Wildfire Suppression Cost Forecasts for the US Forest Service

costs, pure autoregressive models of historical costs, and more complex regression models that may include historical costs but also external information. The first approach is the simplest, and one example is the 10-year moving average of the most recent observed costs. This model gives each year an equal weight when making forecasts and implicitly assumes that expected costs change slowly. In addition, relatively simple are autoregressive models, which are similar to moving average models except that they allow previous years’ costs to have differing weights. These two approaches require no data other than the time series of costs and may have an added benefit of relative transparency, but they may ignore potentially useful external information. The more complex regression models take advantage of this additional external information. For this analysis, the cost of fire suppression is defined as the group of expenditures that fall under the US Forest Service budget category of Wildland Fire Suppression. This includes money expended to suppress wildland fires, to monitor naturally occurring fires that are being allowed to burn for resource benefits, and emergency rehabilitation of burned areas. This definition does not include physical or economic damages to trees, forests, structures, or the impacts on the local economy from either the wildfire or suppression operations. In this article, we use the terms suppression cost and suppression expenditure interchangeably. Our expenditure data are from a database of US Forest Service fire management expenditures first developed by Schuster et al. (1997) and maintained by the Rocky Mountain Research Station. The data, which extend for 31 years (1977–2007), are annual by US Federal FY, which begins October 1 and ends September 30. For example, FY 2007 began Oct. 1, 2006 and ended Sept. 30, 2007. Suppression expenditures are influenced by a number of factors. Anything that affects the number, size, and intensity of wildfires will affect the costs of suppression. Our regressions include ocean temperatures and ocean pressure indices that have been shown to influence fire in many parts of the United States. Sea surface temperatures and atmospheric pressure affect circulation patterns, including frontal systems and thunderstorms, thus affecting fire-related weather and hazardous forest fuels by altering lightning, precipitation, temperature, and wind. These include the El NinoSouthern Oscillation (Swetnam and Betancourt 1990, Barnett and Brenner 1992, McKenzie et al. 2004, Schoennagel et al. 2005, Kitzberger et al. 2007), the Pacific Decadal Oscillation (McKenzie et al. 2004, Schoennagel et al. 2005, Collins et al. 2006, Kitzberger et al. 2007), the Arctic Oscillation, the Southern Oscillation Index (Simard et al. 1985, Swetnam and Betancourt 1990, Brenner 1991), and the North Atlantic Oscillation (Collins et al. 2006, Kitzberger et al. 2007). Although larger climatic patterns are represented by the ocean temperature and pressure measures, we also incorporate localized measures of drought to capture the local effects of changes in precipitation and temperature on fuel conditions. Drought indices have been shown to explain observed fire activity (Westerling 2003, Crimmins and Comrie 2004, Gedalof et al. 2005, Collins et