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

作者：Cheick Doumbia, Alain N. Rousseau, Hakan Başağaoğlu, Michel Baraër, Debaditya Chakraborty · 发表于：Journal of Hydrology · 年份：2024 · DOI：10.1016/j.jhydrol.2024.132519 · 被引用次数：10 · 研究领域：Cryospheric studies and observations、Geophysics and Gravity Measurements、Hydrology and Watershed Management Studies

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 cli...