Glacier evolution models based on temperature-index approaches are commonly used to assess hydrological impacts of glacier changes. However, current model calibration frameworks cannot efficiently transfer information from sparse high-resolution observations across glaciers. This limits their ability to resolve seasonal mass changes on unmonitored glaciers in large-scale applications. Machine learning approaches can potentially address this limitation by learning relationships from sparse data that are transferable in space and time, including to unmonitored glaciers. Here, we present the Mass Balance Machine (MBM), a data-driven mass balance model based on the XGBoost architecture, designed to provide accurate and high spatio-Temporal resolution regional-scale reconstructions of glacier mass balance. We trained and tested MBM using a dataset of approximately 4000 seasonal and annual point mass balance measurements from 32 glaciers across heterogeneous climate settings in mainland Norway, spanning from 1962 to 2021. To assess the advantage of MBM's generalisation capabilities, we compared its predictions on independent test glaciers at various spatio-Temporal scales with those of regional-scale simulations from three glacier evolution models. MBM successfully predicted annual and seasonal point mass balance on the test glaciers (RMSE of 0.59-1.00 m w.e. and bias of-0.01 to 0.04 m w.e.). On seasonal mass balance, MBM outperformed the other models across spatial scales, reducing RMSE by up to 46 % and 25 % on glacier-wide winter and summer mass balance, respectively. Our results demonstrate the capability of machine learning models to generalise across glaciers and climatic settings from relatively sparse mass balance data, highlighting their potential for a wide range of applications.

Arsenic contamination in shallow aquifers of Holocene alluvial basins is a serious health risk affecting millions of people [1]. Detection of arsenic hotspots is a slow and tedious process based on the analysis of groundwater samples. This study improves arsenic risk prediction by incorporating geomorphological features such as oxbow lakes and clay plugs into a machine learning (ML) approach. Advances in remote sensing [2], often combined with ML, enable the efficient detection of these and other proxy features, significantly reducing reliance on labour-intensive fieldwork. By combining these features with environmental and demographic data, the approach provides more accurate and cost-effective risk assessments, enabling better-targeted interventions in vulnerable regions and supporting proactive environmental monitoring.