GEMS-GER: A Machine Learning Benchmark Dataset of Long-Term Groundwater Levels in Germany with Meteorological Forcings and Site-Specific Environmental Features

This repository provides the dataset accompanying the GEMS-GER (Groundwater Levels, Environment, Meteorology, Site Properties – Germany) benchmark for machine learning-based groundwater modeling. The dataset includes long-term groundwater level time series, meteorological and hydrological forcing data, site-specific environmental properties, and benchmark model evaluation results. All data originate from official public sources and have been harmonized across the 16 German federal states (Bundesländer).

Contents of this repository:

• Groundwater level time series (GEMS-GER_data/dynamic/MW_*.csv):

  Weekly aggregated groundwater levels (GWL) from 3,207 monitoring wells (1991–2022), including:

  • Daily temperature (mean, min, max)

  • Precipitation and humidity (HYRAS/DWD)

  • Real, potential, and reference evapotranspiration

  • Soil moisture and soil temperature (5 m)

  • Snow water equivalent, snowmelt, and runoff (ERA5-Land)

  • GWL_flag indicating observed vs. imputed values

  Site-specific static descriptors (GEMS-GER_data/static/static_features.csv):

  • Hydrogeology and soil type

  • Land use and climate classification

  • Elevation and derived topographic parameters (e.g. slope, TWI)

  Benchmark model performance (GEMS-GER_data/model_performance/)

  • Median NSE, RMSE, R², and Bias across 10 runs for each of the 3,207 wells:

  • model_performance_singlewell_model.csv – Single-well models (trained individually)
  • model_performance_global_model.csv – Global model (trained jointly on all wells)

  Pre-generated time series plots (GEMS-GER_data_figures/DYN_Feat_MW_*.pdf):

  • Visualizations of groundwater levels and selected forcing variables for all wells

  • Provided separately to reduce the size of the main dataset download

Directory structure:

The dataset is intended for research and benchmarking in hydrogeology, data-driven groundwater modeling, and environmental machine learning. It forms the basis of the GEMS-GER benchmark, as described in the associated preprint. All data originate from public sources and have been harmonized across administrative and institutional boundaries to enable consistent large-scale analysis.

All associated code, documentation, and update announcements are maintained in the project's GitHub repository:  
https://github.com/KITHydrogeology/GEMS-GER ensuring transparency, traceability, and reproducibility.