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
# Option to remove marine-terminating glaciers
option_cal_remove_marine_glaciers = 1
# Reference climate data
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,
cfg.PARAMS['border'] = 10
# Usually we recommend to set dl_verify to True - here it is quite slow
# because of the huge files so we just turn it off.
# Switch it on for real cases!
cfg.PARAMS['dl_verify'] = True
cfg.PARAMS['use_multiple_flowlines'] = False
# temporary directory for testing (deleted on computer restart)
#cfg.PATHS['working_dir'] = utils.get_temp_dir('PyGEM_ex')
cfg.PATHS['working_dir'] = pygem_prms.oggm_gdir_fp
# ===== LOAD GLACIERS =====
if pygem_prms.glac_no is not None:
glac_no = pygem_prms.glac_no
else:
main_glac_rgi_all = modelsetup.selectglaciersrgitable(
rgi_regionsO1=pygem_prms.rgi_regionsO1, rgi_regionsO2=pygem_prms.rgi_regionsO2,
rgi_glac_number=pygem_prms.rgi_glac_number)
glac_no = list(main_glac_rgi_all['rgino_str'].values)
rgi_ids = ['RGI60-' + x.split('.')[0].zfill(2) + '.' + x.split('.')[1] for x in glac_no]
#%% ===== SELECT BEST DEM =====
# Get the pre-processed topography data
# - creates directories from scratch
gdirs = rgitopo.init_glacier_directories_from_rgitopo(rgi_ids)
# ===== FLOWLINES (w debris) =====
# - checks if directories are created (only use if you're on an already prepared directory)
#gdirs = workflow.init_glacier_directories(rgi_ids)
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
netcdf_fn = 'ERA-Interim_2000_2018_masschange_p' + str(
int(degree_size * 100)) + 'deg.nc'
output_ds_all.to_netcdf(sim_netcdf_fp + netcdf_fn, encoding=encoding)
# Close datasets
output_ds_all.close()
print(
np.round(output_ds_all.masschange_monthly[:, :, :].values.sum() / 18,
2), 'Gt/yr')
#%%
if option_trishuli == 1:
glac_no = input.glac_fromcsv(
input.main_directory +
'/../qgis_himat/trishuli_shp/trishuli_RGIIds.csv')
main_glac_rgi = modelsetup.selectglaciersrgitable(glac_no=glac_no)
# ds_new = xr.open_dataset(input.output_sim_fp + 'ERA-Interim/Trishuli_ERA-Interim_c2_ba1_100sets_2000_2017.nc')
# ds_old13 = xr.open_dataset(input.output_sim_fp + 'ERA-Interim/ERA-Interim_1980_2017_nochg/' +
# 'R13_ERA-Interim_c2_ba1_100sets_1980_2017.nc')
# ds_old15 = xr.open_dataset(input.output_sim_fp + 'ERA-Interim/ERA-Interim_1980_2017_nochg/' +
# 'R15_ERA-Interim_c2_ba1_100sets_1980_2017.nc')
# time_old_idx_start = 12*20
# time_new_idx_start = 0
# years = np.arange(2000,2018)
# option_components = 1
ds_new = xr.open_dataset(
input.output_sim_fp + 'IPSL-CM5A-LR/' +
'Trishuli_IPSL-CM5A-LR_rcp85_c2_ba1_100sets_2000_2100.nc')
ds_old13 = xr.open_dataset(
else:
debug = False
# Reference GCM name
print('Reference climate data is:', input.ref_gcm_name)
# RGI glacier number
if args.rgi_glac_number_fn is not None:
with open(args.rgi_glac_number_fn, 'rb') as f:
rgi_glac_number = pickle.load(f)
else:
rgi_glac_number = input.rgi_glac_number
# Select glaciers and define chunks
main_glac_rgi_all = modelsetup.selectglaciersrgitable(
rgi_regionsO1=input.rgi_regionsO1,
rgi_regionsO2='all',
rgi_glac_number=input.rgi_glac_number)
# Define chunk size for parallel processing
if args.option_parallels != 0:
num_cores = int(
np.min(
[main_glac_rgi_all.shape[0], args.num_simultaneous_processes]))
chunk_size = int(np.ceil(main_glac_rgi_all.shape[0] / num_cores))
else:
# if not running in parallel, chunk size is all glaciers
chunk_size = main_glac_rgi_all.shape[0]
# Read GCM names from argument parser
gcm_name = args.gcm_list_fn
if args.gcm_name is not None:
gcm_list = [args.gcm_name]
def mb_bins_to_reg_glacierwide(mb_binned_fp=pygem_prms.mb_binned_fp,
O1Regions=['01']):
# Delete these import
mb_binned_fp = pygem_prms.mb_binned_fp
O1Regions = ['01']
print(
'\n\n SPECIFYING UNCERTAINTY AS 0.3 mwea for model development - needs to be updated from mb providers!\n\n'
)
reg_mb_mwea_err = 0.3
mb_yrfrac_dict = {
'01': [2000.419, 2018.419],
'02': [2000.128, 2012],
'03': [2000.419, 2018.419],
'04': [2000.419, 2018.419],
'05': [2000.419, 2018.419],
'06': [2000.419, 2018.419],
'07': [2000.419, 2018.419],
'08': [2000.419, 2018.419],
'09': [2000.419, 2018.419],
'10': [2000.128, 2012],
'11': [2000.128, 2013],
'12': [2000.128, 2012],
'HMA': [2000.419, 2018.419],
'16': [2000.128, 2013.128],
'17': [2000.128, 2013.128],
'18': [2000.128, 2013]
}
for reg in O1Regions:
reg_fp = mb_binned_fp + reg + '/'
main_glac_rgi = modelsetup.selectglaciersrgitable(
rgi_regionsO1=[reg], rgi_regionsO2='all', rgi_glac_number='all')
reg_binned_fns = []
for i in os.listdir(reg_fp):
if i.endswith('_mb_bins.csv'):
reg_binned_fns.append(i)
reg_binned_fns = sorted(reg_binned_fns)
print('Region ' + reg + ' has binned data for ' +
str(len(reg_binned_fns)) + ' glaciers.')
reg_mb_df_cns = [
'RGIId', 'O1Region', 'O2Region', 'area_km2', 'mb_mwea',
'mb_mwea_err', 't1', 't2', 'perc_valid'
]
reg_mb_df = pd.DataFrame(np.zeros(
(main_glac_rgi.shape[0], len(reg_mb_df_cns))),
columns=reg_mb_df_cns)
reg_mb_df.loc[:, :] = np.nan
reg_mb_df.loc[:, 'RGIId'] = main_glac_rgi['RGIId']
reg_mb_df.loc[:, 'O1Region'] = main_glac_rgi['O1Region']
reg_mb_df.loc[:, 'O2Region'] = main_glac_rgi['O2Region']
reg_mb_df.loc[:, 'area_km2'] = main_glac_rgi['Area']
# Process binned files
for nfn, reg_binned_fn in enumerate(reg_binned_fns):
if nfn % 500 == 0:
print(' ', nfn, reg_binned_fn)
mb_binned_df = pd.read_csv(reg_fp + reg_binned_fn)
glac_str = reg_binned_fn.split('_')[0]
glac_rgiid = 'RGI60-' + glac_str.split('.')[0].zfill(
2) + '.' + glac_str.split('.')[1]
rgi_idx = np.where(main_glac_rgi['RGIId'] == glac_rgiid)[0][0]
area_km2_valid = mb_binned_df['z1_bin_area_valid_km2'].sum()
mb_mwea = (mb_binned_df['z1_bin_area_valid_km2'] *
mb_binned_df['mb_bin_mean_mwea']).sum() / area_km2_valid
mb_mwea_err = reg_mb_mwea_err
t1 = mb_yrfrac_dict[reg][0]
t2 = mb_yrfrac_dict[reg][1]
perc_valid = area_km2_valid / reg_mb_df.loc[rgi_idx,
'area_km2'] * 100
reg_mb_df.loc[rgi_idx, 'mb_mwea'] = mb_mwea
reg_mb_df.loc[rgi_idx, 'mb_mwea_err'] = mb_mwea_err
reg_mb_df.loc[rgi_idx, 't1'] = t1
reg_mb_df.loc[rgi_idx, 't2'] = t2
reg_mb_df.loc[rgi_idx, 'perc_valid'] = perc_valid
#%%
# Quality control
O2Regions = list(set(list(main_glac_rgi['O2Region'].values)))
O2Regions_mb_mwea_dict = {}
rgiid_outliers = []
for O2Region in O2Regions:
reg_mb_df_subset = reg_mb_df[reg_mb_df['O2Region'] == O2Region]
reg_mb_df_subset = reg_mb_df_subset.dropna(subset=['mb_mwea'])
# Use 1.5*IQR to remove outliers
reg_mb_mwea_25 = np.percentile(reg_mb_df_subset['mb_mwea'], 25)
reg_mb_mwea_50 = np.percentile(reg_mb_df_subset['mb_mwea'], 50)
reg_mb_mwea_75 = np.percentile(reg_mb_df_subset['mb_mwea'], 75)
reg_mb_mwea_iqr = reg_mb_mwea_75 - reg_mb_mwea_25
print(np.round(reg_mb_mwea_25, 2), np.round(reg_mb_mwea_50, 2),
np.round(reg_mb_mwea_75, 2), np.round(reg_mb_mwea_iqr, 2))
reg_mb_mwea_bndlow = reg_mb_mwea_25 - 1.5 * reg_mb_mwea_iqr
reg_mb_mwea_bndhigh = reg_mb_mwea_75 + 1.5 * reg_mb_mwea_iqr
# Record RGIIds that are outliers
rgiid_outliers.extend(reg_mb_df_subset[
(reg_mb_df_subset['mb_mwea'] < reg_mb_mwea_bndlow) |
(reg_mb_df_subset['mb_mwea'] > reg_mb_mwea_bndhigh)]
['RGIId'].values)
# Select non-outliers and record mean
reg_mb_df_subset_qc = reg_mb_df_subset[
(reg_mb_df_subset['mb_mwea'] >= reg_mb_mwea_bndlow)
& (reg_mb_df_subset['mb_mwea'] <= reg_mb_mwea_bndhigh)]
reg_mb_mwea_qc_mean = reg_mb_df_subset_qc['mb_mwea'].mean()
O2Regions_mb_mwea_dict[O2Region] = reg_mb_mwea_qc_mean
#%%
print(
'CREATE DICTIONARY FOR RGIIDs with nan values or those that are outliers'
)
# print(A['mb_mwea'].mean(), A['mb_mwea'].std(), A['mb_mwea'].min(), A['mb_mwea'].max())
# print(reg_mb_mwea, reg_mb_mwea_std)
#%%
reg_mb_fn = reg + '_mb_glacwide_all.csv'
reg_mb_df.to_csv(mb_binned_fp + reg_mb_fn, index=False)
print('TO-DO LIST:')
print(' - quality control based on 3-sigma filter like Shean')
print(' - extrapolate for missing or outlier glaciers by region')
binned_fullfns.append(binned_fp + i)
# Sorted files
binned_fullfns = [x for _, x in sorted(zip(rgiids, binned_fullfns))]
rgiids = sorted(rgiids)
# print('\n\nDELETE ME TO RUN ALL!\n\n')
# rgiids = rgiids[0:1]
# binned_fullfns = binned_fullfns[0:1]
# Reference GCM name
gcm_name = args.ref_gcm_name
print('Reference climate data is:', gcm_name)
# Select all glaciers in a region
main_glac_rgi_all = modelsetup.selectglaciersrgitable(glac_no=rgiids)
main_glac_rgi_all['binned_fullfn'] = binned_fullfns
main_glac_rgi_all['CenLon_360'] = main_glac_rgi_all['CenLon']
main_glac_rgi_all.loc[
main_glac_rgi_all['CenLon_360'] < 0, 'CenLon_360'] = (
360 + main_glac_rgi_all.loc[main_glac_rgi_all['CenLon_360'] < 0,
'CenLon_360'])
main_glac_rgi_all['RefYear'] = (main_glac_rgi_all['RefDate'] /
1e4).astype(int)
# print(np.where(main_glac_rgi_all.rgino_str.values == '08.00005'))
# Define chunk size for parallel processing
if args.option_parallels != 0:
num_cores = int(
np.min(
