Crown Fuel Spatial Variability and Predictability of Fire Spread

Fire behavior predictions, as well as measures of uncertainty in those predictions, are essential in operational and strategic fire management decisions. While it is becoming common practice to assess uncertainty in fire behavior predictions arising from variability in weather inputs, uncertainty arising from the fire models themselves is difficult to assess. This is the case with fires in crown fuels and the operational fire behavior models used in the United States, where model assumptions, such as fuel homogeneity and steady-state spread, limit the capability of those models to provide reliable results, possibly leading to uncertainties of unknown magnitude. An emerging body of work with dynamic physics-based fire models illustrates the capabilities of those models to address potentially important factors that are not considered by operational models, such as fuel heterogeneity and transitional behaviors. In this investigation we used a dynamic, physics-based fire model, FIRETEC, to explore variability in the forward spread rate of a fire arising from spatial variability in crown fuels. We generated 25 different spatial configurations of trees with four different clumping patterns, for a total of 100 different simulated forest stands. Using FIRETEC we simulated fire through each of these stands, holding the ignition and weather inputs constant. Analyses assessed differences in spread rates between clumping groups and arising from differences in canopy cover and total fuel, as well as differences in variability in spread rate between clumping groups. Differences in spread rates between groups and due to canopy cover and total fuel were not significant, largely due to high variability within groups; differences in variance in spread rate between clumping groups was statistically significant (p-value 0.0007). Variability in spread rate increased substantially as gaps between tree clumps got larger, with > 60% difference in overall spread rate observed between the lowest rate of spread and the highest rate of spread. Results suggest a certain inherent unpredictability in fire behavior that can largely be attributed to fine scale fire-fuel-atmosphere interactions which are by their nature difficult to predict beforehand. Self-determining physical fire models do not predict, but rather observe outcomes; however, ensemble runs with physical fire models can be used to quantify variability in fire behavior arising from sources that are not considered by operational models. Our study suggests a need for a paradigm shift in fire behavior modeling which emphasizes identifying sources of variability and estimating their magnitude over pure prediction of spread rates.