Enhancing Terrestrial Water Storage Understanding Using Combination of Finite Difference Numerical Method and Remote Sensing Data Integration

Abstract

Terrestrial Water Storage (TWS) plays a vital role in the hydrological cycle, significantly impacting water resource management, agriculture, and climate research. Reliable estimation of TWS is crucial for forecasting droughts, optimizing reservoir management, and assessing the effects of climate change on water resources. However, traditional ground-based methods for estimating TWS are often limited in spatial coverage and are resource-intensive. This article aimed to numerically model these TWS changes through integration of remote sensing data and finite difference method. Finite Difference method. The proposed method utilized the soil moisture, evapotranspiration, vegetation and precipitation data to simulate the changes in water storage over time for the Shelby County. The scripts were run in Google Earth Engine (GEE) and MATLAB environment for remote sensing data analysis and simulation, respectively. Evapotranspiration was calculated as a nonlinear function of temperature and Normalized Difference Vegetation Index (NDVI) using the Newton-Raphson method. The results indicated the increase in TWS anomalies which showed a net gain in water storage. The continuous rise in TWS suggests increased water retention, which could be attributed to higher precipitation, reduced evapotranspiration (ET), or changes in soil moisture and groundwater levels. The outcomes of this article will be helpful to identify the water storage changes which are crucial for water resources management.