High-resolution mass balance reconstructions for Swiss glaciers using machine learning

Glacier retreat poses significant environmental and societal challenges. Understanding the local impacts of climate drivers on glacier evolution is essential, with glacier mass balance being a central concept. This study uses the Mass Balance Machine (MBM; Sjursen et al., 2025), an open-source, data-driven model based on eXtreme Gradient Boosting (XGBoost) that reconstructs glacier mass balance with high spatiotemporal resolution at regional scales. Trained on point mass balance data from multiple glaciers, MBM captures both intra- and inter-glacier variability, enabling the identification of transferable patterns and applications to glaciers without direct observations. Here, we applied MBM to reconstruct the mass balance of Swiss glaciers. The model was trained using a comprehensive dataset of approximately 34,000 winter and annual point mass balance measurements from 35 Swiss glaciers in diverse climate settings from 1951 to 2023. Using MBM, we generated high spatial resolution reconstructions of seasonal and annual mass balance for these 35 glaciers. When validated on independent unseen glaciers, MBM demonstrated robust performance across spatial scales (point to glacier-wide) and temporal scales (monthly to annual). This study underscores how MBM can be effectively used in Switzerland to generalize across diverse glaciers and climatic conditions, highlighting the model's versatility and broad applicability.