Characterizing Aquifer Properties Through a Sparse‐Grids‐Based Bayesian Framework and InSAR Measurements: A Basin‐Scale Application to Alto Guadalentín, Spain

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.