Incorporating Shear Resistance Into Debris Flow Triggering Model Statistics

Author

Noah J. Lyman, California Polytechnic State University, San Luis ObispoFollow

DOI: https://doi.org/10.15368/theses.2020.146
Available at: https://digitalcommons.calpoly.edu/theses/2254

Date of Award

12-2020

Degree Name

MS in Civil and Environmental Engineering

Department/Program

Civil and Environmental Engineering

College

College of Engineering

Advisor

Robb Moss

Advisor Department

Civil and Environmental Engineering

Advisor College

College of Engineering

Abstract

Several regions of the Western United States utilize statistical binary classification models to predict and manage debris flow initiation probability after wildfires. As the occurrence of wildfires and large intensity rainfall events increase, so has the frequency in which development occurs in the steep and mountainous terrain where these events arise. This resulting intersection brings with it an increasing need to derive improved results from existing models, or develop new models, to reduce the economic and human impacts that debris flows may bring. Any development or change to these models could also theoretically increase the ease of collection, processing, and implementation into new areas.

Generally, existing models rely on inputs as a function of rainfall intensity, fire effects, terrain type, and surface characteristics. However, no variable in these models directly accounts for the shear stiffness of the soil. This property when considered with the respect to the state of the loading of the sediment informs the likelihood of particle dislocation, contractive or dilative volume changes, and downslope movement that triggers debris flows. This study proposes incorporating shear wave velocity (in the form of slope-based thirty-meter shear wave velocity, Vs30) to account for this shear stiffness. As commonly used in seismic soil liquefaction analysis, the shear stiffness is measured via shear wave velocity which is the speed of the vertically propagating horizontal shear wave through sediment. This spatially mapped variable allows for broad coverage in the watersheds of interest. A logistic regression is used to then compare the new variable against what is currently used in predictive post-fire debris flow triggering models.

Resulting models indicated improvement in some measures of statistical utility through receiver operating characteristic curves (ROC) and threat score analysis, a method of ranking models based on true/false positive and negative results. However, the integration of Vs30 offers similar utility to current models in additional metrics, suggesting that this input can benefit from further refinement. Further suggestions are additionally offered to further improve the use of Vs30 through in-situ measurements of surface shear wave propagation and integration into Vs30 datasets through a possible transfer function. Additional discussion into input variables and their impact on created models is also included.