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.