GRACE and GRACE-FO Related Publications (no abstracts)

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Interpretation of glacier mass change within the Upper Yukon Watershed from GRACE using Explainable Automated Machine Learning Algorithms

Doumbia, Cheick, Rousseau, Alain N., Ba\csa\ugao\uglu, Hakan, Baraer, Michel, and Chakraborty, Debaditya, 2025. Interpretation of glacier mass change within the Upper Yukon Watershed from GRACE using Explainable Automated Machine Learning Algorithms. Journal of Hydrology, 651:132519, doi:10.1016/j.jhydrol.2024.132519.

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@ARTICLE{2025JHyd..65132519D,
       author = {{Doumbia}, Cheick and {Rousseau}, Alain N. and {Ba{\c{s}}a{\u{g}}ao{\u{g}}lu}, Hakan and {Baraer}, Michel and {Chakraborty}, Debaditya},
        title = "{Interpretation of glacier mass change within the Upper Yukon Watershed from GRACE using Explainable Automated Machine Learning Algorithms}",
      journal = {Journal of Hydrology},
     keywords = {Glacier mass temporal variations, Meteorological features, Hydrological features, Explainable machine learning model, Oceanic-climatic oscillations},
         year = 2025,
        month = apr,
       volume = {651},
          eid = {132519},
        pages = {132519},
     abstract = "{Glaciers play a vital role in providing water resources for drinking,
        agriculture, and hydro-electricity in many mountainous regions.
        As global warming progresses, accurately reconstructing long-
        term glacier mass changes and comprehending their intricate
        dynamic relationships with environmental variables are
        imperative for sustaining livelihoods in these regions. This
        paper presents the use of eXplainable Machine Learning (XML)
        models with GRACE and GRACE-FO data to reconstruct long-term
        monthly glacier mass changes in the Upper Yukon Watershed (UYW),
        Canada. We utilized the H2O-AutoML regression tools to identify
        the best performing Machine Learning (ML) model for filling
        missing data and predicting glacier mass changes from
        hydroclimatic data. The most accurate predictive model in this
        study, the Gradient Boosting Machine, coupled with explanatory
        methods based on SHapley Additive eXplanation (SHAP) and Local
        Interpretable Model-Agnostic Explanations (LIME) analyses, led
        to automated XML models. The XML unveiled and ranked key
        predictors of glacier mass changes in the UYW, indicating a
        decrease since 2014. Analysis showed decreases in snow water
        equivalent, soil moisture storage, and albedo, along with
        increases in rainfall flux and air temperature were the main
        drivers of glacier mass loss. A probabilistic analysis hinging
        on these drivers suggested that the influence of the key
        hydrological features is more critical than the key
        meteorological features. Examination of climatic oscillations
        showed that high positive anomalies in sea surface temperature
        are correlated with rapid depletion in glacier mass and soil
        moisture, as identified by XML. Integrating H2O-AutoML with SHAP
        and LIME not only achieved high prediction accuracy but also
        enhanced the explainability of the underlying hydroclimatic
        processes of glacier mass change reconstruction from GRACE and
        GRACE-FO data in the UYW. This automated XML framework is
        applicable globally, contingent upon sufficient high-quality
        data for model training and validation.}",
          doi = {10.1016/j.jhydrol.2024.132519},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025JHyd..65132519D},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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