1. Equilibrium runs on a subset of an RGI Region

This example shows how to run the OGGM glacier evolution model on a subset of the Alps (the Rofental catchment in the Austrian Alps).

The first part of the script is related to setting up the run, then to select certain glaciers out of the RGI file. If you are not familiar with RGI files, you might want to do our short tutorial first:

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Then, we download the Pre-processed directories for this run. We use the level 4 data, which contain enough files to start a dynamical run, but not much more!

After the download, starting the runs take only one command. We show an example with a temperature bias as well, illustrating the sensitivity of these glaciers to temperature change.

Script

# Python imports
import logging

# Libs
import geopandas as gpd
import shapely.geometry as shpg

# 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

# 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]

# Sort for more efficient parallel computing
rgidf = rgidf.sort_values('Area', ascending=False)

log.workflow('Starting OGGM run')
log.workflow('Number of glaciers: {}'.format(len(rgidf)))

# Go - get the pre-processed glacier directories
gdirs = workflow.init_glacier_regions(rgidf, from_prepro_level=4)

# We can step directly to a new experiment!
# Random climate representative for the recent climate (1985-2015)
# This is a kind of "commitment" run
workflow.execute_entity_task(tasks.run_random_climate, gdirs,
                             nyears=300, y0=2000, seed=1,
                             output_filesuffix='_commitment')
# Now we add a positive and a negative bias to the random temperature series
workflow.execute_entity_task(tasks.run_random_climate, gdirs,
                             nyears=300, y0=2000, seed=2,
                             temperature_bias=0.5,
                             output_filesuffix='_bias_p')
workflow.execute_entity_task(tasks.run_random_climate, gdirs,
                             nyears=300, y0=2000, seed=3,
                             temperature_bias=-0.5,
                             output_filesuffix='_bias_m')

# Write the compiled output
utils.compile_glacier_statistics(gdirs)
utils.compile_run_output(gdirs, filesuffix='_commitment')
utils.compile_run_output(gdirs, filesuffix='_bias_p')
utils.compile_run_output(gdirs, filesuffix='_bias_m')

# 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:

2019-02-16 17:50:51: oggm.cfg: Using configuration file: /home/mowglie/Documents/git/oggm-fork/oggm/params.cfg
2019-02-16 17:50:52: __main__: Starting OGGM run
2019-02-16 17:50:52: __main__: Number of glaciers: 54
2019-02-16 17:50:52: oggm.workflow: init_glacier_regions from prepro level 4 on 54 glaciers.
2019-02-16 17:50:52: oggm.workflow: Execute entity task gdir_from_prepro on 54 glaciers
2019-02-16 17:50:52: oggm.workflow: Multiprocessing: using all available processors (N=8)
2019-02-16 17:50:54: oggm.workflow: Execute entity task run_random_climate on 54 glaciers
2019-02-16 17:51:44: oggm.workflow: Execute entity task run_random_climate on 54 glaciers
2019-02-16 17:52:36: oggm.workflow: Execute entity task run_random_climate on 54 glaciers
2019-02-16 17:54:11: __main__: OGGM is done! Time needed: 0:03:20

Some analyses

The output directory contains many output files, most of them in the individual glacier directories. Most often, users will use the compiled data files, where the most relevant model outputs are stored together:

  • the compile_glacier_statistics.csv file contains various information for each glacier (here the output is limited because we used a preprocessing level of 4 - see other examples for more information).

  • the run_output_*.nc files contain the volume, area and length timeseries of each inddividual glacier.

Let’s have a look at them:

# 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))
# Volume
(ds.volume.sel(rgi_id=rid) * 1e-9).plot(ax=ax1, label='[1985-2015]')
(dsp.volume.sel(rgi_id=rid) * 1e-9).plot(ax=ax1, label='+0.5°C')
(dsm.volume.sel(rgi_id=rid) * 1e-9).plot(ax=ax1, label='-0.5°C')
ax1.legend(loc='best')
# Length
(ds.length.sel(rgi_id=rid) * 1e-3).plot(ax=ax2, label='[1985-2015]')
(dsp.length.sel(rgi_id=rid) * 1e-3).plot(ax=ax2, label='+0.5°C')
(dsm.length.sel(rgi_id=rid) * 1e-3).plot(ax=ax2, label='-0.5°C')
plt.tight_layout()
plt.show()

This code snippet should produce the following plots:

../_images/rgi_region_all.png

The graphics above show the basin sums of glacier volume (km3) and area (km2) in a random climate corresponding to the period [1985-2015] (i.e. each year is picked randomly out of this period). This is a “commitment” run, i.e. showing the expected glacier change even if climate doesn’t change any more in the future. The time axis shows the number of years since the glacier inventory date (here, 2003).

Note

The glacier area and length plots can be noisy in certain conditions because OGGM currently doesn’t differentiate between snow and ice, i.e. occasional years with large snowfall can artificially increase the glacier area. The effect on volume is negligible, though. A good way to deal with this noise is to smooth the time series: see our dedicated tutorial for a possible way to deal with this.

The graphics below are for the Hintereisferner glacier only, and they show glacier volume and glacier length evolution:

../_images/rgi_region_sel.png