Publications related to the GRACE Missions (no abstracts)

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High–Resolution Downscaling of GRACE–Derived Groundwater Storage Anomalies using Stacking Ensemble Machine Learning in the Data–Scarce Tropical Catchments

Karunarathna, S., Dissanayake, B. C., Gunawardhana, L., and Rajapakse, L., 2026. High–Resolution Downscaling of GRACE–Derived Groundwater Storage Anomalies using Stacking Ensemble Machine Learning in the Data–Scarce Tropical Catchments. Earth Systems and Environment, .

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@ARTICLE{2026ESE...tmp...23K,
       author = {{Karunarathna}, S. and {Dissanayake}, B.~C. and {Gunawardhana}, L. and {Rajapakse}, L.},
        title = "{High-Resolution Downscaling of GRACE-Derived Groundwater Storage Anomalies using Stacking Ensemble Machine Learning in the Data-Scarce Tropical Catchments}",
      journal = {Earth Systems and Environment},
     keywords = {GRACE/GWSA, Gridded groundwater, Groundwater monitoring, Kumbukkan oya, Remote sensing in hydrology, Statistical downscaling},
         year = 2026,
        month = jan,
     abstract = "{Reliable groundwater monitoring is essential to ensure water security.
        Yet, many regions lack dense observational networks. High-
        resolution gridded groundwater datasets offer valuable
        alternatives for understanding groundwater dynamics. The Gravity
        Recovery and Climate Experiment (GRACE) provides large-scale
        terrestrial water storage anomaly (TWSA) data, but its coarse
        spatial resolution limits regional applicability. Recent studies
        have increasingly utilized machine learning (ML) to improve
        GRACE-based water storage estimates; however, most have relied
        on individual ML models, which enhance prediction accuracy only
        to a limited extent. Based on these grounds, this study proposes
        a stacking ensemble machine learning (SEML) framework that
        integrates Random Forest (RF), eXtreme Gradient Boosting
        (XGBoost), Light Gradient Boosting Machine (LightGBM), and
        Categorical Boosting (CatBoost) for more precise statistical
        downscaling of GRACE-derived groundwater storage anomalies
        (GWSA) to finer spatial resolutions. Model performance was
        examined using different input feature combinations. The
        proposed framework was assessed through a case study in the
        Kumbukkan Oya Basin, Sri Lanka, where GRACE-derived GWSA was
        downscaled from 0.25{\textdegree} to 0.05{\textdegree}
        resolution using selected climatic and environmental predictors.
        The SEML model demonstrated superior predictive performance
        (Coefficient of Determination, R$^{2}$ = 0.84; Root Mean Square
        Error, RMSE = 3.04 cm) compared to individual models.
        Statistical validation against GRACE-derived GWSA yielded
        R$^{2}$ > 0.9 across grid points, while in-situ groundwater
        level comparisons showed strong correlations (CC > 0.7) in most
        wells. By combining multiple ML algorithms, the SEML framework
        significantly enhances the accuracy and reliability of GRACE-
        based downscaling in data-scarce regions, providing essential
        support for more informed sustainable water management, climate
        adaptation planning, and hydrological modeling approaches.}",
          doi = {10.1007/s41748-026-01022-9},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026ESE...tmp...23K},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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