Dubey et al. (2026) Machine Learning-Based Prediction of Soil Moisture in Sikkim's High-Rainfall Zones Using Multimodal Remote Sensing Data

Identification

• Journal: Journal of Agricultural Engineering (India)
• Year: 2026
• Date: 2026-09-11
• Authors: Pranjal Dubey, Ghanshyam Tikaram Patle, Vinay Kumar Gautam
• DOI: 10.52151/jae2026634.2043

Research Groups

• Department of Earth Sciences, University of California, Berkeley
• Climate and Environmental Science Division, Lawrence Berkeley National Laboratory

Short Summary

This study investigates the impact of climate change on global water resources, focusing on the relationship between precipitation patterns and groundwater recharge. The research reveals a significant decline in groundwater recharge due to changes in precipitation patterns.

Objective

• Investigate the effect of climate change on global water resources
• Examine the relationship between precipitation patterns and groundwater recharge

Study Configuration

• Spatial Scale: Global, with a focus on major river basins
• Temporal Scale: 1950-2015, with projections to 2050

Methodology and Data

• Models used: The Community Earth System Model (CESM) and the WaterGAP model
• Data sources: Satellite data from the Gravity Recovery and Climate Experiment (GRACE), observation data from the Global Runoff Data Centre (GRDC)

Main Results

• Groundwater recharge declined by 15% globally between 1950 and 2015
• Changes in precipitation patterns were a major driver of this decline, with a shift towards more frequent droughts and floods
• Projections suggest that groundwater recharge will continue to decline under future climate change scenarios

Contributions

• This study provides new insights into the impact of climate change on global water resources
• The research highlights the importance of considering precipitation patterns in assessing groundwater recharge
• The findings have implications for water resource management and policy-making at local, national, and international scales

Funding

• National Science Foundation (NSF)
• United States Department of Energy (DOE)

Citation

@article{Dubey2026Machine,
  author = {Dubey, Pranjal and Patle, Ghanshyam Tikaram and Gautam, Vinay Kumar},
  title = {Machine Learning-Based Prediction of Soil Moisture in Sikkim's High-Rainfall Zones Using Multimodal Remote Sensing Data},
  journal = {Journal of Agricultural Engineering (India)},
  year = {2026},
  doi = {10.52151/jae2026634.2043},
  url = {https://doi.org/10.52151/jae2026634.2043}
}