Integrating SBAS-InSAR and Random Forest for Identifying and Controlling Land Subsidence and Uplift in a Multi-Layered Porous System of North China Plain

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Abstract

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.

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Wang, Y., Chen, X., Wang, Z., Gao, M., & Wang, L. (2024). Integrating SBAS-InSAR and Random Forest for Identifying and Controlling Land Subsidence and Uplift in a Multi-Layered Porous System of North China Plain. Remote Sensing, 16(5). https://doi.org/10.3390/rs16050830

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