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RESEARCH ARTICLE
Contents Vol 32(12)

Fighting wildfires: predicting initial attack success across Victoria, Australia

M. P. Plucinski A * , S. Dunstall B , N. F. McCarthy https://orcid.org/0000-0003-3893-0433 C , S. Deutsch D , E. Tartaglia B D , C. Huston B and A. G. Stephenson B
+ Author Affiliations
- Author Affiliations

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

B CSIRO Data61, Private Bag 10, Clayton South, Vic. 3169, Australia.

C Research and Development team, Fire Risk, Research and Community Preparedness Department, Country Fire Authority, Burwood, Vic., Australia.

D Department of Energy, Environment, and Climate Action, Data Insights, PO Box 500, East Melbourne, Vic. 8002, Australia.

* Correspondence to: matt.plucinski@csiro.au

International Journal of Wildland Fire 32(12) 1689-1703 https://doi.org/10.1071/WF23053
Submitted: 19 April 2023  Accepted: 2 November 2023  Published: 2 December 2023

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

Abstract

Background

The small portion of fires that escape initial attack (IA) have the greatest impacts on communities and incur most suppression costs. Early identification of fires with potential for escaping IA can prompt fire managers to order additional suppression resources, issue timely public warnings and plan longer-term containment strategies when they have the greatest potential for reducing a fire’s impact.

Aims

To develop IA models from a state-wide incident dataset containing novel variables that can be used to estimate the probability of IA when a new fire has been reported.

Methods

A large dataset was compiled from bushfire incident records, geographical data and weather observations across the state of Victoria (n = 35 154) and was used to develop logistic regression models predicting the probability of initial attack success in grassland-, forest- and shrubland-dominated vegetation types.

Key results

Models including input variables describing weather conditions, travel delay, slope and distance from roads were able to reasonably discriminate fires contained to 5 ha.

Conclusions and implications

The models can be used to estimate IA success – using information available when the location of a new fire can be estimated – and they can be used to prompt planning for larger fires.

Keywords: containment, containment probability, incident data, initial attack, logistic regression, suppression, suppression effectiveness, wildfires.

References

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.
| Crossref | Google Scholar |

Arienti MC, Cumming SG, Boutin S (2006) Empirical models of forest fire initial attack success probabilities: the effects of fuels, anthropogenic linear features, fire weather, and management. Canadian Journal of Forest Research 36, 3155-3166.
| Crossref | Google Scholar |

Beverly JL (2017) Time since prior wildfire affects subsequent fire containment in black spruce. International Journal of Wildland Fire 26, 919-929.
| Crossref | Google Scholar |

Calkin DE, Venn T, Wibbenmeyer M, Thompson MP (2013) Estimating US federal wildland fire managers’ preferences toward competing strategic suppression objectives. International Journal of Wildland Fire 22, 212-222.
| Crossref | Google Scholar |

CFA (2020) ‘Strategy and outcomes framework 2020-2030.’ (Country Fire Authority) Available at https://www.cfa.vic.gov.au/ArticleDocuments/512/FINAL‐CFA‐Strategy‐Outcomes‐Framework.pdf

Cheney NP, Gould JS, Catchpole WR (1998) Prediction of fire spread in grasslands. International Journal of Wildland Fire 8, 1-13.
| Crossref | Google 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.
| Crossref | Google Scholar | PubMed |

DELWP (2019) ‘Standard of Cover for Bushfire Management on Public Land.’ (Department of Environment, Land, Water and Planning: Melbourne, Australia)

Diniz‐Filho JAF, Rangel TFLVB, Bini LM (2008) Model selection and information theory in geographical ecology. Global Ecology and Biogeography 17, 479-488.
| Crossref | Google Scholar |

Fernandes PM, Pacheco AP, Almeida R, Claro J (2016) The role of fire-suppression force in limiting the spread of extremely large forest fires in Portugal. European Journal of Forest Research 135, 253-262.
| Crossref | Google Scholar |

Finkele K, Mills GA, Beard G, Jones DA (2006) National gridded drought factors and comparison of two soil moisture deficit formulations used in prediction of Forest Fire Danger Index in Australia. Australian Meteorological Magazine 55, 183-197.
| Google Scholar |

Gebert KM, Black AE (2012) Effect of suppression strategies on federal wildland fire expenditures. Journal of Forestry 110, 65-73.
| Crossref | Google Scholar |

Gill AM, King KJ, Moore AD (2010) Australian grassland fire danger using inputs from the GRAZPLAN grassland simulation model. International Journal of Wildland Fire 19, 338-345.
| Crossref | Google Scholar |

Gould JS, Sullivan AL (2022) Initial growth of fires in eucalypt litter, from ignition to steady-state rate of spread: laboratory studies. International Journal of Wildland Fire 31, 163-175.
| Crossref | Google Scholar |

Hosmer JDW, Lemeshow S, Sturdivant RX (2013) ‘Applied Logistic Regression.’ 3rd edn. (John Wiley & Sons, Inc.: Hoboken, NJ, USA)

Keetch JJ, Byram GM (1968) A drought index for forest fire control. Research Paper No. SE-38. (USDA Forest Service, Southeastern Forest Experiment Station: Asheville, NC, USA)

Marshall E, Dorph A, Holyland B, Filkov A, Penman TD (2022) Suppression resources and their influence on containment of forest fires in Victoria. International Journal of Wildland Fire 31, 1144-1154.
| Crossref | Google Scholar |

Matthews S (2006) A process-based model of fine fuel moisture. International Journal of Wildland Fire 15, 155-168.
| Crossref | Google Scholar |

Matthews S, Fox-Hughes P, Grootemaat S, Hollis, JJ, Kenny BJ, Sauvage S (2019) ‘Australian Fire Danger Rating System: Research Prototype.’ (NSW Rural Fire Service: Lidcombe, NSW)

McArthur AG (1966) Weather and grassland fire behaviour. Department of Natural Development, Forestry and Timber Bureau Leaflet No. 100, Canberra, ACT.

McArthur AG (1967) Fire behaviour in eucalypt forests. Commonwealth of Australia Forestry and Timber Bureau Leaflet No. 107, Canberra, ACT.

McCarthy GJ, Tolhurst KG (1998) Effectiveness of firefighting first attack operations by the Department of Natural Resources and Environment from 1991/92 - 1994/95. Department of Natural Resources and Environment Research Report No. 45.

McCarthy GJ, Plucinski MP, Gould JS (2012) Analysis of the resourcing and containment of multiple remote fires: The Great Divide Complex of fires, Victoria, December 2006. Australian Forestry 75, 54-63.
| Crossref | Google Scholar |

McCarthy NF, Duff T, Butler K, Deutsch S (2023) Bottlenecks, feedbacks and thresholds in wildfire suppression. In ‘MODSIM2023, 25th International Congress on Modelling and Simulation’. Darwin, NT. (Eds J Vaze, C Chilcott, L Hutley, SM Cuddy) pp. 577–582. (Modelling and Simulation Society of Australia and New Zealand)

Miller C, Plucinski M, Sullivan A, Stephenson A, Huston C, Charman K, Prakash M, Dunstall S (2017) Electrically caused wildfires in Victoria, Australia are over-represented when fire danger is elevated. Landscape and Urban Planning 167, 267-274.
| Crossref | Google Scholar |

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

Parks GM (1964) Development and application of a model for suppression of forest fires. Management Science 10, 760-766.
| Crossref | Google Scholar |

Penman TD, McColl-Gausden SC, Cirulis BA, Kultaev D, Ababei DA, Bennett LT (2022) Improved accuracy of wildfire simulations using fuel hazard estimates based on environmental data. Journal of Environmental Management 301, 113789.
| Crossref | Google Scholar | PubMed |

Plucinski MP (2012) Factors affecting containment area and time of Australian forest fires featuring aerial suppression. Forest Science 58, 390-398.
| Crossref | Google Scholar |

Plucinski MP (2013) Modelling the probability of Australian grassfires escaping initial attack to aid deployment decisions. International Journal of Wildland Fire 22, 459-468.
| Crossref | Google Scholar |

Plucinski MP (2014) The timing of vegetation fire occurrence in a human landscape. Fire Safety Journal 67, 42-52.
| Crossref | Google Scholar |

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

Plucinski MP, McCarthy GJ, Hollis JJ, Gould JS (2012) The effect of aerial suppression on the containment time of Australian wildfires estimated by fire management personnel. International Journal of Wildland Fire 21, 219-229.
| Crossref | Google Scholar |

Podur JJ, Martell DL (2007) A simulation model of the growth and suppression of large forest fires in Ontario. International Journal of Wildland Fire 16, 285-294.
| Crossref | Google Scholar |

Rodrigues M, Alcasena F, Gelabert P, Vega-García C (2020) Geospatial modeling of containment probability for escaped wildfires in a Mediterranean region. Risk Analysis 40, 1762-1779.
| Crossref | Google Scholar | PubMed |

Sakamoto Y, Ishiguro M, Kitagawa G (1986) ‘Akaike information criterion statistics.’ (D. Reidel Publishing Company: Boston, MA, USA)

Stocks BJ, Martell DL (2016) Forest fire management expenditures in Canada: 1970–2013. The Forestry Chronicle 92, 298-306.
| Crossref | Google Scholar |

Stonesifer CS, Calkin DE, Thompson MP, Belval EJ (2021) Is this flight necessary? The Aviation Use Summary (AUS): a framework for strategic, risk-informed aviation decision support. Forests 12, 1078.
| Crossref | Google Scholar |

Sullivan AL (2010) Grassland fire management in future climate. In ‘Advances in Agronomy. Vol. 106’. (Ed. DL Sparks) pp. 173–208. (Academic Press)

Symonds MRE, Moussalli A (2011) A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behavioral Ecology and Sociobiology 65, 13-21.
| Crossref | Google Scholar |

Tedim F, Leone V, Amraoui M, Bouillon C, Coughlan M, Delogu G, Fernandes P, Ferreira C, McCaffrey S, McGee T, Parente J, Paton D, Pereira M, Ribeiro L, Viegas D, Xanthopoulos G (2018) Defining extreme wildfire events: difficulties, challenges, and impacts. Fire 1, 9.
| Crossref | Google Scholar |

Tibbits A, Whittaker J (2007) Stay and defend or leave early: policy problems and experiences during the 2003 Victorian bushfires. Environmental Hazards 7, 283-290.
| Crossref | Google Scholar |

Tremblay P-O, Duchesne T, Cumming SG (2018) Survival analysis and classification methods for forest fire size. PLoS One 13, e0189860.
| Crossref | Google Scholar | PubMed |

Wheatley M, Wotton BM, Woolford DG, Martell DL, Johnston JM (2022a) Modelling decisions concerning the dispatch of airtankers for initial attack on forest fires in Ontario, Canada. Canadian Journal of Forest Research 53, 217-233.
| Crossref | Google Scholar |

Wheatley M, Wotton BM, Woolford DG, Martell DL, Johnston JM (2022b) Modelling initial attack success on forest fires suppressed by air attack in the province of Ontario, Canada. International Journal of Wildland Fire 31, 774-785.
| Crossref | Google Scholar |

Whittaker J, Taylor M, Bearman C (2020) Why don’t bushfire warnings work as intended? Responses to official warnings during bushfires in New South Wales, Australia. International Journal of Disaster Risk Reduction 45, 101476.
| Crossref | Google Scholar |

Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bulletin 1, 80-83.
| Crossref | Google Scholar |

Youden WJ (1950) Index for rating diagnostic tests. Cancer 3, 32-35.
| Crossref | Google Scholar | PubMed |