Publications related to the GRACE Missions (no abstracts)

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Integrating Satellite Retrievals, Numerical Models, and Machine Learning for Global Groundwater Recharge Estimation

Soylu, M. E., Entekhabi, D., and Bras, R. L., 2026. Integrating Satellite Retrievals, Numerical Models, and Machine Learning for Global Groundwater Recharge Estimation. Water Resources Research, 62(3):e2025WR040312, doi:10.1029/2025WR040312.

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@ARTICLE{2026WRR....6240312S,
       author = {{Soylu}, M.~E. and {Entekhabi}, D. and {Bras}, R.~L.},
        title = "{Integrating Satellite Retrievals, Numerical Models, and Machine Learning for Global Groundwater Recharge Estimation}",
      journal = {Water Resources Research},
     keywords = {recharge, remote sensing, soil moisture, machine learning, groundwater},
         year = 2026,
        month = mar,
       volume = {62},
       number = {3},
          eid = {e2025WR040312},
        pages = {e2025WR040312},
     abstract = "{Knowledge of the groundwater recharge rate determines whether aquifer
        use is sustainable. However, accurately measuring recharge
        globally presents significant challenges due to the complexity
        of subsurface processes and the lack of direct observational
        methods. This study addresses these challenges by developing a
        methodology that integrates satellite data, numerical models,
        and machine learning to estimate groundwater recharge globally.
        The methodology involves two steps. First, we run a numerical
        model, Hydrus-1D, to simulate soil moisture fluxes in the
        unsaturated zone by solving the Richards equation in the
        vertical direction for 235 different points representing various
        climates and soil types across the globe. Second, using
        Hydrus-1D inputs and outputs, we train a supervised ensemble
        machine-learning model, specifically a Gaussian Process
        Regression model, as an emulator to mimic Hydrus-1D. This
        enables us to process satellite observations efficiently to
        estimate annual recharge flux globally. Inputs for the model
        include NASA's SMAP soil moisture and GPM precipitation
        observations, ERA5 climate reanalysis data, and soil hydraulic
        properties. Rainfall, unsaturated hydraulic conductivity, and
        soil moisture are identified as the most significant predictors
        of groundwater recharge. The approach effectively captures
        global recharge patterns, particularly in regions with high
        rainfall, though it shows some limitations in arid areas with
        minimal recharge and heavily irrigated areas. We confirm the
        reasonableness of recharge estimates by comparing them with
        observed changes in subsurface water storage from the GRACE
        satellite mission. The method effectively captures the observed
        trends in water storage, demonstrating the model's capability to
        estimate recharge using large-scale satellite and reanalysis
        data.}",
          doi = {10.1029/2025WR040312},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026WRR....6240312S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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