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 (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.本仓库提供配套于GEMS-GER（德国地下水水位、环境、气象与场地属性，Groundwater Levels, Environment, Meteorology, Site Properties – Germany）机器学习地下水建模基准的数据集。该数据集包含长期地下水水位时间序列、气象与水文强迫数据、场地专属环境属性，以及基准模型评估结果。所有数据均源自官方公开数据源，并已针对德国16个联邦州（Bundesländer）完成统一标准化处理。 仓库内容如下： 地下水水位时间序列（存储路径：GEMS-GER_data/dynamic/MW_*.csv）： 包含1991年至2022年间3207口监测井的周聚合地下水水位（GWL）数据，具体包括： - 日气温数据（均值、最低值、最高值） - 降水与湿度数据（数据源：HYRAS/DWD） - 实际、潜在与参考蒸散发数据 - 5厘米深度的土壤湿度与土壤温度数据 - 雪水当量、融雪量与径流数据（数据源：ERA5-Land） - GWL_flag标记，用于区分观测值与插补值 场地专属静态描述符（存储路径：GEMS-GER_data/static/static_features.csv）： - 水文地质与土壤类型 - 土地利用与气候分类 - 高程及衍生地形参数（如坡度、地形湿度指数TWI） 基准模型性能数据（存储路径：GEMS-GER_data/model_performance/）： 涵盖3207口监测井各10次训练的中位数纳什效率系数（NSE）、均方根误差（RMSE）、决定系数（R²）与偏差（Bias）： - model_performance_single.csv：单井一维卷积神经网络（1D-CNN）模型（单独训练）的性能数据 - model_performance_global_dynonly.csv：仅使用动态特征的全局长短期记忆网络（LSTM）模型（在所有监测井联合训练）的性能数据 - model_performance_global_dynstat.csv：同时使用动态与静态特征的全局LSTM模型（在所有监测井联合训练）的性能数据 预生成的时间序列可视化图（存储路径：GEMS-GER_data_figures/DYN_Feat_MW_*.pdf）： - 所有监测井的地下水水位与选定强迫变量的可视化图像 该部分数据单独提供，以减小主数据集的下载体积。 目录结构： GEMS-GER_data/ ├── dynamic/ # 包含3207口监测井的单井时间序列CSV文件（命名格式为MW_{ID}.csv） │ ├── MW_1.csv │ ├── MW_2.csv │ └── ... ├── static/ # 场地专属静态描述符目录 │ └── static_features.csv # （例如：地质、土地利用、气候统计信息） ├── model_performance/ # 模型性能指标目录（包含10次训练的中位数结果） │ ├── model_performance_single.csv # 单井1D-CNN模型性能数据 │ ├── model_performance_global_dynonly.csv # 仅使用动态输入的全局LSTM模型性能数据 │ └── model_performance_global_dynstat.csv # 同时使用动态与静态输入的全局LSTM模型性能数据 GEMS-GER_figures/ ├── DYN_Feat_MW_1.pdf # 单井输入时间序列可视化图 ├── DYN_Feat_MW_2.pdf └── ... 本数据集适用于水文地质、数据驱动地下水建模以及环境机器学习领域的研究与基准测试。正如相关预印本中所述，本数据集是GEMS-GER基准的核心支撑数据。所有数据均源自公开数据源，并已跨越行政与机构边界完成统一标准化处理，以支持大规模一致性分析。 所有相关代码、文档与更新公告均维护于本项目的GitHub仓库：https://github.com/KITHydrogeology/GEMS-GER，以确保研究的透明度、可追溯性与可复现性。