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Application of random forest modeling to evaluate groundwater storage changes in the Breede Water Management Area, South Africa

Adam, Maal A., Scheiber–Enslin, Stephanie E., and Ali, K. A., 2026. Application of random forest modeling to evaluate groundwater storage changes in the Breede Water Management Area, South Africa. Hydrogeology Journal, 34(5):1339–1354, doi:10.1007/s10040-026-03047-w.

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@ARTICLE{2026HydJ...34.1339A,
       author = {{Adam}, Maal A. and {Scheiber-Enslin}, Stephanie E. and {Ali}, K.~A.},
        title = "{Application of random forest modeling to evaluate groundwater storage changes in the Breede Water Management Area, South Africa}",
      journal = {Hydrogeology Journal},
     keywords = {Groundwater storage, Land surface model, Machine learning models, Downscaling, South Africa, Earth Sciences, Physical Geography and Environmental Geoscience, Information and Computing Sciences, Artificial Intelligence and Image Processing},
         year = 2026,
        month = aug,
       volume = {34},
       number = {5},
        pages = {1339-1354},
     abstract = "{Monitoring groundwater storage anomalies (GWSA) at high spatial
        resolutions is critical for sustainable water resource
        management and hydrological applications. The Gravity Recovery
        and Climate Experiment (GRACE) satellite provides unique
        insights into changes in terrestrial water storage but is
        limited by its coarse spatial resolution. In this study, GRACE
        observations, in situ groundwater levels, and Global Land Data
        Assimilation System (GLDAS)-derived hydrological variables were
        integrated to downscale GWSA from 1{\textdegree} {\texttimes}
        1{\textdegree} to 0.25{\textdegree} {\texttimes}
        0.25{\textdegree} in the Breede Water Management Area, South
        Africa (2002--2022). Three machine learning models, namely,
        random forest (RF), support vector regression (SVR), and
        artificial neural networks (ANN), were tested. RF consistently
        outperformed the others (R$^{2}$ = 0.86, Nash--Sutcliffe
        efficiency (NSE) = 0.75, root mean square error (RMSE) = 0.19)
        and was selected for downscaling. The downscaled GWSA revealed
        spatial heterogeneity, with higher recharge in the eastern
        mountainous regions and persistent depletion in the western
        lowlands. Temporal patterns captured major droughts (2002--2006,
        2015--2021) and recovery phases (2008--2010, 2020--2022),
        consistent with the observed hydroclimatic variability.
        Validation against in situ boreholes (NSE up to 0.55, RMSE =
        19--25 mm, R$^{2}$ = 0.77--0.81) confirmed strong agreement,
        although some discrepancies reflected aquifer heterogeneity and
        effects of groundwater abstractions. Uncertainties remain due to
        GRACE errors, GLDAS predictor biases, and limited borehole
        coverage, which constrain model generalization. Despite these
        limitations, this study demonstrates the potential of RF-based
        downscaling to improve GRACE groundwater monitoring in data-
        scarce regions and to inform adaptive water management under
        increasing climate variability.}",
          doi = {10.1007/s10040-026-03047-w},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026HydJ...34.1339A},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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