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Modeling Containment of Large Wildfires Using Generalized Linear Mixed-Model Analysis

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Billions of dollars are spent annually in the United States to contain large wildland fires, but the factors contributing to suppression success remain poorly understood. We used a regression model (generalized linear mixed-model) to model containment probability of individual fires, assuming that containment was a repeated-measures problem (fixed effect) and individual fires were random effects. Changes in daily fire size from 306 fires occurring in years 2001‐2005 were processed to identify intervals of high spread from those of low spread. The model was tested against independent data from 140 fires in 2006. The analysis suggested that containment was positively related to the number of consecutive days during which the fire grew little and the number of previous intervals. Containment probability was negatively related to the length of intervals during which the fire exhibited high spread and the presence of timber fuel types, but fire size was not a significant predictor. Characterization of containment probability may be a useful component of cost-benefit analysis of large fire management and planning systems.

Keywords: fire suppression; generalized linear mixed models; large wildfires; wildfire containment

Document Type: Research Article

Publication date: 2009-06-01

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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