Abstract
Accelerated cryospheric degradation across the Hindu Kush Himalaya (HKH) has catalyzed the rapid formation and expansion of proglacial lakes, substantially elevating the risk of catastrophic Glacial Lake Outburst Floods (GLOFs). Conventional in-situ monitoring in these remote, high-altitude terrains is severely constrained by rugged topography, extreme weather, and logistical hazards. To address this vulnerability, this study develops a scalable, end-to-end Early Warning System (EWS) that couples multi-sensor satellite remote sensing with deep learning computer vision and 2D hydrodynamic flood propagation modeling. Our architecture utilizes a dual-stream Spatio-Temporal Attention U-Net (STA-UNet) to ingest and fuse cloud-penetrating Sentinel-1 Synthetic Aperture Radar (SAR) and high-resolution Sentinel-2 multispectral imagery, achieving automated lake boundary segmentation and water volume tracking at sub-weekly temporal resolution. Simultaneously, multi-temporal differential interferometric SAR (DInSAR) monitors precursory moraine-dam deformation and slope instability. When anomaly thresholds are breached, an integrated shallow-water hydrodynamic model simulates breach hydrographs and downstream inundation trajectories. Validated against historical GLOF events and retrospective multi-year time-series across 14 high-risk glacial lakes in Nepal, India, and Bhutan, the framework demonstrated a 94.2% segmentation precision (Intersection over Union: 0.89) and detected anomalous lake expansion and dam displacement up to 18 days prior to peak instability. The automated pipeline achieved an alert generation latency of under 35 minutes from satellite data downlink, providing critical actionable lead times for downstream mountain communities and regional infrastructure.
