One-dimensional simulation of land subsidence in vertically-heterogeneous highly compressible aquitards coupled with data assimilation via ensemble Kalman filter

This study presents a methodology for calibrating a nonlinear groundwater flow and consolidation model in highly compressible, heterogeneous aquitards, focusing on vertical heterogeneity. Inspired by conditions in the Mexico basin, where the nature of the aquitard sediments, along with pore pressure monitoring through piezometers, plays a significant role. The model combines a nonlinear one-dimensional groundwater flow algorithm with an Ensemble Kalman Filter (EnKF) for data assimilation, correcting hydraulic head ( h ) and vertical hydraulic conductivity ( K ) distributions. Four reference cases were tested, and three data assimilation strategies were explored: (a) only h measurements, (b) only K measurements, and (c) both. Results show that all strategies provide satisfactory parameter estimations and settlement predictions, with the combined approach yielding the highest accuracy. While the method successfully simulates subsidence, its effectiveness diminishes if data assimilation only occurs in the initial simulation phase. This methodology has strong potential for predicting subsidence in real-world heterogeneous aquitards. • A new way to apply the EnKF to calibrate a 1D nonlinear groundwater flow model is proposed. • The approach allows assimilating measurements of h , K , or both, leading to accurate results. • The most accurate strategy is to assimilate both h and K data. • The methodology allows us to understand and predict land subsidence in heterogeneous aquitards. • The methodology shows strong potential for real-world applications, particularly in the Mexico basin.