Abstract
Glacial lake monitoring is urgently needed across the Himalaya due to the threat of glacial lake outburst floods (GLOFs). Furthermore, both the population and the infrastructure exposed to or dependent on these glacial lakes are increasing. However, there are a substantial number of glacial lakes in the Himalaya with potential transboundary GLOF impacts, and their remote, high-altitude locations make monitoring extremely challenging, so existing field measurements are limited. Here, we propose a benchmark Himalayan glacial lake monitoring network “HiGLMN” that will characterize glacial lakes by combining geomorphological signatures of GLOFs, monitoring triggers and mechanisms of dam failure, and downstream impacts using in situ observations, remote sensing, and hydrodynamic modeling, and feed into early warning for disaster mitigation. We also provide existing practices to support the effectiveness and necessity and propose strategies for future data management. The monitoring network will contribute to robust GLOF risk management, early warning, and mitigation.
| Original language | English |
|---|---|
| Pages (from-to) | E2579-E2597 |
| Journal | Bulletin of the American Meteorological Society |
| Volume | 106 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 1 Dec 2025 |
Keywords
- Artificial intelligence
- Databases
- Gauges
- Remote sensing
- Risk assessment
- Risk modeling
ASJC Scopus subject areas
- Atmospheric Science
Fields of Expertise
- Advanced Materials Science
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