Quantifying the distribution of mountain snow is critical issue to ensure effective water resource management, skillful hydrological prediction, and hazard mitigation. This manuscript presents a highly impactful framework for quantifying meteorological forcing uncertainty in model simulations of mountain snow using novel machine learning applications and large sample emulators. By strictly adhering to FAIR data principles and utilizing open-source, automated workflows, the study provides a compelling demonstration of best practices in hydrological modeling. This paper will serve as an invaluable guide for early-career hydrologists seeking practical examples of rigorous code development and modeling workflows.

This study describes a machine learning workflow that brings together a variety of surface mass balance (SMB) measurements and a SMB model to improve model skill and to evaluate the performance of the trained and untrained model. The research makes a novel contribution to glacier mass balance modelling, with the potential for transferability across diverse climatic and glaciological settings. The workflow also provides a valuable framework that could inform the wider development and application of hybrid modelling approaches to glacier evolution that remain physically grounded.