Low‐Rank Geostatistical Inversion for Spatiotemporal Heterogeneity of Aquitard Hydraulic Parameters

Abstract Many hydrogeological processes are intricately linked to both the spatial heterogeneity and temporal dynamics of the involved parameters. The variability in hydrogeological parameters across both space and time domains is defined as the spatiotemporal heterogeneity. This study introduces a partitioning geostatistical inversion approach to characterize the spatiotemporal heterogeneity, and further applies this methodology to the long‐term multi‐extensometer data from the second aquitard beneath Qingliang Primary School, Changzhou City, China. The Principal Component Geostatistical Approach (PCGA) is used to capture the temporal variability in the hydraulic conductivity ( K ) and specific storage ( S s ) across the three sub‐layers of the aquitard of interest. Comparative analysis reveals that models accounting for spatiotemporal heterogeneity achieve significantly higher accuracy than those considering only spatial heterogeneity. PCGA effectively captures the temporal variations in aquitard K and S s , with sub‐layer 3 dominating the temporal dynamics. Additionally, neglecting the initial delayed drainage within the aquitard leads to a notable overestimation of K . Moreover, utilizing a stage‐wise distribution of parameters as an initial guess significantly enhances inversion accuracy. The proposed methodology not only deepens the understanding of aquitard deformation but also holds broad potential for advancing the characterization of spatiotemporal heterogeneity in 4D hydrogeological modeling.