Project Lead: George Brencher, UW Civil & Environmental Engineering
Data Science Lead: Scott Henderson
Glacial lakes are distributed in alpine terrain worldwide and are frequently dammed by unstable glacial moraines. These moraine dams can fail, causing lakes to rapidly drain and flood downstream valleys. Glacial lake outburst floods (GLOFs) are a significant hazard for high-elevation infrastructure and communities—on October 4, 2023, a GLOF in Sikkim, India, destroyed the Teesta III hydroelectric dam, washed away 15 bridges, affected hundreds of villages, stranded 3,000 tourists, and left at least 74 dead with many more missing. This flood was triggered when a landslide on a glacial moraine catastrophically failed and fell into the South Lhonak Lake, causing it to breach its banks. The landslide had been moving downslope at rates of up to 10 meters per year since at least 2016, but was not identified prior to its collapse, despite multiple flood hazard and risk analyses for the site.
Using satellite synthetic aperture radar remote sensing, we have developed a workflow allowing us to quantify surface changes that can contribute to GLOF likelihood, including landslide movement and moraine dam subsidence. Our approach fuses interferometric synthetic aperture radar (InSAR) and SAR speckle tracking data to accurately capture deformation as fast as hundreds of meters per year and as slow as <1 cm per year. For this incubator project, we hope to improve and scale our workflow to measure surface displacement with high spatial and temporal resolution for the areas surrounding all of the large glacial lakes in Nepal for the length of the Sentinel-1 archive (~2016-present). The resulting multi-year displacement time series will allow us to detect and track intra- and inter-annual changes of dynamic landslide, permafrost, and glacial features. We will also precisely quantify rates of moraine dam subsidence, significantly improving our understanding of GLOF hazard for hundreds of dangerous lakes and providing a critical missing input to existing risk analysis frameworks. In addition, we expect that 1) our scaled approach will be easily transferred to other regions, allowing us to create robust regional displacement time series anywhere on Earth, and 2) our almost decade-long, high spatial and temporal resolution displacement time series will be of broad scientific interest to glaciologists, geomorphologists, engineers, and hydrologists working in mountainous environments.