The China‒Nepal Himalayas (CNH) are threatened by glacial lake outburst floods (GLOFs), which endanger trade and local communities. Currently, glacial lake risk assessments in the CNH lack a reliable, quantitative, and cross-regional comparative approach. To fill this gap, this study presents a detailed and refined GLOF risk assessment using a data-driven machine learning framework. We first mapped lake boundaries in 1990, 2000, 2010, and 2020 using Landsat imagery and analyzed their distribution and temporal changes. We then identified potentially dangerous glacial lakes (PDGLs) among lakes that contact glaciers and are at least 0.1 km 2 in size. A machine learning model trained on High Mountain Asia estimated the likelihood that PDGLs would trigger GLOFs using key factors. We also evaluated impacts on exposed elements through stochastic inundation modeling. The final risk level was determined by combining hazard assessment with downstream impact analysis. Results show that 2377 glacial lakes were identified, mostly at elevations between 4100 m and 5900 m, with a 27% increase in area from 1992 to 2022. The trained model accurately identified all historical GLOFs in the CNH, confirming its high reliability. Downstream exposed elements, including buildings, bridges, roads, and hydropower facilities, remain at risk. Of the 76 lakes identified as potentially hazardous, four are categorized as having a very high risk and 14 as having a high risk. This study provides data to help stakeholders and policymakers pinpoint high-risk lakes and develop effective mitigation strategies.
Wu et al. (2026) studied this question.