Global Land Subsidence Mapping Reveals Widespread Loss of Aquifer Storage Capacity Datasets

Groundwater overdraft gives rise to multiple adverse impacts including land subsidence and permanent groundwater storage loss. Existing methods have been 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) and provides a first-order estimate of aquifer storage loss due to consolidation of ~17 km3/year globally. China, the United States, and Iran account for the majority of groundwater storage loss due to consolidation. The model quantifies key drivers of subsidence and has high predictive accuracy, with an F1-score of 0.83 on the validation set. 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（Groundwater Overdraft）会引发多种不利影响，包括地面沉降（Land Subsidence）与永久性地下水储量损失（Permanent Groundwater Storage Loss）。现有研究方法尚无法在全球尺度下以足够高的空间分辨率刻画地下水储量损失，难以适配局部研究的精度需求。本研究借助遥感（Remote Sensing）与基于模型的数据集，结合机器学习方法，探究地下水胁迫（Groundwater Stress）、含水层耗竭（Aquifer Depletion）与地面沉降之间的内在关联。所构建的模型可在约2千米的高空间分辨率下预测全球地面沉降量，并就全球范围内因土层固结引发的含水层储量损失给出初步估算，年均损失量约为17立方千米。中国、美国与伊朗贡献了该类固结型地下水储量损失的绝大部分。该模型量化了地面沉降的关键驱动因子，且预测精度优异，验证集上的F1分数（F1-score）达0.83。约73%的已测绘沉降区域分布于农田与城区，凸显了针对此类区域落实可持续地下水管理举措的必要性。本研究结果有助于评估已知沉降区域的沉降空间范围，同时可用于定位尚未被发现的地下水胁迫区。