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<p>Glacial Lake Outburst Floods (GLOFs) pose a growing threat to Himalayan communities. Yet, integrated frameworks that link systematic regional-scale susceptibility assessments with quantitative, uncertainty-aware impact modelling are critically lacking. This study presents an integrated approach that combines multi-decadal remote sensing, a Fuzzy-Analytic Hierarchy Process (AHP), and ensemble hydrodynamic modelling to assess GLOF risk in the Garhwal region, India. Our inventory (1990–2023) reveals a 126% increase in total glacial lake area, expanding from 147.3 ± 0.32&#xa0;ha to 332.9 ± 0.11&#xa0;ha, with 21 lakes exhibiting expansion &gt; 100%. The Fuzzy-AHP susceptibility model, which uniquely incorporates decadal lake expansion as a dynamic criterion and propagates measurement uncertainties, identified 11 out of 87 lakes as highly susceptible, characterized by large volumes (e.g., 4.96 ± 0.89 × 10⁶ m³), rapid growth, and unstable moraines in four different river basins, except for the Yamuna. Hydrodynamic (HEC-RAS) modelling of worst-case breach scenarios for the highest-priority lakes projected rapid breach development (&lt; 0.6&#xa0;h), peak discharges ranging from 377 to 1,149&#xa0;m³/s, and flood depths up to 14.3&#xa0;m in downstream settlements, with significant spatial variability across basins. The synthesis of dynamic susceptibility ranking and probabilistic flood impact ranges provides a transformative, actionable basis for risk reduction. We recommend immediate early warning systems at the four highest-risk lakes (e.g., GL-AL47) and dynamic hazard zoning for settlements like Badrinath and Ghansali. This framework advances GLOF risk assessment beyond static indices toward proactive, evidence-based decision-making.</p>

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Fuzzy-AHP-based susceptibility assessment and flood modelling of GLOFs in the Indian Himalaya

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Vikas Rawat,
  • Shekhar Singh,
  • Jigmat Norboo,
  • Mahabir Singh Negi

摘要

Glacial Lake Outburst Floods (GLOFs) pose a growing threat to Himalayan communities. Yet, integrated frameworks that link systematic regional-scale susceptibility assessments with quantitative, uncertainty-aware impact modelling are critically lacking. This study presents an integrated approach that combines multi-decadal remote sensing, a Fuzzy-Analytic Hierarchy Process (AHP), and ensemble hydrodynamic modelling to assess GLOF risk in the Garhwal region, India. Our inventory (1990–2023) reveals a 126% increase in total glacial lake area, expanding from 147.3 ± 0.32 ha to 332.9 ± 0.11 ha, with 21 lakes exhibiting expansion > 100%. The Fuzzy-AHP susceptibility model, which uniquely incorporates decadal lake expansion as a dynamic criterion and propagates measurement uncertainties, identified 11 out of 87 lakes as highly susceptible, characterized by large volumes (e.g., 4.96 ± 0.89 × 10⁶ m³), rapid growth, and unstable moraines in four different river basins, except for the Yamuna. Hydrodynamic (HEC-RAS) modelling of worst-case breach scenarios for the highest-priority lakes projected rapid breach development (< 0.6 h), peak discharges ranging from 377 to 1,149 m³/s, and flood depths up to 14.3 m in downstream settlements, with significant spatial variability across basins. The synthesis of dynamic susceptibility ranking and probabilistic flood impact ranges provides a transformative, actionable basis for risk reduction. We recommend immediate early warning systems at the four highest-risk lakes (e.g., GL-AL47) and dynamic hazard zoning for settlements like Badrinath and Ghansali. This framework advances GLOF risk assessment beyond static indices toward proactive, evidence-based decision-making.