Improved Fire Growth Modeling

Ball, George L.; Guertin, D. Phillip

doi:10.1071/wf9920047

Cited by 32 publications

(23 citation statements)

References 4 publications

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“…This method minimizes distortion to fire shapes that results from cellular automata or gridded contagion algorithms (Ball and Guertin 1992;Peterson et al 2009). The original MTT algorithm was enhanced to permit time-varying burning conditions and include spotting from torching trees (Albini 1979).…”

Section: Fire Growth and Behaviormentioning

confidence: 98%

A simulation of probabilistic wildfire risk components for the continental United States

Finney

McHugh

Grenfell

et al. 2011

Stoch Environ Res Risk Assess

343

290

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This simulation research was conducted in order to develop a large-fire risk assessment system for the contiguous land area of the United States. The modeling system was applied to each of 134 Fire Planning Units (FPUs) to estimate burn probabilities and fire size distributions. To obtain stable estimates of these quantities, fire ignition and growth was simulated for 10,000 to 50,000 ''years'' of artificial weather. The fire growth simulations, when run repeatedly with different weather and ignition locations, produce burn probabilities and fire behavior distributions at each landscape location (e.g., number of times a ''cell'' burns at a given intensity divided by the total years). The artificial weather was generated for each land unit using (1) a fire danger rating index known as the Energy Release Component (ERC) which is a proxy for fuel moisture contents, (2) a time-series analysis of ERC to represent daily and seasonal variability, and (3) distributions of wind speed and direction from weather records. Large fire occurrence was stochastically modeled based on historical relationships to ERC. The simulations also required spatial data on fuel structure and topography which were acquired from the LANDFIRE project (http://www.landfire.gov). Fire suppression effects were represented by a statistical model that yields a probability of fire containment based on independent predictors of fire growth rates and fuel type. The simulated burn probabilities were comparable to observed patterns across the U.S. over the range of four orders of magnitude, generally falling within a factor of 3 or 4 of historical estimates. Close agreement between simulated and historical fire size distributions suggest that fire sizes are determined by the joint distributions of spatial opportunities for fire growth (dependent on fuels and ignition location) and the temporal opportunities produced by conducive weather sequences. The research demonstrates a practical approach to using fire simulations at very broad scales for purposes of operational planning and perhaps ecological research.

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Section: Fire Growth and Behaviormentioning

confidence: 98%

A simulation of probabilistic wildfire risk components for the continental United States

Finney

McHugh

Grenfell

et al. 2011

Stoch Environ Res Risk Assess

343

290

View full text Add to dashboard Cite

show abstract

“…Fire spread has been modeled for a long time using regular networks [1][2][3][4][5], as well as cellular automata [6][7][8], to include site weights. However, as suggested in 1976 by de Gennes [9], such networks did not take into account physical effects beyond the nearest 0010 neighbors of a burning site, such as flame-induced radiative/convective effects or firebrand impact.…”

Section: Introductionmentioning

confidence: 99%

Modeling forest fire spread and spotting process with small world networks

Porterie

Zekri

Clerc

et al. 2007

Combustion and Flame

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“…이러한 시뮬레이션 모델들은 일반적으로 확률적 시뮬레이션과 결정론적 시뮬레이션으로 구분 될 수 있다 [11][12][13] . [15,16,17] . 처음 셀룰라오토마타 방법은 1950년대 초 물리 학, 화학, 생물학, 수학, 컴퓨터공학 분야에서 다양한 동적 현상을 연구하는데 사용되었다.…”

Section: 서 론unclassified

A Simulation Model for the Study on the Forest Fire Pattern

Song¹,

Jeon²,

Lee³

2013

Journal of the Korea Society for Simulation

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Because forest fires are predicted to increase in severity and frequency under global climate change with important environmental implications, an understanding of fire dynamics is critical for mitigation of these negative effects. For the reason, researchers with different background, such as ecologists, physicists, and mathematical biologists, have developed the simulation models to mimic the forest fire spread patterns. In this study, we suggested a novel model considering the wind effect. Our theoretical forest was comprised of two different tree species with varying probabilities of transferring fire that were randomly distributed in space at densities ranging from 0.0 (low) to 1.0 (high). We then studied the distributional patterns of burnt trees using a two-dimensional stochastic cellular automata model with minimized local rules. We investigated the time, T, that the number of burnt trees reaches 25% of the whole trees for different values of the initial tree density, fire transition probability, and the degree of wind strength. Simulation results showed that the values of T decreased with the increase of tree density, and the wind effect decreased in the case of too high or low tree density. We believe that our model can be a useful tool to explore forest fire spreading patterns.

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Improved Fire Growth Modeling

Cited by 32 publications

References 4 publications

A simulation of probabilistic wildfire risk components for the continental United States

A simulation of probabilistic wildfire risk components for the continental United States

Modeling forest fire spread and spotting process with small world networks

A Simulation Model for the Study on the Forest Fire Pattern

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