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<p>Investigating the preferential flow path of a debris flow is crucial for quantifying the risk and developing mitigation strategies. Here, we examined 66 debris flows from the Western Ghats in India employing Rapid Mass Movement Simulation (RAMMS)::Debris Flow software to understand the kinematics of run-out. Our analysis revealed that the debris flow run-out in the study area follow two main routes: 60 along the existing stream channels (SC) and six following the steepest hill slope (SH). We further simulated these debris flows to identify their drivers, and derived a threshold that distinguishes between SC and SH-type debris flows. Our results indicate that the debris flow volumes greater than 7072 cu. m is SH-type, whereas those with smaller volumes are more likely to follow SC paths. The model’s accuracy was validated against field observations, achieving a success rate of 93% for SH-type flows and 85% for SC.</p>

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Predicting debris flow pathways using volume-based thresholds for effective risk assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月10日

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  • A. Rajaneesh,
  • V. K. Krishnapriya,
  • K. S. Sajinkumar,
  • Nikhil Nedumpallile Vasu,
  • Cees van Westen,
  • Thomas Oommen,
  • Ali P. Yunus,
  • Vanessa J. Banks,
  • A. P. Pradeepkumar

摘要

Investigating the preferential flow path of a debris flow is crucial for quantifying the risk and developing mitigation strategies. Here, we examined 66 debris flows from the Western Ghats in India employing Rapid Mass Movement Simulation (RAMMS)::Debris Flow software to understand the kinematics of run-out. Our analysis revealed that the debris flow run-out in the study area follow two main routes: 60 along the existing stream channels (SC) and six following the steepest hill slope (SH). We further simulated these debris flows to identify their drivers, and derived a threshold that distinguishes between SC and SH-type debris flows. Our results indicate that the debris flow volumes greater than 7072 cu. m is SH-type, whereas those with smaller volumes are more likely to follow SC paths. The model’s accuracy was validated against field observations, achieving a success rate of 93% for SH-type flows and 85% for SC.