Articles | Volume 29, issue 15
https://doi.org/10.5194/hess-29-3405-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-3405-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Aug 2025
Research article |
| 01 Aug 2025
| 01 Aug 2025
Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany
Federal Institute for Geosciences and Natural Resources, Berlin, Germany
Alexander Schulz
Berliner Hochschule für Technik, Berlin, Germany
Maria Wetzel
Federal Institute for Geosciences and Natural Resources, Berlin, Germany
Maximilian Nölscher
Federal Institute for Geosciences and Natural Resources, Berlin, Germany
Teodor Chiaburu
Berliner Hochschule für Technik, Berlin, Germany
Felix Biessmann
Berliner Hochschule für Technik, Berlin, Germany
Stefan Broda
Federal Institute for Geosciences and Natural Resources, Berlin, Germany
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With the growing use of machine learning for groundwater level (GWL) prediction, proper performance estimation is crucial. This study compares three validation strategies—blocked cross-validation (bl-CV), repeated out-of-sample (repOOS), and out-of-sample (OOS)—for 1D-CNN models using meteorological inputs. Results show that bl-CV offers the most reliable performance estimates, while OOS is the most uncertain, highlighting the need for careful method selection.
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We present a public dataset of weekly groundwater levels from more than 3,000 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.
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We show the results of the 2022 Groundwater Time Series Modelling Challenge; 15 teams applied data-driven models to simulate hydraulic heads, and three model groups were identified: lumped, machine learning, and deep learning. For all wells, reasonable performance was obtained by at least one team from each group. There was not one team that performed best for all wells. In conclusion, the challenge was a successful initiative to compare different models and learn from each other.
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To understand the impact of external factors on groundwater level modelling using a 1-D convolutional neural network (CNN) model, we train, validate, and tune individual CNN models for 505 wells distributed across Lower Saxony, Germany. We then evaluate the performance of these models against available geospatial and time series features. This study provides new insights into the relationship between these factors and the accuracy of groundwater modelling.
Benedikt Heudorfer, Tanja Liesch, and Stefan Broda
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We build a neural network to predict groundwater levels from monitoring wells. We predict all wells at the same time, by learning the differences between wells with static features, making it an entity-aware global model. This works, but we also test different static features and find that the model does not use them to learn exactly how the wells are different, but only to uniquely identify them. As this model class is not actually entity aware, we suggest further steps to make it so.
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Barite formation is an important factor for many use cases of the geological subsurface because it may change the rock.
In this modelling study, the replacement reaction of celestite to barite is investigated.
The steps that were identified to play a role are celestite dissolution followed by two-step precipitation of barite: spontaneous formation of small crystals and their subsequent growth.
Explicitly including the processes improve the usability of the models for quantified prediction.
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Short summary
Accurate groundwater level predictions are crucial for sustainable management. This study applies two machine learning models – Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS) and the Temporal Fusion Transformer (TFT) – to forecast seasonal groundwater levels for 5288 wells across Germany. N-HiTS outperformed TFT, with both models performing well in diverse hydrogeological settings, particularly in lowlands with distinct seasonal dynamics.
Accurate groundwater level predictions are crucial for sustainable management. This study...
