Incorporating shear stiffness into post-fire debris flow statistical triggering models

R. E. S. Moss () and N. Lyman
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R. E. S. Moss: California Polytechnic State University
N. Lyman: U.S. Army Corps of Engineers

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 113, issue 2, No 5, 913-932

Abstract: Abstract Commonly used post-fire debris flow statistical triggering models consider predictor variables that account for; rainfall intensity, rainfall accumulation, area burned, burned intensity, geology, slope, and others. These models represent the physical process of debris flow initiation and subsequent failure by quantifying near-surface soil characteristics. Shear wave velocity as a proxy for sediment shear stiffness informs the likelihood of particle dislocation, contractive or dilative volume changes, and downslope displacement that result from flow-type failures. This broadly available variable common to other hazard predictions, such as liquefaction analysis, provides good coverage in the watersheds of interest for debris flow predictions. A logistic regression is used to compare the new variable against currently used variables for predictive post-fire debris flow triggering models. We find that the new variable produces slightly improved performance in prediction of triggering while better capturing the physics of flow-type failure. Additional suggestions are presented for utilizing statistical cross-validation methods to advance prediction performance and the utility of different variables for quick assessment of likelihood during post-fire rainfall events.

Keywords: Debris flow; Shear wave velocity; Predictive modeling (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-022-05330-x

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