Preprints
https://doi.org/10.5194/hess-2023-260
https://doi.org/10.5194/hess-2023-260
08 Jan 2024
 | 08 Jan 2024
Status: this preprint is currently under review for the journal HESS.

Assessing national exposure and impact to glacial lake outburst floods considering uncertainty under data sparsity

Huili Chen, Qiuhua Liang, Jiaheng Zhao, and Sudan Bikash Maharjan

Abstract. Glacial Lake Outburst Floods (GLOFs) are widely recognized as one of the most devastating natural hazards in the Himalaya, which may catastrophic consequences including substantial loss of lives. To effectively mitigate these risks and enhance regional resilience, it is imperative to conduct an objective and holistic assessment of GLOFs hazards and their potential impacts of GLOFs 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 utilized with a Random Forest model to extract glacial lake water surfaces. Bayesian models, derived from previous research, are employed to estimate a plausible range of glacial lake water volumes and associated GLOF peak discharges, while accounting for the uncertainty stemming from the limited size of available data and outliers within the data. A significant amount of GLOF scenarios is subsequently generated based on this estimated plausible range of peak discharges. A 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 to different exposures. The evaluation framework is applied to 21 glacial lakes identified potentially dangerous in the Nepal Himalaya. The results indicate that Tsho Rolpa Lake, Lower Barun Lake and Thulagi Lake bear the most serious impacts of GLOFs on buildings and roads, and influence existing hydropower facilities, while Lower Barun Lake, Tsho Rolpa Lake and Lumding Lake will experience the most impacts of GLOFs on agriculture areas. Four anonymous lakes (located at 85°37′51″ E, 28°09′44″ N; 87°44′59″ E, 27°48′57″ N; 87°56′05″ N, 27°47′26″ E; 86°55′41″ E, 27°51′00″ N) have the potential to impact more than 100 buildings, and the first three lakes may even submerge existing hydropower facilities.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Huili Chen, Qiuhua Liang, Jiaheng Zhao, and Sudan Bikash Maharjan

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-260', Adam Emmer, 08 Feb 2024
    • AC3: 'Reply on RC1', Huili Chen, 16 May 2024
  • CC1: 'Comment on hess-2023-260', Taigang Zhang, 02 Mar 2024
    • AC1: 'Reply on CC1', Huili Chen, 15 May 2024
  • RC2: 'Comment on hess-2023-260', Anonymous Referee #2, 21 Mar 2024
    • AC2: 'Reply on RC2', Huili Chen, 16 May 2024
Huili Chen, Qiuhua Liang, Jiaheng Zhao, and Sudan Bikash Maharjan
Huili Chen, Qiuhua Liang, Jiaheng Zhao, and Sudan Bikash Maharjan

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Short summary
Glacial Lake Outburst Floods (GLOFs) can cause serious damage. To assess their risks, we developed an innovative framework using remote sensing, Bayesian models, flood modeling, and open-source data. This enables us to evaluate GLOFs on a national scale, despite limited data and challenges accessing high-altitude lakes. We evaluated dangerous lakes in Nepal, identifying those most at risk. This work is crucial for understanding GLOF risks and the framework can be transferred to other areas.