Mon Feb 16, F. Flechtner
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Varma, Vipul and Gandhi, Fenil, 2026. A hybrid framework for projecting 21st–century groundwater replenishment and its amplified seasonal cycle. Journal of Hydrology, 666:134814, doi:10.1016/j.jhydrol.2025.134814.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026JHyd..66634814V,
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},
keywords = {Groundwater recharge, Climate change, Machine learning, CMIP6, Environmental modeling, Water security, GRACE satellite, Risk assessment},
year = 2026,
month = feb,
volume = {666},
eid = {134814},
pages = {134814},
abstract = "{Groundwater recharge, a critical component of the terrestrial water
cycle, faces profound uncertainty under future climate change.
This study develops a novel, hybrid framework to address key
gaps in current projection capabilities by synergistically
integrating satellite remote sensing, process-based modeling,
and machine learning. A physically constrained Water Balance
Model (WBM), uniquely parameterized with GRACE-derived specific
yield, was used to generate a robust historical training dataset
for a high-fidelity XGBoost emulator (Test R$^{2}$ > 0.98). The
emulator, driven by a downscaled CMIP6 climate model ensemble
for a network of monitoring wells in the eastern United States,
produced probabilistic daily recharge projections under multiple
Shared Socioeconomic Pathways (SSPs). The projections reveal a
robust reorganization of the seasonal recharge cycle,
characterized by a tendency towards drier winters and wetter
summers in many areas. Spatiotemporal analysis of mean annual
change identifies an emergent spatial dipole, with a projected
net decrease in recharge across the central and southern
portions of the study area and a net increase in the Northeast.
This research provides a transferable, process-informed
framework for translating global climate projections into
actionable, high-resolution risk assessments.}",
doi = {10.1016/j.jhydrol.2025.134814},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026JHyd..66634814V},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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