DOI: 10.5281/zenodo.16736908
title: GEMS-GER: A Machine Learning Benchmark Dataset of Long-Term Groundwater Levels in Germany with Meteorological Forcings and Site-Specific Environmental Features
title: Groundwater Levels, Environment, Meteorology, Site Properties

description: 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 (at 5 cm depth)




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_single.csv – Single-well 1D-CNN models (trained individually)

model_performance_global_dynonly.csv – Global LSTM model (trained jointly on all wells)  using dynamic features

model_performance_global_dynstat.csv – Global LSTM model (trained jointly on all wells) using both dynamic and static features


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:





GEMS-GER_data/├── dynamic/                                  # 3,207 CSV files containing time series per monitoring well (MW_{ID}.csv)│   ├── MW_1.csv│   ├── MW_2.csv│   └── ...│├── static/                                   # Static site-specific descriptors│   └── static_features.csv                   # (e.g., geology, land use, climate statistics)│├── model_performance/                        # Model performance metrics (median across 10 runs)│   ├── model_performance_single.csv          # Single-well CNN models│   ├── model_performance_global_dynonly.csv  # Global LSTM (dynamic inputs only)│   └── model_performance_global_dynstat.csv  # Global LSTM (dynamic + static inputs)

GEMS-GER_figures/├── DYN_Feat_MW_1.pdf                         # Input time series visualization per well├── DYN_Feat_MW_2.pdf└── ...



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.