Mustafi et al. (2026) Multi-Sensor Monitoring and Flood Pathway Analysis of Glacial Lake Outburst Floods (GLOFs)

Identification

• Journal: Lecture notes in networks and systems
• Year: 2026
• Date: 2026-01-01
• Authors: Subhranil Mustafi, R. Maity, Ayoti Banerjee, Sarbani Palit, Rajat Pal, Supratim Guha
• DOI: 10.1007/978-981-96-8998-9_14

Research Groups

• Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India
• Geological Studies Unit, Indian Statistical Institute, Kolkata, India
• Department of Computer Science and Engineering, University of Calcutta, Kolkata, India
• Center of Remote Sensing and GIS, Korea Polar Research Institute, Yeonsu-gu Incheon, Republic of Korea

Short Summary

This study utilized multi-sensor data, specifically Synthetic Aperture Radar (SAR) and Digital Elevation Models (DEMs), to monitor changes in the Thyanbo glacial lake's extent and delineate floodwater pathways following a Glacial Lake Outburst Flood (GLOF). The research successfully identified a peak lake area of 0.048 km² before the GLOF and demonstrated SAR's potential as an all-weather tool for GLOF monitoring and disaster mitigation.

Objective

• To monitor changes in the extent of the Thyanbo glacial lake and trace the pathways of floodwater following a Glacial Lake Outburst Flood (GLOF) using multi-sensor data.

Study Configuration

• Spatial Scale: Thyanbo glacial lake in the Solukhumbu region of Nepal.
• Temporal Scale: Focused on a GLOF event that occurred on 16th August 2024, with monitoring of the lake's critical stage immediately prior to the moraine dam failure.

Methodology and Data

• Models used: A comprehensive methodology combining digital elevation models (DEMs) and backscatter analysis of Synthetic Aperture Radar (SAR) data was employed.
• Data sources: Synthetic Aperture Radar (SAR) data from the Sentinel-1 satellite, Digital Elevation Models (DEMs), and ground truth sources for corroboration.

Main Results

• A peak lake area of 0.048 km² was observed and corroborated with ground truth sources immediately before the GLOF event.
• The study successfully delineated the floodwater pathways and estimated changes in the lake area.
• The findings provide significant insights into the vulnerability of glacial lake systems and enable accurate identification of floodwater pathways.
• The research demonstrates the potential of SAR-based monitoring as a reliable, all-weather tool for early warning systems in high-altitude regions prone to GLOFs.

Contributions

• Demonstrated the effectiveness of SAR-based monitoring as a reliable, all-weather tool for early detection and monitoring systems in high-altitude, GLOF-prone areas.
• Provided significant insights into the vulnerability of glacial lake systems and facilitated accurate identification of floodwater pathways, directly aiding in disaster preparedness and risk mitigation efforts.
• Applied a comprehensive methodology integrating Digital Elevation Models (DEMs) and Synthetic Aperture Radar (SAR) backscatter analysis for detailed GLOF monitoring.

Funding

No specific funding projects, programs, or reference codes were provided in the paper.

Citation

@article{Mustafi2026MultiSensor,
  author = {Mustafi, Subhranil and Maity, R. and Banerjee, Ayoti and Palit, Sarbani and Pal, Rajat and Guha, Supratim},
  title = {Multi-Sensor Monitoring and Flood Pathway Analysis of Glacial Lake Outburst Floods (GLOFs)},
  journal = {Lecture notes in networks and systems},
  year = {2026},
  doi = {10.1007/978-981-96-8998-9_14},
  url = {https://doi.org/10.1007/978-981-96-8998-9_14}
}