def get_mean_temps_2k(rgi, return_prcp):
from oggm import cfg, utils, workflow, tasks
from oggm.core.massbalance import PastMassBalance
# Initialize OGGM
cfg.initialize()
wd = utils.gettempdir(reset=True)
cfg.PATHS['working_dir'] = wd
utils.mkdir(wd, reset=True)
cfg.PARAMS['baseline_climate'] = 'HISTALP'
# and set standard histalp values
cfg.PARAMS['temp_melt'] = -1.75
cfg.PARAMS['prcp_scaling_factor'] = 1.75
gdir = workflow.init_glacier_regions(rgidf=rgi.split('_')[0],
from_prepro_level=3,
prepro_border=10)[0]
# run histalp climate on glacier!
tasks.process_histalp_data(gdir)
f = gdir.get_filepath('climate_historical')
with utils.ncDataset(f) as nc:
refhgt = nc.ref_hgt
mb = PastMassBalance(gdir, check_calib_params=False)
df = pd.DataFrame()
df2 = pd.DataFrame()
for y in np.arange(1870, 2015):
for i in np.arange(9, 12):
flyear = utils.date_to_floatyear(y, i)
tmp = mb.get_monthly_climate([refhgt], flyear)[0]
df.loc[y, i] = tmp.mean()
if return_prcp:
for i in np.arange(3, 6):
flyear = utils.date_to_floatyear(y, i)
pcp = mb.get_monthly_climate([refhgt], flyear)[3]
df2.loc[y, i] = tmp.mean()
t99 = df.loc[1984:2014, :].mean().mean()
t85 = df.loc[1870:1900, :].mean().mean()
t2k = df.loc[1900:2000, :].mean().mean()
if return_prcp:
p99 = df2.loc[1984:2014, :].mean().mean()
p85 = df2.loc[1870:1900, :].mean().mean()
p2k = df2.loc[1900:2000, :].mean().mean()
return t85, t99, t2k, p85, p99, p2k
return t85, t99, t2k
def gdir():
cfg.initialize()
test_dir = '/home/lilianschuster/Schreibtisch/PhD/oggm_files/MBsandbox_tests'
if not os.path.exists(test_dir):
test_dir = utils.gettempdir(dirname='OGGM_MBsandbox_test', reset=True)
cfg.PATHS['working_dir'] = test_dir
base_url = ('https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/'
'L1-L2_files/elev_bands')
df = ['RGI60-11.00897']
gdirs = workflow.init_glacier_directories(df,
from_prepro_level=2,
prepro_border=10,
prepro_base_url=base_url,
prepro_rgi_version='62')
return gdirs[0]
def test_geodetic_glaciologicalMB_HEF():
cfg.initialize()
cfg.PARAMS['use_multiprocessing'] = False
working_dir = '/home/lilianschuster/Schreibtisch/PhD/oggm_files/oneFlowline'
# this needs to be changed if working on another computer
if not os.path.exists(working_dir):
working_dir = utils.gettempdir(dirname='OGGM_mb_type_intercomparison', reset=True)
cfg.PATHS['working_dir'] = working_dir
# use Huss flowlines
base_url = ('https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/'
'L1-L2_files/elev_bands')
# get HEF glacier
df = utils.get_rgi_glacier_entities(['RGI60-11.00897'])
gdirs = workflow.init_glacier_directories(df, from_prepro_level=2,
prepro_border=10,
prepro_base_url=base_url,
prepro_rgi_version='62')
gd = gdirs[0]
h, w = gd.get_inversion_flowline_hw()
fls = gd.read_pickle('inversion_flowlines')
cfg.PARAMS['baseline_climate'] = 'ERA5dr'
oggm.shop.ecmwf.process_ecmwf_data(gd, dataset='ERA5dr')
######################
# test area is similar to OGGM gdir HEF area
np.testing.assert_allclose(pd_geodetic_alps.loc['RGI60-11.00897'].area, gd.rgi_area_km2, rtol=0.01)
glaciological_mb_data_HEF_mean = gd.get_ref_mb_data()['ANNUAL_BALANCE'].loc[2000:].mean() #.sel(2000,2020)
# test if mass balance in period 2000 and 2020 of glaciological method is similar to the geodetic one
np.testing.assert_allclose(pd_geodetic_alps.loc['RGI60-11.00897'].dmdtda*1000,
glaciological_mb_data_HEF_mean, rtol = 0.05 )
def gdir():
""" Provides a copy of the base Hintereisenferner glacier directory in
a case directory specific to the current test class using the single
elev_bands as flowlines. All cases in
the test class will use the same copy of this glacier directory.
"""
cfg.initialize()
cfg.PARAMS['use_multiprocessing'] = False
test_dir = '/home/lilianschuster/Schreibtisch/PhD/oggm_files/MBsandbox_tests'
if not os.path.exists(test_dir):
test_dir = utils.gettempdir(dirname='OGGM_MBsandbox_test',
reset=True)
cfg.PATHS['working_dir'] = test_dir
base_url = ('https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/'
'L1-L2_files/elev_bands')
df = ['RGI60-11.00897']
gdirs = workflow.init_glacier_directories(df, from_prepro_level=2,
prepro_border=10,
prepro_base_url=base_url,
prepro_rgi_version='62')
return gdirs[0]
def test_ssp585_problem():
from oggm import cfg, utils, workflow, tasks, graphics
from oggm.core import massbalance, flowline, climate
import logging
log = logging.getLogger(__name__)
cfg.initialize()
cfg.PATHS['working_dir'] = utils.gettempdir(dirname='test', reset=False)
gdirs = workflow.init_glacier_directories(
['RGI60-11.03238'], #'RGI60-11.03677'], #'RGI60-11.03471'],
from_prepro_level=2,
prepro_border=10,
prepro_base_url=base_url,
prepro_rgi_version='62',
)
gdir = gdirs[0]
cfg.PARAMS['hydro_month_nh'] = 1
ssp = 'ssp585'
process_w5e5_data(gdir, temporal_resol='monthly', climate_type='WFDE5_CRU')
process_isimip_data(gdir,
ensemble=ensemble,
ssp=ssp,
climate_historical_filesuffix='_monthly_WFDE5_CRU',
temporal_resol='monthly')
'entity_intersects.shp')
new_intersects.to_file(entity_intersects_path)
# set intersect file to use
cfg.set_intersects_db(new_intersects)
# year where outlines are valid
outline_year = os.path.basename(fp)[:4]
# check geometries
#for idx, row in entity.iterrows():
# if entity.geometry.iloc[idx].type != 'Polygon':
# print(row['RGIId'] + row.geometry.type)
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('User_past_mb', reset=True)
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
#cfg.PATHS['working_dir'] = utils.gettempdir('user', reset=True)
#cfg.PATHS['working_dir'] = utils.gettempdir(dirname='OGGM-GettingStarted', reset=True)
# glacier directory
gdirs = workflow.init_glacier_directories(
entity
) #, prepro_base_url=base_url) # <- from prepro_level uses preprocessed files
#rgi_id = gdir.rgi_id
#gdir.rgi_date = outline_year
# update gdir outline year
for gdir in gdirs:
from oggm import utils, workflow, tasks
# For timing the run
import time
start = time.time()
# Module logger
log = logging.getLogger(__name__)
# Initialize OGGM and set up the default run parameters
cfg.initialize(logging_level='WORKFLOW')
rgi_version = '61'
rgi_region = '11' # Region Central Europe
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('OGGM_Inversion')
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
# RGI file
path = utils.get_rgi_region_file(rgi_region, version=rgi_version)
rgidf = gpd.read_file(path)
# Select the glaciers in the Pyrenees
rgidf = rgidf.loc[rgidf['O2Region'] == '2']
# Sort for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)
log.workflow('Starting OGGM inversion run')
log.workflow('Number of glaciers: {}'.format(len(rgidf)))
# Locals
import oggm.cfg as cfg
from oggm import tasks, utils, workflow
from oggm.workflow import execute_entity_task
# Module logger
log = logging.getLogger(__name__)
# For timing the run
start = time.time()
# Initialize OGGM and set up the run parameters
cfg.initialize()
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('OGGM_precalibrated_run')
cfg.PATHS['working_dir'] = WORKING_DIR
# Use multiprocessing?
cfg.PARAMS['use_multiprocessing'] = True
# Initialize from existing directories, no need for shapefiles
gdirs = workflow.init_glacier_regions()
log.info('Starting OGGM run')
log.info('Number of glaciers: {}'.format(len(gdirs)))
# We can step directly to a new experiment!
# Random climate representative for the recent climate (1985-2015)
# This is a kind of "commitment" run
execute_entity_task(tasks.run_random_climate,
import geopandas as gpd
import oggm
from oggm import cfg, tasks
from oggm.utils import get_demo_file, gettempdir
cfg.initialize()
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
base_dir = gettempdir('Flowlines_Docs')
entity = gpd.read_file(get_demo_file('HEF_MajDivide.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=base_dir)
tasks.define_glacier_region(gdir, entity=entity)
tasks.glacier_masks(gdir)
tasks.compute_centerlines(gdir)
tasks.initialize_flowlines(gdir)
tasks.compute_downstream_line(gdir)
# Imports
from os import path
import geopandas as gpd
import matplotlib.pyplot as plt
from oggm.utils import get_demo_file, gettempdir
# Local working directory (where OGGM wrote its output)
WORKING_DIR = gettempdir('OGGM_Rofental')
# Plot: the basin, the outlines and the centerlines
basin = gpd.read_file(get_demo_file('rofental_hydrosheds.shp'))
rgi = gpd.read_file(path.join(WORKING_DIR, 'rgi_rofental.shp'))
centerlines = gpd.read_file(path.join(WORKING_DIR, 'glacier_centerlines.shp'))
f, ax = plt.subplots()
basin.plot(ax=ax, color='k', alpha=0.2)
rgi.plot(ax=ax, color='C0')
centerlines.plot(ax=ax, color='C3')
plt.title('Rofental glaciers and centerlines')
plt.tight_layout()
plt.show()
# Locals
from oggm import cfg, utils, workflow
from oggm.core import climate, gis
import oggm_vas as vascaling
# Initialize OGGM and set up the default run parameters
vascaling.initialize()
rgi_version = '62'
# 10 is only for OGGM-VAS, OGGM needs 80 to run
cfg.PARAMS['border'] = 80
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('moritz')
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
# RGI glaciers: her you usually pick a region or yours
rgi_ids = ['RGI60-11.00897', 'RGI60-11.00892']
rgi_region = (rgi_ids[0].split('-')[-1]).split('.')[0]
# The important bit: update with the folder with CRU files in it when available
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(rgi_ids,
from_prepro_level=3,
prepro_base_url=base_url,
prepro_rgi_version=rgi_version)
gdf = gpd.read_file(fp53) # read one of the outline files to use as 'default'
# exclude glaciers smaller than 0.1 km2
#gdf = entity[entity['Area'] >= 0.1]
# select or deselect ids
excludeIDs = [
'RGI60-05.01920', 'RGI60-05.01987_1', 'RGI60-05.02213', 'RGI60-05.02328_1',
'RGI60-05.02280_1'
]
gdf = gdf[gdf.RGIId.isin(excludeIDs)]
#outline_year = os.path.basename(fp)[:4]
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('User_surf_change', reset=True)
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
# empty GeoDataFrame to save flowlines
flowlines = gpd.GeoDataFrame()
for rgiid in gdf['RGIId']:
# select glacier
entity = gdf[gdf['RGIId'] == rgiid]
# glacier directory
gdirs = workflow.init_glacier_directories(
entity
) #, prepro_base_url=base_url) # <- from prepro_level uses preprocessed files
def run_and_plot_merged_montmine(pout):
# Set-up
cfg.initialize(logging_level='WORKFLOW')
cfg.PATHS['working_dir'] = utils.gettempdir(dirname='OGGM-merging',
reset=True)
# Use a suitable border size for your domain
cfg.PARAMS['border'] = 80
cfg.PARAMS['use_intersects'] = False
montmine = workflow.init_glacier_directories(['RGI60-11.02709'],
from_prepro_level=3)[0]
gdirs = workflow.init_glacier_directories(['RGI60-11.02709',
'RGI60-11.02715'],
from_prepro_level=3)
workflow.execute_entity_task(tasks.init_present_time_glacier, gdirs)
gdirs_merged = workflow.merge_glacier_tasks(gdirs, 'RGI60-11.02709',
return_all=False,
filename='climate_monthly',
buffer=2.5)
# plot centerlines
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[20, 10])
plot_centerlines(montmine, ax=ax1, use_flowlines=True)
xt = ax1.get_xticks()
ax1.set_xticks(xt[::2])
ax1.tick_params(axis='both', which='major', labelsize=20)
ax1.set_title('entity glacier', fontsize=24)
plot_centerlines(gdirs_merged, ax=ax2, use_model_flowlines=True)
ax2.tick_params(axis='both', which='major', labelsize=20)
ax2.set_title('merged with Glacier de Ferpecle', fontsize=24)
axs = fig.get_axes()
axs[3].remove()
axs[2].tick_params(axis='y', labelsize=16)
axs[2].set_ylabel('Altitude [m]', fontsize=18)
fig.suptitle('Glacier du Mont Mine', fontsize=24)
fig.subplots_adjust(left=0.04, right=0.99, bottom=0.08, top=0.89,
wspace=0.3)
fn = os.path.join(pout, 'merged_montmine.png')
fig.savefig(fn)
# run glaciers with negative t bias
# some model settings
years = 125
tbias = -1.5
# model Mont Mine glacier as entity and complile the output
tasks.run_constant_climate(montmine, nyears=years,
output_filesuffix='_entity',
temperature_bias=tbias)
ds_entity = utils.compile_run_output([montmine], path=False,
filesuffix='_entity')
# model the merged glacier and complile the output
tasks.run_constant_climate(gdirs_merged, nyears=years,
output_filesuffix='_merged',
temperature_bias=tbias,
climate_filename='climate_monthly')
ds_merged = utils.compile_run_output([gdirs_merged], path=False,
filesuffix='_merged')
#
# bring them to same size again
tbias = -2.2
years = 125
tasks.run_constant_climate(montmine, nyears=years,
output_filesuffix='_entity1',
temperature_bias=tbias)
ds_entity1 = utils.compile_run_output([montmine], path=False,
filesuffix='_entity1')
# and let them shrink again
# some model settings
tbias = -0.5
years = 100
# load the previous entity run
tmp_mine = FileModel(
montmine.get_filepath('model_run', filesuffix='_entity1'))
tmp_mine.run_until(years)
tasks.run_constant_climate(montmine, nyears=years,
output_filesuffix='_entity2',
init_model_fls=tmp_mine.fls,
temperature_bias=tbias)
ds_entity2 = utils.compile_run_output([montmine], path=False,
filesuffix='_entity2')
# model the merged glacier and complile the output
tmp_merged = FileModel(
gdirs_merged.get_filepath('model_run', filesuffix='_merged'))
tmp_merged.run_until(years)
tasks.run_constant_climate(gdirs_merged, nyears=years,
output_filesuffix='_merged2',
init_model_fls=tmp_merged.fls,
temperature_bias=tbias,
climate_filename='climate_monthly')
ds_merged2 = utils.compile_run_output([gdirs_merged], path=False,
filesuffix='_merged2')
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[20, 7])
dse = ds_entity.length.to_series().rolling(5, center=True).mean()
dsm = ds_merged.length.to_series().rolling(5, center=True).mean()
ax1.plot(dse.values, 'C1', label='Entity glacier', linewidth=3)
ax1.plot(dsm.values, 'C2', label='Merged glacier', linewidth=3)
ax1.set_xlabel('Simulation time [yr]', fontsize=20)
ax1.set_ylabel('Glacier length[m]', fontsize=20)
ax1.grid(True)
ax1.legend(loc=2, fontsize=18)
dse2 = ds_entity2.length.to_series().rolling(5, center=True).mean()
dsm2 = ds_merged2.length.to_series().rolling(5, center=True).mean()
ax2.plot(dse2.values, 'C1', label='Entity glacier', linewidth=3)
ax2.plot(dsm2.values, 'C2', label='Merged glacier', linewidth=3)
ax2.set_xlabel('Simulation time [yr]', fontsize=22)
ax2.set_ylabel('Glacier length [m]', fontsize=22)
ax2.grid(True)
ax2.legend(loc=1, fontsize=18)
ax1.set_xlim([0, 120])
ax2.set_xlim([0, 100])
ax1.set_ylim([7500, 12000])
ax2.set_ylim([7500, 12000])
ax1.tick_params(axis='both', which='major', labelsize=20)
ax2.tick_params(axis='both', which='major', labelsize=20)
fig.subplots_adjust(left=0.08, right=0.96, bottom=0.11, top=0.93,
wspace=0.3)
fn = os.path.join(pout, 'merged_montmine_timeseries.png')
fig.savefig(fn)
def get_mean_temps_eq(rgi, histalp_storage, comit_storage, ensmembers):
from oggm import cfg, utils, GlacierDirectory
from oggm.core.massbalance import MultipleFlowlineMassBalance
from oggm.core.flowline import FileModel
import shutil
# 1. get mean surface heights
df85 = pd.DataFrame([])
df99 = pd.DataFrame([])
for i in range(ensmembers):
fnc1 = os.path.join(comit_storage, rgi,
'model_run_commitment1885_{:02d}.nc'.format(i))
fnc2 = os.path.join(comit_storage, rgi,
'model_run_commitment1999_{:02d}.nc'.format(i))
tmpmod1 = FileModel(fnc1)
tmpmod2 = FileModel(fnc2)
for j in np.arange(270, 301):
tmpmod1.run_until(j)
df85.loc[:, '{}{}'.format(i, j)] = tmpmod1.fls[-1].surface_h
tmpmod2.run_until(j)
df99.loc[:, '{}{}'.format(i, j)] = tmpmod2.fls[-1].surface_h
meanhgt99 = df99.mean(axis=1).values
meanhgt85 = df85.mean(axis=1).values
# 2. get the climate
# Initialize OGGM
cfg.initialize()
wd = utils.gettempdir(reset=True)
cfg.PATHS['working_dir'] = wd
utils.mkdir(wd, reset=True)
cfg.PARAMS['baseline_climate'] = 'HISTALP'
# and set standard histalp values
cfg.PARAMS['temp_melt'] = -1.75
i = 0
storage_dir = os.path.join(histalp_storage, rgi, '{:02d}'.format(i),
rgi[:8], rgi[:11], rgi)
new_dir = os.path.join(cfg.PATHS['working_dir'], 'per_glacier',
rgi[:8], rgi[:11], rgi)
shutil.copytree(storage_dir, new_dir)
gdir = GlacierDirectory(rgi)
mb = MultipleFlowlineMassBalance(gdir, filename='climate_monthly',
check_calib_params=False)
# need to do the above for every ensemble member if I consider PRECIP!
# and set cfg.PARAMS['prcp_scaling_factor'] = pdict['prcp_scaling_factor']
df99_2 = pd.DataFrame()
df85_2 = pd.DataFrame()
for i in np.arange(9, 12):
for y in np.arange(1870, 1901):
flyear = utils.date_to_floatyear(y, i)
tmp = mb.flowline_mb_models[-1].get_monthly_climate(meanhgt85,
flyear)[0]
df85_2.loc[y, i] = tmp.mean()
for y in np.arange(1984, 2015):
tmp = mb.flowline_mb_models[-1].get_monthly_climate(meanhgt99,
flyear)[0]
df99_2.loc[y, i] = tmp.mean()
t99 = df99_2.mean().mean()
t85 = df85_2.mean().mean()
return t85, t99
# CRU, HISTALP, ERA5, ERA5L, CERA, CERA+ERA5, CERA+ERA5L?
baseline = 'CERA+ERA5L'
# Initialize OGGM and set up the run parameters
cfg.initialize()
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
# Local paths (where to find the OGGM run output)
dirname = 'OGGM_ref_mb_{}_RGIV{}_OGGM{}'.format(baseline, rgi_version,
oggm.__version__)
WORKING_DIR = utils.gettempdir(dirname, home=True)
cfg.PATHS['working_dir'] = WORKING_DIR
# Read the rgi ids of the reference glaciers
rids = pd.read_csv(os.path.join(WORKING_DIR, 'mb_ref_glaciers.csv'),
index_col=0, squeeze=True)
# Go - initialize glacier directories
gdirs = workflow.init_glacier_directories(rids)
# Cross-validation
file = os.path.join(cfg.PATHS['working_dir'], 'ref_tstars.csv')
ref_df = pd.read_csv(file, index_col=0)
for i, gdir in enumerate(gdirs):
print('Cross-validation iteration {} of {}'.format(i + 1, len(ref_df)))
dsc = xr.open_dataset(smb_path, decode_times=False)#.chunk({'time':20})
dsc.attrs['pyproj_srs'] = proj.srs
dsc['time'] = np.append(pd.period_range(start='2018.01.01',
end='2018.12.01', freq='M').to_timestamp(),
pd.period_range(start='1958.01.01',
end='2017.12.01', freq='M').to_timestamp())
ds_smb_two = dsc.isel(time=slice(48,12*34))
see = ds_smb_two.chunk({'time':2})
avg = see.SMB_rec.mean(dim='time').compute()
# OGGM run
cfg.initialize()
cfg.initialize(logging_level='WORKFLOW')
cfg.PATHS['working_dir'] = utils.gettempdir(dirname='OGGM-k-plots',
reset=True)
cfg.PARAMS['border'] = 10
# Find a glacier with good coverage of both data
gdirs = workflow.init_glacier_regions(['RGI60-05.00800'],
from_prepro_level=3)
gdir=gdirs[0]
utils_vel.write_flowlines_to_shape(gdir, path=gdir.dir)
shp_path = os.path.join(gdir.dir, 'RGI60-05.shp')
shp = gpd.read_file(shp_path)
# Crop velocity raster
dvel_sel, derr_sel = utils_vel.crop_vel_data_to_glacier_grid(gdir,
dvel, derr)
import pandas as pd
import xarray as xr
import oggm
from oggm import cfg, tasks
from oggm.core.climate import (mb_yearly_climate_on_glacier,
t_star_from_refmb,
local_t_star, mu_star_calibration)
from oggm.core.massbalance import (ConstantMassBalance)
from oggm.utils import get_demo_file, gettempdir
cfg.initialize()
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
base_dir = gettempdir('Climate_docs')
entity = gpd.read_file(get_demo_file('HEF_MajDivide.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=base_dir)
tasks.define_glacier_region(gdir, entity=entity)
tasks.glacier_masks(gdir)
tasks.compute_centerlines(gdir)
tasks.initialize_flowlines(gdir)
tasks.compute_downstream_line(gdir)
tasks.catchment_area(gdir)
tasks.catchment_width_geom(gdir)
tasks.catchment_width_correction(gdir)
data_dir = get_demo_file('HISTALP_precipitation_all_abs_1801-2014.nc')
cfg.PATHS['cru_dir'] = os.path.dirname(data_dir)
cfg.PARAMS['baseline_climate'] = 'HISTALP'
cfg.PARAMS['baseline_y0'] = 1850
# Module logger
log = logging.getLogger(__name__)
# Initialize OGGM and set up the default run parameters
cfg.initialize(logging_level='WORKFLOW')
rgi_version = '61'
rgi_region = '11' # Region Central Europe
# Here we override some of the default parameters
# How many grid points around the glacier?
# Make it large if you expect your glaciers to grow large:
# here, 80 is more than enough
cfg.PARAMS['border'] = 80
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('OGGM_Rofental')
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
# RGI file
path = utils.get_rgi_region_file(rgi_region, version=rgi_version)
rgidf = gpd.read_file(path)
# Get the Rofental Basin file
path = utils.get_demo_file('rofental_hydrosheds.shp')
basin = gpd.read_file(path)
# Take all glaciers in the Rofental Basin
in_bas = [basin.geometry.contains(shpg.Point(x, y))[0] for
(x, y) in zip(rgidf.CenLon, rgidf.CenLat)]
rgidf = rgidf.loc[in_bas]
# set intersect file to use
#cfg.set_intersects_db(new_intersects)
# select subset
entity = entity[~entity.RGIId.isin(sel)]
# year where outlines are valid
outline_year = os.path.basename(fp)[:4]
# check geometries
#for idx, row in entity.iterrows():
# if entity.geometry.iloc[idx].type != 'Polygon':
# print(row['RGIId'] + row.geometry.type)
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('User_gcm_data', reset=True)
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
#cfg.PATHS['working_dir'] = utils.gettempdir('user', reset=True)
#cfg.PATHS['working_dir'] = utils.gettempdir(dirname='OGGM-GettingStarted', reset=True)
# test with one glacier
#entity = entity[entity['RGIId'] == 'RGI60-05.02114']
# rgi_ids for default data
#rgi_ids = ['RGI60-05.01916']
# glacier directory
#base_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/L3-L5_files/CRU/centerlines/qc3/pcp2.5/no_match/'
gdirs = workflow.init_glacier_directories(
import oggm.cfg as cfg
from oggm import tasks, utils, workflow
from oggm.workflow import execute_entity_task
from oggm.utils import get_demo_file
# Module logger
log = logging.getLogger(__name__)
# For timing the run
start = time.time()
# Initialize OGGM and set up the default run parameters
cfg.initialize()
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('OGGM_spinup_run')
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
# Use multiprocessing?
cfg.PARAMS['use_multiprocessing'] = True
# How many grid points around the glacier?
# Make it large if you expect your glaciers to grow large
cfg.PARAMS['border'] = 80
# Go - initialize glacier directories
gdirs = workflow.init_glacier_regions(['RGI60-11.00897'], from_prepro_level=4)
# Additional climate file (CESM)
cfg.PATHS['cesm_temp_file'] = get_demo_file('cesm.TREFHT.160001-200512'
import oggm.cfg as cfg
from oggm import tasks, utils, workflow
from oggm.workflow import execute_entity_task
from oggm.utils import get_demo_file
# Module logger
log = logging.getLogger(__name__)
# For timing the run
start = time.time()
# Initialize OGGM and set up the default run parameters
cfg.initialize()
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('OGGM_spinup_run')
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
# Use multiprocessing?
cfg.PARAMS['use_multiprocessing'] = True
# How many grid points around the glacier?
# Make it large if you expect your glaciers to grow large
cfg.PARAMS['border'] = 80
# Go - initialize glacier directories
gdirs = workflow.init_glacier_regions(['RGI60-11.00897'], from_prepro_level=4)
# Additional climate file (CESM)
cfg.PATHS['cesm_temp_file'] = get_demo_file('cesm.TREFHT.160001-200512'
# Initialize OGGM and set up the default run parameters
cfg.initialize(logging_level='WORKFLOW')
# Here we override some of the default parameters
# How many grid points around the glacier?
# We make it small because we want the model to error because
# of flowing out of the domain
cfg.PARAMS['border'] = 80
# This is the important bit!
# We tell OGGM to continue despite of errors
cfg.PARAMS['continue_on_error'] = True
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('OGGM_Errors')
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
rgi_ids = ['RGI60-11.00897', 'RGI60-11.01450', 'RGI60-11.03295']
log.workflow('Starting OGGM run')
log.workflow('Number of glaciers: {}'.format(len(rgi_ids)))
# Go - get the pre-processed glacier directories
gdirs = workflow.init_glacier_directories(rgi_ids, from_prepro_level=4)
# We can step directly to the experiment!
# Random climate representative for the recent climate (1985-2015)
# with a negative bias added to the random temperature series
workflow.execute_entity_task(tasks.run_random_climate, gdirs,
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)
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm as colormap
import xarray as xr
try:
import salemf
except ImportError:
pass
# Read the default parameter file and make them available to other OGGM tools via cfg.PARAMS
cfg.initialize(logging_level='WORKFLOW')
cfg.PARAMS['prcp_scaling_factor'], cfg.PARAMS['ice_density'], cfg.PARAMS['continue_on_error']
cfg.PATHS['working_dir'] = utils.gettempdir(dirname='/home/nirab/OGGM/OGGM-GettingStarted', reset=True)
rgi_ids = ['RGI60-11.01328', 'RGI60-11.00897']
#graphics.plot_domain(gdirs, figsize=(8, 7))
#gdir = rgi_ids[1]
# Plotting the glacier over the DEM file
for rgi in rgi_ids:
f, axs = plt.subplots(2, 2, figsize=(8, 6))
for ax, border in zip(np.array(axs).flatten(), [10, 80, 160, 250]):
gdir = workflow.init_glacier_directories(rgi,
from_prepro_level=1,prepro_border=border)
graphics.plot_domain(gdir, ax=ax, title='Border: {}'.format(border),
def test_process_era5_daily_data():
cfg.initialize()
test_dir = '/home/lilianschuster/Schreibtisch/PhD/oggm_files/tests'
if not os.path.exists(test_dir):
test_dir = utils.gettempdir(dirname='OGGM_era5_daily_test', reset=True)
cfg.PATHS['working_dir'] = test_dir
b_url_ = 'https://cluster.klima.uni-bremen.de/~fmaussion'
base_url = b_url_ + '/gdirs/prepro_l2_202010/elevbands_fl_with_consensus'
df = utils.get_rgi_glacier_entities(['RGI60-11.00897'])
gdirs = workflow.init_glacier_directories(df,
from_prepro_level=2,
prepro_border=40,
prepro_base_url=base_url,
prepro_rgi_version='62')
gdir = gdirs[0]
process_era5_daily_data(gdir, y0=1979, y1=2018)
filename = 'climate_historical_daily'
fpath = gdir.get_filepath(filename)
# check the climate files of an individual glacier (Hintereisferner)
xr_nc = xr.open_dataset(fpath)
assert np.all(xr_nc.prcp) > 0
# to be sure that there are no erroneaous filling values inside
assert np.all(xr_nc.prcp) < 10000
# temperature values are in °C and in the right range
assert np.all(xr_nc.temp) > -100
assert np.all(xr_nc.temp) < 100
# temperature gradient should be in the following range
assert np.all(xr_nc.gradient > -0.015)
assert np.all(xr_nc.gradient < -0.002)
# all lapse rates/ precipitation values in one month should be equal
# because only temperature is on daily basis
np.testing.assert_allclose(xr_nc.resample(time='1M').std().prcp,
0,
atol=1e-3)
np.testing.assert_allclose(xr_nc.resample(time='1M').std().gradient,
0,
atol=1e-3)
# summed up monthly precipitation from daily dataset
xr_nc_prcp_m = xr_nc.prcp.resample(time='1M').sum()
oggm.shop.ecmwf.process_ecmwf_data(gdir, dataset="ERA5", y0=1979, y1=2018)
filename = 'climate_historical'
fpath = gdir.get_filepath(filename)
xr_nc_monthly = xr.open_dataset(fpath)
# check if summed up monthly precipitation from daily
# dataset equals approx. to the ERA5 monthly prcp
np.testing.assert_allclose(xr_nc_prcp_m.values,
xr_nc_monthly.prcp.values,
rtol=1e-4)
xr_nc_temp_m = xr_nc.temp.resample(time='1M').mean()
# check if mean temperature from daily dataset equals
# approx. to the ERA5 monthly temp.
np.testing.assert_allclose(xr_nc_temp_m.values,
xr_nc_monthly.temp.values,
atol=0.05)
with pytest.raises(InvalidParamsError):
# dataset only goes from 1979--2018
process_era5_daily_data(gdir, y0=1979, y1=2019)
# in cfg.PARAMS that is initiated during testing,
# cfg.PARAMS[hydro_month_nh = 10], this is in conflict with 8
process_era5_daily_data(gdir, y0=1979, y1=2018, hydro_month_nh=8)
# Locals
import oggm.cfg as cfg
from oggm import utils, workflow
# For timing the run
import time
start = time.time()
# Initialize OGGM and set up the default run parameters
cfg.initialize()
rgi_version = '61'
rgi_region = '11' # Alps
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('OGGM_Rofental')
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
# We use intersects
path = utils.get_rgi_intersects_region_file(rgi_region, version=rgi_version)
cfg.set_intersects_db(path)
# RGI file
path = utils.get_rgi_region_file(rgi_region, version=rgi_version)
rgidf = gpd.read_file(path)
# Get the Rofental Basin file
path = utils.get_demo_file('rofental_hydrosheds.shp')
basin = gpd.read_file(path)
# Imports
import os
import xarray as xr
import matplotlib.pyplot as plt
from oggm.utils import get_demo_file, gettempdir
# Local working directory (where OGGM wrote its output)
WORKING_DIR = gettempdir('OGGM_Rofental')
# Read the files using xarray
ds = xr.open_dataset(os.path.join(WORKING_DIR, 'run_output_commitment.nc'))
dsp = xr.open_dataset(os.path.join(WORKING_DIR, 'run_output_bias_p.nc'))
dsm = xr.open_dataset(os.path.join(WORKING_DIR, 'run_output_bias_m.nc'))
# Compute and plot the regional sums
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4))
# Volume
(ds.volume.sum(dim='rgi_id') * 1e-9).plot(ax=ax1, label='[1985-2015]')
(dsp.volume.sum(dim='rgi_id') * 1e-9).plot(ax=ax1, label='+0.5°C')
(dsm.volume.sum(dim='rgi_id') * 1e-9).plot(ax=ax1, label='-0.5°C')
ax1.legend(loc='best')
# Area
(ds.area.sum(dim='rgi_id') * 1e-6).plot(ax=ax2, label='[1985-2015]')
(dsp.area.sum(dim='rgi_id') * 1e-6).plot(ax=ax2, label='+0.5°C')
(dsm.area.sum(dim='rgi_id') * 1e-6).plot(ax=ax2, label='-0.5°C')
plt.tight_layout()
# Pick a specific glacier (Hintereisferner)
rid = 'RGI60-11.00897'
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
import logging
log = logging.getLogger(__name__)
# RGI Version
rgi_version = '61'
# CRU or HISTALP?
baseline = 'CRU'
# Initialize OGGM and set up the run parameters
cfg.initialize()
# Local paths (where to write the OGGM run output)
dirname = 'OGGM_ref_mb_{}_RGIV{}_OGGM{}'.format(baseline, rgi_version,
oggm.__version__)
WORKING_DIR = utils.gettempdir(dirname, home=True)
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
# We are running the calibration ourselves
cfg.PARAMS['run_mb_calibration'] = True
# We are using which baseline data?
cfg.PARAMS['baseline_climate'] = baseline
# Use multiprocessing?
cfg.PARAMS['use_multiprocessing'] = True
# Set to True for operational runs - here we want all glaciers to run
cfg.PARAMS['continue_on_error'] = False
# This script will plot the workflow for the columbia glacier and the LeConte
# glacier.
"""Useful plotting functions"""
import os
import geopandas as gpd
import shutil
from oggm import cfg, graphics, utils, workflow, tasks
from oggm.workflow import execute_entity_task
from matplotlib import rcParams
import matplotlib.pyplot as plt
cfg.initialize(logging_level='WORKFLOW')
WORKING_DIR = utils.gettempdir(dirname='OGGM-workflow', reset=False)
cfg.PATHS['working_dir'] = WORKING_DIR
MAIN_PATH = os.path.expanduser('~/cryo_calving_2019/')
plot_path = os.path.join(MAIN_PATH, 'plots/')
RGI_FILE = os.path.join(MAIN_PATH,
'input_data/01_rgi60_Alaska_modify/01_rgi60_Alaska.shp')
#This path needs to be change
Columbia_prepro = os.path.join(MAIN_PATH,
'output_data/1_Columbia_Glacier_runs/1_preprocessing/per_glacier/')
Columbia_dir = os.path.join(Columbia_prepro,
'RGI60-01/RGI60-01.10/RGI60-01.10689')
import oggm
from oggm import cfg, tasks
from oggm.core.climate import (mb_yearly_climate_on_glacier, t_star_from_refmb,
local_t_star, mu_star_calibration)
from oggm.core.massbalance import (ConstantMassBalance)
from oggm.utils import get_demo_file, gettempdir
from oggm.shop import histalp
cfg.initialize()
cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp'))
cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif')
histalp.set_histalp_url('https://cluster.klima.uni-bremen.de/~oggm/'
'test_climate/histalp/')
base_dir = gettempdir('Climate_docs')
cfg.PATHS['working_dir'] = base_dir
entity = gpd.read_file(get_demo_file('HEF_MajDivide.shp')).iloc[0]
gdir = oggm.GlacierDirectory(entity, base_dir=base_dir, reset=True)
tasks.define_glacier_region(gdir)
tasks.glacier_masks(gdir)
tasks.compute_centerlines(gdir)
tasks.initialize_flowlines(gdir)
tasks.compute_downstream_line(gdir)
tasks.catchment_area(gdir)
tasks.catchment_width_geom(gdir)
tasks.catchment_width_correction(gdir)
cfg.PARAMS['baseline_climate'] = 'HISTALP'
cfg.PARAMS['baseline_y0'] = 1850
tasks.process_histalp_data(gdir)
from oggm import utils, workflow, tasks
# For timing the run
import time
start = time.time()
# Module logger
log = logging.getLogger(__name__)
# Initialize OGGM and set up the default run parameters
cfg.initialize(logging_level='WORKFLOW')
rgi_version = '61'
rgi_region = '11' # Region Central Europe
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('OGGM_Inversion')
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
# RGI file
path = utils.get_rgi_region_file(rgi_region, version=rgi_version)
rgidf = gpd.read_file(path)
# Select the glaciers in the Pyrenees
rgidf = rgidf.loc[rgidf['O2Region'] == '2']
# Sort for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)
log.workflow('Starting OGGM inversion run')
log.workflow('Number of glaciers: {}'.format(len(rgidf)))
def single_flowline_glacier_directory(rgi_id, reset=False, prepro_border=80):
"""Prepare a GlacierDirectory for PyGEM (single flowline to start with)
Parameters
----------
rgi_id : str
the rgi id of the glacier
reset : bool
set to true to delete any pre-existing files. If false (the default),
the directory won't be re-downloaded if already available locally in
order to spare time.
prepro_border : int
the size of the glacier map: 10, 80, 160, 250
Returns
-------
a GlacierDirectory object
"""
if type(rgi_id) != str:
raise ValueError('We expect rgi_id to be a string')
if 'RGI60-' not in rgi_id:
raise ValueError('OGGM currently expects IDs to start with RGI60-')
cfg.initialize()
wd = utils.gettempdir(dirname='pygem-{}-b{}'.format(rgi_id, prepro_border),
reset=reset)
cfg.PATHS['working_dir'] = wd
cfg.PARAMS['use_multiple_flowlines'] = False
# Check if folder is already processed
try:
gdir = utils.GlacierDirectory(rgi_id)
gdir.read_pickle('model_flowlines')
# If the above works the directory is already processed, return
return gdir
except OSError:
pass
# If not ready, we download the preprocessed data for this glacier
gdirs = workflow.init_glacier_regions([rgi_id],
from_prepro_level=2,
prepro_border=prepro_border)
# Compute all the stuff
list_talks = [
tasks.glacier_masks,
tasks.compute_centerlines,
tasks.initialize_flowlines,
tasks.compute_downstream_line,
tasks.catchment_area,
tasks.catchment_width_geom,
tasks.catchment_width_correction,
tasks.compute_downstream_bedshape,
tasks.local_t_star,
tasks.mu_star_calibration,
tasks.prepare_for_inversion,
tasks.mass_conservation_inversion,
tasks.filter_inversion_output,
tasks.init_present_time_glacier,
]
for task in list_talks:
# The order matters!
workflow.execute_entity_task(task, gdirs)
return gdirs[0]
# Initialize OGGM and set up the default run parameters
cfg.initialize(logging_level='WORKFLOW')
# Here we override some of the default parameters
# How many grid points around the glacier?
# We make it small because we want the model to error because
# of flowing out of the domain
cfg.PARAMS['border'] = 80
# This is the important bit!
# We tell OGGM to continue despite of errors
cfg.PARAMS['continue_on_error'] = True
# Local working directory (where OGGM will write its output)
WORKING_DIR = utils.gettempdir('OGGM_Errors')
utils.mkdir(WORKING_DIR, reset=True)
cfg.PATHS['working_dir'] = WORKING_DIR
rgi_ids = ['RGI60-11.00897', 'RGI60-11.01450', 'RGI60-11.03295']
log.workflow('Starting OGGM run')
log.workflow('Number of glaciers: {}'.format(len(rgi_ids)))
# Go - get the pre-processed glacier directories
gdirs = workflow.init_glacier_regions(rgi_ids, from_prepro_level=4)
# We can step directly to the experiment!
# Random climate representative for the recent climate (1985-2015)
# with a negative bias added to the random temperature series
workflow.execute_entity_task(tasks.run_random_climate, gdirs,
