Quantitative analysis of forest fire extinction efficiency

Miguel E. Castillo Soto, Francisco Rodriguez y Silva

Abstract


Aim of study: Evaluate the economic extinction efficiency of forest fires, based on the study of fire combat undertaken by aerial and terrestrial means.

Area of study, materials and methods: Approximately 112,000 hectares in Chile.   Records of 5,876 forest fires that occurred between 1998 and 2009 were analyzed. The area further provides a validation sector for results, by incorporating databases for the years 2010 and 2012. The criteria used for measuring extinction efficiency were economic value of forestry resources,  Contraction Factor analysis and definition of the extinction costs function.

Main results: It is possible to establish a relationship between burnt area, extinction costs and economic losses. The method proposed may be used and adapted to other fire situations, requiring unit costs for aerial and terrestrial operations, economic value of the property to be protected and speed attributes of fire spread in free advance.

Research highlights: The determination of extinction efficiency in containment works of forest fires and potential projection of losses, different types of plant fuel and local conditions favoring the spread of fire broaden the admissible ranges of a, φ and Ce considerably.

Keywords: Forest fire; Combat efficiency; Productivity analysis.

Abbreviations: FCS; Superficial Contraction Factor.


Full Text:

PDF HTML XML

References


References

Alexander M, 2001. Fire Behaviour as a Factor in Forest and Rural Fire Suppresion. Fire Research Report. Forest Research Bulletin 197. 35 pp.

Alexandrian D, Esnault F, Calabri G, 1999. Forest fires in the Mediterranean area. In Unasylva (FAO). 0041-6436, Volume 50, 197: 35-41.

Andrews P, Bevins C, Seli R, 2003. BehavePlus fire modelling system, version 2.0: Users Guide. USDA Forest Service, Rocky Mountain Research Station, (Ogden, UT). General Technical Report RMRS- GTR-106WWW.

Andrews P, Queen P, 2001. Fire modelling and information system technology. Int J Wildland Fire 10(4): 343–352. http://dx.doi.org/10.1071/WF01033

Andrews P, 1986. BEHAVE: fire behaviour prediction and fuel modelling system – BURN subsystem, Part I. USDA Forest Service, Intermountain Research Station, (Ogden, UT). General Technical Report GTR-INT-194.

Burgan R, Rothermel R, 1984. BEHAVE: Fire behaviour prediction and fuel modelling system–FUEL subsystem. USDA Forest Service, Inter- mountain Forest and Range Experiment Station, (Ogden, UT). General Technical Report GTR-INT-167.

Castillo M, Julio G, Garfias R, 2014. Current status of risk and prognosis of forest fires in Chile. Progress and future challenges. Wildfire Hazards and Disasters. Book. Elsevier Inc. Chapter. 4. Pp. 59-75. 284 pp.

Castillo M, Molina-Martínez J, Rodríguez y Silva F, Julio G, 2013. A territorial fire vulnerability model for Mediterranean ecosystems in South America. Ecol Informatics 13: 106-113. http://dx.doi.org/10.1016/j.ecoinf.2012.06.004

Castillo M, 1998. Método de Validación para el Simulador de Expansión de Incendios Forestales del Sistema KITRAL. Memoria de Título. Facultad de Ciencias Forestales, Universidad de Chile. 123 pp.

de Torres Curth M, Biscayart C, Ghermandi L, Pfister G, 2012. Wildland-urban interface fires and socioeconomic conditions: A case study of a Northwestern Patagonia city. Env Manag 49: 876-891. http://dx.doi.org/10.1007/s00267-012-9825-6

González-Cabán A, 2013. The Economic Dimension of Wildland Fires. Vegetation Fires and Global Change – Challenges for Concerted International Action. Global Fire Monitoring Center (GFMC). 229-237.

Homes T, Calkin D, 2013. Econometric Analysis of fire suppression production functions for large wildland fires. Int J Wildland Fire 22: 246-255. http://dx.doi.org/10.1071/WF11098

Koutsias N, Martinez J, Chuvieco E, Allgower B, 2005. Modelling Wildland Fire Occurrence in Southern Europe by Geographically Weighted Regression Approach, in: De la Riva J, Perez-Cabello F, Chuvieco E, (Eds.), Fifth International Workshop on Remote Sensing and GIS Applications to Forest Fire Management: Fire Effects Assessment, Zaragoza, Spain. pp. 57-60.

Leone V, Lovreglio R, Martin M, Martinez J, Vilar L, 2009. Human factors of fire occurrence in the Mediterranean, in: Chuvieco E (Ed.), Earth observation of wildland fires in Mediterranean ecosystems. Springer, Berlin. pp. 149-170. http://dx.doi.org/10.1007/978-3-642-01754-4_11

Leone V, Lovreglio R, Martínez-Fernandez J, 2002. Forest fires and anthropic influences: a study case (Gargano National Park, Italy), in: Viegas X (Ed.), Forest fire research and wild-land fire safety. Mill Press, Rotterdam. pp. 11-28.

Le Houérou H, 1987. Vegetation wildfires in the Mediterranean basin: evolution and trends. Ecología Mediterránea. 13(4): 13-24.

Lloret F, Calvo E, Pons X, Diaz-Delgado R, 2002. Wildfires and landscape patterns in the Eastern Iberian Peninsula. Land. Ecology 17: 745-759. http://dx.doi.org/10.1023/A:1022966930861

Mavsar R, González-Cabán A, Varela E, 2013. The state of development of fire management decisión suppport systems in America and Europe. For P Economics 29: 45-55.

Molina Martínez J, Herrera M, Zamora R, Rodríguez y Silva F, González-Cabán A, 2011. Economic losses to Iberian swine production from forest fires. For P Economics 13: 614–621.

Pedernera P, Julio G, 1999. Improving the Economic Efficiency of Combatting Forest Fires in Chile: The KITRAL System. USDA Forest Service Gen. Tech. Rep. PSW-GTR-173. 1999. 149-155.

Prestemon J, Mercer D, Pye J, 2008. Natural disturbance production functions. In 'The Economics of Forest Disturbances: Wildfires, Storms, and Invasive Species'. (Eds TP Holmes, JP Prestemon, KL Abt), Springer: Dordrecht, the Netherlands. pp. 35–58. http://dx.doi.org/10.1007/978-1-4020-4370-3_3

Reams M, Haines T, Renner C, Wascom M, Kingre H, 2005. Goals, obstacles and effective strategies of wildfire mitigation programs in the ildland–urban interface. For P Economics 7: 818–826.

Rodríguez y Silva F, Molina-Martínez J, González-Cabán A, 2014. Methodology for determining operational priorities for prevention and suppression of wildland fires. Int J of Wildland Fire 23, 544–554. http://dx.doi.org/10.1071/WF13063

Rodríguez y Silva F, Julio G, Castillo M, Molina J, Herrera M, Toral M, Cerda C, González L, 2010. Aplicación y adaptación del Modelo SEVEIF para la evaluación socioeconómica del impacto de incendios forestales en la Provincia de Valparaíso, Chile. Agencia Española de Cooperación Internacional para el Desarrollo (AECID). 52 pp.

Rodríguez y Silva F, González-Cabán A, 2012. La predicción de la productividad en las operaciones de extinción de incendios forestales: una aproximación metodológica desde el análisis de la dificultad de extinción y el registro de la experiencia. En: IV Simposio Internacional en Economía, Planificación y Políticas en Incendios Forestales, México. En actas.

Rodríguez y Silva F, González-Cabán A, 2010. 'SINAMI': a tool for the economic evaluation of forest fire management programs in mediterranean ecosystems. Int J Wildland Fire 19: 927-936. http://dx.doi.org/10.1071/WF09015

Rodríguez y Silva F, 1999. A forest fire simulation tool for economic planning in fire management models: an application of the Arc-Cardin strategic model. In 'Proceedings of the Symposium on Fire Economics, Planning and Policy: Bottom Lines', 5–9 April 1999, San Diego, CA. (Eds A Gonza’lez-Caba’n, P Omi) USDA Forest Service, Pacific Southwest Research Station, PSW-GTR-173, Albany, CA. pp. 143–148.

Rothermel R, 1972. A mathematical model for predicting fire spread inwildland fuels. USDA Forest Service, Intermountain Forest and Range Experiment Station, (Ogden, UT). Research Paper INT-115.




DOI: 10.5424/fs/2015242-06644

Webpage: www.inia.es/Forestsystems