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@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}
}