Abstract
Due to a unique combination of environmental conditions, the chaparral shrublands of southern California are prone to large, intense wildland fires. There is ongoing work in the fire research community to establish whether fuel accumulation or weather conditions are the determining factor in the prevalence of large chaparral fires. This study introduces a framework for contributing a modeling perspective to understanding these alternative hypotheses. As models formalize our understanding of the physical process of fire spread, the sensitivity of the models to the meteorological and fuel inputs should be indicators of their relative importance. A global sensitivity analysis (GSA) was conducted on HFire, a spatially explicit raster model developed for modeling fire spread in chaparral fuels, based on the Rothermel spread equations. The GSA provided a quantitative measure of the importance of each of the model inputs on the predicted fire size. The results indicate that, under extreme weather conditions, wind speed was over three times more influential on predicted fire sizes than any other single model input. This finding supports the idea that fires burning under Santa Ana conditions are primarily driven by high wind speeds. Future research will involve extending the GSA methodology to quantify the relative importance of these inputs in terms of the long-term fire regime in chaparral ecosystems.
Highlights
Due to a unique combination of environmental conditions, the chaparral shrublands of southern California are prone to large, intense wildland fires
As models formalize our understanding of the physical process of fire spread, the sensitivity of the models to the meteorological and fuel inputs should be indicators of their relative importance
The results indicate that, under extreme weather conditions, wind speed was over three times more influential on predicted fire sizes than any other single model input
Summary
Tests of the age-dependent hypothesis have analyzed similar historical data but have concluded that there is not a direct relationship between large fires and fuel accumulation, and that large fires are a natural feature of the chaparral fire regime (Keeley et al 1999, Keeley and Fotheringham 2001a, Keeley and Fotheringham 2001b, Moritz 2003, Moritz et al 2004) In all of these studies, one point of agreement has been that Santa Ana winds can be the driving force behind large fire events. The objective was to use SA techniques to quantitatively establish the way in which the output of HFire is dependent on the inputs of fuel and weather conditions This analysis was framed in terms of predicting the size of fires during potential Santa Ana events, and the research hypothesis being tested was that wind speed accounts for more variability in simulated fire size than fuel-related variables such as fine dead fuel moisture and fuel model. Sensitivity analysis was implemented by 1) identifying a study area suited to testing the experimental hypotheses; 2) collecting the spatial information needed to run the model; 3) collecting the temporal input data; 4) defining a distribution for each input factor based on statistical analysis of the data; 5) sampling from the distributions of the input factors, and executing the model using these samples; 6) performing the sensitivity analysis in order to quantify the relative importance each input factor has on the output of the model; and 7) evaluating the hypotheses based on the results of the sensitivity analysis
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