A Mapping Firewall
G E O W O R L D / O C T O B E R 2 O O 9 20 BY DAVID BUCKLEY, JOSEPH K. BERRY AND JASON BATCHELOR Disaster Mapping I n the 2007 fire season, San Diego County alone saw 360,000 acres burned, more than $1 billion in losses, more than 1,200 homes destroyed, many buildings and critical infrastructure lost, and significant amounts of commodity agriculture ruined. Suppression costs at the federal level have surpassed $1 billion annually for the last several years, and state and local costs are believed to be more than double that. The consequences of wildfires have never been greater as more people move into wildfire-prone areas. And there's an increasing need for fuel treatments, mitigation planning, prevention awareness and recov-ery preparedness to reduce wildfire risk and impacts to these communities. But where is the greatest risk? What are the poten-tial economic, social and environmental impacts? What and where are mitigation actions most needed? How can alternatives be quantified, compared and prioritized? Are we spending our budgets effectively and efficiently? This article focuses on the utility of geotechnology, map-analysis procedures, and Web-based visualization and delivery options to identify areas of greatest jeop-ardy as well as quantify the dollar impact of wildfire loss and proposed mitigation efforts. Wildfire Threat and Risk Modeling Previous wildfire risk models developed a relative scale, such as the low, medium, high and extreme fire-danger levels seen at the entrances of national forests. Although this scale is useful for informing the public and guiding broad fire planning, it doesn't fully express wildfire risk. Comprehensive risk modeling involves three distinct elements: 1. Wildfire Threat estimating the probability and intensity of a wildfire occurring at a location. 2. Wildfire Effects quantifying the impact of the potential loss. 3. Wildfire Risk combining the threat and effects into a measure of probable loss over time. The Wildfire Threat portion integrates numerous mapped data layers such as weather factors, histori-cal fire occurrence, surface and canopy fuels, terrain, and suppression effectiveness based on historic Modeling and Visualizations Assess Wildfire Threats, Risks and Economic Exposure