask_land_boolean, landmask_land_file)
# set the landmask for river and land
landmask_river_and_land = vos.readPCRmapClone(v = filename_for_land_river_mask_at_global_extent, \
cloneMapFileName = clonemap_mask_file,
tmpDir = tmp_folder, \
absolutePath = None, isLddMap = False, cover = None, isNomMap = True)
landmask_river_and_land_boolean = pcr.ifthen(
pcr.scalar(landmask_river_and_land) > 0.0, pcr.boolean(1.0))
landmask_river_and_land_boolean = pcr.ifthen(
landmask_river_and_land_boolean, landmask_river_and_land_boolean)
landmask_river_and_land_file = "landmask_river_and_land_mask_%s.map" % (
str(nr))
pcr.report(landmask_river_and_land_boolean,
landmask_river_and_land_file)
# kill all aguila processes if exist
os.system('killall aguila')
# report a global nominal map for land
pcr.setclone(global_clone_map)
filename_for_nominal_land_mask_at_global_extent = "global_landmask_land_mask_all.map"
pcr.report(landmask_land_all,
filename_for_nominal_land_mask_at_global_extent)
pcr.aguila(landmask_land_all)
# report a global nominal map for river and and land
pcr.setclone(global_clone_map)
filename_for_nominal_land_river_mask_at_global_extent = "global_landmask_river_and_land_mask_all.map"
pcr.report(landmask_river_and_land_all,
filename_for_nominal_land_river_mask_at_global_extent)
pcr.aguila(landmask_river_and_land_all)
print("\n\n Done \n\n")
'cost_correction.csv'),
index_col=0,
comment='#')
#%% Test the cost calculation
msr_name = 'sidechannel_everywhere'
label = 'everywhere'
msr_name = 'lowering_everywhere'
label = 'everywhere'
#msr_name = 'minemblowering_dummy-ID'
#msr_name = 'groynelowering_dummy-ID'
msr = read_measure(os.path.join(msr_map_dir, msr_name))
printing = True
dh = dem - msr.dem
vtot = pcr.maptotal(dh * 25**2)
pcr.aguila(dh)
### Smoothing
c_sm = CostSmoothing('dummy', smoothing_distr)
sm_cost, sm_std = c_sm.overview(msr)
if printing == True:
print(sm_cost)
print(sm_std)
### Earth work
c_ew = CostEarthwork(label, minemb_ext_use_distr, minemb_int_use_distr,
minemb_polluted_distr, dem, minemb, groyne,
pollution_zones, cost_input_ew)
ew_cost, ew_std = c_ew.overview(msr, pollution_threshold=2)
if printing == True:
print(ew_cost)
print(ew_std)
def plot(self):
pcr.aguila(self.mean, self.stddev)
# calculate the average value/map of the local model
msg = "Calculate the average value/map of the local model!"
print(msg)
# - Using the top layer of the local model
local_model_folder = "/scratch-shared/edwinhs/colombia_model_results/head/l1_top/"
i_month = 0
cum_map = pcr.scalar(0.0)
for year in (str_year, end_year + 1, 1):
for month in (1, 12 + 1, 1):
i_month = i_month + 1
file_name = local_model_folder + "/head_%04i%02i" + "01_l1.idf.map" %(year, month)
print(file_name)
cum_map = cum_map + pcr.readmap(local_model_folder + "")
average_local = cum_map / i_month
pcr.aguila(average_local)
#~ # calculate the average value/map of the global model
#~ # - Using the upper layer of the global model
#~ global_model_folder = "/scratch-shared/edwinhs/modflow_results_in_pcraster/upper_layer/regional/"
#~ i_month = 0
#~ for year in (str_year, end_year + 1, 1):
#~ for month in (1, 12 + 1, 1):
#~ average_global =
#~
#~ # calculate the anomaly value of the local model
#~ anomaly_local = {}
#~
#~
#~ # calculate the anomaly value of the global model
#~ anomaly_global = {}
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")
msr_friction = pcr.cover(hwat_free_friction, 0) +\
pcr.cover(buildings_friction, 0) +\
pcr.cover(waste_friction, 0) +\
pcr.cover(directives_friction, 0) +\
pcr.cover(owner_friction, 0)
msr_friction = pcr.ifthen(pcr.defined(outline), msr_friction)
pcr.report(veg_friction, 'veg_friction.map')
pcr.report(flow_path_friction, 'flow_path_friction.map')
pcr.report(buildings_friction, 'buildings_friction.map')
pcr.report(directives_friction, 'directives_friction.map')
pcr.report(waste_friction, 'waste_friction.map')
pcr.report(owner_friction, 'owner_friction.map')
pcr.report(msr_friction, 'msr_friction.map')
pcr.aguila(msr_friction)
#%%
useDoy = None,
cloneMapFileName = None,\
LatitudeLongitude = True,\
specificFillValue = None)
areal_groundwater_recharge = pcr.areatotal(groundwater_recharge * cell_area, class_map)/pcr.areatotal(cell_area, class_map)
# - ignore negative groundwater recharge (due to capillary rise)
areal_groundwater_recharge = pcr.max(0.0, areal_groundwater_recharge)
# areal groundwater contribution to meet enviromental flow (unit: m/year)
groundwater_contribution_to_environmental_flow = pcr.max(0.10, fraction_reserved_recharge * groundwater_recharge)
groundwater_contribution_to_environmental_flow_filename = output_directory + "/" + "groundwater_contribution_to_environmental_flow.m.per.year.map"
pcr.report(pcr.ifthen(landmask, groundwater_contribution_to_environmental_flow), groundwater_contribution_to_environmental_flow_filename)
areal_groundwater_contribution_to_environmental_flow = pcr.areatotal(groundwater_contribution_to_environmental_flow * cell_area, class_map)/pcr.areatotal(cell_area, class_map)
areal_groundwater_contribution_to_environmental_flow = pcr.min(0.75 * areal_groundwater_recharge, areal_groundwater_contribution_to_environmental_flow)
# groundwater stress map (dimensionless)
groundwater_stress_map = pcr.ifthen(landmask, \
areal_groundwater_abstraction/(pcr.cover(pcr.max(0.001, areal_groundwater_recharge - areal_groundwater_contribution_to_environmental_flow), 0.001)))
# fill in the entire cell
groundwater_stress_map = pcr.ifthen(landmask, \
pcr.areamaximum(groundwater_stress_map, class_map_all))
groundwater_stress_map_filename = output_directory + "/" + str(sys.argv[2]) + "_" + str(start_year) + "to" + str(end_year) + ".groundwater_stress.map"
pcr.report(groundwater_stress_map, groundwater_stress_map_filename)
pcr.aguila(groundwater_stress_map)
# groundwater footprint map (km2)
groundwater_footprint_map = groundwater_stress_map * pcr.cover(pcr.areatotal(cell_area, class_map), 0.0) / (1000. * 1000.)
groundwater_footprint_map_filename = output_directory + "/" + str(sys.argv[2]) + "_" + str(start_year) + "to" + str(end_year) + ".groundwater_footprint.km2.map"
pcr.report(groundwater_footprint_map, groundwater_footprint_map_filename)
pcr.aguila(groundwater_footprint_map)
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
