Wildland surface fire spread modelling, 1990–2007. 3: Simulation and mathematical analogue models
Andrew L. Sullivan
+ Author Affiliations
- Author Affiliations
CSIRO Sustainable Ecosystems and CSIRO Climate Adaptation Flagship, GPO Box 284, Canberra, ACT 2601, Australia. Email: andrew.sullivan@csiro.au
International Journal of Wildland Fire 18(4) 387-403 https://doi.org/10.1071/WF06144
Submitted: 1 November 2006 Accepted: 17 January 2008 Published: 29 June 2009
Abstract
In recent years, advances in computational power have led to an increase in attempts to model the behaviour of wildland fires and to simulate their spread across landscape. The present series of articles endeavours to comprehensively survey and précis all types of surface fire spread models developed during the period 1990–2007. The present paper surveys models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one-dimensional models to two dimensions and then simulate the propagation of a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical concept (rather than a physical representation of fire spread) that coincidentally represents the spread of fire. Other papers in the series survey models of a physical or quasi-physical nature, and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.
Acknowledgements
I would like to acknowledge the CSIRO Sustainable Ecosystems Bushfire Dynamics and Applications (BDA) Team and the CSIRO Centre for Complex Systems Science for supporting the present project; Jim Gould and Rowena Ball for comments on the draft manuscript; Miguel Cruz and Ian Knight for internally reviewing the draft; and the anonymous journal referees who assisted in making this a much better article.
References
Achtemeier GL (2003) ‘Rabbit Rules’ – an application of Stephen Wolfram’s ‘New Kind of Science’ to fire spread modelling. In ‘Fifth Symposium on Fire and Forest Meteorology’, 16–20 November 2003, Orlando, FL. (American Meteorological Society: Boston, MA)
Albinet G, Searby G , Stauffer D
(1986) Fire propagation in a 2-D random medium. Journal of Physics 47, 1–7.
| Crossref | GoogleScholarGoogle Scholar |
Albini FA (1979) Spot fire distance from burning trees – a predictive model. USDA Forest Service, Intermountain Forest and Range Experimental Station, General Technical Report INT-56. (Odgen, UT)
Albini FA , Reinhardt ED
(1995) Modeling ignition and burning rate of large woody natural fuels. International Journal of Wildland Fire 5, 81–91.
| Crossref | GoogleScholarGoogle Scholar |
Alexander ME (1985) Estimating the length to breadth ratio of elliptical forest fire patterns. In ‘Proceedings of the Eighth Conference on Forest and Fire Meteorology’, 29 April–2 May 1985, Detroit, MI. (Eds LR Donoghue, RE Martin) pp. 287–304. (Society of American Foresters: Bethesda, MD)
Anderson DH, Catchpole EA, de Mestre NJ , Parkes T
(1982) Modelling the spread of grass fires. Journal of Australian Mathematics Society B 23, 451–466.
| Crossref | GoogleScholarGoogle Scholar |
Andrews P (1986) BEHAVE: fire behaviour prediction and fuel modelling system – BURN subsystem, Part 1. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-194. (Ogden, UT)
Asensio M , Ferragut L
(2002) On a wildland fire model with radiation. International Journal for Numerical Methods in Engineering 54(1), 137–157.
| Crossref | GoogleScholarGoogle Scholar |
Bak P (1996) ‘How Nature Works: the Science of Self-organised Criticality.’ (Springer: New York)
Bak P, Tang C , Wiesenfeld K
(1987) Self-organised criticality: an explanation of 1/f noise. Physical Review Letters 59(4), 381–384.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Butler BW, Finney M, Bradshaw L, Forthofer J, McHugh C, Stratton R, Jimenez D (2006b) Wind Wizard: a new tool for fire management decision support. In ‘Fuels Management – How to Measure Success: Conference Proceedings’, 28–30 March 2006, Fort Collins, CO. (Eds PL Andrews, BW Butler) USDA Forest Service, Rocky Mountain Research Station, RMRS-P-41, pp. 787–796. (Portland, OR)
Caldarelli G, Frondoni R, Gabrielli A, Montuori M, Retzlaff R , Ricotta C
(2001) Percolation in real wildfires. Europhysics Letters 56(4), 510–516.
| Crossref | GoogleScholarGoogle Scholar |
CAS |
Cheney NP (1981) Fire behaviour. In ‘Fire and the Australian Biota’, Ch. 5. (Eds AM Gill, R Groves, I Noble) pp. 151–175. (Australian Academy of Science: Canberra)
Cheney NP, Gould JS , Catchpole WR
(1993) The influence of fuel, weather and fire shape variables on fire-spread in grasslands. International Journal of Wildland Fire 3(1), 31–44.
| Crossref | GoogleScholarGoogle Scholar |
Clark T, Coen J, Radke L, Reeder M, Packham D (1998) Coupled atmosphere-fire dynamics. In ‘III International Conference on Forest Fire Research and 14th Conference on Fire and Forest Meteorology’, 16–20 November 1998, Luso, Portugal. (Ed. DX Viegas) Vol. 1, pp. 67–82. (DX Viegas: Coimbra, Portugal)
Clark TL, Coen J , Latham D
(2004) Description of a coupled atmosphere–fire model. International Journal of Wildland Fire 13(1), 49–63.
| Crossref | GoogleScholarGoogle Scholar |
Coen JL, Clark TL (2000) Coupled atmosphere–fire model dynamics of a fireline crossing a hill. In ‘Third Symposium on Fire and Forest Meteorology’, 9–14 January 2000, Long Beach, CA. pp. 7–10. (American Meteorological Society: Boston, MA)
Coleman JR , Sullivan AL
(1996) A real-time computer application for the prediction of fire spread across the Australian landscape. Simulation 67(4), 230–240.
| Crossref | GoogleScholarGoogle Scholar |
CWFGM Steering Committee (2004) ‘Prometheus User Manual v. 3.0.1.’ (Canadian Forest Service)
Dercole F , Maggi S
(2005) Detection and continuation of a border collision bifurcation in a forest fire model. Applied Mathematics and Computation 168, 623–635.
| Crossref | GoogleScholarGoogle Scholar |
Dunn A (2007) A model of wildfire propagation using the interacting spatial automata formalism. PhD thesis, University of Western Australia.
Dunn A, Milne G (2004) Modelling wildfire dynamics via interacting automata. In ‘Cellular Automata, 6th International Conference on Cellular Automata for Research and Industry, ACRI 2004, Amsterdam, the Netherlands, October 25-28, 2004. Proceedings’, Lecture Notes in Computer Science, Vol. 3305. (Eds PMA Sloot, B Chopard, AG Hoekstra) pp. 395–404. (Springer-Verlag: Berlin, Germany)
Eklund P
(2001) A distributed spatial architecture for bushfire simulation. International Journal of Geographical Information Science 15(4), 363–378.
| Crossref | GoogleScholarGoogle Scholar |
Finney MA (1994) Modeling the spread and behaviour of prescribed natural fires. In ‘Proceedings of the 12th Conference on Fire and Forest Meteorology’, 26–28 October 1993, Jekyll Island, GA. pp. 138–143. (American Society of Foresters: Bethesda, MD)
Finney MA (1998) FARSITE: Fire area simulator – model development and evaluation. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP-4. (Fort Collins, CO)
Finney MA
(2002) Fire growth using minimum travel time methods. Canadian Journal of Forest Research 32(8), 1420–1424.
| Crossref | GoogleScholarGoogle Scholar |
Forestry Canada Fire Danger Group (1992) Development and structure of the Canadian Forest Fire Behavior Prediction System. Forestry Canada Science and Sustainable Development Directorate, Information Report ST-X-3. (Ottawa, ON)
Frandsen W, Andrews P (1979) Fire behaviour in non-uniform fuels. USDA Forest Service, Intermountain Forest and Range Experiment Station, Research Paper INT-232. (Ogden, UT)
French I (1992) Visualisation techniques for the computer simulation of bushfires in two dimensions. MSc thesis, University of New South Wales, Australian Defence Force Academy, Canberra.
French IA, Anderson DH , Catchpole EA
(1990) Graphical simulation of bushfire spread. Mathematical and Computer Modelling 13(12), 67–71.
| Crossref | GoogleScholarGoogle Scholar |
Guariso G, Baracani M (2002) A simulation software of forest fires based on two-level cellular automata. In ‘Proceedings of the IV International Conference on Forest Fire Research, 2002 Wildland Fire Safety Summit’, 18–23 November 2002, Luso, Portugal. (Ed. DX Viegas) p. 100. (Millpress Science Publishers: Rotterdam, the Netherlands)
Gurer K, Georgopoulos PG (1998) Numerical modeling of forest fires within a 3D meteorological/dispersion model. In ‘Second Symposium on Fire and Forest Meteorology’, 11–16 January 1998, Phoenix, AZ. pp. 144–148. (American Meteorological Society: Boston, MA)
Hargrove WW, Gardner RH, Turner MG, Romme WH , Despain DG
(2000) Simulating fire patterns in heterogeneous landscapes. Ecological Modelling 135(2–3), 243–263.
| Crossref | GoogleScholarGoogle Scholar |
Jenkins MA, Clark T, Coen J (2001) Coupling atmospheric and fire models. In ‘Forest Fires: Behaviour and Ecological Effects’, Ch. 5, 1st edn. (Eds E Johnson, K Miyanishi) pp. 257–302. (Academic Press: San Diego, CA)
Johnston P, Milne G , Kelso J
(2006) A heat transfer simulation model for wildfire spread. Forest Ecology and Management 234, S78.
| Crossref | GoogleScholarGoogle Scholar |
Kalabokidis KD, Hay CM, Hussin YA (1991) Spatially resolved fire growth simulation. In ‘Proceedings of the 11th Conference on Fire and Forest Meteorology’, 16–19 April 1991, Missoula, MT. (Eds PL Andrews, DF Potts) pp. 188–195. (Society of American Foresters: Bethesda, MD)
Karafyllidis I
(1999) Acceleration of cellular automata algorithms using genetic algorithms. Advances in Engineering Software 30(6), 419–437.
| Crossref | GoogleScholarGoogle Scholar |
Kourtz PH, Nozaki S, O’Regan WG (1977) Forest fires in a computer: a model to predict the perimeter location of a forest fire. Fisheries and Environment Canada, Information Report FF-X-65. (Ottawa, ON)
Langton CG
(1990) Computation at the edge of chaos: phase transitions and emergent computation. Physica. D, Nonlinear Phenomena 42(1–3), 12–37.
| Crossref | GoogleScholarGoogle Scholar |
Li X, Magill W (2000) Modelling fire spread under environmental influence using a cellular automaton approach. Complexity International 08, li01. Available at http://www.complexity.org.au/ci/vol08/li01/ [Verified 18 June 2009]
Li X , Magill W
(2003) Critical density in a fire spread model with varied environmental conditions. International Journal of Computational Intelligence and Applications 3(2), 145–155.
| Crossref | GoogleScholarGoogle Scholar |
Lopes A, Cruz M, Viegas D (1998) Firestation – an integrated system for the simulation of wind flow and fire spread over complex topography. In ‘III International Conference on Forest Fire Research and 14th Conference on Fire and Forest Meteorology’, 16–20 November 1998, Luso, Portugal. (Ed. DX Viegas) Vol. 1, pp. 741–754. (DX Viegas: Coimbra, Portugal)
Lopes AMG, Cruz MG , Viegas DX
(2002) Firestation – an integrated software system for the numerical simulation of fire spread on complex topography. Environmental Modelling & Software 17(3), 269–285.
| Crossref | GoogleScholarGoogle Scholar |
Margerit J, Sero-Guillaume O (1998) Richards’ model, Hamilton–Jacobi equations and temperature field equations of forest fires. In ‘III International Conference on Forest Fire Research and 14th Conference on Fire and Forest Meteorology’, 16–20 November 1998, Luso, Portugal. (Ed. DX Viegas) Vol. 1, pp. 281–294. (DX Viegas: Coimbra, Portugal)
McAlpine R , Wotton B
(1993) The use of fractal dimension to improve wildland fire perimeter predictions. Canadian Journal of Forest Research 23, 1073–1077.
| Crossref | GoogleScholarGoogle Scholar |
McArthur AG (1966) Weather and grassland fire behaviour. Commonwealth Department of National Development, Forestry and Timber Bureau Leaflet 100. (Canberra, ACT)
McArthur AG (1967) Fire behaviour in eucalypt forests. Commonwealth Department of National Development, Forestry and Timber Bureau Leaflet 107. (Canberra, ACT)
McCormick RJ, Brandner TA, Allen TFH (1999) Toward a theory of meso-scale wildfire modeling – a complex systems approach using artificial neural networks. In ‘Proceedings of the Joint Fire Science Conference and Workshop’, 15–17 June 1999, Boise, ID. (Eds LF Neuenschwander, KC Ryan) (University of Idaho and International Association of Wildland Fire: Moscow, ID) Available at http://jfsp.nifc.gov/conferenceproc/ [Verified 27 May 2009]
Mendez V , Llebot JE
(1997) Hyperbolic reaction-diffusion equations for a forest fire model. Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 56(6), 6557–6563.
|
CAS |
Muzy A, Innocenti E, Aiello A, Santucci J-F, Wainer G (2002) Cell-DEVS quantization techniques in a fire spreading application. In ‘Proceedings of the 34th Winter Simulation Conference: Exploring New Frontiers’, December 8–11 2002, San Diego, CA, USA. (Eds JL Snowdon, JM Charnes) pp. 542–549. (Association for Computing Machinery: New York)
Muzy A, Innocenti E, Santucci JF, Hill DRC (2003) Optimization of cell spaces simulation for the modeling of fire spreading. In ‘Proceedings 36th Annual Simulation Symposium (ANSS-36 2003)’, 30 March–2 April 2003, Orlando, FL, USA. pp. 289–296. (IEEE Computer Society: Washington, DC)
Muzy A, Innocenti E, Aïello A, Santucci J, Santoni P , Hill D
(2005a) Modelling and simulation of ecological propagation processes: application to fire spread. Environmental Modelling & Software 20(7), 827–842.
| Crossref | GoogleScholarGoogle Scholar |
Opperman T, Gould J, Finney M, Tymstra C (2006) Applying fire spread simulators in New Zealand and Australia: results from an international seminar. In ‘Fuels Management – How to Measure Success: Conference Proceedings’, 28–30 March 2006, Portland, OR. (Eds PL Andrews, BW Butler) USDA Forest Service, Rocky Mountain Research Station, RMRS-P-41, pp. 201–212. (Fort Collins, CO)
Pastor-Satorras R , Vespignani A
(2000) Corrections to the scaling in the forest-fire model. Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 61, 4854–4859.
|
CAS |
Peet GB (1965) A fire danger rating and controlled burning guide for the northern jarrah (Euc. marginata Sm.) forest of Western Australia. Forests Department, Bulletin No 74. (Perth, WA)
Perry GL, Sparrow AD , Owens IF
(1999) A GIS-supported model for the simulation of the spatial structure of wildland fire, Cass Basin, New Zealand. Journal of Applied Ecology 36(4), 502–518.
| Crossref | GoogleScholarGoogle Scholar |
Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Intermountain Forest and Range Experimental Station, Research Paper INT-115. (Odgen, UT)
Rothermel RC (1991) Predicting behavior and size of crown fires in the northern Rocky Mountains. USDA Forest Service, Intermountain Forest and Range Experimental Station, Research Paper INT-438. (Odgen, UT)
Sanderlin J, Sunderson J (1975) Simulation for wildland fire management planning support (Fireman): executive summary. Mission Research Corp., Contract No. 21–343, Spec. 200. (Santa Barbara, CA)
Sanderlin JC, Van Gelder R (1977) A simulation of fire behavior and suppression effectiveness for operational support. In ‘Wildland Fire Management. Proceedings of the First International Conference on Mathematical Modeling’, 29 August–1 September 1977, University of Missouri, Rolla, MO. (Ed. XJR Avula) Vol. 2, pp. 619–630.
Schenk K, Drossel B, Clar S , Schwabl F
(2000) Finite-size effects in the self-organised critical forest-fire model. The European Physical Journal B 15, 177–185.
| Crossref | GoogleScholarGoogle Scholar |
CAS |
Stevenson AE, Schermerhorn DA, Miller SC (1974) Simulation of southern California forest fires. In ‘Fifteenth Symposium (International) on Combustion’, Tokyo, Japan. pp. 147–155. (The Combustion Institute: Pittsburgh, PA)
Sullivan AL
(2009a) Wildland surface fire spread modelling, 1990–2007. 1: Physical and quasi-physical models. International Journal of Wildland Fire 18, 349–368.
| Crossref | GoogleScholarGoogle Scholar |
Sullivan AL, Knight IK (2004) A hybrid cellular automata/semi-physical model of fire growth. In ‘Proceedings of the 7th Asia–Pacific Conference on Complex Systems’, 6–10 December 2004, Cairns, QLD, Australia. pp. 64–73. (Central Queensland University: Rockhampton, QLD)
Taplin RH
(1993) Sources of variation for fire spread rate in non-homogeneous fuel. Ecological Modelling 68, 205–211.
| Crossref | GoogleScholarGoogle Scholar |
CAS |
Trunfio GA (2004) Predicting wildfire spreading through a hexagonal cellular automata model. In ‘Cellular Automata, 6th International Conference on Cellular Automata for Research and Industry, ACRI 2004, Amsterdam, the Netherlands, 25–28 October 2004. Proceedings’, Lecture Notes in Computer Science, Vol. 3305. (Eds PMA Sloot, B Chopard, AG Hoekstra) pp. 385–394. (Springer-Verlag: Berlin, Germany)
Vakalis D, Sarimveis H, Kiranoudis C, Alexandridis A , Bafas G
(2004) A GIS-based operational system for wildland fire crisis management I. Mathematical modelling and simulation. Applied Mathematical Modelling 28(4), 389–410.
| Crossref | GoogleScholarGoogle Scholar |
Vasconcelos MJ, Guertin P, Zwolinski M (1990) FIREMAP: simulations of fire behaviour, a GIS-supported system. In ‘Effects of Fire Management of South-western Natural Resources, Proceedings of the Symposium’, 15–17 November 1988, Tucson, AZ. (Ed. JS Krammes) USDA Forest Service, General Technical Report RM-GTR-191, pp. 217–221. (Fort Collins, CO)
Viegas D (Ed.) (2002) Forest Fire Research and Wildland Fire Safety. In ‘Proceedings of the IV International Conference on Forest Fire Research’, 18–23 November 2002, Luso, Coimbra, Portugal. (Millpress: Rotterdam, the Netherlands)
von Niessen W , Blumen A
(1988) Dynamic simulation of forest fires. Canadian Journal of Forest Research 18, 807–814.
| Crossref | GoogleScholarGoogle Scholar |
Wolfram S (1986) Theory and application of cellular automata. In ‘Advanced Series on Complex Systems, Vol. 1’. (World Scientific: Singapore)
Wolfram SD
(1983) Statistical mechanics of cellular automata. Reviews of Modern Physics 55, 601–644.
| Crossref | GoogleScholarGoogle Scholar |
1 Dr K. G. Tolhurst, Senior Lecturer, University of Melbourne, VIC, Australia.
2 In vector data, fuel is generally stored as polygons represented by a series of data points representing the vertices of the outline of the fuel and the fuel attributes for the whole polygon. Very large areas can be stored in this fashion at little cost but with increased overhead in processing to determine if a point is inside a polygon.
3 In a 2-D lattice, the cells sharing boundaries form the von Neumann neighbourhood (four neighbours), cells sharing boundaries and vertices form the Moore neighbourhood (eight neighbours) (
Albinet et al. 1986).
4 A fractal is a geometric shape that is recursively self-similar (i.e. the same on all scales), defining an associated length scale such that its dimension is not an integer, i.e. fractional.
5 Dr M. A. Finney, Research Forester, USDA Forest Service, Missoula, MT, USA.