Crown fuel consumption in Canadian boreal forest fires

Abstract

Predictive crown fuel consumption models were developed using empirical data from experimental burning projects. Crown fuel load for foliage, bark, branchwood and stemwood were calculated for live overstorey and understorey trees in each plot using nationally derived tree biomass algorithms. Standing dead tree branchwood and stemwood biomass were similarly calculated. Crown bulk density values were calculated for all non-stemwood fuel components. Factors that affect the initiation and spread of crown fires (live crown base height, foliar moisture content, surface fuel consumption, critical surface fire spread rate, critical surface fire intensity) and components of the Canadian Forest Fire Weather Index System were not statistically significant variables. Crown bulk density was moderately correlated with crown fuel consumption but was not an influential factor. A new crown fuel consumption model was developed by regression analysis using fuel load of overstorey tree foliage and standing dead tree branchwood, and fire rate of spread through crown fraction burned. A simpler model was developed using only overstorey tree foliage fuel load and fire rate of spread. Both models provide forest and fire management agencies with enhanced ability to determine crown fuel consumption, fire behaviour and carbon emissions in boreal fires using basic forest inventory or biomass/carbon datasets.

Improved logistic models of crown fire probability in Canadian conifer forests

“…Moderately closed pine stands (~50-60% canopy closure) were assigned to the 'moderate' density class, in parallel with the dominant class found in the litter moisture data (Wotton and Beverly 2007). The 'dense' class was assigned to sites with estimated canopy closure of >60% or particularly high crown fuel loading values (Forestry Canada Fire Danger Group 1992;Cruz 1999;de Groot et al 2022). 'Light' density class was assigned to sites with obvious low-density structure (Alexander et al 1991), as well as to individual plots at other sites with <45% canopy closure (Table 1).…”

“…1) have been summarised in part in previous publications (e.g. Cruz 1999;de Groot et al 2022). Our present database (Table 1) includes the majority of conifer experimental fire data between 1960 and 1986, along with 21 more recent observations (the latest being 2019), for a total of 42 more fires than were used in the development of previous crown fire occurrence models (Cruz et al 2004).…”

“…1j) was a result of the stem mortality associated with natural stand development. A separate set of plots at the site had been hand-thinned 15 years prior to their burning (SC-TH) and had much lower overstorey tree density (Stocks 1987a;de Groot et al 2022). Earlier studies have treated these experimental plots as separate fuel types (Forestry Canada Fire Danger Group 1992; de Groot et al 2022), 1 or estimated that the standing dead trees at SC-IM reduced the FMC (Van Wagner 1993).…”

A Case Study Featuring the Time Evolution of a Fire‐Induced Plume Jet Over the Rum Creek Fire: Mechanisms, Processes, and Dynamical Interplay

“…The principal goal was to observe dynamics and emissions within the vicinity of a fire front, and the coupling of dynamics between the atmosphere and flames that ultimately effects the ROS in boreal forests (Stocks et al., 2004). Experimental burns were conducted between 1995 and 2001 with many modeling studies using data collected on fuels, winds from towers, and head fire intensities to understand relationships between wind, fuel consumption and moisture, and ROS (e.g., Abbott et al., 2007; de Groot et al., 2022; Linn et al., 2012). Another experiment conducted in the late 1990s, but focused on wildfire impacts on climate from boreal forest burning, was FROSTFIRE (Harden et al., 2004; Hinzman et al., 2003).…”

Development of an Index for Forest Fire Risk Assessment Considering Hazard Factors and the Hazard-Formative Environment

“…The Canadian Forest Fire Danger Rating System (CFFDRS) is widely used and has been applied in several countries, including parts of the United States, New Zealand, Fiji, Indonesia, and Malaysia [28]. This system calculates multiple flammability and fire behavior indicators based on four meteorological factors and derives a risk index for quantitatively guiding forest fire management activities [29,30]. In China, the adaptability of the CFFDRS was verified in the Daxing'anling region [31].…”