Investigation of Post-Fire Debris Flows in Montecito

Yifei Cui,Deqiang Cheng,Dave Chan

doi:10.3390/ijgi8010005

Abstract

Debris flows in a burned area, post-fire debris flows, are considered as one of the most dangerous geo-hazards due to their high velocity, long run-out distance, and huge destruction to infrastructures. The rainfall threshold to trigger such hazards is often reduced compared with normal debris flow because ashes generated by mountain fires reduce the permeability of the top soil layer, thus increasing surface runoff. At the same time, burnt material and residual debris have very poor geo-mechanical characteristics, e.g., their internal friction angle and cohesion are typically low, and thus an intense rainfall can easily trigger some debris flows. Studying post-fire debris flow enables us to get a deeper understanding of disaster management. In this paper, the debris flow that occurred in Montecito, California, USA, and was affected by the Thomas Fire was used as a case study. Five major watersheds were extracted based on the digital elevation model (DEM). Remote sensing images were used to analyze the wildfire process, the extent of the burned areas, and the burn severity. The hypsometric integral (HI) and short-duration rainfall records of the watersheds around Montecito when the post-fire debris flows occurred were analyzed. Steep terrain, loose and abundant deposits, and sufficient water supply are the important conditions affecting the formation of debris flows. Taking watersheds as the research objects, HI was used to describe the geomorphic and topographic features, open-access rainfall data was used to represent the water supply, and burn severity represented the abundance of material sources. An occurrence probability model of post-fire debris flow based on HI, short-duration heavy rainfall, and burn severity was developed by using a logistic regression model in post-fire areas. By using this model, the occurrence probability of the post-fire debris flow in different watersheds around Montecito was analyzed based on the precipitation with time. Especially, the change characteristics of occurrence probability of debris flows over time based on the model bring a new perspective to observe the obvious change of the danger of post-fire debris flows and it is very useful for early warning of post-fire debris flows.

Highlights

Vegetation in the forest has the function of conserving water and preserving soil erosion
Cannon et al [4] found that most post-fire debris flows are generated through the process of progressive entrainment of material eroded from hillslopes and channels
Fire Analysis Using the Shortwave Infrared (SWIR) and Normalized Burn Ratio (NBR) 3.2.1S. hFoirretwAanvaelyinsifsraUresdin(gSWthIeRS)hcoanrtwpeanveetrIantferahraezdeoakned, sNooSrWmIaRliizmedagBeusrcnanRabteious(NedBtRo)identify and aSnhaolrytzweaavcetivinefrfaorreedst,4ca0n]. pTeankeintrgatLeahnadzseata8ndastma oaskea,nseoxSaWmpIRle,imthaegfiesrecsamn obkeeucsaendbteo siedeenntiinfythaentdruaenacloylzoer iamctaivgee f(oFrigesutrefir3ea)[.39H,4o0w].eTvaekr,initgisLdanifdfiscautlt8todaidtaenastifayntehxeabmuprnlee,dthaerefairse. sInmtohkee fcaalsneb-ceosloerencoinmtphoestitreueimcoalgoer oimf sahgoer(tFwigavuerein3far)a. rHedowdeavtaer(,Rit=isSdWifIfRic2u,ltGto=iSdWenItRif1y, tahnedbBur=neSdWaIrRe2a)s,. tIhne tbhuernfaeldsea-rceoalsorarceormevpeoaslietde aims paginekocfolsohro(rFtwigauvre 3inb)f.rared data (R = SWIR2, G = SWIR1, and B = SWIR2), the burned areas are revealed as pink color (Figure 3b)

Summary

Introduction

Vegetation in the forest has the function of conserving water and preserving soil erosion. Cannon et al [17] used areal burned extent, soil properties, basin morphology, and rainfall from short-duration and low-recurrence-interval convective rainstorms to construct models to describe post-fire debris-flow probability. Gartner et al [18] proposed a prediction function of erosion probability for post-fire debris flows based on channel slope, plane curvature, and channel length by conducting a comparison among factors, including contributing drainage basin area, channel slope, planform curvature, burn severity, and the upstream channel length, resulting in good accuracy and precision. HI, burn severity, and short-duration rainfall characteristics of the watersheds around Montecito were analyzed firstly After that, these three factors were selected to conduct the logistic regression model of occurrence probability of the debris flows in post-fire areas. Using the ArcGIS “watershed” tool, in which the flow-direction raster and pour-point data were used as input data, the contributing areas above different pour points in a raster data were determined as ranges of watersheds

Hypsometric Integral

Logistic Regression Model

Model Application around Montecito

Findings

Discussion and Conclusions