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The demand for water resources during urbanization forces the continuous exploitation of groundwater, resulting in dramatic piezometric drawdown and inducing regional land subsidence (LS). This has greatly threatened sustainable development in the long run. LS modeling helps understanding the factors responsible for the ongoing loss of land elevation and hence enhances the development of prevention strategies. Data-driven LS models perform well with fewer variables and faster convergence than physically-based hydrogeological models. However, the former models often cannot simultaneously reflect the temporal nonlinearity and spatial correlation (SC) characteristics of LS under complex variables. We proposed a LS spatiotemporal model which considers both nonlinear and spatial correlations between LS and groundwater level change of exploited aquifers. It is based on deep learning method and LS time series detected by permanent scatterer-interferometric synthetic aperture radar (PS-InSAR). The LS time series and hydrogeological properties are constructed as a spatiotemporal dataset for model training. The spatiotemporal LS model, geographically weighted long short-term memory (GW-LSTM), is constructed by integrating SC with LSTM. This latter is a deep recurrent neural network approach incorporating sequential data. The model is validated by a case study in the Beijing plain. The results show that the accuracy of the proposed model can be greatly improved considering the spatial correlation between LS and influencing factors. Furthermore, the comparison between the LSTM and GW-LSTM models reveals that groundwater level variation is not a unique causation of LS in the study area. The developed model deals with the spatiotemporal characteristics of LS under multiple variables and can be used to predict LS under different scenarios of groundwater level variations for the purpose of monitoring and providing evidence to support the prevention of future LS., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Depletion of groundwater aquifers along with all of the associated quality and quantity problems which affect profitability of direct agricultural and urban users and linked groundwater-ecosystems have been recognized globally. During recent years, attention has been devoted to land subsidence-the loss of land elevation that occurs in areas with certain geological characteristics associated with aquifer exploitation. Despite the large socioeconomic impacts of land subsidence most of these effects are still not well analyzed and not properly recognized and quantified globally. In this paper we developed a land subsidence impact extent (LSIE) index that is based on 10 land subsidence attributes, and applied it to 113 sites located around the world with reported land subsidence effects. We used statistical means to map physical, human, and policy variables to the regions affected by land subsidence and quantified their impact on the index. Our main findings suggest that LSIE increases between 0.1 and 6.5% by changes in natural processes, regulatory policy interventions, and groundwater usage, while holding all other variables unchanged. Effectiveness of regulatory policy interventions varies depending on the lithology of the aquifer system, in particular its stiffness. Our findings suggest also that developing countries are more prone to land subsidence due to lower performance of their existing water governance and institutions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The original morphology and hydrogeology of many low-lying coastlands worldwide have been significantly modified over the last century through river diversion, embankment built-up, and large-scale land reclamation projects. This led to a progressive shifting of the groundwater-surficial water exchanges from naturally to anthropogenically driven. In this human-influenced hydrologic landscape, the saltwater contamination usually jeopardizes the soil productivity. In the coastland south of Venice (Italy), several well log measurements, chemical and isotope analyses have been performed over the last decade to characterize the occurrence of the salt contamination. The processing of this huge dataset highlights a permanent variously-shaped saline contamination up to 20km inland, with different conditions in relation with the various geomorphological features of the area. The results point out the important role of the land reclamation in shaping the present-day salt contamination and reveal the contribution of precipitation, river discharge, lagoon and sea water to the shallow groundwater in the various coastal sectors. Moreover, an original vulnerability map to salt contamination in relation to the farmland productivity has been developed taking into account the electrical conductivity of the upper aquifer in the worst condition, the ground elevation, and the distance from salt and fresh surface water sources. Finally, the study allows highlighting the limit of traditional investigations in monitoring saltwater contamination at the regional scale in managed Holocene coastal environments. Possible improvements are outlined., (Copyright © 2015 Elsevier B.V. All rights reserved.)