ABSTRACT

The advent of Gravity Recovery and Climate Experiment (GRACE) has opened the doors for remote monitoring of gravitational changes and its derivatives across the globe, but received less attention due to poor spatial and temporal representation. Statistical models of varying complexity are commonly employed to downscale the GRACE datasets for use with local to regional applications. This study presents the application of two commonly employed machine learning models, multi-linear regression (MLR) and random forest (RF), in spatially downscaling (from 1° to 0.25°) the GRACE-derived terrestrial water storage anomalies (TWSA) by establishing a correlation with various land surface and hydroclimatic variables. The downscaled TWSA was further converted into groundwater storage anomalies. Applicability of the proposed methods was tested on four contrasting hydrogeological basins of India. For each basin, the significant predictor variables were considered to establish the relations. Seasonal groundwater levels observed in 236 wells during 2006–2015 were used for method validation and accuracy assessment. We observed a close match between GRACE-derived groundwater levels and the measurements for three of the four basins (r = 0.40–0.92, Root mean square error (RMSE) = 3.6–10.5 cm). Our results indicate that the predictor variables to downscale TWSA should be considered cautiously based on the hydrogeological, topographical, and meteorological characteristics of the basin.

摘要

重力恢复和气候实验（GRACE）的出现为全球范围内的重力变化及其衍生物的远程监控打开了大门，但由于时空表现差而受到的关注较少。复杂程度不同的统计模型通常用于缩小GRACE数据集，以供本地到区域应用程序使用。这项研究介绍了两种常用的机器学习模型，即多线性回归（MLR）和随机森林（RF）在空间尺度缩小（从1°到0.25°）的GRACE衍生的地面水存储异常（TWSA）中的应用建立与各种陆地表面和水文气候变量的相关性。缩小的TWSA进一步转换为地下水存储异常。在印度的四个对比水文地质盆地上测试了所提方法的适用性。对于每个盆地，均考虑了重要的预测变量以建立这种关系。使用2006年至2015年间在236口井中观测到的季节性地下水位进行方法验证和准确性评估。我们观察到GRACE派生的地下水位与四个流域中三个流域的测量值非常接近（r = 0.40–0.92，均方根误差（RMSE）= 3.6–10.5 cm）。我们的结果表明，应根据流域的水文地质，地形和气象特征谨慎考虑下TWSA的预测变量。使用2006年至2015年间在236口井中观测到的季节性地下水位进行方法验证和准确性评估。我们观察到GRACE派生的地下水位与四个流域中三个流域的测量值非常接近（r = 0.40–0.92，均方根误差（RMSE）= 3.6–10.5 cm）。我们的结果表明，应根据流域的水文地质，地形和气象特征谨慎考虑下TWSA的预测变量。使用2006年至2015年间在236口井中观测到的季节性地下水位进行方法验证和准确性评估。我们观察到GRACE派生的地下水位与四个流域中三个流域的测量值非常接近（r = 0.40–0.92，均方根误差（RMSE）= 3.6–10.5 cm）。我们的结果表明，应根据流域的水文地质，地形和气象特征谨慎考虑下TWSA的预测变量。