Cited by 85Fire effects on ecosystemsForest Management and PolicyRangeland and Wildlife Management

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

Houtman, Rachel M., Montgomery, Claire A., Gagnon, Aaron R., Calkin, David E., Dietterich, Thomas G., McGregor, Sean, Crowley, Mark·2013·International Journal of Wildland Fire

Abstract

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.

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Registry

Crossref · 10.1071/wf12157

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  • RL is used to predict the spatial progression of fire, which helps firefighters decide when to let a fire burn and when to stop it.

    “In [266,267], RL is used to predict the spatial progression of fire.This helps firefighters decide when to let a fire burn and when to stop it [355].”

    From Tackling climate change with machine learning (2023) · cites this paper as [355]

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Authors
Houtman, R. M., Montgomery, C. A., Gagnon, A. R., Calkin, D. E., Dietterich, T. G., McGregor, S., & Crowley, M.
Affiliations
  • Oregon State University (US)
  • BUSDA Rocky Mountain Research Station, Forest Sciences Laboratory, 800 East Beckwith Avenue, Missoula, MT 59801, USA.
Corresponding author
Houtman, Rachel M.
Venue
International Journal of Wildland Fire
Published
2013-05-09
Volume
22
Issue
7
Pages
871-882
ISSN
  • 1049-8001 (ISSN-L)
  • 1448-5516
OpenAlex
W2141948736
Cited by
85
References
44
Citations by year
2026: 2 · 2025: 1 · 2024: 9 · 2023: 6 · 2022: 12 · 2021: 5 · 2020: 5 · 2019: 10 · 2018: 8 · 2017: 7 · 2016: 5 · 2015: 9 · 2014: 4 · 2013: 1 · 2012: 1
FWCI
4.82
Type
journal article
Language
en
Open access
closed
Locations
International Journal of Wildland Fire (publishedVersion)
Funders
  • National Science Foundation
  • U.S. Forest Service
  • Rocky Mountain Research Station
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Fire effects on ecosystems
  • Physical Sciences › Environmental Science › Global and Planetary Change › Forest Management and Policy
  • Physical Sciences › Environmental Science › Ecology › Rangeland and Wildlife Management
Keywords
wildfire suppression costs, fuel treatment, fire behavior modeling, central Oregon

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