def mb_calibration(rgi_version, baseline):
"""Run the mass balance calibration for the VAS model. RGI version and
baseline climate must be given.
Parameters
----------
rgi_version : str
Version (and subversion) of the RGI, e.g., '62'
baseline : str
'HISTALP' or 'CRU', name of the baseline climate
"""
# initialize OGGM and set up the run parameters
vascaling.initialize(logging_level='WORKFLOW')
# LOCAL paths (where to write the OGGM run output)
# dirname = 'VAS_ref_mb_{}_RGIV{}'.format(baseline, rgi_version)
# wdir = utils.gettempdir(dirname, home=True, reset=True)
# utils.mkdir(wdir, reset=True)
# cfg.PATHS['working_dir'] = wdir
# CLUSTER paths
wdir = os.environ.get('WORKDIR', '')
cfg.PATHS['working_dir'] = wdir
# we are running the calibration ourselves
cfg.PARAMS['run_mb_calibration'] = True
# we are using which baseline data?
cfg.PARAMS['baseline_climate'] = baseline
# no need for intersects since this has an effect on the inversion only
cfg.PARAMS['use_intersects'] = False
# use multiprocessing?
cfg.PARAMS['use_multiprocessing'] = True
# set to True for operational runs
cfg.PARAMS['continue_on_error'] = True
# 10 is only for OGGM-VAS, OGGM needs 80 to run
cfg.PARAMS['border'] = 80
if baseline == 'HISTALP':
# OGGM HISTALP PARAMETERS from Matthias Dusch
# see https://oggm.org/2018/08/10/histalp-parameters/
# cfg.PARAMS['prcp_scaling_factor'] = 1.75
# cfg.PARAMS['temp_melt'] = -1.75
# cfg.PARAMS['temp_all_solid'] = 0
# cfg.PARAMS['prcp_default_gradient'] = 0
# VAS HISTALP PARAMETERS from x-validation
cfg.PARAMS['prcp_scaling_factor'] = 2.5
cfg.PARAMS['temp_melt'] = -0.5
cfg.PARAMS['temp_all_solid'] = 0
cfg.PARAMS['prcp_default_gradient'] = 0
elif baseline == 'CRU':
# using the parameters from Marzeion et al. (2012)
# cfg.PARAMS['prcp_scaling_factor'] = 2.5
# cfg.PARAMS['temp_melt'] = 1
# cfg.PARAMS['temp_all_solid'] = 3
# cfg.PARAMS['prcp_default_gradient'] = 3e-4
# using the parameters from Malles and Marzeion 2020
cfg.PARAMS['prcp_scaling_factor'] = 3
cfg.PARAMS['temp_melt'] = 0
cfg.PARAMS['temp_all_solid'] = 4
cfg.PARAMS['prcp_default_gradient'] = 4e-4
# the next step is to get all the reference glaciers,
# i.e. glaciers with mass balance measurements.
# get the reference glacier ids (they are different for each RGI version)
df, _ = utils.get_wgms_files()
rids = df['RGI{}0_ID'.format(rgi_version[0])]
# we can't do Antarctica
rids = [rid for rid in rids if not ('-19.' in rid)]
# For HISTALP only RGI reg 11.01 (ALPS)
if baseline == 'HISTALP':
rids = [rid for rid in rids if '-11' in rid]
# initialize the glacier regions
base_url = "https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/" \
"L3-L5_files/CRU/elev_bands/qc3/pcp2.5/match_geod"
# Go - get the pre-processed glacier directories
gdirs = workflow.init_glacier_directories(rids, from_prepro_level=3,
prepro_base_url=base_url,
prepro_rgi_version=rgi_version)
# Some glaciers in RGI Region 11 are not inside the HISTALP domain
if baseline == 'HISTALP':
gdirs = [gdir for gdir in gdirs if gdir.rgi_subregion == '11-01']
# get reference glaciers with mass balance measurements
gdirs = utils.get_ref_mb_glaciers(gdirs)
# make a new dataframe with those (this takes a while)
print('For RGIV{} we have {} candidate reference '
'glaciers.'.format(rgi_version, len(gdirs)))
# run climate tasks
vascaling.compute_ref_t_stars(gdirs)
# execute_entity_task(vascaling.local_t_star, gdirs)
# we store the associated params
mb_calib = gdirs[0].read_pickle('climate_info')['mb_calib_params']
with open(os.path.join(wdir, 'mb_calib_params.json'), 'w') as fp:
json.dump(mb_calib, fp)
def mb_calibration(rgi_version, baseline):
""" Run the mass balance calibration for the VAS model. RGI version and
baseline cliamte must be given.
:param rgi_version: int, RGI version
:param baseline: str, baseline climate 'HISTALP' or 'CRU'
"""
# initialize OGGM and set up the run parameters
vascaling.initialize(logging_level='WORKFLOW')
# LOCAL paths (where to write the OGGM run output)
# dirname = 'VAS_ref_mb_{}_RGIV{}'.format(baseline, rgi_version)
# wdir = utils.gettempdir(dirname, home=True, reset=True)
# utils.mkdir(wdir, reset=True)
# cfg.PATHS['working_dir'] = wdir
# CLUSTER paths
wdir = os.environ.get('WORKDIR', '')
cfg.PATHS['working_dir'] = wdir
# we are running the calibration ourselves
cfg.PARAMS['run_mb_calibration'] = True
# we are using which baseline data?
cfg.PARAMS['baseline_climate'] = baseline
# no need for intersects since this has an effect on the inversion only
cfg.PARAMS['use_intersects'] = False
# use multiprocessing?
cfg.PARAMS['use_multiprocessing'] = True
# set to True for operational runs
cfg.PARAMS['continue_on_error'] = True
# 10 is only for OGGM-VAS, OGGM needs 80 to run
cfg.PARAMS['border'] = 80
if baseline == 'HISTALP':
# other params: see https://oggm.org/2018/08/10/histalp-parameters/
# cfg.PARAMS['prcp_scaling_factor'] = 1.75
# cfg.PARAMS['temp_melt'] = -1.75
cfg.PARAMS['prcp_scaling_factor'] = 2.5
cfg.PARAMS['temp_melt'] = -0.5
elif baseline == 'CRU':
# using the parameters from Marzeion et al. (2012)
# cfg.PARAMS['prcp_scaling_factor'] = 2.5
# cfg.PARAMS['temp_melt'] = 1
# cfg.PARAMS['temp_all_solid'] = 3
# using the parameters from Malles and Marzeion 2020
cfg.PARAMS['prcp_scaling_factor'] = 3
cfg.PARAMS['temp_melt'] = 0
cfg.PARAMS['temp_all_solid'] = 4
# cfg.PARAMS['prcp_gradient'] = 4
# the next step is to get all the reference glaciers,
# i.e. glaciers with mass balance measurements.
# get the reference glacier ids (they are different for each RGI version)
df, _ = utils.get_wgms_files()
rids = df['RGI{}0_ID'.format(rgi_version[0])]
# we can't do Antarctica
rids = [rid for rid in rids if not ('-19.' in rid)]
# For HISTALP only RGI reg 11.01 (ALPS)
if baseline == 'HISTALP':
rids = [rid for rid in rids if '-11' in rid]
# make a new dataframe with those (this takes a while)
print('Reading the RGI shapefiles...')
rgidf = utils.get_rgi_glacier_entities(rids, version=rgi_version)
print('For RGIV{} we have {} candidate reference '
'glaciers.'.format(rgi_version, len(rgidf)))
# initialize the glacier regions
base_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/' \
'L3-L5_files/RGIV62_fleb_qc3_CRU_pcp2.5'
# Go - get the pre-processed glacier directories
gdirs = workflow.init_glacier_directories(rids,
from_prepro_level=3,
prepro_base_url=base_url,
prepro_rgi_version=rgi_version)
# Some glaciers in RGI Region 11 are not inside the HISTALP domain
if baseline == 'HISTALP':
gdirs = [gdir for gdir in gdirs if gdir.rgi_subregion == '11-01']
# get reference glaciers with mass balance measurements
gdirs = utils.get_ref_mb_glaciers(gdirs)
# keep only these glaciers
rgidf = rgidf.loc[rgidf.RGIId.isin([g.rgi_id for g in gdirs])]
# save to file
rgidf.to_file(os.path.join(wdir, 'mb_ref_glaciers.shp'))
print('For RGIV{} and {} we have {} reference glaciers'.format(
rgi_version, baseline, len(rgidf)))
# sort for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)
# newly initialize glacier directories
gdirs = workflow.init_glacier_directories(rgidf, reset=False, force=True)
workflow.execute_entity_task(gis.define_glacier_region, gdirs)
workflow.execute_entity_task(gis.glacier_masks, gdirs)
# run climate tasks
vascaling.compute_ref_t_stars(gdirs)
execute_entity_task(vascaling.local_t_star, gdirs)
# we store the associated params
mb_calib = gdirs[0].read_pickle('climate_info')['mb_calib_params']
with open(os.path.join(wdir, 'mb_calib_params.json'), 'w') as fp:
json.dump(mb_calib, fp)
rgi_version, baseline, len(rgidf)))
# sort for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)
# initialize glacier directories
gdirs = workflow.init_glacier_regions(rgidf)
# specify needed prepro tasks
task_list = [
tasks.glacier_masks,
tasks.compute_centerlines,
tasks.initialize_flowlines,
tasks.catchment_area,
tasks.catchment_intersections,
tasks.catchment_width_geom,
tasks.catchment_width_correction,
]
# execute all prepro tasks
for task in task_list:
execute_entity_task(task, gdirs)
# run climate tasks
vascaling.compute_ref_t_stars(gdirs)
execute_entity_task(vascaling.local_t_star, gdirs)
# we store the associated params
mb_calib = gdirs[0].read_pickle('climate_info')['mb_calib_params']
with open(os.path.join(WORKING_DIR, 'mb_calib_params.json'), 'w') as fp:
json.dump(mb_calib, fp)
def mb_calibration(rgi_version, baseline):
""" Run the mass balance calibration for the VAS model. RGI version and
baseline cliamte must be given.
:param rgi_version: int, RGI version
:param baseline: str, baseline climate 'HISTALP' or 'CRU'
"""
# initialize OGGM and set up the run parameters
vascaling.initialize(logging_level='WORKFLOW')
# local paths (where to write the OGGM run output)
# dirname = 'VAS_ref_mb_{}_RGIV{}'.format(baseline, rgi_version)
# wdir = utils.gettempdir(dirname, home=True, reset=True)
# utils.mkdir(wdir, reset=True)
wdir = os.environ['WORKDIR']
cfg.PATHS['working_dir'] = wdir
# we are running the calibration ourselves
cfg.PARAMS['run_mb_calibration'] = True
# we are using which baseline data?
cfg.PARAMS['baseline_climate'] = baseline
# no need for intersects since this has an effect on the inversion only
cfg.PARAMS['use_intersects'] = False
# use multiprocessing?
cfg.PARAMS['use_multiprocessing'] = True
# set to True for operational runs
cfg.PARAMS['continue_on_error'] = True
if baseline == 'HISTALP':
# other params: see https://oggm.org/2018/08/10/histalp-parameters/
# cfg.PARAMS['prcp_scaling_factor'] = 1.75
# cfg.PARAMS['temp_melt'] = -1.75
cfg.PARAMS['prcp_scaling_factor'] = 2.5
cfg.PARAMS['temp_melt'] = -0.5
elif baseline == 'CRU':
# using the parameters from Marzeion et al. (2012)
cfg.PARAMS['prcp_scaling_factor'] = 2.5
cfg.PARAMS['temp_melt'] = 1
cfg.PARAMS['temp_all_solid'] = 3
# the next step is to get all the reference glaciers,
# i.e. glaciers with mass balance measurements.
# get the reference glacier ids (they are different for each RGI version)
rgi_dir = utils.get_rgi_dir(version=rgi_version)
df, _ = utils.get_wgms_files()
rids = df['RGI{}0_ID'.format(rgi_version[0])]
# we can't do Antarctica
rids = [rid for rid in rids if not ('-19.' in rid)]
# For HISTALP only RGI reg 11.01 (ALPS)
if baseline == 'HISTALP' or True:
rids = [rid for rid in rids if '-11' in rid]
debug = False
if debug:
print("==================================\n"
+ "DEBUG MODE: only RGI60-11.00897\n"
+ "==================================")
rids = [rid for rid in rids if '-11.00897' in rid]
cfg.PARAMS['use_multiprocessing'] = False
# make a new dataframe with those (this takes a while)
print('Reading the RGI shapefiles...')
rgidf = utils.get_rgi_glacier_entities(rids, version=rgi_version)
print('For RGIV{} we have {} candidate reference '
'glaciers.'.format(rgi_version, len(rgidf)))
# initialize the glacier regions
gdirs = workflow.init_glacier_directories(rgidf, reset=False, force=True)
workflow.execute_entity_task(gis.define_glacier_region, gdirs)
workflow.execute_entity_task(gis.glacier_masks, gdirs)
# we need to know which period we have data for
print('Process the climate data...')
if baseline == 'CRU':
execute_entity_task(tasks.process_cru_data, gdirs, print_log=False)
elif baseline == 'HISTALP':
# Some glaciers are not in Alps
gdirs = [gdir for gdir in gdirs if gdir.rgi_subregion == '11-01']
# cfg.PARAMS['continue_on_error'] = True
execute_entity_task(tasks.process_histalp_data, gdirs, print_log=False,
y0=1850)
# cfg.PARAMS['continue_on_error'] = False
else:
execute_entity_task(tasks.process_custom_climate_data,
gdirs, print_log=False)
# get reference glaciers with mass balance measurements
gdirs = utils.get_ref_mb_glaciers(gdirs)
# keep only these glaciers
rgidf = rgidf.loc[rgidf.RGIId.isin([g.rgi_id for g in gdirs])]
# save to file
rgidf.to_file(os.path.join(wdir, 'mb_ref_glaciers.shp'))
print('For RGIV{} and {} we have {} reference glaciers'.format(rgi_version,
baseline,
len(rgidf)))
# sort for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)
# newly initialize glacier directories
gdirs = workflow.init_glacier_directories(rgidf, reset=False, force=True)
workflow.execute_entity_task(gis.define_glacier_region, gdirs)
workflow.execute_entity_task(gis.glacier_masks, gdirs)
# run climate tasks
vascaling.compute_ref_t_stars(gdirs)
execute_entity_task(vascaling.local_t_star, gdirs)
# we store the associated params
mb_calib = gdirs[0].read_pickle('climate_info')['mb_calib_params']
with open(os.path.join(wdir, 'mb_calib_params.json'), 'w') as fp:
json.dump(mb_calib, fp)