Articles | Volume 26, issue 15
https://doi.org/10.5194/hess-26-4033-2022
© Author(s) 2022. 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-26-4033-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Aug 2022
Research article |
| 05 Aug 2022
| 05 Aug 2022
Spatiotemporal optimization of groundwater monitoring networks using data-driven sparse sensing methods
Institute of Applied Geosciences, Division of Hydrogeology, Karlsruhe Institute of Technology, Karlsruhe, Germany
Tanja Liesch
Institute of Applied Geosciences, Division of Hydrogeology, Karlsruhe Institute of Technology, Karlsruhe, Germany
Andreas Wunsch
Institute of Applied Geosciences, Division of Hydrogeology, Karlsruhe Institute of Technology, Karlsruhe, Germany
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Cited
18 citations as recorded by crossref.
- A generalized approach for predicting and mitigating total dissolved gas supersaturation at data-scarce dams S. Li et al. https://doi.org/10.1016/j.watres.2025.125282
- Balancing Results from AI-Based Geostatistics versus Fuzzy Inference by Game Theory Bargaining to Improve a Groundwater Monitoring Network M. Hashemi et al. https://doi.org/10.3390/make6030092
- Bridging data scarcity in groundwater quality studies: A systematic evaluation of statistical and deep learning-based generators C. Aju et al. https://doi.org/10.1016/j.pce.2026.104327
- Feasibility Screening of River Basin Management Plan Delivery Under War-Driven Uncertainty: The Ukrainian Tisza Sub-Basin (2025–2030) S. Lousada et al. https://doi.org/10.3390/w18101178
- Reconstruction of Sparse Stream Flow and Concentration Time‐Series Through Compressed Sensing K. Zhang et al. https://doi.org/10.1029/2022GL101177
- A novel groundwater monitoring network design framework for long-term and economical data monitoring S. Jena https://doi.org/10.1016/j.gsd.2024.101252
- Estimating Regional Groundwater Level by Combining Satellite, Model, and Large-Sample Observations Inputs Y. Cao et al. https://doi.org/10.3390/rs18101622
- Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing K. Zhang et al. https://doi.org/10.1029/2022WR034092
- A machine learning approach to site groundwater contamination monitoring wells V. Gómez-Escalonilla et al. https://doi.org/10.1007/s13201-024-02320-1
- A Remote Sensing-Based Groundwater Level Monitoring System Using Machine Learning X. Cheng et al. https://doi.org/10.3390/rs18142372
- Spatiotemporal prediction for groundwater heavy metal contamination using Soft-DTW-based clustering and graph neural network framework Y. He et al. https://doi.org/10.1016/j.watres.2025.125245
- Surrogate modeling for rapid estimation of spatially-resolved flood damage: Application to a coastal region S. Li et al. https://doi.org/10.1016/j.jhydrol.2025.134763
- Climate-hydrology-topography-anthropogenic factors jointly drive the evolution of vegetation coverage in semi-arid regions: A downscaling approach based on random forest and nonlinear residual correction J. Sun et al. https://doi.org/10.1016/j.eiar.2026.108457
- A digital ecohydraulic twin for riverine habitat prediction and optimization S. Li et al. https://doi.org/10.1016/j.jenvman.2025.128445
- Urban geological assessment for SDG 11 alignment: empirical evidence from 11 cities in Zhejiang, China L. Zhou et al. https://doi.org/10.1016/j.tust.2026.107799
- Agricultural and energy sectors dominate Irans water crisis: A GRACE-GLDAS quantification (2002_2023) X. Yuan et al. https://doi.org/10.13168/AGG.2025.0027
- NiMo 4.0 – Enabling advanced data analytics with AI for environmental governance in the water domain M. Budde et al. https://doi.org/10.1515/auto-2024-0034
- Data-driven insights into urban groundwater contamination: A framework for SDG 6 implementation in developing countries M. Hamza et al. https://doi.org/10.1016/j.clwas.2025.100351
18 citations as recorded by crossref.
- A generalized approach for predicting and mitigating total dissolved gas supersaturation at data-scarce dams S. Li et al. https://doi.org/10.1016/j.watres.2025.125282
- Balancing Results from AI-Based Geostatistics versus Fuzzy Inference by Game Theory Bargaining to Improve a Groundwater Monitoring Network M. Hashemi et al. https://doi.org/10.3390/make6030092
- Bridging data scarcity in groundwater quality studies: A systematic evaluation of statistical and deep learning-based generators C. Aju et al. https://doi.org/10.1016/j.pce.2026.104327
- Feasibility Screening of River Basin Management Plan Delivery Under War-Driven Uncertainty: The Ukrainian Tisza Sub-Basin (2025–2030) S. Lousada et al. https://doi.org/10.3390/w18101178
- Reconstruction of Sparse Stream Flow and Concentration Time‐Series Through Compressed Sensing K. Zhang et al. https://doi.org/10.1029/2022GL101177
- A novel groundwater monitoring network design framework for long-term and economical data monitoring S. Jena https://doi.org/10.1016/j.gsd.2024.101252
- Estimating Regional Groundwater Level by Combining Satellite, Model, and Large-Sample Observations Inputs Y. Cao et al. https://doi.org/10.3390/rs18101622
- Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing K. Zhang et al. https://doi.org/10.1029/2022WR034092
- A machine learning approach to site groundwater contamination monitoring wells V. Gómez-Escalonilla et al. https://doi.org/10.1007/s13201-024-02320-1
- A Remote Sensing-Based Groundwater Level Monitoring System Using Machine Learning X. Cheng et al. https://doi.org/10.3390/rs18142372
- Spatiotemporal prediction for groundwater heavy metal contamination using Soft-DTW-based clustering and graph neural network framework Y. He et al. https://doi.org/10.1016/j.watres.2025.125245
- Surrogate modeling for rapid estimation of spatially-resolved flood damage: Application to a coastal region S. Li et al. https://doi.org/10.1016/j.jhydrol.2025.134763
- Climate-hydrology-topography-anthropogenic factors jointly drive the evolution of vegetation coverage in semi-arid regions: A downscaling approach based on random forest and nonlinear residual correction J. Sun et al. https://doi.org/10.1016/j.eiar.2026.108457
- A digital ecohydraulic twin for riverine habitat prediction and optimization S. Li et al. https://doi.org/10.1016/j.jenvman.2025.128445
- Urban geological assessment for SDG 11 alignment: empirical evidence from 11 cities in Zhejiang, China L. Zhou et al. https://doi.org/10.1016/j.tust.2026.107799
- Agricultural and energy sectors dominate Irans water crisis: A GRACE-GLDAS quantification (2002_2023) X. Yuan et al. https://doi.org/10.13168/AGG.2025.0027
- NiMo 4.0 – Enabling advanced data analytics with AI for environmental governance in the water domain M. Budde et al. https://doi.org/10.1515/auto-2024-0034
- Data-driven insights into urban groundwater contamination: A framework for SDG 6 implementation in developing countries M. Hamza et al. https://doi.org/10.1016/j.clwas.2025.100351
Saved (final revised paper)
Latest update: 07 Aug 2026
Short summary
We present a data-driven approach to select optimal locations for groundwater monitoring wells. The applied approach can optimize the number of wells and their location for a network reduction (by ranking wells in order of their information content and reducing redundant) and extension (finding sites with great information gain) or both. It allows us to include a cost function to account for more/less suitable areas for new wells and can help to obtain maximum information content for a budget.
We present a data-driven approach to select optimal locations for groundwater monitoring wells....
