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Article << Previous     |     Next >>   Contents Vol 22(2)

Field validation of a free-agent cellular automata model of fire spread with fire–atmosphere coupling

Gary L. Achtemeier

Center for Forest Disturbance Science, USDA Forest Service, Athens, GA, 30602, USA. Email: gachtemeier@fs.fed.us

International Journal of Wildland Fire 22(2) 148-156 http://dx.doi.org/10.1071/WF11055
Submitted: 23 April 2011  Accepted: 3 July 2012   Published: 25 September 2012

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A cellular automata fire model represents ‘elements’ of fire by autonomous agents. A few simple algebraic expressions substituted for complex physical and meteorological processes and solved iteratively yield simulations for ‘super-diffusive’ fire spread and coupled surface-layer (2-m) fire–atmosphere processes. Pressure anomalies, which are integrals of the thermal properties of the overlying heated plume, drive the surface winds around and through the fire. Five simulations with differing fuel and wind conditions were compared with fire and meteorological data from an experimental grassfire (FireFlux). The fire model accurately simulated bulk patterns of measured time-series of 2-m winds at two towers and observed fire behaviour (spread rate, flaming depth and heat released). Fidelity to spatial windfields in the vicinity of the fire was similar to results from full-physics fire models for other grassfires. Accurate predictions of fire spread depend critically on accurate wind speeds and directions at the location of the fire. Simulated fire–atmosphere coupling using FireFlux data increased wind speeds across the fire line by up to a factor of three. With its computational speed relative to full-physics models, the fire model can inform full-physics modellers regarding problems of interest. Although the fire model is tested for homogeneous fuels on flat terrain, the model is designed for simulating complex distributions of fire within heterogeneous distributions of fuels over complex landscapes.


Achtemeier GL (2003) ‘Rabbit Rules’ – an application of Stephen Wolfram's ‘New Kind of Science’ to fire spread modeling. In ‘Fifth Symposium on Fire and Forest Meteorology’, 16–20 November 2003, Orlando, FL. (American Meteorological Society: Boston, MA)

Achtemeier GL (2005) Planned Burn–Piedmont. A local operational numerical meteorological model for tracking smoke on the ground at night: model development and sensitivity tests. International Journal of Wildland Fire 14, 85–98.
CrossRef |

Adou JK, Billaud Y, Brou DA, Clerc J-P, Consalvi J-L, Fuentes A, Kaiss A, Nmira F, Porterie B, Zekri L, Zekri N (2010) Simulating wildfire patterns using a small-world network model. Ecological Modelling 221, 1463–1471.
CrossRef |

Andrews PL (2008) BehavePlus fire modeling system, version 4.0: variables. USDA Forest Service, Rocky Mountain Research Station, Report RMRS-GTR-213WWW. (Fort Collins, CO)

Andrews PL, Bevins CD, Seli RC (2005) BehavePlus fire modeling system, version 3.0: User's Guide. USDA Forest Service, Rocky Mountain Research Station, Report RMRS-GTR-106WWW Revised. (Ogden, UT)

Beezley JD, Chakraborty S, Coen JL, Douglas CC, Mandel J, Vodacek A, Wang Z (2008) Real-time data driven wildland fire modelling. Lecture Notes in Computer Science 5103, 46–53.
CrossRef |

Berjak SG, Hearne JW (2002) An improved cellular automaton model for simulating fire in a spatially heterogeneous Savanna system. Ecological Modelling 148, 133–151.
CrossRef |

Bouchaud J-P, Georges A (1990) Anomalous diffusion in random media: models, statistical mechanisms and physical realizations. Physics Reports 195, 127–293.
CrossRef |

Byram GM (1959) Combustion of forest fuels. In ‘Forest Fire control and Use’. (Ed. KP Davis) pp. 155–182. (McGraw Hill: New York)

Clark TL, Jenkins MA, Coen J, Packham D (1996) A coupled atmospheric-fire model: convective Froude number and dynamic fingering. International Journal of Wildland Fire 6, 177–190.
CrossRef |

Clarke KC, Olsen G (1996) Refining a cellular automaton model for wildfire propagation and extinction. In ‘GIS and Environmental Modeling: Progress and Research Issues’. (Eds MF Goodchild, LT Steyaert, BO Parks, C Johnston) pp. 333–338. (Wiley: Hoboken, NJ)

Clarke KC, Brass JA, Riggan PJ (1994) A cellular automaton model of wildfire propagation and extinction. Photogrammetric Engineering and Remote Sensing 60, 1355–1367.

Clements CB (2010) Thermodynamic structure of a grass fire plume. International Journal of Wildland Fire 19, 895–902.
CrossRef |

Clements CB, Zhong S, Goodrick S, Li J, Potter BE, Bian X, Heilman WE, Charney JJ, Perna R, Jang M, Lee M, Patel M, Street S, Aumann G (2007) Observing the dynamics of wildland grass fires: FireFlux – a field validation experiment. Bulletin of the American Meteorological Society 88, 1369–1382.
CrossRef |

Cunningham P, Linn RR (2007) Numerical simulation of grass fires using a coupled atmosphere–fire model: dynamics of fire spread. Journal of Geophysical Research – Atmospheres 112, D05108
CrossRef |

Hernández Encinas A, Hernández Encinas L, White SH, Martín del Rey A, Rodríguez Sánchez G (2007) Simulation of forest fire fronts using cellular automata. Advances in Engineering Software 38, 372–378.
CrossRef |

Flakes GW (2000) ‘The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems, and Adaptation.’ (MIT Press: Cambridge, MA)

Grell GA, Dudhia J, Stauffer DR (1994) A description of the fifth-generation Penn State/NCAR mesoscale model (MM5). National Center for Atmospheric Research, Technical Note NCAR/TN-3921STR. (Boulder, CO)

Hargrove WW, Gardner RH, Turner MG, Romme WH, Despain DG (2000) Simulating fire patterns in heterogeneous landscapes. Ecological Modelling 135, 243–263.
CrossRef |

Karafyllidis I, Thanailakis A (1997) A model for predicting forest fire spreading using cellular automata. Ecological Modelling 99, 87–97.
CrossRef |

Linn RR (1997) Transport model for prediction of wildfire behavior. Los Alamos National Laboratory, Scientific Report LA13334-T. (Los Alamos, NM)

Linn RR, Cunningham P (2005) Numerical simulations of grass fires using a coupled atmosphere–fire model: basic fire behavior and dependence on wind speed. Journal of Geophysical Research 110, D13107
CrossRef |

Linn RR, Harlow FH (1998) FIRETEC: a transport description of wildfire behavior. In ‘Second Symposium on Forest and Fire Meteorology’, 11–16 January 1998, Phoenix, AZ. (Eds FM Fujioka, DW Goens) pp. 14–19. (American Meteorological Society: Boston, MA)

Linn RR, Winterkamp J, Edminster C, Colman JJ, Smith WS (2007) Coupled influences of topography and wind on wildland fire behaviour. International Journal of Wildland Fire 16, 183–195.
CrossRef |

Mell W, Jenkins MA, Gould J, Cheney P (2007) A physics-based approach to modeling grassland fires. International Journal of Wildland Fire 16, 1–22.
CrossRef |

Mercer GN, Weber RO (2001) ‘Fire Plumes in Forest Fires: Behavioral and Ecological Effects.’ (Eds EA Johnson, K Miyanishi) pp. 225–256. (Academic Press: New York)

Metzler R, Klafter J (2000) The random walk’s guide to anomalous diffusion: a fractional dynamics approach. Physics Reports 339, 1–77.
CrossRef | CAS |

Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Intermountain Research Station, Research Paper INT-115. (Ogden, UT),

Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2005) A description of the advanced research WRF Version 2. National Center for Atmospheric Research, Technical Note, NCAR/TN-468+STR. (Boulder, CO)

Sullivan AL (2009a) Wildland surface fire spread modeling, 1990–2007: 1. Physical and quasi-physical models. International Journal of Wildland Fire 18, 349–368.
CrossRef |

Sullivan AL (2009b) Wildland surface fire spread modeling, 1990–2007: 2. Empirical and quasi-empirical models. International Journal of Wildland Fire 18, 369–386.
CrossRef |

Sullivan AL, Knight IK (2004) A hybrid cellular automata/semi-physical model of fire growth. In ‘Proceedings of the Seventh Asia–Pacific Conference on Complex Systems’, 6–10 December 2004, Cairns, Qld. (Eds R Stoner, Q Han, W Li) pp. 64–73. Available at http://www.dar.csiro.au/css/research/sullivan/complex2004%20sullivan%20and%20knight.pdf [Verified 12 September 2012]

Yassemi S, Dragicevic S, Schmidt M (2008) Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour. Ecological Modelling 210, 71–84.
CrossRef |

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