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.
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Acknowledgements
Integral to the success of this work was the generous support of the Warren J. Baker Endowment in Project-Based Learning and Robert D. Koob Endowment for Student Success. Additional resources were offered by the California Polytechnic State University – San Luis Obispo Graduate Assistantship Fund. We also want to thank research at the USGS, NOAA, and the California Geologic Survey for their data collection, and there works of debris flows. Finally, we would like to thank Dennis Staley, Jason Kean, and the many other collaborators for their work and models that this work builds upon.
Funding
Financial support came from the Warren J. Baker Endowment in Project-Based Learning and Robert D. Koob Endowment for Student Success at Cal Poly. Additional resources were offered by the California Polytechnic State University – San Luis Obispo Graduate Assistantship Fund.
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Both authors contributed to the completion of this research. Robb Moss conceived of the idea and developed the proof of concept, including initial statistical analysis and modeling. Noah Lyman performed data collection and thorough statistical analysis to fill out the study. Portions of this manuscript were taken from the MS Thesis of Noah Lyman, which was edited and commented on by Robb Moss. The manuscript as a whole was drafted by Robb Moss with edits, comments, and contributions by Noah Lyman. Both authors approve of the final manuscript.
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Moss, R.E.S., Lyman, N. Incorporating shear stiffness into post-fire debris flow statistical triggering models. Nat Hazards 113, 913–932 (2022). https://doi.org/10.1007/s11069-022-05330-x
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DOI: https://doi.org/10.1007/s11069-022-05330-x