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Debris-flow susceptibility of upland catchments

Author

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  • Mélanie Bertrand
  • Frédéric Liébault
  • Hervé Piégay

Abstract

Over the last three decades, many regional studies in mountain ranges under temperate climate revealed that it is possible to discriminate debris-flow and fluvial fans from morphometric indicators measured at the scale of the catchment and the fan itself. The most commonly used indicators are the Melton index (R), a normalized index of the gravitational energy of the catchment, and the fan slope (S). A wide range of thresholds have been proposed for discriminating purpose, but these are generally based on a small population of catchments and may be highly influenced by ambiguous fans included in the data set. A database of 620 upland catchments from several mountain ranges under temperate climate was compiled from the literature to propose robust discriminant morphometric thresholds for debris-flow versus fluvial responses. Linear discriminant analysis (LDA) and logistic regression (LR) were performed using the whole data set, and a leave-one-out cross-validation was used to evaluate performances of the models. Sensitivity and specificity scores obtained for LDA and LR were 0.96 and 0.73, and 0.95 and 0.75, respectively. It is also shown that the channel slope above which debris-flow is observed decreases with the gravitational energy of the catchment. Limitations of the morphometric discrimination are discussed. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Mélanie Bertrand & Frédéric Liébault & Hervé Piégay, 2013. "Debris-flow susceptibility of upland catchments," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 67(2), pages 497-511, June.
    • Handle: RePEc:spr:nathaz:v:67:y:2013:i:2:p:497-511
    DOI: 10.1007/s11069-013-0575-4
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    Cited by:

    1. M. Ponziani & D. Ponziani & A. Giorgi & H. Stevenin & S. M. Ratto, 2023. "The use of machine learning techniques for a predictive model of debris flows triggered by short intense rainfall," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 143-162, May.
    2. María Isabel Arango & Edier Aristizábal & Federico Gómez, 2021. "Morphometrical analysis of torrential flows-prone catchments in tropical and mountainous terrain of the Colombian Andes by machine learning techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 983-1012, January.
    3. Jiazhi Qie & Adrien Favillier & Frédéric Liébault & Juan Antonio Ballesteros Cánovas & Jérôme Lopez-Saez & Sébastien Guillet & Loïc Francon & Yihua Zhong & Markus Stoffel & Christophe Corona, 2024. "A supply-limited torrent that does not feel the heat of climate change," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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