Extreme value analysis of wildfires in Canadian boreal forest ecosystems

Abstract

Large fires are a major disturbance in Canadian forests and exert significant effects on both the climate system and ecosystems. During the last century, extremely large fires accounted for the majority of Canadian burned area. By making an instaneous change over a vast area of ecosystems, extreme fires often have significant social, economic, and ecological consequences. Since extreme values of fire size always situate in the upper tail of a cumulative probability distribution, the mean and variance alone are not sufficient to fully characterize those extreme events. To characterize the large fire behaviors in the upper tail, the authors in this study applied three extreme value distribution functions: (i) the generalized extreme value (GEV) distribution, (ii) the generalized Pareto distribution (GPD), and (iii) the GEV distribution with a Poisson point process (PP) representation to fit the Canadian historical fire data of the period 1959–2010. The analysis was conducted with the whole data set and different portions of the data set according to ignition sources (lightning-caused or human-caused) and ecozone classification. It is found that (i) all three extreme statistical models perform well to characterize extreme fire events, but the GPD and PP models need extra care to fit the nonstationary fire data, (ii) anthropogenic and natural extreme fires have significantly different extreme statistics, and (iii) fires in different ecozones exhibit very different characteristics in the view of statistics. Further, estimated fire return levels are comparable with observations in terms of the magnitude and frequency of an extreme event. These statistics of extreme values provide valuable information for future quantification of large fire risks and forest management in the region.