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
# 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)
cal_data = cal_data.sort_values(['glacno', 't1_idx'])
cal_data.reset_index(drop=True, inplace=True)
ds2 = cal_data
#ds2 = pd.read_csv(os.getcwd() + '/../DEMs/larsen/larsen2015_supplementdata_wRGIIds.csv')
#ds2 = pd.read_csv(os.getcwd() + '/../DEMs/McNabb_data/wgms_dv/Alaska_dV_17jun_preprocessed.csv')
larsen_glac = input.get_same_glaciers(os.getcwd() + '/../Output/cal_opt1/reg1')
larsen_glac = ['RGI60-01.' + x for x in larsen_glac]
#glac_idxs = ds2.RGIId
#glac_idxs = glac_idxs.index.tolist(larsen_glac)
mb_sim = ds.variables['massbaltotal_glac_monthly'].values[:, :, 0]
mb_cal = ds2.mb_mwe
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