Exploring machine-learning extrapolation of glacier elevation change in High Mountain Asia derived from ICESat-2 data

Glacier elevation change is a fundamental measure for quantifying glacier mass balance and assessing glacier–climate interactions. Large-scale estimates are commonly derived either from satellite altimetry, which provides robust but spatially sparse measurements, or from digital elevation model (DEM) differencing, which enables spatially continuous mapping but is more sensitive to noise and bias in complex mountain terrain. Machine learning (ML) approaches have increasingly been used to bridge this gap by correcting or reconstructing elevation measurements using climate and topographic predictors. However, because ML-based prediction inherently involves extrapolation beyond directly sampled glaciers, its reliability across heterogeneous glacier systems such as existing in High Mountain Asia (HMA)remains poorly constrained.

In this study, we explore the behaviour of ML-based glacier elevation change predictions trained with ICESat-2 elevation measurements combined with climate and terrain variables across multiple HMA subregions. ICESat-2 footprints provide dense elevation change observations over only a limited subset of glaciers within each subregion. We train subregion-specific XGBoost models and evaluate their performance in relation to glacier sampling characteristics, feature importance, and elevation-dependent behavior.

The results reveal pronounced regional contrasts despite comparable glacier size and sample coverage across regions. In the Karakoram for example, ML-based extrapolation produces spatially coherent and elevation-dependent patterns of glacier elevation change, with predicted dh systematically decreasing from lower to higher elevations, consistent with expected glacier-scale behavior. These structured predictions are associated with robust model performance (R² ≈ 0.7). In contrast, in West Kunlun Shan, extrapolated elevation change fields are spatially uniform and weakly structured, showing little sensitivity to the applied climate and terrain predictors. These results indicate that the effectiveness of ML-based glacier elevation change modeling depends less on sample size or glacier extent alone than on the presence of stable and internally consistent response structures within glacier systems.