glathida Glacier Thickness Database

Worldwide dataset of glacier thickness observations aggregated from literature review, data submissions, and published data.

Versions

• v3.1.0: The latest published version of glathida is available from the GTN-G website (https://doi.org/10.5904/wgms-glathida-2020-10). It is described in:

  • Welty et al. (2020). Worldwide version-controlled database of glacier thickness observations. Earth System Science Data, 12, 3039–3055. https://doi.org/10.5194/essd-12-3039-2020

• v4-beta: The next version is in development and available from this GitLab repository (https://gitlab.com/wgms/glathida). There is yet no timeline for publication of this version.

Note that the data structure has changed (see documentation). Tables T, TT, and TTT are now glacier, band, and point. An additional survey table makes it possible to submit point measurements without a corresponding entry in glacier.

Contribute

Bug reports, data submissions, and other issues can be posted to the issue tracker at https://gitlab.com/wgms/glathida/-/issues. Submitters are encouraged to validate their data before submission (see the Developer guide further below).

Submission: as issue

For users who are not familiar with Git, data can be submitted as an issue.

• Create a new issue: https://gitlab.com/wgms/glathida/-/issues/new
• In the Choose a template dropdown, select Data submission.
• Fill out and follow the instructions in the issue template.
• Link to your data or attach it directly to the issue. Ideally, the data should be a spreadsheet or CSV files with column names and values as described in the documentation.
• Click Create issue.

Your submission will be reviewed by a maintainer, who will create a merge request.

Submission: as merge request

Users familiar with Git are encouraged to submit a merge request directly.

• Fork the repository.
• Create a new branch in your fork.
• Add data to a new subdirectory of submissions (for example, submissions/{investigator name}-{survey or publication year}-{glacier name}). Data should be CSV files structured as described in the documentation.
• Modify or remove existing data (in /data) as needed.
• Create a merge request.

Continuous integrations tests will automatically check your submission. If they fail, you are encouraged to commit further changes until they pass. The merge request will be reviewed by a maintainer and hopefully merged!

Developer guide

Clone the repository and move into the directory.

git clone https://gitlab.com/wgms/glathida.git
cd glathida

Create the glathida conda environment and activate it.

conda env create --file environment.yaml
conda activate glathida

Run tests on the metadata (datapackage.yaml) and data (data/*).

pytest

Or test a data submission.

python -m tests.check_submission path/to/submission
# For example:
# python -m tests.check_submission data/24k-glacier-2019

User guide (Python)

Read data

The legacy (v3) data is stored as CSV files directly in the data subdirectory (e.g. data/point.csv), but new data is stored in subdirectories of data (for example, data/24k-glacier-2019/point.csv). To read all data as unified tables, the provided helpers.read_data function can be used. This assumes the glathida Python environment has been activated (see above).

from tests import helpers

dfs = helpers.read_data()
dfs.keys()
# dict_keys(['glacier', 'point', 'survey', 'band'])

Otherwise, the following can be used in a typical Python environment.

from collections import defaultdict
from pathlib import Path

import pandas as pd


paths = Path('data').rglob('*.csv')
dfs = defaultdict(list)
for path in paths:
  df = pd.read_csv(path, low_memory=False)
  dfs[path.stem].append(df)
dfs = {
  name: pd.concat(df_list, ignore_index=True)
  for name, df_list in dfs.items()
}

Assign RGI ID to points

Assumes the data are already loaded in dfs as above.

import geopandas as gpd

# Convert points DataFrame to a GeoDataFrame
df = dfs['point']
points = gpd.GeoDataFrame(
  df, geometry=gpd.points_from_xy(df['longitude'], df['latitude'], crs=4326)
)

# Read RGI inventory
rgi = gpd.read_file('path/to/rgi')

# Add RGI ID to points using spatial indexing (for speed)
# NOTE: points.sjoin(rgi, how='left', predicate='within') may be as fast
# NOTE: Assumes RGI ID is in column 'rgi_id' (v7). Use 'RGIId' for v6.
points['rgi_id'] = None
i_rgi, i_points = points.sindex.query(rgi.geometry, predicate='intersects')
points.loc[points.index[i_points], 'rgi_id'] = rgi.iloc[i_rgi]['RGIId'].values