Adaptation of QES-Fire, a dynamically coupled fast response wildfire model for heterogeneous environments
Matthew J. Moody A * , Rob Stoll
B and Brian N. Bailey A
+ Author Affiliations
- Author Affiliations
A Department of Plant Sciences, University of California Davis, Davis, CA 95616, USA.
B Department of Mechanical Engineering, University of Utah, Salt Lake City, UT 84112, USA.
* Correspondence to: mmoody@ucdavis.edu
International Journal of Wildland Fire 32(5) 749-766 https://doi.org/10.1071/WF22190
Submitted: 2 September 2022 Accepted: 6 March 2023 Published: 6 April 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF.
Abstract
Background: Modelling of fire front progression is challenging due to the large range of spatial and temporal scales involved in the interactions between the atmosphere and fire fronts. Further modelling complications arise when heterogeneous terrain and fuels are considered.
Aims: The aim of this study was to create a new parameterisation for wildfire-induced winds that accounts for the effects of heterogeneous terrain and fuels within the QES-Fire modelling framework – a fast-response wildfire model.
Methods: QES-Fire’s new turbulent plume merging model allows for distinct plumes to be merged together from fires burning in heterogeneous terrain with heterogeneous fuels. Additionally, fuel inputs from the LANDFIRE database developed for the Rothermel rate of spread (ROS) model, are translated to the Balbi ROS model.
Key results: The model was evaluated against the forested RxCADRE field experiment, with and without the effects of heterogeneity. Inclusion of heterogeneity reduced the relative error in burned area from 36 to 6%.
Conclusions: Small variations in terrain and fuel heterogeneity lead to large errors in rate and direction of fire front spread.
Implications: The modelled effects of terrain and fuel heterogeneity indicated the importance of capturing the complex coupled wildfire–atmospheric dynamics at the fire front.
Keywords: coupled wildfire-atmospheric dynamics, fast-response model, fire-fuel model, heterogeneous fuels, heterogeneous terrain, merging buoyant plumes, rate of spread, simplified physics model, wildfire model.
References
Achtemeier GL (2013) Field validation of a free-agent cellular automata model of fire spread with fire–atmosphere coupling. International Journal of Wildland Fire 22, 148–156.| Field validation of a free-agent cellular automata model of fire spread with fire–atmosphere coupling.Crossref | GoogleScholarGoogle Scholar |
Balbi JH, Morandini F, Silvani X, Filippi JB, Rinieri F (2009) A physical model for wildland fires. Combustion and Flame 156, 2217–2230.
| A physical model for wildland fires.Crossref | GoogleScholarGoogle Scholar |
Balbi JH, Chatelon FJ, Morvan D, Rossi JL, Marcelli T, Morandini F (2020) A convective–radiative propagation model for wildland fires. International Journal of Wildland Fire 29, 723–738.
| A convective–radiative propagation model for wildland fires.Crossref | GoogleScholarGoogle Scholar |
Barbano F, DiSabatino S, Stoll R, Pardyjak ER (2020) A numerical study of the impact of vegetation on mean and turbulence fields in a European-city neighbourhood. Building and Environment 186, 107293
| A numerical study of the impact of vegetation on mean and turbulence fields in a European-city neighbourhood.Crossref | GoogleScholarGoogle Scholar |
Batchelor GK (1954) Heat convection and buoyancy effects in fluids. Quarterly Journal of the Royal Meteorological Society 80, 339–358.
| Heat convection and buoyancy effects in fluids.Crossref | GoogleScholarGoogle Scholar |
Baum HR, McCaffrey BJ (1989) Fire induced flow field – theory and experiment. Fire Safety Science 2, 129–148.
| Fire induced flow field – theory and experiment.Crossref | GoogleScholarGoogle Scholar |
Bozorgmehr B (2022) QES-Winds: Development of a GPU based fast-response wind model to simulate flow over complex terrain and urban areas. PhD thesis, University of Utah, Salt Lake City, UT, USA.
Bozorgmehr B, Willemsen P, Gibbs JA, Stoll R, Kim JJ, Pardyjak ER (2021a) Utilizing dynamic parallelism in CUDA to accelerate a 3D red-black successive over relaxation wind-field solver. Environmental Modelling & Software 137, 104958
| Utilizing dynamic parallelism in CUDA to accelerate a 3D red-black successive over relaxation wind-field solver.Crossref | GoogleScholarGoogle Scholar |
Bozorgmehr B, Willemsen P, Margairaz F, Gibbs JA, Patterson Z, Stoll R, Pardyjak ER (2021b) ‘QES-Winds v1.0: Theory and User’s Guide.’ (University of Utah: Salt Lake City, UT, USA)
Burgan RE (1979) Estimating live fuel moisture for the 1978 national fire danger rating system. Research Paper INT-RP-226. USDA Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT, USA.
Butler B, Teske C, Jimenez D, O’Brien J, Sopko P, Wold C, Vosburgh M, Hornsby B, Loudermilk E (2016) Observations of energy transport and rate of spreads from low-intensity fires in longleaf pine habitat – RxCADRE 2012. International Journal of Wildland Fire 25, 76–89.
| Observations of energy transport and rate of spreads from low-intensity fires in longleaf pine habitat – RxCADRE 2012.Crossref | GoogleScholarGoogle Scholar |
Byram GM (1959) Forest fire behavior. In ‘Forest Fire: Control and Use’. (Ed. KP Davis) pp. 90–123. (McGraw-Hill: New York, NY, USA)
Chatelon FJ, Balbi JH, Morvan D, Rossi JL, Marcelli T (2017) A convective model for laboratory fires with well-ordered vertically-oriented fuel beds. Fire Safety Journal 90, 54–61.
| A convective model for laboratory fires with well-ordered vertically-oriented fuel beds.Crossref | GoogleScholarGoogle Scholar |
Cionco RM (1965) A mathematical model for air flow in a vegetative canopy. Journal of Applied Meteorology and Climatology 4, 517–522.
| A mathematical model for air flow in a vegetative canopy.Crossref | GoogleScholarGoogle Scholar |
Clark TL, Coen J, Latham D (2004) Description of a coupled atmosphere–fire model. International Journal of Wildland Fire 13, 49–63.
| Description of a coupled atmosphere–fire model.Crossref | GoogleScholarGoogle Scholar |
Clements CB, Lareau NP, Seto D, Contezac J, Davis B, Teske C, Zajkowski TJ, Hudak AT, Bright BC, Dickinson MB, Butler BW, Jimenez D, Hiers JK (2016) Fire weather conditions and fire–atmosphere interactions observed during low-intensity prescribed fires – RxCADRE 2012. International Journal of Wildland Fire 25, 90–101.
| Fire weather conditions and fire–atmosphere interactions observed during low-intensity prescribed fires – RxCADRE 2012.Crossref | GoogleScholarGoogle Scholar |
Coen JL, Cameron M, Michalakes J, Patton EG, Riggan PJ, Yedinak KM (2013) WRF-Fire: coupled weather–wildland fire modeling with the weather research and forecasting model. Journal of Applied Meteorology and Climatology 52, 16–38.
| WRF-Fire: coupled weather–wildland fire modeling with the weather research and forecasting model.Crossref | GoogleScholarGoogle Scholar |
Filippi JB, Bosseur F, Pialat X, Santoni PA, Strada S, Mari C (2011) Simulation of coupled fire/atmosphere interaction with the MesoNh–ForeFire models. Journal of Combustion 2011, 540390
| Simulation of coupled fire/atmosphere interaction with the MesoNh–ForeFire models.Crossref | GoogleScholarGoogle Scholar |
Finney MA, Weise DR, Martin RE (1995) FARSITE: a fire area simulator for fire managers. Technical Report PSW-GTR-158. Department of Agriculture, Forest Service, Pacific Southwest Research Station, Portland, OR, USA
Finney MA, Cohen JD, Forthofer JM, McAllister SS, Gollner MJ, Gorham DJ, Saito K, Akafuah NK, Adam BA, English JD (2015) Role of buoyant flame dynamics in wildfire spread. Proceedings of the National Academy of Sciences 112, 9833–9838.
| Role of buoyant flame dynamics in wildfire spread.Crossref | GoogleScholarGoogle Scholar |
Fons WL (1946) Analysis of fire spread in light forest fuels. Journal of Agricultural Research 72, 92–121.
Fosberg MA (1971) Derivation of the 1-and 10-hour timelag fuel moisture calculations for fire-danger rating. Research Note RM-RN-207. USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, CO, USA
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 |
Halubok M, Kochanski AK, Stoll R, Bailey BN (2022) Errors in the estimation of leaf area density from aerial LiDAR data: Influence of statistical sampling and heterogeneity. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14.
| Errors in the estimation of leaf area density from aerial LiDAR data: Influence of statistical sampling and heterogeneity.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 |
Hilton JE, Sullivan AL, Swedosh W, Sharples J, Thomas C (2018) Incorporating convective feedback in wildfire simulations using pyrogenic potential. Environmental Modelling & Software 107, 12–24.
| Incorporating convective feedback in wildfire simulations using pyrogenic potential.Crossref | GoogleScholarGoogle Scholar |
Hoffman C, Sieg C, Linn R, Mell W, Parsons R, Ziegler J, Hiers J (2018) Advancing the science of wildland fire dynamics using process-based models. Fire 1, 32
| Advancing the science of wildland fire dynamics using process-based models.Crossref | GoogleScholarGoogle Scholar |
Kaye NB, Linden PF (2004) Coalescing axisymmetric turbulent plumes. Journal of Fluid Mechanics 502, 41–63.
| Coalescing axisymmetric turbulent plumes.Crossref | GoogleScholarGoogle Scholar |
LANDFIRE (2012a) 40 Scott and Burgan fire behavior fuel models. Available at LANDFIRE Program: Data Products - Fuel [verified 25 August 2022]
LANDFIRE (2012b) Elevation. Available at LANDFIRE Program: Data Products - Fuel [verified 25 August 2022]
LANDFIRE (2012c) Forest canopy density. Available at LANDFIRE Program: Data Products - Fuel [verified 25 August 2022]
LANDFIRE (2012d) Forest canopy height. Available at LANDFIRE Program: Data Products - Fuel [verified 25 August 2022]
Lautenberger C (2013) Wildland fire modeling with an Eulerian level set method and automated calibration. Fire Safety Journal 62, 289–298.
| Wildland fire modeling with an Eulerian level set method and automated calibration.Crossref | GoogleScholarGoogle Scholar |
Linn R, Reisner J, Colman JJ, Winterkamp J (2002) Studying wildfire behavior using FIRETEC. International Journal of Wildland Fire 11, 233–246.
| Studying wildfire behavior using FIRETEC.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 |
Linn RR, Canfield JM, Cunningham P, Edminster C, Dupuy JL, Pimont F (2012) Using periodic line fires to gain a new perspective on multi-dimensional aspects of forward fire spread. Agricultural and Forest Meteorology 157, 60–76.
| Using periodic line fires to gain a new perspective on multi-dimensional aspects of forward fire spread.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 |
Mallia DV, Kochanski AK, Urbanski SP, Mandel J, Farguell A, Krueger SK (2020) Incorporating a canopy parameterization within a coupled fire–atmosphere model to improve a smoke simulation for a prescribed burn. Atmosphere 11, 832
| Incorporating a canopy parameterization within a coupled fire–atmosphere model to improve a smoke simulation for a prescribed burn.Crossref | GoogleScholarGoogle Scholar |
Mandel J, Beezley JD, Kochanski AK (2011) Coupled atmosphere–wildland fire modeling with WRF 3.3 and SFIRE 2011. Geoscientific Model Development 4, 591–610.
| Coupled atmosphere–wildland fire modeling with WRF 3.3 and SFIRE 2011.Crossref | GoogleScholarGoogle Scholar |
Mandel J, Amram S, Beezley JD, Kelman G, Kochanski AK, Kondratenko VY, Lynn BH, Regev B, Vejmelka M (2014) Recent advances and applications of WRF–SFIRE. Natural Hazards and Earth System Sciences 14, 2829–2845.
| Recent advances and applications of WRF–SFIRE.Crossref | GoogleScholarGoogle Scholar |
Margairaz F, Eshagh H, Hayati AN, Pardyjak ER, Stoll R (2022) Development and evaluation of an isolated-tree flow model for neutral–stability conditions. Urban Climate 42, 101083
| Development and evaluation of an isolated-tree flow model for neutral–stability conditions.Crossref | GoogleScholarGoogle Scholar |
McArthur AG (1966) Weather and grassland fire behaviour. Forestry and Timber Bureau leaflet No. 100. Department of National Development, Canberra, ACT, Australia.
McCaffrey BJ (1983) Momentum implications for buoyant diffusion flames. Combustion and Flame 52, 149–167.
| Momentum implications for buoyant diffusion flames.Crossref | GoogleScholarGoogle Scholar |
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 |
Miller C, Hilton J, Sullivan A, Prakash M (2015) SPARK – a bushfire spread prediction tool. In ‘International Symposium on Environmental Software Systems’. (Eds R Denzer, RM Argent, G Schimak, J Hřebíček) pp. 262–271. (Springer Cham: Edinburgh, UK) https://doi.org/10.1007/978-3-319-15994-2_26
Moody MJ, Gibbs JA, Krueger S, Mallia D, Pardyjak ER, Kochanski AK, Bailey BN, Stoll R (2022) QES-Fire: a dynamically-coupled fast-response wildfire model. International Journal of Wildland Fire 31, 306–325.
| QES-Fire: a dynamically-coupled fast-response wildfire model.Crossref | GoogleScholarGoogle Scholar |
Morvan D (2011) Physical phenomena and length scales governing the behaviour of wildfires: a case for physical modelling. Fire Technology 47, 437–460.
| Physical phenomena and length scales governing the behaviour of wildfires: a case for physical modelling.Crossref | GoogleScholarGoogle Scholar |
Morvan D, Dupuy JL, Rigolot E, Valette JC (2006) FIRESTAR: a physically based model to study wildfire behaviour. Forest Ecology and Management 234, S114–S114.
| FIRESTAR: a physically based model to study wildfire behaviour.Crossref | GoogleScholarGoogle Scholar |
O’Brien JJ, Loudermilk EL, Hornsby B, Hudak AT, Bright BC, Dickinson MB, Hiers JK, Teske C, Ottmar RD (2016) High-resolution infrared thermography for capturing wildland fire behaviour: RxCADRE 2012. International Journal of Wildland Fire 25, 62–75.
| High-resolution infrared thermography for capturing wildland fire behaviour: RxCADRE 2012.Crossref | GoogleScholarGoogle Scholar |
Ottmar RD, Hiers JK, Butler BW, Clements CB, Dickinson MB, Hudak AT, O’Brien JJ, Potter BE, Rowell EM, Strand TM, et al. (2016a) Measurements, datasets and preliminary results from the RxCADRE project – 2008, 2011 and 2012. International Journal of Wildland Fire 25, 1–9.
| Measurements, datasets and preliminary results from the RxCADRE project – 2008, 2011 and 2012.Crossref | GoogleScholarGoogle Scholar |
Ottmar RD, Hudak AT, Prichard SJ, Wright CS, Restaino JC, Kennedy MC, Vihnanek RE (2016b) Pre-fire and post-fire surface fuel and cover measurements collected in the south-eastern United States for model evaluation and development – RxCADRE 2008, 2011 and 2012. International Journal of Wildland Fire 25, 10–24.
| Pre-fire and post-fire surface fuel and cover measurements collected in the south-eastern United States for model evaluation and development – RxCADRE 2008, 2011 and 2012.Crossref | GoogleScholarGoogle Scholar |
Pardyjak ER, Brown MJ (2001) Evaluation of a fast-response urban wind model – comparison to single-building wind tunnel data (No. LA-UR-01-4028). Los Alamos National Lab. (LANL), Los Alamos, NM, USA.
Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. Technical Report INT-115. USDA Forests Service, Intermountain Forest and Range Experiment Station, Ogden, UT, USA.
Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. Gen. Tech. Rep. RMRS-GTR-153. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USA
Sethian JA (1999) ‘Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Vol 3.’ (Cambridge University Press: Cambridge, UK)
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 |
Stull RB (2003) ‘An Introduction to Boundary Layer Meteorology.’ (Kluwer Academic Publishers: Amsterdam, The Netherlands)
Trelles JJ (1999) Mass fire modeling of the 20 October 1991 Oakland Hills Fire. PhD thesis, University of California, Berkeley, CA, USA.
Ulmer L, Margairaz F, Bailey BN, Mahaffee WF, Pardyjak ER, Stoll R (2023) A fast-response, wind angle-sensitive model for predicting mean winds in row-organized canopies. Agricultural and Forest Meteorology 329, 109273
| A fast-response, wind angle-sensitive model for predicting mean winds in row-organized canopies.Crossref | GoogleScholarGoogle Scholar |
Viegas DX, Pita LP (2004) Fire spread in canyons. International Journal of Wildland Fire 13, 253–274.
| Fire spread in canyons.Crossref | GoogleScholarGoogle Scholar |
