Land subsidence caused by groundwater extraction has numerous negative consequences, such as loss of groundwater storage and damage to infrastructure. Understanding the magnitude, timing, and locations of land subsidence, as well as the mechanisms driving it, is crucial to implementing mitigation strategies, yet the complex, nonlinear processes causing subsidence are difficult to quantify. Physical models relating groundwater flux to aquifer compaction exist but require substantial hydrological data sets and are time consuming to calibrate. Land deformation can be measured using interferometric synthetic aperture radar (InSAR) and GPS, but the former is computationally expensive to estimate at scale and is subject to tropospheric and ionospheric errors, and the latter leaves many temporal and spatial gaps. In this study, we apply for the first time a machine learning approach that quantifies the relationships of various widely available input data, including evapotranspiration, land use, and sediment thickness, with land subsidence. We apply this method over the Western United States and estimate that from 2015 to 2016, ~2.0 km³/yr of groundwater storage was lost due to groundwater pumping‐induced compaction of sediments. Subsidence is concentrated in the Central Valley of California, and the state of California accounts for 75% of total subsidence in the Western United States. Other significant areas of subsidence occur in cultivated regions of the Basin and Range province. This study demonstrates that widely available ancillary data can be used to estimate subsidence at a larger scale than has been previously possible.

中文翻译：

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使用机器学习映射的美国西部与地面沉降相关的地下水存储损失

地下水开采造成的地面沉降具有许多负面影响，例如地下水储量的损失和基础设施的破坏。了解地面沉降的大小，时间和位置，以及驱动地面沉降的机制，对于实施减灾策略至关重要，但是导致沉降的复杂，非线性过程很难量化。存在将地下水通量与含水层压实相关的物理模型，但需要大量的水文数据集，并且校准耗时。土地变形可以使用干涉合成孔径雷达（InSAR）和GPS进行测量，但前者在规模上估算成本高昂，并且受对流层和电离层误差的影响，而后者则留下了许多时空差异。在这个研究中，我们首次应用了一种机器学习方法，该方法可量化各种广泛可用的输入数据（包括蒸散量，土地利用和沉积物厚度）与地面沉降之间的关系。我们将此方法应用于美国西部，并估计从2015年到2016年，约2.0公里由于地下水泵送引起的沉积物压实，导致每年³ /的地下水存储量损失。沉降集中在加利福尼亚州的中央山谷，加利福尼亚州占美国西部沉降总量的75％。沉降的其他重要区域也发生在盆地和兰格省的耕种地区。这项研究表明，广泛使用的辅助数据可用于以比以前更大的规模估算沉降。