Predicting the rate of spread of mixed-fuel surface fires in northeastern China using the Rothermel wildfire behaviour model: a laboratory study

doi:10.1007/s11676-024-01730-w

Abstract

Abstract:

The rate of fire spread is a key indicator for assessing forest fire risk and developing fire management plans. The Rothermel model is the most widely used fire spread model, established through laboratory experiments on homogeneous fuels but has not been validated for conifer-deciduous mixed fuel. In this study, Pinus koraiensis and Quercus mongolica litter was used in a laboratory burning experiment to simulate surface fire spread in the field. The effects of fuel moisture content, mixed fuel ratio and slope on spread rate were analyzed. The optimum packing ratio, moisture-damping coefficient and slope parameters in the Rothermel model were modified using the measured spread rate which was positively correlated with slope and negatively with fuel moisture content. As the Q. mongolica load increased, the spread rate increased and was highest at a fuel ratio of 4:6. The model with modified optimal packing ratio and slope parameters has a significantly lower spread rate prediction error than the unmodified model. The spread rate prediction accuracy was significantly improved after modifying the model parameters based on spread rates from laboratory burning simulations.

Key words: Rothermel model, Mixed fuel, Fuel moisture content, Slope, Parameter modification

Hui Yang, Huiying Cai, Guang Yang, Daotong Geng, Long Sun. Predicting the rate of spread of mixed-fuel surface fires in northeastern China using the Rothermel wildfire behaviour model: a laboratory study[J]. JOURNAL OF FORESTRY RESEARCH, 2024, 35(1): 108-.

Hui Yang, Huiying Cai, Guang Yang, Daotong Geng, Long Sun. [J]. 林业研究(英文版), 2024, 35(1): 108-.

References 48

1	Awad C, Morvan D, Rossi JL, Marcelli T, Chatelon FJ, Morandini F, Balbi JH. Fuel moisture content threshold leading to fire extinction under marginal conditions. Fire Saf J, 2020, 118: 103226, DOI
2	Burton JE, Cawson JG, Filkov AI, Penman TD. Leaf traits predict global patterns in the structure and flammability of forest litter beds. J Ecol, 2021, 109(3): 1344-1355, DOI
3	Butler BW, Anderson WR, Catchpole EA (2007) Influence of slope on fire spread rate. In: BW Butler, W Cook (eds) ‘The Fire Environment - Innovations, Management, and Policy’. USDA Forest Service, Rocky Mountain Research Station, Proceedings RMRS-P-46CD, pp.75–82
4	Cai LY, He HS, Wu ZW, Lewis BL, Liang Y. Development of standard fuel models in boreal forests of northeast China through calibration and validation. PLoS ONE, 2014, 9(4): e94043, DOI
5	Campbell-Lochrie Z, Walker-Ravena C, Gallagher M, Skowronski N, Mueller EV, Hadden RM. Investigation of the role of bulk properties and in-bed structure in the flow regime of buoyancy-dominated flame spread in porous fuel beds. Fire Saf J, 2021, 120: 103035, DOI
6	Cardil A, Monedero S, SeLegue P, Navarrete MA, De-Miguel S, Purdy S, Marshall G, Chavez T, Allison K, Quilez R, Ortega M, Silva CA, Ramirez J. Performance of operational fire spread models in California. Int J Wildland Fire, 2023, 32(11): 1492-1502, DOI
7	Chernkhunthod C, Hioki Y. Fuel characteristics and fire behavior in mixed deciduous forest areas with different fire frequencies in Doi Suthep-Pui National Park Northern, Thailand. Landsc Ecol Eng, 2020, 16: 289-297, DOI
8	Cruz MG, Alexander ME. Assessing crown fire potential in coniferous forests of western North America: a critique of current approaches and recent simulation studies. Int J Wildland Fire, 2010, 19(4): 377-398, DOI
9	Cruz MG, Gould JS, Alexander ME, Sullivan AL, McCaw WL, Matthews S. Empirical-based models for predicting head-fire rate of spread in Australian fuel types. Aust for, 2015, 78(3): 118-158, DOI
10	Cruz MG, Alexander ME, Sullivan AL. Mantras of wildland fire behaviour modelling: facts or fallacies?. Int J Wildland Fire, 2017, 26(11): 973-981, DOI
11	Drysdale DD, Macmillan AJR. Flame spread on inclined surfaces. Fire Saf J, 1992, 18(3): 245-254, DOI
12	Dupuy JL, Maréchal J. Slope effect on laboratory fire spread: contribution of radiation and convection to fuel bed preheating. Int J Wildland Fire, 2011, 20(2): 289-307, DOI
13	Dupuy JL, Marechal J, Morvan D. Fires from a cylindrical forest fuel burner: combustion dynamics and flame properties. Combust Flame, 2003, 135(1–2): 65-76, DOI
14	Geng DT, Ning JB, Li ZG, Yu HZ, Di XY, Yang G. Spread rate and parameter correction of surface fuel in Pinus koraiensis plantation based on Rothermel model. J Beijing for Univ, 2021, 43(11): 79-88 in Chinese
15	Guo HW, Kong LY, Gao YJ, Xiang D, Li ZS, Gong L, Zhang YC. Transition from surface fire to crown fire and effects of crown height, moisture content and tree flower. Fire Technol, 2022, DOI
16	Guo HW, Xiang D, Kong LY, Gao YJ, Zhang YC. Upslope fire spread and heat transfer mechanism over a pine needle fuel bed with different slopes and winds. App Therm Eng, 2023, 229: 120605, DOI
17	He QQ, Liu NA, Xie XD, Zhang LH, Zhang Y, Yan WD. Experimental study on fire spread over discrete fuel bed-Part I: effects of packing ratio. Fire Saf J, 2021, 126: 103470, DOI
18	Jimenez E, Hussaini MY, Goodrick S. Quantifying parametric uncertainty in the Rothermel model. Int J Wildland Fire, 2008, 17(5): 638-649, DOI
19	Kreye JK, Kobziar LN, Zipperer WC. Effects of fuel load and moisture content on fire behaviour and heating in masticated litter-dominated fuels. Int J Wildland Fire, 2012, 22(4): 440-445, DOI
20	Liu NA, Wu JM, Chen HX, Zhang LH, Deng ZH, Satoh K, Viegas DX, Raposo JR. Upslope spread of a linear flame front over a pine needle fuel bed: the role of convection cooling. Pro Combust Inst, 2015, 35(3): 2691-2698, DOI
21	Liu YN, Hussaini MY, Ökten G. Global sensitivity analysis for the Rothermel model based on high-dimensional model representation. Can J Forest Res, 2015, 45(11): 1474-1479, DOI
22	Man ZY, Sun L, Hu HQ, Zhang YL. Prediction model of the spread rate of eight typical surface dead fuel in Southern China under windless and flat land. Sci Silvae Sin, 2019, 55(7): 197-204, in Chinese DOI
23	Marino E, Dupuy JL, Pimont F, Guijarro M, Hemando C, Linn R. Fuel bulk density and fuel moisture content effects on fire rate of spread: a comparison between FIRETEC model predictions and experimental results in shrub fuels. J Fire Sci, 2012, 30(4): 277-299, DOI
24	Matvienko OV, Kasymov DP, Filkov AL, Daneyko OL, Gorbatov DA. Simulation of fuel bed ignition by wildland firebrands. Int J Wildland Fire, 2018, 27(8): 550-561, DOI
25	McArthur AG (1967) Fire behaviour in eucalypt forests. Commonwealth of Australia Forest and Timber Bureau, Leaflet Number 107, Canberra, Australian Capital Territory
26	Morandini F, Santoni PA, Balbi JH. Fire front width effects on fire spread across a laboratory scale sloping fuel bed. Combust Sci Technol, 2001, 166(1): 67-90, DOI
27	Morvan D. Numerical study of the effect of fuel moisture content (FMC) upon the propagation of a surface fire on a flat terrain. Fire Saf J, 2013, 58: 121-131, DOI
28	Nelson JRM. An effective wind speed for models of fire spread. Int J Wildland Fire, 2002, 11(2): 153-161, DOI
29	Ning JB, Di XY, Yu HZ, Yuan SB, Yang G. Spatial distribution of particulate matter 2.5 released from surface fuel combustion of Pinus koraiensis–A laboratory simulation study. Environ Pollut, 2021, 287: 117282, DOI
30	Overholt KJ, Cabrera J, Kurzawski A, Koopersmith M, Ezekoye OA. Characterization of fuel properties and fire spread rates for little bluestem grass. Fire Technol, 2014, 50: 9-3, DOI
31	Possell M, Bell TL. The influence of fuel moisture content on the combustion of Eucalyptus foliage. Int J Wildland Fire, 2012, 22(3): 343-352, DOI
32	Qing LH, Liu QJ, Sun Z, Xu ZZ, Siqing B. Leaf litter decomposition rate of main tree species in broad-leaved Korean pine forest and its relationship with leaf traits. Acta Ecol Sin, 2022, 42(14): 5894-5905, in Chinese DOI
33	Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. Research Paper INT-115. (USDA Forest Service, Intermountain Forest and Range Experiment Station: Ogden, UT)
34	Sánchez-Monroy X, Mell W, Torres-Arenas J, Butler BW. Fire spread upslope: numerical simulation of laboratory experiments. Fire Saf J, 2019, 108: 102844, DOI
35	Sandberg DV, Riccardi CL, Schaaf MD. Reformulation of Rothermel’s wildland fire behaviour model for heterogeneous fuel beds. Can J Forest Res, 2007, 37(12): 2438-2455, DOI
36	Sharples JJ. An overview of mountain meteorological effects relevant to fire behaviour and bushfire risk. Int J Wildland Fire, 2009, 18(7): 737-754, DOI
37	Simeoni A, Bartoli P, Torero JL, Santoni PA. On the role of bulk properties and fuel species on the burning dynamics of pine forest litters. Fire Saf Sci, 2011, 10: 1401-1414, DOI
38	Sullivan AL. Wildland surface fire spread modelling, 1990–2007: 1: physical and quasi-physical models. Int J Wildland Fire, 2009, 18(4): 349-368, DOI
39	Sullivan AL. Wildland surface fire spread modelling, 1990–2007: 2: empirical and quasi-empirical models. Int J Wildland Fire, 2009, 18(4): 369-386, DOI
40	Sullivan AL. Wildland surface fire spread modelling, 1990–2007: 3: simulation and mathematical analogue models. Int J Wildland Fire, 2009, 18(4): 387-403, DOI
41	Terrah SM, Sabi FZ, Mosbah O, Dilem A, Hamamousse N, Sahila A, Harrouz O, Boutchiche H, Chaib F, Zekri N, Kaiss A, Clerc JP, Giroud F, Viegas DX. Nonexistence of critical fuel moisture content for flammability. Fire Safy J, 2020, 111: 102928, DOI
42	Viegas DX, Almeida M, Miranda AI, Ribeiro LM. Linear model for spread rate and mass loss rate for mixed-size fuel beds. Int J Wildland Fire, 2010, 19(5): 531-540, DOI
43	Walker XJ, Baltzer JL, Cumming SG, Day NJ, Ebert C, Goetz S, Johnstone JF, Potter S, Rogers BM, Schuur EAG, Turetsky MR, Mack MC. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature, 2019, 572(7770): 520-523, DOI
44	Watanabe Y, Torikai H, Ito A. Flame spread along a thin solid randomly distributed combustible and noncombustible areas. P Combust Inst, 2011, 33(2): 2449-2455, DOI
45	Wilson RA. Observations of extinction and marginal burning states in free burning porous fuel beds. Combust Sci Technol, 1985, 44(3–4): 179-193, DOI
46	Yashwanth BL, Shotorban B, Mahalingam S, Lautenberger CW, Weise DR. A numerical investigation of the influence of radiation and moisture content on pyrolysis and ignition of a leaf-like fuel element. Combust Flame, 2016, 163: 301-316, DOI
47	Zhang YL, Tian LL. Examining and reforming the Rothermel surface fire spread model under no-wind and zero-slope conditions for the karst ecosystems. Forests, 2023, 14(6): 1088, DOI
48	Zhang JL, Liu BF, Chu TF, Di XY, Jin S. Fire behavior of ground surface fuels in Pinus koraiensis and Quercus mongolica mixed forest under no wind and zero slope condition: A prediction with extended Rothermel model. Chin J Appl Ecol, 2012, 23(06): 1495-1502, in Chinese DOI

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