No abstract

Current post-fire debris flow triggering models consider predictor variables accounting 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 shear failure by quantifying near-surface soil characteristics. By including shear wave velocity as a proxy for sediment shear stiffness, models can better inform the likelihood of particle dislocation, contractive or dilative volume changes, and downslope displacement that results from debris flows. 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 improved performance in prediction of triggering while capturing the physics of sediment failing in a shearing and flow-type response. 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 eminent high intensity rainfall events.