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Groundwater overexploitation and loading of buildings have been the main factors triggering land subsidence along the west coast of Bohai Bay, China, since the 2000s. Uneven subsidence has been causing damage to buildings and civil facilities, loss of elevation, increasing the risk of flood and seawater intrusion, and threatening the safety of people's lives and property. This paper analyzed the spatial and temporal features of land subsidence along the coastal area from 2003 to 2010 and from 2015 to 2020, respectively. The relations between the initiating factors and land subsidence were explored. Then, the simulation model of land subsidence was constructed through a deep learning method. During the process, multiple data were collected, including land satellite (Landsat), environmental satellite advanced synthetic aperture radar (ENVISAT ASAR) and Sentinel-1 images, leveling data, lithological data, and groundwater level data. The area occupied by buildings and vertical displacement were extracted by using supervised classification, small baseline subset (SBAS), and persistent scatterer interferometry (PSI) technologies. The gated recurrent unit (GRU) neural network was adopted to simulate the evolution of land subsidence. Results showed that the maximum annual vertical displacement rate decreased from −94 mm/yr during 2003–2010 to −87 mm/yr during 2015–2020. The correlation efficiency between the groundwater level of the third confined aquifer group and land subsidence was larger than the area occupied by buildings and the compressible layer thickness with subsidence. The constructed GRU neural network model can simulate subsidence from September 2019 to December 2019, with the overall RMSE and MAE being 3.16 mm and 2.19 mm, respectively. This work can facilitate an understanding of the evolution and prevention of land subsidence along the west coast of Bohai Bay, which will provide information for policy decisions and flood-fighting plans of the worldwide coastal cities. [ABSTRACT FROM AUTHOR]

The hydrogeologic systems of alluvial fan are characterized by a heterogeneous distribution of various lithological units/facies. The structure (integral scale and volumetric proportion) of the hydrofacies distribution and the values of the hydrogeomechanical parameters of each facies can play a major role on the system response to groundwater withdrawal in term of land subsidence. We propose a novel approach where stochastically simulated hydrofacies distributions are coupled with 3-D finite element groundwater flow and geomechanical simulations to characterize land subsidence and horizontal movements due to groundwater withdrawal under a statistical framework. The integrated approach is applied on the Chaobai alluvial plain, China, an area of about 1,100 km2 where the main wellfields supplying water to Beijing are located. Groundwater pumping from the 1960s to now caused a land subsidence larger than 1 m and the present subsidence rate peaks to 70 mm/year. A Monte Carlo simulation with 100 hydrofacies generations is used. The model outcomes highlight how the heterogeneous structure of the hydrofacies fan reflects into the computed displacement fields. The standard deviation associated to the mean displacement field amounts up to 1/10 of the displacement components. The larger coefficient of variation (CV up to 0.5) is associated to the zone characterized by longer integral scale and with localized groundwater withdrawals. The computed variability of the subsidence rate, in the range of 1 to 3 mm/year, reflecting the intrinsic heterogeneous nature of an alluvial fan, corresponds to the short-distance variability of land subsidence measured by persistent scatterer interferometry. [ABSTRACT FROM AUTHOR]