Lorenzo Tamellini

Urban hydrological models require robust calibration strategies to accurately simulate rainfall–runoff processes, yet parameter sensitivity and the choice of objective functions remain key challenges. This study applied the SWMM nonlinear reservoir approach to an urban catchment in Pavia, Italy, using high-resolution rainfall–runoff data from multiple events. Sensitivity analysis showed that surface storage and hydraulic resistance dominated runoff response, while infiltration had limited influence. Calibration was performed with a genetic algorithm, testing six objective functions. The results indicated that the Index of Agreement and NSE-sqrt provided the most consistent performance across calibration and validation, supporting their use in urban drainage modeling.

This study is a first step towards using data-informed differential models to predict the dynamics of cancer-on-chip experiments. We consider a conceptualized one-dimensional device, containing a cancer and a population of white blood cells. The interaction between cancer and cells is described by a chemotaxis model inspired by Keller-Segel-type equations, solved by a Hybridizable Discontinuous Galerkin method. Our goal is using (synthetic) data to tune the parameters of the model and assessing the uncertainty on the predictions of the dynamics due to the uncertainty on the parameters remaining after the tuning procedure. To this end, we first perform a sensitivity analysis using both Sobol and Morris indices to assess how parameters impacts model predictions, and fix the value of parameters with negligible impact. Subsequently, we conduct an inverse uncertainty quantification analysis by Bayesian techniques to compute a data-informed probability distribution of the remaining parameters. Finally, we carry out a forward uncertainty quantification analysis to compute the impact of the residual parametric uncertainties on the quantities of interest of the model. The whole procedure is sped up by sparse-grids surrogate models approximating the mapping of the uncertain parameters to the quantities of interest.

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.

• A variably saturated coupled poroelastic model is developed to simulate soil deformation caused by groundwater depletion within a phreatic aquifer system at a basin scale. • A sparse grid-based Bayesian framework is proposed to infer hydraulic conductivity and compressbility by integrating the poroelastic model with observed data. • Utilization of InSAR-derived displacement datasets significantly reduces the uncertainty associated with aquifer properties, facilitating a comprehensive understanding of hydrogeological processes in an over-exploited aquifer system in Spain.