Matthew Thompson, Rocky Mountain Research Station, USFS, firstname.lastname@example.org (Presenter)
Michael Hand, Rocky Mountain Research Station, USFS, email@example.com
Darek Nalle, Rocky Mountain Research Station, USFS, firstname.lastname@example.org
Land management agencies face uncertain tradeoffs regarding investments in preparedness and fuels management versus future suppression costs and impacts to valued resources and assets. The expected suppression costs and impacts of fire are not equal across landscapes or across regions, suggesting opportunities for efficiency gains by prioritizing funding where net wildfire management costs and detrimental impacts are lowest. It is critical, therefore, to have the ability to estimate the likely socioeconomic and ecological consequences of escaped large wildland fires. We present an approach to quantify likely consequences by sampling from a set of simulated empirical distributions. The foundation of this approach is the simulation of escaped large wildland fire occurrence and growth across the landscape of interest. With this dataset we can estimate likely suppression costs on a per-fire basis, based on historical fire cost data and characteristics associated with each ignition. We can also estimate likely impacts of wildfire, by overlaying simulated fire perimeters with geospatial maps of valued resources and assets. Using bootstrap sampling techniques we can then estimate the consequences of any given number of escapes in any given location. Through a case study on the Deschutes National Forest, we illustrate how our approach can be coupled with information on initial attack success and fuel treatment effectiveness to analyze investments across the wildfire management spectrum.