4.3 Article

Quantifying the Potential Impacts of Fuel Treatments on Wildfire Suppression Costs

期刊

JOURNAL OF FORESTRY
卷 111, 期 1, 页码 49-58

出版社

OXFORD UNIV PRESS INC
DOI: 10.5849/jof.12-027

关键词

wildfire management; hazardous fuels; suppression cost; risk assessment

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资金

  1. National Fire Decision Support Center

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Modeling the impacts and effects of hazardous fuel reduction treatments is a pressing issue within the wildfire management community. Prospective evaluation of fuel treatment effectiveness allows for comparison of alternative treatment strategies in terms of socioeconomic and ecological impacts and facilitates analysis of tradeoffs across land-management objectives. Studies have yet to rigorously examine potential impacts to fire suppression expenditures associated with prior hazardous fuel reduction treatments. Such information would be helpful for federal land-management agencies struggling to contain escalating wildfire management costs. In this article we establish a methodology for estimating potential reductions in wildfire suppression costs. Our approach pairs wildfire simulation outputs with a regression cost model and quantifies the influence of fuel treatments on distributions of wildfire sizes and suppression costs. Our case study focuses on a landscape within the Deschutes National Forest in central Oregon that was selected to receive funding under the auspices of the Collaborative Forest Landscape Restoration Program. Results suggest substantial reductions in distributions of wildfire size and suppression cost on a per fire basis. Furthermore, because fewer ignitions become large fires on the treated landscape, distributions of annual area burned and annual suppression costs also shift downward because of fuel treatments. Results are contingent on four key factors: large-scale implementation of fuel treatments across the landscape, assumed treatment effectiveness over the duration of the analysis period, accuracy of wildfire and cost models, and accuracy of projected changes to fire behavior fuel models resulting from fuel treatments. We discuss strengths and limitations of the modeling approach and offer suggestions for future improvements and applications.

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