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Background <p>Numerical simulation of debris flow runout at regional scales remains challenging, particularly when a large number of slope-failure initiation points must be treated simultaneously within a single computational domain. Existing numerical approaches often face increasing computational demand and practical limitations when applied consistently from laboratory to wide-area simulations.</p> Objective <p>This study evaluates the depth-integrated particle method (DIPM) for simulating debris flows across different spatial scales, using a structured validation approach that includes flume-scale experiments, real-world case studies, and a wide-area regional application.</p> Methods <p>DIPM models debris flow as collection of discrete soil columns governed by Manning’s coefficient (<i>n</i>) and the critical deposition angle (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({i}_{cr}\)</EquationSource> </InlineEquation>), with interactions driven by hydraulic pressure gradients. The method was first validated using large-scale USGS debris flow flume experiments, where it reproduced front velocity and depositional area. It was then applied to two documented debris flow case study areas, where parameter values were calibrated based on agreement with observed deposition patterns.</p> Results <p>For the case study areas, a suitable range of <i>n</i> (0.05–0.15) and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\({i}_{cr}\)</EquationSource> </InlineEquation> (3°–10°) was identified, with Critical Success Index (CSI) values ranging from 0.63 to 0.68. This parameter range was subsequently applied to a wide-area simulation involving 2249 landslide scarps to evaluate the broader applicability of DIPM, resulting in an overall Critical Success Index (CSI) of approximately 0.60, with spatial variability influenced by topography and parameter sensitivity.</p> Conclusion <p>These findings confirm that DIPM can reproduce debris flow behavior at both events and regional scales with moderate computational cost, supporting its potential for scalable hazard mapping.</p>

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Quantitative evaluation and wide-area simulation of post-failure debris flows using the depth-integrated particle method

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月24日

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  • Fazlul Habib Chowdhury,
  • Takashi Matsushima

摘要

Background

Numerical simulation of debris flow runout at regional scales remains challenging, particularly when a large number of slope-failure initiation points must be treated simultaneously within a single computational domain. Existing numerical approaches often face increasing computational demand and practical limitations when applied consistently from laboratory to wide-area simulations.

Objective

This study evaluates the depth-integrated particle method (DIPM) for simulating debris flows across different spatial scales, using a structured validation approach that includes flume-scale experiments, real-world case studies, and a wide-area regional application.

Methods

DIPM models debris flow as collection of discrete soil columns governed by Manning’s coefficient (n) and the critical deposition angle ( \({i}_{cr}\) ), with interactions driven by hydraulic pressure gradients. The method was first validated using large-scale USGS debris flow flume experiments, where it reproduced front velocity and depositional area. It was then applied to two documented debris flow case study areas, where parameter values were calibrated based on agreement with observed deposition patterns.

Results

For the case study areas, a suitable range of n (0.05–0.15) and \({i}_{cr}\) (3°–10°) was identified, with Critical Success Index (CSI) values ranging from 0.63 to 0.68. This parameter range was subsequently applied to a wide-area simulation involving 2249 landslide scarps to evaluate the broader applicability of DIPM, resulting in an overall Critical Success Index (CSI) of approximately 0.60, with spatial variability influenced by topography and parameter sensitivity.

Conclusion

These findings confirm that DIPM can reproduce debris flow behavior at both events and regional scales with moderate computational cost, supporting its potential for scalable hazard mapping.