Improved logistic models of crown fire probability in Canadian conifer forests

Abstract

Background Crown fires are an ecologically necessary but hazardous process in conifer forests. Prediction of their behaviour in Canada has largely depended on the Canadian Forest Fire Behaviour Prediction System, in which fire weather indices drive primarily fixed fuel type models. The Crown Fire Initiation and Spread (CFIS) system presents a more flexible approach to predicting crown fire occurrence than fixed fuel type models. Aims Using a multi-decadal database of experimental fires carried out in conifer plots (1960–2019, n = 113), our aim was to develop updated models based on the CFIS system approach, fitting crown fire occurrence models to fire environment variables using logistic regression. Methods We tested alternative fuel moisture estimates and compared various model forms using repeated cross-validation. In two-storeyed stands, crown fire occurrence was defined as the involvement of lower canopy stratum fuels. Key results Final models based on wind speed, fuel strata gap, litter moisture and surface fuel consumption predicted crowning events correctly in up to 92% of cases in training data (89% in cross-validation). Conclusions and implications These new models offer improved accuracy and flexibility that will help users assess how competing environmental factors interact under different fuel treatments and wildfire scenarios.

Fuel types misrepresent forest structure and composition in interior British Columbia: a way forward

“…In both provincial and national decision trees and fuel typing layers (Perrakis et al 2018 ; Natural Resources Canada 2019 ), mismatches were frequently associated with branches of the decision tree that assign fuel type based on species composition (e.g., Douglas-fir as the leading overstory canopy species). This compositional approach limits the inclusion of structural fuels attributes, which are emphasized over composition in the FBP System and are important for predicting the likelihood of short or long flame lengths, active or passive tree torching, and crown fire initiation and spread (Cruz et al 2005 ; Alexander and Cruz 2006 , 2011 ; Cruz and Alexander 2013 ; Perrakis et al 2023 ). For example, in the provincial decision tree, eight dry-zone Douglas-fir stands that had not been logged in the past 6 years, with canopy heights over 12 m and canopy cover greater than 55%, were assigned as C-7 based on composition (Perrakis et al 2018 ), whereas the field assigned fuel type was C-4 based on structure (Table S1 ).…”

Linking crown fire likelihood with post-fire spectral variability in Mediterranean fire-prone ecosystems

“…Extreme wildfire events involving crown fires usually occur under severe fire weather conditions, resulting in hazardous and erratic fire behaviour (Mitsopoulos and Dimitrakopoulos 2007) over a wide variety of forest ecosystems throughout the western Mediterranean Basin (Fernández-Guisuraga et al 2019, 2023a. A crown fire in forest spreads much faster (up to two to four times) than a surface fire burning in the same conditions (Fernandes et al 2004;Perrakis et al 2023), and its control efforts by direct attack are ineffective (Erni et al 2020;Frost et al 2022). These characteristics, together with increased fire intensity and frequent long-range spotting (Albini et al 2012), may entail a serious threat to property and life in the wildland-urban interface (Fiorini et al 2023).…”

Comparison of Different Models to Simulate Forest Fire Spread: A Case Study

“…As well as the established traditional physical and empirical models, more types of forest fire behavior models have been established, with progress made in their abilities [27]; flame geometry modeling has focused on the effect of the wind on flame characteristics [28]. Based on logistics, the established probabilistic models are based more on the occurrence of extreme fire behavior such as spotting fires and canopy fires [29].…”