An ensemble machine learning approach for filling voids in surface elevation change maps over glacier surfaces

Abstract. Glacier mass balance assessments in mountainous regions often rely on digital elevation models (DEMs) to estimate surface elevation change. However, these DEMs are prone to spatial data voids, particularly during historical reconstructions using older imagery. These voids, which are most common in glacier accumulation zones, introduce uncertainty into estimates of glacier mass balance and surface elevation change. Traditional void-filling methods, such as constant and hypsometric interpolation, have limitations in capturing spatial variability in elevation change. This study introduces a machine-learning- based approach using gradient-boosted tree regression (XGBoost) to estimate glacier surface-elevation change across voids. High Mountain Asia (HMA) is an ideal study area for assessing the accuracy of different void-filling approaches across glaciers with varying morphology and climatic settings. We compare XGBoost predictions to traditional void-filling methods across the Western and Eastern Himalayas using a dataset of DEM-derived elevation changes. Results indicate that XGBoost consistently outperforms simpler methods, reducing root mean square error (RMSE) and mean absolute error (MAE) while improving alignment with observed elevation changes. The study highlights the advantages of integrating multiple glaciological and topographic predictors, demonstrating the potential of machine learning to improve assessments of glacier mass balance and elevation change. Future research should explore additional predictors, such as climate data, to further enhance predictive accuracy.