Register      Login
International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

Physics-based simulations of grassfire propagation on sloped terrain at field scale: motivations, model reliability, rate of spread and fire intensity

Jasmine Innocent A B , Duncan Sutherland B C , Nazmul Khan https://orcid.org/0000-0001-8483-7171 A B and Khalid Moinuddin A B *
+ Author Affiliations
- Author Affiliations

A Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, 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(4) 496-512 https://doi.org/10.1071/WF21124
Submitted: 3 September 2021  Accepted: 5 December 2022   Published: 20 January 2023

© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF.

Abstract

This study focuses on physics-based modelling of grassfire behaviour over flat and sloped terrains through a set of field-scale simulations performed using the Wildland–urban Interface Fire Dynamics Simulator (WFDS), with varying wind speeds (12.5, 6 and 3 m s−1) and slope angles (−30° to +30°). To ensure the accuracy of this Large Eddy Simulation (LES), a sensitivity study was carried out to select the converged domain and grid sizes. Fire isochrones, locations of fire front, dynamic and quasi-steady rates of spread (RoS), and fire intensity results from the simulations are presented. Within the simulations conducted, the RoS and fire intensity were found to be higher with increasing slope angles, as well as with wind velocity. RoS comparisons are made with various empirical models. At different slope angles and driving wind velocities, different empirical quasi-steady RoS broadly match with particular dynamic maximum, minimum and averaged RoS values from this study. It appears that the ideal nature of grassfire propagation simulation and challenges related to measuring quasi-steady values in experimental studies are likely reasons for the observed differences. Additionally, for lower wind velocities, the RoS–fire intensity relationship (Byram’s) deviates from linearity for greater upslopes.

Keywords: fire behaviour, fire front, grass fire, intensity, rate of spread (RoS) of fire, simulations, slope, wind speed.


References

Andrews PL (2018) ‘The Rothermel Surface Fire Spread Model and Associated Developments: A Comprehensive Explanation.’ (USDA)

Beck JA (1995) Equations for the Forest Fire Behaviour Tables for Western Australia. CALM Science 1, 325–348.

Burgan RE, Rothermel RC (1984) BEHAVE: Fire Behavior Prediction and Fuel Modeling System – FUEL Subsystem. PMS 439-1, NFES 0275. (USDA Forest Service) Available at https://www.fs.usda.gov/rm/pubs_int/int_gtr167.pdf

Burrows N (1994) Experimental development of a fire management model for Jarrah (Eucalyptus marginata Donn ex Sm.) forest. PhD Thesis, Australian National University, Canberra. Available at
| Crossref |

Byram GM (1959) Combustion of forest fuels. In ‘Forest fire: control and use’. (Ed. KP Davis) pp. 61–89. (McGraw-Hill: New York)

Chen TBY, Yuen ACY, Wang C, Yeoh GH, Timchenko V, Cheung SCP, Chan QN, Yang W (2018) Predicting the fire spread rate of a sloped pine needle board utilizing pyrolysis modelling with detailed gas-phase combustion. International Journal of Heat and Mass Transfer 125, 310–322.
Predicting the fire spread rate of a sloped pine needle board utilizing pyrolysis modelling with detailed gas-phase combustion.Crossref | GoogleScholarGoogle Scholar |

Cheney NP, Gould JS, Catchpole WR (1993) The influence of fuel, weather and fire shape variables on fire spread in grasslands. International Journal of Wildland Fire 3, 31–44.
The influence of fuel, weather and fire shape variables on fire spread in grasslands.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 |

Cruz MG, Alexander ME (2013) Uncertainty associated with model predictions of surface and crown fire rates of spread. Environmental Modelling & Software 47, 16–28.
Uncertainty associated with model predictions of surface and crown fire rates of spread.Crossref | GoogleScholarGoogle Scholar |

Cruz MG, Alexander ME, Sullivan AL (2017) Mantras of wildland fire behaviour modelling: facts or fallacies? International Journal of Wildland Fire 26, 973–981.
Mantras of wildland fire behaviour modelling: facts or fallacies?Crossref | GoogleScholarGoogle Scholar |

Davis T, Sigmon K (2005) ‘MATLAB Premier’, 7th edn. (Chapman & Hall/CRC)

Eftekharian E, Ghodrat M, He Y, Ong RH, Kwok KCS, Zhao M, Samali B (2019) Investigation of terrain slope effects on wind enhancement by a line source fire. Case Studies in Thermal Engineering 14, 100467
Investigation of terrain slope effects on wind enhancement by a line source fire.Crossref | GoogleScholarGoogle Scholar |

FCFDG (Forestry Canada Fire Danger Group) (1992) Development and structure of the Canadian Forest Fire Behavior Prediction System. Information Report. (Forestry Canada, Science and Sustainable Development Directorate)

Jackson WJ, Argent RM, Bax NJ, Clark GF, Coleman S, Cresswell ID, Emmerson KM, Evans K, Hibberd MF, Johnston EL, Keywood MD, Klekociuk A, Mackay R, Metcalfe D, Murphy H, Rankin A, Smith DC, Wienecke B (2017). Australia State of the Environment 2016. Independent report to the Australian Government Minister for the Environment and Energy. Australian Government Department of the Environment and Energy, Canberra. Available at https://apo.org.au/node/74267

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 |

Linn R, Winterkamp J, Edminster C, Colman JJ, Smith WS (2007) Coupled influences of topography and wind on wildland fire behaviour. International Journal of Wildland Fire 16, 183–195.
Coupled influences of topography and wind on wildland fire behaviour.Crossref | GoogleScholarGoogle Scholar |

McArthur AG (1966) ‘Weather and grassland fire behaviour.’ (Forestry and Timber Bureau, Department of National Development, Commonwealth of Australia)

McArthur AG (1967) ‘Fire behaviour in eucalypt forests.’ (Forestry and Timber Bureau, Department of National Development, Commonwealth of Australia)

McGrattan, K, Hostikka, S, McDermott, R, Floyd, J, Weinschenk, C, Overholt, K (2015) Fire Dynamics Simulator Technical Reference Guide Volume 1: Mathematical Model. NIST special publication 1018-1. National Institute of Standards and Technology.
| Crossref |

McGrattan KB, Forney GP, Hostikka S, McDermott R, Weinschenk C (2013) Fire Dynamics Simulator, User’s Guide, 6th edn (original version 2013, revised version 6.3.2 in 2015). NIST special publication 1019. National Institute of Standards and Technology.
| Crossref |

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 |

Mell W, Maranghides A, McDermott R, Manzello SL (2009) Numerical simulation and experiments of burning Douglas fir trees. Combustion and Flame 156, 2023–2041.
Numerical simulation and experiments of burning Douglas fir trees.Crossref | GoogleScholarGoogle Scholar |

Mell W, Simeoni A, Morvan D, Hiers JK, Skowronski N, Hadden RM (2018) Clarifying the meaning of mantras in wildland fire behaviour modelling: reply to Cruz et al. (2017). International Journal of Wildland Fire 27, 770–775.
Clarifying the meaning of mantras in wildland fire behaviour modelling: reply to Cruz et al. (2017).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 |

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 |

Noble IR, Gill AM, Bary GAV (1980) McArthur’s fire-danger meters expressed as equations. Australian Journal of Ecology 5, 201–203.
McArthur’s fire-danger meters expressed as equations.Crossref | GoogleScholarGoogle Scholar |

Perez-Ramirez Y, Mell WE, Santoni PA, Tramoni JB, Bosseur F (2017) Examination of WFDS in modeling spreading fires in a furniture calorimeter. Fire Technology 53, 1795–1832.
Examination of WFDS in modeling spreading fires in a furniture calorimeter.Crossref | GoogleScholarGoogle Scholar |

Pimont F, Dupuy J-L, Linn RR (2012) Coupled slope and wind effects on fire spread with influences of fire size: a numerical study using FIRETEC. International Journal of Wildland Fire 21, 828–842.
Coupled slope and wind effects on fire spread with influences of fire size: a numerical study using FIRETEC.Crossref | GoogleScholarGoogle Scholar |

Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. General Technical Report INT-115. (USDA Forest Service, Intermountain Forest and Range Research Station: Ogden, UT)

Sánchez-Monroy X, Mell W, Torres-Arenas J, Butler BW (2019) Fire spread upslope: Numerical simulation of laboratory experiments. Fire Safety Journal 108, 102844
Fire spread upslope: Numerical simulation of laboratory experiments.Crossref | GoogleScholarGoogle Scholar |

Sharples JJ (2008) Review of formal methodologies for wind–slope correction of wildfire rate of spread. International Journal of Wildland Fire 17, 179–193.
Review of formal methodologies for wind–slope correction of wildfire rate of spread.Crossref | GoogleScholarGoogle Scholar |

Sharples JJ (2017) Risk implications of dynamic fire propagation. A case study of the Ginninderry region. Preliminary Report, June 2017. (Ginninderra Falls Association)

Sharples JJ, Vegas DX, MaRae RHD, Raposo JRN, Farinha HAS (2011) Lateral bushfire propagation driven by the interaction of wind, terrain and fire. In ‘19th International Congress on Modelling and Simulation, Perth, Australia, 2011’. pp. 12–16. Available at https://www.mssanz.org.au/modsim2011/A2/sharples.pdf

Standards Australia (2018) AS 3959-2018: Construction of buildings in bushfire-prone areas. Standards Australia Ltd.

Stull RB, Arhens CD (2000) ‘Meteorology for Scientists and Engineers’, 2nd edn. (Brooks/Cole, Pacific Grove: California)

Sullivan AL (2009) Wildland surface fire spread modelling, 1990–2007. 3: Simulation and mathematical analogue models. International Journal of Wildland Fire 18, 387–403.
Wildland surface fire spread modelling, 1990–2007. 3: Simulation and mathematical analogue models.Crossref | GoogleScholarGoogle Scholar |

Sullivan AL, Sharples JJ, Matthews S, Plucinski MP (2014) A downslope fire spread correction factor based on landscape-scale fire behaviour. Environmental Modelling & Software 62, 153–163.
A downslope fire spread correction factor based on landscape-scale fire behaviour.Crossref | GoogleScholarGoogle Scholar |

Sutherland D, Sharples JJ, Moinuddin KAM (2020a) A response to comments of Cruz et al. on: ‘The effect of ignition protocol on the spread rate of grass fires’. International Journal of Wildland Fire 29, 1139–1141.
A response to comments of Cruz et al. on: ‘The effect of ignition protocol on the spread rate of grass fires’.Crossref | GoogleScholarGoogle Scholar |

Sutherland D, Sharples JJ, Moinuddin KAM (2020b) 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 |

Taylor SW, Wotton BM, Alexander ME, Dalrymple GN (2004) Variation in wind and crown fire behaviour in a northern jack pine–black spruce forest. Canadian Journal of Forest Research 34, 1561–1576.
Variation in wind and crown fire behaviour in a northern jack pine–black spruce forest.Crossref | GoogleScholarGoogle Scholar |

Weise DR, Biging GS (1997) A qualitative comparison of fire spread models incorporating wind and slope effects. Forest Science 43, 170–180.

Wilson R (1990) Reexamination of Rothermel's fire spread equations in no-wind and no-slope conditions. Research Paper INT-434. (USDA Forest Service, Intermountain Forest and Range Research Station: Ogden, UT)