The numerical investigation of air humidity effects on the fire spread in forests (case study: Malekrod-Siahkal forest)

Document Type : Research Paper

Authors

1 Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran.

2 Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.

10.22059/jfwp.2023.350562.1227

Abstract

Forest fires are a natural part of the ecosystem, but their uncontrolled spread can result in severe economic and environmental damage. Therefore, it is vital to take measures to prevent and manage their spread. However, given the intricate and complex nature of fires, studying real fires in their environmental context can be challenging, if not impractical. As an alternative, researchers can use computer simulation models based on field studies to better understand fire behavior and its effects on the environment. The FARSITE simulator, based on the Rothermel model, was used to conduct numerical simulations of fire spread with respect to fuel, topography, and weather conditions such as humidity. Simulation results showed that the Sorensen and kappa coefficients were at their highest at 0.80 and 0.77, respectively, in air humidity scenarios; with average values of fire spread rate, flame length, and fireline intensity being 0.58 m/min, 0.54 m, and 74.54 kW/m, respectively. Based on these results, it can be concluded that increasing air humidity is a natural way to prevent fire spread. Furthermore, numerical studies and simulations of fire behavior and spread are appropriate alternatives to experimental studies in this field due to their lower costs and fewer limitations.

Keywords

Main Subjects

References

[1]. Adab, H., Devikanniah, K., & Solaimani, K. (2013). Modeling fire risk in northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65, 1723-1743.
[2]. Liu, W., Wang, S., Zhou, Y., Wang, L., & Zhange, S. (2010). Analysis of forest potential fire Environment Based on GIS and RS. Proceedings of 18th International conference on geo-information. Beijing, China, pp: 18-20.
[3]. North, M.P., Stephens, S.L., Collins, B.M., Agee, J.K., Aplet, G., Franklin, J.F., & Fulé, P.Z. (2015). Reform forest fire management: Agency incentives undermine policy effectiveness. Science, 349, 1280-1281.
[4]. Vilar, L., Garrido, J., Echavarría, P., Martínez-Vega, J. & Martín, M. P. (2019). Comparative analysis of CORINE and climate change initiative land cover maps in Europe: Implications for wildfire occurrence estimation at regional and local scales. International Journal of Applied Earth Observation and Geoinformation, 78; 102-117.
[5]. Nami, M. H., Jaafari, A., Fallah, M. & Nabiuni, S. (2018). Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS. International Journal of Environmental Science and Technology, 15; 373–384.
[6]. Finney, M. A. (1994). FARSITE: a Fire area simulator for fire manager. Fire Issues and solutions in urban ecosystems. Pacific Southwest Research Station, Forest Service, U.S. Department of Agriculture; p. 55-56
[7]. Finney, M. (2004). FARSITE: Fire Area simulator-model development and evaluation united states Department of Agriculture, Forest Service, Rocky Mountain Research Station. 47 p.
[8]. Duane, A., Kelly, L., Giljohann, K., Batllori, E., McCarthy, M. & Brotons, L. (2019). Disentangling the Influence of Past Fires on Subsequent Fires in Mediterranean Landscapes. Ecosystems, 22; 1338–1351.
[9]. Cruz, M. G., and Fernandes, P. M,. (2008). Development of fuel models for fire behaviour prediction in maritime pine (Pinus pinaster Ait.) stands. International Journal of Wildland Fire, 17; 194–204.
[10]. Cai, L,. He, H.S., Wu, Z., Lewis, B.L. & Liang, Y. (2014). Development of standard fuel models in boreal forests of Northeast China through calibration and validation. PLoS One, 9(4): e94043.
[11]. Cai, L,. S.He, H,. Liang, Y,. Wu, Z. & Huang, C. (2019). Analysis of the uncertainty of fuel model parameters in wildland fire modelling of a boreal forest in north-east China, International Journal of Wildland Fire, 28; 205-215.
[12]. Sadat Razavi, A,. Shafiepoor motlagh, M,. Noorpoor, A, & Ehasani, A. (2022). Modelling the Effect of Temperature Increments on Wildfires. Pollution, 8(1), 193-209. (In Persian)
[13]. Duguy, B., Alloza, J. A., Roder, A., Vallejo, R. & Pastor, F. (2007). modeling the effects of landscape fuel treatment on fire growth and behaviour in a mediterranean landscape (eastern spain). International Journal of Wildland Fire, 16, 619-632.
[14]. Jahdi, R., darvishsefat, A., & Etemad, V. (2014). predicting forest fire spread using fire behavior model (case study: malekroud forest-siahkal). Iranian Journal of Forest, 5 (4), 419-430. (In Persian)
[15]. Zinger, K., Carvalho, L. M., Peterson, S., Fujioka, F., Dunie, G.J., Jones, C. & Moritz, M. (2020). evaluating the Ability of FARSITE to simulate wildfires influenced by Extreme Downslope winds in santa Barbara, california. Fire, 3, 29.
[16]. Weather Underground’s WunderMap, https://www.wunderground.com/ (accessed November 7, 2021).
[17]. Arca, B., Bacciu, V., Duce, P., Pellizzaro, G., Salis, M. & Spano, D. (2015). Use of simulator to produce fire probability Maps in a Mediterranean Area.
[18]. Hamadeh, N., Karouni, A., Daya, B. & Chauvet, P. (2017). Using correlative data analysis to develop weather index that estimates the risk of forest fires in Lebanon & Mediterranean: Assessment versus prevalent meteorological indices. Case Studies in Fire Safety, 7; 8-22.
[19]. Shinneman, D. J., Germino, M. J., Pilliod, D. S., Aldridge, C. L., Vaillant, N. M., & Coates, P. S. (2019). The ecological uncertainty of wildfire fuel breaks: Examples from the sagebrush steppe. Frontiers Ecology Environment, 279–288.
[20]. Toivanen, J., Engel, C. B., Reeder, M. J., Lane, T. P., Davies, L., & Webster, S. (2019). Coupled atmosphere-fire simulations of the Black Saturday Kilmore East wildfires with the Unified Model. Journal of Advances in Modeling Earth Systems, 11, 210–230.

Files

Share

How to cite

Statistics