No abstract

The rate of spread of crown fires advancing over level to gently undulating terrain was modeled through nonlinear regression analysis based on an experimental data set pertaining primarily to boreal forest fuel types. The data set covered a significant spectrum of fuel complex and fire behavior characteristics. Crown fire rate of spread was modeled separately for fires spreading in active and passive crown fire regimes. The active crown fire rate of spread model encompassing the effects of 10-m open wind speed, estimated fine fuel moisture content, and canopy bulk density explained 61% of the variability in the data set. Passive crown fire spread was modeled through a correction factor based on a criterion for active crowning related to canopy bulk density. The models were evaluated against independent data sets originating from experimental fires. The active crown fire rate of spread model predicted 42% of the independent experimental crown fire data with an error lower then 25% and a mean absolute percent error of 26%. While the models have some shortcomings and areas in need of improvement, they can be readily utilized in support of fire management decision making and other fire research studies.

Application of crown fire behavior models in fire management decision-making have been limited by the difficulty of quantitatively describing fuel complexes, specifically characteristics of the canopy fuel stratum. To estimate canopy fuel stratum characteristics of four broad fuel types found in the western United States and adjacent areas of Canada, namely Douglas-fir, ponderosa pine, mixed conifer, and lodgepole pine forest stands, data from the USDA Forest Service's Forest Inventory and Analysis (FIA) database were analysed and linked with tree-level foliage dry weight equations. Models to predict canopy base height (CBH), canopy fuel load (CFL) and canopy bulk density (CBD) were developed through linear regression analysis and using common stand descriptors (e.g. stand density, basal area, stand height) as explanatory variables. The models developed were fuel type specific and coefficients of determination ranged from 0.90 to 0.95 for CFL, between 0.84 and 0.92 for CBD and from 0.64 to 0.88 for CBH. Although not formally evaluated, the models seem to give a reasonable characterization of the canopy fuel stratum for use in fire management applications.

A model was developed to predict the ignition of forest crown fuels above a surface fire based on heat transfer theory. The crown fuel ignition model (hereafter referred to as CFIM) is based on first principles, integrating: (i) the characteristics of the energy source as defined by surface fire flame front properties; (ii) buoyant plume dynamics; (iii) heat sink as described by the crown fuel particle characteristics; and (iv) energy transfer (gain and losses) to the crown fuels. Fuel particle temperature increase is determined through an energy balance relating heat absorption to fuel particle temperature. The final model output is the temperature of the crown fuel particles, which upon reaching ignition temperature are assumed to ignite. CFIM predicts the ignition of crown fuels but does not determine the onset of crown fire spread per se. The coupling of the CFIM with models determining the rate of propagation of crown fires allows for the prediction of the potential for sustained crowning. CFIM has the potential to be implemented in fire management decision support systems.

The effect of convection column air temperature and live needle moisture content on ignitability of tree branches was verified and quantified by exposing branches of three conifer species to a hot-air convection column, at temperatures between 400 and 640 °C, and measuring time to ignition. The three species were ponderosa pine (Pinusponderosa Laws.), Douglas-fir (Pseudotsugamenziesii var glauca (Beissn.) Franco), and lodgepole pine (Pinuscontorta Dougl.). The experiment was repeated monthly over the course of a year, taking advantage of the natural fluctuation of live needle moisture content. Three multiple regression equations for the prediction of time to ignition with air temperature and needle moisture as the independent variables were developed.