Assessing Groundwater Storage Anomalies using GRACE Measurements: Implications for Sustainable Water Resource Management

Agriculture is vital in the Indian economy as it contributes approximately 17 percent to the total Gross Domestic Product (GDP). Wells and tubewells supply more than 61 percent of India's net irrigated land. The rapidly expanding population, increasing the demand for drinking, irrigation purposes, and industrial applications, leads to over-exploitation of groundwater and a rapid decline in groundwater levels. Due to the apparent relevance of groundwater, it is necessary to monitor it continuously. Monitoring the dynamics of groundwater as a valuable natural resource is difficult, whereas insufficient and uneven observation wells are a problem. Therefore, groundwater fluctuation for time and space is considered for planning for the sustainable management of water resources. The present study assesses the terrestrial water storage (TWS) changes across the region of India using Gravity Recovery and Climate Experiment (GRACE) measurements. By obtaining information on soil moisture and surface water, it is possible to derive groundwater storage changes using TWS anomalies. Groundwater storage anomalies are calculated for the entire Indian region from GRACE TWS data using surface water and soil moisture data simulated by the Global Land Data Assimilation System (GLDAS) model. GRACE data is a valuable tool for assessing regional groundwater storage anomalies. The study found that the negative GWS anomalies are associated with drought, and positive anomalies are associated with flooding events. Also the regionalization of GRACE data for modeling GW using in situ measurements, as precipitation & observed GW data. The comparisons of GWS anomalies with monthly groundwater levels and correlation with the rainfall data from the IMD (2003-2015) indicated that reasonable GWS estimates using the GRACE satellite followed the observed pattern closely, showing the potential for the GRACE mission for groundwater monitoring.

Keywords: Groundwater, GRACE, Land surface model, Groundwater anomalies, GLDAS, Soil Moisture.