A Comparison Study of Grace-Based Groundwater Modeling for Data-Rich and Data-Scarce Regions

Abstract

Gravity Recovery and Climate Experiment (GRACE) modeling in water resources is an emerging field in hydrology. Investigation of groundwater change using remote sensing data helps overcome data limitation at a regional scale. We present a GRACE modeling approach to estimate the variations of groundwater for two case studies, the Upper Mississippi Basin in the US as a relatively data-rich region and the Ngadda catchment of the Lake Chad Basin in Africa as a data-poor region. It is critical to understand whether GRACE data is capable of analyzing groundwater change in data-poor regions as much as in data-rich regions. The GRACE data is applied first to analyze groundwater changes at the Upper Mississippi Basin, and compare it with ground truth data. The modeling conditions that affect the model accuracy are soil moisture models, groundwater fluctuations in the monitoring well, and the matter of the aquifer. The most successful GRACE modeling approach determined the effect of soil moisture model and aquifer. The strong correlation of 86.1% and 73.4%, respectively, verifies a good match between GRACE-based and ground truth time series. After the successful modeling approach is verified for the data-rich region, the technique was employed for the Ngadda Catchment of the Lake Chad Basin, as a data-poor region, to analyze groundwater changes. We investigated the effect of soil moisture models, scales, groundwater fluctuations in the individual cell, and the coverage area parameters in the GRACE modeling for the data-poor region. The most successful GRACE modeling approach determined the effect of soil moisture model