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RESEARCH ARTICLE
Contents Vol 14(1)

Fire spread in chaparral—‘go or no-go?’

David R. Weise A C , Xiangyang Zhou B , Lulu Sun B and Shankar Mahalingam B
+ Author Affiliations
- Author Affiliations

A Forest Fire Laboratory, Pacific Southwest Research Station, USDA Forest Service, 4955 Canyon Crest Drive, Riverside, CA 92507, USA.

B Department of Mechanical Engineering, University of California-Riverside, Riverside, CA 92521, USA. Telephone: +1 951 787 2134; email: xzhou@engr.ucr.edu; lsun@engr.ucr.edu; shankar.mahalingam@ucr.edu

C Corresponding author. Telephone: +1 951 680 1500; fax: +1 951 680 1501; email: dweise@fs.fed.us

International Journal of Wildland Fire 14(1) 99-106 https://doi.org/10.1071/WF04049
Submitted: 24 August 2004  Accepted: 11 October 2004   Published: 7 March 2005

Abstract

Current fire models are designed to model the spread of a linear fire front in dead, small-diameter fuels. Fires in predominantly living vegetation account for a large proportion of annual burned area in the United States. Prescribed burning is used to manage living fuels; however, prescribed burning is currently conducted under conditions that result in marginal burning. We do not understand quantitatively the relative importance of the fuel and environmental variables that determine spread in live vegetation. To address these weaknesses, laboratory fires have been burned to determine the effects of wind, slope, moisture content and fuel characteristics on fire spread in fuel beds of common chaparral species. Four species (Adenostoma fasciculatum, Ceanothus crassifolius, Quercus berberidifolia, Arctostaphylos parryana), two wind velocities (0 and 2 m s−1) and two fuel bed depths (20 and 40 cm) were used. Oven-dry moisture content of fine fuels (<0.63 cm diameter) ranged from 0.09 to 1.06. Seventy of 125 fires successfully propagated the length (2.0 m) of the elevated fuel bed. A logistic model to predict the probability of successful fire spread was developed using stepwise logistic regression. The variables selected to predict propagation were wind velocity, slope percent, moisture content, fuel loading, species and air temperature. Air temperature and species terms were removed from the model for parsimony. The final model correctly classified 94% of the observations. Comparison of results with an empirical decision matrix for prescribed burning in chaparral suggested some agreement between the laboratory data and the empirical tool.


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