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Allowing a wildfire to burn: estimating the effect on future fire suppression costs

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https://ir.library.oregonstate.edu/concern/articles/fj2362891

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  • Where a legacy of aggressive wildland fire suppression has left forests in need of fuel reduction, allowing wildland fire to burn may provide fuel treatment benefits, thereby reducing suppression costs from subsequent fires. The least-cost-plus-net-value-change model of wildland fire economics includes benefits of wildfire in a framework for evaluating suppression options. In this study, we estimated one component of that benefit – the expected present value of the reduction in suppression costs for subsequent fires arising from the fuel treatment effect of a current fire. To that end, we employed Monte Carlo methods to generate a set of scenarios for subsequent fire ignition and weather events, which are referred to as sample paths, for a study area in central Oregon. We simulated fire on the landscape over a 100-year time horizon using existing models of fire behaviour, vegetation and fuels development, and suppression effectiveness, and we estimated suppression costs using an existing suppression cost model. Our estimates suggest that the potential cost savings may be substantial. Further research is needed to estimate the full least-cost-plus-net-value-change model. This line of research will extend the set of tools available for developing wildfire management plans for forested landscapes.
  • Keywords: bio-economic modelling, wildland fire management, forest economics, forest fire policy
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  • Houtman, R., Montgomery, C., Gagnon, A., Calkin, D., Dietterich, T., McGregor, S., & Crowley, M. (2013). Allowing a wildfire to burn: Estimating the effect on future fire suppression costs. International Journal of Wildland Fire, 22(7), 871-882. doi:10.1071/WF12157
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  • 22
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  • 7
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  • This research was supported by the USDA Forest Service Rocky MountainResearch Station (joint venture agreement 09-JV-11221636–307) and by aNational Science Foundation Expeditions in Computing award forComputational Sustainability (grant 083278).
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