Paper No. 11
Presentation Time: 10:50 AM

ANALYSIS OF ECONOMIC RISK FROM POST-WILDFIRE DEBRIS FLOWS


MCCOY, Kevin1, SANTI, Paul1, KAFFINE, Daniel2, REBENNACK, Steffen2, KRASKO, Vitaliy2 and LOHMANN, Timo2, (1)Department of Geology & Geological Engineering, Colorado School of Mines, Golden, CO 80401, (2)Division of Economics and Business, Colorado School of Mines, Golden, CO 80401, kemccoy@mines.edu

Post-wildfire debris flows are a serious hazard in the western United States. Potential damage from these events includes destruction of structures, degradation of habitat and water quality, and loss of human life. Understanding economic risk from post-wildfire debris flows will allow cost optimization modeling for selection of natural hazard management strategies following a fire. The first step in this process is an analysis of damage cost estimates and related probabilities, conducted for the area impacted by the 2009 Jesusita Fire near Santa Barbara, California. Debris flow hazards and impacts were analyzed in ArcGIS, utilizing existing models and publicly available data. Next, probability of occurrence and volume of post-wildfire debris flows were estimated for a range of plausible storm rainfall scenarios using equations developed in Gartner et al. (2008) and Cannon et al. (2010). Debris flow inundation areas were estimated in ArcGIS using a modified version of the LAHARZ program (Schilling, 1998), adjusted by the USGS to model post-wildfire debris flows. A series of inundation maps was produced to cover the expected range of debris flow volumes. Features potentially impacted by debris flows were identified from the inundation maps using geoprocessing tools in ArcGIS. Damage cost estimates for individual impacted features were generated and totaled to develop curves of damage cost as a function of expected debris flow volume for each basin within the burned area. Economic risk in the form of expected costs was estimated by multiplying the probability function by the damage function. Preliminary results suggest that this process can identify the most vulnerable rainfall-probability-damage scenario, which can then be used to optimize debris flow management strategies.