def load_glacier_data(glac_no=None, rgi_regionsO1=None, rgi_regionsO2='all', rgi_glac_number='all',
load_caldata=0, startyear=2000, endyear=2018, option_wateryear=3):
"""
Load glacier data (main_glac_rgi, hyps, and ice thickness)
"""
# Load glaciers
main_glac_rgi_all = modelsetup.selectglaciersrgitable(
rgi_regionsO1=rgi_regionsO1, rgi_regionsO2 =rgi_regionsO2, rgi_glac_number=rgi_glac_number,
glac_no=glac_no)
# Glacier hypsometry [km**2], total area
main_glac_hyps_all = modelsetup.import_Husstable(main_glac_rgi_all, pygem_prms.hyps_filepath, pygem_prms.hyps_filedict,
pygem_prms.hyps_colsdrop)
# Ice thickness [m], average
main_glac_icethickness_all = modelsetup.import_Husstable(main_glac_rgi_all, pygem_prms.thickness_filepath,
pygem_prms.thickness_filedict, pygem_prms.thickness_colsdrop)
# Additional processing
main_glac_hyps_all[main_glac_icethickness_all == 0] = 0
main_glac_hyps_all = main_glac_hyps_all.fillna(0)
main_glac_icethickness_all = main_glac_icethickness_all.fillna(0)
# Add degree groups to main_glac_rgi_all
# Degrees
main_glac_rgi_all['CenLon_round'] = np.floor(main_glac_rgi_all.CenLon.values/degree_size) * degree_size
main_glac_rgi_all['CenLat_round'] = np.floor(main_glac_rgi_all.CenLat.values/degree_size) * degree_size
deg_groups = main_glac_rgi_all.groupby(['CenLon_round', 'CenLat_round']).size().index.values.tolist()
deg_dict = dict(zip(deg_groups, np.arange(0,len(deg_groups))))
main_glac_rgi_all.reset_index(drop=True, inplace=True)
cenlon_cenlat = [(main_glac_rgi_all.loc[x,'CenLon_round'], main_glac_rgi_all.loc[x,'CenLat_round'])
for x in range(len(main_glac_rgi_all))]
main_glac_rgi_all['CenLon_CenLat'] = cenlon_cenlat
main_glac_rgi_all['deg_id'] = main_glac_rgi_all.CenLon_CenLat.map(deg_dict)
if load_caldata == 1:
cal_datasets = ['shean']
startyear=2000
dates_table = modelsetup.datesmodelrun(startyear=startyear, endyear=endyear, spinupyears=0,
option_wateryear=option_wateryear)
# Calibration data
cal_data_all = pd.DataFrame()
for dataset in cal_datasets:
cal_subset = class_mbdata.MBData(name=dataset)
cal_subset_data = cal_subset.retrieve_mb(main_glac_rgi_all, main_glac_hyps_all, dates_table)
cal_data_all = cal_data_all.append(cal_subset_data, ignore_index=True)
cal_data_all = cal_data_all.sort_values(['glacno', 't1_idx'])
cal_data_all.reset_index(drop=True, inplace=True)
if load_caldata == 0:
return main_glac_rgi_all, main_glac_hyps_all, main_glac_icethickness_all
else:
return main_glac_rgi_all, main_glac_hyps_all, main_glac_icethickness_all, cal_data_all
gcm_name = 'ERA-Interim'
option_gcm_downscale = 2
option_lapserate_fromgcm = 1
option_export = 1
time_start = time.time()
#%% ===== LOAD GLACIER DATA =====
# RGI glacier attributes
main_glac_rgi = modelsetup.selectglaciersrgitable(rgi_regionsO1=rgi_regionsO1,
rgi_regionsO2='all',
rgi_glac_number='all')
# Glacier hypsometry [km**2], total area
main_glac_hyps = modelsetup.import_Husstable(main_glac_rgi,
pygem_prms.hyps_filepath,
pygem_prms.hyps_filedict,
pygem_prms.hyps_colsdrop)
elev_bins = main_glac_hyps.columns.values.astype(int)
# Ice thickness [m], average
main_glac_icethickness = modelsetup.import_Husstable(
main_glac_rgi, pygem_prms.thickness_filepath,
pygem_prms.thickness_filedict, pygem_prms.thickness_colsdrop)
main_glac_hyps[main_glac_icethickness == 0] = 0
# Width [km], average
main_glac_width = modelsetup.import_Husstable(main_glac_rgi,
pygem_prms.width_filepath,
pygem_prms.width_filedict,
pygem_prms.width_colsdrop)
# Add volume [km**3] and mean elevation [m a.s.l.] to the main glaciers table
main_glac_rgi['Volume'], main_glac_rgi['Zmean'] = modelsetup.hypsometrystats(
main_glac_hyps, main_glac_icethickness)
# Glacier selection
rgi_regionsO1 = [1]
#rgi_glac_number = ['14683']
rgi_glac_number = input.get_same_glaciers(os.getcwd() + '/../Output/cal_opt4/')
startyear = 1980
endyear = 2018
# Select glaciers
main_glac_rgi = modelsetup.selectglaciersrgitable(
rgi_regionsO1=rgi_regionsO1,
rgi_regionsO2='all',
rgi_glac_number=rgi_glac_number)
# Glacier hypsometry [km**2], total area
main_glac_hyps = modelsetup.import_Husstable(main_glac_rgi,
input.hyps_filepath,
input.hyps_filedict,
input.hyps_colsdrop)
# Determine dates_table_idx that coincides with data rgi_regionsO1
dates_table = modelsetup.datesmodelrun(startyear, endyear, spinupyears=0)
elev_bins = main_glac_hyps.columns.values.astype(int)
elev_bin_interval = elev_bins[1] - elev_bins[0]
#cal_datasets = ['larsen']
cal_data = pd.DataFrame()
#for dataset in cal_datasets:
cal_subset = class_mbdata.MBData(name='braun') #, rgi_region=rgi_regionsO1[0]
cal_subset_data = cal_subset.retrieve_mb(main_glac_rgi, main_glac_hyps,
dates_table)
cal_data = cal_data.append(cal_subset_data, ignore_index=True)
gcm_name = 'ERA-Interim'
option_gcm_downscale = 2
option_lapserate_fromgcm = 1
option_export = 1
time_start = time.time()
#%% ===== LOAD GLACIER DATA =====
# RGI glacier attributes
main_glac_rgi = modelsetup.selectglaciersrgitable(rgi_regionsO1=rgi_regionsO1,
rgi_regionsO2='all',
rgi_glac_number='all')
# Glacier hypsometry [km**2], total area
main_glac_hyps = modelsetup.import_Husstable(main_glac_rgi,
input.hyps_filepath,
input.hyps_filedict,
input.hyps_colsdrop)
elev_bins = main_glac_hyps.columns.values.astype(int)
# Ice thickness [m], average
main_glac_icethickness = modelsetup.import_Husstable(main_glac_rgi,
input.thickness_filepath,
input.thickness_filedict,
input.thickness_colsdrop)
main_glac_hyps[main_glac_icethickness == 0] = 0
# Width [km], average
main_glac_width = modelsetup.import_Husstable(main_glac_rgi,
input.width_filepath,
input.width_filedict,
input.width_colsdrop)
# Add volume [km**3] and mean elevation [m a.s.l.] to the main glaciers table
main_glac_rgi['Volume'], main_glac_rgi['Zmean'] = modelsetup.hypsometrystats(
def load_masschange_monthly(regions,
ds_ending,
netcdf_fp=sim_netcdf_fp,
option_add_caldata=0):
""" Load monthly mass change data """
count = 0
for region in regions:
count += 1
# Load datasets
ds_fn = 'R' + str(region) + ds_ending
ds = xr.open_dataset(netcdf_fp + ds_fn)
main_glac_rgi_region_ds = pd.DataFrame(ds.glacier_table.values,
columns=ds.glac_attrs)
glac_wide_massbaltotal_region = ds.massbaltotal_glac_monthly.values[:, :,
0]
glac_wide_area_annual_region = ds.area_glac_annual.values[:, :, 0]
time_values = pd.Series(
ds.massbaltotal_glac_monthly.coords['time'].values)
# ===== GLACIER DATA =====
main_glac_rgi_region = modelsetup.selectglaciersrgitable(
rgi_regionsO1=[region], rgi_regionsO2='all', rgi_glac_number='all')
if (main_glac_rgi_region['glacno'] -
main_glac_rgi_region_ds['glacno']).sum() == 0:
print('Region', str(region), ': number of glaciers match')
# Glacier hypsometry
main_glac_hyps_region = modelsetup.import_Husstable(
main_glac_rgi_region, pygem_prms.hyps_filepath,
pygem_prms.hyps_filedict, pygem_prms.hyps_colsdrop)
# Ice thickness [m], average
main_glac_icethickness_region = modelsetup.import_Husstable(
main_glac_rgi_region, input.thickness_filepath,
input.thickness_filedict, input.thickness_colsdrop)
main_glac_hyps_region[main_glac_icethickness_region == 0] = 0
# ===== CALIBRATION DATA =====
if option_add_caldata == 1:
dates_table_nospinup = modelsetup.datesmodelrun(
startyear=input.startyear,
endyear=input.endyear,
spinupyears=0)
cal_data_region = pd.DataFrame()
for dataset in cal_datasets:
cal_subset = class_mbdata.MBData(name=dataset)
cal_subset_data = cal_subset.retrieve_mb(
main_glac_rgi_region, main_glac_hyps_region,
dates_table_nospinup)
cal_data_region = cal_data_region.append(cal_subset_data,
ignore_index=True)
cal_data_region = cal_data_region.sort_values(['glacno', 't1_idx'])
cal_data_region.reset_index(drop=True, inplace=True)
# ===== APPEND DATASETS =====
if count == 1:
main_glac_rgi = main_glac_rgi_region
main_glac_hyps = main_glac_hyps_region
main_glac_icethickness = main_glac_icethickness_region
glac_wide_massbaltotal = glac_wide_massbaltotal_region
glac_wide_area_annual = glac_wide_area_annual_region
if option_add_caldata == 1:
cal_data = cal_data_region
else:
main_glac_rgi = main_glac_rgi.append(main_glac_rgi_region)
glac_wide_massbaltotal = np.concatenate(
[glac_wide_massbaltotal, glac_wide_massbaltotal_region])
glac_wide_area_annual = np.concatenate(
[glac_wide_area_annual, glac_wide_area_annual_region])
if option_add_caldata == 1:
cal_data = cal_data.append(cal_data_region)
# If more columns in region, then need to expand existing dataset
if main_glac_hyps_region.shape[1] > main_glac_hyps.shape[1]:
all_col = list(main_glac_hyps.columns.values)
reg_col = list(main_glac_hyps_region.columns.values)
new_cols = [item for item in reg_col if item not in all_col]
for new_col in new_cols:
main_glac_hyps[new_col] = 0
main_glac_icethickness[new_col] = 0
elif main_glac_hyps_region.shape[1] < main_glac_hyps.shape[1]:
all_col = list(main_glac_hyps.columns.values)
reg_col = list(main_glac_hyps_region.columns.values)
new_cols = [item for item in all_col if item not in reg_col]
for new_col in new_cols:
main_glac_hyps_region[new_col] = 0
main_glac_icethickness_region[new_col] = 0
main_glac_hyps = main_glac_hyps.append(main_glac_hyps_region)
main_glac_icethickness = main_glac_icethickness.append(
main_glac_icethickness_region)
# reset index
main_glac_rgi.reset_index(inplace=True, drop=True)
main_glac_hyps.reset_index(inplace=True, drop=True)
main_glac_icethickness.reset_index(inplace=True, drop=True)
if option_add_caldata == 1:
cal_data.reset_index(inplace=True, drop=True)
# Volume [km**3] and mean elevation [m a.s.l.]
main_glac_rgi['Volume'], main_glac_rgi[
'Zmean'] = modelsetup.hypsometrystats(main_glac_hyps,
main_glac_icethickness)
# ===== MASS CHANGE CALCULATIONS =====
# Compute glacier volume change for every time step and use this to compute mass balance
glac_wide_area = np.repeat(glac_wide_area_annual[:, :-1], 12, axis=1)
# Mass change [km3 mwe]
# mb [mwea] * (1 km / 1000 m) * area [km2]
glac_wide_masschange = glac_wide_massbaltotal / 1000 * glac_wide_area
if option_add_caldata == 1:
return main_glac_rgi, glac_wide_masschange, glac_wide_area, time_values, cal_data
else:
return main_glac_rgi, glac_wide_masschange, glac_wide_area, time_values
def main(list_packed_vars):
"""
Climate data bias adjustment
Parameters
----------
list_packed_vars : list
list of packed variables that enable the use of parallels
Returns
-------
csv files of bias adjustment output
The bias adjustment parameters are output instead of the actual temperature and precipitation to reduce file
sizes. Additionally, using the bias adjustment will cause the GCM climate data to use the reference elevation
since the adjustments were made from the GCM climate data to be consistent with the reference dataset.
"""
# Unpack variables
count = list_packed_vars[0]
chunk = list_packed_vars[1]
main_glac_rgi_all = list_packed_vars[2]
chunk_size = list_packed_vars[3]
gcm_name = list_packed_vars[4]
time_start = time.time()
parser = getparser()
args = parser.parse_args()
if (gcm_name != pygem_prms.ref_gcm_name) and (args.rcp is None):
rcp_scenario = os.path.basename(args.gcm_list_fn).split('_')[1]
elif args.rcp is not None:
rcp_scenario = args.rcp
# rcp_scenario = os.path.basename(args.gcm_file).split('_')[1]
# ===== LOAD OTHER GLACIER DATA =====
main_glac_rgi = main_glac_rgi_all.iloc[chunk:chunk + chunk_size, :]
# Glacier hypsometry [km**2], total area
main_glac_hyps = modelsetup.import_Husstable(main_glac_rgi, pygem_prms.hyps_filepath,
pygem_prms.hyps_filedict, pygem_prms.hyps_colsdrop)
# Ice thickness [m], average
main_glac_icethickness = modelsetup.import_Husstable(main_glac_rgi, pygem_prms.thickness_filepath,
pygem_prms.thickness_filedict, pygem_prms.thickness_colsdrop)
main_glac_hyps[main_glac_icethickness == 0] = 0
# Width [km], average
main_glac_width = modelsetup.import_Husstable(main_glac_rgi, pygem_prms.width_filepath,
pygem_prms.width_filedict, pygem_prms.width_colsdrop)
elev_bins = main_glac_hyps.columns.values.astype(int)
# Select dates including future projections
# If reference climate data starts or ends before or after the GCM data, then adjust reference climate data such
# that the reference and GCM span the same period of time.
if pygem_prms.startyear >= pygem_prms.gcm_startyear:
ref_startyear = pygem_prms.startyear
else:
ref_startyear = pygem_prms.gcm_startyear
if pygem_prms.endyear <= pygem_prms.gcm_endyear:
ref_endyear = pygem_prms.endyear
else:
ref_endyear = pygem_prms.gcm_endyear
dates_table_ref = modelsetup.datesmodelrun(startyear=ref_startyear, endyear=ref_endyear,
spinupyears=pygem_prms.ref_spinupyears,
option_wateryear=pygem_prms.ref_wateryear)
dates_table = modelsetup.datesmodelrun(startyear=pygem_prms.gcm_startyear, endyear=pygem_prms.gcm_endyear,
spinupyears=pygem_prms.gcm_spinupyears,
option_wateryear=pygem_prms.gcm_wateryear)
# ===== LOAD CLIMATE DATA =====
# Reference climate data
ref_gcm = class_climate.GCM(name=pygem_prms.ref_gcm_name)
# Air temperature [degC], Precipitation [m], Elevation [masl], Lapse rate [K m-1]
ref_temp, ref_dates = ref_gcm.importGCMvarnearestneighbor_xarray(ref_gcm.temp_fn, ref_gcm.temp_vn, main_glac_rgi,
dates_table_ref)
ref_prec, ref_dates = ref_gcm.importGCMvarnearestneighbor_xarray(ref_gcm.prec_fn, ref_gcm.prec_vn, main_glac_rgi,
dates_table_ref)
ref_elev = ref_gcm.importGCMfxnearestneighbor_xarray(ref_gcm.elev_fn, ref_gcm.elev_vn, main_glac_rgi)
ref_lr, ref_dates = ref_gcm.importGCMvarnearestneighbor_xarray(ref_gcm.lr_fn, ref_gcm.lr_vn, main_glac_rgi,
dates_table_ref)
ref_lr_monthly_avg = (ref_lr.reshape(-1,12).transpose().reshape(-1,int(ref_temp.shape[1]/12)).mean(1)
.reshape(12,-1).transpose())
# GCM climate data
if gcm_name == 'ERA-Interim' or gcm_name == 'COAWST':
gcm = class_climate.GCM(name=gcm_name)
else:
gcm = class_climate.GCM(name=gcm_name, rcp_scenario=rcp_scenario)
# Air temperature [degC], Precipitation [m], Elevation [masl], Lapse rate [K m-1]
gcm_temp, gcm_dates = gcm.importGCMvarnearestneighbor_xarray(gcm.temp_fn, gcm.temp_vn, main_glac_rgi, dates_table)
gcm_prec, gcm_dates = gcm.importGCMvarnearestneighbor_xarray(gcm.prec_fn, gcm.prec_vn, main_glac_rgi, dates_table)
gcm_elev = gcm.importGCMfxnearestneighbor_xarray(gcm.elev_fn, gcm.elev_vn, main_glac_rgi)
if gcm_name == 'ERA-Interim':
gcm_lr, gcm_dates = gcm.importGCMvarnearestneighbor_xarray(gcm.lr_fn, gcm.lr_vn, main_glac_rgi, dates_table)
else:
gcm_lr = monthly_avg_array_rolled(ref_lr, dates_table_ref, dates_table)
# COAWST data has two domains, so need to merge the two domains
if gcm_name == 'COAWST':
gcm_temp_d01, gcm_dates = gcm.importGCMvarnearestneighbor_xarray(gcm.temp_fn_d01, gcm.temp_vn, main_glac_rgi,
dates_table)
gcm_prec_d01, gcm_dates = gcm.importGCMvarnearestneighbor_xarray(gcm.prec_fn_d01, gcm.prec_vn, main_glac_rgi,
dates_table)
gcm_elev_d01 = gcm.importGCMfxnearestneighbor_xarray(gcm.elev_fn_d01, gcm.elev_vn, main_glac_rgi)
# Check if glacier outside of high-res (d02) domain
for glac in range(main_glac_rgi.shape[0]):
glac_lat = main_glac_rgi.loc[glac,pygem_prms.rgi_lat_colname]
glac_lon = main_glac_rgi.loc[glac,pygem_prms.rgi_lon_colname]
if (~(pygem_prms.coawst_d02_lat_min <= glac_lat <= pygem_prms.coawst_d02_lat_max) or
~(pygem_prms.coawst_d02_lon_min <= glac_lon <= pygem_prms.coawst_d02_lon_max)):
gcm_prec[glac,:] = gcm_prec_d01[glac,:]
gcm_temp[glac,:] = gcm_temp_d01[glac,:]
gcm_elev[glac] = gcm_elev_d01[glac]
#%% ===== BIAS CORRECTIONS =====
# OPTION 1: Adjust temp and prec similar to Huss and Hock (2015) but limit maximum precipitation
# - temperature accounts for means and interannual variability
# - precipitation corrects
if pygem_prms.option_bias_adjustment == 1:
# Temperature bias correction
gcm_temp_biasadj, gcm_elev_biasadj = temp_biasadj_HH2015(ref_temp, ref_elev, gcm_temp, dates_table_ref,
dates_table)
# Precipitation bias correction
gcm_prec_biasadj, gcm_elev_biasadj = prec_biasadj_opt1(ref_prec, ref_elev, gcm_prec, dates_table_ref,
dates_table)
# OPTION 2: Adjust temp and prec according to Huss and Hock (2015) accounts for means and interannual variability
elif pygem_prms.option_bias_adjustment == 2:
# Temperature bias correction
gcm_temp_biasadj, gcm_elev_biasadj = temp_biasadj_HH2015(ref_temp, ref_elev, gcm_temp, dates_table_ref,
dates_table)
# Precipitation bias correction
gcm_prec_biasadj, gcm_elev_biasadj = prec_biasadj_HH2015(ref_prec, ref_elev, gcm_prec, dates_table_ref,
dates_table)
if gcm_prec_biasadj.max() > 10:
print('precipitation bias too high, needs to be modified')
print(np.where(gcm_prec_biasadj > 10))
elif gcm_prec_biasadj.min() < 0:
print('Negative precipitation value')
print(np.where(gcm_prec_biasadj < 0))
#%% PLOT BIAS ADJUSTED DATA
if option_plot_adj:
print('plotting')
plot_biasadj(ref_temp, gcm_temp_biasadj, ref_prec, gcm_prec, gcm_prec_biasadj, dates_table_ref, dates_table)
#%% Export variables as global to view in variable explorer
if args.option_parallels == 0:
global main_vars
main_vars = inspect.currentframe().f_locals
print('\nProcessing time of', gcm_name, 'for', count,':',time.time()-time_start, 's')
str(x)[:4] for x in larsen_summary.date1.values
]
# Replace Mendenhall with '2000F', '2012F'
# Lemon Creek with '1993F', '2012F'
# Taku '1993F', '2012F'
# Nizina is not there
# Yanert is not available for the given time periods - others are
# Load RGI attributes
rgi_regionsO1 = [1]
main_glac_rgi = modelsetup.selectglaciersrgitable(rgi_regionsO1=rgi_regionsO1,
rgi_regionsO2='all',
rgi_glac_number=glacno)
main_glac_hyps_10m = modelsetup.import_Husstable(main_glac_rgi,
pygem_prms.hyps_filepath,
pygem_prms.hyps_filedict,
pygem_prms.hyps_colsdrop)
binsize_rgi = int(main_glac_hyps_10m.columns[1]) - int(
main_glac_hyps_10m.columns[0])
#%%
# Quick quality control check on Huss product
huss_area_ones = main_glac_hyps_10m.copy().values
huss_area_ones[huss_area_ones > 0] = 1
huss_area_ones_idxmax = np.argmax(huss_area_ones, axis=1)
main_glac_rgi['huss_min_elev'] = [
int(main_glac_hyps_10m.columns.values[x])
for x in list(huss_area_ones_idxmax)
]
main_glac_rgi[
'dif_min_elev'] = main_glac_rgi.Zmin - main_glac_rgi.huss_min_elev
