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8 - Coupled Fire–Atmosphere Model Evaluation and Challenges

Published online by Cambridge University Press:  16 June 2022

Kevin Speer
Affiliation:
Florida State University
Scott Goodrick
Affiliation:
US Forest Service
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Summary

Coupled fire–atmosphere feedback is essential for modeling wildland fire spread, especially extreme fire phenomena. In this chapter, the suite of current and emerging tools capable of modeling this complexity is examined; these tools now dominate fundamental wildland fire research and are starting to be applied to fire operations, training, and planning. Some of the barriers to progress and challenges to validating these tools highlighted in this chapter suggest more emphasis on three areas: a scale-dependent and purposeful approach to comparing model results with appropriate observations, recognizing the limitations of each; the quantification of the errors and under-specifications in fuel properties and the impact of each; and assessing large-scale simulations and directing observations to address priority research gaps, from a position informed by the vast catalog of atmospheric scientific research.

Type
Chapter
Information
Wildland Fire Dynamics
Fire Effects and Behavior from a Fluid Dynamics Perspective
, pp. 209 - 249
Publisher: Cambridge University Press
Print publication year: 2022

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References

Agee, JK, Bahro, B, Finney, MA, Omi, PN, Sapsis, DB, Skinner, CN, van Wagtendonk, JW, Weatherspoon, CP (2000) The use of shaded fuel breaks in landscape fire management. Forest Ecology and Management 127(1–3), 5566.Google Scholar
Agee, JK, Wright, CS, Williamson, N, Huff, MH (2002) Foliar moisture content of Pacific Northwest vegetation and its relation to wildland fire behavior. Forest Ecology and Management 167(1–3), 5766.Google Scholar
Albini, FA (1976) Estimating Wildfire Behaviour and Effects. USDA Forest Service, Intermountain Forest and Range Experimental Station General Technical Report No. INT-30, Ogden, UT.Google Scholar
Albini, FA, Baughman, RG (1979) Estimating Wind Speeds for Predicting Wildland Fire Behavior. Research Paper INT-RP-221. Ogden, UT: USDA Forest Service, Intermountain Forest and Range Experiment Station.Google Scholar
Alexander, ME, Cruz, MG (2006) Evaluating a model for predicting active crown fire rate of spread using wildfire observations. Canadian Journal of Forest Research 36(11), 30153028.Google Scholar
Alexander, ME, Cruz, MG (2013a) Are the applications of wildland fire behaviour models getting ahead of their evaluation again? Environmental Modelling & Software 41, 6571.Google Scholar
Alexander, ME, Cruz, MG (2013b) Limitations on the accuracy of model predictions of wildland fire behaviour: a state-of-the-knowledge overview. The Forestry Chronicle 89(3), 372383.CrossRefGoogle Scholar
Alexander, ME, Stefner, CN, Mason, JA, Stocks, BJ, Hartley, GR, Maffey, ME, Wotton, BM, Taylor, SW, Lavoie, N, Dalrymple, GN (2004) Characterizing the Jack Pine–Black Spruce Fuel Complex of the International Crown Fire Modelling Experiment (ICFME). National Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, AB. Information Report NOR-X-393.Google Scholar
Alexandrov, GA, Ames, D, Bellocchi, G, Bruen, M, Crout, N, Erechtchoukova, M, Hildebrandt, A, Hoffman, F, Jackisch, C, Khaiter, P, Mannina, G, Matsunaga, T, Purucker, ST, Rivington, M, Samaniego, L (2011) Technical assessment and evaluation of environmental models and software: Letter to the Editor. Environmental Modelling & Software 26(3), 328336.CrossRefGoogle Scholar
Andela, N, Morton, DC, Giglio, L, Paugam, R, Chen, Y, Hantson, S, van der Werf, GR, Randerson, JT (2019) The Global Fire Atlas of individual fire size, duration, speed and direction. Earth System Science Data 11, 529552.CrossRefGoogle Scholar
Anderson, HE (1982) Aids to Determining Fuel Models for Estimating Fire Behavior. General Technical Report INT-122. USDA Forest Service Intermountain Forest and Range Experiment Station, Ogden, UT.Google Scholar
Aumond, P, Masson, V, Lac, C, Gauvreau, B, Dupont, S, Berengier, M (2013) Including the drag effects of canopies: Real case large-eddy simulation studies. Boundary-Layer Meteorology 146, 6580.Google Scholar
Bacciu, V, Arca, B, Pellizzaro, G, Salis, M, Ventura, A, Spano, D, Duce, P (2009) Mediterranean maquis fuel model development and mapping to support fire modeling. EGU General Assembly Conference Abstracts, vol. 11.Google Scholar
Balbi, J-H, Rossi, J-L, Marcelli, T, Chatelon, F-J (2010) Physical modeling of surface fire under nonparallel wind and slope conditions. Combustion Science and Technology 182(7), 922939.Google Scholar
Blocken, B, Gualtieri, C (2012) Ten iterative steps for model development and evaluation applied to computational fluid dynamics for environmental fluid mechanics. Environmental Modelling & Software 33, 122.Google Scholar
Brown, A, Bruns, M, Gollner, M, Hewson, J, Maragkos, G, Marshall, A, McDermott, R, Merci, B, Rogaume, T, Stoliarov, S, Torero, J, Trouve, A, Wang, Y, Weckman, E (2018) Proceedings of the First Workshop Organized by the IAFSS Working Group on Measurement and Computation of Fire Phenomena (MaCFP). Fire Safety Journal 101, 117.Google Scholar
Brown, JK, Bevins, CD (1986) Surface Fuel Koadings and Predicted Fire Behavior for Vegetation Types in the Northern Rocky Mountains. USDA Forest Service Research Note INT-358, Intermountain Forest and Range Experiment Station, Ogden UT.Google Scholar
Brown, JK, See, TE (1981) Downed Dead Woody Fuel and Biomass in the Northern Rocky Mountains. USDA Forest Service Technical Report, INT-117, Intermountain Forest and Range Experiment Station, Ogden UT.Google Scholar
Burgan, RE (1987) Concepts and Interpreted Examples in Advanced Fuel Modeling. USDA For. Serv. Res. Pap. INT-238, Intermountain Forest and Range Experiment Station, Ogden, UT.Google Scholar
Burrows, N, Ward, B, Robinson, A (1991) Fire behaviour in spinifex fuels on the Gibson Desert Nature Reserve, Western AustraliaJournal of Arid Environments 20(2), 189204.Google Scholar
Butler, B, Cohen, J, Latham, D, Schuette, R, Sopko, P, Shannon, K, Jimenez, D, Bradshaw, L (2004) Measurements of radiant emissive power and temperatures in crown fires. Canadian Journal of Forest Research 34, 15771587.Google Scholar
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(1), 7689.Google Scholar
Canfield, JM, Linn, RR, Sauer, JA, Finney, M, Forthofer, JA (2014) A numerical investigation of the interplay between fireline length, geometry, and rate of spread. Agricultural and Forest Meteorology 189 –190, 4859.Google Scholar
Cardil, A, Monedero, S, Silva, CA, Ramirez, J (2019) Adjusting the rate of spread of fire simulations in real-time. Ecological Modelling 395, 3944.Google Scholar
Cary, GJ, Keane, RE, Gardner, RH, Lavorel, S, Flannigan, MD, Davies, ID, Li, C, Lenihan, JM, Rupp, TS, Mouillot, F (2006) Comparison of the sensitivity of landscape-fire-succession models to variation in terrain, fuel pattern, climate and weather. Landscape Ecology 21(1), 121137.Google Scholar
Charland, AM, Clements, CB (2013) Kinematic structure of a wildland fire plume observed by Doppler lidar. Journal of Geophysical Research: Atmospheres 118, 32003212.Google Scholar
Cheney, NP, Gould, JS (1995) Fire growth in grassland fuels. International Journal of Wildland Fire 5, 237247.Google 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, 3144.Google Scholar
Cheney, NP, Gould, JS, McCaw, WL, Anderson, WR (2012) Predicting fire behaviour in dry eucalypt forest in southern Australia. Forest Ecology and Management 280, 120131.Google Scholar
Clark, TL, Coen, JL, Latham, D (2004) Description of a coupled atmosphere–fire model. International Journal of Wildland Fire 13, 4963.CrossRefGoogle Scholar
Clark, TL, Jenkins, MA, Coen, J, Packham, D (1996a) A coupled atmospheric–fire model: Convective feedback on fire line dynamics. Journal of Applied Meteorology 35(6), 875901.Google Scholar
Clark, TL, Jenkins, MA, Coen, J, Packham, D (1996b) A coupled atmospheric–fire model: Convective Froude number and dynamic fingering. International Journal of Wildland Fire 6(4), 177190.Google Scholar
Clark, TL, Radke, LF, Coen, JL, Middleton, D (1999) Analysis of small-scale convective dynamics in a crown fire using infrared video camera imagery. Journal of Applied Meteorology 38(10), 14011420.Google Scholar
Clements, CB (2010) Thermodynamic structure of a grass fire plume. International Journal of Wildland Fire 19(7), 895902.Google Scholar
Clements, CB, Lareau, NP, Seto, D, Contezac, J, Davis, B, Teske, C, Zajkowski, TJ, Hudak, AT, Bright, BC, Dickinson, MB, Butler, BW, Jimenez, DM, Hiers, JK (2016) Fire weather conditions and fire–atmosphere interactions observed during low-intensity prescribed fires–RxCADRE 2012. International Journal of Wildland Fire 25(1), 90101.Google Scholar
Clements, CB, Seto, D (2015) Observations of fire–atmosphere interactions and near-surface heat transport on a slope. Boundary-Layer Meteorology 154, 409426.Google Scholar
Clements, CB, Zhong, S, Bian, X, Heilman, WE, Byun, DW (2008) First observations of turbulence generated by grass fires. Journal of Geophysical Research: Atmospheres 113(D22), D22102.Google Scholar
Clements, CB, Zhong, S, Goodrick, S, Li, J, Potter, BE, Bian, X, Heilman, WE, Charney, JJ, Perna, R, Jang, M, Lee, D, Patel, M, Street, S, Aumann, G (2007) Observing the dynamics of wildland grass fires: FireFlux: A field validation experiment. Bulletin of the American Meteorological Society 88(9), 13691382.Google Scholar
Coen, JL (2005) Simulation of the Big Elk Fire using coupled atmosphere–fire modeling. International Journal of Wildland Fire 14(1), 4959.CrossRefGoogle Scholar
Coen, JL (2013) Modeling Wildland Fires: A Description of the Coupled Atmosphere–Wildland Fire Environment Model (CAWFE). NCAR Technical Note NCAR/TN-500+STR.Google Scholar
Coen, JL (2018) Some requirements for simulating wildland fire behavior using insight from coupled weather-wildland fire models. Fire 1(1), 6.Google 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(1), 1638.Google Scholar
Coen, JL, Mahalingam, S, Daily, JW (2004) Infrared imagery of crown-fire dynamics during FROSTFIRE. Journal of Applied Meteorology 43(9), 12411259.Google Scholar
Coen, JL, Riggan, PJ (2014) Simulation and thermal imaging of the 2006 Esperanza wildfire in southern California: Application of a coupled weather-wildland fire model. International Journal of Wildland Fire 23(6), 755770.Google Scholar
Coen, JL, Schroeder, W (2013) Use of spatially refined satellite remote sensing fire detection data to initialize and evaluate coupled weather–wildfire growth model simulations. Geophysical Research Letters 40(20), 55365541.CrossRefGoogle Scholar
Coen, JL, Schroeder, W (2015) The High Park fire: Coupled weather–wildland fire model simulation of a windstorm‐driven wildfire in Colorado’s Front Range. Journal of Geophysical Research: Atmospheres 120(1), 131146.Google Scholar
Coen, JL, Schroeder, W (2017) Coupled weather–fire modeling: From research to operational forecasting. Fire Management Today 75(1), 3945.Google Scholar
Coen, JL, Schroeder, W, Conway, S, Tarnay, L (2020) Computational modeling of extreme wildland fire events: a synthesis of scientific understanding with applications to forecasting, land management, and firefighter safety. Journal of Computational Science 45, 101152.Google Scholar
Coen, JL, Schroeder, W, Quayle, B (2018a) The generation and forecast of extreme winds during the origin and progression of the 2017 Tubbs Fire. Atmosphere 9(12), 462.Google Scholar
Coen, JL, Stavros, EN, Fites-Kaufman, JA (2018b) Deconstructing the King megafire. Ecological Applications 28(6), 15651580.CrossRefGoogle Scholar
Countryman, CM (1969) Project Flambeau … An Investigation of Mass Fire (1964–1967), final report volume I. Prepared for Office of Civil Defense under OCD Work Order No. OCD-PS-65-26. Berkeley, CA: USDA Forest Service, Pacific Southwest Forest and Range Experiment Station.Google Scholar
Cruz, MG, Alexander, ME (2013) Uncertainty associated with model predictions of surface and crown fire rates of spread. Environmental Modelling & Software 47, 1628.Google Scholar
Cruz, MG, Alexander, ME, Sullivan, AL (2017) Mantras of wildland fire behaviour modelling: Facts or fallacies? International Journal of Wildland Fire 26(11), 973981.Google Scholar
Cruz, MG, Gould, JS, Kidnie, S, Bessell, R, Nichols, D, Slijepcevic, A (2015) Effects of curing on grass fires: II. Effect of grass senescence on the rate of fire spread. International Journal of Wildland Fire 24(6), 838848.Google Scholar
Cruz, MG, McCaw, WL, Anderson, WR, Gould, JS (2013) Fire behaviour modelling in semi-arid mallee-heath shrublands of southern Australia. Environmental Modelling & Software 40, 2134.CrossRefGoogle Scholar
Cruz, MG, Sullivan, AL, Gould, JS, Sims, NC, Bannister, AJ, Hollis, JJ, Hurley, RJ (2012) Anatomy of a catastrophic wildfire: The Black Saturday Kilmore East fire in Victoria, Australia. Forest Ecology and Management 284, 269285.Google Scholar
Davis, C, Brown, B, Bullock, R (2006) Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas. Monthly Weather Review 134(7), 17721784.Google Scholar
Dennison, PE, Brewer, SC, Arnold, JD, Moritz, MA (2014) Large wildfire trends in the western United States, 1984–2011. Geophysical Research Letters 41(8), 29282933.Google Scholar
Di Virgilio, G, Evans, JP, Blake, SAP, Armstrong, M, Dowdy, AJ, Sharples, J, McRae, R (2019) Climate change increases the potential for extreme wildfires. Geophysical Research Letters 46(14), 85178526.Google Scholar
Doucet, A, Freitas, N, Gordon, N (2001) Sequential Monte Carlo Methods in Practice. New York: Springer.Google Scholar
Duff, TJ, Cawson, JG, Cirulis, B, Nyman, P, Sheridan, GJ, Tolhurst, KG (2018) Conditional performance evaluation: using wildfire observations for systematic fire simulator development. Forests 9(4), 189.Google Scholar
Dupuy, JL, Morvan, D (2005) Numerical study of a crown fire spreading toward a fuel break using a multiphase physical model. International Journal of Wildland Fire 14(2), 141151.CrossRefGoogle Scholar
Dupuy, J, Pimont, F, Linn, R, Clements, C (2014) FIRETEC evaluation against the FireFlux experiment: preliminary results. In: Viegas, DX, ed. Advances in Forest Fire Research. Coimbra, Portugal: University of Coimbra, pp. 261274.Google Scholar
Durran, DR (1990) Mountain waves and downslope winds. In: Blumen, W, ed. Atmospheric Processes over Complex Terrain, Meteorological Monographs series, vol. 23. Boston, MA: American Meteorological Society, pp. 5983.Google Scholar
Eliassen, A, Palm, E (1960) On the transfer of energy in stationary mountain waves. Geofysiske Publikasjoner 22(3), 123.Google Scholar
Fahnestock, GR, Key, WK (1971) Weight of brushy forest fire fuels from photographs. Forest Science 17(1), 119124.Google Scholar
Fernandes, PM, Catchpole, WR, Rego, FC (2000) Shrubland fire behaviour modelling with microplot data. Canadian Journal of Forest Research 30(6), 889899.Google Scholar
Filippi, J-B, Bosseur, F, Mari, C, Lac, C (2018) Simulation of a large wildfire in a coupled fire-atmosphere model. Atmosphere 9(6), 218.Google Scholar
Filippi, J-B, Bosseur, F, Mari, C, Lac, C, Le Moigne, P, Cuenot, B, Veynante, D, Cariolle, D, Balbi, JH (2009) Coupled atmosphere-wildland fire modelling. Journal of Advances in Modeling Earth Systems 1(4), 19.Google Scholar
Filippi, J-B, Mallet, V, Nader, B (2014) Representation and evaluation of wildfire propagation simulations. International Journal of Wildland Fire 23(1), 4657.Google Scholar
Filippi, J-B, Pialat, X, Clements, CB (2013) Assessment of ForeFire/Meso-NH for wildland fire/atmosphere coupled simulation of the FireFlux experiment. Proceedings of the Combustion Institute 34(2), 26332640.Google Scholar
Finney, MA (1998) FARSITE: Fire Area Simulator: Model Development and Evaluation. US Department of Agriculture Forest Service: Ogden, UT, USA.CrossRefGoogle Scholar
Fons, WL (1946) Analysis of fire spread in light forest fuels. Journal of Agricultural Research 72(3), 93121.Google Scholar
Frankman, D, Webb, BW, Butler, BW, Jimenez, D, Forthofer, JM, Sopko, P, Shannon, KS, Hiers, JK, Ottmar, RD (2013) Measurements of convective and radiative heating in wildland fires. International Journal of Wildland Fire 22(2), 157167.Google Scholar
Fujioka, FM (2002) A new method for the analysis of fire spread modeling errors. International Journal of Wildland Fire 11(3–4), 193203.Google Scholar
de Gennaro, M, Billaud, Y, Pizzo, Y, Garivait, S, Loraud, JC, El Hajj, M, Porterie, B (2017) Real-time wildland fire spread modeling using tabulated flame properties. Fire Safety Journal 91, 872881.Google Scholar
Gilleland, E, Ahijevych, D, Brown, BG, Casati, B, Ebert, EE (2009) Intercomparison of spatial forecast verification methods. Weather Forecasting 24(5), 14161430.Google Scholar
Gould, JS, McCaw, WL, Cheney, NP, Ellis, PF, Knight, IK, Sullivan, AL (2008) Project Vesta: Fire in Dry Eucalypt Forest: Fuel Structure, Fuel Dynamics and Fire Behaviour. Canberra, ACT: CSIRO Publishing.Google Scholar
Habeeb, RL, Trebilco, J, Wotherspoon, S, Johnson, CR (2005) Determining natural scales of ecological systems. Ecological Monographs 75(4), 467487.Google Scholar
Hardy, C, Heilman, W, Weise, D, Goodrick, S, Ottmar, R (2008) Fire Behavior Advancement Plan; a Plan for Addressing Physical Fire Processes within the Core Fire Science Portfolio. Final report to the Joint Fire Sciences Program Board of Governors.Google Scholar
Hiers, JK, O’Brien, JJ, Mitchell, RJ, Grego, JM, Loudermilk, EL (2009) The wildland fuel cell concept: An approach to characterize fine-scale variation in fuels and fire in frequently burned longleaf pine forests. International Journal of Wildland Fire 18(3), 315325.CrossRefGoogle Scholar
Hoffman, CM, Morgan, P, Mell, W, Parsons, R, Strand, E, Cook, S (2013) Surface fire intensity influences simulated crown fire behavior in lodgepole pine forests with recent mountain pine beetle-caused tree mortality. Forest Science 59(4), 390399.CrossRefGoogle Scholar
Hoffman, CM, Sieg, CH, Linn, RR, Mell, W, Parsons, RA, Ziegler, JP, Hiers, JK (2018) Advancing the science of wildland fire dynamics using process-based models. Fire 1(2), 32.Google Scholar
Hornby, LG (1936) Fire control planning in the northern Rocky Mountain region. Progress Report No. 1. Missoula, MT: US Department of Agriculture, Forest Service, Northern Rocky Mountain Forest and Range Experiment Station.Google Scholar
Jakeman, AJ, Letcher, RA, Norton, JP (2006) Ten iterative steps in development and evaluation of environmental models. Environmental Modelling & Software 21(5), 602614.Google Scholar
Johnson, EA, Miyanishi, K, Weir, JMH (1998) Wildfires in the western Canadian boreal forest: Landscape patterns and ecosystem management. Journal of Vegetation Science 9(4), 603610.Google Scholar
Kalabokidis, K, Omi, P (1992) Quadrat analysis of wildland fuel spatial variability. International Journal of Wildland Fire 2(4), 145152.Google Scholar
Keane, RE (2008) Biophysical controls on surface fuel litterfall and decomposition in the northern Rocky Mountains, USA. Canadian Journal of Forest Research 38(6), 14311445.Google Scholar
Keane, RE (2013) Describing wildland surface fuel loading for fire management: A review of approaches, methods and systems. International Journal of Wildland Fire 22(1), 5162.Google Scholar
Keane, RE, Burgan, R, van Wagtendonk, J (2001) Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire 10(4), 301319.Google Scholar
Keane, RE, Gray, K, Bacciu, V, Leirfallom, S (2012) Spatial scaling of wildland fuels for six forest and rangeland ecosystems of the northern Rocky Mountains, USA. Landscape Ecology 27(8), 12131234.Google Scholar
Keane, RE, Reeves, M (2012) Use of expert knowledge to develop fuel maps for wildland fire management. In: Perera, AH, Drew, C, Johnson, CJ, eds. Expert Knowledge and Its Application in Landscape Ecology. New York: Springer, pp. 211228.Google Scholar
King, KJ, Bradstock, RA, Cary, GJ, Chapman, J, Marsden-Smedley, JB (2008) The relative importance of fine-scale fuel mosiacs on reducing fire risk in south-west Tasmania, Australia. International Journal of Wildland Fire 17(3), 421430.Google Scholar
Lagouvardos, K, Kotroni, V, Giannaros, ΤΜ, Dafis, S (2019) Meteorological conditions conducive to the rapid spread of the deadly wildfire in eastern Attica, Greece. Bulletin of the American Meteorological Society 100(11), 21372145.Google Scholar
Lenschow, DH, Lothon, M, Mayor, SD, Sullivan, PP, Canut, G (2012) A comparison of higher-order vertical velocity moments in the convective boundary layer from lidar with in situ measurements and LES. Boundary-Layer Meteorology 143, 107123.Google Scholar
Leventon, I, Batiot, B, Bruns, M, Hostikka, S, Nakamura, Y, Reszka, P, Rogaume, T, Stoliarov, S (2021) The MaCFP Condensed Phase Working Group: A Structured, Global Effort towards Pyrolysis Model Development, ASTM Selected Technical Papers (STP), Atlanta, GA, US [online]. Available from https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933681 (last accessed November 26, 2021).Google Scholar
Linn, RR (1997) A Transport Model for Prediction of Wildfire Behavior. PhD Thesis, New Mexico State University, Los Alamos National Laboratory, Scientific Report LA13334-T.Google Scholar
Linn, RR, Anderson, K, Winterkamp, J, Brooks, A, Wotton, M, Dupuy, JL, Wotton, M, 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(5), 879898.Google 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(D13), D13107.Google 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.Google Scholar
Linn, RR, Reisner, J, Colman, JJ, Winterkamp, J (2002) Studying wildfire behavior using FIRETEC. International Journal of Wildland Fire 11(3–4), 233246.Google Scholar
Linn, RR, Winterkamp, J, Colman, J, Edminster, C, Bailey, JD (2005) Modeling interactions between fire and atmosphere in discrete element fuel beds. International Journal of Wildland Fire 14(1) 3748.Google Scholar
Liu, Y, Kochanski, A, Baker, KR, Mell, W, Linn, R, Paugam, R, Mandel, J, Fournier, A, Jenkins, M-A, Goodrick, S, Achtemeier, G, Zhao, F, Ottmar, R, French, NHF, Larkin, N, Brown, T, Hudak, A, Dickinson, M, Potter, B, Clements, C, Urbanski, S, Prichard, S, Watts, A, McNamara, D (2019) Fire behaviour and smoke modelling: model improvement and measurement needs for next-generation smoke research and forecasting systems. International Journal of Wildland Fire 28(8), 570588.Google Scholar
Lopes, AMG, Sousa, ACM, Viegas, DX (1995) Numerical simulation of turbulent flow and fire propagation in complex topography. Numerical Heat Transfer, Part A: Applications 27(2), 229253.Google Scholar
Lorenz, EN (1969) The predictability of a flow which possesses many scales of motion. Tellus 21(3), 289307.Google Scholar
Loudermilk, EL, O’Brien, J, Mitchell, RJ, Hiers, JK, Cropper, WP, Grunwald, S, Grego, J, Fernandez, J (2012) Linking complex forest fuel structure and fire behaviour at fine scales. International Journal of Wildland Fire 21(7), 882893.Google Scholar
Mandel, J, Beezley, J, Coen, J, Kim, M (2009) Data assimilation for wildland fires: Ensemble Kalman Filters in coupled atmosphere–surface models. IEEE Control Systems Magazine 29(3), 4765.Google Scholar
Mandel, J, Beezley, JD, Kochanski, AK (2011) Coupled atmosphere–wildland fire modeling with WRF-Fire version 3.3. Geoscientific Model Development 4, 591610.Google Scholar
Mandel, J, Bennethum, LS, Beezley, JD, Coen, JL, Douglas, CC, Kim, M, Vodacek, A (2008) A wildland fire model with data assimilation. Mathematics and Computers in Simulation 79(3), 584606.Google Scholar
Marino, E, Dupuy, JL, Pimont, F, Guijarro, M, Hernando, C, Linn, R (2012) 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. Journal of Fire Sciences 30(4), 277299.Google Scholar
McArthur, AG (1967) Fire Behaviour in Eucalypt Forests. Commonwealth of Australia, Forestry and Timber Bureau No. Leaflet 107, Canberra, ACT.Google Scholar
McCaw, WL, Gould, JS, Cheney, NP, Ellis, PFM, Anderson, WR (2012) Changes in behaviour of fire in dry eucalypt forest as fuel increases with age. Forest Ecology and Management 271, 170181.Google Scholar
Mell, WE, Jenkins, MA, Gould, J, Cheney, P (2007) A physics-based approach to modelling grassland fires. International Journal of Wildland Fire 16(1), 122.Google Scholar
Mell, WE, McGrattan, KB, Baum, HR (1996) Numerical simulation of combustion in fire plumes. Symposium (International) on Combustion, 26(1), 15231530.Google Scholar
Menage, D, Chetehouna, K, Mell, W (2012) Numerical simulations of fire spread in a Pinus pinaster needles fuel bed. Journal of Physics: Conference Series 395(1), 012011.Google 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(12), 800814.Google Scholar
Morandini, F, Simeoni, A, Santoni, PA, Balbi, J-H (2005) A model for the spread of fire across a fuel bed incorporating the effects of wind and slope. Combustion Science and Technology 177(7), 13811418.Google Scholar
Morvan, D, Dupuy, J (2001) Modeling of fire spread through a forest fuel bed using a multiphase formulation. Combustion and Flame 127(1–2), 19811994.CrossRefGoogle Scholar
Mukherjee, S, Schalkwuk, J, Jonker, HJJ (2016) Predictability of dry convective boundary layers: An LES study. Journal of the Atmospheric Science 73(7), 27152727.Google Scholar
Niu, S, Luo, Y, Dietze, M, Keenan, TF, Shi, Z, Li, J, ChapinIII, FS (2014) The role of data assimilation in predictive ecology. Ecosphere 5(5), 116.Google 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(1), 6275.Google Scholar
Ottmar, RD, Hiers, JK, Butler, BW, Clements, CB, Dickinson, MB, Hudak, AT, O’Brien, JO, Potter, BE, Rowell, EM, Strand, TM, Zajkowski, TJ (2016) Measurements, datasets and preliminary results from the RxCADRE project–2008, 2011 and 2012. International Journal of Wildland Fire 25(1), 19.Google Scholar
Ottmar, RD, Sandberg, DV, Riccardi, CL, Prichard, SJ (2007) An overview of the fuel characteristic classification system: Quantifying, classifying, and creating fuelbed for resource planning. Canadian Journal of Forest Research 37(12), 23832393.Google Scholar
Parsons, RA, Mell, WE, McCauley, P (2011) Linking 3D spatial models of fuels and fire: Effects of spatial heterogeneity on fire behavior. Ecological Modelling 222(3), 679691.Google Scholar
Parsons, RA, Pimont, F, Wells, L, Cohn, G, Jolly, WM, de Coligny, F, Rigolet, E, Dupuy, J-L, Mell, W, Linn, RR (2018) Modeling thinning effects on fire behavior with STANDFIRE. Annals of Forest Science 75(1), 7.Google Scholar
Peace, M, Mattner, T, Mills, G, Kepert, J, McCaw, L (2015) Fire-modified meteorology in a coupled fire–atmosphere model. Journal of Applied Meteorology and Climatology, 54(3), 704720.Google Scholar
Pimont, F, Dupuy, JL, Linn, RR, Dupont, S (2009) Validation of FIRETEC wind-flows over a canopy and a fuel-break. International Journal of Wildland Fire 18(7), 775790.Google Scholar
Pimont, F, Dupuy, J-L, Linn, RR, Dupont, S (2011) Impacts of tree canopy structure on wind flows and fire propagation simulated with FIRETEC. Annals of Forest Science 68, 523530.Google Scholar
Pimont, F, Dupuy, JL, Linn, RR, Parsons, R, Martin-StPaul, N (2017) Representativeness of wind measurements in fire experiments: Lessons learned from large-eddy simulations in a homogeneous forest. Agricultural and Forest Meteorology 232, 479488.CrossRefGoogle Scholar
Prichard, S, Larkin, NS, Ottmar, R, French, NHF, Baker, K, Brown, T, Clements, C, Dickinson, M, Hudak, A, Kochanski, A, Linn, R, Liu, Y, Potter, B, Mell, W, Tanzer, D, Urbanski, S, Watts, A (2019) The fire and smoke model evaluation experiment: A plan for integrated, large fire–atmosphere field campaigns. Atmosphere 10(2), 66.Google Scholar
Rabin, SS, Melton, JR, Lasslop, G, Bachelet, D, Forrest, M, Hantson, S, Kaplan, JO, Li, F, Mangeon, S, Ward, DS, Yue, C, Arora, VK, Hickler, T, Kloster, S, Knorr, W, Nieradzik, L, Spessa, A, Folberth, GA, Sheehan, T, Voulgarakis, A, Kelley, DI, Prentice, IC, Sitch, S, Harrison, S, Arneth, A (2017) The Fire Modeling Intercomparison Project (FireMIP), phase 1: Experimental and analytical protocols with detailed model descriptions. Geoscientific Model Development 10, 11751197.Google Scholar
Radke, LR, Clark, TL, Coen, JL, Walther, C, Lockwood, RN, Riggin, PJ, Brass, J, Higgins, R (2000) The WildFire Experiment (WiFE): Observations with airborne remote sensors. Canadian Journal of Remote Sensing 26(5), 406417.Google Scholar
Reeves, MC, Ryan, KC, Rollins, MG, Thompson, TG (2009) Spatial fuel data products of the LANDFIRE project. International Journal of Wildland Fire 18(3), 250267.Google Scholar
Reich, RM, Lundquist, JE, Bravo, VA (2004) Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA. International Journal of Wildland Fire 13(1), 119129.Google Scholar
Riccardi, CL, Ottmar, RD, Sandberg, DV, Andreu, A, Elman, E, Kopper, K, Long, J (2007) The fuelbed: A key element of the Fuel Characteristic Classification System. Canadian Journal of Forest Research 37(12), 23942412.Google Scholar
Rochoux, MC, Collin, A, Zhang, C, Trouvé, A, Lucor, D, Moireau, P (2018) Front shape similarity measure for shape-oriented sensitivity analysis and data assimilation for Eikonal equation. ESAIM: Proceedings and Surveys 63(ESAIM: ProcS), 258279.Google Scholar
Rocca, ME (2009) Fine-scale patchiness in fuel load can influence initial post-fire understory composition in a mixed conifer forest, Sequoia National Park, California. Natural Areas Journal 29(2), 126133.Google Scholar
Rothermel, RC (1972) A Mathematical Model for Predicting Fire Spread in Wildland Fuels. USDA Forest Service No. Research Paper INT-115, Ogden, UT.Google Scholar
Rothermel, RC, Rinehart, GC (1983) Field Procedures for Verification and Adjustment of Fire Behavior Predictions. Research report. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station: Ogden, UT.Google Scholar
Rykiel, EJ (1996) Testing ecological models: the meaning of validation. Ecological Modelling 90(3), 229244.Google Scholar
Sandberg, DV, Ottmar, RD, Cushon, GH (2001) Characterizing fuels in the 21st century. International Journal of Wildland Fire 10(3–4), 381387.Google Scholar
Sandberg, DV, Riccardi, CL, Schaaf, MD (2007) Reformulation of Rothermel’s wildland fire behaviour model for heterogeneous fuelbeds. Canadian Journal of Forestry Research 37(12), 24382455.Google Scholar
Santoni, P-A, Filippi, J-B, Balbi, J-H, Bosseur, F (2011) Wildland fire behaviour case studies and fuel models for landscape-scale fire modeling. Journal of Combustion Article ID 613424.Google Scholar
Schag, GM, Stow, DA, Riggan, PJ, Tissell, RG, Coen, JL (2021) Examining landscape-scale fuel and terrain controls of wildfire spread rates using repetitive airborne thermal infrared (ATIR) imagery. Fire 4(1), 6.Google Scholar
Schroeder, W, Oliva, P, Giglio, L, Csiszar, I (2014) The new VIIRS 375 m active fire detection data product: Algorithm description and initial assessment. Remote Sensing of Environment 143, 8596.Google Scholar
Scott, JH, Burgan, RE (2005) Standard Fire Behavior Fuel Models: A Comprehensive Set for Use with Rothermel’s Surface Fire Spread Model. General Technical Report RMRS-GTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.Google Scholar
Simpson, CC, Sharples, JJ, Evans, JP (2016) Sensitivity of atypical lateral fire spread to wind and slope. Geophysical Research Letters 43(4), 17441751.Google Scholar
Stavros, EN, Coen, J, Peterson, B, Singh, H, Kennedy, K, Ramirez, C, Schimel, D (2018) Use of imaging spectroscopy and LIDAR to characterize fuels for fire behavior prediction. Remote Sensing Applications: Society and Environment 11, 4150.Google Scholar
Stephens, SL, Lydersen, JM, Collins, BM, Fry, DL, Meyer, MD (2015) Historical and current landscape-scale ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada. Ecosphere 6(5), 163.Google Scholar
Stocks, BJ (1987) Fire behavior in immature jack pine. Canadian Journal of Forest Research 17(1), 8086.Google Scholar
Stocks, BJ, Alexander, ME, Wotton, BM, Stefner, CN, Flannigan, MD, Taylor, SW, Lavoie, N, Mason, JA, Hartley, GR, Maffey, ME, Dalrymple, GN, Blake, TW, Cruz, MG, Lanoville, RA (2004) Crown fire behaviour in a northern jack pine – Black spruce forest. Canadian Journal of Forest Research 34(8), 15481560.Google Scholar
Stow, D, Riggan, P, Schag, G, Brewer, W, Tissell, R, Coen, J, Storey, E (2019) Assessing uncertainty and demonstrating potential for estimating fire rate of spread at landscape scales based on time sequential airborne thermal infrared imaging. International Journal of Remote Sensing 40(13), 48764897.Google Scholar
Stratton, RD (2006) Guidance on Spatial Wildland Fire Analysis: Models, Tools, and Techniques. Research report. Department of Agriculture, Forest Service, Rocky Mountain Research Station: Fort Collins, CO.Google Scholar
Sullivan, AL (2017) Inside the inferno: Fundamental processes of wildland fire behaviour part 1: Combustion chemistry and heat release. Current Forestry Reports 3, 132149.CrossRefGoogle 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(1), 5060.Google Scholar
Tarnay, L, Coen, J, Kennedy, K, McElhaney, M, Evans, K, Ramirez, C (2020) Modeled effects of fuel reduction on rim fire daily smoke emissions. Virtual Conference. International Association of Wildland Fire 3rd International Smoke Symposium ISS3#, April, Rayleigh, NC and Davis, CA.Google 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(8), 15611576.Google Scholar
Thurston, W, Fawcett, RJ, Tory, KJ, Kepert, JD (2016) Simulating boundary‐layer rolls with a numerical weather prediction model. Quarterly Journal of the Royal Meteorological Society 142(694), 211223.Google Scholar
Turner, MG, Romme, WH (1994) Landscape dynamics in crown fire ecosystems. Landscape Ecology 9, 5977.Google Scholar
Van Wagdendonk, JW, Benedict, JM, Sydoriak, WM (1996) Physical properties of woody fuel particles of Sierra Nevada conifers. International Journal of Wildland Fire 6(3), 117123.Google Scholar
Van Wagdendonk, JW, Sydoriak, WM, Benedict, JM (1998) Heat content variation of Sierra Nevada conifers. International Journal of Wildland Fire 8(3), 147158.Google Scholar
Watts, JM (1987) Validating fire models. Fire Technology 23, 9394.Google Scholar
Whiteman, CD (2000) Mountain Meteorology: Fundamentals and Applications. New York: Oxford University Press.Google Scholar
Wotton, BM, Gould, JS, McCaw, WL, Cheney, NP, Taylor, SW (2012) Flame temperature and residence time of fires in dry eucalypt forest. International Journal of Wildland Fire 21(3), 270281.Google Scholar
Xue, HD, Gu, F, Hu, XL (2012) Data assimilation using sequential Monte Carlo methods in wildfire spread simulation. ACM Transactions on Modeling and Computer Simulation 22(4), 125.Google Scholar
Zhang, C, Collin, A, Moireau, P, Trouvé, A, Rochoux, MC (2019) State-parameter estimation approach for data-driven wildland fire spread modeling: Application to the 2012 RxCADRE S5 field-scale experiment. Fire Safety Journal 105, 286299.Google Scholar
Zhang, C, Rochoux, MC, Tang, W, Gollner, M, Filippi, JB, Trouvé, A (2017) Evaluation of a data-driven wildland fire spread forecast model with spatially-distributed parameter estimation in simulations of the Fireflux field-scale experiment. Fire Safety Journal 91, 758767.Google Scholar
Zhou, X, Mahalingam, S, Weise, D (2007) Experimental study and large eddy simulation of effect of terrain slope on marginal burning in shrub fuel beds. Proceedings of the Combustion Institute 31(2), 25472555.Google Scholar
Ziegler, JP, Hoffman, C, Battaglia, M, Mell, W (2017) Spatially explicit measurements of forest structure and fire behavior following restoration treatments in dry forests. Forest Ecology and Management 386, 112.Google Scholar

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