pygeoglim 1.3.1

GLiM lithology + GLHYMPS hydrogeology attributes for watersheds and regions

pygeoglim

pygeoglim is a Python package for fetching raw lithology and hydrogeology data from GLiM and GLHYMPS 2.0 for any watershed on Earth. It returns GeoDataFrames of geology polygons that callers can analyse freely, with CAMELS-style attribute summaries as an integrated convenience layer. Built for hydrological modelling, large-sample hydrology, and Earth system research.

📋 Table of Contents

• Installation
• Quick Start
• Extracted Attributes
• Data Sources
• Requirements
• Citation
• License
• Author

📦 Installation

From PyPI (Recommended)

pip install pygeoglim

From GitHub

pip install git+https://github.com/galib9690/pygeoglim.git

Development Mode

git clone https://github.com/galib9690/pygeoglim.git
cd pygeoglim
pip install -e .

🚀 Quick Start

Basic Usage

from shapely.geometry import box
from pygeoglim import fetch_glim, fetch_glhymps, glim_attributes, glhymps_attributes

# Define a watershed bounding box (lon_min, lat_min, lon_max, lat_max)
watershed = box(-85.5, 39.5, -85.0, 40.0)

# Fetch raw geology polygons (GeoDataFrames)
lithology  = fetch_glim(watershed)      # GLiM — lithology polygons
hydrogeol  = fetch_glhymps(watershed)   # GLHYMPS — permeability polygons

# CAMELS-style attribute summaries
glim   = glim_attributes(lithology)
glhymp = glhymps_attributes(hydrogeol)

print(glim)    # {'geol_1st_class': ..., 'carbonate_rocks_frac': ..., ...}
print(glhymp)  # {'geol_porosity': ..., 'geol_permeability': ..., ...}

Global Watersheds (non-CONUS)

from shapely.geometry import box
from pygeoglim import fetch_glim, fetch_glhymps

# Rhine headwaters, Germany — pass region="global" for non-CONUS
rhine = box(6.0, 46.5, 8.5, 48.5)

lithology = fetch_glim(rhine, region="global")
hydrogeol = fetch_glhymps(rhine, region="global")

Requires a HuggingFace token for global tiles. Run huggingface-cli login once or set the HF_TOKEN environment variable.

Using Shapefile Input

from pygeoglim import load_geometry, fetch_glim

# Load geometry from a shapefile
geom = load_geometry(shapefile="path/to/watershed.shp")
lithology = fetch_glim(geom)

📊 Extracted Attributes

Lithology (GLiM Dataset)

Attribute	Description
geol_1st_class	Dominant lithology class
glim_1st_class_frac	Fraction of dominant class
geol_2nd_class	Second most common lithology class
glim_2nd_class_frac	Fraction of second most common class
carbonate_rocks_frac	Fraction of carbonate rocks

Hydrogeology (GLHYMPS Dataset)

Attribute	Description	Units
geol_porosity	Area-weighted porosity	fraction
geol_permeability	Area-weighted permeability	log₁₀ m²
geol_permeability_linear	Permeability (linear scale)	m²
hydraulic_conductivity	Hydraulic conductivity	m/s

🌍 Data Sources

GLiM – Global Lithological Map

• Citation: Hartmann, J. & Moosdorf, N. (2012). The new global lithological map database GLiM: A representation of rock properties at the Earth surface. Geochemistry, Geophysics, Geosystems, 13. doi:10.1029/2012GC004370
• Dataset DOI: 10.1594/PANGAEA.788537
• License: Personal research use only — redistribution requires written permission from the Commission for the Geological Map of the World (CCGM).

GLHYMPS 2.0 – Global Hydrogeology Maps

• Citation: Gleeson, T. et al. (2014). Mapping permeability over the surface of the Earth. Geophysical Research Letters, 41(14), 4896–4900. doi:10.1002/2014GL059856
• Dataset: Huscroft, J. et al. GLHYMPS 2.0. doi:10.5683/SP2/TTJNIU
• License: Open Database License (ODbL) — redistribution with attribution permitted.

📋 Requirements

• Python ≥ 3.9
• geopandas ≥ 0.13
• shapely ≥ 2.0
• numpy ≥ 1.24
• pyproj ≥ 3.6
• huggingface_hub ≥ 0.20

📖 Citation

If you use this package in your research, please cite:

@software{galib2025pygeoglim,
  author = {Galib, Mohammad and Merwade, Venkatesh},
  title  = {pygeoglim: A Python package for extracting geological attributes from GLiM and GLHYMPS datasets},
  url    = {https://github.com/galib9690/pygeoglim},
  doi    = {10.5281/zenodo.17314746},
  year   = {2025}
}

Please also cite the original datasets (GLiM and GLHYMPS) as referenced in the Data Sources section.

🐛 Issues and Support

If you encounter any problems or have questions:

• Check the Issues page
• Create a new issue with a detailed description
• Include your Python version, package version, and error messages

🤝 License

Distributed under the MIT License. See LICENSE for more information.

👨‍💻 Author

Mohammad Galib
Purdue University

• 📧 Email: [[e-mail]]
• 🌐 GitHub: @galib9690
• 🏛️ Institution: Purdue University

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