Assessing national exposure to and impact of glacial lake outburst floods considering uncertainty under data sparsity
Guardado en:
| Udgivet i: | Hydrology and Earth System Sciences vol. 29, no. 3 (2025), p. 733 |
|---|---|
| Hovedforfatter: | |
| Andre forfattere: | , , |
| Udgivet: |
Copernicus GmbH
|
| Fag: | |
| Online adgang: | Citation/Abstract Full Text Full Text - PDF |
| Tags: |
Ingen Tags, Vær først til at tagge denne postø!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3164225925 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1027-5606 | ||
| 022 | |a 1607-7938 | ||
| 024 | 7 | |a 10.5194/hess-29-733-2025 |2 doi | |
| 035 | |a 3164225925 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 123631 |2 nlm | ||
| 100 | 1 | |a Chen, Huili |u School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK | |
| 245 | 1 | |a Assessing national exposure to and impact of glacial lake outburst floods considering uncertainty under data sparsity | |
| 260 | |b Copernicus GmbH |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Glacial lake outburst floods (GLOFs) are widely recognised as one of the most devastating natural hazards in the Himalayas, with catastrophic consequences, including substantial loss of life. To effectively mitigate these risks and enhance regional resilience, it is imperative to conduct an objective and holistic assessment of GLOF hazards and their potential impacts over a large spatial scale. However, this is challenged by the limited availability of data and the inaccessibility to most of the glacial lakes in high-altitude areas. The data challenge is exacerbated when dealing with multiple lakes across an expansive spatial area. This study aims to exploit remote sensing techniques, well-established Bayesian regression models for estimating glacial lake conditions, cutting-edge flood modelling technology, and open data from various sources to innovate a framework for assessing the national exposure and impact of GLOFs. In the innovative framework, multi-temporal imagery is utilised with a random forest model to extract glacial lake water surfaces. Bayesian models are employed to estimate a plausible range of glacial lake water volumes and the associated GLOF peak discharges while accounting for the uncertainty stemming from the limited sizes of the available data and outliers within the data. A significant number of GLOF scenarios is subsequently generated based on this estimated plausible range of peak discharges. A graphics processing unit (GPU)-based hydrodynamic model is then adopted to simulate the resulting flood hydrodynamics in different GLOF scenarios. Necessary socio-economic information is collected and processed from multiple sources, including OpenStreetMap, Google Earth, local archives, and global data products, to support exposure analysis. Established depth–damage curves are used to assess the GLOF damage extents for different exposures. The evaluation framework is applied to 21 glacial lakes identified as potentially dangerous in the Nepalese Himalayas. The results indicate that, in the scenario of a complete breach of dam height across 21 lakes, Tsho Rolpa Lake, Thulagi Lake, and Lower Barun Lake bear the most serious impacts of GLOFs on buildings, roads, and agricultural areas, while Thulagi Lake could influence existing hydropower facilities. One unnamed lake in the Trishuli River basin, two unnamed lakes in the Tamor River basin, and three unnamed lakes in the Dudh River basin have the potential to impact more than 200 buildings. Moreover, the unnamed lake in the Trishuli River basin has the potential to inundate existing hydropower facilities. | |
| 651 | 4 | |a Nepal | |
| 651 | 4 | |a Himalaya Mountains | |
| 653 | |a Sparsity | ||
| 653 | |a River basins | ||
| 653 | |a Outliers (statistics) | ||
| 653 | |a Regression models | ||
| 653 | |a Hydrodynamics | ||
| 653 | |a High altitude | ||
| 653 | |a Glacial lakes | ||
| 653 | |a Regression analysis | ||
| 653 | |a Socioeconomic aspects | ||
| 653 | |a Remote sensing | ||
| 653 | |a Rivers | ||
| 653 | |a Damage detection | ||
| 653 | |a Damage assessment | ||
| 653 | |a Availability | ||
| 653 | |a Sensing techniques | ||
| 653 | |a Hydroelectric power | ||
| 653 | |a Uncertainty | ||
| 653 | |a Floods | ||
| 653 | |a Buildings | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Spatial data | ||
| 653 | |a Lakes | ||
| 653 | |a Graphics processing units | ||
| 653 | |a Exposure | ||
| 653 | |a Remote sensing techniques | ||
| 653 | |a Open data | ||
| 653 | |a Outbursts | ||
| 653 | |a Information processing | ||
| 653 | |a Probability theory | ||
| 653 | |a Digital mapping | ||
| 653 | |a Glacial lake outburst floods | ||
| 653 | |a Water discharge | ||
| 653 | |a Hydrology | ||
| 653 | |a Hazard assessment | ||
| 653 | |a Lake water | ||
| 653 | |a Risk assessment | ||
| 653 | |a Hazard mitigation | ||
| 653 | |a Graphics | ||
| 653 | |a Infrastructure | ||
| 653 | |a Satellites | ||
| 653 | |a Mathematical models | ||
| 653 | |a Bayesian theory | ||
| 653 | |a Hydrodynamic models | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Liang, Qiuhua |u School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK | |
| 700 | 1 | |a Zhao, Jiaheng |u FM Research Division, FM Center, 288 Pasir Panjang Road, 117369, Singapore | |
| 700 | 1 | |a Sudan, Bikash Maharjan |u International Centre for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal | |
| 773 | 0 | |t Hydrology and Earth System Sciences |g vol. 29, no. 3 (2025), p. 733 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3164225925/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3164225925/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3164225925/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |