Hasan Md Fahim, Smith Ryan, Vajedian Sanaz, Pommerenke Rahel, Majumdar Sayantan
Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, 80523, USA.
Department of Geosciences and Geological and Petroleum Engineering, Missouri University of Science and Technology, Rolla, MO, 65409, USA.
Nat Commun. 2023 Oct 4;14(1):6180. doi: 10.1038/s41467-023-41933-z.
Groundwater overdraft gives rise to multiple adverse impacts including land subsidence and permanent groundwater storage loss. Existing methods are unable to characterize groundwater storage loss at the global scale with sufficient resolution to be relevant for local studies. Here we explore the interrelation between groundwater stress, aquifer depletion, and land subsidence using remote sensing and model-based datasets with a machine learning approach. The developed model predicts global land subsidence magnitude at high spatial resolution (~2 km), provides a first-order estimate of aquifer storage loss due to consolidation of ~17 km/year globally, and quantifies key drivers of subsidence. Roughly 73% of the mapped subsidence occurs over cropland and urban areas, highlighting the need for sustainable groundwater management practices over these areas. The results of this study aid in assessing the spatial extents of subsidence in known subsiding areas, and in locating unknown groundwater stressed regions.
地下水超采会引发多种不利影响，包括地面沉降和地下水资源永久性损失。现有方法无法在全球尺度上以足够的分辨率来表征地下水资源损失，从而难以用于当地研究。在此，我们采用机器学习方法，利用遥感和基于模型的数据集，探索地下水压力、含水层枯竭与地面沉降之间的相互关系。所开发的模型能够以高空间分辨率（约2公里）预测全球地面沉降幅度，对全球范围内因压实作用导致的每年约17立方千米的含水层储量损失提供一阶估计，并量化沉降的关键驱动因素。约73%的已测绘沉降发生在农田和城市地区，这凸显了在这些地区实施可持续地下水管理措施的必要性。本研究结果有助于评估已知沉降地区的沉降空间范围，并定位未知的地下水压力区域。
