2. Ice thickness inversion

This example shows how to run the OGGM ice thickness inversion model with various ice parameters: the deformation parameter A and a sliding parameter.

There is currently no “best” set of parameters for the ice thickness inversion model. As shown in Maussion et al. (2019), the default parameter set results in global volume estimates which are a bit larger than previous values. For the consensus estimate of Farinotti et al. (2018), OGGM participated with a deformation parameter 1.5 times larger than the generally accepted default value.

There is no reason to think that the ice parameters are the same between neighboring glaciers. There is currently no “good” way to calibrate them. We won’t discuss the details here, but we provide a script to illustrate the sensitivity of the model to this choice.

Script

Here, we run the inversion algorithm for all glaciers in the Pyrenees.

# Python imports
import logging

# Libs
import geopandas as gpd

# Locals
import oggm.cfg as cfg
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)))

# Go - get the pre-processed glacier directories
# We start at level 3, because we need all data for the inversion
gdirs = workflow.init_glacier_directories(rgidf,
                                          from_prepro_level=3,
                                          prepro_border=10)

# Default parameters
# Deformation: from Cuffey and Patterson 2010
glen_a = 2.4e-24
# Sliding: from Oerlemans 1997
fs = 5.7e-20

# Correction factors
factors = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
factors += [1.1, 1.2, 1.3, 1.5, 1.7, 2, 2.5, 3, 4, 5]
factors += [6, 7, 8, 9, 10]

# Run the inversions tasks with the given factors
for f in factors:
    # Without sliding
    suf = '_{:03d}_without_fs'.format(int(f * 10))
    workflow.execute_entity_task(tasks.mass_conservation_inversion, gdirs,
                                 glen_a=glen_a*f, fs=0)
    workflow.execute_entity_task(tasks.filter_inversion_output, gdirs)
    # Store the results of the inversion only
    utils.compile_glacier_statistics(gdirs, filesuffix=suf,
                                     inversion_only=True)

    # With sliding
    suf = '_{:03d}_with_fs'.format(int(f * 10))
    workflow.execute_entity_task(tasks.mass_conservation_inversion, gdirs,
                                 glen_a=glen_a*f, fs=fs)
    workflow.execute_entity_task(tasks.filter_inversion_output, gdirs)
    # Store the results of the inversion only
    utils.compile_glacier_statistics(gdirs, filesuffix=suf,
                                     inversion_only=True)

# Log
m, s = divmod(time.time() - start, 60)
h, m = divmod(m, 60)
log.workflow('OGGM is done! Time needed: %d:%02d:%02d' % (h, m, s))

If everything went well, you should see an output similar to:

2020-03-25 12:31:52: oggm.cfg: Using configuration file: /home/mowglie/disk/Dropbox/HomeDocs/git/oggm-fork/oggm/params.cfg
2020-03-25 12:31:52: oggm.cfg: Multiprocessing switched ON according to the parameter file.
2020-03-25 12:31:52: oggm.cfg: Multiprocessing: using all available processors (N=8)
2020-03-25 12:31:53: __main__: Starting OGGM inversion run
2020-03-25 12:31:53: __main__: Number of glaciers: 35
2020-03-25 12:31:53: oggm.workflow: init_glacier_directories from prepro level 3 on 35 glaciers.
2020-03-25 12:31:55: oggm.workflow: Execute entity task gdir_from_prepro on 35 glaciers
2020-03-25 12:32:00: oggm.workflow: Execute entity task mass_conservation_inversion on 35 glaciers
(...)
2020-03-25 12:32:33: oggm.workflow: Execute entity task filter_inversion_output on 35 glaciers
2020-03-25 12:32:33: oggm.workflow: Execute entity task glacier_statistics on 35 glaciers
2020-03-25 12:32:34: __main__: OGGM is done! Time needed: 0:00:41

Analysing the output

The output directory contains the glacier_statistics_*.csv files, which compile the output of each inversion experiment. Let’s run the analysis in a jupyter notebook, it’s so much easier!

https://img.shields.io/badge/Launch-OGGM%20tutorials-579ACA.svg?style=popout&logo=data:image/png;base64,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