Simulating fire patterns in heterogeneous landscapes

A broad-scale probabilistic model of forest fires, EMBYR, has been developed to simulate the effects of large fires burning through heterogeneous landscapes. Fire ignition and spread are simulated on a gridded landscape by (1) examining each burning site at each time step, (2) independently evaluating the probability of spread to eight neighbors based on fuel type, fuel moisture, wind speed and direction, and (3) distributing firebrands to downwind sites, where the probability of ignition of new fires is a function of fuel type and moisture conditions. Low values for the probability of spread, I, produce a dendritic burn pattern resembling a slow, meandering fire, whereas higher values of I produce solid patterns similar to a rapidly moving, intensely burning fire. I had to be greater than a critical value, ic, estimated to lie between 0.250 and 0.251, to have a 50% chance of propagating across the landscape by adjacent spread alone. The rate of spread of fire at I=0.30 was nearly four times faster when firebrands were included in the simulations, and nearly eight times faster in the presence of moderate wind. Given the importance of firebrands in projecting fire spread, there is a need for better empirical information on fire spotting. A set of model parameters was developed to represent the weather conditions and fuel types on the subalpine plateau of Yellowstone National Park, WY, USA. Simulation experiments were performed to reveal relationships between fire and landscape-scale heterogeneity of fuels. In addition, EMBYR was used to explore fire patterns in the subalpine plateau by simulating four scenarios of weather and fuel conditions. The results of repeated simulations were compared by evaluating risk (the cumulative frequency distribution of the area burned) as a function of the change in weather conditions. Estimates of risk summarized the high degree of variability experienced in natural systems, the difficulty of predicting fire behavior when conditions are near critical thresholds, a quantification of uncertainties concerning future weather conditions, and useful tool for assessing potential wildfire effects. © 2000 Elsevier Science B.V. All rights reserved.