Fire spread in chaparral – a comparison of laboratory data and model predictions in burning live fuels
David R. Weise A F , Eunmo Koo B , Xiangyang Zhou C , Shankar Mahalingam D , Frédéric Morandini E and Jacques-Henri Balbi E
+ Author Affiliations
- Author Affiliations
A USDA Forest Service, Pacific Southwest Research Station, Fire and Fuels Program, Riverside, CA 92506-6071, USA.
B Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87544, USA.
C FM Global, Inc., 1175 Boston-Providence Turnpike, PO Box 9102, Norwood, MA 02062-5019, USA.
D Department of Mechanical and Aerospace Engineering, University of Alabama in Huntsville, AL 35899, USA.
E Unité Mixte de Recherche (UMR) CNRS (Centre National de la Recherche Scientifique) 6134 – Sciences Pour l’Environnement (SPE), University of Corsica, BP 52, F-20250 Corte, France.
F Corresponding author. Email: dweise@fs.fed.us
International Journal of Wildland Fire 25(9) 980-994 https://doi.org/10.1071/WF15177
Submitted: 3 October 2015 Accepted: 20 April 2016 Published: 16 June 2016
Abstract
Fire behaviour data from 240 laboratory fires in high-density live chaparral fuel beds were compared with model predictions. Logistic regression was used to develop a model to predict fire spread success in the fuel beds and linear regression was used to predict rate of spread. Predictions from the Rothermel equation and three proposed changes as well as two physically based models were compared with observed spread rates of spread. Flame length–fireline intensity relationships were compared with flame length data. Wind was the most important variable related to spread success. Air temperature, live fuel moisture content, slope angle and fuel bed bulk density were significantly related to spread rate. A flame length–fireline intensity model for Galician shrub fuels was similar to the chaparral data. The Rothermel model failed to predict fire spread in nearly all of the fires that spread using default values. Increasing the moisture of extinction marginally improved its performance. Modifications proposed by Cohen, Wilson and Catchpole also improved predictions. The models successfully predicted fire spread 49 to 69% of the time. Only the physical model predictions fell within a factor of two of actual rates. Mean bias of most models was close to zero. Physically based models generally performed better than empirical models and are recommended for further study.
Additional keywords: Adenostoma fasciculatum, Arctostaphylos glandulosa, Ceanothus crassifolius, Quercus berberidifolia.
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