Automated Multi-Sensor Framework for Glacial Lake Dynamics and Unreported GLOF Detection across High Mountain Asia

Current GLOF (Glacial Lake Outburst Floods) risk assessments in High Mountain Asia (HMA) are often limited by static glacial lake inventories and unreliable Area-Volume (A-V) scaling models, which are not widely validated in the region. In this study, we developed a fully automated framework integrating multi-source remote sensing (e.g. Landsat, Sentinel-1/2, Copernicus DEM) and satellite altimetry (ICESat-2) to monitor glacial lake dynamics. We map nearly 32,000 glacial lakes across HMA for 2022 with a detection accuracy of 96% and relative boundary accuracy of 98% for lakes >20,000 m², offering a significant improvement over manual inventories. Our analysis shows three important results. First, regional heterogeneity is pronounced across the HMA sub-regions. For instance, East Kun Lun exhibited the highest expansion rate (8% area increase, 2016–2022), highlighting the need for targeted hazard assessment, while West Himalaya showed minor change (0.04%). Second, by processing ICESat-2 data for >16,000 lakes, we validated widely used A-V scaling models. Our results demonstrate that large supraglacial and extra-glacial lakes exhibit volume estimation errors exceeding 1500% in standard models, highlighting a significant bias in current flood volume potential estimates. Third, our automated temporal monitoring identified four GLOF events during the study period that were not previously documented. Analysis of these events reveals distinct area peaks before GLOFs, providing quantifiable indicators for early warning. This framework utilizes open-source remote sensing data on the Google Earth Engine cloud platform for regular monitoring of glacial lakes with higher accuracy while providing a reproducible, scalable method to correct volume estimates and detect hidden GLOF events in high-mountain valleys.