Abstract
ABSTRACTIt is well known that global climate change causes an increase in forest fire frequency and severity. Thus, understanding fire dynamics is necessary to comprehend the mitigation of the negative effects of forest fires. Our objective was to inform how fire spreads in a simulated two-species forest with varying wind strengths. The forest in this study was comprised of two different tree species with varying probabilities of transferring fire that was randomly distributed in space at densities (Ctot) ranging from 0.0 (low) to 1.0 (high). We studied the distribution pattern of burnt trees by using local rules of the two-dimensional model. This model incorporated wind blowing from south to north with strength (Pw) ranging from 0.0 (low) to 1.0 (high). Simulation results showed that when Ctot > 0.45 the fire covered the entire forest, but when Ctot ≤ 0.45 the fire did not spread. The wind effect on the variation of the amount of the burnt tree was maximized at the critical density and dramatically decreased with increasing Ctot. Additionally, we found that the term of Ctot and Pw plays an important role in determining the distribution.
Highlights
Biomass burning, in which both living and dead vegetation are burned, can be a process that emits greenhouse gases, reactive gases, and aerosols (e.g. CO2, CH4, and CO) into the atmosphere
We used a cellular automata (CA) model to understand the fire spread in a theoretical forest comprised of two tree species that varied in their capacities to transfer fire
We determined that a total tree density of 0.45 represented a critical value; when tree density was above this level, fire was able to spread throughout the entire landscape unhindered
Summary
In which both living and dead vegetation are burned, can be a process that emits greenhouse gases, reactive gases, and aerosols (e.g. CO2, CH4, and CO) into the atmosphere. Stochastic models are typically based on observational data and can incorporate variations in relevant burning parameters (e.g. fuel type, fuel moisture, wind) using statistical methodologies. These models have been used to explore the consequences of theoretical or assumed conditions on various processes. The processes can provide the ways to incorporate variables inherent in the systems (e.g. animal trajectories, weather) In other words, this approach allows for the development of effective operational tools for forest fire simulations under real conditions. It may be said that the difference between the two types of models is that the stochastic models can provide a range of potential outcomes with associated probabilities, whereas the deterministic models provide a single forecast based on a set of initial conditions and deterministic biophysical processes
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