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A derivation of the statistical characteristics of forest fires

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  • Lin, Jianyi
  • Rinaldi, Sergio

Abstract

The analysis of large data sets concerning fires in various forested areas of the world has pointed out that burned areas can often be described by different power-law distributions for small, medium and large fires and that a scaling law for the time intervals separating successive fires is fulfilled. The attempts of deriving such statistical laws from purely theoretical arguments have not been fully successful so far, most likely because important physical and/or biological factors controlling forest fires were not taken into account. By contrast, the two-layer spatially extended forest model we propose in this paper encapsulates the main characteristics of vegetational growth and fire ignition and propagation, and supports the empirically discovered statistical laws. Since the model is fully deterministic and spatially homogeneous, the emergence of the power and scaling laws does not seem to necessarily require meteorological randomness and geophysical heterogeneity, although these factors certainly amplify the chaoticity of the fires. Moreover, the analysis suggests that the existence of different power-laws for fires of various scale might be due to the two-layer structure of the forest which allows the formation of different kinds of fires, i.e. surface, crown, and mixed fires.

Suggested Citation

  • Lin, Jianyi & Rinaldi, Sergio, 2009. "A derivation of the statistical characteristics of forest fires," Ecological Modelling, Elsevier, vol. 220(7), pages 898-903.
  • Handle: RePEc:eee:ecomod:v:220:y:2009:i:7:p:898-903
    DOI: 10.1016/j.ecolmodel.2009.01.011
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    References listed on IDEAS

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    1. K. Schenk & B. Drossel & S. Clar & F. Schwabl, 2000. "Finite-size effects in the self-organized critical forest-fire model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 15(1), pages 177-185, May.
    2. Albano, Ezequiel V., 1995. "Spreading analysis and finite-size scaling study of the critical behavior of a forest fire model with immune trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 216(3), pages 213-226.
    3. Drossel, B. & Schwabl, F., 1992. "Self-organized criticality in a forest-fire model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 191(1), pages 47-50.
    4. Drossel, B. & Schwabl, F., 1993. "Forest-fire model with immune trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 199(2), pages 183-197.
    5. Lasaponara, Rosa & Santulli, Adriano & Telesca, Luciano, 2005. "Time-clustering analysis of forest-fire sequences in southern Italy," Chaos, Solitons & Fractals, Elsevier, vol. 24(1), pages 139-149.
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    Cited by:

    1. de Benicio, Rosilda B. & Stošić, Tatijana & de Figueirêdo, P.H. & Stošić, Borko D., 2013. "Multifractal behavior of wild-land and forest fire time series in Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6367-6374.
    2. Nadjla Bentekhici & Sid-Ahmed Bellal & Ahmed Zegrar, 2020. "Contribution of remote sensing and GIS to mapping the fire risk of Mediterranean forest case of the forest massif of Tlemcen (North-West Algeria)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 811-831, October.
    3. Kwadwo YEBOAH BOTAH, 2023. "Forest Fires In A Changing Climate: Risk Assessment And Management In Leiria National Forest, Portugal," Eastern European Journal for Regional Studies (EEJRS), Center for Studies in European Integration (CSEI), Academy of Economic Studies of Moldova (ASEM), vol. 9(2), pages 169-191, December.

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