Improved estimation of global-scale changes in groundwater storage using machine learning.

在气候变化中，地下水作为可靠的淡水来源的重要性预计会增加，因为它对水文循环的短期和中期波动有更大的适应能力。在过去二十年中，重力恢复和气候实验(GRACE)使大规模卫星监测全球陆地总储水量的变化成为可能。到目前为止，地下水储量的变化是通过扣除土壤水分、地表水和冰雪等其他陆地水储量的质量贡献，从总的陆地水储量计算出来的。对这些陆地储水量变化的估计主要来自不充分约束的陆地表面模型(LSM)，偶尔也来自现场观测。来自未校准的全球规模的LSM的陆地水存储的估计变化仍然存在很大的不确定性，阻碍了GRACE对地下水存储的强有力监测。该项目旨在设计一个数据驱动的框架，利用新获得的全球范围测压观测数据，根据陆地水总储量计算地下水储量。下一步，由于基于全球过程的模型过度简化了水存储组件，将创建用于全球地下水存储的替代深度学习模型，以捕捉预测变量和地下水趋势中的非线性关系，并将学习到的知识转移到观测有限的区域。改进对地下水储量的估计将有助于为可持续利用淡水提供流域规模的管理信息。

The importance of groundwater as a reliable freshwater source is expected to increase amidst climate change, given its greater resilience to short- and mid-term fluctuations in the hydrological cycle. Over the last two decades, the Gravity Recovery and Climate Experiment (GRACE) has enabled large-scale satellite monitoring of changes in total terrestrial water storage globally. Changes in groundwater storage have, to date, been computed from total terrestrial water storage by deducting mass contributions from other terrestrial water stores: soil moisture, surface water, and snow and ice. Estimates of changes in these terrestrial water stores derive primarily from inadequately constrained land surface models (LSMs) and occasionally from in situ observations. Substantial uncertainty persists in the estimated changes in terrestrial water stores from uncalibrated, global-scale LSMs hindering robust monitoring of groundwater storage from GRACE. The project aims to devise a data-driven framework to calculate groundwater storage from total terrestrial water storage, leveraging newly accessible global-scale piezometric observations. Next, as the global process-based models oversimplify water storage components, a surrogate deep learning model for global ground water storage will be created to capture non-linear relationships in predictive variables and groundwater trends, and to transfer the learned knowledge to the regions with limited observations. Improved estimates of groundwater storage will help to inform basin-scale management for sustainable freshwater use.