Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires

Living Edition
| Editors: Samuel L. Manzello

Cost of Suppression

  • Veronique FlorecEmail author
  • Matthew P. Thompson
  • Francisco Rodríguez y Silva
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-51727-8_96-1

Synonyms

Definition

Cost of suppression is defined as the money spent in suppression activities. Suppression is defined as the activities aimed at restricting the spread of a wildfire after its detection. The term suppression is broadly defined to encompass all activities aimed at putting the fire out and minimizing the area burned.

Introduction

Suppression, or firefighting, is a fire agency’s response to a wildfire using a range of resources to limit its spread. Other expenditures incurred prior to the start of the fire (e.g., land management planning, fuel treatments, prepositioning of firefighting resources, detection systems, etc.) are considered to be part of the presuppression stage (Gebert et al. 2008). The type of resources needed for suppressing a wildfire and how long they are used for depend on where the fire occurs (e.g., close or far away from human habitations), what assets are located close to the ignition point (e.g., critical infrastructure, habitat for threatened species, towns), time of the year, weather conditions, and the predicted rate of spread (Katuwal et al. 2017).

The costs of suppression are a function of the amount and type of resources used over the duration of the fire. These may include money spent on firefighting equipment, vehicle or machinery use/hire and maintenance, aircraft use/hire and maintenance, salaries, payroll overheads, staff costs (e.g., travel, accommodation, and food costs), overtime, contracts paid to external organizations, and any other expenditures associated with the suppression of the fire. There are a range of responses to wildfires, all of which incur costs, but their implementation is contextual, and how frequently they are used may depend on the policy in effect, the fire agency’s capacity, the value of assets at risk, and other factors (Canton-Thompson et al. 2008; Calkin et al. 2013; Thompson 2014). Fire management policies vary substantially across and within countries. And although similar principles apply with regard to the protection of life and property, the way suppression resources are budgeted, allocated, and deployed can vary dramatically from one fire-prone region to another.

The costs of suppression have been the subject of extensive research and have increasingly been the focus of fire economists because of the dramatic increase in suppression expenditures in many countries since the mid-1980s, including the USA (Thompson et al. 2013a; Hand et al. 2016), Australia (Morgan 2009), Canada (Flannigan et al. 2005), and countries in Europe and Africa (Williams et al. 2011). Some of the questions that researchers have considered include (1) the likelihood of success of initial attack and (2) the drivers of suppression costs. The next section, “Different Suppression Resources: Types, Uses, and Costs”, provides a general overview of different resources used for suppression and how they affect total suppression costs. Sections “Initial Attack vs. Extended Attack” and “Drivers of Suppression Costs” address the two research questions above. Section “Suppression Cost Examples” presents examples from Australia, the USA, and Spain. Finally, section “Challenges and Concerns” outlines some of the key challenges for the economics of wildfire suppression.

Different Suppression Resources: Types, Uses, and Costs

Ground Resources

The costs of ground resources vary with size. Usually the larger the resource, the more expensive it is to operate. Fire trucks are usually the least expensive resource to operate in terms of costs per hour. Machinery or earthmoving equipment (i.e., front loaders and bulldozers) are generally more expensive to operate than fire trucks but remain within the same order of magnitude (approx. USD 100 to 400 per hour) (DFES 2017; FAMWEB 2018). In addition to hourly rates, there are travel costs and standby costs (i.e., the costs of having the resource ready and waiting to be deployed if needed). Travel costs range within a few dollars per kilometer, while standby costs range between USD 200 to 1,300 per day for fire trucks and USD 500 to 2,000 per day for earthmoving equipment (DFES 2017; FAMWEB 2018). How, when, and where ground resources are used depend on the location of the fire and the characteristics of the nearby landscape, the policies in place within fire agencies, weather conditions, and the availability of different resources.

Ground resources also include hand crews (i.e., firefighters), who operate the machinery and firefighting equipment. Firefighters can either be government employees with permanent positions (career firefighters) or people in the community with firefighting training that form part of a local fire brigade (volunteer firefighters). Depending on whether an agency uses mostly career or volunteer firefighters, suppression labor costs are very different, career firefighters being a lot more expensive. Because of sparse human settlement outside Australian capital cities, Australian communities rely heavily on volunteer firefighters (McLennan et al. 2009), and only about 7% of the 235,000 firefighters in the country are career firefighters (SCRGSP 2015). As a result, firefighting labor costs are less expensive in Australia compared to other countries. In contrast, US fire agencies rely more heavily on career firefighters and sustain higher suppression labor costs. About 30% of the country’s 1.2 million local firefighters are career firefighters, many of whom have wildland fire suppression duties (Haynes and Stein 2017).

Given the significant difference in labor costs between volunteer and career firefighters, some authors (e.g., Donovan 2006) have investigated the optimal number of volunteer or career firefighters an agency should have. The optimal number of each type depends on the severity of the fire season and the flexibility in ordering additional contract crews when needed (Donovan 2006). However, optimal numbers are sensitive to prior assumptions about fire season severity and whether there is other work available for career firefighters on days with no fire events.

Aerial Resources

Aerial resources are the most expensive resource, costing in the order of thousands or tens of thousands of dollars per hour to operate (approx. USD 1,000 to 12,000 per hour) (Thompson et al. 2013b; DFES 2017). The cost of aerial resources varies significantly depending on the type of resource (fixed wing aircraft vs. helicopter) and the size or water lifting capacity of the resource. In addition, the total cost of aerial resources may be substantially increased by standby costs, which are much higher for aerial resources than for ground resources (approx. USD 1,000 to 20,000 per day).

Aerial resources perform tasks such as ferrying crew and equipment, reconnaissance, and dropping retardant to slow the spread of the fire. An advantage of aerial resources is their ability to rapidly reach fires, and for this reason, they may be activated early to manage the fire and minimize its spread while ground resources get to it. However, even though their impact on fire spread during extreme weather conditions may be minimal (Plucinski et al. 2007; Calkin et al. 2014a), they are often used for extended periods of time in large fires with the aim to protect high value assets (Thompson et al. 2013b; Calkin et al. 2014b).

Initial Attack vs. Extended Attack

Suppression is best seen as a continuum where managers select the amount and type of suppression resources in response to the changing fire environment. Fire managers will typically order additional suppression resources with increasing fire size or increasing damage potential. For a variety of reasons, however, suppression is usually divided into two phases: initial attack and extended attack. Many fire management agencies establish targets and measure performance on the basis of initial attack. Agencies also often plan a “standard response” for initial attack, i.e., a common package of suppression resources in response to detected ignitions, whereas managers may use a wide variety of resource packages for extended attack (Thompson et al. 2013b; Stonesifer et al. 2016; Katuwal et al. 2017).

Initial attack is defined as the beginning of suppression activities, i.e., the actions carried out by the first resources to arrive at the fire. The first resources deployed to the fire are generally trying to contain a relatively small fire; thus suppression costs for initial attack tend to be relatively low. Most fires are stopped at the initial attack phase and the area burned is kept relatively small. However, when a fire cannot be extinguished by initial attack resources, the fire is considered to have escaped, and suppression activities go into the next phase, namely, extended attack. Several interacting factors affect the likelihood of success of initial attack, including weather conditions, fuel moisture, fuel load, vegetation type, fire danger index, distance to population centers, and initial fire area (Arienti et al. 2006; Plucinski 2012, 2013). In severe or more extreme fire conditions, the likelihood of suppression success is lower, and thus more resources may be deployed during the initial attack phase.

When fires escape initial attack and enter the extended attack phase, they are much more difficult to control, their size can extend to very large areas (Liang et al. 2008), and they have the potential for a much longer duration, especially in areas with vast wildlands with continuous fuels to burn (Plucinski 2013; Christman and Rollins 2015). During extended attack, more resources are deployed, often resulting in a large number of firefighters, fire trucks, machinery, evacuation personnel, and large aerial resources operating concurrently. This generates a heavy load in terms of operations, planning, logistics, and analytics and also requires the coordination between several government agencies (e.g., roads management, power distribution, police, etc.). Consequently, suppression costs for extended attack are usually significantly higher.

Drivers of Suppression Costs

Suppression costs generally account for more than half of total wildland fire management costs (SCRGSP 2015; Forest Service 2017). Given the considerable weight of suppression costs in wildfire management and their dramatic increase over the last two decades, a large body of research has focused on investigating the key drivers of suppression costs and what has contributed to their increase over time (Donahue 2004; Calkin et al. 2005; Gebert et al. 2007; Canton-Thompson et al. 2008; Donovan et al. 2008, 2011; Liang et al. 2008; Prestemon et al. 2008; Prestemon and Donovan 2008; Abt et al. 2009; Gebert and Black 2012; Gude et al. 2013; Thompson et al. 2013b; Hand et al. 2017).

One of the main drivers of suppression costs is fire size. The larger the fire, the longer it lasts and the more resources are needed to contain it. Fire size alone has been found to explain between 25% and 70% of suppression costs (Calkin et al. 2005; Gebert et al. 2007; Liang et al. 2008). And as annual area burned has increased and become more erratic since the 1990s, so have annual suppression costs (Calkin et al. 2005). Another important driver of suppression costs is private land or proximity to houses (Gebert et al. 2007; Liang et al. 2008; Gude et al. 2013). Fire managers deploy more resources when a fire approaches houses in an effort to protect life and property. Models predicting suppression expenditures have estimated that the percentage of private land burned explains about 58% of variation in suppression expenditures (Liang et al. 2008). Other models have estimated that a 1% change in the number of houses close to the fire increases daily suppression costs by 7% (Gude et al. 2013). In fact, it has been found that when fire size and private land are considered together, other variables have little effect on suppression expenditures (Liang et al. 2008).

Other factors have also been shown to influence suppression costs, including the suppression strategy implemented, fire agency management style, fire managers’ risk aversion, media and political pressure, and fuel and terrain conditions. For instance, after controlling for a variety of fire and landscape characteristics, Hand et al. (2017) found that some incident management teams (IMTs) in the USA implement suppression strategies that consistently use more suppression resources compared to other ITMs. Differences between ITMs’ suppression strategies account for ∼14% of variation in resource use. Gebert and Black (2012) also showed that management strategies affect suppression costs. Donahue (2004) showed that managerial factors, such as the degree of participatory management, the amount of firefighting training conducted, and the use of cost and performance measures, also influence suppression costs.

Increased risk aversion among fire managers has also contributed to the increase in suppression costs (Canton-Thompson et al. 2008) along with increased newspaper coverage of wildfires and greater political pressure (Donovan et al. 2011). Calkin et al. (2013) showed that, all else being equal, managers actually prefer higher cost strategies in light of social and institutional expectations and tend to use more expensive resources (e.g., aerial resources). Thus social and political pressures promote more aggressive wildfire management strategies (Donovan and Brown 2005, 2007).

Extreme terrain conditions may require the use of more expensive (aerial) resources or additional equipment for firefighters to be able to get to the fire, effectively increasing suppression expenditures (Gebert et al. 2007). Fuel type can also affect suppression expenditures. In general, fires starting in pine forests are more expensive than fires starting in other fuel types (Gebert et al. 2007).

Research shows that the increasing trend in suppression costs is likely to continue. Increases in the number of extreme weather days due to climate change is likely to result in more intense fires that will pose a serious challenge to fire agencies (Bowman et al. 2009; Barbero et al. 2015). Population growth in the wildland urban interface (WUI) may also contribute to further increases in suppression costs (Mell et al. 2010). Today, fires that do not involve people and property are rare (Thomas and Butry 2014), and since proximity to the WUI has been shown to influence suppression costs, some authors have hypothesized that the expansion of the WUI will continue to challenge fire managers and their budgets (Stockmann et al. 2010; Chas-Amil et al. 2013).

Suppression Cost Examples

In this section, some examples are presented that illustrate the similarities/differences in suppression policies and decision-making, suppression resource types and use, and suppression costs in three different fire-prone environments: Western Australia, the USA, and Spain.

Western Australia

The state of Western Australia (WA) is an interesting example because of its diverse vegetation, its size, and its small pockets of highly populated areas surrounded by large (mostly) uninhabited areas. Most suppression efforts and expenditures are concentrated in the southwest of WA, where the vast majority of the population is located. Fire management in this area is commonly characterized by early detection systems, rapid initial attack, and the application of prescribed burning during the cooler months of the year (Burrows and McCaw 2013).

In WA, fire location has a significant influence on the type of resources first sent to a fire, thereby affecting suppression costs. If a fire starts in the WUI (threatening human life and property), it automatically triggers the activation of aerial resources, which are left on standby until their deployment is deemed necessary. However, if a fire starts in rural areas, usually small (less expensive) units are dispatched to determine whether additional resources are needed. More recently, however, aerial resources have been increasingly used in many rural areas for initial attack (Plucinski and Pastor 2013).

Suppression expenditures in the southwest of WA have increased substantially in recent years (see Fig. 1), mostly due to (a) population growth, fires are more likely to happen close to population centers, intensifying suppression activities to protect people and property; (b) increasingly fragmented landscapes, making suppression strategies more complex and more difficult to implement (Burrows and McCaw 2013); (c) climate change, causing decreases in average annual rainfall and increases in temperature in the region (McCaw 2013); (d) aggressive suppression strategies, resulting in fuel accumulation that causes more intense wildfires (Williams et al. 2011); and (e) greater use of aerial resources, partly due to community pressures and expectations (Morgan 2009).
Fig 1

DBCA bushfire suppression costs 2003/2004 to 2012/2013. (Source: DPaW 2014)

USA

In the USA, the costs of wildfire suppression have increased remarkably in the past decades. This is particularly true for the U.S.D.A. Forest Service, which manages over 78 million hectares of national forests and grasslands, and is responsible for the majority of federal expenses on wildfire suppression (Hand et al. 2014). Over time, the escalating costs of suppression have resulted in reduced agency budgets for other programs, such as vegetation and watershed management, roads and trail maintenance, and land and fire management planning (Forest Service 2017). As a result, there has been a growing interest in determining ways to reduce suppression costs. One area of focus has been examining how fire managers make decisions surrounding suppression resource use. Based on this body of research, some authors have recommended that agencies strengthen incentives for cost containment, in part to counterbalance sociopolitical pressures that can push managers toward more aggressive suppression strategies (e.g., Donovan and Brown 2005; Donovan et al. 2011; Thompson et al. 2013a, 2015).

Increasingly, policymakers and managers have focused on how preventative management may reduce future costs, particularly in the realm of fuels and vegetation management. Several model-based studies have demonstrated the potential for fuel treatments to reduce future suppression costs, based on changes in factors such as fire size and burn severity (Thompson et al. 2013c; Taylor et al. 2015; Barnett et al. 2016). However, due to the relative rarity of wildfire in any given place at any given time, the rate at which fuel treatments encounter wildfires can be very low (Barnett et al. 2016; Thompson et al. 2017). The lower the odds a fuel treatment strategy will ever interact with fire, the lower the odds the upfront costs of treatment will be offset by future savings (Thompson and Anderson 2015). Further, fire economics theory does not necessarily indicate that increasing fuel treatment investments will always lead to reducing suppression costs. For example, treated areas may increase the marginal effectiveness of suppression, leading managers to invest in more suppression effort and thereby increasing cost (Rideout et al. 2008). Recent empirical research suggests this may be the case, finding that daily management costs increased when fires burned into previously treated areas (Naughton and Barnett 2017).

Although the costs of suppression remain important to the U.S.D.A. Forest Service, the agency’s focus in recent years has evolved from better managing costs to better managing firefighter exposure. This shift in emphasis reflects the priority of protecting human life and safety, and the agency has offered a new definition of success that stresses achieving objectives with the “least exposure necessary” (Forest Service 2017). Thus fire managers need to evaluate and balance trade-offs between suppression costs, firefighter exposure, and fire impacts. To help them design suppression response strategies that minimize firefighter exposure while achieving land and fire management objectives, recent research innovations include the development of tools that help managers better identify operationally relevant features such as safety zones, egress routes, potential fire control locations, and areas of high suppression difficulty (Rodriguez y Silva et al. 2014; Campbell et al. 2017; O’Connor et al. 2017).

Spain

In the last two decades, a substantial increase in suppression costs has been observed in Spain. Rodriguez y Silva and Molina (2016) compiled data for major fires occurring in the south of Spain between 1993 and 2012 and showed that, despite significant reductions in area burned (or only moderate increases), suppression costs have more than doubled. For instance, suppression costs for a large fire in Beas in 1993 were estimated at €180,660 (in 2012 €, USD 205,750) for a fire that burned 6180 hectares, while suppression costs for a fire in Coín-Marbella in 2012 were estimated at €510,000 (USD 580,829) for a fire that burned 8230 hectares. This increase in suppression costs is largely due to an increase in the use of aerial resources, not only in terms of the number of aircrafts used per fire but also in the number of hours flown by each aircraft (Rodriguez y Silva and Molina 2016). Furthermore, the cost per hour of fire duration and the cost per hectare burned have also increased, indicating an increase in the complexity and difficulty of suppression activities for large forest fires in the last 20 years.

Today, new challenges arise for incident-level operational management due to higher levels of uncertainty associated with the more intense and severe forest fires occurring in southern Spain. Operational management tends to respond to more severe fires with higher numbers of both ground and aerial resources in an effort to reduce operational uncertainty (see Table 1). As a result, suppression costs continue to rise. However, Spain is not an isolated case. A similar situation can be observed in other fire-prone countries in the EU (Montiel-Molina 2013).
Table 1

Average suppression costs and resource use per fire size

Fire size (ha)

Average number of hours to control the fire

Average area controlled per hour (ha/h)

Average suppression costs (euro)

Average suppression costs/ha (euro/ha)

Average number of ground resourcesa

Average number of aircrafts

1–100

13

6

66,971

1,706

22

8

101–200

18

12

78,006

562

25

8

201–300

18

14

140,820

526

28

10

301–400

18

26

157,574

456

34

11

401–500

16

38

177,467

394

41

12

501–600

19

32

197,995

343

42

14

601–700

21

29

182,645

318

43

16

701–800

53

15

261,179

291

61

18

801–900

61

18

312,433

279

69

22

901–1000

70

19

387,179

265

71

24

1001–3000

85

24

393,007

222

74

25

3001–5000

93

31

436,889

189

78

26

5001–8000

115

39

451,992

85

94

27

8001–10000

133

42

483,775

72

103

28

aGround resources include fire trucks, bulldozers and fire brigades.

Challenges and Concerns

Despite numerous studies investigating the costs of suppression, there is a need for more econometric analyses to better understand the effectiveness and productivity of different suppression resources and help fire managers with the allocation of resources (Plucinski et al. 2007; Plucinski 2012; Calkin et al. 2014b; Duff and Tolhurst 2015). The effectiveness of suppression efforts in containing wildfires is still poorly understood, particularly for large wildfires (Finney et al. 2009). To advance this field of research, fire agencies need to improve their data collection and reporting standards and improve their monitoring and evaluation of suppression strategies and associated costs (Thompson et al. 2013b). Without continuous monitoring and evaluation, and better data for economic analyses, fire agencies will not have the capacity to determine what is effective other than through anecdotal accounts. For improved decision-making, this needs to be determined through data-driven evidence. The lack of incentives for fire managers to limit costs is also related to this lack of monitoring (Thompson et al. 2013a; Calkin et al. 2014a).

In recent years, the use of aerial resources in wildfire suppression has increased in many fire-prone regions (Calkin et al. 2014b), with the aim being to stop the fires as soon as possible. However, in landscapes that have evolved with frequent fire (e.g., Australian eucalypt forests, US dry pine, and mixed conifer forests), there may be some disadvantages to aiming for the immediate suppression of all fires. In these landscapes, large investments in initial attack generate immediate benefits but may result in considerable fuel buildup, leading to larger and more expensive wildfires in the future. Thus, this strategy may not generate benefits in the long term, and large expenditures in initial attack may be defeating the purpose of fire management, which is to reduce the long-term impacts of wildfires on life, property, and the environment. In some landscapes, previous wildfires reduce the ignition and spread of subsequent wildfires (Parks et al. 2016) and allowing wildfires to burn may actually reduce future suppression costs and damages (Houtman et al. 2013). However, this may not be applicable to all fire-prone landscapes, where fire may not have been as frequent in the past (e.g., Canadian boreal forests), but forest fire activity is likely to increase due to climate change (Wotton et al. 2010).

Another issue is that the economic studies that have been conducted so far have rarely been applied. In essence, they provide a motivating or guiding basis for discussion and evaluation alternatives (Thompson et al. 2017), but they have not had a significant influence on how resource mix decisions are made at the incident or strategic levels (Duff and Tolhurst 2015). For this to happen, it will be necessary for these models to be expanded from experimental to broad operational implementation and have a degree of localized measurements, especially since costs and fire behavior drivers vary greatly between jurisdictions (Duff and Tolhurst 2015). Also, despite a large body of literature looking at the optimal use of suppression resources (i.e., optimal fleet size, most efficient mix of firefighting resources to be deployed) or investigating the key factors that affect initial attack success (Donovan and Rideout 2003; Fried et al. 2006; Plucinski 2012), none of these studies have included economics in their analysis, and only a handful of papers have looked at the implications for suppression expenditures and the short- and long-term benefits generated by different resource mixes (Gebert et al. 2007; Petrovic and Carlson 2012; Calkin et al. 2014b). Therefore, there is a need to better incorporate economic efficiency criteria into optimization modelling efforts, for the research to be applied more and influence resource mix decisions.

Summary

This contribution explored the cost of wildfire suppression activities, examining the different types of resources used, the two stages of suppression, the drivers of suppression costs, and the current challenges in wildfire suppression economics research. One key observation in suppression economics research is that suppression costs have increased substantially in most fire-prone landscapes in the last two decades, mostly due to increases in the frequency and severity of large wildfires and increases in the use of aerial resources (the most expensive type of resource) to suppress these fires. As a result, this topic has been the subject of extensive research. But despite extensive research on the costs of suppression and the drivers of suppression costs, operational adoption of the frameworks and decision support systems developed remains limited. Existing models may need to be expanded to include broader operational implementation approaches before efficiency gains in suppression are observed.

Cross-References

References

  1. Abt KL, Prestemon JP, Gebert KM (2009) Wildfire suppression cost forecasts for the US Forest Service. J For 107(4):173–178Google Scholar
  2. 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. Can J For Res 36(12):3155–3166Google Scholar
  3. Barbero R, Abatzoglou JT, Larkin NK, Kolden CA, Stocks B (2015) Climate change presents increased potential for very large fires in the contiguous United States. Int J Wildland Fire 24(7):892–899Google Scholar
  4. Barnett K, Parks SA, Miller C, Naughton HT (2016) Beyond fuel treatment effectiveness: characterizing interactions between fire and treatments in the US. Forests 7(10):237Google Scholar
  5. Bowman DMJS, Balch JK, Artaxo P, Bond WJ, Carlson JM, Cochrane MA, D’Antonio CM, DeFries RS, Doyle JC, Harrison SP, Johnston FH, Keeley JE, Krawchuk MA, Kull CA, Marston JB, Moritz MA, Prentice IC, Roos CI, Scott AC, Swetnam TW, van der Werf GR, Pyne SJ (2009) Fire in the earth system. Science 324(5926):481–484Google Scholar
  6. Burrows N, McCaw L (2013) Prescribed burning in southwestern Australian forests. Front Ecol Environ 11(s1):e25–e34Google Scholar
  7. Calkin DE, Gebert KM, Jones G, Neilson RP (2005) Forest service large fire area burned and suppression expenditure trends, 1970–2002. J For 103(4):179–183Google Scholar
  8. Calkin DE, Venn T, Wibbenmeyer M, Thompson MP (2013) Estimating US federal wildland fire managers’ preferences toward competing strategic suppression objectives. Int J Wildland Fire 22(2):212–222Google Scholar
  9. Calkin DE, Cohen JD, Finney MA, Thompson MP (2014a) How risk management can prevent future wildfire disasters in the wildland-urban interface. Proc Natl Acad Sci U S A 111(2):746–751Google Scholar
  10. Calkin DE, Stonesifer CS, Thompson MP, McHugh CW (2014b) Large airtanker use and outcomes in suppressing wildland fires in the United States. Int J Wildland Fire 23(2):259–271Google Scholar
  11. Campbell MJ, Dennison PE, Butler BW (2017) Safe separation distance score: a new metric for evaluating wildland firefighter safety zones using LiDAR. Int J Geogr Inf Sci 31(7):1448–1466Google Scholar
  12. Canton-Thompson J, Gebert KM, Thompson B, Jones G, Calkin D, Donovan GH (2008) External human factors in incident management team decision-making and their effect on large fire suppression expenditures. J For 106(8):416–424Google Scholar
  13. Chas-Amil ML, Touza J, Garcia-Martinez E (2013) Forest fires in the wildland-urban interface: A spatial analysis of forest fragmentation and human impacts. Appl Geogr 43:127–137Google Scholar
  14. Christman L, Rollins K (2015) The economic benefit of localised, short-term, wildfire-potential information. Int J Wildland Fire 24(7):974–982Google Scholar
  15. Department of Fire and Emergency Services (DFES) (2017) Emergency operations equipment costs. Unpublished dataGoogle Scholar
  16. Department of Parks and Wildlife (DPaW) (2014) Bushfire suppression expenditures in the three forest regions of the south-west of WA. Unpublished data. Government of Western Australia, Perth. DPaW is now the Department of Biodiversity, Conservation and Attractions (DBCA)Google Scholar
  17. Donahue AK (2004) The influence of management on the cost of fire protection. J Policy Anal Manage 23(1):71–92MathSciNetGoogle Scholar
  18. Donovan GH (2006) Determining the optimal mix of federal and contract fire crews: a case study from the Pacific Northwest. Ecol Model 194(4):372–378Google Scholar
  19. Donovan GH, Brown TC (2005) An alternative incentive structure for wildfire management on National Forest Land. For Sci 51(5):387–395Google Scholar
  20. Donovan GH, Brown TC (2007) Be careful what you wish for: the legacy of Smokey bear. Front Ecol Environ 5(2):73–79Google Scholar
  21. Donovan GH, Rideout DB (2003) An integer programming model to optimize resource allocation for wildfire containment. For Sci 49(2):331–335Google Scholar
  22. Donovan GH, Noordijk P, Radeloff VC Gonzalez-Caban A (ed) (2008) Estimating the impact of proximity of houses on wildfire suppression costs in Oregon and Washington, proceedings of the second international symposium on fire economics, planning, and policy: a global view, Albany, 19–22 Apr 2004. (U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, General Technical Report PSW-GTR-208Google Scholar
  23. Donovan GH, Prestemon JP, Gebert K (2011) The effect of newspaper coverage and political pressure on wildfire suppression costs. Soc Nat Resour 24(8):785–798Google Scholar
  24. Duff TJ, Tolhurst KG (2015) Operational wildfire suppression modelling: A review evaluating development, state of the art and future directions. Int J Wildland Fire 24(6):735–748Google Scholar
  25. FAMWEB (2018) National Fire and Aviation Management Web Applications: SIT Reports. Available at https://fam.nwcg.gov/fam-web/. Accessed Dec 2018
  26. Finney MA, Grenfell IC, McHugh CW (2009) Modeling containment of large wildfires using generalized linear mixed-model analysis. For Sci 55(3):249–255Google Scholar
  27. Flannigan M, Logan K, Amiro B, Skinner W, Stocks B (2005) Future area burned in Canada. Clim Chang 72(1):1–16Google Scholar
  28. Forest Service – U.S. Department of Agriculture (2017) Forest Service Manual (FSM) Directive Issuances, Series 5000 — Protection and Development. Available at: https://www.fs.fed.us/im/directives/dughtml/fsm_5000.html. Accessed on 10 Apr 2018
  29. Fried JS, Gilless JK, Spero J (2006) Analysing initial attack on wildland fires using stochastic simulation. Int J Wildland Fire 15(1):137–146Google Scholar
  30. Gebert KM, Black AE (2012) Effect of suppression strategies on federal wildland fire expenditures. J For 110(2):65–73Google Scholar
  31. Gebert KM, Calkin DE, Yoder J (2007) Estimating suppression expenditures for individual large wildland fires. West J Appl For 22(3):188–196Google Scholar
  32. Gebert KM, Calkin DE, Huggett RJ, Abt KL (2008) Economic analysis of federal wildfire management programs. In: Holmes TP, Prestemon JP, Abt K (eds) The economics of forest disturbances: wildfires, storms and invasive species. Springer, Dordrecht, pp 295–322Google Scholar
  33. Gude PH, Jones K, Rasker R, Greenwood MC (2013) Evidence for the effect of homes on wildfire suppression costs. Int J Wildland Fire 22(4):537–548Google Scholar
  34. Hand MS, Gebert KM, Liang J, Calkin DE, Thompson MP, Zhou M (2014) Economics of wildfire management: the development and application of suppression expenditure models. Springer, DordrechtGoogle Scholar
  35. Hand MS, Thompson MP, Calkin DE (2016) Examining heterogeneity and wildfire management expenditures using spatially and temporally descriptive data. J For Econ 22:80–102Google Scholar
  36. Hand M, Katuwal H, Calkin DE, Thompson MP (2017) The influence of incident management teams on the deployment of wildfire suppression resources. Int J Wildland Fire 26(7):615–629Google Scholar
  37. Haynes HJG, Stein GP (2017) U.S. Fire Department Profile–2015. National Fire Protection Association, QuincyGoogle Scholar
  38. Houtman RM, Montgomery CA, Gagnon AR, Calkin DE, Dietterich TG, McGregor S, Crowley M (2013) Allowing a wildfire to burn: estimating the effect on future fire suppression costs. Int J Wildland Fire 22(7):871–882Google Scholar
  39. Katuwal H, Dunn CJ, Calkin DE (2017) Characterising resource use and potential inefficiencies during large-fire suppression in the western US. Int J Wildland Fire 26(7):604–614Google Scholar
  40. Liang J, Calkin DE, Gebert KM, Venn TJ, Silverstein RP (2008) Factors influencing large wildland fire suppression expenditures. Int J Wildland Fire 17(5):650–659Google Scholar
  41. McCaw WL (2013) Managing forest fuels using prescribed fire – a perspective from southern Australia. For Ecol Manag 294:217–224Google Scholar
  42. McLennan J, Birch A, Cowlishaw S, Hayes P (2009) Maintaining volunteer firefighter numbers: Adding value to the retention coin. Aust J Emerg Manage 24(2):40–47Google Scholar
  43. Mell WE, Manzello SL, Maranghides A, Butry D, Rehm RG (2010) The wildland–urban interface fire problem – current approaches and research needs. Int J Wildland Fire 19(2):238–251Google Scholar
  44. Montiel-Molina C (2013) Comparative assessment of wildland fire legislation and policies in the European Union: towards a fire framework directive. Forest Policy Econ 29:1–6Google Scholar
  45. Morgan G (2009) Asia and Australasia wildfire management: a regional perspective. In: Gonzalez-Caban A (Technical Coordinator) Proceedings of the third international symposium on fire economics, planning, and policy: common problems and approaches, Carolina, Puerto Rico, 29 Apr−2 May 2008, pp 8–23. (General Technical Report PSW-GTR-227, U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station)Google Scholar
  46. Naughton HT, Barnett K (2017) Final report: Spatiotemporal evaluation of fuel treatment and previous wildfire effects on suppression costs. Joint fire science program, project ID: 14-5-01-25. U.S. GovernmentGoogle Scholar
  47. O’Connor CD, Calkin DE, Thompson MP (2017) An empirical machine learning method for predicting potential fire control locations for pre-fire planning and operational fire management. Int J Wildland Fire 26(7):587–597Google Scholar
  48. Parks SA, Miller C, Holsinger LM, Baggett LS, Bird BJ (2016) Wildland fire limits subsequent fire occurrence. Int J Wildland Fire 25(2):182–190Google Scholar
  49. Petrovic N, Carlson JM (2012) A decision-making framework for wildfire suppression. Int J Wildland Fire 21(8):927–937Google Scholar
  50. Plucinski MP (2012) Factors affecting containment area and time of Australian Forest fires featuring aerial suppression. For Sci 58(4):390–398Google Scholar
  51. Plucinski MP (2013) Modelling the probability of Australian grassfires escaping initial attack to aid deployment decisions. Int J Wildland Fire 22(4):459–468Google Scholar
  52. Plucinski MP, Pastor E (2013) Criteria and methodology for evaluating aerial wildfire suppression. Int J Wildland Fire 22(8):1144–1154Google Scholar
  53. Plucinski MP, Gould JS, McCarthy GJ, Hollis JJ (2007) The effectiveness and efficiency of aerial fire-fighting in Australia: Part 1. Bushfire CRC Technical Report No A0701. Available at http://www.bushfirecrc.com/sites/default/files/managed/resource/aerial_suppression_report_final_web.pdf.
  54. Prestemon JP, Donovan GH (2008) Forecasting resource-allocation decisions under climate uncertainty: fire suppression with assessment of net benefits of research. Am J Agric Econ 90(4):1118–1129Google Scholar
  55. Prestemon JP, Abt K, Gebert K (2008) Suppression cost forecasts in advance of wildfire seasons. For Sci 54(4):381–396Google Scholar
  56. Rideout DB, Wei Y, Kirsch AG, Botti SJ (2008) Toward a unified economic theory of fire program analysis with strategies for empirical modelling. In: Holmes TP, Prestemon JP, Abt K (eds) The economics of forest disturbances: wildfires, storms and invasive species. Springer, Dordrecht, pp 361–380Google Scholar
  57. Rodriguez y Silva F, Molina JR (2016) Los incendios forestales en España en un contexto de cambio climático: Información y herramientas para la adaptación (INFOADAPT). Memoria final del proyecto. Fundación Biodiversidad. Ministerio de Agricultura, Pesca, Alimentación y Medio Ambiente. Universidad de Castilla-La ManchaGoogle Scholar
  58. Rodriguez y Silva F, Molina JR, Rodriguez J (2014) The efficiency analysis of the fire control operations using VISUAL-SEVEIF tool. In: Viegas DX (ed) Advances in forest fire research. Imprensa da Universidade de Coimbra, Coimbra, pp 1883–1894Google Scholar
  59. Steering Committee for the Review of Government Service Provision (SCRGSP) (2015) Report on Government Services 2015, vol. D, Chapter 9, Fire and ambulance services. Productivity Commission, Canberra ACT. Available at https://www.pc.gov.au/research/ongoing/report-on-government-services/2015
  60. Stockmann KD, Burchfield J, Calkin DE, Venn TJ (2010) Guiding preventative wildland fire mitigation policy and decisions with an economic modeling system. Forest Policy Econ 12(2):147–154Google Scholar
  61. Stonesifer CS, Calkin DE, Thompson MP, Stockmann KD (2016) Fighting fire in the heat of the day: an analysis of operational and environmental conditions of use for large airtankers in United States fire suppression. Int J Wildland Fire 25(5):520–533Google Scholar
  62. Taylor MH, Meador AJS, Kim Y-S, Rollins K, Will H (2015) The economics of ecological restoration and hazardous fuel reduction treatments in the ponderosa pine Forest ecosystem. For Sci 61(6):988–1008Google Scholar
  63. Thomas DS, Butry DT (2014) Areas of the U.S. wildland-urban interface threatened by wildfire during the 2001-2010 decade. Nat Hazards 71(3):1561–1585Google Scholar
  64. Thompson MP (2014) Social, institutional, and psychological factors affecting wildfire incident decision making. Soc Nat Resour 27(6):1–9MathSciNetGoogle Scholar
  65. Thompson MP, Anderson NM (2015) Modeling fuel treatment impacts on fire suppression cost savings: a review. Calif Agric 69(3):164–170Google Scholar
  66. Thompson MP, Calkin DE, Finney MA, Gebert KM, Hand MS (2013a) A risk-based approach to Wildland fire budgetary planning. For Sci 59(1):63–77Google Scholar
  67. Thompson MP, Calkin DE, Herynk J, McHugh CW, Short KC (2013b) Airtankers and wildfire management in the US Forest Service: examining data availability and exploring usage and cost trends. Int J Wildland Fire 22(2):223–233Google Scholar
  68. Thompson MP, Vaillant NM, Haas JR, Gebert KM, Stockmann KD (2013c) Quantifying the potential impacts of fuel treatments on wildfire suppression costs. J For 111(1):49–58Google Scholar
  69. Thompson MP, Haas JR, Finney MA, Calkin DE, Hand MS, Browne MJ, Halek M, Short KC, Grenfell IC (2015) Development and application of a probabilistic method for wildfire suppression cost modeling. Forest Policy Econ 50:249–258Google Scholar
  70. Thompson MP, Riley KL, Loeffler D, Haas JR (2017) Modeling fuel treatment leverage: Encounter rates, risk reduction, and suppression cost impacts. Forests 8(12):469Google Scholar
  71. Williams J, Albright D, Hoffmann AA, Eritsov A, Moore PF, Mendes De Morais JC, Leonard M, San Miguel-Ayanz J, Xanthopoulos G, van Lierop P (2011) Findings and implications from a coarse-scale global assessment of recent selected mega-fires. In: FAO at the Vth international Wildland fire conference, Sun City, 9–13 May 2011, pp 27–40. (Forestry Department, Fire Management Division Working Paper FM/27/E, Food and Agriculture Organization of the United NationsGoogle Scholar
  72. Wotton BM, Nock CA, Flannigan MD (2010) Forest fire occurrence and climate change in Canada. Int J Wildland Fire 19(3):253–271Google Scholar

Copyright information

© This is a U.S. Government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  • Veronique Florec
    • 1
    Email author
  • Matthew P. Thompson
    • 2
  • Francisco Rodríguez y Silva
    • 3
  1. 1.Centre for Environmental Economics and PolicyThe University of Western AustraliaPerthAustralia
  2. 2.U.S.D.A. Forest Service (Rocky Mountain Research Station)Fort CollinsUSA
  3. 3.Forest Engineering Department, Forest Fire LaboratoryUniversity of CordobaCórdobaSpain

Section editors and affiliations

  • Raphaele Blanchi
    • 1
  1. 1.Land & WaterCSIROMelbourneAustralia