SafeFlow: Glacial Lake Outburst Floods Early Warning System With Probability-Based Risk Scoring
Year: 2026
Type: article
Abstract: Glacial Lake Outburst Floods (GLOFs) occur when ice dams break and glacial lakes suddenly burst open. These floods cause massive destruction as huge volumes of water rush downstream. Climate change is accelerating the formation and growth of glacial lakes, making GLOFs more frequent and increasingly unpredictable. However, traditional risk assessment techniques, such as field inventory and fixed hazard maps, are not capable of representing the dynamic nature of glacial hazards over space and time. To address this issue, the authors propose a new end-to-end deep learning framework called SafeFlow for GLOF risk estimation. The proposed framework combines meteorological information from ERA5, ice velocity information from NASA ITS_LIVE, and land surface temperature information from MODIS with local lake ablation characteristics such as area, elevation, and distance to upstream glaciers. The proposed framework provides a probabilistic risk score through a dashboard, which helps to effectively detect high-risk regions compared to traditional techniques.
Authors: Asmit Patel, Asha Rani Mishra, Vedanshi ., Krishna Yadav, Molisha Agarwal
Institution: Galgotias College of Engineering & Technology
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: hybrid
Estimated APC paid:
OpenAlex ID: W7151069754