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RESEARCH ARTICLE (Open Access)

Evidence for lack of a fuel effect on forest and shrubland fire rates of spread under elevated fire danger conditions: implications for modelling and management

Miguel G. Cruz https://orcid.org/0000-0003-3311-7582 A * , Martin E. Alexander B and Paulo M. Fernandes C
+ Author Affiliations
- Author Affiliations

A CSIRO, GPO Box 1700, Canberra, ACT 2601, Australia.

B Wild Rose Fire Behaviour, 180 – 50434 Range Road 232, Leduc County, AB T4X 0L1, Canada.

C Centro de Investigação e de Tecnologias Agroambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal.

* Correspondence to: miguel.cruz@csiro.au

International Journal of Wildland Fire 31(5) 471-479 https://doi.org/10.1071/WF21171
Submitted: 26 November 2021  Accepted: 8 March 2022   Published: 20 April 2022

© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

The suggestion has been made within the wildland fire community that the rate of spread in the upper portion of the fire danger spectrum is largely independent of the physical fuel characteristics in certain forest ecosystem types. Our review and analysis of the relevant scientific literature on the subject suggest that fuel characteristics have a gradual diminishing effect on the rate of fire spread in forest and shrubland fuel types with increasing fire danger, with the effect not being observable under extreme fire danger conditions. Empirical-based fire spread models with multiplicative fuel functions generally do not capture this effect adequately. The implications of this outcome on fire spread modelling and fuels management are discussed.

Keywords: dead fuel moisture contents, fire behaviour, fire propagation, fire spread modelling, fire weather, forest fuels management, fuel characteristics, fuel model, fuel type, wind speed.


References

Agee JK (1997) The severe weather wildfire – too hot to handle? Northwest Science 71, 153–156.

Alexander ME (1982) Calculating and interpreting forest fire intensities. Canadian Journal of Botany 60, 349–357.
Calculating and interpreting forest fire intensities.Crossref | GoogleScholarGoogle 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, 3015–3028.
Evaluating a model for predicting active crown fire rate of spread using wildfire observations.Crossref | GoogleScholarGoogle Scholar |

Alexander ME, Cruz MG (2012) Interdependencies between flame length and fireline intensity in predicting crown fire initiation and crown scorch height. International Journal of Wildland Fire 21, 95–113.
Interdependencies between flame length and fireline intensity in predicting crown fire initiation and crown scorch height.Crossref | GoogleScholarGoogle Scholar |

Alexander ME, Cruz MG (2020) Fireline intensity. In ‘Encyclopedia of wildfires and wildland–urban interface (WUI) fires’. (Ed. SL Manzello) pp. 453–460. (Springer: Cham, Switzerland)

Alexander ME, Mutch RW, Davis KM, Bucks CM (2017) Wildland fires: dangers and survival. In ‘Auerbach’s wilderness Medicine’, Vol. 1, 7th edn. (Ed. PS Auerbach) pp. 276–318. (Elsevier: Philadelphia, PA, USA)

Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-122. (Ogden, UT, USA)

Anderson WR, Cruz MG, Fernandes PM, McCaw L, Vega JA, Bradstock RA, Fogarty L, Gould J, McCarthy G, Marsden-Smedley JB, Matthews S, Mattingley G, Pearce HG, van Wilgen BW (2015) A generic, empirical-based model for predicting rate of fire spread in shrublands. International Journal of Wildland Fire 24, 443–460.
A generic, empirical-based model for predicting rate of fire spread in shrublands.Crossref | GoogleScholarGoogle Scholar |

Andrews PL (2014) Current status and future needs of the BehavePlus Fire Modeling System. International Journal of Wildland Fire 23, 21–33.
Current status and future needs of the BehavePlus Fire Modeling System.Crossref | GoogleScholarGoogle Scholar |

Andrews PL (2018) The Rothermel surface fire spread model and associated developments: a comprehensive explanation. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-371. (Fort Collins, CO, USA)

Ascoli D, Vacchiano G, Motta R, Bovio G (2015) Building Rothermel fire behaviour fuel models by genetic algorithm optimisation. International Journal of Wildland Fire 24, 317–328.
Building Rothermel fire behaviour fuel models by genetic algorithm optimisation.Crossref | GoogleScholarGoogle Scholar |

Bessie WC, Johnson EA (1995) The relative importance of fuels and weather on fire behavior in subalpine forests. Ecology 76, 747–762.
The relative importance of fuels and weather on fire behavior in subalpine forests.Crossref | GoogleScholarGoogle Scholar |

Bradstock RA, Hammill KA, Collins L, Price O (2010) Effects of weather, fuel and terrain on fire severity in topographically diverse landscapes of south-eastern Australia. Landscape Ecology 25, 607–619.
Effects of weather, fuel and terrain on fire severity in topographically diverse landscapes of south-eastern Australia.Crossref | GoogleScholarGoogle Scholar |

Burgan RE (1987) Concepts and interpreted examples in advanced fuel modelling. USDA Forest Service, Intermountain Research Station, General Technical Report INT-238. (Ogden, UT, USA)

Burgan RE, Rothermel RC (1984) BEHAVE: Fire behavior prediction and fuel modeling system – FUEL subsystem. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-167. (Ogden, UT, USA)

Burrows N, McCaw L (2013) Prescribed burning in southwestern Australian forests. Frontiers in Ecology and the Environment 11, e25–e34.
Prescribed burning in southwestern Australian forests.Crossref | GoogleScholarGoogle Scholar |

Burrows ND, Stephens C, Wills A, Densmore V (2021) Fire mosaics in south-west Australian forest landscapes. International Journal of Wildland Fire 30, 933–945.
Fire mosaics in south-west Australian forest landscapes.Crossref | GoogleScholarGoogle Scholar |

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

Catchpole EA, Alexander ME, Gill AM (1992) Elliptical-fire perimeter- and area-intensity distributions. Canadian Journal of Forest Research 22, 968–972.
Elliptical-fire perimeter- and area-intensity distributions.Crossref | GoogleScholarGoogle Scholar |

Cheney NP (1981) Fire behaviour. In ‘Fire and the Australian biota’. (Eds. AM Gill, RH Groves, IR Noble) pp. 151–175. (Australian Academy of Science: Canberra, ACT, Australia)

Cheney P, Sullivan A (2008) ‘Grassfires: fuel, weather and fire behaviour’, 2nd edn. (CSIRO Publishing: Collingwood, SA, Australia)

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 |

Cheney NP, Gould JS, McCaw WL, Anderson WR (2012) Predicting fire behaviour in dry eucalypt forest in southern Australia. Forest Ecology and Management 280, 120–131.
Predicting fire behaviour in dry eucalypt forest in southern Australia.Crossref | GoogleScholarGoogle Scholar |

Clarke PJ, Lawes MJ, Murphy BP, Russell-Smith J, Nano CEM, Bradstock R, Enright NJ, Fontaine JB, Gosper CR, Radford I, Midgley JJ, Gunton RM (2015) A synthesis of postfire recovery traits of woody plants in Australian ecosystems. Science of the Total Environment 534, 31–42.
A synthesis of postfire recovery traits of woody plants in Australian ecosystems.Crossref | GoogleScholarGoogle Scholar |

Collins KM, Price OF, Penman TD (2018) Suppression resource decisions are the dominant influence on containment of Australian forest and grass fires. Journal of Environmental Management 228, 373–382.
Suppression resource decisions are the dominant influence on containment of Australian forest and grass fires.Crossref | GoogleScholarGoogle Scholar | 30243073PubMed |

Cruz MG, Alexander ME (2019) The 10% wind speed rule of thumb for estimating a wildfire’s forward rate of spread in forests and shrublands. Annals of Forest Science 76, 44
The 10% wind speed rule of thumb for estimating a wildfire’s forward rate of spread in forests and shrublands.Crossref | GoogleScholarGoogle Scholar |

Cruz MG, Alexander ME, Wakimoto RH (2005) Development and testing of models for predicting crown fire rate of spread in conifer forest stands. Canadian Journal of Forest Research 35, 1626–1639.
Development and testing of models for predicting crown fire rate of spread in conifer forest stands.Crossref | GoogleScholarGoogle Scholar |

Cruz MG, Gould JS, Alexander ME, Sullivan AL, McCaw WL, Matthews S (2015) Empirical-based models for predicting head-fire rate of spread in Australian fuel types. Australian Forestry 78, 118–158.
Empirical-based models for predicting head-fire rate of spread in Australian fuel types.Crossref | GoogleScholarGoogle Scholar |

Cruz MG, Alexander ME, Fernandes PM, Kilinc M, Sil  (2020) Evaluating the 10% wind speed rule of thumb for estimating a wildfire’s forward rate of spread against an extensive independent set of observations. Environmental Modelling & Software 133, 104818
Evaluating the 10% wind speed rule of thumb for estimating a wildfire’s forward rate of spread against an extensive independent set of observations.Crossref | GoogleScholarGoogle Scholar |

Cruz MG, Cheney NP, Gould JS, McCaw WL, Kilinc M, Sullivan AL (2022) An empirical-based model for predicting the forward spread rate of wildfires in eucalypt forests. International Journal of Wildland Fire 31, 81–95.
An empirical-based model for predicting the forward spread rate of wildfires in eucalypt forests.Crossref | GoogleScholarGoogle Scholar |

Deeming JE, Brown JK (1975) Fuel models in the National Fire-Danger Rating System. Journal of Forestry 73, 347–350.
Fuel models in the National Fire-Danger Rating System.Crossref | GoogleScholarGoogle Scholar |

FCFDG (1992) Development and structure of the Canadian Forest Fire Behavior Prediction System. Forestry Canada Fire Danger Group, Information Report ST-X-3. (Ottawa, ON, Canada).

Fernandes PM (2015) Empirical support for the use of prescribed burning as a fuel treatment. Current Forestry Reports 1, 118–127.
Empirical support for the use of prescribed burning as a fuel treatment.Crossref | GoogleScholarGoogle Scholar |

Fernandes PM, Botelho HS, Rego FC, Loureiro C (2009) Empirical modelling of surface fire behaviour in maritime pine stands. International Journal of Wildland Fire 18, 698–710.
Empirical modelling of surface fire behaviour in maritime pine stands.Crossref | GoogleScholarGoogle Scholar |

Fernandes PM, Sil Â, Ascoli D, Cruz MG, Rossa CG, Alexander ME (2020) Characterizing fire behavior across the globe. In ‘Proceedings of the Fire Continuum Conference – Preparing for the Future of Wildland Fire’, 21–24 May 2018. Missoula, MT, USA. (Eds SM Hood, S Drury, T Steelman, R Steffens) USDA Forest Service, Rocky Mountain Research Station, Proceedings RMRS-P-78, pp. 258–263. (Fort Collins, CO, USA)

Finney MA (2004) FARSITE: fire area simulator – model development and evaluation. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP-4 Revised. (Ogden, UT, USA).

Finney MA (2016) The essential role of prescribed fire in fuel hazard reduction. In ‘Actas V Jornadas Forestales Patagónicas/III Jornadas Forestales de Patagonia Sur/Ecofuego II’, 9–13 November 2016, Esquel, Chubut, Argentina. pp. 434–439. (Centro de Investigación y Extensión Forestal Andino Patagónico: Esquel, Chubut, Argentina)

Gould JS, McCaw WL, Cheney NP, Ellis PF, Knight IK, Sullivan AL (2007) ‘Project Vesta: fire in dry eucalypt forest: fuel structure, fuel dynamics and fire behaviour.’ (Ensis-CSIRO and Department of Environment and Conservation: Canberra, ACT and Perth, WA, Australia)

Hislop S, Stone C, Haywood A, Skidmore A (2020) The effectiveness of fuel reduction burning for wildfire mitigation in sclerophyll forests. Australian Forestry 83, 255–264.
The effectiveness of fuel reduction burning for wildfire mitigation in sclerophyll forests.Crossref | GoogleScholarGoogle Scholar |

Keane RE (2014) ‘Wildland fuel fundamentals and application.’ (Springer: Cham, Switzerland)

Keetch JJ, Byram GM (1968) A drought index for forest fire control. USDA Forest Service, Southeast Forest Experiment Station, Research Paper SE-38; revised November 1988. (Asheville, NC, USA)

Kilinc M, Anderson W, Price B (2012) The applicability of bushfire behaviour models in Australia. Victorian Government, Department of Sustainability and Environment, DSE Schedule 5: Fire Severity Rating Project, Technical Report 1. (Melbourne, VIC, Australia)

Leavesley A, Woulters M, Thornton R (Eds) (2020) ‘Prescribed burning in Australasia: the science, practice and politics of burning the bush.’ (Australasian Fire Authorities Council: Melbourne, VIC, Australia)

Luke RH, McArthur AG (1978) ‘Bushfires in Australia.’ (Australian Government Publishing Service: Canberra, ACT, Australia)

Lydersen JM, Collins BM, Brooks ML, Matchett JR, Shive KL, Povak NA, Kane VR, Smith DF (2017) Evidence of fuels management and fire weather influencing fire severity in an extreme fire event. Ecological Applications 27, 2013–2030.
Evidence of fuels management and fire weather influencing fire severity in an extreme fire event.Crossref | GoogleScholarGoogle Scholar | 28644577PubMed |

Marsden-Smedley JB, Catchpole WR (1995) Fire modelling in Tasmanian buttongrass moorlands II. Fire behaviour. International Journal of Wildland Fire 5, 215–228.
Fire modelling in Tasmanian buttongrass moorlands II. Fire behaviour.Crossref | GoogleScholarGoogle Scholar |

Martinson EJ, Omi PN (2013) Fuel treatments and fire severity: a meta-analysis. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP-103WWW. (Fort Collins, CO, USA)

McArthur AG (1962) Control burning in eucalypt forests. Commonwealth of Australia, Forestry and Timber Bureau, Leaflet 80. (Canberra, ACT, Australia)

McArthur AG (1967) Fire behaviour in eucalypt forests. Commonwealth of Australia, Forestry and Timber Bureau, Leaflet 107. (Canberra, ACT, Australia)

McCarthy GJ, Tolhurst KG (2001) Effectiveness of broadscale fuel reduction burning in assisting with wildfire control in parks and forests in Victoria. State of Victoria, Department of Natural Resources and Environment, Research Report 51. (Melbourne, VIC, Australia)

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, 170–181.
Changes in behaviour of fire in dry eucalypt forest as fuel increases with age.Crossref | GoogleScholarGoogle Scholar |

Merrill DF, Alexander ME (Eds) (1987) ‘Glossary of forest fire management terms’, 4th edn. (National Research Council of Canada, Canadian Committee on Forest Fire Management, Publication NRCC 26516: Ottawa, ON, Canada)

Miller C, Hilton J, Sullivan A, Prakash M (2015) SPARK – a bushfire spread prediction tool. In ‘Environmental Software Systems. Infrastructures, Services and Applications’. (Eds R Denzer, R Argent, G Schimak, J Hřebíček) pp. 262–271. IFIP Advances in Information and Communication Technology, Vol. 448. (Springer International Publishing: Cham, Switzerland)

Monedero S, Ramirez J, Cardil A (2019) Predicting fire spread and behaviour on the fireline. Wildfire Analyst Pocket: a mobile app for wildland fire prediction. Ecological Modelling 392, 103–107.
Predicting fire spread and behaviour on the fireline. Wildfire Analyst Pocket: a mobile app for wildland fire prediction.Crossref | GoogleScholarGoogle Scholar |

Moreira F, Ascoli D, Safford H, Adams MA, Moreno JM, Pereira JMC, Catry FX, Armesto J, Bond W, González ME, Curt T, Koutsias N, McCaw L, Price O, Pausas JG, Rigolot E, Stephens S, Tavsanoglu C, Vallejo VR, Van Wilgen BW, Xanthopoulos G, Fernandes PM (2020) Wildfire management in Mediterranean-type regions: paradigm change needed. Environmental Research Letters 15, 011001
Wildfire management in Mediterranean-type regions: paradigm change needed.Crossref | GoogleScholarGoogle Scholar |

Moritz MA, Keeley JE, Johnson EA, Schaffner AA (2004) Testing a basic assumption of shrubland fire management: how important is fuel age? Frontiers in Ecology and the Environment 2, 67–72.
Testing a basic assumption of shrubland fire management: how important is fuel age?Crossref | GoogleScholarGoogle Scholar |

Neale T, May D (2020) Fuzzy boundaries: simulation and expertise in bushfire prediction. Social Studies in Science 50, 837–859.
Fuzzy boundaries: simulation and expertise in bushfire prediction.Crossref | GoogleScholarGoogle Scholar |

Noble IR, Bary GAV, Gill AM (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 |

Omi PN (2015) Theory and practice of wildland fuels management. Current Forestry Reports 1, 100–117.
Theory and practice of wildland fuels management.Crossref | GoogleScholarGoogle Scholar |

Outcalt KW, Wade DD (2004) Fuels management reduces tree mortality from wildfires in south-eastern United States. Southern Journal of Applied Forestry 28, 28–34.
Fuels management reduces tree mortality from wildfires in south-eastern United States.Crossref | GoogleScholarGoogle Scholar |

Page WG, Alexander ME, Jenkins MJ (2013) Wildfire’s resistance to control in mountain pine beetle-attacked lodgepole pine forests. The Forestry Chronicle 89, 783–794.
Wildfire’s resistance to control in mountain pine beetle-attacked lodgepole pine forests.Crossref | GoogleScholarGoogle Scholar |

Parks SA, Holsinger LM, Miller C, Nelson CR (2015) Wildland fire as a self-regulating mechanism: the role of previous burns and weather in limiting fire progression. Ecological Applications 25, 1478–1492.
Wildland fire as a self-regulating mechanism: the role of previous burns and weather in limiting fire progression.Crossref | GoogleScholarGoogle Scholar | 26552258PubMed |

Plucinski MP (2019a) Fighting flames and forging firelines: wildfire suppression effectiveness at the fire edge. Current Forestry Reports 5, 1–19.
Fighting flames and forging firelines: wildfire suppression effectiveness at the fire edge.Crossref | GoogleScholarGoogle Scholar |

Plucinski MP (2019b) Contain and control: wildfire suppression effectiveness at incidents and across landscapes. Current Forestry Reports 5, 20–40.
Contain and control: wildfire suppression effectiveness at incidents and across landscapes.Crossref | GoogleScholarGoogle Scholar |

Plucinski MP, Sullivan AL, Rucinski CJ, Prakash M (2017) Improving the reliability and utility of operational bushfire behaviour predictions in Australian vegetation. Environmental Modelling & Software 91, 1–12.
Improving the reliability and utility of operational bushfire behaviour predictions in Australian vegetation.Crossref | GoogleScholarGoogle Scholar |

Price OF, Bradstock RA (2010) The effect of fuel age on the spread of fire in sclerophyll forest in the Sydney region of Australia. International Journal of Wildland Fire 19, 35–45.
The effect of fuel age on the spread of fire in sclerophyll forest in the Sydney region of Australia.Crossref | GoogleScholarGoogle Scholar |

Price OF, Bradstock RA (2012) The efficacy of fuel treatment in mitigating property loss during wildfires: insights from analysis of the severity of the catastrophic fires in 2009 in Victoria, Australia. Journal of Environmental Management 113, 146–157.
The efficacy of fuel treatment in mitigating property loss during wildfires: insights from analysis of the severity of the catastrophic fires in 2009 in Victoria, Australia.Crossref | GoogleScholarGoogle Scholar | 23025983PubMed |

Prichard SJ, Peterson DL, Jacobson K (2010) Fuel treatments reduce the severity of wildfire effects in dry mixed conifer forest, Washington, USA. Canadian Journal of Forest Research 40, 1615–1626.
Fuel treatments reduce the severity of wildfire effects in dry mixed conifer forest, Washington, USA.Crossref | GoogleScholarGoogle Scholar |

Rego FC, Morgan P, Fernandes PM, Hoffman C (2021) ‘Fire science: from chemistry to landscape management.’ (Springer: Cham, Switzerland)

Reinhardt ED, Keane RE, Calkin DE, Cohen JD (2008) Objectives and considerations for wildland fuel treatment in forested ecosystems of the interior western United States. Forest Ecology and Management 256, 1997–2006.
Objectives and considerations for wildland fuel treatment in forested ecosystems of the interior western United States.Crossref | GoogleScholarGoogle Scholar |

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

Sandberg DV, Ottmar RD, Cushon GH (2001) Characterizing fuels in the 21st century. International Journal of Wildland Fire 10, 381–387.
Characterizing fuels in the 21st century.Crossref | GoogleScholarGoogle Scholar |

Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-155. (Fort Collins, CO, USA)

Scott JH, Reinhardt ED (2001) Assessing crown fire potential by linking models of surface and crown fire behavior. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP-29. (Fort Collins, CO, USA)

Scott AC, Bowman DMJS, Bond WJ, Pyne SJ, Alexander ME (2014) ‘Fire on Earth: an introduction.’ (Wiley-Blackwell: Chichester, England)

Stevens JT, Safford HD, Latimer AM (2014) Wildfire-contingent effects of fuel treatments can promote ecological resilience in seasonally dry conifer forests. Canadian Journal of Forest Research 44, 843–854.
Wildfire-contingent effects of fuel treatments can promote ecological resilience in seasonally dry conifer forests.Crossref | GoogleScholarGoogle Scholar |

Stevens-Rumann C, Shive K, Fulé P, Sieg CH (2013) Pre-wildfire fuel reduction treatments result in more resilient forest structure a decade after wildfire. International Journal of Wildland Fire 22, 1108–1117.
Pre-wildfire fuel reduction treatments result in more resilient forest structure a decade after wildfire.Crossref | GoogleScholarGoogle Scholar |

Stocks BJ (1987) Fire behavior in immature jack pine. Canadian Journal of Forest Research 17, 80–86.
Fire behavior in immature jack pine.Crossref | GoogleScholarGoogle Scholar |

Storey M, Price O, Tasker E (2016) The role of weather, past fire and topography in crown fire occurrence in eastern Australia. International Journal of Wildland Fire 25, 1048–1060.
The role of weather, past fire and topography in crown fire occurrence in eastern Australia.Crossref | GoogleScholarGoogle Scholar |

Storey MA, Bedward M, Price OF, Bradstock RA, Sharples JJ (2021) Derivation of a Bayesian fire spread model using large-scale wildfire observations. Environmental Modelling and Software 144, 105127
Derivation of a Bayesian fire spread model using large-scale wildfire observations.Crossref | GoogleScholarGoogle Scholar |

Sullivan AL (2009) Wildland surface fire spread modelling, 1990-2007. 2: Empirical and quasi-empirical models. International Journal of Wildland Fire 18, 369–386.
Wildland surface fire spread modelling, 1990-2007. 2: Empirical and quasi-empirical models.Crossref | GoogleScholarGoogle Scholar |

Tedim F, Leone V, McGee TK (Eds) (2020) ‘Extreme wildfire events and disasters: root causes and new management strategies.’ (Elsevier: Amsterdam, The Netherlands)

Tolhurst KG, McCarthy G (2016) Effect of prescribed burning on wildfire severity: a landscape-scale case study from the 2003 fires in Victoria. Australian Forestry 79, 1–14.
Effect of prescribed burning on wildfire severity: a landscape-scale case study from the 2003 fires in Victoria.Crossref | GoogleScholarGoogle Scholar |

Tolhurst KG, Shields B, Chong D (2008) Phoenix: development and application of a bushfire risk management tool. Australian Journal of Emergency Management 23, 47–54.

Tymstra C, Bryce RW, Wotton BM, Taylor SW, Armitage OB (2010) Development and structure of Prometheus: the Canadian wildland fire growth simulation model. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Information Report NOR-X-417. (Edmonton, AB, Canada)

Van Wagner CE (1977) Conditions for the start and spread of crown fire. Canadian Journal of Forest Research 7, 23–34.
Conditions for the start and spread of crown fire.Crossref | GoogleScholarGoogle Scholar |

Van Wagner CE (1987) Development and structure of the Canadian Forest Fire Weather Index System. Government of Canada, Canadian Forestry Service, Forestry Technical Report 35. (Ottawa, ON, Canada)

Van Wagner CE (1993) Prediction of crown fire behavior in two stands of jack pine. Canadian Journal of Forest Research 23, 442–449.
Prediction of crown fire behavior in two stands of jack pine.Crossref | GoogleScholarGoogle Scholar |

Waltz AEM, Stoddard MT, Kalies EL, Springer JD, Huffman DW, Meador AS (2014) Effectiveness of fuel reduction treatments: assessing metrics of forest resiliency and wildfire severity after the Wallow Fire, AZ. Forest Ecology and Management 334, 43–52.
Effectiveness of fuel reduction treatments: assessing metrics of forest resiliency and wildfire severity after the Wallow Fire, AZ.Crossref | GoogleScholarGoogle Scholar |