Using your our own glacier inventory with OGGM#

The Randolph Glacier Inventory is a key dataset to model glaciers at any scale: it includes outlines of the extent of each glacier in the world, an information that is critical in figuring out how much a particular glacier might contribute to rising sea level. These glacier outlines are the starting point of any simulation in OGGM. The RGI’s latest version (v6), as well as v5, are provided and supported by OGGM (see the documentation). However, there are several issues in the RGI that might make you want to use your own corrected glacier outlines.

This notebook describes how to feed OGGM with them. We will show you three case studies about how to give any geometry to OGGM and avoid errors of incompatibility between your shapefile and the model framework.

We have three case studies which should cover a number of applications:

  1. Dividing a glacier into smaller entities (common case, useful for poorly outlined glaciers, which are in reality separate dynamical entities)

  2. Merging two glaciers together (useful for tidewater glaciers in particular, not much elsewhere)

  3. Start from a completely independent inventory

TLDR;#

If you want to use custom data to feed OGGM with, you should:

  • make a shapefile that resembles the RGI one: same attributes, and the glacier geometries should be in lon/lat projection. The most important attribute is geometry, of course, but others are used by OGGM as well: refer to the OGGM documentation to decide which ones. The RGI documentation (found in the RGI directory after download) is useful as well! We also have a useful function (oggm.utils.cook_rgidf) which can help you with that.

  • compute and use a new glacier interesects file, or make sure you don’t need one and disable this option in OGGM.

Structure of an RGI file#

import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import oggm
import os
from oggm import cfg, utils, workflow, tasks, graphics
from oggm.core import inversion
cfg.initialize(logging_level='WARNING')

2023-03-07 12:47:00: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2023-03-07 12:47:00: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2023-03-07 12:47:00: oggm.cfg: Multiprocessing: using all available processors (N=2)

Let’s read a file from the standard RGI:

utils.get_rgi_dir(version='61')

'/github/home/OGGM/rgi/RGIV61'

sh = utils.get_rgi_region_file('11', version='61')
sh

'/github/home/OGGM/rgi/RGIV61/11_rgi61_CentralEurope/11_rgi61_CentralEurope.shp'

Shapefiles are best read an manipulated with geopandas in python (see also our working_with_rgi.ipynb tutorial):

gdf = gpd.read_file(sh)
gdf.head()
RGIId GLIMSId BgnDate EndDate CenLon CenLat O1Region O2Region Area Zmin ... Lmax Status Connect Form TermType Surging Linkages Name check_geom geometry
0 RGI60-11.00001 G013599E47495N 20030799 20030999 13.5987 47.4949 11 1 0.122 2191 ... 461 0 0 0 0 9 9 NaN NaN POLYGON ((13.60035 47.49330, 13.59995 47.49332...
1 RGI60-11.00002 G013614E47485N 20030799 20030999 13.6135 47.4845 11 1 2.292 2203 ... 1853 0 0 0 0 9 9 NaN NaN POLYGON ((13.60638 47.47578, 13.60599 47.47579...
2 RGI60-11.00003 G013596E47484N 20030799 20030999 13.5960 47.4835 11 1 0.851 2280 ... 1140 0 0 0 0 9 9 NaN NaN POLYGON ((13.59765 47.47613, 13.59726 47.47614...
3 RGI60-11.00004 G013583E47481N 20030799 20030999 13.5829 47.4807 11 1 0.053 2319 ... 382 0 0 0 0 9 9 NaN NaN POLYGON ((13.58283 47.47969, 13.58243 47.47971...
4 RGI60-11.00005 G013603E47477N 20030799 20030999 13.6026 47.4774 11 1 0.057 2656 ... 202 0 0 0 0 9 9 NaN NaN POLYGON ((13.60076 47.47519, 13.60036 47.47521...

5 rows × 24 columns

An RGI file contains the actual glacier geometries, but also a number of attribute which are used by OGGM afterwards. Let’s learn how to make our own file now.

Case 1: dividing a glacier into smaller entities#

A typical example of wrongly divided glacier is Hintereisferner, in Austria:

# OGGM set-up
cfg.PATHS['working_dir'] = utils.gettempdir(dirname='rgi-case-1', reset=True)
cfg.PARAMS['border'] = 10

# Get the HEF geometry and plot it
gl = utils.get_rgi_glacier_entities(['RGI60-11.00897'])
gl.plot(edgecolor='k');

2023-03-07 12:47:02: oggm.cfg: PARAMS['border'] changed from `40` to `10`.
../_images/use_your_own_inventory_14_1.png

Obviously, the two smaller tongues used to flow in the main one but this is not the case anymore today. We need updated geometries.

Make a new “RGI file”#

There is no simple way to automate the process of finding these bad geometries, but we are working on this (don’t hold your breath, this has been in development since a long time). Here we use a geometry that we prepared in QGis:

# We simulate the case where we only have the geometry, nothing else
divides = gpd.read_file(utils.get_demo_file('divides_alps.shp'))
divides = divides.loc[divides.RGIId == 'RGI50-11.00897'][['geometry']]
divides
geometry
1 POLYGON ((10.74505 46.80376, 10.74610 46.80293...
93 POLYGON ((10.74860 46.80493, 10.74717 46.80529...
94 POLYGON ((10.74602 46.80561, 10.74717 46.80529... 
divides.plot(edgecolor='k');
../_images/use_your_own_inventory_19_0.png

Now we use the RGI entity as template - it’s good to use the same attributes as the original RGI glacier, because most of them are already correct:

template = pd.concat([gl]*3, ignore_index=True)
template
RGIId GLIMSId BgnDate EndDate CenLon CenLat O1Region O2Region Area Zmin ... Aspect Lmax Status Connect Form TermType Surging Linkages Name geometry
0 RGI60-11.00897 G010758E46800N 20030799 20030999 10.7584 46.8003 11 1 8.036 2430 ... 71 7178 0 0 0 0 9 1 Hintereisferner POLYGON ((10.75085 46.81381, 10.75112 46.81397...
1 RGI60-11.00897 G010758E46800N 20030799 20030999 10.7584 46.8003 11 1 8.036 2430 ... 71 7178 0 0 0 0 9 1 Hintereisferner POLYGON ((10.75085 46.81381, 10.75112 46.81397...
2 RGI60-11.00897 G010758E46800N 20030799 20030999 10.7584 46.8003 11 1 8.036 2430 ... 71 7178 0 0 0 0 9 1 Hintereisferner POLYGON ((10.75085 46.81381, 10.75112 46.81397...

3 rows × 23 columns

We change the important ones:

# Attributes
template['RGIId'] = ['RGI60-11.00897_d01', 'RGI60-11.00897_d02', 'RGI60-11.00897_d03']
template['Name'] = ['Hintereisferner d01', 'Hintereisferner d02', 'Hintereisferner d03']
# Geometries
template['geometry'] = divides['geometry'].values
# Center point
for i, geom in template[['geometry']].iterrows():
    cenlon, cenlat = geom.geometry.centroid.xy
    template.loc[i, 'CenLon'] = np.array(cenlon)
    template.loc[i, 'CenLat'] = np.array(cenlat)
# This is important to properly georeference the file
import salem
template.crs = salem.wgs84.srs

template.plot(edgecolor='k');
../_images/use_your_own_inventory_24_0.png

Save it:

hef_new_shape_path = os.path.join(cfg.PATHS['working_dir'], 'hef_divided.shp')
template.to_file(hef_new_shape_path)

Compute the intersects#

Hintereisferner has a divide with another glacier as well! Let’s find out which:

intersects_alps = gpd.read_file(utils.get_rgi_intersects_region_file('11'))
intersects_hef = intersects_alps.loc[(intersects_alps.RGIId_1 == 'RGI60-11.00897') | (intersects_alps.RGIId_2 == 'RGI60-11.00897')]
intersects_hef
RGIId_1 RGIId_2 geometry
119 RGI60-11.00846 RGI60-11.00897 LINESTRING (10.74826 46.81077, 10.74840 46.810...
143 RGI60-11.00897 RGI60-11.00846 LINESTRING (10.72557 46.79781, 10.72557 46.797...

Ok, we can now create a file which has all the glaciers we need to compute the relevant intersects (note that we could also use the full standard RGI with just HEF replaced):

for_intersects = pd.concat([template, utils.get_rgi_glacier_entities(['RGI60-11.00846'])], ignore_index=True)
for_intersects.crs = salem.wgs84.srs
for_intersects.plot(edgecolor='k');

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[13], line 1
----> 1 for_intersects = pd.concat([template, utils.get_rgi_glacier_entities(['RGI60-11.00846'])], ignore_index=True)
      2 for_intersects.crs = salem.wgs84.srs
      3 for_intersects.plot(edgecolor='k');

File /usr/local/pyenv/versions/3.10.10/lib/python3.10/site-packages/pandas/util/_decorators.py:331, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    325 if len(args) > num_allow_args:
    326     warnings.warn(
    327         msg.format(arguments=_format_argument_list(allow_args)),
    328         FutureWarning,
    329         stacklevel=find_stack_level(),
    330     )
--> 331 return func(*args, **kwargs)

File /usr/local/pyenv/versions/3.10.10/lib/python3.10/site-packages/pandas/core/reshape/concat.py:381, in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
    159 """
    160 Concatenate pandas objects along a particular axis.
    161 
   (...)
    366 1   3   4
    367 """
    368 op = _Concatenator(
    369     objs,
    370     axis=axis,
   (...)
    378     sort=sort,
    379 )
--> 381 return op.get_result()

File /usr/local/pyenv/versions/3.10.10/lib/python3.10/site-packages/pandas/core/reshape/concat.py:616, in _Concatenator.get_result(self)
    612             indexers[ax] = obj_labels.get_indexer(new_labels)
    614     mgrs_indexers.append((obj._mgr, indexers))
--> 616 new_data = concatenate_managers(
    617     mgrs_indexers, self.new_axes, concat_axis=self.bm_axis, copy=self.copy
    618 )
    619 if not self.copy:
    620     new_data._consolidate_inplace()

File /usr/local/pyenv/versions/3.10.10/lib/python3.10/site-packages/pandas/core/internals/concat.py:226, in concatenate_managers(mgrs_indexers, axes, concat_axis, copy)
    223     values = np.concatenate(vals, axis=1)
    224 else:
    225     # TODO(EA2D): special-casing not needed with 2D EAs
--> 226     values = concat_compat(vals, axis=1)
    227     values = ensure_block_shape(values, ndim=2)
    229 values = ensure_wrapped_if_datetimelike(values)

File /usr/local/pyenv/versions/3.10.10/lib/python3.10/site-packages/pandas/core/dtypes/concat.py:133, in concat_compat(to_concat, axis, ea_compat_axis)
    130 if isinstance(to_concat[0], ABCExtensionArray):
    131     # TODO: what about EA-backed Index?
    132     cls = type(to_concat[0])
--> 133     return cls._concat_same_type(to_concat)
    134 else:
    135     return np.concatenate(to_concat)

File /usr/local/pyenv/versions/3.10.10/lib/python3.10/site-packages/geopandas/array.py:1323, in GeometryArray._concat_same_type(cls, to_concat)
   1311 """
   1312 Concatenate multiple array
   1313 
   (...)
   1320 ExtensionArray
   1321 """
   1322 data = np.concatenate([ga.data for ga in to_concat])
-> 1323 return GeometryArray(data, crs=_get_common_crs(to_concat))

File /usr/local/pyenv/versions/3.10.10/lib/python3.10/site-packages/geopandas/array.py:1413, in _get_common_crs(arr_seq)
   1406         warnings.warn(
   1407             "CRS not set for some of the concatenation inputs. "
   1408             f"Setting output's CRS as {names[0]} "
   1409             "(the single non-null crs provided)."
   1410         )
   1411     return crs_not_none[0]
-> 1413 raise ValueError(
   1414     f"Cannot determine common CRS for concatenation inputs, got {names}. "
   1415     "Use `to_crs()` to transform geometries to the same CRS before merging."
   1416 )

ValueError: Cannot determine common CRS for concatenation inputs, got ['unknown', 'WGS 84']. Use `to_crs()` to transform geometries to the same CRS before merging.

Good! Let’s use rgitools to compute the intersects for this new situation:

from rgitools.funcs import compute_intersects
new_intersects = compute_intersects(for_intersects)

If this raises an error, you might have to install rgitools first (e.g. pip install git+https://github.com/OGGM/rgitools.git) and also networkx (pip install networkx)

f, ax = plt.subplots()
for_intersects.plot(ax=ax, edgecolor='k');
new_intersects.plot(ax=ax, edgecolor='r');

Good! We can store our intersects to use them with OGGM afterwards:

hef_intersects_path = os.path.join(cfg.PATHS['working_dir'], 'hef_divided_intersects.shp')
new_intersects.to_file(hef_intersects_path)

Finally: the OGGM run#

# This is important! We tell OGGM to recompute the glacier area for us
cfg.PARAMS['use_rgi_area'] = False
# This is the default anyway, but we set it here to be sure
cfg.PARAMS['use_intersects'] = True

# This is important!
cfg.set_intersects_db(hef_intersects_path)

# This is to avoid a download in the tutorial, you dont' need do this at home
cfg.PATHS['dem_file'] = utils.get_demo_file('hef_srtm.tif')

# This is important again - standard OGGM 
rgidf = gpd.read_file(hef_new_shape_path)
gdirs = workflow.init_glacier_directories(rgidf, reset=True, force=True)

workflow.execute_entity_task(tasks.define_glacier_region, gdirs);
workflow.execute_entity_task(tasks.glacier_masks, gdirs);
workflow.execute_entity_task(tasks.compute_centerlines, gdirs);
workflow.execute_entity_task(tasks.initialize_flowlines, gdirs);
workflow.execute_entity_task(tasks.catchment_area, gdirs);
workflow.execute_entity_task(tasks.catchment_width_geom, gdirs);
workflow.execute_entity_task(tasks.catchment_width_correction, gdirs);

If you get “NameError ‘skdraw’ is not defined”, you have to install the python image processing package scikit-image first (e.g. via conda install scikit-image)

graphics.plot_catchment_width(gdirs, add_intersects=True, corrected=True)

It works!

The intersects in OGGM are used for two main things:

  • when a grid-point glacier section touches an intersect, it will be attributed a rectangular bed (instead of a parabolic one)

  • when interpolating the ice thickness to a 2D grid, the boundary condition thickness=0 at the glacier outline is removed where there are intersects

We recommend to use intersects for your runs as well.

Case 2: merging glaciers#

Sometimes, you may want to merge glaciers together. This case is less frequent than Case 1, but might be useful for calving glaciers, which are sometimes divided in the RGI.

Original RGI outlines#

We use a case study for two marine-terminating glaciers in Alaska that have to be merged into a single outline in order to model a correct calving flux for these glaciers (following the methods described in Recinos et al., (2019)). The resulting shapefile is a new one that needs to be adapted in order for OGGM to run.

We will study the Sawyer Glacier (RGI60-01.03890) that is actually connected via the calving front with this other entity (RGI60-01.23664). Visit this link to learn more about the retreat of the Sawyer Glacier and see images illustrating this connection.

gl = utils.get_rgi_glacier_entities(['RGI60-01.03890', 'RGI60-01.23664'])

Here OGGM downloaded the outlines for both glaciers. If we plot them together, we can see that both glaciers drain into the same fjord. See the google map below:

cfg.initialize(logging_level='WARNING')
cfg.PATHS['working_dir'] = utils.gettempdir(dirname='rgi-case-2-example', reset=True)
cfg.PARAMS['border'] = 10
gdirs = workflow.init_glacier_directories(['RGI60-01.03890', 'RGI60-01.23664'], from_prepro_level=3, reset=True, force=True)
graphics.plot_googlemap(gdirs, figsize=(6, 6));

The upper glacier map is a zoom version of the plot below. They share the same glaciers terminus. Therefore, to estimate a calving flux for these glaciers we need them connected.

Let’s merge these two outlines using geopandas#

merged = gl.dissolve(by='O2Region', as_index=False) 
merged = merged[gl.columns]
merged.plot(edgecolor='k');

RGI attributes#

We now have a new shapefile, which resembles an RGI one but has wrong attributes. Some aren’t relevant, but some are. See the documentation for a list.

The important ones are: RGIId, CenLon, CenLat, TermType, Area. Area and CenLon, Cenlat can be calculated by OGGM later, as we have seen earlier. Here, we prefer to keep the Area computed by the RGI for consistency.

# We use the RGI as template (this avoids strange IO issues)
template = gl.iloc[[0]].copy()
template['geometry'] = merged['geometry'].values

# Change CenLon, CenLat
cenlon, cenlat = merged.iloc[0].geometry.representative_point().xy
template['CenLon'] = cenlon
template['CenLat'] = cenlat

# We sum up the areas
template['Area'] = gl.Area.sum()
template['Area']

In Recinos et al., (2019) we wanted to estimate a frontal ablation flux for this new outline and compare it with previous estimates found in the literature for the Sawyer Glacier (McNabb et al., 2015).

For this reason we kept the Sawyer glacier attributes to the following variables: RGIId, GLIMSId, Name

The TermType should be equal to 1, for Marine-terminating.

template['TermType'] = 1
template['Name'] = 'Sawyer Glacier merged with RGI60-01.23664'

Now we can write a new shapefile for this glacier. We recommend doing this if you have to make several model runs. You can also integrate this outline to your main RGI shapefile:

cfg.initialize()
cfg.PATHS['working_dir'] = utils.gettempdir(dirname='rgi-case-2', reset=True)
template.crs = salem.wgs84.srs
template.to_file(os.path.join(cfg.PATHS['working_dir'], 'merged.shp'))

Run OGGM with this new glacier#

For simplicity, we do not compute the intersects in this case: however, we recommend you do do so (see above). In all cases, do not use the intersects provided automatically with OGGM when using custom inventories, as they are likely to be wrong.

# Set-up the run
cfg.PARAMS['border'] = 10

# We don't use intersects here
cfg.PARAMS['use_intersects'] = False

# We prefer OGGM to use the area we computed ourselves
cfg.PARAMS['use_rgi_area'] = True

# Use our merged file
rgidf = gpd.read_file(os.path.join(cfg.PATHS['working_dir'], 'merged.shp'))
gdirs = workflow.init_glacier_directories(rgidf, reset=True, force=True)
gdirs

Here we are not able to use the Pre-processed directories and the respective Processing levels that OGGM provides for a easy run set up. We can’t use this workflow simply because we have a different beginning than OGGM, we have a different RGI! We just need to type more and run all the model task one by one:

from oggm.workflow import execute_entity_task

# Calculate the DEMs and the masks
execute_entity_task(tasks.define_glacier_region, gdirs, source = 'SRTM')
execute_entity_task(tasks.glacier_masks, gdirs)

# Calculate the Pre-processing tasks
task_list = [
    tasks.compute_centerlines,
    tasks.initialize_flowlines,
    tasks.catchment_area,
    tasks.catchment_width_geom,
    tasks.catchment_width_correction,
]

for task in task_list:
    execute_entity_task(task, gdirs)

note that we used in tasks.define_glacier_region SRTM as DEM source which is not the default one in OGGM. The default one is NASADEM, but for that you have to register at NASA Earthdata more info in dem_sources.ipynb/register

graphics.plot_googlemap(gdirs, figsize=(6, 6));

graphics.plot_catchment_width(gdirs, corrected=True);

Case 3: start from a completely independent inventory#

OGGM (since version 1.5.3) now offers a function (utils.cook_rgidf()) to make it easier of using a non-RGI glacier inventory in OGGM. Now, let’s use a non-RGI glacier inventory from the second Chinese glacier inventory (CGI2, https://doi.org/10.3189/2015JoG14J209) to show how it works.

# Let's get the sample CGI2 glacier inventory and see what it looks like
from oggm import utils
import geopandas as gpd
cgidf = gpd.read_file(utils.get_demo_file('cgi2.shp'))
cgidf

Ha! There are some Chinese characters! But it should not influence our work. Now, let’s try with the simplest case:

rgidf_simple = utils.cook_rgidf(cgidf, o1_region='13')
rgidf_simple

In this case, we fake all of the columns values except for geometry. With this rgidf_simple, we can handle most of the OGGM procedure after set cfg.PARAMS['use_rgi_area'] = False. Let’s have a try:

from oggm import cfg, workflow

cfg.initialize()
cfg.PARAMS['use_multiprocessing'] = True
cfg.PARAMS['use_rgi_area'] = False
cfg.PARAMS['use_intersects'] = False
cfg.PATHS['working_dir'] = utils.gettempdir(dirname='cook_rgidf', reset=True)
gdirs = workflow.init_glacier_directories(rgidf_simple)

# The tasks below require downloading new data - we comment them for the tutorial, but it should work for you!
# workflow.gis_prepro_tasks(gdirs)
# workflow.download_ref_tstars('https://cluster.klima.uni-bremen.de/~oggm/ref_mb_params/oggm_v1.4/RGIV62/CRU/centerlines/qc3/pcp2.5')
# workflow.climate_tasks(gdirs)
# workflow.inversion_tasks(gdirs)

utils.compile_glacier_statistics(gdirs)

Sometimes, the information in the original glacier inventory also covered what RGI need, but with different name. For example, both CGI2 and RGI include Lon-lat coordinate to indicate the location of the glacier but with different names. CGI2 used Glc_Long and Glc_Lati, while RGI6 used CenLon and CenLat. Also, CGI2 include glacier area information with name Glc_Area, while RGI named glacier area as Area. If we want to keep those values but rename them following RGI, we can use the assign_column_values keyword argument:

rgidf_save_columns = utils.cook_rgidf(cgidf, o1_region='13', assign_column_values={'Glc_Long': 'CenLon', 'Glc_Lati': 'CenLat', 'Glc_Area': 'Area', 'GLIMS_ID': 'GLIMSId'})
rgidf_save_columns

Seems perfect! However, the glacier area in CGI2 is in \(m^2\), but it is in \(km^2\) for RGI. So, we need to correct the Area values right:

rgidf_save_columns['Area'] = rgidf_save_columns.Area * 1e-6
rgidf_save_columns.Area

Note:#

Despite that cook_rgidf() can handle most of the cases for OGGM, there are some limitations. Here, we try to point out some of cases in which the rgidf sourced from cook_rgidf() might get you in trouble:

  • in cook_rgidf(), we assign the glacier form with ‘0’ (Glacier) for all of the glaciers in the original data. OGGM assigns different parameters for glaciers (form ‘0’) and ice caps (form ‘1’).

  • termtype was also assign as ‘0’ which means ‘land-terminating’. Here again, OGGM treats ‘Marine-terminating’ glaciers differently (see ‘Frontal ablation’).

For these kinds of attribution, there is nothing we can do automatically. Users need to assign the right values according the actual condition of their glaciers, if the attribution is important to their use cases.

What’s next?#