Fire spread in chaparral – a comparison of laboratory data and model predictions in burning live fuels

Abstract

International audienceFire behaviour data from 240 laboratory fires in high-density live chaparral fuel beds were compared withmodel predictions. Logistic regression was used to develop a model to predict fire spread success in the fuel beds and linearregression was used to predict rate of spread. Predictions from the Rothermel equation and three proposed changes as wellas two physically based models were compared with observed spread rates of spread. Flame length–fireline intensityrelationships were compared with flame length data. Wind was the most important variable related to spread success. Airtemperature, 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 modelfailed to predict fire spread in nearly all of the fires that spread using default values. Increasing the moisture of extinctionmarginally improved its performance. Modifications proposed by Cohen, Wilson and Catchpole also improvedpredictions. The models successfully predicted fire spread 49 to 69% of the time. Only the physical model predictionsfell within a factor of two of actual rates. Mean bias of most models was close to zero. Physically based models generallyperformed better than empirical models and are recommended for further study

Fire Behavior Predicting Models Efficiency in Brazilian Commercial Eucalypt Plantations

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