Varma et al. (2025) A hybrid framework for projecting 21st-century groundwater replenishment and its amplified seasonal cycle

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-12-21
• Authors: Vipul Varma, Fenil Gandhi
• DOI: 10.1016/j.jhydrol.2025.134814

Research Groups

Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India

Short Summary

This study develops a novel hybrid framework integrating satellite remote sensing, process-based modeling, and machine learning to project 21st-century groundwater replenishment. Projections for the eastern United States reveal a robust reorganization of the seasonal recharge cycle towards drier winters and wetter summers, alongside an emergent spatial dipole in mean annual change.

Objective

• To develop a transferable, process-informed hybrid framework for projecting 21st-century groundwater replenishment and its amplified seasonal cycle, addressing critical uncertainties in current projection capabilities under climate change.

Study Configuration

• Spatial Scale: Eastern United States, focusing on a network of monitoring wells.
• Temporal Scale: 21st-century (daily projections).

Methodology and Data

• Models used: Physically constrained Water Balance Model (WBM), XGBoost emulator, CMIP6 climate model ensemble (downscaled).
• Data sources: Satellite remote sensing (GRACE-derived specific yield), historical training dataset, climate model outputs, monitoring well data.

Main Results

• The developed XGBoost emulator achieved high fidelity (Test R² > 0.98) in replicating historical recharge.
• Projections indicate a robust reorganization of the seasonal recharge cycle, characterized by a tendency towards drier winters and wetter summers across many areas.
• Spatiotemporal analysis of mean annual change identifies an emergent spatial dipole: a projected net decrease in recharge across the central and southern portions of the study area, and a net increase in the Northeast.

Contributions

• Development of a novel, hybrid framework synergistically integrating satellite remote sensing, process-based modeling, and machine learning for groundwater recharge projection.
• Unique parameterization of a Water Balance Model using GRACE-derived specific yield, enhancing physical constraint.
• Provides daily timescale projections, resolving sub-seasonal dynamics crucial for understanding aquifer resilience and baseflow stability.
• Offers a transferable, process-informed framework for translating global climate projections into actionable, high-resolution risk assessments for water security.

Funding

Not specified in the provided text.

Citation

@article{Varma2025hybrid,
  author = {Varma, Vipul and Gandhi, Fenil},
  title = {A hybrid framework for projecting 21st-century groundwater replenishment and its amplified seasonal cycle},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134814},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134814}
}