GEMS-GER: a machine learning benchmark dataset of long-term groundwater levels in Germany with meteorological forcings and site-specific environmental features

Ohmer, Marc; Liesch, Tanja; Habbel, Bastian; Heudorfer, Benedikt; Gomez, Mariana; Clos, Patrick; Nölscher, Maximilian; Broda, Stefan

doi:10.5194/essd-18-77-2026

Articles | Volume 18, issue 1

https://doi.org/10.5194/essd-18-77-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-18-77-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 1

Data description paper

|

05 Jan 2026

Data description paper |

| 05 Jan 2026

GEMS-GER: a machine learning benchmark dataset of long-term groundwater levels in Germany with meteorological forcings and site-specific environmental features

Marc Ohmer, Tanja Liesch, Bastian Habbel, Benedikt Heudorfer, Mariana Gomez, Patrick Clos, Maximilian Nölscher, and Stefan Broda

Data sets

GEMS-GER: A Machine Learning Benchmark Dataset of Long-Term Groundwater Levels in Germany with Meteorological Forcings and Site-Specific Environmental Features Marc Ohmer et al. https://doi.org/10.5281/zenodo.15530171

Model code and software

GEMS-GER code and benchmark models for the publicly available groundwater monitoring dataset of Germany. Marc Ohmer and Tanja Liesch https://doi.org/10.5281/zenodo.17855212

Short summary

We present a public dataset of weekly groundwater levels from more than 3000 wells across Germany, spanning 32 years. It combines weather data and site-specific environmental information to support forecasting groundwater changes. Three benchmark models of varying complexity show how data and modeling approaches influence predictions. This resource promotes open, reproducible research and helps guide future water management decisions.