Controlling groundwater table decline could mitigate land subsidence and induced environmental hazards in over-explored areas. Nevertheless, this becomes a challenge in the multi-layered porous system as (in)elastic deformation simultaneously occurs due to vast spatiotemporal variability in the groundwater table. In this study, SBAS-InSAR was used to estimate annual land deformation during 2017–2022 in a specific region of North China Plain, in which aquifers are composed of many layers of fine-grained compressible sediments and the groundwater table has experienced a prolonged decline. The random forest (RF) was applied to establish the nonlinear relationship between accumulated deformation and its potential driving factors, including the depth to the groundwater table (GWD) and its change rate, and the compressible sediment thickness. Results show that the marked subsidence and uplift co-exist in the region even though the groundwater table has risen widely since the South–North Water Diversion Project. The land subsidence is attributed to inelastic compaction of the thick compressible deposits in depression cone centers, where the GWD is over 40 m and 90 m in the shallow and deep aquifers, respectively. In contrast, the marked uplift is primarily attributed to fast rising of the groundwater table (e.g., −2.44 m/a). The RF predictions suggest that, to control the subsidence, the GWD should be less than 20 and 70 m in the shallow and deep aquifers, respectively, and the rising rate of the GWD should increase to 2–5 times of current rates in the depression cones. To mitigate the marked uplift, the rising rate of the GWD should reduce to 1/2–1/5 of the current rates in the shallow aquifers. The uneven deformations of sediments in the depression cone centers and uplift in their boundaries may exacerbate geohazards. Therefore, it is vital to implement appropriate governance of groundwater recovery in the multi-layered porous system.

Earlier, hydrological simulation calibration and validation relied on flow observations at hydrological stations, but multisource observations changed the basin hydrological simulation from single-flow validation to multivariate validation, including evaporation, soil water, and runoff. This study used the Soil and Water Assessment Tool (SWAT) distributed hydrological model to simulate and investigate hydrological processes in the Jinghe River Basin in China. After a single-station, single-variable calibration using flow observation data at the Zhangjiashan Hydrological Station, multisource data were used to validate actual evaporation, soil water, and runoff. Using the flow station data from Zhangjiashan station for parameter calibration and validation, the simulated values of R², NSE, and KGE were all above 0.64, the PBIAS was within 20%, and the values of all the metrics in the calibration period were better than those in the validation period. The results show that the model performed satisfactorily, proving its regional applicability. Qingyang, Yangjiaping, and Zhangjiazhan stations had R², NSE, and KGE values above 0.57 and PBIAS within 25% during regional calibration, considering spatial variability. Additionally, simulation accuracy downstream increased. R², NSE, and KGE were above 0.50, and PBIAS was within 25% throughout validation, except for Qingyang, where the validation period was better than the calibration period. The Zhangjiashan station monthly runoff simulation improved after regional calibration. Runoff validation performed highest in the multivariate validation of evaporation–soil water–runoff, followed by actual evaporation and soil water content in China. The evaluation results for each hydrological variable improved after additional manual calibration. Multivariate verification based on multisource data improved the hydrological simulation at the basin scale.