Spatially explicit forecasts of large wildland fire probability and suppression costs for CaliforniaHaiganoush K. Preisler A G , Anthony L. Westerling B , Krista M. Gebert C F , Francisco Munoz-Arriola D and Thomas P. Holmes E
A USDA Forest Service, Pacific Southwest Research Station, 800 Buchanan Street, West Annex Building, Albany, CA 94710, USA.
B University of California – Merced, 5200 N. Lake Road, Merced, CA 95343, USA.
C USDA Forest Service, Rocky Mountain Research Station, PO Box 7669, Missoula, MT 59807, USA.
D University of California – San Diego, 9500 Gilman Drive, San Diego, CA 92093-5004, USA.
E USDA Forest Service, Southern Research Station, Forestry Sciences Lab, PO Box 12254, Research Triangle Park, NC 27709, USA.
F Present address: USDA Forest Service, Northern Region, 200 East Broadway, PO Box 7669, Missoula, MT 59807, USA.
G Corresponding author. Email: email@example.com
International Journal of Wildland Fire 20(4) 508-517 http://dx.doi.org/10.1071/WF09087
Submitted: 8 August 2009 Accepted: 13 August 2010 Published: 20 June 2011
In the last decade, increases in fire activity and suppression expenditures have caused budgetary problems for federal land management agencies. Spatial forecasts of upcoming fire activity and costs have the potential to help reduce expenditures, and increase the efficiency of suppression efforts, by enabling them to focus resources where they have the greatest effect. In this paper, we present statistical models for estimating 1–6 months ahead spatially explicit forecasts of expected numbers, locations and costs of large fires on a 0.125° grid with vegetation, topography and hydroclimate data used as predictors. As an example, forecasts for California Federal and State protection responsibility are produced for historic dates and compared with recorded fire occurrence and cost data. The results seem promising in that the spatially explicit forecasts of large fire probabilities seem to match the actual occurrence of large fires, with the exception of years with widespread lightning events, which remain elusive. Forecasts of suppression expenditures did seem to differentiate between low- and high-cost fire years. Maps of forecast levels of expenditures provide managers with a spatial representation of where costly fires are most likely to occur. Additionally, the statistical models provide scientists with a tool for evaluating the skill of spatially explicit fire risk products.
Additional keywords: fire simulations, generalised Pareto distribution, hydroclimate, logistic regression, moisture deficit, spline functions.
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