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Wildfire Suppression Cost Forecasts for the US Forest Service

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Abstract:

The US Forest Service and other land-management agencies seek better tools for anticipating future expenditures for wildfire suppression. We developed regression models for forecasting US Forest Service suppression spending at 1-, 2-, and 3-year lead times. We compared these models to another readily available forecast model, the 10-year moving average model, and found that the regression models do a better job of forecasting the expenditures for all three time horizons. When evaluated against the historical data, our models were particularly better at forecasting the more recent years (2000‐2007) than the less sophisticated models. The regression models also allowed us to generate, using simulation methods, forecast statistics such as the means, medians, and confidence intervals of costs. These additional statistics provide policymakers, wildfire managers, and planners more information than a single forecast value.

Keywords: budget; expenditures; wildland fire management

Document Type: Research Article

Publication date: 2009-06-01

More about this publication?
  • The Journal of Forestry is the most widely circulated scholarly forestry journal in the world. In print since 1902, the Journal has received several national awards for excellence. The mission of the Journal of Forestry is to advance the profession of forestry by keeping forest management professionals informed about significant developments and ideas in the many facets of forestry: economics, education and communication, entomology and pathology, fire, forest ecology, geospatial technologies, history, international forestry, measurements, policy, recreation, silviculture, social sciences, soils and hydrology, urban and community forestry, utilization and engineering, and wildlife management. The Journal is published bimonthly: January, March, May, July, September, and November.

    2015 Impact Factor: 1.476
    Ranking: 22 of 66 in forestry

    Average time from submission to first decision: 39.6 days*
    June 1, 2016 to Feb. 28, 2017

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    Forest Science
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