Simulated behaviour of wildland fire spreading through idealised heterogeneous fuels
Nazmul Khan
A B , Duncan Sutherland B C and Khalid Moinuddin A B *
+ Author Affiliations
- Author Affiliations
A Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Vic., Australia.
B Bushfire and Natural Hazards Cooperative Research Centre (CRC), East Melbourne, Vic. 3002, Australia.
C School of Science, University of New South Wales, Canberra, ACT 2610, Australia.
* Correspondence to: Khalid.Moinuddin@vu.edu.au
International Journal of Wildland Fire 32(5) 738-748 https://doi.org/10.1071/WF22009
Submitted: 4 February 2022 Accepted: 31 January 2023 Published: 21 February 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF.
Abstract
Homogeneous vegetation is widely used in wildland fire behaviour models, although real vegetation is heterogeneous in nature and composed of different kinds of fuels and non-combustible parts. Many features of fires can arise from this heterogeneity. For land management and firefighting, creating heterogeneous fuel areas may be useful to reduce fire intensity and rate of spread (ROS), and alter fire geometry. Recently, an empirical model for fire spread in spinifex grasslands was developed and validated against experimental measurements. In this study, physics-based grassland fire behaviour simulations were conducted with varying percentages of fuel cover and alternating square and rectangular patches of burnable and non-burnable material. The environmental conditions and thermophysical properties of the grassland were kept constant throughout the simulation to separate the effects of fuel heterogeneities from other parameters. For three sets of nominal wind velocities, 3, 5.6 and 10 m s−1, we identified ‘go’ and ‘no go’ fires. Reasonable agreement between the non-dimensionalised simulated ROS and observed ROS in spinifex was found. There is a significant reduction of fire intensity, ROS, flame length, fire width and fire line length due to the heterogeneous effect of vegetation.
Keywords: fire line length, flame length, heterogeneity, homogeneous vegetation, rate of spread, spinifex, wildland fire, wind speed.
References
Apte VB, Bilger RW, Green AR, Quintiere JG (1991) Wind-aided turbulent flame spread and burning over large-scale horizontal PMMA surfaces. Combustion and Flame 85, 169–184.| Wind-aided turbulent flame spread and burning over large-scale horizontal PMMA surfaces.Crossref | GoogleScholarGoogle Scholar |
Atchley AL, Linn R, Jonko A, Hoffman C, Hyman JD, Pimont F, Sieg C, Middleton RS (2021) Effects of fuel spatial distribution on wildland fire behaviour. International Journal of Wildland Fire 30, 179–189.
| Effects of fuel spatial distribution on wildland fire behaviour.Crossref | GoogleScholarGoogle Scholar |
Benali A, Sá ACL, Ervilha AR, Trigo RM, Fernandes PM, Pereira JMC (2017) Fire spread predictions: Sweeping uncertainty under the rug. Science of the Total Environment 592, 187–196.
| Fire spread predictions: Sweeping uncertainty under the rug.Crossref | GoogleScholarGoogle Scholar |
Berjak SG, Hearne JW (2002) An improved cellular automaton model for simulating fire in a spatially heterogeneous savanna system. Ecological Modelling 148, 133–151.
| An improved cellular automaton model for simulating fire in a spatially heterogeneous savanna system.Crossref | GoogleScholarGoogle Scholar |
Bossert JE, Harlow FH, Linn RR, Reisner JM, White AB, Winterkamp JL (1997) Coupled weather and wildfire behavior modeling at Los Alamos: an overview. United States. Available at https://www.osti.gov/biblio/635205
Bou-Zeid E, Meneveau C, Parlange MB (2004) Large-eddy simulation of neutral atmospheric boundary layer flow over heterogeneous surfaces: Blending height and effective surface roughness. Water Resources Research 40, W02505
| Large-eddy simulation of neutral atmospheric boundary layer flow over heterogeneous surfaces: Blending height and effective surface roughness.Crossref | GoogleScholarGoogle Scholar |
Bradstock RA, Gill AM (1993) Fire in semiarid, mallee shrublands – size of flames from discrete fuel arrays and their role in the spread of fire. International Journal of Wildland Fire 3, 3–12.
| Fire in semiarid, mallee shrublands – size of flames from discrete fuel arrays and their role in the spread of fire.Crossref | GoogleScholarGoogle Scholar |
Brown JK (1982) ‘Fuel and fire behavior prediction in big sagebrush. Vol. 290’. 10 p. (USDA Forest Service, Intermountain Forest and Range Experiment Station)
Burrows N, Ward B, Robinson A (2000) Behaviour and some impacts of a large wildfire in the Gnangara maritime pine (Pinus pinaster) plantation, Western Australia. CALMScience 3, 251–260.
Burrows N, Gill M, Sharples J (2018) Development and validation of a model for predicting fire behaviour in spinifex grasslands of arid Australia. International Journal of Wildland Fire 27, 271–279.
| Development and validation of a model for predicting fire behaviour in spinifex grasslands of arid Australia.Crossref | GoogleScholarGoogle Scholar |
Cheney NP, Gould JS, Catchpole WR (1998) Prediction of fire spread in grasslands. International Journal of Wildland Fire 8, 1–13.
| Prediction of fire spread in grasslands.Crossref | GoogleScholarGoogle Scholar |
Cobian-Iñiguez J, Aminfar A, Weise DR, Princevac M (2019) On the Use of semi-empirical flame models for spreading chaparral crown fire. Frontiers in Mechanical Engineering 5, 50
| On the Use of semi-empirical flame models for spreading chaparral crown fire.Crossref | GoogleScholarGoogle Scholar |
Duarte JAMS, Carvalho JM, Ruskin HJ (1992) The direction of maximum spread in anisotropic forest fires and its critical properties. Physica A: Statistical Mechanics and its Applications 183, 411–421.
| The direction of maximum spread in anisotropic forest fires and its critical properties.Crossref | GoogleScholarGoogle Scholar |
Dupont S, Brunet Y (2008) Edge flow and canopy structure: a large-eddy simulation study. Boundary-Layer Meteorology 126, 51–71.
| Edge flow and canopy structure: a large-eddy simulation study.Crossref | GoogleScholarGoogle Scholar |
Finney MA (2001) Design of regular landscape fuel treatment patterns for modifying fire growth and behavior. Forest Science 47, 219–228.
| Design of regular landscape fuel treatment patterns for modifying fire growth and behavior.Crossref | GoogleScholarGoogle Scholar |
Frangieh N, Morvan D, Meradji S, Accary G, Bessonov O (2018) Numerical simulation of grassland fires behavior using an implicit physical multiphase model. Fire Safety Journal 102, 37–47.
| Numerical simulation of grassland fires behavior using an implicit physical multiphase model.Crossref | GoogleScholarGoogle Scholar |
Gao W, Shaw R, Paw U KT (1989) Observation of organized structure in turbulent flow within and above a forest canopy. In ‘Boundary Layer Studies and Applications’. (Ed. RE Munn) pp. 349–377. (Springer)
Hiers JK, O’Brien JJ, Varner JM, Butler BW, Dickinson M, Furman J, Gallagher M, Godwin D, Goodrick SL, Hood SM, Hudak A, Kobziar LN, Linn R, Loudermilk EL, McCaffrey S, Robertson K, Rowell EM, Skowronski N, Watts AC, Yedinak KM (2020) Prescribed fire science: the case for a refined research agenda. Fire Ecology 16, 11
| Prescribed fire science: the case for a refined research agenda.Crossref | GoogleScholarGoogle Scholar |
Hilton JE, Miller C, Sullivan AL, Rucinski C (2015) Effects of spatial and temporal variation in environmental conditions on simulation of wildfire spread. Environmental Modelling & Software 67, 118–127.
| Effects of spatial and temporal variation in environmental conditions on simulation of wildfire spread.Crossref | GoogleScholarGoogle Scholar |
Hoffman CM, Canfield J, Linn RR, Mell W, Sieg CH, Pimont F, Ziegler J (2016) Evaluating crown fire rate of spread predictions from physics-based models. Fire Technology 52, 221–237.
| Evaluating crown fire rate of spread predictions from physics-based models.Crossref | GoogleScholarGoogle Scholar |
Jarrin N, Benhamadouche S, Laurence D, Prosser R (2006) A synthetic-eddy-method for generating inflow conditions for large-eddy simulations. International Journal of Heat and Fluid Flow 27, 585–593.
| A synthetic-eddy-method for generating inflow conditions for large-eddy simulations.Crossref | GoogleScholarGoogle Scholar |
Khan N, Moinuddin K (2021) The role of heat flux in an idealised firebreak built in surface and crown fires. Atmosphere 12, 1395
| The role of heat flux in an idealised firebreak built in surface and crown fires.Crossref | GoogleScholarGoogle Scholar |
Khan N, Sutherland D, Wadhwani R, Moinuddin K (2019) Physics-based simulation of heat load on structures for improving construction standards for bushfire-prone areas. Frontiers in Mechanical Engineering 5, 35
| Physics-based simulation of heat load on structures for improving construction standards for bushfire-prone areas.Crossref | GoogleScholarGoogle Scholar |
Kiefer MT, Heilman WE, Zhong S, Charney JJ, Bian X (2016) A study of the influence of forest gaps on fire–atmosphere interactions. Atmospheric Chemistry and Physics 16, 8499–8509.
| A study of the influence of forest gaps on fire–atmosphere interactions.Crossref | GoogleScholarGoogle Scholar |
King KJ, Bradstock RA, Cary GJ, Chapman J, Marsden-Smedley JB (2008) The relative importance of fine-scale fuel mosaics on reducing fire risk in south-west Tasmania, Australia. International Journal of Wildland Fire 17, 421–430.
| The relative importance of fine-scale fuel mosaics on reducing fire risk in south-west Tasmania, Australia.Crossref | GoogleScholarGoogle Scholar |
Linn R, Anderson K, Winterkamp J, Brooks A, Wotton M, Dupuy J-L, Pimont F, Edminster C (2012) Incorporating field wind data into FIRETEC simulations of the International Crown Fire Modeling Experiment (ICFME): preliminary lessons learned. Canadian Journal of Forest Research 42, 879–898.
| Incorporating field wind data into FIRETEC simulations of the International Crown Fire Modeling Experiment (ICFME): preliminary lessons learned.Crossref | GoogleScholarGoogle Scholar |
Linn RR, Cunningham P (2005) Numerical simulations of grass fires using a coupled atmosphere–fire model: basic fire behavior and dependence on wind speed. Journal of Geophysical Research: Atmospheres 110, D13107
| Numerical simulations of grass fires using a coupled atmosphere–fire model: basic fire behavior and dependence on wind speed.Crossref | GoogleScholarGoogle Scholar |
Linn RR, Goodrick SL, Brambilla S, Brown MJ, Middleton RS, O’Brien JJ, Hiers JK (2020) QUIC-fire: A fast-running simulation tool for prescribed fire planning. Environmental Modelling & Software 125, 104616
| QUIC-fire: A fast-running simulation tool for prescribed fire planning.Crossref | GoogleScholarGoogle Scholar |
McGrattan K, Hostikka S, McDermott R, Floyd J, Weinschenk C, Overholt K (2013) ‘Fire dynamics simulator technical reference guide volume 1: mathematical model. Vol. 1018(1)’. NIST Special Publication. 175 p. (US Department of Commerce, National Institute of Standards and Technology, US Government Printing Office: Washington, DC, USA)
Mell W, Jenkins MA, Gould J, Cheney P (2007) A physics-based approach to modelling grassland fires. International Journal of Wildland Fire 16, 1–22.
| A physics-based approach to modelling grassland fires.Crossref | GoogleScholarGoogle Scholar |
Moinuddin K, Khan N, Sutherland D (2021) Numerical study on effect of relative humidity (and fuel moisture) on modes of grassfire propagation. Fire Safety Journal 125, 103422
| Numerical study on effect of relative humidity (and fuel moisture) on modes of grassfire propagation.Crossref | GoogleScholarGoogle Scholar |
Moinuddin KAM, Sutherland D, Mell W (2018) Simulation study of grass fire using a physics-based model: striving towards numerical rigour and the effect of grass height on the rate of spread. International Journal of Wildland Fire 27, 800–814.
| Simulation study of grass fire using a physics-based model: striving towards numerical rigour and the effect of grass height on the rate of spread.Crossref | GoogleScholarGoogle Scholar |
Moon K, Duff TJ, Tolhurst KG (2019) Sub-canopy forest winds: understanding wind profiles for fire behaviour simulation. Fire Safety Journal 105, 320–329.
| Sub-canopy forest winds: understanding wind profiles for fire behaviour simulation.Crossref | GoogleScholarGoogle Scholar |
Morvan D (2015) Numerical study of the behaviour of a surface fire propagating through a firebreak built in a Mediterranean shrub layer. Fire Safety Journal 71, 34–48.
| Numerical study of the behaviour of a surface fire propagating through a firebreak built in a Mediterranean shrub layer.Crossref | GoogleScholarGoogle Scholar |
Morvan D, Dupuy J-L (2004) Modeling the propagation of a wildfire through a Mediterranean shrub using a multiphase formulation. Combustion and Flame 138, 199–210.
| Modeling the propagation of a wildfire through a Mediterranean shrub using a multiphase formulation.Crossref | GoogleScholarGoogle Scholar |
Morvan D, Frangieh N (2018) Corrigendum 1 (published 19 Sep 2018) and Corrigendum 2 (published 11 Dec 2018) to: Wildland fires behaviour: wind effect versus Byram’s convective number and consequences upon the regime of propagation. International Journal of Wildland Fire 27, 857
| Corrigendum 1 (published 19 Sep 2018) and Corrigendum 2 (published 11 Dec 2018) to: Wildland fires behaviour: wind effect versus Byram’s convective number and consequences upon the regime of propagation.Crossref | GoogleScholarGoogle Scholar |
Pastor E, Zárate L, Planas E, Arnaldos J (2003) Mathematical models and calculation systems for the study of wildland fire behaviour. Progress in Energy and Combustion Science 29, 139–153.
| Mathematical models and calculation systems for the study of wildland fire behaviour.Crossref | GoogleScholarGoogle Scholar |
Pimont F, Dupuy J-L, Linn RR, Dupont S (2009) Validation of FIRETEC wind-flows over a canopy and a fuel-break. International Journal of Wildland Fire 18, 775–790.
| Validation of FIRETEC wind-flows over a canopy and a fuel-break.Crossref | GoogleScholarGoogle Scholar |
Simeoni A, Salinesi P, Morandini F (2011) Physical modelling of forest fire spreading through heterogeneous fuel beds. International Journal of Wildland Fire 20, 625–632.
| Physical modelling of forest fire spreading through heterogeneous fuel beds.Crossref | GoogleScholarGoogle Scholar |
Sun R, Krueger SK, Jenkins MA, Zulauf MA, Charney JJ (2009) The importance of fire–atmosphere coupling and boundary-layer turbulence to wildfire spread. International Journal of Wildland Fire 18, 50–60.
| The importance of fire–atmosphere coupling and boundary-layer turbulence to wildfire spread.Crossref | GoogleScholarGoogle Scholar |
Sutherland D, Sharples JJ, Moinuddin KAM (2020) The effect of ignition protocol on grassfire development. International Journal of Wildland Fire 29, 70–80.
| The effect of ignition protocol on grassfire development.Crossref | GoogleScholarGoogle Scholar |
Turner MG, Romme WH (1994) Landscape dynamics in crown fire ecosystems. Landscape Ecology 9, 59–77.
| Landscape dynamics in crown fire ecosystems.Crossref | GoogleScholarGoogle Scholar |
