Characterizing Aquifer Properties Through a Sparse‐Grids‐Based Bayesian Framework and InSAR Measurements: A Basin‐Scale Application to Alto Guadalentín, Spain
Abstract Aquifer characterization is essential for predicting aquifer responses and ensuring sustainable groundwater management. In this study we develop a sparse‐grids‐based Bayesian framework to infer the hydraulic conductivity and the soil compressibility of over‐exploited aquifer systems using I...
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR038543 |
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| author | Yueting Li Claudia Zoccarato Chiara Piazzola Lorenzo Tamellini Guadalupe Bru Carolina Guardiola‐Albert Pietro Teatini |
| author_facet | Yueting Li Claudia Zoccarato Chiara Piazzola Lorenzo Tamellini Guadalupe Bru Carolina Guardiola‐Albert Pietro Teatini |
| author_sort | Yueting Li |
| collection | DOAJ |
| description | Abstract Aquifer characterization is essential for predicting aquifer responses and ensuring sustainable groundwater management. In this study we develop a sparse‐grids‐based Bayesian framework to infer the hydraulic conductivity and the soil compressibility of over‐exploited aquifer systems using Interferometric Synthetic Aperture Radar (InSAR) ground displacement data sets and piezometric records. The framework integrates a three‐dimensional (3D) coupled variably saturated poromechanical model, accounting for the complex interplay between groundwater depletion and soil deformation through the explicit quantification of the porosity change. The Bayesian inversion approach enables a probabilistic characterization of parameters in the form of a posterior distribution. However, exploring this posterior using Markov chain Monte Carlo (MCMC) methods is computationally prohibitive due to the substantial cost of solving the nonlinear poromechanical forward problem. To overcome this issue, we propose the use of sparse‐grid surrogate models to approximate the forward solutions. The methodology is applied to the Alto Guadalentín basin, Spain, where long‐term aquifer exploitation has led to a lowering of the water table larger than 100 m causing impressive land subsidence, with rates up to 15 cm/yr as evidenced by InSAR. The results demonstrate that integrating InSAR data significantly enhances the characterization of the aquifer properties, with the resulting numerical simulations aligning well with available observations. |
| format | Article |
| id | doaj-art-53505e24dbfb432ea7f2e21531c546d6 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-53505e24dbfb432ea7f2e21531c546d62025-08-26T12:02:53ZengWileyWater Resources Research0043-13971944-79732025-08-01618n/an/a10.1029/2024WR038543Characterizing Aquifer Properties Through a Sparse‐Grids‐Based Bayesian Framework and InSAR Measurements: A Basin‐Scale Application to Alto Guadalentín, SpainYueting Li0Claudia Zoccarato1Chiara Piazzola2Lorenzo Tamellini3Guadalupe Bru4Carolina Guardiola‐Albert5Pietro Teatini6Department of Civil Environmental and Architectural Engineering University of Padova Padova ItalyDepartment of Civil Environmental and Architectural Engineering University of Padova Padova ItalyDepartment of Mathematics Technical University of Munich München GermanyConsiglio Nazionale delle Ricerche Istituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes” (CNR‐IMATI) Pavia ItalyGeohazards InSAR Laboratory and Modeling Group (InSARlab) Geological and Mining Institute of Spain (IGME) CSIC Madrid SpainGeohazards InSAR Laboratory and Modeling Group (InSARlab) Geological and Mining Institute of Spain (IGME) CSIC Madrid SpainDepartment of Civil Environmental and Architectural Engineering University of Padova Padova ItalyAbstract Aquifer characterization is essential for predicting aquifer responses and ensuring sustainable groundwater management. In this study we develop a sparse‐grids‐based Bayesian framework to infer the hydraulic conductivity and the soil compressibility of over‐exploited aquifer systems using Interferometric Synthetic Aperture Radar (InSAR) ground displacement data sets and piezometric records. The framework integrates a three‐dimensional (3D) coupled variably saturated poromechanical model, accounting for the complex interplay between groundwater depletion and soil deformation through the explicit quantification of the porosity change. The Bayesian inversion approach enables a probabilistic characterization of parameters in the form of a posterior distribution. However, exploring this posterior using Markov chain Monte Carlo (MCMC) methods is computationally prohibitive due to the substantial cost of solving the nonlinear poromechanical forward problem. To overcome this issue, we propose the use of sparse‐grid surrogate models to approximate the forward solutions. The methodology is applied to the Alto Guadalentín basin, Spain, where long‐term aquifer exploitation has led to a lowering of the water table larger than 100 m causing impressive land subsidence, with rates up to 15 cm/yr as evidenced by InSAR. The results demonstrate that integrating InSAR data significantly enhances the characterization of the aquifer properties, with the resulting numerical simulations aligning well with available observations.https://doi.org/10.1029/2024WR038543poromechanical modelBayesian inversionsparse grid collocationInSAR |
| spellingShingle | Yueting Li Claudia Zoccarato Chiara Piazzola Lorenzo Tamellini Guadalupe Bru Carolina Guardiola‐Albert Pietro Teatini Characterizing Aquifer Properties Through a Sparse‐Grids‐Based Bayesian Framework and InSAR Measurements: A Basin‐Scale Application to Alto Guadalentín, Spain Water Resources Research poromechanical model Bayesian inversion sparse grid collocation InSAR |
| title | Characterizing Aquifer Properties Through a Sparse‐Grids‐Based Bayesian Framework and InSAR Measurements: A Basin‐Scale Application to Alto Guadalentín, Spain |
| title_full | Characterizing Aquifer Properties Through a Sparse‐Grids‐Based Bayesian Framework and InSAR Measurements: A Basin‐Scale Application to Alto Guadalentín, Spain |
| title_fullStr | Characterizing Aquifer Properties Through a Sparse‐Grids‐Based Bayesian Framework and InSAR Measurements: A Basin‐Scale Application to Alto Guadalentín, Spain |
| title_full_unstemmed | Characterizing Aquifer Properties Through a Sparse‐Grids‐Based Bayesian Framework and InSAR Measurements: A Basin‐Scale Application to Alto Guadalentín, Spain |
| title_short | Characterizing Aquifer Properties Through a Sparse‐Grids‐Based Bayesian Framework and InSAR Measurements: A Basin‐Scale Application to Alto Guadalentín, Spain |
| title_sort | characterizing aquifer properties through a sparse grids based bayesian framework and insar measurements a basin scale application to alto guadalentin spain |
| topic | poromechanical model Bayesian inversion sparse grid collocation InSAR |
| url | https://doi.org/10.1029/2024WR038543 |
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