The depletion and variations of groundwater storage~(GWS) are of critical importance for sustainable groundwater management. In this work, we use Gravity Recovery and Climate Experiment (GRACE) to estimate variations in the terrestrial water storage~(TWS) and use it in conjunction with the Global Land Data Assimilation System~(GLDAS) data to extract GWS variations over time for Indus river basin~(IRB). We present a data processing framework that processes and combines these data-sets to provide an estimate of GWS changes. We also present the design of a band-limited optimally concentrated window function for spatial localization of the data in the region of interest. We construct the so-called optimal window for the IRB region and use it in our processing framework to analyze the GWS variations from 2005 to 2015. Our analysis reveals the expected seasonal variations in GWS and signifies groundwater depletion on average over the time period. Our proposed processing framework can be used to analyze spatio-temporal variations in TWS and GWS for any region of interest.
翻译:地下水储存的耗竭和变异对于可持续的地下水管理至关重要。在这项工作中,我们使用重力恢复和气候实验(GRACE)来估计陆地水储存的变异,并与全球土地数据同化系统(GLDAS)的数据一起,利用这些数据来为印度河流域逐年提取GWS变异。我们提出了一个数据处理框架,用于处理和合并这些数据集,以提供GWS变化的估计。我们还提出设计一个带的有限、最佳集中的窗口功能,以便在感兴趣的区域对数据进行空间定位。我们为IRB区域建造了所谓的最佳窗口,并在我们的处理框架中使用它来分析2005年至2015年期间GWS变化的预期季节性变化,并表明在任何感兴趣的区域平均耗尽地下水。我们提议的处理框架可用于分析TWS和GWS的任何区域的水量变化。