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

Suppression resources and their influence on containment of forest fires in Victoria

Erica Marshall https://orcid.org/0000-0002-7297-5777 A * , Annalie Dorph A B , Brendan Holyland https://orcid.org/0000-0003-4080-0419 A , Alex Filkov https://orcid.org/0000-0001-5927-9083 A and Trent D. Penman https://orcid.org/0000-0002-5203-9818 A
+ Author Affiliations
- Author Affiliations

A FLARE Wildfire Research, School of Ecosystem and Forest Sciences, University of Melbourne, Creswick, Vic., Australia.

B School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia.

* Correspondence to: erica.marshall@unimelb.edu.au

International Journal of Wildland Fire 31(12) 1144-1154 https://doi.org/10.1071/WF22029
Submitted: 9 March 2022  Accepted: 8 October 2022   Published: 28 October 2022

© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF.

Abstract

Background: Wildfire suppression is becoming more costly and dangerous as the scale and severity of impacts from fires increase under climate change.

Aims: We aim to identify the key environmental and management variables influencing containment probability for forest fires in Victoria and determine how these change over time.

Methods: We developed Random Forest models to identify variables driving fire containment within the first 24 h of response. We used a database of ~12 000 incident records collected across Victoria, Australia.

Key results: Response time, fire size at first attack, number of ground resources deployed (e.g. fire fighters), ignition cause, and environmental factors that influence fire spread (e.g. elevation, humidity, wind, and fuel hazard) were key drivers of suppression success within the first 24 h. However, certainty about the factors influencing suppression reduced as the containment period increased.

Conclusions: Suppression success hinges on a balance between the environmental factors that drive fire spread and the rapid deployment of sufficient resources to limit fire perimeter growth.

Implications: Decreasing the period between an ignition and the time of arrival at the fire will allow first responders to begin suppression before the fire size has exceeded their capability to construct a control line.

Keywords: containment, containment probability, fire, management, Random Forest, suppression, suppression resources, wildfire.


References

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.
Empirical models of forest fire initial attack success probabilities: The effects of fuels, anthropogenic linear features, fire weather, and management.Crossref | GoogleScholarGoogle Scholar |

Barmpoutis P, Papaioannou P, Dimitropoulos K, Grammalidis N (2020) A review on early forest fire detection systems using optical remote sensing. Sensors 20, 6442
A review on early forest fire detection systems using optical remote sensing.Crossref | GoogleScholarGoogle Scholar |

Biddle N, Bryant C, Gray M, Marasinghe D (2020) Measuring the economic impact of early bushfire detection ANU Centre for Social Research and Methods. Available at https://csrm.cass.anu.edu.au/research/publications/measuring-economic-impact-early-bushfire-detection

Breiman L (2001) Random forests. Machine Learning 45, 5–32.
Random forests.Crossref | GoogleScholarGoogle Scholar |

Brotons L, Aquilué N, de Cáceres M, Fortin MJ, Fall A (2013) How Fire History, Fire Suppression Practices and Climate Change Affect Wildfire Regimes in Mediterranean Landscapes. PLoS One 8, e62392
How Fire History, Fire Suppression Practices and Climate Change Affect Wildfire Regimes in Mediterranean Landscapes.Crossref | GoogleScholarGoogle Scholar |

Brown T, Mills G, Harris S, Podnar D, Reinbold H, Fearon M (2016) A bias corrected WRF mesoscale fire weather dataset for Victoria, Australia 1972-2012. Journal of Southern Hemisphere Earth Systems Science 66, 281–313.
A bias corrected WRF mesoscale fire weather dataset for Victoria, Australia 1972-2012.Crossref | GoogleScholarGoogle Scholar |

Carlisle DM, Falcone J, Meador MR (2009) Predicting the biological condition of streams: Use of geospatial indicators of natural and anthropogenic characteristics of watersheds. Environmental Monitoring and Assessment 151, 143–160.
Predicting the biological condition of streams: Use of geospatial indicators of natural and anthropogenic characteristics of watersheds.Crossref | GoogleScholarGoogle Scholar |

Clarke H, Gibson R, Cirulis B, Bradstock RA, Penman TD (2019) Developing and testing models of the drivers of anthropogenic and lightning-caused wildfire ignitions in south-eastern Australia. Journal of Environmental Management 235, 34–41.
Developing and testing models of the drivers of anthropogenic and lightning-caused wildfire ignitions in south-eastern Australia.Crossref | GoogleScholarGoogle Scholar |

Clarke H, Penman T, Boer M, Cary GJ, Fontaine JB, Price O, Bradstock R (2020) The Proximal Drivers of Large Fires: A Pyrogeographic Study. Frontiers in Earth Science 8, 90
The Proximal Drivers of Large Fires: A Pyrogeographic Study.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 |

Cutler DR, Edwards Jr TC, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random Forests for Classification in Ecology. Ecology 88, 2783–2792.
Random Forests for Classification in Ecology.Crossref | GoogleScholarGoogle Scholar |

DELWP (2021) National Vegetation Information System (NVIS). Available at https://www.awe.gov.au/agriculture-land/land/native-vegetation/national-vegetation-information-system

Dewilde L, Chapin FS (2006) Human Impacts on the Fire Regime of Interior Alaska : Interactions among Fuels, Ignition Sources, and Fire Suppression. Ecosystems 9, 1342–1353.
Human Impacts on the Fire Regime of Interior Alaska : Interactions among Fuels, Ignition Sources, and Fire Suppression.Crossref | GoogleScholarGoogle Scholar |

Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, Marquéz JRG, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, Mcclean C, Osborne PE, Reineking B, Schröder B, Skidmore AK, Zurell D, Lautenbach S (2013) Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46.
Collinearity: A review of methods to deal with it and a simulation study evaluating their performance.Crossref | GoogleScholarGoogle Scholar |

Duff TJ, Tolhurst KG (2015) Operational wildfire suppression modelling: A review evaluating development, state of the art and future directions. International Journal of Wildland Fire 24, 735–748.
Operational wildfire suppression modelling: A review evaluating development, state of the art and future directions.Crossref | GoogleScholarGoogle Scholar |

Dunn CJ, Thompson MP, Calkin DE (2017) A framework for developing safe and effective large-fire response in a new fire management paradigm. Forest Ecology and Management 404, 184–196.
A framework for developing safe and effective large-fire response in a new fire management paradigm.Crossref | GoogleScholarGoogle Scholar |

Elith J (2019) 15-Machine Learning, Random Forests, and Boosted Regression Trees. In ‘Quantitative analyses in wildlife science’. (Eds LA Brennan, AN Tri, BG Marcot) p. 281. (Johns Hopkins University Press).

Frangieh N, Accary G, Rossi JL, Morvan D, Meradji S, Marcelli T, Chatelon FJ (2021) Fuelbreak effectiveness against wind-driven and plume-dominated fires: A 3D numerical study. Fire Safety Journal 124, 103383
Fuelbreak effectiveness against wind-driven and plume-dominated fires: A 3D numerical study.Crossref | GoogleScholarGoogle Scholar |

Friedman JH (2001) Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics 29, 1189–1232.
Greedy Function Approximation: A Gradient Boosting Machine.Crossref | GoogleScholarGoogle Scholar |

Gannon BM, Thompson MP, Deming KZ, Bayham J, Wei Y, O’Connor CD (2020) A geospatial framework to assess fireline effectiveness for large wildfires in the western USA. Fire 3, 43
A geospatial framework to assess fireline effectiveness for large wildfires in the western USA.Crossref | GoogleScholarGoogle Scholar |

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

Gill AM, Allan G (2008) Large fires, fire effects and the fire-regime concept. International Journal of Wildland Fire 17, 688–695.
Large fires, fire effects and the fire-regime concept.Crossref | GoogleScholarGoogle Scholar |

Gill AM, Stephens SL, Cary GJ (2013) The worldwide “wildfire” problem. Ecological Applications 23, 438–454.
The worldwide “wildfire” problem.Crossref | GoogleScholarGoogle Scholar |

Greenwell BM (2017) pdp: An R Package for Constructing Partial Dependence Plots. The R Journal 9, 421–436.
pdp: An R Package for Constructing Partial Dependence Plots.Crossref | GoogleScholarGoogle Scholar |

Greenwell BM, Boehmke BC, Mccarthy AJ (2018) A Simple and Effective Model-Based Variable Importance Measure. arXiv.
A Simple and Effective Model-Based Variable Importance Measure.Crossref | GoogleScholarGoogle Scholar |

Harris S, Mills G, Brown T (2019) Victorian fire weather trends and variability. In ‘MODSIM2019, 23rd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2019’(Ed S. Elsawah) pp. 747–753. (MODSIM)
| Crossref |

Heyerdahl EK, Brubaker LB, Agee JK (2001) Spatial controls of historical fire regimes: a multiscale example from the interior west, USA. Ecology 82, 660–678.
Spatial controls of historical fire regimes: a multiscale example from the interior west, USA.Crossref | GoogleScholarGoogle Scholar |

Hines F, Victoria. Fire and Adaptive Management Branch, Victoria. Department of Sustainability and Environment (2010) ‘Overall fuel hazard assessment guide.’ (Fire Management Branch, Dept of Natural Resources and Environment)

Huang BFF, Boutros PC (2016) The parameter sensitivity of random forests. BMC Bioinformatics 17, 331
The parameter sensitivity of random forests.Crossref | GoogleScholarGoogle Scholar |

Huber-Stearns H, Moseley C, Bone C, Mosurinjohn N, Lyon KM (2019) An initial look at contracted wildfire response capacity in the American west. Journal of Forestry 117, 1–8.
An initial look at contracted wildfire response capacity in the American west.Crossref | GoogleScholarGoogle Scholar |

Liaw A, Wiener M (2002) Classification and Regression by randomForest. R News 2, 18–22.

McArthur AG (1967) ‘Fire behaviour in eucalypt forests. Vol. 107.’ (Commonwealth of Australia Forestry and Timber Bureau Leaflet No.)

McCarthy GJ, Tolhurst KG, Wouters MA, Victoria. Fire Management. (2003) ‘Prediction of firefighting resources for suppression operations in Victoria’s parks and forests.’ (Dept. of Sustainability and Environment)

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.
Analysis of the resourcing and containment of multiple remote fires: The great divide complex of fires, Victoria, December 2006.Crossref | GoogleScholarGoogle Scholar |

McColl-Gausden SC, Bennett LT, Duff TJ, Cawson JG, Penman TD (2020) Climatic and edaphic gradients predict variation in wildland fuel hazard in south-eastern Australia. Ecography 43, 443–455.
Climatic and edaphic gradients predict variation in wildland fuel hazard in south-eastern Australia.Crossref | GoogleScholarGoogle Scholar |

Moon K, Duff TJ, Tolhurst KG (2019) Sub-canopy forest winds: understanding wind profiles for fire behaviour simulation. Fire Safety Journal 105, 320–329.
Sub-canopy forest winds: understanding wind profiles for fire behaviour simulation.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 |

Olson JS (1963) Energy Storage and the Balance of Producers and Decomposers in Ecological Systems. Ecology 44, 322–331.
Energy Storage and the Balance of Producers and Decomposers in Ecological Systems.Crossref | GoogleScholarGoogle Scholar |

Papadopoulos S, Azar E, Woon WL, Kontokosta CE (2018) Evaluation of tree-based ensemble learning algorithms for building energy performance estimation. Journal of Building Performance Simulation 11, 322–332.
Evaluation of tree-based ensemble learning algorithms for building energy performance estimation.Crossref | GoogleScholarGoogle Scholar |

Penman TD, Collins L, Price OF, Bradstock RA, Metcalf S, Chong DMO (2013) Examining the relative effects of fire weather, suppression and fuel treatment on fire behaviour – A simulation study. Journal of Environmental Management 131, 325–333.
Examining the relative effects of fire weather, suppression and fuel treatment on fire behaviour – A simulation study.Crossref | GoogleScholarGoogle Scholar |

Penman TD, Nicholson AE, Bradstock RA, Collins L, Penman SH, Price OF (2015) Reducing the risk of house loss due to wildfires. Environmental Modelling & Software 67, 12–25.
Reducing the risk of house loss due to wildfires.Crossref | GoogleScholarGoogle Scholar |

Petrovic N, Alderson DL, Carlson JM (2012) Dynamic resource allocation in disaster response: tradeoffs in wildfire suppression. PLoS One 7, e33285
Dynamic resource allocation in disaster response: tradeoffs in wildfire suppression.Crossref | GoogleScholarGoogle Scholar |

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

Plucinski MP (2019a) 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 (2019b) 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, 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.
The effect of aerial suppression on the containment time of Australian wildfires estimated by fire management personnel.Crossref | GoogleScholarGoogle Scholar |

Plucinski MP, Hurley R, Bessell R, Nichols D (2019) Assessing gel control lines for controlling grassfires. In ‘Proceedings for the 6th International Fire Behavior and Fuels Conference’. (International Association of Wildland Fire: Missoula, Montana, USA). 5pp.

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.
A simulation model of the growth and suppression of large forest fires in Ontario.Crossref | GoogleScholarGoogle Scholar |

Prasad AM, Iverson LR, Liaw A (2006) Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems 9, 181–199.
Newer classification and regression tree techniques: Bagging and random forests for ecological prediction.Crossref | GoogleScholarGoogle Scholar |

Read N, Duff TJ, Taylor PG (2018) A lightning-caused wildfire ignition forecasting model for operational use. Agricultural and Forest Meteorology 253–254, 233–246.
A lightning-caused wildfire ignition forecasting model for operational use.Crossref | GoogleScholarGoogle Scholar |

Riley KL, Thompson MP, Scott JH, Gilbertson-Day JW (2018) A model-based framework to evaluate alternative wildfire suppression strategies. Resources 7, 4
A model-based framework to evaluate alternative wildfire suppression strategies.Crossref | GoogleScholarGoogle Scholar |

Simpson H, Bradstock R, Price O (2019) A temporal framework of large wildfire suppression in practice, a qualitative descriptive study. Forests 10, 884
A temporal framework of large wildfire suppression in practice, a qualitative descriptive study.Crossref | GoogleScholarGoogle Scholar |

Simpson H, Bradstock R, Price O (2021) Quantifying the prevalence and practice of suppression firing with operational data from large fires in Victoria, Australia. Fire 4, 63
Quantifying the prevalence and practice of suppression firing with operational data from large fires in Victoria, Australia.Crossref | GoogleScholarGoogle Scholar |

Strobl C, Malley J, Tutz G (2009) Supplemental Material for An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests. Psychological Methods 14, 323–348.
Supplemental Material for An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests.Crossref | GoogleScholarGoogle Scholar |

Thuiller W, Araújo MB, Lavorel S (2003) Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales. Journal of Vegetation Science 14, 669–680.
Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales.Crossref | GoogleScholarGoogle Scholar |

Tymstra C, Stocks BJ, Cai X, Flannigan MD (2020) Wildfire management in Canada: Review, challenges and opportunities. Progress in Disaster Science 5, 100045
Wildfire management in Canada: Review, challenges and opportunities.Crossref | GoogleScholarGoogle Scholar |

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2021) Modelling species presence-only data with random forests. Ecography 44, 1731–1742.
Modelling species presence-only data with random forests.Crossref | GoogleScholarGoogle Scholar |

Wollstein K, O’Connor C, Gear J, Hoagland R (2022) Minimize the bad days: Wildland fire response and suppression success. Rangelands 44, 187–193.
Minimize the bad days: Wildland fire response and suppression success.Crossref | GoogleScholarGoogle Scholar |

Wotton BM, McAlpine RS, Hobbs MW (1999) The Effect of Fire Front Width on Surface Fire Behaviour. International Journal of Wildland Fire 9, 247–253.
The Effect of Fire Front Width on Surface Fire Behaviour.Crossref | GoogleScholarGoogle Scholar |

Zhao Q, Hastie T (2021) Causal Interpretations of Black-Box Models. Journal of Business & Economic Statistics 39, 272–281.
Causal Interpretations of Black-Box Models.Crossref | GoogleScholarGoogle Scholar |

Zylstra P, Bradstock RA, Bedward M, Penman TD, Doherty MD, Weber RO, Gill AM, Cary GJ (2016) Biophysical mechanistic modelling quantifies the effects of plant traits on fire severity: Species, not surface fuel loads, determine flame dimensions in eucalypt forests. PLoS One 11, e0160715
Biophysical mechanistic modelling quantifies the effects of plant traits on fire severity: Species, not surface fuel loads, determine flame dimensions in eucalypt forests.Crossref | GoogleScholarGoogle Scholar |