Raw data for "Low-rank geostatistical inverse modeling of spatiotemporal heterogeneity in aquitard hydraulic parameters"

Many hydrogeological processes are intricately linked to both 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 (Ss) across the three sub-layers of the aquitard of interest. Comparative analysis reveals that models accounting for spatiotemporal heterogeneity leads to significantly improved accuracy compared to those considering only spatial heterogeneity. PCGA effectively captures the temporal variability in aquitard K and Ss, with sub-layer 3 playing a dominant role in the inverted temporal dynamics. Additionally, neglecting the initially delayed drainage within the aquitard leads to a notable underestimation 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.