Statistical quality-control methods were used to detect significant changes in the mean and variance of the annual fire occurrence and area burned in Canada (19182000), Ontario (19172000), and northwestern Ontario (19172000). The quality-control chart method employed uses the first half of the record of a process as a baseline to test for significant changes in the mean or variance of the process in the second half of the record. Significant increases were detected in annual area burned and in fire occurrence in Canada, Ontario, and northwestern Ontario.
Abstract. Forest fire managers have long understood that most of a fire's growth typically occurs on a small number of days when burning conditions are conducive for spread. Fires either grow very slowly at low intensity or burn considerable area in a 'run'. A simple classification of days into 'spread events' and 'non-spread events' can greatly improve estimates of area burned. Studies with fire-growth models suggest that the Canadian Forest Fire Behaviour Prediction System (FBP System) seems to predict growth well during high-intensity 'spread events' but tends to overpredict rate of spread for non-spread events. In this study, we provide an objective weather-based definition of 'spread events', making it possible to assess the probability of having a spread event on any particular day. We demonstrate the benefit of incorporating this 'spread event' day concept into a fire-growth model based on the Canadian FBP System.
Most of the area burned by forest fires in Canada is due to the few fires that escape initial attack and become large. We developed a discrete event simulation model of the growth and suppression of large fires in the province of Ontario. Based on fire, weather and suppression data from the Ontario Ministry of Natural Resources, our model includes a logistic regression component to predict the probability that a fire will escape initial attack and burn more than 100 ha, a component that simulates the growth of large fires based on weather and forest vegetation, and a component that simulates fire suppression by firefighters and aircraft. We used our model to predict area burned under mild and severe weather with varying levels of fire suppression resources. We found that, although severe weather limits fire suppression effectiveness, suppression has a significant effect on area burned even during severe fire seasons.
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