Factors Affecting Containment Area and Time of Australian ForestFires Featuring Aerial Suppression

Matt P. Plucinski

Abstract: The performance of wildfire suppression is often monitored using statistics related to area burned andtime to contain a fire. Potential factors affecting the probability of initial attack (IA) success and the probabilityof large fires were examined in a data set composed of 334 Australian wildfires that burned in forest andshrubland vegetation and used aerial- and tanker-based suppression during the IA phase. Logistic regressionanalysis was used to determine the most significant predictor variables for a range of area- and time-baseddefinitions for these measures. The variables that were found to be the best predictors of IA success were firearea at IA, fuel hazard, and aircraft response time. The probability of large fires was related to fuel hazard, areaat IA, and the Forest Fire Danger Index. Fire area at IA was strongly linked with aerial suppression time delayand was also influenced by weather and fuel hazard score. Fire management practices can influence IA area,response timing, and fuel hazard. IA area and response times can be minimized through efficient fire detectionand by deploying appropriate suppression resources rapidly from bases in locations that provide optimizedgeographical coverage. Fuel hazard can be moderated through management actions such as fuel reductionburning. FOR. SCI. 58(4):390–398.

Keywords: wildfire suppression, initial attack, wildfire response, wildfire containment, aerial suppression

F IRE MANAGEMENT operations aim to reduce the ad-verse effects of fires on people, property, and theenvironment. Suppression strategies have tended to

focus on rapid initial attack (IA) of fires to minimize theduration and area burned by wildfires. Fires that are ac-cessed quickly have smaller perimeters and are of a lowerintensity when suppression commences than fires that areaccessed later and are more likely to be contained to smallerareas and in shorter time frames (e.g., Parks 1964, McCar-thy and Tolhurst 1998, McCarthy 2003, Hirsch et al. 2004,Arienti et al. 2006, Plucinski et al. 2007). Traditionally,suppression of Australian wildfires has been conducted byground suppression resources such as tankers, crews withhand tools, and earth-moving machinery. Aircraft have be-come increasingly available for wildfire suppression overthe last decade. Firefighting aircraft are well suited to IAbecause of their ability to travel quickly and to attack firesthat are difficult or dangerous to access from the ground. Inthis role they assist ground suppression resources by reduc-ing fire growth and intensity.

The effectiveness of wildfire containment operations canbe gauged using statistics related to the proportion of firesthat have desirable or undesirable outcomes. These statisticsmay relate to damage caused by fires, which can be ex-pressed by figures such as house and infrastructure losses,estimates of threatened assets saved or proportions of firescontained within defined area or time limits. Although sta-

tistics related to asset destruction and damage can providean indication of costs associated with a fire, it can bedifficult to use these to make comparisons between firesbecause of their geographical variation. The number ofthreatened assets saved by suppression is not routinelyestimated in most parts of Australia, and a range of meth-odologies may be used to estimate such figures. Concisedefinitions of outcome statistics on area burned and the timetaken to contain fires are able to be used for comparisons ofmultiple fires. These fire outcome statistics are often used asperformance measures and featured in reports of seasonalsuppression operations. In the present study, area burnedand containment time statistics are used to explore factorsthat influence IA success, the occurrence of large fires, andresponse success.

The success of IA has traditionally been used as one ofthe main measures of fire management agencies achievingtheir goals (Wotton et al. 2010). A range of IA successdefinitions have been used by fire agencies and suppressionstudies. These are normally based on final fire area or timeto containment. A range of cutoff thresholds are used todefine success because spatial differences in land use andthe fire environment result in a range of acceptable impactsof the undesirable effects of wildfires. Wildfires are mostundesirable in areas in which there is the greatest risk ofsocial, economic, and environmental damage. These high-risk areas tend to have more suppression resources than

Manuscript received August 10, 2010; accepted July 6, 2011; published online February 2, 2012; http://dx.doi.org/10.5849/forsci.10-096.

Matt P. Plucinski, CSIRO Ecosystem Sciences and CSIRO Climate Adaption Flagship, Bushfire Cooperative Research Centre, GPO Box 1700, Canberra,ACT 2601, Australia—Phone: 61-2-62464242; Fax: 61-2-62464000; matt.plucinski@csiro.au.

Acknowledgments: This project was funded by the Bushfire Cooperative Research Centre. Many operations personnel from Australian wildfire response andland management agencies generously provided data from their fires and openly discussed operational procedures and decision-making processes. GregMcCarthy (University of Melbourne), Jennifer Hollis (Western Australian Department of Environment and Conservation), and Jim Gould (CSIRO) assistedwith data collection, collation, and project management. Wendy Anderson, Stuart Matthews, Chris Beadle, and Miguel Cruz made helpful comments on draftversions of the manuscript. Comments from the anonymous reviewers have significantly improved the article.

Copyright © 2012 by the Society of American Foresters.

390 Forest Science 58(4) 2012

lower-risk areas and can therefore be expected have a betterIA performance. Thresholds defining IA success do notneed to be as stringent in areas in which the impacts ofwildfires are not as severe. IA performance is also affectedby local environmental conditions, such as vegetation, ter-rain, weather, and access to water, which influence firebehavior and suppression effectiveness.

Some Australian land and fire management agencieshave defined IA success for forested conservation areas andareas near population centers as fire containment within 5ha (e.g., Department of Sustainability and Environment2003, Department of Environment and Climate Change2008, Department of Environment and Conservation 2008,ACT Government 2009). Larger threshold areas, e.g., 20 hain Western Australia (Department of Environment and Con-servation 2008), are used in areas with lower populationdensities or conservation values. Some Canadian studies(e.g., Cumming 2001, 2005, Arienti et al. 2006) have used3 ha in boreal forests. The proportional increase in areaburned between IA and containment has also been used asan IA success measure. McCarthy (2003) and McCarthy andTolhurst (1998) considered fires that grew more than threetimes the IA area to be containment failures.

Area burned is not always suitable as a measure ofsuppression success. Direct attack is not always used duringIA, and control lines are sometimes constructed at a distancefrom the fire perimeter, resulting in a large final fire area.Indirect attack is often a preferred and less costly option inareas with limited ground access to the fire edge or whenfire intensity is beyond that suitable for safe direct attack. Inthese cases, the final fire area reflects the location of fall-back lines and is not a good indicator of suppression effec-tiveness, and the time taken to contain a fire is a moreappropriate measure. This can be defined as the durationbetween detection or the start of IA and containment. Mc-Carthy (2003), McCarthy and Tolhurst (1998), and Plucin-ski et al. (2007) have used a threshold of 8 hours from IA.Another time-based method of defining IA success uses afixed point in time. This method has been used in Canadawith the specific definition of containment by 10:00 am(Quintilio and Anderson 1976) or noon (Ontario Govern-ment 2004) on the day after detection.

The incidence of large fires can indicate the efficiency offire management in an area (Cheney 1976). Fires that arelarge in area present the greatest risk of damage and are themost costly to suppress. These are undesirable, and theirfrequency in a region can be used as a comparative measureof seasonal severity. A range of final fire area thresholdshave been used to distinguish large fires. These include 40ha (Podur and Martell 2009, National Interagency FireCentre 2011), 100 ha (Podur and Martell 2007, Bermudez etal. 2009), 200 ha (Cumming 2001), and 1,000 ha (Bradstocket al. 2009).

The adequacy of response to a fire can also be measuredin terms of IA fire area and time elapsed between detectionand IA. IA fire area reflects the effectiveness of detectionand response and environmental conditions. Arienti et al.(2006) used a 3-ha IA area definition to discriminate re-sponse successes and failures.

Response time is the sum of the delay time between

detection and deployment of resources and the time takenfor a given resource to travel to a fire. Fire agencies often settimes for resources to depart base after notification of a firebased on weather conditions. Travel time is highly depen-dant on the distances between bases and fires and the speedat which resources travel. A number of studies have focusedon optimizing the placement of resources to minimize traveltimes to fires (e.g., Islam and Martell 1998, Greulich 2003,2008, Islam et al. 2009, Provost et al. 2009).

The objectives of this article are to determine the mostinfluential predictor variables for simple time and area-based definitions of IA success using a data set of Austra-lian wildfires that occurred in forest- and shrub-dominatedvegetation types and involved aerial suppression. The arti-cle also seeks to determine predictors for large fire occur-rence defined using final fire area and response successdefined using the area of fire at IA.

MethodsSource Data

Fire incident data were collected from 334 wildfires thatburned in forest- or shrubland-dominated vegetation andoccurred between November 2004 and February 2008 fireseasons in populated regions of southern Australia. Theseregions included all of Victoria, Tasmania, and the Austra-lian Capital Territory, eastern New South Wales, southwestWestern Australia, and the southeast corners of South Aus-tralia and Queensland. For all fires analyzed in this study,aircraft were used to drop suppressants or retardants duringthe early stages of IA and involved tanker-based groundsuppression. Fires that involved aerial suppression weretargeted because they tend to be the highest priority andhave better records for cross-checking data. The fires thatinvolve aerial suppression are likely to be more difficult tocontain on average than those that do not involve aerialsuppression and will therefore have lower probabilities ofIA success and higher probabilities of being large or burn-ing for a long time than those burning in milder conditions.Fires that occurred in grasslands were not considered forthis study because of differences in fire behavior and sup-pression tactics (Luke and McArthur 1978, Cheney andSullivan 2008). Fires that involved the transport of groundcrews by aircraft were excluded from the analysis becausethese fires tended to occur in remote locations where groundsuppression was mainly limited to crews with hand tools,and suppression tactics were therefore different.

Fire incident data had to be collected for this studybecause there were no existing databases containing fireresponse variables. The data were collected from fire man-agement personnel from the majority of Australian landmanagement and wildfire response agencies who had per-formed roles such as incident controller, operations officer,divisional commander, and air attack supervisor. The datawere collected using survey forms designed to cover a rangeof readily obtainable fire-related statistics as listed in Table1 and described below. Forms were distributed before fireseasons and soon after incidents. The information obtainedwas verified using follow-up interviews and checking offi-cial agency records. The fires included in this study were

Forest Science 58(4) 2012 391

those for which a survey questionnaire was returned, whichis a subset of all the fires that occurred during the period.

Data Fields

The variables available to this data set were limited tothose that could be obtained from data providers or second-ary sources (e.g., weather data from the Bureau of Meteo-rology) (Table 1). Some independent variables, includingfire area at IA and fuel hazard scores, rely on the judgmentof those providing the data. The use of expert judgments inwildfire operations studies can be subject to an immeasur-able amount of bias affecting data accuracy and reliability(Simard et al. 1973, Hirsch and Martell 1996). Wildfireoperations studies have often had to rely on data that arepotentially subjective because this is usually the only meansof obtaining information on specific variables or from asufficient quantity of fires (e.g., Hirsch and Martell 1996,Hirsch et al. 1998, 2004, McCarthy and Tolhurst 1998,McCarthy 2003, Plucinski et al. 2007). Here subjectiveestimates were used for variables such as fire area at IA andfuel hazard scores.

Timing data for each fire included the times of detection,IA, and fire containment. These were used to calculateresponse time period variables and containment time mea-sures. The response time variables were the time periodsbetween detection and first aircraft suppression work (ta)and first ground suppression work (tg). The containmenttime measure used was the period between IA and contain-ment (tic). Here the time of containment is defined as thetime when fire growth stopped. This may have preceded thecompletion of a control line around the full perimeter andother suppression tasks such as mop-up and patrol.

The weather data collected for each fire included maxi-mum Forest Fire Danger Index (FFDI) (McArthur 1967),

maximum air temperature (T), maximum hourly averagewind speed at 10 m in an open area (U), and minimumrelative humidity (H) between detection and containment orfor the first 24 hours for fires that burned for longer than 24hours from detection. FFDI, like other fire danger ratingsystems, is based on the principal that fire danger is deter-mined by wind speed, fuel moisture content, and fuel avail-ability (Matthews 2009) and is calculated from temperature,relative humidity, wind speed, and a drought factor based onthe Keetch-Byram Drought Index (Keetch and Byram1968). The use of weather variables that represent the peakfire danger conditions experienced during IA may not givethe best representation of average conditions experiencedduring IA; however, information at peak fire danger condi-tions was available for all fires and reflects the nature offorecasts used by fire suppression agencies. Weather datawere obtained from the most representative Bureau of Me-teorology weather station. Manual weather measurementstaken close to the fire ground were used for six fires forwhich they were available.

Visual fuel hazard ratings (McCarthy et al. 1999, Gouldet al. 2007a, Hines et al. 2010) were used to describe fuelcharacteristics. Fuel hazard guides are used operationally todescribe fuels in nongrassland parts of Australia. The fuelhazard rating method allows for quick descriptions of fuellayers based on the continuity, depth, height, and portion ofdead fuel and are not specific to vegetation type. Fuelhazard ratings were recorded for the four fuel layers: surface(litter), near-surface fuel (suspended low fuel), elevated fuel(shrubs), and bark fuels. Overall fuel hazard rating wasdetermined using the method of McCarthy et al. (1999).Fuel hazard ratings were estimated in the field by attendingfirefighters and are provided as an average across the areaburned before containment. Ordinal values (1–5) were as-signed for the five rating classes (low, moderate, high, veryhigh, and extreme, respectively) for analysis.

A binary categorical variable (V) was used to separatefires into two vegetation structure groups. Fires that burnedin vegetation with a prominent shrub layer, incorporatingvegetation types such as shrublands, heathlands, and scrub,as defined by Specht’s (1970) classification, as well aswoodlands with a shrub-dominated understory, were as-signed a value of 1. Fires that burned in mainly forest andwoodland vegetation with substantial litter layers or grassyunderstories were given the value 0.

The average slope incline (S) for the fire area at IA wasplaced into one of four classes: 0 � level ground (0°); 1 �low (�5°); 2 � moderate (5–15°); or 3 � steep (�15°).These were either estimated by the data provider or deter-mined from a topographical map.

Estimates of initial fire area (Ai) were obtained from thefirst arriving personnel. First arriving suppression crewsprovide an estimate of fire area in a situation report trans-mitted by radio to fire controllers when they first arrived ata fire. The details of this report are recorded in radio logsand official incident reports kept on file within the fireagencies. The accuracy of fire area estimates depends on theexperience of those making them. The precision of the areaestimates in this data set was limited, with most fires lessthan 10 ha rounded to the nearest hectare and many larger

Table 1. List of symbols and abbreviations used for variablesin the data set.

Variable Description

ta Time delay between detection and first aerialsuppression work (hours)

tg Time delay between detection and first groundsuppression work (hours)

tic Time period (hours) between first suppressionwork and containment

FFDI Maximum Forest Fire Danger IndexT Maximum air temperature (°C)H Minimum relative humidity (%)U Maximum wind speed (km h�1)SFHS Surface fuel hazard scoreNSFHS Near-surface fuel hazard scoreEFHS Elevated fuel hazard scoreBFHS Bark fuel hazard scoreOFHS Overall fuel hazard scoreS Slope incline class: 0 � flat (0°), 1 � low (�5°),

2 � moderate (5–15°), 3 � steep (�15°)Ai Fire area at initial attack (ha)Af Final fire area (ha)UI Urban interface category: 1 � fires ignited within

1 km of built areas, 0 � fires ignited outside ofbuilt areas

V Presence of shrub-dominated vegetation at IA: 1� present, 0 � absent

392 Forest Science 58(4) 2012

fires rounded to the nearest 5 ha. Estimates of Ai werechecked against figures for final fire area (Af), determinedby fire agency mapping for official reporting, as well as theburning conditions and likely ignition time. Five fires withquestionable initial fire area estimates were not included inthe data set.

The location of fires with respect to urban interface areaswas categorized using the variable urban interface (UI).Fires that started within 1 km of a built area, includingsuburbs, towns, and industrial estates, were assigned a valueof 1, whereas those that started outside of this area weregiven a value of 0. One kilometer was used for this defini-tion because it could be applied to all fires in the data setwith confidence, whereas larger distances could not.

Information on the ignition source was gathered wherepossible. Ignition sources were grouped into four nominalcategories: lightning, accidental, intentional, and unknown.The lightning category was used for fires for which light-ning was determined to be the ignition source. No othernatural ignition sources had been identified. The accidentalcategory included anthropogenic ignitions when there wasno intentional intent, whereas the intentional category wasused for fires when the suspected cause was arson. Of thefires, 142 were ignited by lightning, 40 were accidental, 70were intentional, and 82 were of unknown origin.

In this study, it was assumed that an appropriate type andquantity of resources were sent to all fires because thenumber of resources deployed to fires was not available forthe analysis. Comparisons of the number of resources de-ployed to fires could not have been readily made herebecause there were many different types of resources used,and arrival data are not routinely recorded. This analysisalso assumes that suppression resources have been sent tothe fires with the aim of containment. Although this is likelyto be true in most cases, it is possible that resources are sentto fires with other motives, such as the protection ofinfrastructure.

Analysis

Logistic regression modeling was used to identify thevariables that have the greatest influence on IA success,response success, and the occurrence of large fires. Logisticmodels were formulated using a forward stepwise methodbased on likelihood ratio tests (Lehmann 1986) and in-cluded testing for two-way interactions between selectedvariables. The inclusion of variables in the models wasdecided using the Akaike information criterion (AIC)(Sakamoto et al. 1986), with variables only added whenthey reduced the AIC by 1 or more. Logistic regressionmodels were of the form

ln�p/�1 � p�� � b0 � b1 f1 � . . . � bn fn (1)

where p is the probability of IA success, response success,or the occurrence of large fires, b0, b1, and bn are regressionconstants, and f1 and fn are predictor variables. All statisticaltests and modeling were performed using the statistics pro-gram R version 2.11.1 (R Development Core Team 2010).

The goodness of fit of the logistic models was comparedusing Nagelkerke’s pseudo R2 statistic (R2

N) (Nagelkerke

1991), the fraction of correctly classified predictions (accu-racy), and the Matthews correlation coefficient (MCC)(Baldi et al. 2000). For the two latter measures, the pre-dicted probability of success of 0.5 was used as a cutoff.The MCC was used because it gives a more balancedmeasure of goodness of fit than accuracy when models havelow sensitivity (fraction true positive prediction) or speci-ficity (fraction true negative prediction) or when the classgroups are of uneven size. An MCC of 1 indicates perfectprediction, whereas MCCs of 0 and �1 indicate random andinverse prediction, respectively. The area under the receiveroperating characteristic curve was used to determine thediscriminative ability of the model over a range of cutoffpoints (for details, see Hosmer and Lemeshow 2000). Theinfluence of variables within models was compared usingAIC, which is the increase in AIC when each term isremoved from the model.

The IA success definitions used for the analysis includedtwo area-based definitions (Af �5 and 20 ha) and one timeperiod definition (tic �8 hours). One large fire definition (Af

�100) and one response success definition (Ai �1 ha) werealso investigated. Operational versions of the models werealso developed for each definition. The operational modelswere based on the stepwise models; however, the onlyweather variable used in these models was FFDI and theonly fuel variable was overall fuel hazard score (OFHS).Both of these are summary variables that are determinedusing the other weather and fuel variables (McArthur 1967,McCarthy et al. 1999). FFDI is generally more available inweather forecasts than other variables and is used for otherfire management activities. Modeled OFHS is available insome agency mapping, whereas other fuel variables are not.

Results

The ranges of the variables in the data set are presentedin Table 2. Fires with the slowest response times (ta, tg � 8hours) were those that were not attacked until the morningafter detection. The fires that were the largest at IA werelow-priority fires that occurred when many concurrent fireswere burning in the same region. Of the 334 fires, 43 camefrom UI areas. Five fires that started outside of UI areas

Table 2. Range of variables in the data set.

Variable Median Mean SD Minimum Maximum

ta 1.00 1.81 2.99 0.02 22.00tg 0.67 1.19 1.63 0.05 11.50tic 4.50 26.31 66.20 0.08 605.10FFDI 23.24 26.41 18.48 1.22 107.00T 30 29.54 5.98 13 43H 26 31.03 17.91 5 94U 17 20.07 15.45 0 67SFHS 3 2.66 0.91 1 5NSFHS 3 2.55 0.91 1 5EFHS 3 2.78 0.93 1 5BFHS 2 2.40 1.07 0 5OFHS 3 2.95 0.88 1 5Ai 2 5.24 9.41 0.003 80Af 9 1,485.3 9,332.7 0.003 130,231

Symbols are defined in Table 1.

Forest Science 58(4) 2012 393

burned into them. Most (235) of the fires burned in forest-dominated fuel types during IA. Fifty-five fires occurred ina flat location, 103 in locations with a low slope incline(�5°), 86 in locations with a moderate slope incline(5–15°), and 90 in areas with a steep slope inclines (�15°).

The two main outcome variables (Af and tic) were cor-related (r � 0.459, P � 0.001). The correlation was stronger(r � 0.662, P � 0.001) when the two largest fires (45% oftotal area burned) were removed; however, the correlationwas weaker, although still significant, for fires less than 100ha (r � 0.281, P � 0.001). A correlation matrix of input andoutput variables is presented in Table 3.

Nearly half of the fires in the data set were attributed tolightning (Table 4). Lightning fires burned more than 80%of the cumulative area and required 70% of the total con-tainment time. The two largest fires were ignited by light-ning and burned a combined 45% of the total area burned byall fires. The three longest running fires were also ignited bylightning and made up 16% of the total containment time ofall of the fires. These three fires occurred in the BlueMountains region of New South Wales, which has a ruggedlandscape that limits ground accessibility and responsetime.

IA Success

The proportion of fires at different Af, and tic thresholdsin the data set are shown in Figure 1a and b. This figure alsoshows the majority of the cumulative area burned and thefact that containment time could be attributed to a relativelysmall number of fires.

Of the 334 fires, 149 were contained to 5 ha or less. Thestepwise model using this definition of IA success containedAi and near-surface fuel hazard score (NSFHS) with nointeraction (Table 5). Ai has a much higher AIC (159) thanNSFHS (15) in this model. Substituting NSFHS with OFHSfor an operational model increases the AIC by 10.7 and onlyslightly reduces the fit (from R2

N � 0.632 to R2N � 0.608)

(Table 5).Of the fires, 203 were contained to 20 ha or less. The

stepwise model for this IA success definition contained Ai,

OFHS, H, ta, surface fuel hazard score (SFHS), and U, withno interactions. Ai was also the most important variable inthis model with a much higher AIC (101) than that for theother variables (3, 6, 7, 5, and 2, respectively). The replace-ment of either fuel variable with NSFHS did not consider-ably affect the model fit (from R2

N � 0.575 to R2N � 0.569

and 0.563 for OFHS and SFHS, respectively) and onlymarginally increased the AIC by 2 when SFHS was re-placed and 5 when OFHS was replaced. An operationalmodel for this definition used FFDI in place of H and U andomitted SFHS. This resulted in the AIC increasing by 10and a marginal decline in fit (from R2

N � 0.575 to R2N �

0.542) (Table 5).Of the fires, 229 were contained within 8 hours of IA. A

stepwise model applied to this definition included ta, H,NSFHS, Ai, S, bark fuel hazard score (BFHS), and U andhad no significant interactions (Table 5). The most impor-tant variables in this model were ta and Ai (AIC � 38 and13, respectively). AIC values for the other variables in thismodel ranged from 2 to 10. The operational model for thisIA definition substituted OFHS in place of NSFHS andBFHS and FFDI in place of H and U. These changes causedthe AIC to increase by 20.5 and reduced the fit (from R2

N �0.489 to R2

N � 0.419) (Table 5).

Large Fires

There were 80 fires that burned more than 100 ha in thedata set. These fires accounted for 99.34% of the total areaburned (Figure 1a). A higher percentage of lightning-ignitedfires became large fires (39%) than did those from othersources (27%). This is probably because lightning was themost common ignition source in locations where the terrainwas more complex, and ground access was slower andbecause many of the lightning fires occurred simultane-ously, and resources were limited. Lightning-ignited firesrepresent a small proportion of those that occur in interfaceareas where the suppression response is rapid.

The stepwise model applied to the large fire definition(Af � 100) included OFHS, Ai, FFDI, and SFHS with nointeractions (Table 5). The AIC for these variables were12, 19, 15, and 6, respectively. SFHS was removed from thestepwise model to make the operational model. This pro-duced a small decline in fit (from R2

N � 0.467 to R2N �

0.423) (Table 5).

Response Success

The distribution of Ai in the data set can be seen in Figure1c. This figure shows that fires that were larger at IAaccounted for large portions of the total area burned and

Table 3. Correlation between variables.

Variable Af tic Ai

FFDI 0.211c 0.225c 0.180aT 0.159a 0.068 0.136H �0.133 �0.203b �0.117U 0.096 0.191b 0.108SFHS 0.046 0.167a �0.078NSFHS 0.153a 0.189b 0.028EFHS 0.130 0.200b 0.143BFHS 0.128 0.161a 0.014OFHS 0.122 0.276c 0.127S 0.117 0.205c 0.053UI �0.055 �0.072 0.026V �0.063 �0.067 0.046ta 0.030 0.415c 0.251tg 0.043 0.295 0.027Ai 0.256c 0.190b

Symbols are defined in Table 1. a, b, and c indicate correlationssignificant at the 0.05, 0.01, and 0.001 levels, respectively.

Table 4. Fire outcome statistics based on ignition cause.

Ignition sourceNo.fires

%fires

% totalarea burned

% totalcontainment

time

Lightning 142 42.5 82.8 70.1Accidental 40 12.0 3.2 9.7Intentional 70 21.0 11.6 10.3Unknown 82 24.6 2.4 9.9

394 Forest Science 58(4) 2012

total containment time. Of the fires in the data set, 198 hadburned more than 1 ha at IA. A model predicting theprobability of a fire being �1 ha at IA included ta, U, andelevated fuel hazard score (EFHS) (Table 5). The AICs forthese variables were 13, 7, and 5, respectively. SubstitutingEFHS with OFHS only increases the AIC by 3 and reducesthe R2

N from 0.223 to 0.203, whereas substituting U withFFDI increases the AIC by 5 and reduces the R2

N to 0.193.The operational model for Ai �1 ha included both of thesesubstitutions and resulted in an AIC increase of 7 and a

small decline in the fit (R2N � 0.180) (Table 5). The fits for

these models were much lower than those for the IA andlarge fire models. Stepwise models that were fit to larger Ai

thresholds were simple (generally only including ta) and hadvery poor fits (R2

N � 0.150).

Discussion

This analysis has shown that the containment success ofAustralian wildfires is strongly related to the size of fires atIA, the arrival delay of aircraft, fuel hazard, and weather.Fire area at IA was the most important variable in thestepwise models predicting IA success and large fires here.It has also been linked with final area burned in previousstudies (e.g., Hirsch et al. 1998, Arienti et al. 2006). Thisvariable is used by fire controllers to determine whethermore resources should be dispatched to a fire. The influenceof IA fire area is illustrated in Figure 2a. The IA fire areawas found to be largely influenced by response timing andto a lesser extent by weather and fuel variables. It is alsolikely that it would be affected by other factors related todetection, travel distance, and site accessibility that were notable to be tested here. Arienti et al. (2006) found initial firearea to be influenced by fire cause, season, fuel type,weather, and response time. The poor fits of the responsemodels developed here probably reflect complexities indeployment protocols and limitations across the wide vari-ety of fire environments that these data were sourced from.Wildfire response effectiveness using area- and time-basedperformance measures deserves further study at more pre-cise scales.

Fuel hazard score variables featured in all of the stepwisemodels developed here. Near-surface fuel hazard score fea-tured in two of the three IA success models and could besubstituted into the other model without reducing the fit.The near-surface fuel layer is an important fuel componentin Australian eucalypt forests and is used in the recent firespread model of Gould et al. (2007b). OFHS featured in thestepwise model for large fires and was used as the fuelvariable in all operational models. Previous studies thathave investigated fuel effects on fire outcome statistics haveconsidered fuel and vegetation classifications based on spe-cies present (e.g., Arienti et al. 2006, Fried et al. 2006,Martell and Sun 2008) rather than fuel attributes, such as thefuel hazard scores investigated here. Fuel attributes, in theform of fuel mass and fuel hazard scores, are used inempirically derived Australian fire spread models (e.g.,McArthur 1967, Gould et al. 2007b).

Fuel hazard is an important predictor of the probabilitiesof IA success and large fires because it can be modified bymanagement practices, such as fuel reduction burning. Theinfluence of fuel hazard on IA success and large fire occur-rence is illustrated in Figure 2. This figure shows thatreducing overall fuel hazard from extreme to high levelswill improve the probability of fire containment within 5 haby up to 36% and decrease the probability of fires exceeding100 ha by up to 55%. Fuel management through prescribedburning is an important risk reduction strategy used byAustralian fire agencies. Fuel management programs needto be conducted over large areas to cover the potential

Figure 1. Cumulative proportion of number of fires, areaburned, and containment time with (a) final fire area, (b)containment time, and (c) IA area. Vertical lines correspondwith the definitions used in the analysis.

Forest Science 58(4) 2012 395

ignition locations and minimize the area burned bywildfires.

Weather variables were selected for all but one of thestepwise models presented here. Two IA stepwise modelsincluded both relative humidity and wind speed as vari-ables, whereas the large fire model contained FFDI and theresponse model contained wind speed. FFDI, which com-bines the other weather variables, had the highest correla-

tions with fire area and containment time (Table 3) of theweather variables. FFDI was used in the operational modelsbecause it was the most important of the weather variablesand because it is used operationally to predict fire potentialand suppression difficulty, to set resource response levels,and to determine public fire restrictions. Previous studieshave also linked fire weather indices with area burned (e.g.,McCarthy 2003, Arienti et al. 2006, Podur and Martell2007, Bradstock et al. 2009). The influence of FFDI on theprobability of large fires can be seen in Figure 2b.

The time delay between detection and aerial suppressionwas the most important variable in the response model andwas used in two of the stepwise models for IA. The timedelay between detection and ground suppression was notincluded in any models. This variable (tg) was negativelycorrelated with the severity of fire weather conditions (e.g.,FFDI, r � �0.185, P � 0.01), probably due to higherreadiness levels set by agencies. This correlation may havemasked the effect of ground response. Aerial suppressiontime delay was considerably more variable than groundsuppression time delay in the data set (Table 2). Groundsuppression resources arrived at fires before aircraft in most(63%) of the incidents because they are much more com-mon, are deployed routinely to all fires on notification, andare spread across the landscape and thus are usually closerto fire locations than aircraft. However, their slower traveltimes often prevent them from accessing fires first whenthey are in distant or difficult-to-access locations. In thesesituations, aircraft can be used to reduce the response timeand can reduce fire spread, so that fires are smaller and lessintense when ground crews can get to them than they wouldhave been otherwise. Management actions that make crewsavailable for quick deployment will enhance IA success.

Slope incline was only included in the time-based IAmodel (tic �8 h). This variable was correlated with groundsuppression time delay (r � 0.24, P � 0.001), indicatingthat ground crews have more difficulty accessing and sup-pressing fires in steep areas. Fires on steep slopes are morelikely to benefit from the use of aerial suppression thanthose that occur on flat terrain because of the difficultyaccessing them from the ground.

A number of variables that were not able to be investi-gated here have been found to influence wildfire outcomes

Table 5. Stepwise logistic regression models and operational models with fit statistics (AIC, R2N, prediction accuracy, MCC, and

ROC).

Definition and type Model AIC R2N Accuracy MCC ROC

Af �5 ha stepwise 3.88 � 0.93Ai � 0.76NSFHS 251.9 0.632 0.843 0.694 0.910Af �5 ha operational 3.38 � 0.88Ai � 0.51OFHS 262.6 0.608 0.803 0.620 0.902Af �20 ha stepwise 4.82 � 0.35Ai � 0.49OFHS 0.03H � 0.26ta �

0.54SFHS � 0.02U277.0 0.575 0.818 0.611 0.892

Af �20 ha operational 5.44 � 0.34Ai � 0.81OFHS 0.25ta � 0.03FFDI 286.7 0.542 0.803 0.579 0.876tic �8 h stepwise 4.89 � 0.47ta 0.03H � 0.36NSFHS � 0.07Ai �

0.53S � 0.53BFHS � 0.03U289.0 0.489 0.806 0.558 0.868

tic �8 h operational 5.22 � 0.40ta � 0.46OFHS � 0.07Ai � 0.65S �0.03FFDI

309.5 0.419 0.768 0.451 0.836

Af �100 ha stepwise �8.66 1.05OFHS 0.14Ai 0.05FFDI 0.73SFHS 155.1 0.467 0.768 0.450 0.863Af �100 ha operational �6.95 1.26OFHS 0.12Ai 0.04FFDI 161.3 0.423 0.782 0.447 0.852Ai �1 ha stepwise 2.26 � 0.55 ta � 0.03U � 0.50EFHS 219.6 0.223 0.695 0.357 0.764Ai �1 ha operational 2.02 � 0.56 ta � 0.02FFDI � 0.44OFHS 226.4 0.180 0.641 0.239 0.735

Models are of the form given by Equation 1. Symbols are defined in Table 1. ROC, area under the receiver operating characteristic curve.

Figure 2. The influence of OFHS on (a) the probability of IAsuccess (Af <5 ha) and (b) the probability of large fire occur-rence (Af >100 ha) using the operational equations in Table 5.Ai is set at 2 ha for b.

396 Forest Science 58(4) 2012

in previous studies. These include the number of resourcesdeployed, fire load, and fire behavior. The number of re-sources has been linked to productivity in containmentsimulation studies (e.g., Fried and Fried 1996), whereascrew size has been found to influence productivity in anelicitation study (Hirsch et al. 2004). The number of re-sources available for IA firefighting is proportional to thenumber of wildfires occurring at a given time (fire load).Podur and Martell (2007) found fire load to be a significantpredictor of the probability of large fires in Ontario. Firebehavior, in terms of intensity, has been linked to crewproductivity during IA (Hirsch et al. 1998, 2004). Periods ofmoderate fire behavior have been found to be importantduring large fire containment (Finney et al. 2009).

Containment and response performance statistics can beused to demonstrate the effectiveness of fire suppressionprograms and justify operations; however it is importantthat they are considered in the context of interseason vari-ability in fire danger and ignition frequency. The promi-nence of performance statistics, such as frequency of IAsuccess, diverts attention from the positive benefits of sup-pression actions, such as reducing fire impact on commu-nities and infrastructure, which can occur at any fire. Avariety of definitions for performance success measuresneed to be used for different locations because of the rangeof resource types and numbers and tolerances to wildfireimpact. The most appropriate thresholds for performancemeasures, such as IA success, are also likely to depend onvegetation type and land use. Area-based thresholds of IAsuccess are not appropriate for fires for which direct attackis not feasible, because large areas can be burned by back-burning operations.

Operational data sets can be used to develop models toassist fire management personnel with decisions related toresource deployment and planning. Operational data col-lected over long periods can be used to monitor trends infire occurrence, suppression response, and fire impact (e.g.,Cumming 2005, Martell and Sun 2008, Wotton et al. 2010).Operational data containing information on fire response,outcome, and the characteristics of influential factors, suchas those used here, are not routinely collected by Australianwildfire suppression agencies. Such data could be used formore detailed analyses in investigating the causal factors offire suppression outcomes and over the longer term theimpact of policy and resourcing.

Conclusions

The analysis presented here showed that the outcomes offire containment operations are affected by IA area, fuelhazard, response timing, weather, and terrain variables. IAarea was the most important variable for predicting IAsuccess and, being a function of response timing, is a goodmeasure of response success. Fire management practicescan influence IA area, response timing, and fuel hazard. IAarea and response times can be minimized by deployingappropriate suppression resources as rapidly as possible andstaging resources in optimal locations to give adequategeographical coverage. Efficient fire detection will alsohelp minimize the IA area. Fuel management strategies,

such as hazard reduction burning, can help increase theprobability of IA success and lower the probability of largefires. Operational data sets, such as that used here, can beanalyzed to monitor long-term trends in fire occurrence andsuppression performance and can be used to develop oper-ational tools and guidelines to aid operational decisions andstrategies. Fire response data should be routinely collectedand stored by fire suppression agencies.

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