economics
Effect of Suppression Strategies on Federal
Wildland Fire Expenditures
ABSTRACT
Krista M. Gebert and Anne E. Black
Policymakers and decisionmakers alike have suggested that the use of less aggressive suppression
strategies for wildland fires might help stem the tide of rising emergency wildland fire expenditures.
However, the interplay of wildland fire management decisions and expenditures is not well understood.
In this study, we assess the effect of different fire management objectives and strategies on
expenditures. Analyses of 1,330 US Forest Service and US Department of Interior fires from fiscal years
(FYs) 2006 –2008 indicate that management objectives and strategies do affect costs, but the results
vary both by agency and by the cost metric used. For instance, although less aggressive protection
strategies may result in a lower cost per acre or daily cost, increased acreages or longer duration
associated with less aggressive strategies may lead to total fire management costs that are either higher
than or equal to more aggressive strategies.
Keywords: wildland fire, regression analysis, costs, protection, resource benefit
T
he federal land-management agencies of the United States (the US
Forest Service and the US Department of Interior [USDOI]) are struggling to
deal with a changing wildland fire environment. Over the past 20 years, both the magnitude and the variability of the area burned
by wildfire have increased substantially
(Calkin et al. 2005, Westering et al. 2006,
Gebert et al. 2007). The causal factors, however, are somewhat under debate with the
increase attributed to past suppression efforts (Arno and Brown 1991), drought conditions (Westerling et al. 2002, 2003, Crimmins and Comrie 2004, Gedalof et al. 2005,
Collins et al. 2006), and climate change
(Flannigan et al. 2000, Westerling et al.
2006). Rapid population growth in the
wildland– urban interface creates further
suppression challenges (Cardille et al. 2001,
Gill and Stephens 2009, Mozumder et al.
2009). Coincident with these trends, 10year average federal suppression expenditures have increased from $620 million 10
years ago (1990 through 1999) to $1,580
million (2000 –2009); inflated to constant
2009 dollars.
Increasing expenditures have increased
scrutiny of the federal fire management programs by the public, Congress, and government oversight agencies such as the Office of
Management and Budget, the Government
Accountability Office, and the Office of the
Inspector General (OIG). In the past several
years, a number of reports/audits by these
oversight agencies have recommended that
the land-management agencies reexamine
increased efficiency in fire management ef-
forts. Both policymakers and decisionmakers have suggested that the appropriate use of
less aggressive fire management strategies
may reduce overall costs (OIG 2007, USDA
and USDOI 2006, Northern Rockies Coordination Group 2007) while enabling land
managers to meet land-management objectives, such as reducing hazardous fuels and
restoring ecosystems. For instance, in fiscal
year (FY) 2007, the US Forest Service reemphasized the 1995 Federal Wildland Policy’s
(Zimmerman 1999) direction for managers
to use the full array of fire management responses to achieve efficiency—from monitoring through full perimeter control—instead of defaulting to full perimeter control.
Little information exists regarding the
influence of fire management strategy on
wildland fire suppression expenditures. Existing studies have focused on estimating
suppression expenditures, either in aggregate or for individual wildfires (GonzalesCaban 1984, Gebert et al. 2007, Prestemon
et al. 2008, Abt et al. 2009), or assessing the
effect of various biophysical and human factors on suppression costs (Donovan 2004,
Donovan et al. 2011). None have included
variables related to the fire management
strategy used on the fire. This study (funded
by the Joint Fire Science Program) sought to
assess the effect of fire management strategies on federal emergency wildland fire management expenditures. Specifically, do less
Manuscript received August 19, 2010; accepted June 15, 2011; published online February 2, 2012; http://dx.doi.org/10.5849/jof.10-068.
Krista M. Gebert (kgebert@fs.fed.us) is Regional Economist, US Forest Service, Northern Region, 200 East Broadway, PO Box 7669, Missoula, MT 59802. Anne
E. Black (aeblack@fs.fed.us) is Social Science Analyst, US Forest Service, Rocky Mountain Research Station, 800 East Beckwith, PO Box 8089, Missoula, MT
59807. The research reported here was conducted as part of a broader study to assess “The interplay of Appropriate Management Response, suppression costs,
community interaction, and organizational performance” undertaken by several researchers from the US Forest Service Rocky Mountain Research Station, the
Northern Research Station, and North Carolina State University (Black et al. 2010). Funding for this project was provided by the Joint Fire Science Program, and
the US Forest Service.
Journal of Forestry • March 2012
65
aggressive strategies result in lower costs for
the federal agencies than does a strategy of
full perimeter control?
Methods
To answer this question, we assigned
federally managed large fires (300⫹ ac;
USDA: US Forest Service; USDOI: Bureau
of Land Management [BLM], National
Park Service [NPS], and Fish and Wildlife
Service [FWS]) between FY 2006 and 2008
to one of five fire management strategies. Because of inaccuracies in records indicating
daily strategy, we failed to accept the individual fire as the unit of observation [1]. We
analyzed the effect of suppression strategy
on costs using both a comparison of means
approach and a regression analysis. Both of
these follow preexisting work that group
fires geographically for the US Forest Service
(eastern and western United States) and by
each of the listed DOI agencies. This also
allowed us to take advantage of and build
on regression models developed for predicting costs of individual large wildland fires
(Gebert et al. 2007).
Data Collection
Our data set is drawn from the US Forest Service’s Rocky Mountain Research Station’s (RMRS) database of fire expenditure
and fire characteristic information. The
RMRS has collected federal wildland fire
suppression expenditure data for large wildland fires (300⫹ ac) since 1998 for the US
Forest Service and since 2004 for the DOI
(see Gebert et al. 2007 for a description of
the data collection process and data collected). Although many fires may be managed in cooperation with state fire agencies,
this database captures only federal expenditures; therefore, fires with a great deal of
state involvement were removed from the
analysis set. In addition, because emphasis
has been on suppression, few resource benefit objective fires are captured in the data set.
Our subset (the most current information at
the time the study was conducted) includes
1,397 fires (574 US Forest Service fires and
823 DOI fires).
To develop our classification scheme,
we worked closely with fire managers. After
national fire managers from the US Forest
Service and DOI identified a short list of fire
management objectives and strategies, we
validated these with a larger set of fire management personnel (DOI Fire Directors and
US Forest Service Regional Fire Planners).
The result was two primary objectives—pro66
Journal of Forestry • March 2012
tection and resource benefit—and five strategies. (See Appendix for the full description
of the objectives and strategies.)
Briefly, the protection objective centers
on exclusion of unwanted fire. The three
strategies associated with a protection objective ranged from aggressively fighting the
fire (direct suppression) to more of a “herding” approach (limited suppression). The
objective of direct suppression is to minimize burned area. Modified suppression
seeks control of an unwanted fire but not
necessarily minimizing burned area. Attention to probability of success, cost-effectiveness, and so forth, weigh heavily. Limited
suppression seeks to limit activities to those
necessary to protect a specific point or specific area from fire, usually by directing the
fire movement away from or around these
areas.
The resource benefit objective centers
on achieving beneficial ecological effects
from fire. The two associated strategies, area
management and area monitoring, differ according to whether or not actions were taken
to affect fire spread. Under a strategy of area
management, line construction or other tactics to delay, direct, or check fire spread may
be used. Under an area monitoring strategy,
no actions are taken.
We asked field personnel to classify
each fire in the database within their jurisdiction, using a spreadsheet containing
identifying information for each fire (e.g.,
fire name, location, date, and acreage) followed by drop down boxes for predominant
objective/strategy. The spreadsheet, which
included detailed instructions and definitions, was sent out to the field as a formal
data request by the US Forest Service’s
Washington office and DOI fire management personnel at the National Interagency
Fire Center.
Analyses
Separate analyses were performed for
the western US Forest Service regions (Regions 1– 6), the eastern US Forest Service
regions (Regions 8 and 9), and each of three
DOI agencies. This article focuses primarily
on the models and results for the western US
Forest Service regions. US Forest Service
fires in the western United States are responsible for the vast majority of federal wildland
suppression expenditures. From 1985 to
2009, US Forest Service suppression expenditures in the western United States accounted for 80% of US Forest Service suppression expenditures, expenditures in the
eastern United States accounted for 6%, and
the remaining 14% was associated with national shared resources. When looking at
total federal suppression expenditures (US
Forest Service and DOI), US Forest Service
suppression expenditures in the eastern
United States combined with all DOI suppression expenditures accounted for less
than 30% of total expenditures. Additionally, the results for the eastern US Forest Service regions and the DOI agencies were often insignificant.
To assess the effect of differing management strategies on costs, we used two different approaches: an analysis of means (using a
general linear mixed model approach) and
regression analysis. Regression analysis allows one to assess the effect of a variable
(such as fire management strategy) on the
dependent variable (cost), holding all other
variables constant. Therefore, one can assess
the effect of strategy on expenditures, independently of the other explanatory variables;
i.e., what is the effect of strategy on fires with
similar characteristics in terms of size, duration, and so forth. Although this is an important question, fires are extremely heterogeneous, and in the real world, all other
things are not held constant. In fact, in another part of our study (Black et al. 2010),
those interviewed (incident commanders,
line officers, and stakeholders) felt that less
aggressive strategies may not save money
overall because fires become larger and last
longer. Therefore, we first assessed the effect
of strategy by looking at how average costs,
size, and duration differed by strategy.
Determining Affect of Strategy on
Average Costs. To assess the affect of strategy on average costs (per fire, per acre, and
per day) as well as on size and duration of the
incidents, we used the Generalized Linear
Mixed Models (GLIMMIX) procedure in
SAS because our data did not meet the necessary assumptions for using analysis of variance (ANOVA; data was not normally distributed and there was not equal variance
among groups; McCulloch and Searle
2001). General linear models (GLM) allow
more flexibility in the distribution of the dependent variable than does ANOVA analysis. Specifically, data modeled as a GLM can
be a member of any continuous or discrete
exponential family distribution (e.g., normal, exponential, lognormal, Poisson, binomial, negative binomial, and more).
Conventional wisdom holds that the
use of less aggressive strategies will “save” the
federal agencies money— generally through
Table 1. Breakdown of large (300ⴙ ac) US Forest Service fires in study data set by US Forest Service region and objective/strategy
type.
Geographic area US Forest Service region
Objective and
strategy
Protection
Direct suppression
Protection
Modified suppression
Protection
Limited suppression
Resource benefita
Area management
Resource benefita
Area monitoring
Missing
Total
a
1
2
Western
3
4
Eastern
5
6
22
28
79
23
163
141
6
147
310
12
7
17
22
42
21
121
—
22
22
143
28
2
6
31
14
9
90
—
2
2
92
—
2
4
8
2
—
16
—
—
—
16
—
—
—
2
3
—
5
—
—
—
5
1
46
2
19
2
51
—
91
3
143
—
53
8
403
—
141
—
30
—
171
8
574
5
6
Western total
8
9
Eastern total
National
Only included resource benefit fires that at some point were also a suppression event.
less use of aviation and ground resources.
Commonsense suggests that cost per acre
should decrease as strategies become less aggressive for two reasons. First, more aggressive strategies are intended to minimize the
burned area whereas less aggressive protection strategies are not. Under full perimeter
control (or direct suppression), we should
see smaller fires than under other strategies.
This alone would cause the relative cost per
acre to go up even if the total cost of managing the fire were held constant. Additionally, less aggressive strategies likely mean
fewer resources (equipment and human) are
used, leading to lower per acre costs even if
acreage is similar. Therefore, we hypothesized that cost per acre would be lower for
less aggressive strategies and that acreage
would increase as strategies became less aggressive (moving down the objective spectrum from direct suppression to resource
benefit). We also hypothesized that daily
cost would be lower for less aggressive strategies (for similar reasons as per acre costs)
and that duration would likely be longer.
Regarding total cost, conventional wisdom,
such as that espoused by the oversight agencies, is that less aggressive strategies will decrease suppression costs, not just per unit
costs. Therefore, going with conventional
wisdom, our hypothesis was that average fire
cost would decrease for less aggressive strategies, although we were skeptical given the
results of the qualitative interviews mentioned previously.
Determining Effect of Strategy on
Costs When Other Variables Are Held
Constant. To test the effect of strategy for
fires with similar characteristics, we used
two-stage least squares (2SLS) regression
analysis. We used the basic model specification and variables used by Gebert et. al.
(2007) and Donovan et al. (2011). Variations of these models are currently used in
both the Wildland Fire Decision Support
System (WFDSS) and as an after-season performance measure [2]. Separate equations
have been built for each of the DOI agencies
and the US Forest Service (western and eastern United States). The dependent variable
for each is federal wildland fire suppression
expenditures per acre. The independent
variables consist of environmental characteristics of the fire environment (slope, elevation, aspect, fire intensity level, energy release component, and fuel type), values at
risk (distance to the nearest town, total
housing value within 20 mi of ignition, and
whether or not the fire started in a wilderness
area and the distance to the boundary), and
geographic region. We modified these to test
the effect of strategy on federal expenditures
by creating an additional set of dummy (zero/one) variables reflecting strategy type.
The 2SLS regression models were estimated to account for problems that arise
when the dependent variable and one (or
more) of the independent variables used in
the regression are hypothesized to have a
two-way relationship. In conventional regression analysis (ordinary least squares
[OLS]), causation is assumed to flow from
the independent variables to the dependent
variable. The 2SLS is a statistical technique
used when the effect is bidirectional. For instance, in this case, because fire size and cost
per acre are likely simultaneously determined, fire size is termed an endogenous
variable. That is, more acres should affect
management costs, and more management
effort per acre is intended to affect fire size.
In many cases, if OLS is used to estimate
such equations, the results will be biased. In
2SLS, instruments (variables that are correlated with the endogenous variable [size],
but not with the dependent variable [cost
per acre]) are used in the estimation procedure to account for the variation in the endogenous variable, thus producing less biased estimates (see Greene 1993). Statistical
tests, such as the Hausman test (Hausman
1978), can be used to determine whether or
not OLS or 2SLS should be used. In this
study, there were three variables (or classes of
variable) that were potentially endogenous:
size, duration, and suppression strategy, so
each were tested (jointly and independently)
for possible endogeneity.
Final model specification used a natural
log transformation for the dependent variable (federal management expenditures per
acre) as well as for most of the independent
variables, with the exception of categorical
variables. This model provided the best fit of
the data and mitigated problems with heteroskedaticity among residuals. The GLM was
Ln($/ac) ⫽ B0 ⫹ Bi ⴱ ln共X兲 ⫹ Bj ⴱ Z,
where X are the fire characteristics to which
we applied the natural log transformation
(e.g., acres and distances), and Z are the variables that were not transformed. Models
were also tested for multicollinearity, using
the variance inflation factor (VIF) values. All
VIFs were less than 5 (most below 2), indicating that multicollinearity was not strong
enough to warrant correction (Kleinbaum et
al. 2008). For all analyses, statistical significance was assessed at the 0.05 level.
Our hypothesis was that cost per acre
Journal of Forestry • March 2012
67
would decrease as fire management strategies became less aggressive, especially because other variables affecting cost were being held constant. We also hypothesized that
acres and duration were endogenous. We
were more skeptical about the endogeneity
of strategy because although there is pressure
from oversight agencies to affect costs by use
of less aggressive strategies, the objective and
strategy chosen is based on that which is expected to best meet land-management objectives— generally ecological condition,
plus human safety and protection of private
property. Although the strategy chosen to
meet management objectives may lead to a
higher or lower cost per acre (indeed, the
central question here), in practice, the cost
per acre should not affect the strategy
chosen.
Table 2. Breakdown of large (300ⴙ ac) DOI fires in data set by agency, objective, and
strategy.
Agency
Objective and strategy
Protection
Direct suppression
Modified suppression
Limited suppression
Resource benefita
Area management
Area monitoring
Missing
Total
a
NPS
FWS
BLM
Total
36
27
14
68
39
11
448
36
2
552
102
27
5
1
47
130
—
1
57
176
29
2
—
517
34
4
104
823
Only included resource benefit fires that at some point were also a suppression event.
Results
The response rate to the data request
was extremely good, probably because it was
sent out as a formal request by fire management rather than as a research request—
98% for the US Forest Service and 87% for
the DOI. Because the master data set includes only fires that at some point were
managed as under a protection objective, the
only resource benefit fires in our analysis are
those that were eventually converted to protection objective or managed for multiple
objectives. Thus, we lumped both area management and area monitoring objectives into
a “mixed resource benefit” category.
Tables 1 and 2 show the breakdown of
fires by objective/strategy for each of the
agencies. For both the US Forest Service and
the DOI, the majority of fires were classified
as having a predominant objective of protection and strategy of direct suppression (55
and 77%, respectively). However, the breakdown varies a great deal by region (for the
US Forest Service) and agency (for the
DOI). For the US Forest Service, the high
proportion of the most aggressive strategy
was largely influenced by the East, where
147 of the 171 fires were classified as direct
suppression. For the western US Forest Service fires, there was more of a mix of strategies, with 41% of the fires classified as direct
suppression, 31% as modified suppression,
23% as limited suppression, and 5% as
mixed resource benefit. For the DOI, the
predominance of direct suppression was
driven by the BLM, which classified 448 of
their 517 fires as direct suppression. The
classifications for the NPS and FWS were
more varied.
68
Journal of Forestry • March 2012
Figure 1. Average total suppression expenditures by predominant strategy (large [300ⴙ ac
fires] US Forest Service Regions 1– 6).
Effect of Strategy on Average
Fire Cost, Duration, and Size
To begin to understand the effect of
differing fire management strategies on
costs, we analyzed the differences in means
among strategies for several fire outcomes—
cost, size, and duration. Figure 1 shows the
differences in average total fire costs for the
western US Forest Service regions by strategy. Because of the small sample size and
large variation in costs for the mixed resource benefit fires, the confidence interval
for that strategy is very large, leading to statistically insignificant results when compared with the other strategies (this can be
seen by the overlap in the confidence interval of the mixed resource benefit fires as op-
posed to the other strategies). However, we
still included the mixed resource benefit to
show average cost for that category. As we
hypothesized, strategy had a statistically significant effect on total fire cost (P ⫽
0.0106), driven by the modified suppression
events. Modified suppression events had a
much higher average cost ($7.3 million)
than the other strategies, which ranged from
$4.3 million for direct suppression to $3.6
million for mixed resource benefit fires. Differences between modified suppression and
direct suppression and modified suppression and limited suppression were highly
significant (P ⫽ 0.02 or less). Direct suppression, limited suppression, and mixed
resource benefit, with similar average total
costs, were not statistically different from
one another.
We also analyzed per unit costs (per
acre and per day) and the associated units
(size and duration). Figures 2 and 3 show
cost per acre and size by strategy. As hypothesized, per acre costs decreased and acreage
increased with less aggressive strategies, although the differences were not always statistically significant. The average cost per
acre for direct suppression events was much
higher ($1,805/ac), and statistically different
from modified ($998) and limited suppression
events ($747). Average cost of mixed resource
benefit fires were much lower ($315/ac), although because of the large variation in
costs not statistically different. The decreasing trend in per acre costs from modified to
limited to mixed resource benefits fires also
were not statistically different.
Turning to size, the much lower average
acreage of direct suppression incidents
(7,262 ac) was statistically significant,
whereas none of the other strategies indicate
a statistical difference. In fact, the average
acreage for modified and limited suppression events was extremely similar at around
14,000 ac.
Perhaps a more interesting story is
shown by considering daily cost and duration (Figures 4 and 5). Contrary to our hypothesis, this analysis showed no statistically
significant difference in daily costs for any of
the strategies. Although the average daily
cost for direct suppression events is the highest ($335,000), it is only 1.2 times higher
than the next highest cost (modified suppression). Comparing this with Figure 5,
note that although the durations for the different strategies are not only statistically different, the modified suppression events last
nearly twice as long as direct suppression
events. Therefore, it appears that duration of
the events drive the differences (or lack of
differences) in total cost, particularly the
much higher cost of a modified suppression
strategy. Results were similar whether duration was measured as ignition date to control
date or measured as days of fire growth.
With the exception of Region 9 US
Forest Service fires, results for the other geographic areas and agencies are somewhat
similar in that modified suppression events
had higher average fire costs than the other
strategies, although the results were not statistically significant (Table 3). For the Region 9 analysis (Region 8 was excluded from
the analysis because all fires were categorized
as direct suppression), two uncharacteristi-
Figure 2. Average suppression expenditures per acre by predominant strategy (large 300ⴙ
ac fires) US Forest Service Regions 1– 6).
Figure 3. Average fire size by predominant strategy (large 300ⴙ ac fires, US Forest Service
Regions 1– 6).
cally large and expensive fires were dropped
from the data set (Ham and Cavity Lake).
Results for Region 9 showed direct suppression events to be more expensive than modified suppression, although the results were
not statistically significant because of the
large variation in the total cost of fires classified
as modified suppression. However, there were
statistically significant differences between
strategies with regard to per unit costs and
number of acres or days, with direct suppression being associated with higher per unit costs
and increased number of units.
Regression Results
Regression analysis allows an assessment of the effect of an independent variable
(such as fire management strategy) on a dependent variable (i.e., cost), holding all else
constant. It allows us to control for other
Journal of Forestry • March 2012
69
factors that likely affect management costs
(such as size and other fire characteristics) to
see how strategy affects the cost of otherwise
similar fires. Table 4 gives a brief description
of independent variables used in this analysis. Here, we present full results of the model
developed for the western geographic area of
the US Forest Service. We focused on the
western region of the Forest Service because
it accounts for the majority of federal suppression expenditures, and it is the area of
the county that allows managers the most
flexibility in choosing suppression strategies
because of lower human population density
in many areas.
A Hausman test was run to test for the
endogeneity of three of our explanatory variables: size, duration, and strategy. Five instrumental variables were used in the initial
analysis: SON, SON5, ln(dist), ln(slope),
and ERCSQ. SON is a binary variable, taking on the value of one if the fire occurred in
September, October, or November, and
zero otherwise, as was done in a study by
Donovan et al. 2011, to account for smaller
fires that started during these months. Likewise, to accommodate for a different fire season in California, where fall is often a peak
fire season, we interacted SON with the
dummy variable for fires occurring in Region 5. The square of energy release component, the natural log of slope, and the natural log of the distance to the nearest town
were included as instrumental variables because they were correlated with the potential
endogenous variables, but not with the dependent variable, cost per acre. The Hausman test indicated that none of the potential
endogenous variables were independently or
jointly endogenous. Jointly, a partial F-test
gave a P-value of 0.44 for the three potentially endogenous variables. Independently,
the residuals for the first stage equations had
the following P-values (acres ⫽ 0.13, duration ⫽ 0.33, modified suppression ⫽ 0.53,
limited suppression ⫽ 0.06, and mixed resource benefit ⫽ 0.11). Because the duration variable had little or no correlation with
our independent variable (cost per acre) and
we did not believe that in practice that strategy should be endogenous, we retested the
model including only acres as an endogenous variable and including duration as an
instrumental variable, because it was highly
correlated with acres but not correlated with
cost per acre.
The second Hausman test, based on
only acres being endogenous, showed some
indication that size was endogenous (code70
Journal of Forestry • March 2012
Figure 4. Average daily suppression cost by predominant strategy (large 300ⴙ ac fires, US
Forest Service Regions 1– 6).
Figure 5. Average duration by predominant strategy (large 300ⴙ ac fires, US Forest Service
Regions 1– 6).
termined) with cost per acre (P ⫽ 0.09). We,
therefore, estimated the model using 2SLS
because theory would suggest that size and
cost are codetermined. Table 5 shows the
2SLS regression results. Focusing on strat-
egy, holding all else constant, a management
strategy of limited suppression has a negative
and highly significant effect on cost per acre
(P ⫽ 0.0001). The interpretation of the coefficients on dummy variables in a model
with a log-transformed dependent variable is
not straightforward. The percent impact of
dummy variables is calculated following
Kennedy (1981) [3]. In this case, a strategy
of limited suppression reduces expenditures
per acre by 52%. The mixed resource benefit
strategy variable has a negative sign but is
not statistically significant (P ⫽ 0.1166).
Modified suppression was also not statistically different from direct suppression (P ⫽
0.9845). In the case of mixed resource benefit strategy, this was likely caused by the
large variation in cost for these fires and
probably driven in part by low sample size.
However, the results for the coefficient on
modified suppression indicate that even for
fires with similar characteristics (size, values
at risk, topographic features, and more),
modified suppression events have per acre
costs that are very similar to those of direct
suppression. Other variables indicating a
positive force on costs include fuel type, high
fire danger (energy release component and
fire intensity level), housing value, and Regions 5 and 6. Costs decreased with greater
distances from a wilderness boundary.
Discussion
Given the magnitude of fire management expenditures in the past 10 years, federal land-management agencies have come
under increased pressure to reduce the costs
of managing large wildland fires. One suggestion is greater use of less aggressive strategies when and where appropriate.
This study highlights the importance of
looking at fire management performance
through multiple lenses. If assessed solely
through a current year “cost” lens, these results might suggest that the best option for
saving money is to fight every fire aggressively. The analysis of average fire cost suggests that the old adage of “keep it small”
through aggressive suppression does result in
lower total expenditures due to smaller size
and shorter duration. The regression analysis indicates that per unit costs are indeed
lower for some less aggressive strategies (limited suppression) but not for other (modified suppression) when compared with the
cost of direct suppression. The regression
analysis, however, holds all other variables
constant, so the lower unit costs of limited
suppression pertain to fires of similar characteristics (size and duration, to name of
few) and, therefore, does not reflect the fact
that in reality, other factors such as size and
duration are not held constant and are im-
Table 3. Average total suppression cost, per unit cost, and units (acres and days) by
strategy and agency.
Agency and strategy
US Forest Service Region 8
Direct suppression
US Forest Service Region 9a
Direct suppression
Modified suppression
BLM
Direct suppression
Modified suppression
Mixed resource benefit
NPS
Direct suppression
Modified suppression
Limited suppression
Mixed resource benefit
FWS
Direct suppression
Modified suppression
Limited suppression
Total cost
Cost per acre
Acres
Cost per day
Days
128,421
146
543
16,774
6
241,905
12,485
397
50
568
294
35,639
10,990
6
1
358,076
446,949
307,214
265
330
237
7,350
6,071
3,997
143,200
203,109
92,171
4
12
6
889,309
1,051,155
807,340
557,936
942
299
792
127
3,814
5,789
2,520
2,063
68,337
42,104
25,550
36,080
15
16
25
6
336,549
483,981
15,509
112
275
38
3,812
10,468
5,222
190,937
388,326
4,760
4
9
12
Values shown in bold had statistically significant differences between strategies.
a
Two uncharacteristically large and expensive wildfires in Region 9 were removed from the analysis (Ham and Cavity Lake).
portant considerations when choosing a
management strategy.
However, as we now know, in the long
term and in some ecosystems, a legacy of
direct suppression can lead to fuels conditions less amenable to keeping fires small.
Moreover, environmental conditions do not
always favor success of aggressive suppression. Trying to pursue direct suppression on
these fires, particularly if unsuccessful in
keeping fires small, could result in even
higher total costs. This is one interpretation
of the larger fire size and higher total cost
found for the modified suppression strategy.
Unfortunately, we were unable to parse out
modified suppression events that began with
an objective of direct suppression from fires
that were managed as modified suppression
from the onset. Likely, our data set includes
both. Our analysis is based on the predominant strategy used on the fire, a default classification due to lack of confidence in the
day-by-day strategy data captured in the
daily ICS-209 form (National Interagency
Fire Center 2008). Our initial investigations
found the ICS-209 data were not always updated in response to a change in objective or
strategy. To fully test this interpretation, we
would need consistent and high-quality
daily strategy data and a similarly consistent
understanding of the rationale behind objective and strategy selection. To test across the
complete management spectrum, we would
need similar information for true resource
benefit fires (i.e., those managed from beginning to end for resource benefit). In the fu-
ture, it may be possible to extract this information from the WFDSS.
Very few fires were classified as mixed
resource benefit in our data set—21 for the
US Forest Service and 38 for the DOI—and
the majority of these were classified as area
management (the more aggressive of the two
resource benefit objectives). However, our
data set did not include purely resource benefit fires. Therefore, the fires in our data set
are probably more like limited suppression
events than what is typically thought of as a
resource benefit fire. This should be kept in
mind when interpreting the results of this
study.
Although evaluation of fires solely
through a “cost” frame, particularly a “current year cost” frame is incomplete, our analysis does provide insight into the options for
some cost performance measures, the mechanisms that drive costs, and the tradeoffs incurred when selecting a given performance
measure. If the aim is to compare the cost
performance of two similar fires (size, duration, values at risk, and more), cost per acre
or cost per day appear to be appropriate
measures. Developing a complete assessment of fire management objectives and
strategy requires information about additional objectives, such as of the temporal
cost issues, the safety implications, and ecological outcomes.
This study shows that less aggressive
strategies lead to more acres burned today.
How this affects ecological objectives today
and tomorrow, and whether this leads to less
Journal of Forestry • March 2012
71
Table 4. Variables used in development of regression equation for US Forest Service Regions 1– 6.
Fire characteristics
Variable definition
Ln(total acres burned)
Ln(duration)
Fire environment
Aspect
Elevation
Fuel type
Fire intensity level
Energy release component
Values at risk
Ln(total housing value 20)
Wilderness area
Ln(distance to wilderness area boundary)
Region
Objective/strategy
Source
Natural log of total acres within the wildfire perimeter
Natural log of fire duration (days)
NIFMIDa
Calcuated
Sine and cosine of aspect at point of origin in 45 degree increments
Elevation at point of origin
Dummy variables representing fuel type at point of origin. Grass ⫽ NFDRS fuel model A, L,
S, C, T, and N; Brush ⫽ NFDRS fuel model F, Q; Slash ⫽ NFDRS fuel model J, K, and
I; Timber ⫽ NFDRS fuel model H, R, E, P, U, and G; brush4(reference category) ⫽
NFDRS fuel model B and O
Dummy variable for fire intensity level category 1–6 (fil 1 ⫽ reference category)
Energy release component calculated from ignition point using nearest weather station
information (cumulative frequency)
NIFMID
NIFMID
NIFMID
Natural log of total housing value in 20-mi radius from point of origin (census data)/100,000
Dummy variables indicating whether the fire started in a wilderness area (reference category
⫽ not in wilderness area)
If in a wilderness area, natural log of distance to area boundary
Dummy variables for National Forest System region (reference category for western model ⫽
Region 1)
Dummy variables for suppression objective strategy (protection/direct suppression (reference
category), protection/modified suppression, protection/limited suppression, resource
benefit/area management, resource benefit/area monitoring)
Calculated
Calculated
NIFMID
Calculated
Calculated
NIFMID
Collected
Dependent variable ⫽ Ln(federal wildland fire suppression expenditures/acre).
a
NIFMID (National Interagency Fire Management Integrated Database) is the fire occurrence database for the US Forest Service.
Table 5. The 2SLS regression model,
western US Forest Service Regions 1– 6.
Variable
Coefficient
P-value
Ln(total acres burned)
Ln(duration)
Brush
Brush 4
Timber
Slash
Fire intensity level 2
Fire intensity level 3
Fire intensity level 4
Fire intensity level 5
Fire intensity level 6
Ln(total housing value 20)
In a wilderness area
Ln(distance to wilderness
area boundary)
⫻ wilderness dummy
Ln(elevation)
Aspect (cosine)
Aspect (sine)
Energy release component
Region 2
Region 3
Region 4
Region 5
Region 6
Modified suppression
Limited suppression
Mixed resource benefit
(Constant)
⫺0.5291
0.2297
0.2974
0.5367
0.8745
0.7572
0.1196
0.3384
0.3228
0.8316
0.7455
0.0505
0.5159
⫺0.7807
0.0042
0.0858
0.2235
0.1352
0.0021
0.2262
0.6960
0.2554
0.3498
0.0148
0.0193
0.0728
0.0430
0.0004
0.1736
⫺0.0778
0.0430
0.2103
0.00410
⫺0.1199
0.1419
1.0727
0.8025
0.1073
⫺0.7190
⫺0.4207
4.7544
0.1222
0.4008
0.5987
0.0000
0.9905
0.6502
0.5207
0.0000
0.0013
0.5329
0.0004
0.2358
0.0019
Dependent variable ⫽ ln(wildland fire suppression expenditures/acre); R2 ⫽ 0.47; n ⫽ 368; root MSE ⫽ 1.10.
fire and lower costs in the future, are important future research directions. Additional
research directions include comparing cost
of various strategies to mechanical fuel treat72
Journal of Forestry • March 2012
ments; capturing impacts on firefighter
safety (e.g., through such processes as decision/risk management); investigating preferences for identifying and prioritizing potentially conflicting goals such as of
duration, firefighter exposure risk, and fatigue; ecological outcomes; and developing
effective processes to monitor decisionmaking for purposes of organizational benchmarking and continuous improvement.
To truly assess the “cost” performance
of federal agencies, these questions will need
to be addressed and performance measures
developed that encompass the full complexity of fire management. One idea being explored is use of a “balanced scorecard” approach that would look at fire management
performance from a variety of aspects.
Endnotes
[1] We had also hoped to look at the strategies
used on a daily basis, with the unit of observation being a day or a group of days with
similar strategies. However, shortly into the
project, we dropped the second method because data on the strategies used by day was
not readily available and an effort to collect
this type of detailed data on past fires was not
deemed feasible.
[2] These equations have been modified somewhat since the 2007 publication but the basic
variables and methods have remained the
same.
[3] The numerical interpretation of the coefficient on the dummy variable is calculated as:
100 ⴱ (Exp[␤ ⫺ 0.5 ⴱ var{␤}] ⫺ 1).
Literature Cited
ABT, K.L., J.P. PRESTEMON, AND K.M. GEBERT.
2009. Wildfire suppression cost forecasts for
the US Forest Service. J. For. 107(4):173–178.
ARNO, S.F., AND J.K. BROWN. 1991. Overcoming
the paradox in managing wildland fire. West.
Wildl. 17(1):40 – 46.
BLACK, A., S. MCCAFFREY, K. GEBERT, AND T.
STEELMAN. 2010. The interplay of AMR, suppression costs, community interaction, and organizational performance—A multi-disciplinary
approach. Final Rep. to Joint Fire Sciences,
Boise, ID. 23 p.
CALKIN, D.E., K.M. GEBERT, J.G. JONES, AND
R.P. NEILSON. 2005. Forest Service large fire
area burned and suppression expenditure
trends, 1970 –2002. J. For. 103:179 –183.
CARDILLE, J.A., S.J. VENTURA, AND M.G.
TURNER. 2001. Environmental and social factors influencing wildfires in the upper Midwest, United States. Ecol. Applic. 11:111–127.
COLLINS, B.M., P.N. OMNI, AND P.L. CHAPMAN.
2006. Regional relationships between climate
and wildfire-burned area in the Interior West,
USA. Can. J. For. Res. 36(3):699 –709.
CRIMMINS, M.A., AND A.C. COMRIE. 2004. Interactions between antecedent climate and wildfire variability across south-eastern Arizona.
Int. J. Wildl. Fire 13(4):455– 466.
DONOVAN, G.H., P. NOORDIJK, AND V. RADELOFF. 2004. Estimating the impact of proximity of houses on wildfire suppression costs in
Oregon and Washington. P. 697–701 in Proc.
of 2nd symp. on Fire economics, planning and
policy: A global view, Cordoba, Spain, Apr. 19 –
22, 2004. [CD Rom.].
DONOVAN, G.H, J.P. PRESTEMON, AND K.M.
GEBERT. 2011. The effect of newspaper coverage and political pressure on wildfire suppres-
sion costs. Soc. Nat. Resour. 24:8, 785–798.
(First published on Mar. 22, 2011 [iFirst]).
FLANNIGAN, M.D., B.J. STOCKS, AND B.M. WOTTEN. 2000. Climate change and forest fires. Sci.
Total Environ. 262:221–229.
GEBERT, K.M., D.E. CALKIN, AND J. YODER.
2007. Estimating suppression expenditures for
individual large wildland fires. West. J. Appl.
For. 22:188 –196.
GEDALOF, Z.M., D.L. PETERSON, AND N.J. MANTUA. 2005. Atmospheric, climatic and ecological controls on extreme wildfire years in the
northwestern United States. Ecol. Applic.
15(1):154 –174.
GILL, A.M., AND S.L. STEPHENS. 2009. Scientific
and social challenges for the management of
fire-prone wildland– urban interfaces. Environ. Res. Lett. 4:10. (doi:10.1088/1748-326/
4/3/034014.)
GONZALEZ-CABAN, A. 1984. Costs of firefighting
mopup activities. US For. Serv. Res. Note
PSW-367. 5 p.
GREENE, W.H. 1993. Econometric analysis. McMillan Publishing Company, New York.
791 p.
HAUSMAN, J.A. 1978. Specification tests in
econometrics. Econometrica 46:1251–1271.
KENNEDY, P. 1981. Estimation with correctly interpreted dummy variables in semilogarithmic
equations. Am. Econ. Rev. 71(4):801.
KLEINBAUM, D.G., L.L. KUPPER, A. NIZAM, AND
K.E. JULLER. 2008. Applied regression analysis
and other multivariate methods. Thomas
Brooks/Cole, Belmont, CA. 906 p.
MCCULLOCH, C., AND S. SEARLE. 2001. Generalized, linear, and mixed models. Wiley-Interscience, New York. 358 p.
MOZUMDER, P., R. HELFOR, AND R.P. BERRENS.
2009. Provision of a wildfire risk map: Informing residents in the wildland urban interface.
Risk Anal. 29:1588 –1600.
NATIONAL INTERAGENCY FIRE CENTER (NIFC).
2008. 209 Program users guide (03/06/08).
Predictive Services. Available online at www.
gacc.nifc.gov/predictive_services/intelligence/
intell_working_group/committees/training/
program_notes_and_user_guides/209_
program/209_program_users_guide/
documents/ics_209_users_guide_030608.pdf;
last accessed Aug. 2010. 150 p.
NORTHERN ROCKIES COORDINATION GROUP
(NRCG). 2007. Appropriate management response summary for the Northern Rockies.
Available online at www.fs.fed.us/r1/fire/nrcg/
BulletinBoard/AMRsummary.pdf; last accessed Aug. 2010.
OFFICE OF INSPECTOR GENERAL (OIG). 2006.
Audit report: Forest Service large fire suppression
costs. Rep. 08601-44-SF. 47 p.
PRESTEMON, J.P., K.L. ABT, AND K.M. GEBERT.
2008. Suppression cost forecasts in advance of
wildfire seasons. For. Sci. 54:381–396.
USDA AND US DEPARTMENT OF INTERIOR (USDOI). 2007. Direction to leaders: Federal
Fire and Aviation Program. Available online
at www.nifc.gov/nicc/administrative/nmac/
strategy/NMAC_Apx_1.pdf; last accessed
Aug. 2010.
WESTERLING, A.L., T.J. BROWN, A. GERSHUNOV,
D.R. CAYAN, AND M.D. DETTINGER. 2002.
Climate and wildfire in the Western United
States. Bull. Am. Meteorol. Soc. 84(5):595–
604.
WESTERLING, A.J., A. GERSHUNOV, D.R. CAYAN,
AND T.P. BARNETT. 2003. Long lead statistical
forecasts of area burned in western U.S. wildfires by ecosystem province. Int. J. Wildl. Fire
11(3– 4):257–266.
WESTERLING, A.L., H.G. HIDALGO, D.R. CAYAN,
AND T.W. SWETNAM. 2006. Warming and earlier Spring increase Western U.S. forest wildfire activity. Science 313:940 –943.
ZIMMERMAN, G.T. 1999. Cost of appropriate
management response. P. 255–268 in Proc. of
the symp. on Fire economics, planning and policy:
Bottom lines, Gonzalez-Caban, A., and P.N.
Omi (coord.). US For. Serv. Gen. Tech. Rep.
PSW-GTR-173, Pac. Southw. Res. Stn., Albany, CA.
Appendix
Objective/Strategy Definitions
Objective Definitions
1. Protection—Objectives derived from
land-management direction to achieve
protection of sensitive natural and cultural resources, facilities, and values from
negative effects through the exclusion of
unwanted fire.
2. Resource benefit—Objectives derived
from land-management direction to
achieve positive benefits from the presence of fire in a specific area. Benefits
range across a wide scale from site specific
to landscape and affect resources and
community values.
Protection Strategy Definitions
1. Direct suppression—Strategy developed
to achieve the minimum burned area.
Would involve checking fire spread
through direct perimeter control by line
construction and use of fuel breaks and
other barriers to fire spread that are immediately adjacent to active fire.
2. Modified suppression—Strategy developed to achieve control of a fire where
fire is unwanted but minimizing burned
area is not the primary goal. Unsafe conditions for firefighers, low values at risk,
and/or concerns about total potential
suppression costs influence specific tac-
tics and foster use of perimeter confinement by both direct and indirect line
construction and use of natural barriers
and fuelbreaks, both adjacent to active
fire and some distance away from the fire.
This strategy would result in use of a
wider range of tactics than direct suppression.
3. Limited suppression—Strategy developed where conditions of the fire environment, resource availability, values at
risk, and/or cost indicate this is the most
effective strategy. These situations would
involve the use of protection management by implementing more active monitoring and limiting suppression activities to those necessary to protect a specific
point or specific area from fire, usually by
directing the fire movement away from
these areas or around these areas. This
strategy would not result in a continuous
control line around a fire.
Resource Benefit Strategy Definitions
1. Area monitoring—Strategy developed to
achieve resource benefit objectives by allowing the fire to burn freely within the
defined planning area with management
attention focused on monitoring the fire.
2. Area management—Strategy developed
to manage the fire to accomplish resource
benefit objectives. Management actions
consist of monitoring plus varying
amounts and intensities of operational
actions to delay, direct, or check fire
spread to protect a defined area within
the planning area or along the planning
area perimeter. Actions taken depend on
the identified values at risk in and around
the fire planning area and the natural defensibility of the planning area perimeter
and might include:
• Delaying actions prevent the fire from
reaching values or the planning area
perimeter at a certain time (although it
may spread there later) or delay fire
spread until the onset of a fire slowing
or season ending event.
• Directing actions cause the fire to
spread in a different direction, away
from values of concern or away from
the planning area perimeter.
• Checking actions include line construction to stop fire from spreading to
values or the planning area perimeter.
Journal of Forestry • March 2012
73