Evaluation of Groundwater Storage Anomalies in Major River Basins of India using GRACE Satellite and In-Situ Groundwater Level Data

Murudkar, Ankita Chandrakant

Evaluation of Groundwater Storage Anomalies in Major River Basins of India using GRACE Satellite and In-Situ Groundwater Level Data

Files

Ankita Chandrakant Murudkar, Id.No. 58429, M.Tech..pdf (6.26 MB)

Date

2024-06

Authors

Murudkar, Ankita Chandrakant

Publisher

G. B. Pant University of Agriculture & Technology, Pantnagar-263145

Abstract

Groundwater, a significant source of fresh water and often referred to as an invisible resource, plays a crucial role in sustaining various aspects of human life. However, excessive exploitation of groundwater can lead to adverse consequences such as droughts, water scarcity, and hindered social and economic development. Therefore, it is essential to analyze groundwater levels to effectively manage this vital resource. This study focuses on understanding groundwater depletion by investigating 14 different locations across five basins (Ganga, Krishna, Godavari, Mahanadi, and Narmada), strategically selected to be 300 km apart. In the Ganga Basin, the locations of Badarwas, Daryaganj, Prayagraj and Bahera were chosen, while Adlur and Amarapuram were selected in the Krishna Basin. For the Godavari River, Jalgaon, Dhoki, and Akolebazar selected as the study locations. Bodeli, Handia, and Banjari represented the Narmada River, and BLD-022-OW and Abhimanpur were selected in the Mahanadi Basin. A comprehensive dataset spanning 17 years from 2002 to 2019 was utilized, incorporating GRACE (Gravity Recovery and Climate Experiment) and GLDAS (Global Land Data Assimilation System) data to calculate groundwater storage anomalies (GWSA). The Mann-Kendall and Sen's slope methods were utilized to conduct trend analysis. Weaker correlations were observed between GWSAWELL and GWSAGRACE in the Ganga, Krishna, Narmada, and Godavari basins. To improve correlation with in-situ measurements (GWSAWELL), machine learning techniques, namely Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM), were employed. Spatial and temporal trend analysis revealed depletion patterns during specific seasons. Among the machine learning models, the RF model consistently demonstrated strong correlation levels (<0.90) with GWSAWELL. This research enhances groundwater estimation and facilitates effective water resource planning in densely populated areas through the application of machine learning techniques and analysis of long-term datasets.