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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.


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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.