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Predicting groundwater storage from seasonal managed aquifer recharge: insights from machine learning and explainable AI techniques.

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Title: Predicting groundwater storage from seasonal managed aquifer recharge: insights from machine learning and explainable AI techniques.
Authors: Fernandes, Valdrich J.1 (AUTHOR) valdrich.fernandes@wur.nl, de Louw, Perry G. B.1,2 (AUTHOR), Ritsema, Coen J.1 (AUTHOR), Bartholomeus, Ruud P.1,3 (AUTHOR)
Source: Environmental Earth Sciences. Mar2026, Vol. 85 Issue 5, p1-20. 20p.
Subjects: Groundwater recharge, Machine learning, Ensemble learning, Artificial intelligence, Artificial neural networks, Water management, Aquifer storage recovery
Geographic Terms: Netherlands
Abstract: Managed Aquifer Recharge (MAR) is widely used to enhance groundwater storage and support sustainable water use. To support site selection and planning, Machine Learning (ML) models are increasingly used as computationally efficient surrogates for traditional numerical models. While ML has shown promise for steady-state simulations, capturing transient responses remains a challenge, yet these are essential for understanding how recharged water is retained through dry periods. In this study, we use ML to model transient MAR effects by decomposing the groundwater storage time series after recharge ceases into two components: the MAR-response and a decay coefficient, assuming exponential storage decline. This simplified representation captures long-term storage dynamics, with U-Net and XGBoost accurately predict these components (R2 > 0.82) for the Baakse Beek catchment in the sandy, drought-sensitive soils of the Netherlands. The trained models are computationally efficient, making large-scale scenario testing and optimization feasible. Explainable AI techniques, specifically SHAP values, were used to identify key site management decisions and surface water properties that control MAR effectiveness. These findings illustrate the potential of explainable AI and ML surrogate models to enhance the planning and optimization of MAR. [ABSTRACT FROM AUTHOR]
Copyright of Environmental Earth Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 192480427
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Managed Aquifer Recharge (MAR) is widely used to enhance groundwater storage and support sustainable water use. To support site selection and planning, Machine Learning (ML) models are increasingly used as computationally efficient surrogates for traditional numerical models. While ML has shown promise for steady-state simulations, capturing transient responses remains a challenge, yet these are essential for understanding how recharged water is retained through dry periods. In this study, we use ML to model transient MAR effects by decomposing the groundwater storage time series after recharge ceases into two components: the MAR-response and a decay coefficient, assuming exponential storage decline. This simplified representation captures long-term storage dynamics, with U-Net and XGBoost accurately predict these components (R2 > 0.82) for the Baakse Beek catchment in the sandy, drought-sensitive soils of the Netherlands. The trained models are computationally efficient, making large-scale scenario testing and optimization feasible. Explainable AI techniques, specifically SHAP values, were used to identify key site management decisions and surface water properties that control MAR effectiveness. These findings illustrate the potential of explainable AI and ML surrogate models to enhance the planning and optimization of MAR. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Environmental Earth Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: Aquifer storage recovery
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      – SubjectFull: Netherlands
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      – TitleFull: Predicting groundwater storage from seasonal managed aquifer recharge: insights from machine learning and explainable AI techniques.
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              Text: Mar2026
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