Wildland Fire Management
Better Information and a Systematic Process Could Improve Agencies' Approach to Allocating Fuel Reduction Funds and Selecting Projects Gao ID: GAO-07-1168 September 28, 2007Recognizing that millions of acres are at risk from wildland fire, the federal government expends substantial resources on thinning brush, trees, and other potentially hazardous fuels to reduce the fire risk to communities and the environment. However, questions have been raised about how the agencies responsible for wildland fire management--the Department of Agriculture's Forest Service and the Department of the Interior's (Interior) Bureau of Indian Affairs (BIA), Bureau of Land Management (BLM), Fish and Wildlife Service (FWS), and National Park Service (NPS)--allocate their fuel reduction budgets and select projects. GAO was asked to report on the agencies' processes for allocating funds and selecting projects, and on how, if at all, these processes could be improved to better ensure that they contribute to the agencies' overall goal of reducing risk. To obtain this information, GAO visited headquarters and field offices of all five agencies; obtained data on fuel reduction funding and accomplishments; and reviewed previous evaluations of the fuel reduction program.
In allocating fuel reduction funds and selecting projects, the Forest Service, Interior, and the four Interior agencies use both quantitative processes (such as computer models or scoring systems) and professional judgment. At the national level, the Forest Service uses a computer model to help determine the amount of each regional office's allocation, although the model is being refined and the agency still relies largely on past funding levels. Interior and BLM are also developing computer models--based in part on the Forest Service's--to help allocate funds; of Interior's other agencies, BIA allocates funds based on past regional performance in reducing fuels, FWS uses a computer model, and NPS relies on historical funding levels that were based on a now-discontinued model. At the regional and local levels, the agencies use a variety of quantitative and judgmental processes. Although the Forest Service and Interior are taking steps to enhance their funding allocation and project selection processes, there are several improvements they could make to better ensure that they allocate fuel reduction funds to effectively reduce risk. Specifically, when allocating funds and selecting projects, the agencies could improve their processes by (1) consistently assessing all elements of wildland fire risk--including hazard, risk, and values--at the national, regional, and local levels, in order to identify those lands at highest risk from wildland fire and incorporate this information in the allocation and project selection process; (2) developing and using measures of the effectiveness of fuel reduction treatments in order to estimate how much risk reduction is likely to be achieved through particular treatments and for how long; (3) using this information on effectiveness, once developed, in combination with existing information on treatment costs, to assess and compare the cost-effectiveness of potential treatments in deciding how to optimally allocate funds; (4) clarifying the relative importance of the numerous factors they use in allocating funds, including those factors (such as funding stability and the use of forest products resulting from fuel reduction activities) that are unrelated to risk, treatment effectiveness, or cost effectiveness; and (5) following a more systematic process in allocating funds--that is, a process that is methodical, based on criteria, and applied consistently--to ensure that funds are directed to locations where risk can be reduced most effectively.
RecommendationsOur recommendations from this work are listed below with a Contact for more information. Status will change from "In process" to "Open," "Closed - implemented," or "Closed - not implemented" based on our follow up work.
Director: Team: Phone:GAO-07-1168, Wildland Fire Management: Better Information and a Systematic Process Could Improve Agencies' Approach to Allocating Fuel Reduction Funds and Selecting Projects
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United States Government Accountability Office:
GAO:
Report to Congressional Requesters:
September 2007:
Wildland Fire Management:
Better Information and a Systematic Process Could Improve Agencies'
Approach to Allocating Fuel Reduction Funds and Selecting Projects:
GAO-07-1168:
GAO Highlights:
Highlights of GAO-07-1168, a report to congressional requesters.
Why GAO Did This Study:
Recognizing that millions of acres are at risk from wildland fire, the
federal government expends substantial resources on thinning brush,
trees, and other potentially hazardous fuels to reduce the fire risk to
communities and the environment. However, questions have been raised
about how the agencies responsible for wildland fire management”the
Department of Agriculture‘s Forest Service and the Department of the
Interior‘s (Interior) Bureau of Indian Affairs (BIA), Bureau of Land
Management (BLM), Fish and Wildlife Service (FWS), and National Park
Service (NPS)”allocate their fuel reduction budgets and select projects.
GAO was asked to report on the agencies‘ processes for allocating funds
and selecting projects, and on how, if at all, these processes could be
improved to better ensure that they contribute to the agencies‘ overall
goal of reducing risk. To obtain this information, GAO visited
headquarters and field offices of all five agencies; obtained data on
fuel reduction funding and accomplishments; and reviewed previous
evaluations of the fuel reduction program.
What GAO Found:
In allocating fuel reduction funds and selecting projects, the Forest
Service, Interior, and the four Interior agencies use both quantitative
processes (such as computer models or scoring systems) and professional
judgment. At the national level, the Forest Service uses a computer
model to help determine the amount of each regional office‘s
allocation, although the model is being refined and the agency still
relies largely on past funding levels. Interior and BLM are also
developing computer models”based in part on the Forest Service‘s”to
help allocate funds; of Interior‘s other agencies, BIA allocates funds
based on past regional performance in reducing fuels, FWS uses a
computer model, and NPS relies on historical funding levels that were
based on a now-discontinued model. At the regional and local levels,
the agencies use a variety of quantitative and judgmental processes.
Although the Forest Service and Interior are taking steps to enhance
their funding allocation and project selection processes, there are
several improvements they could make to better ensure that they
allocate fuel reduction funds to effectively reduce risk. Specifically,
when allocating funds and selecting projects, the agencies could
improve their processes by:
* consistently assessing all elements of wildland fire risk”including
hazard, risk, and values”at the national, regional, and local levels,
in order to identify those lands at highest risk from wildland fire and
incorporate this information in the allocation and project selection
process;
* developing and using measures of the effectiveness of fuel reduction
treatments in order to estimate how much risk reduction is likely to be
achieved through particular treatments and for how long;
* using this information on effectiveness, once developed, in
combination with existing information on treatment costs, to assess and
compare the cost-effectiveness of potential treatments in deciding how
to optimally allocate funds;
* clarifying the relative importance of the numerous factors they use
in allocating funds, including those factors (such as funding stability
and the use of forest products resulting from fuel reduction
activities) that are unrelated to risk, treatment effectiveness, or
cost effectiveness; and
* following a more systematic process in allocating funds”that is, a
process that is methodical, based on criteria, and applied
consistently”to ensure that funds are directed to locations where risk
can be reduced most effectively.
What GAO Recommends:
GAO is recommending a number of actions to improve the agencies‘
ability to ensure that fuel reduction funds are directed where they
will most effectively reduce risk from wildland fire. In commenting on
a draft of this report, the Forest Service and Interior agreed with its
findings and recommendations.
To view the full product, including the scope and methodology, click on
[hyperlink, http://www.GAO-07-1168]. For more information, contact
Robin M. Nazzaro at (202) 512-3841 or nazzaror@gao.gov.
Contents:
Letter:
Results in Brief:
Background:
The Forest Service Uses a Mix of Quantitative and Judgmental Processes
and Considers a Range of Factors in Allocating Funds and Selecting
Projects:
Interior and Its Agencies Use a Mix of Quantitative and Judgmental
Processes and Consider a Range of Factors in Allocating Funds and
Selecting Projects:
Several Improvements Could Help Better Ensure That Fuel Reduction Funds
Are Allocated to Effectively Reduce Risk:
Conclusions:
Recommendations for Executive Action:
Agency Comments and Our Evaluation:
Appendix I: Objectives, Scope, and Methodology:
Appendix II: Forest Service and Interior Fuel Reduction Funding
Allocations, Fiscal Years 2005, 2006, and 2007:
Appendix III: Summary of Fuel Treatment Accomplishments for the Forest
Service and Interior, Fiscal Years 2005 and 2006:
Appendix IV: Comments from the Department of the Interior and the
Forest Service:
Appendix V: GAO Contact and Staff Acknowledgments:
Related GAO Products:
Tables:
Table 1: Factors Considered in Forest Service Fuel Reduction Funding
Allocation Model:
Table 2: Forest Service Regions' Fiscal Year 2007 Fuel Reduction
Priority Scores and Funding Allocations:
Table 3: BLM Allocations to State Offices, Fiscal Year 2007:
Table 4: Factors and Factor Categories BLM Considers in BLM Fuel
Reduction Funding Allocation Model:
Table 5: Regional Offices GAO Visited:
Table 6: Field Units GAO Visited:
Table 7: Total Appropriations to Forest Service, and Allocations to
Interior Agencies, Fiscal Years 2005, 2006, and 2007:
Table 8: Forest Service Allocations to Regions and Headquarters, Fiscal
Years 2005, 2006, and 2007:
Table 9: Interior Allocations to BLM, BIA, FWS, and NPS, Including WUI
and Non-WUI Allocations, Fiscal Years 2005, 2006, and 2007:
Table 10: BLM Allocations to State Offices and Headquarters, Fiscal
Years 2005, 2006, and 2007:
Table 11: BIA Allocations to Regions and the National Interagency Fire
Center, Fiscal Years 2005, 2006, and 2007:
Table 12: NPS Allocations to Regions and the Washington Office, Fiscal
Years 2005, 2006, and 2007:
Table 13: FWS Allocations to Regions and Headquarters, Fiscal Years
2005, 2006, and 2007:
Table 14: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
Accomplishments for Interior and Forest Service:
Table 15: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
Accomplishments for Forest Service Regions:
Table 16: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
Accomplishments for BLM State Offices:
Table 17: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
Accomplishments for BIA Regions:
Table 18: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
Accomplishments for NPS Regions:
Table 19: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
Accomplishments for FWS Regions:
Figures:
Figure 1: Annual Appropriation and Allocation Process for Fuel
Reduction Funds:
Figure 2: Distribution of Total Land Managed by the Forest Service,
BIA, BLM, FWS, and NPS:
Figure 3: A Mechanical Thinning Project for Fuel Reduction on BLM Land
in California:
Figure 4: Prescribed Fire for Fuel Reduction on Forest Service Land in
South Carolina:
Figure 5: Forest Service Regions' Fuel Reduction Priority Scores as a
Percentage of Total, Compared to Regions' Funding Allocations as a
Percentage of Total Allocations, Fiscal Year 2007:
Figure 6: Percentage of Interior's Total Fuel Reduction Funds Allocated
to the Interior Agencies, Fiscal Years 2001 through 2007:
Figure 7: Density of Wildland-Urban Interface Treatments and Population
Density, by ZIP Code:
Figure 8: Location of Federal Lands and Populated Areas in the
Continental United States:
Figure 9: Map of Los Angeles County Wildland-Urban Interface Fuel
Reduction Treatments Completed in 2005 and 2006, and Population Density:
Figure 10: Map of Rio Blanco County Wildland-Urban Interface Fuel
Reduction Treatments Completed in 2005 and 2006, and Population Density:
Figure 11: Agency Funding Levels as a Percentage of Total Fuel
Reduction Funding, Fiscal Year 2007:
Abbreviations:
BIA: Bureau of Indian Affairs:
BLM: Bureau of Land Management:
FPA: Fire Program Analysis:
FWS: Fish and Wildlife Service:
GAO: Government Accountability Office:
GIS: Geographic information system:
HFI: Healthy Forests Initiative:
HFRA: Healthy Forests Restoration Act:
NEPA: National Environmental Policy Act:
NPS: National Park Service:
USDA: United States Department of Agriculture:
WUI: Wildland-urban interface:
[End of section]
United States Government Accountability Office:
Washington, DC 20548:
September 28, 2007:
The Honorable Norman D. Dicks:
Chairman:
The Honorable Todd Tiahrt:
Ranking Member:
Subcommittee on Interior, Environment, and Related Agencies:
Committee on Appropriations:
House of Representatives:
The Honorable Raśl M. Grijalva:
Chairman:
The Honorable Rob Bishop:
Ranking Member:
Subcommittee on National Parks, Forests, and Public Lands:
Committee on Natural Resources:
House of Representatives:
The Honorable Greg Walden:
House of Representatives:
Decades of fire suppression in the nation's forests, together with such
practices as logging followed by dense tree planting, have resulted in
the accumulation of brush, small trees, and other vegetation that can
fuel wildland fires. Similarly, the nation's rangelands have suffered
from decades of fire suppression and livestock overgrazing, which have
degraded ecosystems and made the rangelands vulnerable to the invasion
of flammable, nonnative species, such as cheat grass. This accumulation
and alteration of vegetation, as well as drought and other stresses
related to climate change, have fueled wildland fires. Collectively,
these fires have cost billions of dollars to suppress, forced thousands
from their homes, and damaged cultural and natural resources. The
impacts of these fires have intensified as more and more communities
develop in areas that are adjacent to fire-prone lands--the wildland-
urban interface.
In response to the increasing threat of wildland fires, the federal
agencies responsible for wildland fire management developed the
National Fire Plan.[Footnote 1] These agencies are the Department of
Agriculture's Forest Service and the Department of the Interior's
(Interior) Bureau of Indian Affairs (BIA), Bureau of Land Management
(BLM), Fish and Wildlife Service (FWS), and National Park Service
(NPS). Two components of the National Fire Plan are a 10-year strategy
and an implementation plan for protecting communities and the
environment that were developed in 2001 and 2002, respectively, by the
Secretaries of Agriculture and of the Interior, along with governors of
western states and other interested parties, and updated in December
2006. The 2002 plan emphasized reducing hazardous fuel in forests and
rangelands to mitigate the risk from wildland fire. In 2003, Congress
passed the Healthy Forests Restoration Act (HFRA),[Footnote 2] with the
stated purpose of reducing wildland fire risk to communities, municipal
water supplies, and other at-risk federal land through a collaborative
process of planning, setting priorities, and implementing fuel
reduction projects.[Footnote 3] HFRA also authorized grants to
commercial facilities that use biomass--that is, small-diameter trees
and branches--to offset the costs incurred to purchase biomass. Fuel
reduction projects can generate substantial amounts of biomass.
According to the updated 10-Year Strategy Implementation Plan, the goal
of the fuel reduction program is to reduce the risk of wildland fire to
communities and the environment. Fuel reduction projects--using
prescribed fire, mechanical thinning, herbicides, grazing, or
combinations of these methods--are intended to remove or modify
wildland fuel to reduce the potential for severe wildland fires, lessen
the damage caused by fires, limit the spread of flammable invasive
species, and restore and maintain healthy ecosystems. Local land
management units, such as national forests and parks, are typically
responsible for selecting and implementing fuel reduction projects.
Since 2001, Congress has appropriated more than $3.2 billion in fuel
reduction funds to the Forest Service and Interior. For 2007, the
Forest Service received about $300 million, and Interior received about
$200 million.[Footnote 4] After receiving its annual appropriation, the
Forest Service allocates funds to its nine regional offices which in
turn allocate funds to individual national forests and grasslands.
Interior, upon receiving its annual appropriation, allocates funds to
BIA, BLM, FWS, and NPS. BLM generally receives about 50 percent of
Interior's funding, BIA about 20 percent, and FWS and NPS about 15
percent each. These agencies then allocate funds to their regional or
state offices, which, in turn, allocate funds to individual field
units, such as national parks or wildlife refuges. (BIA, FWS, and NPS
have regional offices, while BLM has state offices. For the purposes of
this report, we refer to all of these as regional offices when we
discuss the Interior agencies collectively.) Figure 1 shows the annual
appropriation and allocation process.
Figure 1: Annual Appropriation and Allocation Process for Fuel
Reduction Funds:
This figure is an illustration of an organizational chart indicating
the flow of allocations as follows:
* Congress to Interior and Forest Service;
* Interior to BIA, BLM, FWS, NPS;
* Forest Service to Regional Offices;
* BIA, BLM, FWS, NPS to Regional Offices;
* Regional Offices to Field Units.
[See PDF for image]
Source: GAO (data) and Art Explosion (clipart).
[End of figure]
Recognizing that treating all of the land in need of fuel reduction may
take decades, the agencies have acknowledged the importance of setting
priorities for which lands are to receive treatment so that they can
select those treatments that will be the most effective at reducing the
risks from wildland fire. However, we have found a long-standing
pattern of shortcomings in the processes the Forest Service and
Interior agencies use to identify and set priorities for lands needing
fuel reduction. Between 1999 and 2003, we reported that the Forest
Service and Interior had made it a priority to treat lands at the
highest risk from wildland fire, but they had not identified the amount
or location of such lands and had not issued guidance specific enough
for field staff to set priorities for individual projects. We concluded
that the agencies needed a cohesive strategy outlining long-term
options and associated costs for reducing fuel.[Footnote 5] In
subsequent reports, we noted, among other things, the progress the
agencies had made in improving their data, but reiterated that they
needed to complete ongoing efforts to identify lands at risk from
wildland fire--by collecting information on the hazards, the likelihood
of fire occurring, and the values at risk--so funds could be targeted
to such lands. We also reiterated the need to develop a cohesive
strategy that included long-term options and associated costs so that
Congress could make informed decisions about cost-effective approaches
to fuel reduction.[Footnote 6]
In this context, you asked us to report on the agencies' current
processes for identifying and setting priorities for fuel reduction.
Specifically, you asked us to (1) identify the processes the Forest
Service, Interior, and the four Interior agencies use to allocate fuel
reduction funds and select projects for implementation, including the
factors that influence these processes, and (2) determine how, if at
all, these processes could be improved to better ensure that they
contribute to the agencies' goal of effectively reducing the risk of
wildland fire to communities and the environment.
To address these objectives, we met with national, regional, state, and
local officials of the Forest Service, Interior, and Interior agencies.
At the national level, we met with agency officials at their
Washington, D.C., headquarters, as well as at the National Interagency
Fire Center in Boise, Idaho. At the regional and state levels, we used
a structured interview guide to speak, in person or by telephone, with
officials in all Forest Service regional and BLM state offices, as well
as with officials in selected BIA, FWS, and NPS regional offices that
collectively received a substantial portion of each agency's fuel
reduction funds. At the local level, we visited 20 local units, such as
national forests and BLM field offices, in eight states to gain a
better understanding of their processes for selecting fuel reduction
projects for implementation. We selected local units that are diverse
in geographic location, predominant vegetation type, and proximity to
communities and development. We also obtained and reviewed applicable
laws, regulations, and agencywide and regional policies; agency data on
funding allocations; and electronic data on the extent of agency fuel
treatment activities. We tested these data and found that they were
sufficiently reliable for the purposes of this review. Finally, we
interviewed several nonfederal parties, including representatives from
environmental groups and the Western Governors' Association.[Footnote
7] We conducted our work from August 2006 to September 2007 in
accordance with generally accepted government auditing standards. See
appendix I for a detailed description of our methodology.
Results in Brief:
In allocating fuel reduction funds and selecting projects, the Forest
Service--at the national, regional, and local levels--uses both
quantitative processes (such as computer models or scoring systems) and
professional judgment and, in doing so, considers multiple factors,
such as risk assessments, treatment cost per acre, and collaboration
with communities or other entities. Specifically, for 2007, we found
the following:
* At headquarters, the Forest Service began using a computer model to
influence funding allocations to regions. To set priorities for each
region's fuel reduction funding, the model considers multiple factors,
including some intended to assess risk, such as the potential for fires
occurring in each region and their expected severity, as well as other
factors, such as regional use of biomass removed in fuel treatments and
treatment cost per acre. However, the Forest Service's funding
allocations to its regions were not consistent in all cases with the
priority scores resulting from the model, with some high-scoring
regions receiving less funding than some lower-scoring regions. These
disparities occurred for a number of reasons, such as the higher costs
of fuel reduction in some areas. However, the model did not
substantially influence the agency's 2007 allocations; instead, the
Service relied largely on prior year funding levels and used results
from the model only to make minor adjustments. Officials said they
expect the model's results to have more influence on future allocation
decisions, but curbed this influence initially because they were still
refining the model and wanted to maintain relatively stable regional
funding levels.
* In the regions, each region determined how to allocate funds to
national forests and what factors to consider, as long as they were
consistent with the factors used in the national allocation model. Four
of the Service's nine regions relied primarily on quantitative data in
their allocation processes, while five relied primarily on professional
judgment. For example, the Rocky Mountain region used a computer model
that evaluated data on multiple factors, such as vegetative conditions
and areas of insect-killed trees, while the Southern region convened a
group of officials who used professional judgment and considered
historical funding levels, the capabilities of the forests, per-acre
treatment cost, local priorities, and acreage targets when allocating
funds. Beginning with the 2008 allocations, the Forest Service plans to
require regions to use the headquarters model to inform allocation
decisions.
* Locally, national forests had discretion in determining how to select
projects. Some used quantitative, data-driven processes, while others
relied primarily on professional judgment or collaborative processes
involving other agencies and local communities. Forests considered a
range of factors--similar to those used at the national and regional
levels--when selecting projects.
Like the Forest Service, Interior and its agencies' national, regional,
and local offices used both quantitative and judgmental processes for
allocating fuel reduction funds and selecting projects and considered
multiple factors that are similar to those the Forest Service uses.
More specifically, for fiscal year 2007, we found the following:
* Interior allocated funds to its four agencies primarily on the basis
of historical funding levels; however, Interior is developing a
computer model similar to the Forest Service's, and it used the model
to allocate 5 percent of its funds in 2007. Interior agencies, in turn,
had the flexibility to determine how to allocate funds, within the
parameters of departmental guidance.
* BLM headquarters allocated funds primarily on the basis of historical
funding levels, but officials told us that, starting in 2008, they plan
to use a quantitative process incorporating multiple factors, such as
the potential for fires to occur, treatment cost, and local risk
ratings.
* BIA headquarters allocated funds using quantitative processes, but
these processes generally emphasized a single factor--BIA units' past
performance (measured in acres treated) in carrying out fuel reduction
activities.
* FWS headquarters allocated funds to regional offices using a
quantitative process--a model that considers a range of factors, such
as the history of fires, fuel conditions, and communities at risk.
* NPS headquarters allocated funds to regions largely on the basis of
historical funding levels. These funding levels were originally
determined using a model that assessed the risk from wildland fire,
among other factors.
At the regional and local levels, regional offices and field units in
all four agencies used a variety of processes to allocate funds and
select projects for implementation. Some processes emphasized
quantitative data, while others emphasized professional judgment. The
regional and local offices also considered a range of factors,
consistent with departmental direction.
Although the Forest Service and Interior have begun taking action to
enhance their funding allocation processes, there are additional steps
they could take to improve these processes to better ensure they
advance the agencies' goal of effectively reducing the risk of wildland
fire to communities and the environment. Specifically, the agencies
could improve their processes by taking the following five steps:
* Consistently using risk assessments. The agencies did not
consistently use risk assessments in their 2007 allocation processes at
the national, regional, and local levels, in some cases because
national or regional offices expected local units to do so. However,
agency officials cannot be sure that projects identified as high risk
locally would likewise be the highest risk from a regional or national
perspective. Even when the agencies did conduct risk assessments, they
found it difficult to meaningfully distinguish between higher-and lower-
priority locations because one key value at risk--the wildland- urban
interface--is broadly defined and many different areas are classified
as interface. Although the agencies' guidance sets a priority on
projects in the interface, it does not specify whether some of the
areas classified as interface ought to be higher priority than others.
As a result, projects as diverse as those protecting remote power
lines, individual ranch houses, or large suburban subdivisions can all
fall within the wildland-urban interface category and, thus, be
designated high priority--complicating agency officials' attempts to
identify and direct their limited resources toward the highest-priority
areas.
* Developing information on treatment effectiveness. The agencies did
not consider treatment effectiveness--that is, how much risk reduction
can be achieved, and for how long--when making allocation decisions
because they currently have no measure for effectiveness, although they
are working to develop such a measure. Without information on treatment
effectiveness, the agencies could be funding treatments that have
little effect on reducing risk.
* Developing information on cost effectiveness. The agencies often
considered costs when allocating funds, but not cost effectiveness--
primarily because they lack information on treatment effectiveness.
Without such information, it is difficult to know whether a treatment's
cost is warranted or to compare the cost effectiveness of different
potential treatments to decide how to optimally allocate funds.
* Clarifying the importance of factors unrelated to risk or
effectiveness. The agencies often considered factors other than risk,
treatment effectiveness, and cost effectiveness when allocating funds
and selecting projects. When these external factors--such as funding
stability and the use of biomass resulting from fuel reduction
treatments--have considerable influence, it is difficult for the
agencies to ensure that they are allocating funds so that treatments
will most effectively reduce risk.
* Applying more systematic processes. The agencies sometimes relied
exclusively on professional judgment when allocating funds or selecting
projects. Although judgmental processes might result in allocations
that maximize risk reduction, the agencies cannot be assured that they
routinely do because such processes are not necessarily systematic--
that is, methodical, based on criteria, and applied consistently.
To improve the agencies' ability to ensure that fuel reduction funds
are directed to most effectively reduce the risk from wildland fire, we
are recommending that the Secretaries of Agriculture and of the
Interior take actions to implement a more systematic allocation
process; develop additional information on risk, treatment
effectiveness, and cost-effectiveness to support the process; and
clarify the relative importance of multiple criteria for setting
priorities in allocation and project selection decisions. We provided a
draft of this report to the Secretaries of Agriculture and of the
Interior for review and comment. The Forest Service and the Department
of the Interior generally agreed with our report; their joint comment
letter is presented in appendix IV.
Background:
Wildland Fire Is a Natural Process:
Although its effect on communities can be devastating, wildland fire is
a natural and necessary process that provides many benefits to
ecosystems, such as maintaining habitat diversity, recycling soil
nutrients, limiting the spread of insects and disease, and promoting
new growth by causing the seeds of fire-dependent species to germinate.
Wildland fire also periodically removes brush, small trees, and other
vegetation that can otherwise accumulate and increase the size,
intensity, and duration of subsequent fires. However, human uses and
land management practices--including decades of wildland fire
suppression--have excluded fire from ecosystems, reducing the normal
frequency of wildland fire and subsequently causing an accumulation of
vegetation. Federal researchers have estimated that unnaturally dense
fuel accumulations on 90 million to 200 million acres of federal lands
in the contiguous United States place these lands at an elevated risk
of severe wildland fire and that these conditions also hold true for
many nonfederal lands.
Most lands in the United States evolved with fire, and each ecosystem
has a characteristic fire regime that describes the role fire plays in
the ecosystem, including typical fire frequency, scale, intensity, and
duration. These regimes are numbered I through V, with fire regime I
characterized by low-severity fires that historically occurred every 35
years or less, fire regime II characterized by high-severity fires that
historically occurred every 35 years or less, fire regime III
characterized by mixed-severity fires that historically occurred every
35 to 100 or more years, fire regime IV characterized by high-severity
fires that historically occurred every 35 to 100 or more years, and
fire regime V characterized by high-severity fires that historically
occurred every 200 or more years. Many ecosystems--particularly those
in fire regimes I and II--have now missed numerous fire cycles as a
result of past suppression policies and other land management
practices. This departure from the natural fire regime is categorized
by a measure called condition class, which the agencies have used as a
generalized rating for the risk of uncharacteristic wildland fires that
may cause undesirable ecological consequences. Ecosystems in condition
class 1 are generally within their historical fire return interval, so
fires in these areas pose little risk to natural processes--although
fires in such ecosystems may still pose a high risk to communities.
Areas in condition classes 2 and 3 have moderate to significant
departures from historical fire experiences. In such areas, fuel that
would typically have burned periodically has instead accumulated,
posing a higher risk that uncharacteristically large amounts of
vegetation and other natural resources would be lost from wildland
fire; fires in these areas may also pose a high risk to communities.
Five Agencies Are Responsible for Wildland Fire Management:
The Forest Service, BIA, BLM, FWS, and NPS are responsible for wildland
fire management, including fuel reduction. These five agencies manage
about 700 million acres of land in the United States, including
national forests, national grasslands, Indian reservations, national
parks, and national wildlife refuges. The Forest Service and BLM manage
the majority of these lands, with the Forest Service managing about 190
million acres and BLM managing about 260 million acres; BIA, FWS, and
NPS each manage less than 100 million acres. Figure 2 shows the
distribution of land among the five agencies. Each agency has between 7
and 12 regional offices that oversee field units.
Figure 2: Distribution of Total Land Managed by the Forest Service,
BIA, BLM, FWS, and NPS:
This figure is a pie chart depicting percentage of land managed by
agency:
Forest Service: 28%;
BLM (Interior Agency): 38%;
BIA (Interior Agency): 8%;
NPS (Interior Agency): 12%;
FWS (Interior Agency): 14%;
Total equals 700 million acres.
[See PDF for image]
Source: GAO analysis of Forest Service and Interior data.
[End of figure]
Each year, the Forest Service, Interior, and Interior agencies set
performance targets for region-and state-level fuel reduction by
establishing the number of acres the agencies expect to be treated--
both within and outside of the wildland-urban interface--using the
funds allocated. For example, for fiscal year 2007, BLM assigned a
target of almost 90,000 acres to its Oregon/Washington state office and
specified that two-thirds of the acres should be in the wildland-urban
interface. Between 2001 and August 2007, land managers treated more
than 18 million acres under the fuel reduction program, including about
8.5 million acres near communities.[Footnote 8] These acres include
federal, state, and private land, because, in addition to conducting
fuel treatments on federal lands, the agencies work with and grant
funds to local communities to conduct fuel reduction treatments on
state and private lands. These acres also include those that have been
treated more than once.
The agencies generally reduce fuel using either mechanical treatments,
in which equipment--such as chainsaws, chippers, bulldozers, or mowers-
-is used to cut vegetation, or prescribed burning, in which fires are
deliberately set by land managers to restore or maintain desired
vegetation conditions.[Footnote 9] Figure 3 depicts a mechanical
thinning project, and figure 4 depicts a prescribed burn.
Figure 3: A Mechanical Thinning Project for Fuel Reduction on BLM Land
in California (photograph):
[See PDF for image]
[End of figure]
Figure 4: Prescribed Fire for Fuel Reduction on Forest Service Land in
South Carolina (photograph):
[See PDF for image]
[End of figure]
Although prescribed burning can be risky, burning under specified fuel
and weather conditions enables fire to be controlled at a relatively
low intensity level within a confined area. Prescribed burning is very
effective in removing smaller vegetation, such as grasses, leaves, pine
needles, and twigs, but is not as effective in removing larger fuel,
such as trees, or in thinning stands to desired densities. In contrast,
mechanical treatment methods are effective in thinning stands and
removing larger vegetation but may increase the amount of smaller fuel
on the ground, including tree tops and limbs (referred to as slash) and
other debris from thinning. As a result, some fuel reduction projects
use multiple treatment methods and may span several years. For example,
a field unit may first treat an area mechanically to thin accumulated
vegetation and then follow with a prescribed burn to remove remaining
slash and litter on the ground.
In addition to reducing the risk of fire to communities and the
environment, one of the long-term goals of the fuel reduction program
is to allow fire to resume its natural role. By conducting treatments,
including creating fire breaks to help contain the spread of fire, the
agencies increase the amount of land where naturally ignited fires can
safely be allowed to burn. Under wildland fire use policies, land
managers may allow wildland fires that are naturally ignited to
continue to burn, as long as fuel and weather conditions are
appropriate and the fire is located within an area designated for
wildland fire use.[Footnote 10] Managers are thus able to use natural
fire to meet resource objectives, such as removing excess vegetation.
Although the five agencies all reduce fuel in order to reduce risk to
communities and the environment, their fuel reduction programs reflect
differences in their missions, predominant vegetation types, and
allowable land uses. For example, FWS's mission is focused on the
conservation of wildlife habitat, and the agency generally conducts
more prescribed burns than mechanical treatments because such burns
frequently improve habitat as well as reduce risk; the agency has been
conducting prescribed burns since the 1930s. Similarly, prescribed
burns, as well as wildland fire use, are the preferred fuel treatment
methods at NPS and have been used by the agency for decades. NPS
prefers these treatment methods over mechanical treatments because its
mission emphasizes preservation of natural and cultural resources, and
fire is a natural process that better aligns with this mission.
Regarding predominant vegetation types, BLM's lands are largely
rangelands, while lands managed by other agencies, such as the Forest
Service and FWS, include more forests. As a result of this difference,
BLM not only conducts mechanical treatments and prescribed burns, as do
the other agencies, but also uses herbicides to reduce fuel, especially
where rangelands have been invaded by exotic plants such as cheat
grass. Agency differences in allowable land use also affect their fuel
reduction programs. For example, the Forest Service, BIA, and BLM have
active commercial timber programs, and field units may therefore
conduct fuel treatments that benefit both the timber and fuel reduction
programs. BIA and NPS also manage lands with numerous archaeological
sites, which must be considered when conducting treatments. In
contrast, the majority of BLM's land is used for grazing, and, as a
result, BLM coordinates fuel treatments with potentially affected
ranchers.
Expansion into the Wildland-Urban Interface Has Increased, as Has the
Federal Focus on Wildland Fire Management:
Urban and suburban expansion into the wildland-urban interface has
increased the number of communities and structures at risk of wildland
fire near federal lands that the five agencies manage. Experts estimate
that almost 60 percent of all new housing units built in the 1990s were
located in the wildland-urban interface and that this growth trend
continues. They also estimate that more than 30 percent of housing
units overall are located in the wildland-urban interface and that the
interface covers about 9 percent of the nation's land. In addition to
housing units, other types of infrastructure are located in the
wildland-urban interface, including power lines, campgrounds and other
recreation facilities, oil and gas wells, communications towers, and
roads.
After the National Fire Plan was developed, the agencies began
receiving sharp increases in funding for fuel reduction and, since
2001, Congress has appropriated between about $400 million and $500
million annually for fuel reduction under the plan. (App. II shows
agency fuel reduction funding appropriations and allocations for 2005
through 2007; app. III shows the agencies' fuel treatment
accomplishments.) In 2002, the President announced the Healthy Forests
Initiative (HFI), directing the departments of Agriculture and of the
Interior and the Council on Environmental Quality to provide
regulations to ensure more timely decisions, increase efficiency, and
improve results in reducing the risk of catastrophic wildland fires.
In 2003, Congress passed HFRA to reduce wildland fire risk to
communities, municipal water supplies, and other at-risk federal lands
through a collaborative process of planning, setting priorities for,
and implementing fuel reduction projects. In funding authorized fuel
reduction projects on federal land, HFRA requires the agencies to use
at least 50 percent of these funds in the wildland-urban
interface.[Footnote 11] The act also established separate environmental
analysis and administrative review procedures for fuel reduction
projects authorized under HFRA. In providing assistance for fuel
reduction activities on nonfederal lands, HFRA requires the agencies,
to the maximum extent practicable, to give priority to communities that
have adopted a community wildfire protection plan (community plan) or
have taken proactive measures to encourage willing property owners to
reduce fire risk on private property. A community plan identifies and
sets priorities for fuel reduction treatments and recommends the types
and methods of treatment on federal and nonfederal land that will
protect at-risk communities and essential infrastructure; community
plans also recommend measures to reduce structural ignitability
throughout the at-risk community. These plans are to be agreed upon by
the applicable local government, local fire department, and state
forest management agency, in consultation with other interested parties
and the federal land management agencies. As of February 2007, there
were at least 1,100 completed community plans covering almost 3,300
communities throughout the United States, and approximately 450
additional plans in progress, according to the National Association of
State Foresters. A community plan may cover one or more communities,
and some cover entire counties.
According to the 10-Year Strategy Implementation Plan, the goal of the
fuel reduction program is to reduce the risk of wildland fire to
communities and the environment. However, some fuel treatments provide
other benefits in addition to this overall program goal; for example,
agency staff sometimes conduct prescribed burns to both reduce fuel and
enhance wildlife habitat, or conduct mechanical thinning projects
before a commercial timber sale. Similarly, in addition to the
approximately $400 to $500 million appropriated for fuel reduction each
year, funds from other agency programs, such as wildlife management or
timber, often are used to conduct vegetation treatment projects that
reduce fuels as a secondary benefit. In addition, the agencies
sometimes receive partnership funding from outside organizations, such
as the Rocky Mountain Elk Foundation or The Nature Conservancy, to
conduct collaborative treatments.[Footnote 12]
GAO Has Reviewed Agencies' Fuel Treatment Programs:
While the federal agencies acknowledge the importance of setting
priorities for lands needing fuel treatments, we have identified a long-
standing pattern of shortcomings in the processes the Forest Service
and Interior use to identify and set priorities for these lands.
Between 1999 and 2007, we conducted several reviews of the agencies'
wildland fire management efforts, including the fuel reduction program.
We found that, while the agencies aimed to target fuel reduction
efforts to the highest risk areas, they could not ensure that they were
doing so. For example, in 1999, we found that the Forest Service
intended to give priority to treatments in the wildland-urban interface
but was hampered in doing so because it had not fully defined and
mapped such areas.[Footnote 13] We concluded that the Forest Service
needed a cohesive strategy outlining options and associated costs for
reducing fuel. We reiterated the agencies' need for a cohesive strategy
in several additional reports and testimonies issued between 2002 and
2007.[Footnote 14] In 2000 and 2002, we reported that the Forest
Service and Interior did not know how many communities were at high
risk of severe wildland fire or their locations and the cost to treat
them and, therefore, could not set treatment priorities.[Footnote 15]
We further reported in 2002 and 2003 that the agencies did not have
quantifiable long-term and annual performance measures to assess
progress in reducing the risks of wildland fire and that they measured
the performance of the fuel reduction program by number of acres
treated, which does not necessarily correlate to risk
reduction.[Footnote 16]
Similarly, in 2004, we reported that the agencies did not
systematically assess the risks to environmental resources and
ecosystems and, therefore, could not target fuel reduction efforts to
the resources and ecosystems at highest risk. To set priorities for
fuel reduction activities, the agencies must first identify areas at
risk from wildland fire by considering three elements: hazard, risk,
and value.[Footnote 17] A hazard is a potential event, such as a
wildland fire, and the conditions that cause it; in the case of
wildland fire, both the fuel conditions and the fire itself are the
hazard. Risk is the probability that an event such as a wildland fire
will occur. Values are the resources and property that could be lost or
damaged because of a hazard; in the case of wildland fire, values might
include social, economic, or environmental values.[Footnote 18] Without
considering all three elements, the agencies may not be appropriately
setting priorities for areas needing fuel reduction. For example, an
area with high vegetation hazard may not be in an area where fires are
likely to occur, making it a lower priority for treatment; likewise, a
high hazard area might not be near something of value that could be
lost or damaged in a fire, also making it a lower priority for
treatment.
We also found, through multiple reviews, that the agencies could
benefit from coordinating their efforts to manage wildland fires
because wildland fire is a shared problem that transcends
administrative boundaries. For example, in 2001 we reported that
federal policy for managing wildland fire required coordination,
consistency, and agreement among the Forest Service, Interior, and
Interior agencies, but we found that the agencies planned and managed
wildland fire management activities largely on agency-by-agency and
unit-by-unit bases, and could not ensure, among other things, that they
were allocating funds to the highest-risk communities and
ecosystems.[Footnote 19] In a 2002 report, we noted that the Forest
Service and Interior had either developed or were in the process of
developing numerous strategies that had different goals and objectives
and that were not linked, primarily because the agencies had been
managing their lands on an agency-by-agency basis for decades.[Footnote
20] In a subsequent testimony, which emphasized the agencies' need for
a cohesive strategy as well as clearly defined and effective
leadership, we concluded that effectively addressing wildland fire
would require a sustained and coordinated effort between
departments.[Footnote 21] (See Related GAO Products.)
The Forest Service Uses a Mix of Quantitative and Judgmental Processes
and Considers a Range of Factors in Allocating Funds and Selecting
Projects:
The Forest Service uses both quantitative and judgmental processes in
deciding how to allocate fuel reduction funds. At headquarters, the
agency increasingly relies on a quantitative process--reflected in a
computer model--to determine the relative need for fuel reduction funds
in each region. At the regional level, some offices primarily use
quantitative processes to allocate resources while others rely on
professional judgment. Similarly, the national forests use a mix of
quantitative and judgmental processes to select projects.
At the National Level, the Forest Service's Allocation Process
Increasingly Relies on a Quantitative Approach:
At the headquarters level, the Forest Service has developed a computer
model that assesses regions on various factors and assigns a score to
each region reflecting its relative priority for fuel reduction
funds.[Footnote 22] According to Forest Service officials, they
developed the model to address shortcomings that were highlighted by
Congress and that were previously identified by GAO, the Department of
Agriculture's Office of Inspector General, and the Office of Management
and Budget. These shortcomings included inadequate assessment of the
risk of wildland fires to communities, failure to clearly identify fuel
reduction priorities, and little assurance that funding is targeted to
these priorities.[Footnote 23] In addition, agency officials said they
developed the model to provide transparency, so that agency officials
at all levels, as well as Congress and others, can understand the
rationale behind allocation decisions.
In developing the model, the Forest Service brought together an
interdisciplinary group of senior leaders to determine the final list
of factors, which was based on an initial list developed by regional
fuel program managers. To determine the factor weightings, the group
followed a multistep process in which they determined the relative
importance of each factor by comparing it separately to every other
factor, and then synthesized the results to determine overall
weightings.[Footnote 24] The model includes 18 weighted factors, as
shown in table 1.
Table 1: Factors Considered in Forest Service Fuel Reduction Funding
Allocation Model:
Factors: Treatment effectiveness[A];
Weight (percent): 16.7.
Factors: Wildfire potential;
Weight (percent): 12.5.
Factors: Wildland-urban interface;
Weight (percent): 10.3.
Factors: Treatment method availability;
Weight (percent): 8.3.
Factors: Wildlife habitat objectives;
Weight (percent): 7.1.
Factors: Municipal water supply;
Weight (percent): 5.2.
Factors: Ecosystem vulnerability[B];
Weight (percent): 5.2.
Factors: Associated benefits[A,C];
Weight (percent): 4.2.
Factors: Vegetative maintenance;
Weight (percent): 4.2.
Factors: Biomass opportunity;
Weight (percent): 3.6.
Factors: Insects and disease;
Weight (percent): 3.6.
Factors: Invasive species;
Weight (percent): 3.6.
Factors: Vegetation departure[B];
Weight (percent): 3.6.
Factors: Watershed condition;
Weight (percent): 3.6.
Factors: Life cycle cost[A,D];
Weight (percent): 2.8.
Factors: Commercial timber;
Weight (percent): 2.6.
Factors: Smoke emissions[E];
Weight (percent): 1.7.
Factors: Use of legislative tools[F];
Weight (percent): 1.4.
Factors: Total; Weight (percent): 100.
Source: GAO analysis of Forest Service data.
Notes: Totals do not add to 100 percent due to rounding.
[A] No data were available for the 2007 allocation process.
[B] "Ecosystem vulnerability" and "vegetation departure" are measures
of fire regime condition class.
[C] "Associated benefits" is a measure of acres treated with fuel
reduction funds that achieve benefits for other programs, such as
wildlife or watershed.
[D] "Life cycle cost" is intended to measure the cost of treatments per
year.
[E] "Smoke emissions" is a measure of the acres of vegetation that
produces high levels of smoke during a wildland fire.
[F] "Use of legislative tools" is a measure of acres treated in
projects authorized in HFRA or HFI, identified in community wildfire
protection plans, or implemented using stewardship contracts.
Stewardship contracting involves the use of any of several contracting
authorities that were first authorized for use by the Forest Service on
a pilot basis in 1998, and were subsequently extended to BLM. In
practice, stewardship contracts generally involve the exchange of
goods, such as timber, for contract services, such as thinning of brush.
[End of table]
Several of the factors--such as fire potential, ecosystem
vulnerability, and wildland-urban interface--are designed to assess the
potential for severe wildland fires in each region and the likelihood
of damage resulting from such fires. For example, to determine the
potential for severe fires, the model analyzes data such as the number
and size of large wildland fires in each region. To determine the
likelihood of damage resulting from wildland fires, the model includes
data on values at risk, such as the locations of municipal water
supplies and wildland-urban interface.
Other factors are intended to encourage efficiency and effectiveness
within the fuel reduction program and across multiple Forest Service
programs, such as the forest products or wildlife management program,
to take advantage of opportunities to achieve objectives in other
programs. Regarding effectiveness, the model included a factor intended
to assess effectiveness in the regions--method availability. However,
in practice, this factor used data on the total number of acres treated
in each region--in effect rewarding regions for treating a large number
of acres regardless of how well the treatments reduced risk or of the
risk level of the areas treated. In addition, the model was designed to
include a factor to assess how effective individual fuel reduction
treatments are likely to be in reducing risk. However, the Forest
Service does not currently have data to make such an assessment;
consequently, for 2007, this factor did not influence allocations to
the regions.
In 2007, officials used the model's results to inform their decisions
about funding allocations to the regions, although they relied mainly
on the prior year's funding levels along with their professional
judgment. Officials used the model's results only to make minor
adjustments to allocations because the model was still being refined
and because they wanted to phase in funding changes gradually in order
to minimize budget-related disruptions. Headquarters officials said
they expect the model to have a stronger influence on future allocation
decisions.
The model assigned a numerical score to each region that indicated the
region's relative priority for fuel reduction funds, with higher scores
indicating higher priority. However, as shown in table 2 and figure 5,
the Forest Service's funding allocations to its regions are often at
odds with the priority scores resulting from the model, with some high-
scoring regions--such as the Northern and Eastern regions--receiving
less funding than some lower scoring regions such as the Pacific
Southwest and the Southwest regions.
Table 2: Forest Service Regions' Fiscal Year 2007 Fuel Reduction
Priority Scores and Funding Allocations (Dollars in thousands):
Forest Service Region: Southern;
Priority score: 574;
Funding allocation: $29,092.
Forest Service Region: Northern;
Priority score: 455;
Funding allocation: 15,782.
Forest Service Region: Eastern;
Priority score: 416;
Funding allocation: 9,718.
Forest Service Region: Intermountain;
Priority score: 408;
Funding allocation: 16,165.
Forest Service Region: Rocky Mountain;
Priority score: 399;
Funding allocation: 25,445.
Forest Service Region: Pacific Northwest;
Priority score: 389;
Funding allocation: 25,794.
Forest Service Region: Pacific Southwest;
Priority score: 388;
Funding allocation: 43,737.
Forest Service Region: Southwestern;
Priority score: 367;
Funding allocation: 37,341.
Forest Service Region: Alaska[A];
Priority score: [A];
Funding allocation: 805.
Total; Priority score: [Empty];
Funding allocation: $203,879.
Source: Forest Service.
Notes: The Forest Service also allocated $2.265 million to its research
stations and $ 95.109 million to its headquarters office and to cost
pools, which are used for expenses that cannot reasonably be charged to
a single program, including indirect, support, and common services
charges.
[A] The Forest Service excluded the Alaska region from its model
because the region has a small fuel reduction program relative to the
other regions and receives less than 1 percent of the agency's fuel
reduction funds.
[End of table]
Figure 5: Forest Service Regions' Fuel Reduction Priority Scores as a
Percentage of Total, Compared to Regions' Funding Allocations as a
Percentage of Total Allocations, Fiscal Year 2007:
The is a vertical bar graph with Percentage from 0 to 25 on the
vertical axis, and Regions on the horizontal axis. For each region,
two bars are depicted, representing (10 regional priority score as a
percent of total, and (2) regional portion of national allocations.
Percentages noted below are approximated from the graph.
Region: Southern;
Regional priority score: 17;
Regional portion of national allocations: 14.
Region: Northern;
Regional priority score: 13;
Regional portion of national allocations: 7.5.
Region: Eastern;
Regional priority score: 12;
Regional portion of national allocations: 5.
Region: Intermountain;
Regional priority score: 12;
Regional portion of national allocations: 7.5.
Region: Rocky Mountain;
Regional priority score: 12;
Regional portion of national allocations: 12.
Region: Pacific Northwest;
Regional priority score: 11;
Regional portion of national allocations: 13.
Region: Pacific Southwest;
Regional priority score: 11;
Regional portion of national allocations: 21.
Region: Southwestern;
Regional priority score: 11;
Regional portion of national allocations: 18.
[See PDF for image]
Source: GAO analysis of Forest Service data.
[End of figure]
According to Forest Service officials, the allocation amounts were not
more closely correlated with the priority scores for the following
reasons:
* As noted, the officials wanted to temper changes to regions' budget
allocations until they completed revisions to the model and developed
more confidence in its output in order to minimize funding shifts that
might prove inappropriate once the model is refined.[Footnote 25]
Agency officials told us that even when they become confident in the
model's output, they will likely implement changes incrementally in
order to minimize disruption to regions and national forests.
* Until the revisions have been completed, the model's results will be
tentative. An important focus of the revisions will be those 3 of the
model's 18 factors for which the Forest Service had no data sources in
2007. Because of the lack of data, these elements had no effect on the
regions' 2007 priority scores,[Footnote 26] but agency officials hope
to have data to inform these elements for future allocations.
* The relatively high priority score assigned to the Eastern region was
not consistent with agency officials' knowledge of the area--that is,
they believed that, relative to the other regions, there were fewer
destructive wildland fires in the Eastern region and, consequently,
they expected the region's priority score to be lower than it was. When
the officials consulted data on the number of structures burned in
wildland fires, their belief was confirmed. Consequently, agency
officials are reexamining the measures they used to assess risk and
exploring options for refining them.
* Fuel reduction costs vary widely from region to region, and when
making final allocations, the officials made adjustments to accommodate
this variation. For example, the Pacific Southwest region received the
largest allocation of any region, despite its relatively low priority
score, in part because treatment costs in the region are very high
(averaging about $535 per acre in 2006). Therefore, a relatively large
allocation is needed to fund even a moderate amount of work. At the
other end of the spectrum, treatment costs in the Southern region are
low (averaging about $32 per acre in 2006), meaning that needed work
can be accomplished with a smaller allocation. In addition, agency
officials said that, although the Southern region's priority score
might point toward a larger allocation for the region, nonmonetary
constraints--such as the size of the workforce--limit the amount of
work the region can accomplish and, therefore, the amount of funds that
can prudently be invested there. Further, in order to maintain overall
funding stability to the regions, officials coordinated regional fuel
reduction funding allocations with those of other Forest Service
resource programs, such as watershed management or forest products.
This coordination sometimes resulted in officials adjusting fuel
reduction funding allocations in order to compensate for adjustments in
these other programs' funding levels.
* The Forest Service allocated a portion of its fuel reduction funds
according to congressional direction. Specifically, congressional
committee reports accompanying relevant appropriations acts directed
the Forest Service to spend about $34 million (12 percent) of its 2006
fuel reduction funds in certain areas or on certain projects. The
Forest Service accommodated this congressional direction, regardless of
whether doing so was consistent with priority scores.
Some Forest Service Regions Use Quantitative Allocation Processes,
While Others Rely More on Professional Judgment:
For 2007, the Forest Service allowed each region to determine how to
allocate funds to its national forests and what factors to consider in
the process, as long as the factors were consistent with those
considered in the national allocation process. Four of the Service's
nine regions relied primarily on quantitative data in their allocation
processes to national forests, while five relied primarily on
professional judgment. In applying these processes, all nine regions
considered a combination of factors, many of which were similar to
those used at the national level. Beginning with the 2008 allocations,
the Forest Service plans to require regions to use the headquarters
model to inform allocation decisions.
Of the four regions that relied primarily on quantitative processes in
2007, one--the Rocky Mountain region--used a computer model that
analyzed geospatial data on vegetative condition and areas of insect-
killed trees to help assess relative wildland fire risk among the
national forests in the region. Regional officials then used their
judgment to consider other factors, such as lands in the wildland-urban
interface and acreage targets, to refine allocation amounts. Through
the risk assessment process, the region identified 6 emphasis forests
out of the 11 forests in the region and allocated over 70 percent of
the region's fuel reduction funds to these 6 forests. The Pacific
Northwest region also used a model, but its model incorporated regional
data on a number of factors, including the number of acres in fire
regimes I, II, and III; the number of acres identified in community
plans as being in the wildland-urban interface; and per-acre treatment
costs. Using these data, regional officials identified five forests in
the region where an extremely wet climate made the risk of damaging
wildland fires so low that they decided not to allocate any fuel
reduction funds to these forests and excluded them from the model.
Another region that relied on a quantitative process--the Pacific
Southwest region--used a scoring system that ranked forests primarily
on the basis of a risk assessment; the assessment incorporated multiple
factors, such as the number of acres in condition classes 2 and 3 and
in the wildland-urban interface. The region also used other factors,
such as the forests' capacity to conduct fuel treatment work, to make
smaller adjustments. The Intermountain region allocated about 80
percent of its fuel reduction funds in accordance with forests'
historical funding levels. For the remaining funds, the region
delegated priority decisions to collaborative interagency groups in
each of the region's states. These groups scored and ranked proposed
projects against a set of standard criteria and made funding
recommendations to the regional office.
The remaining five Forest Service regions relied primarily on
professional judgment and negotiation among agency officials when
determining funding allocations to national forests. Although these
regions did not use quantitative processes to assign priorities among
forests, they incorporated some of the same information included in
other regions' quantitative processes. For example, the Northern region
conducted a risk assessment for the region, but instead of using the
risk assessment to guide its allocations to the forests, the region
directed forests to use it to identify potential treatments. The region
then allocated funds to forests primarily on the basis of the forests'
proposed annual workloads. In the Southern region, officials used their
professional judgment to decide on allocations largely on the basis of
forests' reported capabilities, per-acre treatment costs, and local
priorities, and how they fit with expected regional targets and budgets.
Factors outside of the formal process influenced allocations, according
to Forest Service officials, but they did not always formally
incorporate these factors into the allocation process. For example, in
several regions, fuel reduction officials said they coordinated with
officials from other resource programs, such as the wildlife management
and vegetation management programs, when deciding on final allocations.
In doing so, they sometimes adjusted fuel reduction allocations to, for
example, prevent multiple programs from reducing allocations to a given
forest in the same year or to take advantage of efficiencies when
different programs' priorities overlapped in a given forest. In
addition, nearly every region reported considering acreage targets when
making allocation decisions--even those that did not report it as an
official part of their allocation processes. Regional officials noted
that pressure to meet the acreage targets established by Forest Service
headquarters sometimes trumped all other factors in allocation
decisions, especially in 2007 when targets increased at a faster rate
than funding levels. Another factor, according to agency officials in
several locations, was direction contained in congressional committee
reports accompanying relevant appropriations acts that a certain amount
of funding be allocated to specific forests or specific districts
within forests. As with headquarters, regional offices allocated funds
according to this direction, apart from any priority-setting process.
For example, in 2006, the Pacific Southwest region allocated nearly $21
million (about 52 percent of the region's budget) on the basis of
congressional committee report direction, in part to treat areas of
insect-killed trees in southern California. Finally, regions reported
shifting funds among forests, after initial allocation decisions had
been made, to accommodate unexpected circumstances during the year,
such as large wildland fires that prevented fuel reduction treatments
from being implemented as planned.
National Forests Select Projects Using Quantitative and Judgmental
Processes:
Like regional offices, national forests are allowed to determine what
processes to use and which factors to consider in selecting fuel
reduction projects to fund and implement, within the parameters of
national and regional direction. In practice, some forests rely more on
quantitative, data-driven processes, while others rely more on
professional judgment. Both consider a mix of factors, as the following
examples, based on our site visits to national forests, illustrate:
* Quantitatively based selection. The Arapaho-Roosevelt National Forest
in Colorado collaborated with another national forest, a national park,
Forest Service research scientists, and the Colorado State Forest
Service to develop a risk assessment that used quantitative data to map
the highest priority locations for fuel reduction treatments in the
area. Forest officials then used the risk assessment to prepare a 10-
year strategy with proposed annual treatments. Each year, forest
officials first consult the strategy and the risk assessment to
identify a list of projects to fund, and then adjust the list to meet
acreage targets within budget constraints. Similarly, officials of the
Angeles National Forest in Southern California convened a diverse group
of stakeholders and followed a step-by-step process to identify
priorities for fuel reduction treatments. During the process, Forest
Service officials provided information, such as the locations of
historical wildland fires and developed areas, as well as places where
fuel reduction was not feasible because, for example, the topography
was too steep to operate needed equipment. They then used fire behavior
models to show where fires could potentially burn and how various
proposed fuel reduction treatments might affect such fires. The end
result was a multiyear list of proposed projects that forest officials
used to select projects each year.
* Judgmental based selection. At the Medicine Bow-Routt National Forest
in Wyoming and the Chattahoochee-Oconee National Forest in Georgia,
officials relied largely on their knowledge and experience about the
area to select fuel reduction projects. Some of these officials had
worked at the same forest for decades. At the Ocala National Forest in
Florida, officials use their professional judgment to select projects,
which are almost all prescribed burns. However, because the forest's
fuel reduction program is so large and the vegetation grows so quickly,
the project selection process is founded on a rotational schedule.
Under this schedule, the forest aims to treat nearly all of its
approximately 130,000 burnable acres over a 4-year period.
Consequently, officials try to treat about a quarter of the acreage--or
slightly over 30,000 acres--each year. Forest officials also said they
consider other factors, such as wind direction, humidity, and human
activity (for example, popular areas for weekend recreation), when
determining the specific timing of a prescribed burn.
In addition to factors that national forests considered, unanticipated
factors influenced project selection decisions at the local level,
sometimes preventing planned projects from being implemented. In such
cases, agency staff frequently carried out lower priority projects in
place of the originally planned projects. For example, wildland fires
sometimes burned in locations planned for fuel reduction treatments,
making the treatments unnecessary; in other cases, litigation prevented
planned treatments from being implemented as scheduled.
Interior and Its Agencies Use a Mix of Quantitative and Judgmental
Processes and Consider a Range of Factors in Allocating Funds and
Selecting Projects:
Interior and its agencies--BLM, BIA, FWS, and NPS--use both
quantitative and judgmental processes for allocating fuel reduction
funds and selecting projects, and consider multiple factors, many of
which are similar to those used by the Forest Service. In 2007,
Interior allocated funds to its four agencies primarily on the basis of
historical funding levels, but it is currently developing a computer
model similar to the Forest Service's. Like Interior, the BLM national
office allocated funds to its state offices primarily on the basis of
historical funding levels in 2007 but is expecting to implement a new
funding allocation model in 2008. The majority of BLM state and local
offices allocated funds and selected projects using quantitative
processes, many of which use scoring systems. The other three Interior
agencies' national, state, and local offices used both quantitative and
judgmental processes to allocate funds and select projects, considering
a range of factors.
Interior Allocates Funds to Its Agencies Primarily on the Basis of
Historical Funding Levels:
Interior's allocations to BLM, BIA, FWS, and NPS have remained fairly
constant from year to year, measured on a percentage basis, because the
department primarily allocates fuel reduction funds on the basis of
past funding levels--what one departmental official called "allocation
by tradition." This funding pattern dates back to 2001, when the
Interior agencies began receiving a sharply increased amount of fuel
reduction funds as a result of the National Fire Plan. Since then, the
percentage of Interior's fuel reduction funding that is allocated to
each of the agencies has remained consistent, with BLM receiving about
50 percent of the funding, BIA receiving about 20 percent, and FWS and
NPS each receiving about 15 percent. Figure 6 shows the percentage of
Interior's total fuel reduction appropriation that was distributed to
each agency from 2001 through 2007.
Figure 6: Percentage of Interior's Total Fuel Reduction Funds Allocated
to the Interior Agencies, Fiscal Years 2001 through 2007:
This figure is a line graph with four lines (BLM, BIA, NPS, and FWS)
depicting fund allocated to agencies by fiscal year. The vertical axis
depicts percentage from 0 to 100. The horizontal axis depicts fiscal
years, 2001 through 2007.
[See PDF for image]
Source: GAO analysis of Interior data.
[End of figure]
In 2001, Interior established initial funding allocations on the basis
of estimates of each agency's infrastructure and capacity (i.e., the
amount of work each could accomplish), which it determined by compiling
field requests from the four agencies. However, at the time, most of
the agencies and their field units had little infrastructure related to
the fuel reduction program--including limited staff--so many units did
not have the resources to collect extensive information on fuel
reduction needs, according to agency officials. As a result, agency
officials had to make allocation decisions based on limited
information. Some agency and departmental officials have stated that
the allocations need to be revisited now that the fuel reduction
program has been in place for several years.
Each year, the department tells the four agencies how much funding the
department has requested for the fuel reduction program and what its
acreage targets are for treatments within and outside of the wildland-
urban interface. The agencies' fuel program leads--the headquarters
officials in charge of each agency's fuel reduction program--then meet
to determine how to divide the funds and set targets for each agency.
However, the fuel program leads do not have the authority to
significantly adjust the funding allocations from previous levels;
rather, such changes would have to be determined at the department
level, according to headquarters officials. The agencies' field units
submit proposed project lists to the regions, which review these lists
before forwarding them to headquarters; these lists provide the fuel
program leads with an idea of each agency's needs and capabilities when
determining funding allocations. After the fuel program leads decide
upon initial allocations, they may shuffle funds within or between
their agencies throughout the year to adapt to uncontrollable
circumstances, such as weather conditions. The majority of fuel
treatments that the Interior agencies conduct depend on the weather,
and sometimes weather conditions prevent work from being completed. For
example, if a drought in the Southeast makes vegetation too dry for
safe prescribed burns, Interior may shift funds to units in the western
United States. In practice, these considerations may result in
Interior's shifting funds from FWS and NPS, which conduct a large
number of fuel treatments in the Southeast, to BLM or BIA, which
conduct most of their fuel treatments in the West.
In 2007, Interior allocated 5 percent of its funds to the agencies
using a model similar to the Forest Service model, and it plans to use
the model to influence a greater portion of allocations in future
years.[Footnote 27] The department developed the model after Interior
and the Forest Service received congressional committee direction in
2005 to develop a common method for setting project priorities.
Interior's 2007 model included a range of factors, such as the amount
of land each agency manages with certain fuel conditions and the degree
to which each agency used biomass, but included fewer factors than the
Forest Service's model because some data were not yet available.
Because Interior does not currently have a good method for measuring
efficiency or effectiveness, its 2007 model used the legislative tools
factor, which measures the extent of use of HFI and HFRA planning
authorities, to measure efficiency, and the number of acres treated to
measure effectiveness. The following provides the complete list of
factors used in Interior's 2007 model:[Footnote 28]
* number of fire starts,
* number of large fires (defined as 500 acres or more),
* fuel conditions,
* biomass utilization,
* number of threatened and endangered species,
* fire regime condition class improvement,
* use of legislative tools (HFI/HFRA),
* number of acres treated, and:
* wildland-urban interface.
Results from the 2007 model were generally consistent with Interior's
allocations to the agencies in previous years. However, Interior is
still making changes to the model, including determining how to weight
the factors, so this may not be the case in future years. According to
departmental officials, Interior intends to be cautious in applying the
new model and making significant changes to current allocations because
Interior and the Forest Service are currently developing the Fire
Program Analysis (FPA) system--an interagency fire management planning
and budgeting model--and they expect information from that system to
inform future allocation decisions.[Footnote 29] By proceeding slowly,
the department hopes to avoid potentially disruptive fluctuations in
regional and field unit allocations.
Once they have received their allocations from the department, Interior
agencies determine how to allocate fuel reduction funds to the regions
within the parameters of departmental and congressional direction.
Interior officials have stated that they would like the agencies to use
more rigorous allocation processes in the future, though one
departmental official noted that he does not want the agencies to
invest substantial funding or time and effort to develop new allocation
processes pending the expected completion of the FPA. Interior guidance
lists the following priorities for selecting projects:
* All projects must result from a collaborative process.
* Funding will be targeted to the wildland-urban interface.
* Within the wildland-urban interface, focus should be on projects near
wildland-urban interface communities at greatest risk of fire;
communities that have completed a community plan or its equivalent; and
communities where there is an active partnership with volunteer
efforts, in-kind services, or partners who contribute funding.
* Outside of the wildland-urban interface, focus should be on areas in
condition class 2 or 3 in fire regimes I, II, or III, or those in
condition class 1 where landscape conditions could quickly deteriorate
to condition 2 or 3.
* Priority should also be given to projects using mechanical
treatments, with special emphasis on projects yielding biomass that can
be sold or traded to companies or the local community; and projects
using contractors, particularly those projects conducted under
contracts that support rural communities' stability.
* Prescribed burning is to be used when weather and resource conditions
permit, where mechanical treatments are not appropriate, and as
maintenance treatments following mechanical work.
* Managers must make maximum practical use of tools provided by HFRA
and HFI.
BLM Increasingly Uses Quantitative Processes in Allocating Funds and
Selecting Projects:
In 2007, BLM headquarters allocated funds to its state offices
primarily on the basis of historical funding levels; however, agency
officials told us that, starting in 2008, BLM plans to use a
quantitative process incorporating factors similar to those used in
Interior's new model, with a greater emphasis on collaboration and
local priorities. BLM headquarters provides flexibility to state
offices and local units when allocating funds and selecting projects
but directs these offices to consider Interior and agency guidance. The
majority of BLM state offices and local units used quantitative
processes to allocate funds and select projects in 2007, frequently
scoring projects against a set of weighted factors.
BLM Allocates Funds to Its State Offices Primarily on the Basis of
Historical Funding Levels but Plans to Use a More Quantitative Approach
in 2008:
In 2007, BLM headquarters allocated funds to its state offices largely
on the basis of past funding levels--as in previous years--as a way to
ensure that funding levels remain relatively stable, but it also
considered proposed projects, national priorities, and the extent to
which state offices met past acreage targets established by BLM. While
the project lists do not largely influence allocations to state
offices, state offices use these lists to allocate funds to field
units, and field units use them to select projects for implementation.
Table 3 shows the 2007 allocations to the BLM state offices. (App. II
also shows 2005 and 2006 allocations.)
Table 3: BLM Allocations to State Offices, Fiscal Year 2007:
State office: Oregon/Washington;
Allocation: $24,878,000;
Percent of BLM total state office allocation: 27.1.
State office: Idaho;
Allocation: 14,598,000;
Percent of BLM total state office allocation: 15.9.
State office: Utah;
Allocation: 10,078,000;
Percent of BLM total state office allocation: 11.0.
State office: California;
Allocation: 7,322,000;
Percent of BLM total state office allocation: 8.0.
State office: Colorado;
Allocation: 6,843,000;
Percent of BLM total state office allocation: 7.5.
State office: Nevada;
Allocation: 6,414,000;
Percent of BLM total state office allocation: 7.0.
State office: New Mexico;
Allocation: 6,412,000;
Percent of BLM total state office allocation: 7.0.
State office: Montana;
Allocation: 5,461,000;
Percent of BLM total state office allocation: 6.0.
State office: Arizona;
Allocation: 4,355,000;
Percent of BLM total state office allocation: 4.7.
State office: Wyoming;
Allocation: 3,684,000;
Percent of BLM total state office allocation: 4.0.
State office: Alaska;
Allocation: 1,556,000;
Percent of BLM total state office allocation: 1.7.
State office: Eastern States;
Allocation: 126,000;
Percent of BLM total state office allocation: 0.1.
State office: Total;
Allocation: 91,727,000[A];
Percent of BLM total state office allocation: 100.0.
Source: GAO analysis of BLM data.
Notes: Total allocation includes the allocation for the current year
plus carryover from the previous fiscal year.
[A] BLM allocated an additional $8,473,000 for BLM headquarters,
science centers, training costs, and other support costs.
[End of table]
As shown in table 3, the Oregon/Washington, Idaho, and Utah state
offices got substantially more funding than the other states--more than
half of BLM's total funding. The Oregon/Washington state office alone
received more than $24 million--27 percent of BLM's state office
funding; one BLM field unit in Oregon, the Medford district office,
received over $9 million in 2007--more than nine state offices each
received in total funding. According to some agency officials, the
relatively high level of fuel reduction funding directed toward the
Oregon/Washington state and Medford district offices is, in part, the
result of BLM's emphasis on providing stable levels of funding to
states and field units. According to these officials, when BLM (along
with other federal agencies) received a sharp increase in fuel
reduction funding in 2001, agency officials sought to identify units
that could implement fuel reduction projects quickly. Because the
Oregon/Washington and Medford offices were identified as having the
capacity to undertake a large number of fuel reduction projects, they
received a substantial portion of the new funding. However, another
agency official told us these large amounts are justified because there
is substantial wildland fire risk in Oregon and, therefore, a great
need for fuel treatments because vegetation grows very quickly in the
western part of the state, there is considerable wildland-urban
interface, and wildland fire suppression costs are high.
Starting in 2008, BLM plans to use a model to influence funding
allocations to state offices for fuel reduction. Use of the model is
intended to ensure that the highest priority work is funded and that
BLM's fuel reduction treatments are integrated with other vegetation
treatments, such as range improvement projects, to effectively achieve
fire and resource management goals and objectives. According to a
headquarters official, the new model is intended to facilitate
comparison of risk and needed work at the national and state levels in
order to set priorities for funding among states and communities.
Headquarters officials will use the model results to make allocation
decisions but will shift no more than 20 percent of the previous year's
allocations to each state in 2008 and 2009.
The model has three components: (1) treatment characteristics; (2) a
measure of the degree of threat; and (3) an efficiency measure. For the
first component--treatment characteristics--the model will score every
proposed project on a set of weighted factors, such as local priority
ratings, the availability of joint funding, and condition class; there
are separate factors and weights for projects within and outside of the
wildland-urban interface. The second component--the measure of the
degree of threat--currently combines three elements: the number of fire
starts, the number of large fires (i.e., fires greater than 300 acres),
and local risk ratings. The third component--efficiency--is currently
measured by past performance on acreage targets, past performance on
estimating treatment costs, and treatment cost per acre. According to
agency officials, they intend to eventually include a measure of
effectiveness in the model, which would indicate how well a treatment
reduces risk or achieves other objectives. However, because BLM does
not currently have a good way to measure effectiveness, it is using
measures of efficiency until it develops a better approach. Table 4
shows the complete list of factors used in the model and their weights.
Table 4: Factors and Factor Categories BLM Considers in BLM Fuel
Reduction Funding Allocation Model:
Funding allocation model components:
Factors evaluated: Community plan or equivalent;
Weights for wildland-urban interface treatments: 0.14;
Weights for treatments outside the wildland-urban interface: 0.01.
Overall weight: [Empty].
Funding allocation model components:
Factors evaluated: High local priority[A];
Weights for wildland-urban interface treatments: 0.14;
Weights for treatments outside the wildland-urban interface: 0.11;
Overall weight: [Empty].
Funding allocation model components:
Factors evaluated: Mechanical treatment;
Weights for wildland-urban interface treatments: 0.12;
Weights for treatments outside the wildland-urban interface: 0.04;
Overall weight: [Empty].
Funding allocation model components:
Factors evaluated: Joint funding available;
Weights for wildland-urban interface treatments: 0.10;
Weights for treatments outside the wildland-urban interface: 0.08;
Overall weight: [Empty].
Factors evaluated: HFRA/HFI NEPA type[B];
Weights for wildland-urban interface treatments: 0.10;
Weights for treatments outside the wildland-urban interface: 0.08;
Overall weight: [Empty].
Funding allocation model components:
Factors evaluated: Stewardship project[C];
Weights for wildland-urban interface treatments: 0.10;
Weights for treatments outside the wildland-urban interface: 0.08;
Overall weight: [Empty].
Funding allocation model components:
Factors evaluated: Multiple land ownership;
Weights for wildland-urban interface treatments: 0.08;
Weights for treatments outside the wildland-urban interface: 0.03;
Overall weight: [Empty].
Treatment characteristics:
Factors evaluated: Moderate local priority[A];
Weights for wildland-urban interface treatments: 0.08;
Weights for treatments outside the wildland-urban interface: 0.06;
Overall weight: 0.45.
Treatment characteristics:
Factors evaluated: Biomass utilized[D];
Weights for wildland-urban interface treatments: 0.05;
Weights for treatments outside the wildland-urban interface: 0.04;
Overall weight: [Empty].
Treatment characteristics:
Factors evaluated: Large-scale treatment[E];
Weights for wildland-urban interface treatments: 0.05;
Weights for treatments outside the wildland-urban interface: 0.10;
Overall weight: Funding allocation model components: [Empty].
Treatment characteristics:
Factors evaluated: Low local priority[A];
Weights for wildland-urban interface treatments: 0.02;
Weights for treatments outside the wildland-urban interface: 0.02;
Overall weight: Funding allocation model components: [Empty].
Treatment characteristics:
Factors evaluated: Condition class 2 or 3;
Weights for wildland-urban interface treatments: 0.01;
Weights for treatments outside the wildland-urban interface: 0.11;
Overall weight: Funding allocation model components: [Empty].
Treatment characteristics:
Factors evaluated: Impacted species;
Weights for wildland-urban interface treatments: 0.01;
Weights for treatments outside the wildland-urban interface: 0.10;
Overall weight: Funding allocation model components: [Empty].
Treatment characteristics:
Factors evaluated: Fire regime I, II, or III;
Weights for wildland-urban interface treatments: [F];
Weights for treatments outside the wildland-urban interface: 0.06;
Overall weight: Funding allocation model components: [Empty].
Treatment characteristics:
Factors evaluated: Fire or other treatment method[G];
Weights for wildland-urban interface treatments: [F];
Weights for treatments outside the wildland-urban interface: 0.08;
Overall weight: [Empty].
Degree of threat:
Factors evaluated: Number of large fires (greater than 300 acres)[H];
Weights for wildland-urban interface treatments: 0.50;
Weights for treatments outside the wildland-urban interface: 0.50;
Overall weight: 0.35.
Degree of threat:
Factors evaluated: Number of fire starts[H];
Weights for wildland-urban interface treatments: 0.25;
Weights for treatments outside the wildland-urban interface: 0.25.
Overall weight: [Empty].
Degree of threat:
Factors evaluated: Local risk rating[I];
Weights for wildland-urban interface treatments: 0.25;
Weights for treatments outside the wildland-urban interface: 0.25.
Overall weight: [Empty].
Efficiency:
Factors evaluated: Past performance on acreage targets[J];
Weights for wildland-urban interface treatments: 0.50;
Weights for treatments outside the wildland-urban interface: 0.50;
Overall weight: 0.20.
Efficiency:
Factors evaluated: Past performance on treatment cost estimates[K];
Weights for wildland-urban interface treatments: 0.40;
Weights for treatments outside the wildland-urban interface: 0.40.
Overall weight: [Empty].
Efficiency:
Factors evaluated: Cost per acre;
Weights for wildland-urban interface treatments: 0.10;
Weights for treatments outside the wildland-urban interface: 0.10.
Overall weight: [Empty].
Source: GAO analysis of BLM data.
[A] The local priority rating is assessed at the local level and is a
way for the field to communicate project priorities that may not be
well-represented by other factors.
[B] The "HFRA/HFI NEPA-type" factor weights projects that use National
Environmental Policy Act (NEPA) planning tools authorized by HFRA or
HFI.
[C] Stewardship projects are accomplished through the use of
stewardship contracting, which involves the use of any of several
contracting authorities that were first authorized for use by the
Forest Service on a pilot basis in 1998, and were subsequently extended
to BLM. In practice, stewardship contracts generally involve the
exchange of goods, such as timber, for contract services, such as
thinning of brush.
[D] The "biomass utilized" factor weights projects that make use of
biomass--small-diameter trees, branches, and other organic material--
removed through fuel reduction.
[E] Large-scale treatments are treatments that are at least 150 percent
larger than the average treatment.
[F] This factor was not used to determine the treatment scores for
wildland-urban interface treatments.
[G] This factor weights projects treated with prescribed fire or other
treatment methods, such as grazing or herbicides.
[H] The "number of large fires" and "number of fire starts" factors are
determined at the field office or district level and applied to all
treatments within that field office or district.
[I] The risk rating is assessed at the local level and is to be
determined from community plans or risk assessment programs.
[J] The "past performance on acreage targets" factor is calculated at
the state level and applied to all treatments within the state.
[K] The "past performance on treatment cost estimates" factor is
calculated at the state level and applied to all treatments within the
state.
[End of table]
The BLM national office also directs state offices and local units to
consider Interior and BLM priorities when allocating funds and
selecting projects. BLM-specific guidance directs state offices and
local units to coordinate fuel treatments with other resource
management activities, such as timber and wildlife habitat; target
funds to wildland-urban interface areas identified through a
collaborative process; target non-wildland-urban interface funds to
ecosystems that have the highest risk-reduction potential; and use HFI
and HFRA planning tools.
The Majority of BLM State Offices Incorporate Quantitative Approaches
in Their Allocation Processes:
The BLM national office allows state offices to choose the approach
they use in allocating funding to field units, as long as they take
into account departmental and BLM priorities, and state offices will
continue to have this flexibility with the implementation of the new
national allocation process, according to headquarters officials. In
2007, 6 of the 11 BLM state offices primarily used quantitative
approaches to inform their allocation processes, and 5 primarily used a
judgmental approach.[Footnote 30] Nine of 11 state offices considered
targets or past performance, and 10 considered at least one factor
related to collaboration, such as community plans. Eight of 11 state
offices considered at least one factor to estimate wildland fire risk,
such as local-or state-level risk assessments or fire regime condition
class.
The six state offices that allocated funds using quantitative processes
in 2007 primarily used weighted scoring systems--similar to the state
scoring component of BLM's new model--to set priorities for projects.
While the specific factors and their weights varied by state, many
factors were commonly used and were similar to those used in BLM's
headquarters system; each of the states had separate lists of factors
for projects within and outside of the wildland-urban interface. For
wildland-urban interface projects, five of the six state offices
emphasized factors such as local risk ratings or community hazard
assessments to estimate risk from wildland fire, and all six offices
considered a variety of other factors, including community support and
joint funding, to measure the extent of collaboration. For projects
outside of the wildland-urban interface, all six offices gave priority
to projects in condition classes 2 or 3, jointly funded or
collaborative projects, and projects that improved threatened and
endangered species habitat, as well as a variety of other factors. Once
the state offices had the field offices' project lists, state and field
offices generally negotiated to determine final funding allocations.
The remaining five state offices primarily used judgmental processes to
allocate funding to field units. For example, in 2007, the Oregon/
Washington state office allocated funding using professional judgment
and negotiation, which included numerous discussions with field units'
fuel program staff to assess the units' priorities and capabilities.
The state office primarily considered capability and past performance
of field offices and BLM's national priorities when making the final
allocations. Starting in 2008, the office plans to use a model to
allocate base funding for fuel reduction, which covers salaries and
other fixed costs, but will continue to allocate project funding using
the current approach, which relies primarily on professional judgment
and negotiation.
BLM officials told us that factors outside of the formal process
influenced allocations. For example, in several states, agency
officials said they coordinated with officials from other resource
programs, such as the range or weeds programs, at the state or local
level when deciding on final allocations or selecting projects. As a
result, they sometimes selected projects that used funding from
multiple resource areas, or benefited these areas, over other projects
in order to take advantage of efficiencies. Many state offices also
reported that they considered acreage targets when making allocation
decisions. According to one state official, acreage targets were the
most influential factor in allocation decisions, and several agency
officials said that lower priority projects were sometimes funded to
meet acreage targets. Finally, state offices reported shuffling funds
among or within field units after allocation decisions had been made to
accommodate uncontrollable circumstances throughout the year, such as
weather conditions that prevented prescribed burns from being
implemented as planned.
BLM state offices also devote substantial effort and funding to assist
in the development of community plans, and allocate a significant
portion of fuel reduction funding to projects on private land. For
example, the Montana state office funded the development of 49 out of
54 completed community plans throughout the state, according to an
agency official. Also, when the Montana state office and its field
units allocate funding to, and select projects in, the wildland-urban
interface, proposed projects on private land--which are submitted to
BLM by counties--are ranked using the same system as BLM projects.
Consequently, BLM projects on federal land essentially compete for the
same funding as projects on private land. In California, the BLM state
office allocates more than half of its wildland-urban interface funding
to a community assistance program, through which fuel treatments on
private, state, or tribal lands adjacent to or in the vicinity of
federal lands are funded through an interagency grant process.
The Majority of BLM Field Units Incorporate Quantitative Approaches
into Their Project Selection Processes:
As with the BLM state offices, in 2007, the majority of BLM field units
used quantitative approaches that incorporated a range of factors--many
of which were similar or identical to the ones used by state offices--
to select and rank projects. For example, the Twin Falls district
office in Idaho scored all projects using a weighted scoring system
developed by the BLM Idaho state office. It then ranked the projects,
considering factors such as project scores and areas identified in
community plans. The Billings field office in Montana also used a
weighted scoring system to rank projects. Field staff initially
identified projects using community plans or the field office's risk
assessment--which analyzed fuel type, fire regime condition class, and
fire occurrence to identify high-risk fire areas--and then scored the
projects using the Montana state office's weighted scoring system to
identify high-, medium-, and low-priority projects.
Also, like national forests, BLM field units were sometimes influenced
by unanticipated factors when selecting projects. For example, agency
officials sometimes deferred planned projects because newly proposed
projects suddenly became a high priority. They pointed to situations in
which nonprofit organizations donated funds to pay for projects and
agency officials gave those projects a higher priority. In Colorado,
recent oil and gas development, as well as construction of new
subdivisions in high-risk areas, have caused field units to shift
priorities to conduct treatments near these developments, according to
a BLM official.
BIA Allocates Funds Largely on the Basis of Units' Performance History,
while FWS and NPS Use Quantitative and Judgmental Processes:
In 2007, the three remaining Interior agencies--BIA, FWS, and NPS--
allocated fuel reduction funds using quantitative and judgmental
processes and considering a variety of factors. Like BLM, these
agencies provide flexibility to regional offices and local units in
determining how to allocate funds and select projects and direct them
to consider departmental priorities. (See app. II for these agencies'
2005 through 2007 allocations to their regional offices.)
BIA and FWS Headquarters Allocate Funds Using Quantitative Processes,
While NPS Headquarters Allocates Funds Primarily on the Basis of
Historical Funding Levels:
In 2007, BIA headquarters allocated fuel reduction funds to its regions
using a formula that considered past performance and proposed work and
that essentially rewarded regions for their accomplishments. The
formula allocated to each region a percentage of the region's total
budget request, based on the percentage of the prior 3 years' acreage
targets that the region met. For example, if a region had met 95
percent of its total acreage target since 2004, the region would
receive about 95 percent of its requested budget for 2007. BIA placed a
cap on the amount of funding that regions could request, based on their
previous year's accomplishments.[Footnote 31] According to a
headquarters official, BIA rewards those regions and units that achieve
acreage targets because, in many instances, units do not meet targets.
FWS headquarters allocated 2007 funds to regional offices using a
quantitative model that considers multiple factors, including
historical fire occurrence, fuel conditions, community assessments of
risk, and field unit past performance. The model has separate modules
for projects within and outside of the wildland-urban interface, and
produces a weighted score for each FWS field unit. In the wildland-
urban interface module, the most influential factors are communities at
risk, local hazard rankings, and fire conditions. For the non-wildland-
urban interface module, the most influential factors are past
performance and proposed work.
NPS headquarters allocated 2007 funding to regional offices primarily
on the basis of historical funding levels. These levels were originally
set by a model that determined funding allocations through a risk
assessment, which considered vegetation types, fuel types, fire return
intervals, and other data, and through an effectiveness measure that
examined treatment success for different vegetation types. According to
an NPS official, the agency maintained funding proportions at the
model's 2005 level after Interior directed it to work on the FPA; NPS
decided that it would have been too much work for field staff to
maintain the model while also preparing data for the FPA. Furthermore,
they believed that the model, initially developed more than 20 years
ago, was outdated and did not merit additional financial investment
while the FPA was being developed.
BIA, FWS, and NPS Regional Offices Allocate Funds to Field Units Using
Quantitative and Judgmental Processes:
BIA, FWS, and NPS allow their regions the flexibility to determine how
to allocate funding to field units, provided the processes and factors
are consistent with departmental and agency guidance. The BIA national
office encourages regions to adopt allocation strategies similar to the
one used at headquarters--which rewards past performance--and some of
BIA's regional offices have done so, such as the Rocky Mountain and
Northwest regions. The Rocky Mountain region, for example, used a
quantitative process to allocate fuel reduction funds, using an
allocation formula similar to the one used by BIA headquarters but
using only the previous year's accomplishment rate, rather than the 3-
year average headquarters used. Likewise, one FWS regional office that
we visited used a quantitative process to allocate fuel reduction funds
in 2007: FWS's Mountain-Prairie region allocated funds to local units
using FWS's national model, but regional officials adjusted the model's
allocations on the basis of their knowledge about local factors, such
as community support for projects and field unit staffing levels.
Other BIA and FWS regional offices, and all of the NPS regional offices
that we visited, allocated fuel reduction funds in 2007 using
judgmental processes that incorporated a range of factors. For example,
BIA's Southwest region allocated funds primarily on the basis of
project rankings (as determined at the local level) and cost
efficiency, according to a regional official. Likewise, FWS's Southeast
region allocated 2007 funds according to a regional official's
assessment of a variety of factors, such as field units' programs of
work and wildland fire activity; this official has many years of
experience managing the region's fuel reduction program. In NPS's
Pacific West region, a group of local and regional fire and fuel
program staff determined funding allocations on the basis of park
priorities, past performance, and conformance with NPS policy, balanced
against regional funding levels and acreage targets.
As in other agencies, officials told us that factors outside of the
formal process also influenced allocations. Several BIA and NPS
officials told us that staffing constraints at field units may affect
allocations. For example, many park units have very small fuel
treatment programs and no staff dedicated solely to the program;
therefore, the fuel reduction programs at such units may be eliminated
if staff, who have numerous collateral duties, no longer have the time
to plan or implement treatments. Furthermore, the location of some
field units makes it difficult to recruit and retain qualified staff;
the field units are located either in areas with high costs of living
or in remote areas. Without dedicated staff to manage fuel reduction
programs at such field units, their capacity to plan and implement
projects and spend any funding allocation is limited, so capacity
becomes the determining factor regardless of other factors considered
in the allocation process, according to agency officials. Some BIA
officials told us that self-determination limits BIA's influence over
the tribes; self-determination provides tribes with the authority to
manage federal programs when they choose to do so, as well as the
authority to choose not to emphasize a given program. In some regions,
acreage targets also affected allocation and project selection
processes, and one agency official told us that projects were sometimes
developed and implemented specifically to meet targets. However, other
BIA, FWS, and NPS regional officials told us that they did not assign
acreage targets to field units or that there was little pressure to
meet targets. Finally, regions reported shifting funds among field
units after allocation decisions had been made to adapt to
uncontrollable circumstances, such as weather conditions that prevented
planned projects from being implemented.
Local BIA, FWS, and NPS Units Select Projects Using Quantitative and
Judgmental Processes:
Some BIA, FWS, and NPS local units selected projects in 2007 using
quantitative processes. For example, in NPS's Sequoia and Kings Canyon
National Parks in California, agency officials identified projects
using a model that determined high-risk areas on the basis of several
factors, such as the risk of a fire starting and the location of the
wildland-urban interface. Park officials used the model information, as
well as additional factors, such as values at risk, sequencing of
treatments, and project accessibility, to select projects. BIA's Zuni
Agency in New Mexico also used a quantitative process to select
projects. The fuels specialist analyzed geographic information--for
example, on housing density and existing vegetation--to identify and
rank projects.
Other BIA, FWS, and NPS units primarily used judgmental processes when
selecting projects for 2007. For example, at FWS's Merritt Island
National Wildlife Refuge in central Florida, field staff selected
projects primarily on the basis of the rotational schedule for
prescribed burns. Refuge officials also considered other factors, such
as wildlife habitat, to select which projects to complete that year.
According to agency officials, the refuge has habitat for the scrub
jay, a threatened species, and while prescribed burns generally improve
this habitat, too much prescribed burning can be disruptive. NPS's Cape
Canaveral National Seashore, which neighbors Merritt Island National
Wildlife Refuge, also selected projects judgmentally, and in
coordination with refuge staff. The process was primarily influenced by
the location of the wildland-urban interface and threatened and
endangered species habitat.
BIA, FWS, and NPS field units, like national forests and BLM field
offices, also adapted to unanticipated events when selecting projects.
In some cases, field units were forced to accommodate unique
circumstances. For example, the Merritt Island National Wildlife Refuge
is adjacent to a National Aeronautics and Space Administration
facility, and, during the days immediately before and during scheduled
rocket or shuttle launches, the refuge must put all prescribed burns on
hold.
Several Improvements Could Help Better Ensure That Fuel Reduction Funds
Are Allocated to Effectively Reduce Risk:
Although the Forest Service and Interior are taking steps to enhance
their funding allocation and project selection processes--for example,
by developing models to assist in making allocation decisions--there
are several improvements they could make to better ensure that they
allocate fuel reduction funds to effectively reduce risk. Specifically,
when allocating funds and selecting projects, the agencies could
improve their processes by (1) consistently assessing all elements of
wildland fire risk, including hazard, risk, and values; (2) developing
and using measures of the effectiveness of fuel reduction treatments;
(3) using this information on effectiveness, once developed, to assess
the cost-effectiveness of potential treatments; (4) clarifying the
relative importance of the numerous factors they use in allocating
funds, including factors unrelated to risk or effectiveness; and (5)
following a more systematic process in allocating funds. While the
agencies have recognized the importance of these elements--particularly
risk, treatment effectiveness, and cost effectiveness--in several
strategy documents, they have not effectively incorporated them into
their allocation processes.
The Agencies Do Not Consistently Assess All Elements of Risk When
Allocating Funds:
The agencies have repeatedly stressed the importance of identifying
high-risk areas in setting priorities and allocating funds for fuel
reduction; for example, in their 2006 document Protecting People and
Natural Resources: A Cohesive Fuels Treatment Strategy (Cohesive
Strategy),[Footnote 32] the Forest Service and Interior declared that
they "expect to ensure that fuel project investments are cost-
effectively allocated to achieve risk reductions." Similarly, in its
2007 budget justification, the Forest Service declared that the fuel
reduction program focuses on reducing the risk of wildland fire and
long-term damage to resources and property; likewise, Interior's 2007
budget justification declared that the department intended to reduce
fuels in order to "provide better risk reduction to communities and
resources."
At the national level, the Forest Service and FWS headquarters
incorporated nationwide risk assessments into their 2007 allocation
processes; Interior did so for only 5 percent of the funds it allocated
to the four Interior agencies; and BIA, BLM, and NPS did not include
risk assessments in their national allocation processes at all,
although BLM officials said they are taking steps to do so in the
future. According to Forest Service and Interior agency officials, it
has been difficult to develop national risk assessments because they
require nationally consistent data, which have not always been
available.[Footnote 33] Furthermore, some of the available national
data on vegetation type and condition were designed for forests and,
consequently, are not as accurate for shrublands and grasslands.
At the regional and local levels, some agency offices used risk
assessments when allocating funds and selecting projects, while others
did not. One of the Forest Service's 9 regions and 2 of BLM's 11 state
offices considered all three elements required for a risk assessment in
their 2007 allocation processes, and several other Forest Service
regions considered two of the three elements--hazard and values--but
did not consider risk. Some, but not all, of the other Interior
agencies' regional offices we visited considered elements of risk
assessments in their allocation processes as well. Regional officials
offered several reasons for not always systematically considering risk
assessments when allocating funds, such as not having the necessary
data for a regionwide risk assessment or only informally considering
risk.
Several agency officials told us that they do not consider the lack of
a formal national or regional risk assessment to be a significant
problem because they rely on field units to assess risk when selecting
projects. However, as with regions, not all local units used risk
assessments when selecting projects; some local units used only partial
assessments or did not use risk assessments at all. Even when field
units do use risk assessments to help select projects in high-risk
areas at the local level, agency officials cannot be confident that
areas designated as high risk locally would still be designated as high
risk at the regional or national level. For example, one BLM field
office in Colorado oversees a rural area with only two communities,
neither of which is at risk from wildland fire, according to BLM
officials. For officials at this office, the most important values at
risk are rural power lines and oil and gas infrastructure; therefore,
they give the highest priority to projects that protect these features.
From a regional or national perspective, however, other projects may be
a higher priority for funding because the values at risk are more
important, the area is at higher risk from fire, the level of hazard is
greater because of fuel conditions, or some combination of these
reasons. Without using national, regional, and local-level risk
assessments that systematically assess hazards, risks, and values, it
is difficult to ensure that allocation decisions are grounded in a
clear understanding of which areas are at the highest risk.
Even when the agencies conduct risk assessments that include hazards,
risks, and values, they may find it difficult to distinguish between
high-and low-priority locations because one key value at risk--the
wildland-urban interface--has multiple definitions that leave
considerable room for interpretation on the part of agency officials.
As a result, many different areas can be classified as wildland-urban
interface, and the term's usefulness in helping agency officials
identify, and direct funds toward, the highest-priority lands is
diminished. In 2001, the agencies--in cooperation with tribes and
states--defined the interface as including three categories: (1) dense
populations (250 or more people per square mile) abutting wildlands;
(2) scattered populations (28 to 250 people per square mile) intermixed
with wildlands, and (3) development surrounding an island of wildland
fuel, such as a park or open space. Agency officials told us that they
developed this definition very quickly, in response to legislative
direction, but later came to believe that it overemphasized population
density and was not flexible enough to accommodate differences in
landscape features such as vegetation, terrain, and prevailing weather
patterns, which can affect the size and shape of areas in the wildland-
urban interface.
In 2003, HFRA defined the wildland-urban interface to include an area
within or adjacent to an at-risk community, that is identified in
project recommendations to a federal agency in a community wildfire
protection plan. For areas not in community plans, HFRA specified that
areas within one-half mile of an at-risk community were to be
considered wildland-urban interface,[Footnote 34] as were areas within
1-1/2-miles of an at-risk community under certain conditions, and areas
adjacent to evacuation routes for at-risk communities. According to
agency officials, this definition offered more flexibility by moving
away from the focus on population density, but it applies only to
projects conducted using HFRA authorities.
Most recently, the 2006 10-Year Strategy Implementation Plan developed
by the agencies, western governors, and others, defined the wildland-
urban interface as the "the zone where structures and other human
development meet at-risk forest and rangelands." While this definition
provided broad flexibility, agency officials told us it did not replace
the 2001 definition (which focused on population densities), and both
the 2001 and 2006 definitions apply to projects other than those
conducted using HFRA authorities. The end result is multiple
definitions that--individually and collectively--allow many different
areas to be classified as wildland-urban interface without specifying
whether some ought to be given higher priority than others.
In part because of this lack of clarity, agency officials we spoke with
reported including several types of locations under the category of
wildland-urban interface. Some units interpreted the interface to mean
only the area surrounding houses, while others also included roads,
power lines, oil and gas development, communications infrastructure,
campgrounds and recreation areas, and other features. For example, the
BLM Colorado state office defined industrial interface as a subcategory
of the wildland-urban interface, including features such as power lines
or oil and gas development, which are common features in or near some
of BLM's rural field units. In contrast, officials for two national
forests near urban areas (Atlanta and Los Angeles) determined that most
or all of their forests were in the wildland-urban interface because,
they estimated, a wildland fire could move into nearby urban and
suburban areas within a single day. In yet another interpretation, a
BIA agency in New Mexico tailored its definition of wildland-urban
interface to accommodate cultural differences between tribes, as the
differences were reflected in the arrangement of their homes: one tribe
built its homes in clusters while another built its homes in a
scattered pattern.
Although each of these interpretations of wildland-urban interface may
have merit given the situations the field units face, the lack of clear
definition effectively allows a wide range of areas to be defined as
wildland-urban interface. The fluid nature of the wildland-urban
interface definition is illustrated by guidance that one FWS region
issued to its local units in 2006, when it notified them that it was
expanding the relatively strict definition of wildland-urban interface
the region had previously used to reflect interagency guidance.
According to this region, "this expanded definition may enhance our
ability to fund a project with [wildland-urban interface] — funding and
will help us meet the [wildland-urban interface] treatment targets
mandated by the Department."
Given the range of definitions available for wildland-urban interface,
it is not surprising to find that in 2005 and 2006 many of the fuel
reduction treatments the agencies identified as being in the wildland-
urban interface were in ZIP code areas with fewer than 28 people per
square mile, on average.[Footnote 35] (See fig. 7.) Specifically, about
2.2 million acres, or 65 percent of all acres treated in areas
identified as the wildland-urban interface during that period, were in
ZIP code areas with fewer than 28 people per square mile. While the
agencies may have had legitimate reasons for some of these treatments-
-for example, to protect a critical evacuation route for a larger
community--it is not clear why, as a whole, so many acres treated are
far from more densely populated areas. Expressing its concern about
this situation in 2006, the Office of Management and Budget noted, "As
the agencies increase their emphasis on [wildland-urban interface]
treatments over time, field staff and/or project proponents may simply
be defining more projects as [wildland-urban interface] projects in
order to increase the likelihood of having their projects funded."
Figure 7: Density of Wildland-Urban Interface Treatments and Population
Density, by ZIP Code:
There are two maps of the Continental United States.
The first map depicts Density of 2005 and 2006 Fuel Reduction
Treatments Identified as Being in the Wildland-Urban Interface, by ZIP
Code, in two densities: 91) Less than 5,000 acres per zip code; and (2)
5,000 or more acres per zip code.
The second map depicts Population Density in 2000 in the Continental
United States, by ZIP Code in three densities: (1) Fewer than 28 people
per square mile; (2) 28 to 249 people per square mile; and (3) 250 or
more people per square mile.
[See PDF for image]
Source: GAO analysis of Forest Service, Interior, and U.S. Census data.
Note: We conducted our analysis using census data on the average
population per square mile across areas defined by ZIP codes. However,
especially in larger ZIP codes, there may be smaller pockets where the
population density is higher or lower than the average used in our
analysis. When mapping the data, we included a 1.5-mile buffer around
the ZIP code areas with 28 or more people per square mile to account
for the 1.5-mile buffer specified in the HFRA definition for wildland-
urban interface.
[End of figure]
Conversely, population density alone may not be sufficient
justification for selecting locations for fuel reduction. One Forest
Service official cautioned against "prioritization by census," because
more densely populated areas are not necessarily at greater risk than
less populated areas. For example, although Chicago is a densely
populated urban area, the Forest Service has not conducted more
treatments in the nearby grassland because the risk of a fire
threatening the urban area is very low, according to agency officials.
In addition, highly populated urban areas are often not as close to
federal lands as are communities with smaller populations, and the
agencies conduct the majority of their fuel reduction work on federal
lands. Figure 8 shows the location of federal lands relative to more
densely populated areas in the continental United States. Even if a
dense urban area is near federal lands, the entire area is not
typically at risk from a fire originating on federal lands; only the
portion of structures closest to federal lands is at risk, according to
Forest Service officials.[Footnote 36] Finally, vegetation and other
conditions on some federal lands make it unlikely that a fire would
burn or that a fire would threaten a nearby population.
Figure 8: Location of Federal Lands and Populated Areas in the
Continental United States:
There are two maps of the Continental United States.
The first map depicts Federal Lands Managed by the Forest Service, BLM,
BIA, FWS, and NPS in the Continental United States.
The second map depicts Population Density in 2000 in the Continental
United States, by ZIP Code in three densities: (1) Fewer than 28 people
per square mile; (2) 28 to 249 people per square mile; and (3) 250 or
more people per square mile.
[See PDF for image]
Source: GAO analysis of U.S. Census and U.S. Geological Survey's
National Atlas Web site data.
Note: We conducted our analysis using census data on the average
population per square mile across areas defined by ZIP codes. However,
especially in larger ZIP codes, there may be smaller pockets where the
population density is higher or lower than the average used in our
analysis. When mapping the data, we included a 1.5-mile buffer around
the ZIP code areas with 28 or more people per square mile to account
for the 1.5-mile buffer specified in the HFRA definition for wildland-
urban interface.
[End of figure]
While many important contextual details are not visible on a national
map, some can be seen at the county level. For example, in Los Angeles
County--the most populous U.S. county--many of the fuel reduction
treatments completed in 2005 and 2006 were adjacent to densely
populated areas, as shown in figure 9, but some were miles away and in
ZIP code areas with relatively low population.
Figure 9: Map of Los Angeles County Wildland-Urban Interface Fuel
Reduction Treatments Completed in 2005 and 2006, and Population Density:
This map depicts five specific entities: (1) Wildland-urban interface
treatment area (these overlay the next four entities); (2) Area with
fewer than 28 people per square mile; (3) 28 to 249 people per square
mile (includes 1.5 mile buffer); (4) 250 or more people per square mile
(includes 1.5 mile buffer); and (5) Federal lands.
Source: GAO analysis of Forest Service, Interior, and U.S. Geological
Survey's National Atlas Web site data.
[See PDF for image]
Note: We conducted our analysis using census data on the average
population per square mile across areas defined by ZIP codes. However,
especially in larger ZIP codes, there may be smaller pockets where the
population density is higher or lower than the average used in our
analysis.
[End of figure]
Treatments occurred in these low-density areas for several reasons.
First, many of the treatments conducted in the county during that
period, while not immediately adjacent to the city of Los Angeles, were
on the federal land closest to the city, the Angeles National Forest,
which of course is not highly populated. Also, while the average
population density for the general area is low, individual communities
with populations ranging from about 1,000 to 3,000 are located inside
the boundaries of the forest, and the Forest Service conducted some
treatments to protect them. Second, developed sites--such as
campgrounds, roads, and recreation areas--where people temporarily
congregate may not be reflected on a census map of population density.
According to officials at the Angeles National Forest, human-caused
wildland fires generally coincide with such areas, making it important
to conduct fuel treatments around these sites. Finally, low-density
areas within the forest were more feasible to treat than some areas
closer to population centers because steep terrain across much of the
forest--including along its southern boundary adjacent to heavily
populated Los Angeles--makes it difficult and expensive to conduct fuel
treatments, and, in some cases, would make treatments ineffective,
according to agency officials.
In contrast to Los Angeles County, Rio Blanco County, Colorado, is a
rural county with a total population of about 6,000 and an average
population density throughout the county of less than 28 people per
square mile. Nevertheless, BLM classified some of its fuel reduction
treatments in this county as wildland-urban interface treatments. As
figure 10 shows, these treatments in 2005 and 2006 were generally
located far from the largest towns in the county--Meeker and Rangely--
which each has a population of about 2,000.
Figure 10: Map of Rio Blanco County Wildland-Urban Interface Fuel
Reduction Treatments Completed in 2005 and 2006, and Population Density:
This map depicts four specific entities: (1) Wildland-urban interface
treatment area (these overlay the next three entities); (2) Area with
fewer than 28 people per square mile; (3) BLM lands; (3) Forest Service
lands.
[See PDF for image]
Source: GAO analysis of Forest Service, Interior, U.S. Census, and U.S.
Geological Survey's National Atlas Web site data.
Note: We conducted our analysis using census data on the average
population per square mile across areas defined by ZIP codes. However,
especially in larger ZIP codes, there may be smaller pockets where the
population density is higher or lower than the average used in our
analysis. In Rio Blanco County, there were no ZIP code areas with an
average population of 28 or more people per square mile.
[End of figure]
According to BLM officials, they did not conduct wildland-urban
interface treatments closer to these towns because the towns are not at
significant risk from wildland fire; they are surrounded in large part
by rocky outcroppings and irrigated agricultural fields, where fires
would not likely start, and area roads serve as fire breaks. In
addition, the county had prepared a community plan identifying its
highest priorities, and the federal lands surrounding the towns were
not among them. Instead, many of the fuel reduction treatments the
agencies did implement--including the five located southwest of Meeker-
-were conducted to protect energy development facilities, such as a
coal mine and oil and gas wells, or power lines that service such
facilities, according to BLM officials. According to the officials,
they selected these projects because they were higher priority than
other potential projects in the county, and because the county's
community plan had identified the protection of energy development and
power lines as its priorities, defining the wildland-urban interface to
include the areas around such infrastructure. While these decisions may
be reasonable given local priorities, it is not clear from a national
perspective whether the values at risk in this case are of higher
priority than the values at risk in other locations--in part because
the definitions of the wildland-urban interface do not distinguish the
relative importance of different values at risk, such as homes, power
lines, or oil and gas wells, among others.
The Agencies Do Not Consider Treatment Effectiveness in Their
Allocation Processes Because They Have No Measure for Effectiveness:
Although the agencies recognize the importance of measuring the
effectiveness of fuel reduction treatments--that is, how much risk
reduction is achieved through a given treatment and for how long--none
of the agencies considered effectiveness when allocating funds in 2007
because they have not yet developed a method for measuring it. Without
understanding the potential effectiveness of fuel reduction treatments,
the agencies cannot ensure that funds are allocated appropriately,
because not all areas that rank high in a risk assessment can be
treated with the same degree of success. For example, parts of southern
California are dominated by chaparral ecosystems, which feature plants
with fire-resistant roots, enabling the plants to re-sprout quickly.
Some of the plants also encourage fire because their leaves are coated
with a flammable resin. Although these areas of chaparral ecosystems
would score high on a risk assessment--because there is a high
vegetation hazard near populated areas with considerable values at
risk--agency officials told us that fuel reduction treatments in
chaparral may be effective for only a short time because the vegetation
often grows back quickly. In addition, many of the damaging fires in
southern California chaparral have been fanned by the warm, dry, and
extremely powerful Santa Ana winds, making it difficult for fuel
treatments to affect fire severity, according to some Forest Service
officials. As a result, some of these areas, though at high risk from
fire, might not be designated as high priority for fuel treatments. In
general, understanding the expected effectiveness of fuel reduction
treatments under different conditions can help the agencies target
their funds toward treatments that will achieve the most risk reduction
for a given cost. The agencies have, on multiple occasions, recognized
the significance of treatment effectiveness; for example, in the 2006
10-Year Strategy Implementation Plan, the agencies identified the need
to "explore the feasibility of developing measures that determine the
degree and longevity of fire hazard reduction achieved by hazardous
fuels treatments."
Although the agencies have not yet developed a measure of
effectiveness, they have designed their allocation models to
accommodate data on effectiveness in the expectation that such data
will eventually become available. The Forest Service's model includes
two elements intended to assess effectiveness, but, because the agency
does not have data on effectiveness, one of the elements serves as a
placeholder--by assigning each region an identical score--and thus does
not influence priority scores, while the other uses data on the total
number of acres treated in each region instead. Forest Service
officials acknowledged that the number of acres treated does not reveal
how effective the treatments are in reducing risk, but told us they
used this information because they wanted a measure that would reflect
the variation in accomplishment levels from one region to the next.
Interior and BLM also plan to include a measure of effectiveness in
their allocation models, but Interior--like the Forest Service--
currently uses total acres treated, and BLM uses data on efficiency,
including total acres treated and average cost per acre, because these
are the only data available. According to agency officials, it is
difficult to develop a single measure of effectiveness for different
geographic locations and vegetation types, because, for example, a
treatment in grass might be effective for 1 year, while a treatment in
some forests might be effective for 30 years. Nevertheless, as long as
the agencies continue to allocate funds without knowing how effective
treatments are likely to be, they cannot be sure that funds are being
spent on projects that substantially reduce overall risk.
According to Forest Service research scientists, developing a measure
of treatment effectiveness would require that the agencies first
determine how to estimate the level of risk in a given location so they
could track any changes in risk resulting from fuel treatments. For
example, they could use data on fire intensity, severity, or
occurrence, or some combination of these and other factors, to evaluate
risk. Once agency officials determined how to estimate risk, they could
use the information to measure treatment effectiveness. However, there
is no consensus on how best to do so and any method would likely
require considerable effort. For example, under one approach described
by the researchers, available scientific studies about fuel reduction
treatments in various vegetation types would be analyzed to ascertain
where fuel treatments are more or less effective, and effectiveness
ratings would be calculated for each vegetation type on the basis of
this information. After establishing the ratings, they would collect
field data to verify their initial conclusions and ratings--a costly
and time-consuming exercise, according to some researchers. Such an
approach would have drawbacks, however; the researchers told us that it
would be difficult to establish a single rating that would apply to
vegetation types under all circumstances because fuel conditions within
a given vegetation type vary widely, depending, for example, on
geographic location and previous fuel reduction activity. In addition,
factors other than vegetation--such as terrain, weather, and soil--also
influence treatment effectiveness. Consequently, some researchers have
proposed alternative approaches, such as one that would consider many
factors, in addition to vegetation type, to assign effectiveness
ratings to individual treatment areas rather than general vegetation
types. However, developing an effectiveness rating scheme using this
approach--or others that incorporate numerous factors--would require
significant research and analysis over a long time period, according to
one researcher.
A less expensive, quicker approach outlined by another Forest Service
researcher would rely on expert opinion rather than field data. Under
this simplified approach, a panel of experts with knowledge about and
experience in fuel reduction treatments and their effectiveness would
use their professional judgment to collectively estimate the extent to
which fuel treatments would be effective in each of several vegetation
or fire regime condition class categories. The experts' estimates could
then be used to inform decisions on allocating funds.
The Agencies Often Consider Costs, but Not Cost-Effectiveness, When
Allocating Funds:
The agencies also do not consider the cost-effectiveness of treatments
when allocating funds, primarily because they do not have data on
treatment effectiveness. Treatment costs can vary widely in different
areas, from as little as $10 per acre to well over $1,000 per acre,
even ranging as high as $30,000 per acre under unusual circumstances,
and allocating funds wisely involves not simply targeting those acres
that can be treated most cheaply, but those acres where treatments
yield the most cost-effective result. While considering costs is an
important step in making allocation decisions, it is equally important
to consider effectiveness in conjunction with costs to avoid funding
ineffective projects simply because they are cheap. However, until the
agencies have data on treatment effectiveness, they will find it
difficult to do so. In support of these considerations, the 2006
Cohesive Strategy emphasized the importance of reducing fuel in the
most cost effective manner possible, because federal funds can support
only a finite number of fuels treatments each year covering a fraction
of the acres at high risk from unusually severe fires.
In practice, the agencies frequently consider costs when allocating
funds and selecting projects. They sometimes give priority to projects
with low per-acre costs in order to leave more funds available for
other projects or to treat more acres within their budgets--an
important factor for agencies trying to meet increasing acreage
targets. Also, agencies sometimes give priority to low-cost treatments
in areas that have previously been treated and are consequently of
relatively low risk, in order to prevent them from becoming higher
risk. According to agency officials, these treatments are a priority
because they are a cost-effective way to maintain low-risk conditions
once achieved; it is generally much cheaper to reduce fuel in areas
that have recently been treated than to do so in areas that have never
been treated or have not been treated for a long time. However, without
knowing the effectiveness of treatments in reducing risk, agency
officials may not be able to compare the relative benefits of potential
projects when deciding where to invest fuel reduction funds--and, thus,
may not know which projects are likely to be the most cost-effective.
In some cases, the agencies also give lower priority to treatments with
very high per-acre costs--even in high-risk areas--because the expected
benefit does not justify the expense. For example, the Desert National
Wildlife Refuge in southern Nevada identified a mechanical thinning
treatment to remove palm trees as its highest-priority fuel reduction
project in 2006. The proposed project was in the wildland-urban
interface and would also improve the habitat of an endangered fish,
according to agency officials. However, it would have cost hundreds of
thousands of dollars--nearly the entire budget for the region--and,
therefore, FWS regional officials did not fund the project.
The Agencies Have Not Established Clear Guidance on the Relative
Importance of Factors Used in Setting Priorities:
In addition to more consistently using information on risk and
developing measures of treatment-and cost-effectiveness, the agencies
could improve their allocation process by clarifying the relative
importance of the different factors they use in setting priorities.
Without such clarification, it is not clear how agency officials are to
resolve conflicts that arise between competing factors. In addition,
when factors other than risk, treatment effectiveness, and cost
effectiveness have considerable influence on allocation decisions, it
is difficult for the agencies to ensure that funds are allocated to
areas where they will most effectively reduce risk.
The agencies consider such factors in part because they are directed to
do so; many of the factors they consider are tied to federal laws or
congressional direction. For example, fuel reduction projects
authorized under HFRA include, among others, projects on federal land
in the wildland-urban interface and certain projects in areas where
ecological restoration is needed because vegetation has departed
significantly from its historical regime. The act requires the agencies
to develop annual programs of work for federal land that give priority
to authorized hazardous fuel reduction projects that provide for the
protection of at-risk communities or watersheds or that implement
community wildfire protection plans. Congressional committee direction
has also called for the agencies to put a priority on fuel reduction
work completed through mechanical treatments and projects that use
biomass.
Some of the factors the agencies consider are also intended to
encourage efficiency in the fuel reduction program, as well as more
broadly in their land management missions. Specifically, the agencies
give priority to projects that achieve benefits not only for the fuel
reduction program but also for other programs such as wildlife
management and watershed improvement--an approach referred to as
integration among programs. Agency officials said implementing such
projects is a way to leverage funds and coordinate resources. The
Forest Service also emphasizes these projects because its
interpretation of the President's HFI calls for a focus on integrated
management, according to agency officials.
In the face of multiple directives and competing agency priorities,
agency officials must balance numerous factors when allocating funds
and selecting projects, as the following examples illustrate:
* Priorities in community plans may not always align with agency-
identified priorities, forcing agencies to choose between them.
According to Montana BLM officials, one community proposed a fuel
reduction project in an area the officials believed was relatively low
risk because it had vegetation that does not burn easily. However, the
officials agreed to implement the project because they are directed to
give priority to locally identified projects and because they did not
want to damage their relationship with the community. Several agency
officials told us that community plans did not always include federal
lands or propose projects in locations where the agencies could
feasibly implement a treatment. In such cases, agency officials
sometimes worked with the communities to identify project locations
agreeable to all, while other times they implemented agency-identified
projects instead of those identified in the plans.
* Direction to give priority to high-risk areas may also conflict with
the agencies' commitment to meet acreage targets. Several agency
officials told us that they sometimes implemented lower-priority
projects with low unit costs because they felt pressure to meet acreage
targets. In some cases, these projects, although low priority for fuel
reduction purposes, were a high priority for other resource programs or
achieved other management objectives.
* Direction to give priority to areas in the wildland-urban interface
may conflict with other agency priorities. For example, NPS officials
told us that giving priority to fuel reduction treatments at the
interface conflicted with the agency's mission to preserve natural
ecosystems and processes, which would call for giving priority to
treatments in undeveloped areas.
* Desire for stable funding and staff levels may make officials
reluctant to shift funds on the basis of risk assessments. When
allocating funds, the agencies frequently emphasized the importance of
maintaining stable funding levels and minimizing disruptions to staff,
which can conflict with the direction to emphasize high-risk areas.
According to agency officials, stable allocations to regions and field
units are needed to ensure predictability and enable regional and field
staff to plan ahead. In addition, a minimum level of funding is needed
to maintain the workforce and infrastructure required to support viable
fuel reduction programs in regions and field units. Several agency
officials told us they were reluctant to shift funding on the basis of
risk assessments because doing so could require staff to relocate--
potentially multiple times--and the officials wanted to avoid uprooting
staff.
Agency guidance offers little in the way of clarification for staff
confronted with numerous, conflicting priorities, as the multiplicity
of priorities in the Forest Service and Interior's 2006 Cohesive
Strategy illustrates. In this strategy, the Forest Service and Interior
outline a set of national fuel treatment priorities but do not
establish a hierarchy of their relative importance. Among the treatment
priorities are areas in the wildland-urban interface as well as some
areas outside the interface in condition classes 2 or 3. In addition,
some areas in condition class 1--the only remaining condition class--
are to be given equal priority, according to the strategy. Similarly,
the strategy calls for priority to be given to mechanical treatments
where appropriate, but also to prescribed burns where appropriate.
After providing a list of priority criteria, the strategy declares that
the more criteria a fuel reduction project meets, the higher its
priority should be for funding. However, it also acknowledges that, in
exercising management discretion, the agencies may need to make
exceptions to the process described for ranking and selecting projects.
Agencies' Allocation Processes Are Not Always Systematic:
Although the agencies are working to develop and implement models that
will allow them to allocate funds more systematically, such systematic
approaches are not used by all agencies or at all levels within the
agencies. By allocating funds using a systematic process--one that is
methodical, based on established criteria, and applied consistently--
the agencies can better ensure that they uniformly consider all
relevant criteria and appropriately apply these criteria in all
decisions.
In particular, when agency officials rely primarily on professional
judgment and negotiation to allocate funds, they do not always follow a
step-by-step approach or consistently apply a predetermined set of
criteria. We recognize that agency decision makers--particularly those
who have served in the same location for many years--often have
detailed knowledge about on-the-ground conditions and a thorough
understanding of fuel reduction needs. Nevertheless, without using a
systematic approach, even knowledgeable and well-meaning decision
makers may be more susceptible to influences that are not intended to
be part of the decisions, as illustrated by the following examples:
* According to several agency officials, they face considerable
pressure to meet acreage targets. Under these circumstances, and with
no pre-determined set of criteria in an allocation process, targets
could have more influence than intended. That is, agency officials
might fund lower priority projects in order to treat more acres.
* In NPS's Southeast region, agency officials told us that the location
of full-time fuel reduction staff has considerable influence on
allocations, even though it is not officially a factor in the
allocation process. Few parks in this region have full-time staff
devoted to fuel reduction, and parks without such staff request and
receive much less fuel reduction funding than do the parks with
dedicated staff--potentially because there are fewer staff to perform
the work necessary to identify fuel reduction needs and request funds.
Consequently, according to agency officials, it is difficult to ensure
that all of the highest-priority areas for fuel reduction across the
region are identified and targeted for funding because some high-
priority areas may not be identified if they are located in parks with
fewer staff. NPS officials in another region expressed a similar
concern, stating that the agency needs to shift fuel reduction funds
within the region to direct them to high-priority locations and
acknowledging that it cannot do so without also shifting personnel to
high-priority locations.
Moving toward more systematic allocation processes also enhances
transparency and accountability. In many of the locations we visited,
the agency offices that relied primarily on professional judgment to
allocate fuel reduction funds and select projects did not document the
rationale for their decisions. As a result, the processes were not
transparent, and neither agency officials nor others--including
Congress and the public--could understand the rationale behind the
decisions or have confidence that the resulting allocations were
directed to the highest-priority areas for reducing risk to communities
and the environment. For example, officials in BLM's Oregon/Washington
state office used their professional judgment to determine allocations
to its 10 district offices. Under this process in 2007, BLM's Medford
district office received an allocation of about $9 million--over 7
times the average allocation received by the other nine district
offices that year. While this disparity may be appropriate, without a
transparent process it is difficult to determine the extent to which
the allocation reflects agency priorities for reducing risk to
communities and the environment, rather than other factors. The
agencies themselves have emphasized the importance of transparency and
accountability; for example, the 10-Year Strategy Implementation Plan
states that the agencies should "strive for maximum transparency in the
decision-making process."
Conclusions:
Our nation's wildland fire problem has been decades in the making and
will not be solved quickly. Nevertheless, with careful choices about
where to spend their limited fuel reduction dollars, federal agencies
can meaningfully, if incrementally, reduce the risks faced by
communities and the environment. Doing so will require the agencies to
continue moving away from allocation by tradition to allocation by
priority. Toward this end, the agencies could improve their current
approaches in three key areas.
First, the agencies would benefit from routinely using an allocation
process that is systematic, and that is common to all the agencies. A
systematic process can help ensure that the agencies apply their
allocation and project selection criteria consistently, and can help
interested parties outside of the process--Congress, local communities,
and other entities--understand the rationale for the funding and
project selection decisions that are made. While the models that some
of the agencies are developing represent substantial steps forward in
this regard and will affect larger portions of funding allocations over
time, not all of the agencies have models, and none consistently uses
models at the national, regional, and local levels. Further, the
models, even where used, often exert only a small influence on
allocation decisions, partly because the agencies do not yet have full
confidence in the models' data. As a result, the agencies often base
decisions mainly on historical funding patterns and professional
judgment. We recognize that professional judgment will always have a
role in the allocation process to account for difficult-to-quantify
factors, such as local priorities or political considerations. However,
the agencies and the public are best served if a systematic process,
such as a model, serves as the foundation for allocation decisions, and
professional judgment plays a supporting, rather than a lead, role.
Also, given that wildland fire is a nationwide problem that does not
respect administrative boundaries, the agencies would do well to
develop and use a common process for allocating fuel reduction funds--
as Congress has called for--that can be customized to accommodate
differences in scale, type of ecosystem, agency mission, and other
criteria.
Second, the agencies could improve the information they use to make
allocation decisions. Because the agencies do not always use risk
assessments and currently lack data on treatment effectiveness, they
often make allocation decisions without knowing, on a broad scale,
where the acres at highest risk are located, which treatments are most
effective at reducing risk, and which areas respond best to treatment.
To improve their allocation decisions, they should continue, over the
long term, to develop and use information on risk and treatment
effectiveness. The agencies can then use this information, in concert
with cost information, to effectively assess tradeoffs among potential
treatments and identify the most cost-effective investments.
Finally, the agencies could strengthen their allocation processes by
sorting through the numerous prioritization factors that have
accumulated over the years and establishing a hierarchy for considering
them. Without such a hierarchy, the exercise of setting priorities can
be frustrating--or even meaningless--because virtually any project can
qualify as high priority. While we recognize that, in some cases, the
agencies are bound by law or congressional direction to give priority
to certain factors, we believe there may remain enough room within
those constraints not only to establish a hierarchy of factors, but
also to clarify the relative importance of categories within some
factors--in particular, various categories of wildland-urban interface.
We do not advocate prioritization by census--simply directing fuel
reduction funds to areas with the highest populations-- but neither do
we believe that the agencies or the public are well- served by the
broad definitions of the interface currently used. However, if the
agencies determine, through further analysis, that laws or
congressional direction create conflicts prohibiting them from
implementing a consistent, systematic approach that distinguishes the
relative importance of various priorities, they should so inform
Congress and seek additional clarification.
It will not be easy to carry out these tasks. As they work to improve
their processes, the agencies will need to devote considerable effort
to developing measures and collecting data on risk and effectiveness
and considerable thought to balancing this information against the many
goals of the fuel reduction program--all in a way that yields
transparent results. And once these steps are carried out, the agencies
face perhaps an even more difficult decision: how best to redirect fuel
reduction funds in a way that improves the agencies' effective use of
their limited funds despite the potentially disruptive consequences for
individual field units or nearby communities. Our findings suggest that
the agencies are increasingly mindful of the merits of such an approach
and that their recent actions have begun to lay the necessary
groundwork. Nevertheless, many challenges remain, and a difficult road
lies ahead.
Recommendations for Executive Action:
We are recommending that the Secretaries of Agriculture and of the
Interior take the following five actions to improve their ability to
allocate fuel reduction funds so that these funds contribute most
effectively to risk reduction.
First, we recommend that the Secretaries of Agriculture and of the
Interior direct the agencies to develop a common, systematic funding
allocation process in order to enhance the transparency and
accountability of their allocation decisions and to ensure a common
federal approach to allocating funds. Such a systematic process should
serve as the foundation of each agency's allocation process and should
be applied at all levels within the agencies. Existing models or those
under development may serve as useful prototypes; for example, while we
have not assessed its accuracy or technical soundness, the Forest
Service's model for allocating funds shows promise as the foundation of
a systematic process.
In addition, we recommend that the Secretaries of Agriculture and of
the Interior direct their agencies to develop information to support
this systematic process. Development of the information should include
the following actions:
* Develop and implement a common approach to risk assessment, to
provide for a broad, national assessment of hazard, risk, and values,
as in the Forest Service's allocation model, as well as more refined
regional and local assessments.
* Devote resources to developing a measure of, and subsequently
collecting data on, fuel reduction effectiveness, so that the agencies
can usefully estimate the extent and duration of risk reduction from
potential fuel treatments. Because developing the measure and
collecting data are likely to be difficult and time-consuming
endeavors, the agencies might find it useful to proceed with convening
a panel of experts to devise a rudimentary framework for estimating
treatment effectiveness.
* Use information on risk and fuel treatment effectiveness, once
available, in concert with information on the cost of treatments, to
assess the cost-effectiveness of various potential fuel reduction
treatments.
Finally, the Secretaries of Agriculture and of the Interior should
provide guidance that clearly distinguishes the relative importance of
the various factors used in allocating funds and selecting projects,
including the importance of risk, effectiveness, and cost in comparison
with other factors. This guidance should also distinguish the relative
priority of different values at risk, especially different elements
within the wildland-urban interface, such as homes, power lines, and
municipal watersheds.
Agency Comments and Our Evaluation:
We provided the Secretaries of Agriculture and of the Interior with a
draft of this report for review and comment. The Forest Service and the
Department of the Interior generally agreed with the findings and
recommendations in the report, noting their ongoing efforts to develop
and implement a risk-informed allocation process, and reiterating the
importance of including state, tribal, and local concerns in the
prioritization process. Their joint comment letter is reproduced in
appendix IV.
We are sending copies of this report to interested congressional
committees, the Secretaries of Agriculture and the Interior, the Chief
of the Forest Service, and other interested parties. We will also make
copies available to others upon request. In addition, the report will
be available at no charge on the GAO Web site at [hyperlink,
http://http://www.gao.gov].
If you or your staffs have any questions about this report, please
contact me at (202) 512-3841 or nazzaror@gao.gov. Contact points for
our Offices of Public Affairs and Congressional Relations may be found
on the last page of this report. GAO staff who made major contributions
to this report are listed in appendix V.
Signed by:
Robin M. Nazzaro:
Director, Natural Resources and Environment:
[End of section]
Appendix I: Objectives, Scope, and Methodology:
We were asked to (1) identify the processes the Forest Service, the
Department of the Interior (Interior), and Interior's agencies--the
Bureau of Indian Affairs (BIA), Bureau of Land Management (BLM), Fish
and Wildlife Service (FWS), and National Park Service (NPS)--use to
allocate fuel reduction funds and select projects for implementation,
including the factors that influence these processes; and (2) determine
how, if at all, the agencies could improve these processes to better
ensure they contribute to their goal of effectively reducing the risk
of wildland fire to communities and the environment. We focused our
review primarily on the Forest Service and BLM because these two
agencies accounted for about 80 percent of the fuel reduction funds
appropriated by Congress for 2005, 2006, and 2007, although we
collected information on the other three agencies as well.[Footnote 37]
We focused our review on fuel reduction work funded through
congressional fuel reduction appropriations; therefore, fuel reduction
work funded by other agency programs or outside organizations is
outside the scope of this review. To gain an understanding of outside
perspectives on the agencies' fuel reduction efforts, we met with
several nonfederal parties, including representatives from the National
Association of State Foresters, The Nature Conservancy, the Western
Governors' Association, and the Wilderness Society.
Fuel Reduction Funding Allocation and Project Selection Processes:
To learn how the agencies allocate fuel reduction funds and select
projects, and to identify the factors that influence these processes,
we first obtained and reviewed documents on policies and procedures
governing the fuel reduction program. These included applicable laws,
administrative initiatives, congressional committee reports, and
interagency agreements, as well as guidance for fuel reduction from the
departments, agency headquarters, and regional offices.[Footnote 38] We
also obtained and analyzed agency data on funding allocations.
To learn about the processes used to allocate fuel reduction funds at
the national level, we met with agency officials from the Forest
Service and Interior at their Washington, D.C., headquarters, and with
officials from all five agencies at the National Interagency Fire
Center in Boise, Idaho. We also met with agency researchers and
modeling experts to better understand the data used in the national
models currently under development by the Forest Service, Interior, and
BLM. We did not, however, assess the accuracy or technical soundness of
these models.
At the regional and state levels, we used a structured interview guide
to speak, in person or by telephone, with officials in all Forest
Service regional and BLM state offices, as well as with officials in
selected BIA, FWS, and NPS regional offices. The structured interview
guide included questions about the processes used to allocate fuel
reduction funds, the factors that influence those processes, the extent
and nature of regional guidance provided to field units, and the amount
of oversight on the part of the regional offices. Because developing
and administering a structured interview guide may introduce errors--
caused by the way a particular question is interpreted, for example--we
included steps in the development and administration of the interview
guide to minimize such errors. We pretested the guide at several
locations and modified it to reflect questions and comments we
received. We also visited a number of the agencies' regional offices to
obtain a greater understanding of the funding allocation processes in
those regions. We selected regional offices that collectively received
a substantial portion of their agency's fuel reduction funds and
represented diversity with respect to fuel reduction funding levels,
fuel reduction acreage accomplishments, predominant vegetation type,
and geographic location. These selection criteria are shown in table 5.
Table 5: Regional Offices GAO Visited:
Agency: Forest Service;
Region/state office: Northern;
Fuel reduction funding level[A]: Greater than average: [Empty];
Fuel reduction funding level[A]: Less than average: [Check];
Acres treated[B]: Greater than average: [Empty];
Acres treated[B]: Less than average: [Check];
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Empty];
Geographic location: Central: [Check];
Geographic location: East: [Empty].
Agency: Forest Service;
Region/state office: Pacific Northwest;
Fuel reduction funding level[A]: Greater than average: [Check];
Fuel reduction funding level[A]: Less than average: [Empty];
Acres treated[B]: Greater than average: [Empty];
Acres treated[B]: Less than average: [Check];
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Check];
Geographic location: Central: [Empty];
Geographic location: East: [Empty].
Agency: Forest Service;
Region/state office: Pacific Southwest;
Fuel reduction funding level[A]: Greater than average: [Check];
Fuel reduction funding level[A]: Less than average: [Empty];
Acres treated[B]: Greater than average: [Empty];
Acres treated[B]: Less than average: [Check];
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Check];
Geographic location: Central: [Empty];
Geographic location: East: [Empty].
Agency: Forest Service;
Region/state office: Rocky Mountain;
Fuel reduction funding level[A]: Greater than average: [Check];
Fuel reduction funding level[A]: Less than average: [Empty];
Acres treated[B]: Greater than average: [Empty];
Acres treated[B]: Less than average: [Check];
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Empty];
Geographic location: Central: [Check];
Geographic location: East: [Empty].
Agency: Forest Service;
Region/state office: Southern;
Fuel reduction funding level[A]: Greater than average: [Check];
Fuel reduction funding level[A]: Less than average: [Empty];
Acres treated[B]: Greater than average: [Check];
Acres treated[B]: Less than average: [Empty];
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Empty];
Geographic location: Central: [Empty];
Geographic location: East: [Check].
Agency: BIA;
Region/state office: Northwest;
Fuel reduction funding level[A]: Greater than average: [Check];
Fuel reduction funding level[A]: Less than average: [Empty];
Acres treated[B]: Greater than average: [Check];
Acres treated[B]: Less than average: [Empty];
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Check];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Check];
Geographic location: Central: [Empty];
Geographic location: East: [Empty].
Agency: BIA;
Region/state office: Pacific (by phone);
Fuel reduction funding level[A]: Greater than average: [Empty];
Fuel reduction funding level[A]: Less than average: [Check];
Acres treated[B]: Greater than average: [Empty];
Acres treated[B]: Less than average: [Check];
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Check];
Geographic location: West: [Check];
Geographic location: Central: [Empty];
Geographic location: East: [Empty].
Agency: BIA;
Region/state office: Rocky Mountain;
Fuel reduction funding level[A]: Greater than average: [Empty];
Fuel reduction funding level[A]: Less than average: [Check];
Acres treated[B]: Greater than average: [Empty];
Acres treated[B]: Less than average: [Check];
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Check];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Empty];
Geographic location: Central: [Check];
Geographic location: East: [Empty].
Agency: BIA;
Region/state office: Southwest;
Fuel reduction funding level[A]: Greater than average: [Check];
Fuel reduction funding level[A]: Less than average: [Empty];
Acres treated[B]: Greater than average: [Check];
Acres treated[B]: Less than average: [Empty];
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Check];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Empty];
Geographic location: Central: [Check];
Geographic location: East: [Empty].
Agency: BLM;
Region/state office: California;
Fuel reduction funding level[A]: Greater than average: [Empty];
Fuel reduction funding level[A]: Less than average: [Check];
Acres treated[B]: Greater than average: [Empty];
Acres treated[B]: Less than average: [Check];
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Check];
Predominant vegetation type: Shrub: [Check];
Geographic location: West: [Check];
Geographic location: Central: [Empty];
Geographic location: East: [Empty].
Agency: BLM;
Region/state office: Colorado;
Fuel reduction funding level[A]: Greater than average: [Empty];
Fuel reduction funding level[A]: Less than average: [Check];
Acres treated[B]: Greater than average: [Empty];
Acres treated[B]: Less than average: [Check];
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Check];
Geographic location: West: [Empty];
Geographic location: Central: [Check];
Geographic location: East: [Empty].
Agency: BLM;
Region/state office: Idaho;
Fuel reduction funding level[A]: Greater than average: [Check];
Fuel reduction funding level[A]: Less than average: [Empty];
Acres treated[B]: Greater than average: [Check];
Acres treated[B]: Less than average: [Empty];
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Check];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Check];
Geographic location: Central: [Empty];
Geographic location: East: [Empty].
Agency: BLM;
Region/state office: Montana;
Fuel reduction funding level[A]: Greater than average: [Empty];
Fuel reduction funding level[A]: Less than average: [Check];
Acres treated[B]: Greater than average: [Empty];
Acres treated[B]: Less than average: [Check];
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Check];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Empty];
Geographic location: Central: [Check];
Geographic location: East: [Empty].
Agency: BLM;
Region/state office: Oregon/Washington;
Fuel reduction funding level[A]: Greater than average: [Check];
Fuel reduction funding level[A]: Less than average: [Empty];
Acres treated[B]: Greater than average: [Check];
Acres treated[B]: Less than average: [Empty];
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Check];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Check];
Geographic location: Central: [Empty];
Geographic location: East: [Empty].
Agency: FWS;
Region/state office: California-Nevada[C];
Fuel reduction funding level[A]: Greater than average: [Empty];
Fuel reduction funding level[A]: Less than average: [Check];
Acres treated[B]: Greater than average: [Empty];
Acres treated[B]: Less than average: [Check];
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Check];
Geographic location: West: [Check];
Geographic location: Central: [Empty];
Geographic location: East: [Empty].
Agency: FWS;
Region/state office: Mountain-Prairie;
Fuel reduction funding level[A]: Greater than average: [Check];
Fuel reduction funding level[A]: Less than average: [Empty];
Acres treated[B]: Greater than average: [Empty];
Acres treated[B]: Less than average: [Check];
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Check];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Empty];
Geographic location: Central: [Check];
Geographic location: East: [Empty].
Agency: FWS;
Region/state office: Southeast;
Fuel reduction funding level[A]: Greater than average: [Check];
Fuel reduction funding level[A]: Less than average: [Empty];
Acres treated[B]: Greater than average: [Check];
Acres treated[B]: Less than average: [Empty];
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Empty];
Geographic location: Central: [Empty];
Geographic location: East: [Check].
Agency: NPS;
Region/state office: Intermountain;
Fuel reduction funding level[A]: Greater than average: [Check];
Fuel reduction funding level[A]: Less than average: [Empty];
Acres treated[B]: Greater than average: [Check];
Acres treated[B]: Less than average: [Empty];
Predominant vegetation type: Forest: [D];
Predominant vegetation type: Grass: [D];
Predominant vegetation type: Shrub: [D];
Geographic location: West: [Empty];
Geographic location: Central: [Check];
Geographic location: East: [Empty].
Agency: NPS;
Region/state office: Pacific West;
Fuel reduction funding level[A]: Greater than average: [Check];
Fuel reduction funding level[A]: Less than average: [Empty];
Acres treated[B]: Greater than average: [Check];
Acres treated[B]: Less than average: [Empty];
Predominant vegetation type: Forest: [D];
Predominant vegetation type: Grass: [D];
Predominant vegetation type: Shrub: [D];
Geographic location: West: [Check];
Geographic location: Central: [Empty];
Geographic location: East: [Empty].
Agency: NPS;
Region/state office: Southeast;
Fuel reduction funding level[A]: Greater than average: [Check];
Fuel reduction funding level[A]: Less than average: [Empty];
Acres treated[B]: Greater than average: [Check];
Acres treated[B]: Less than average: [Empty];
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty];
Geographic location: West: [Empty];
Geographic location: Central: [Empty];
Geographic location: East: [Check].
Source: GAO analysis of Forest Service and Interior data.
[A] "Greater than average" refers to regions that received more than
the average funding amount received by that agency's regions in 2007,
and "less than average" refers to regions that received less than the
average amount in 2007.
[B] "Greater than average" refers to regions that treated more than the
average acres treated by that agency's regions in 2006, and "less than
average" refers to regions that treated less than the average acres
treated in 2006.
[C] FWS's California-Nevada Operations office is officially part of the
Pacific region, but manages its own fuel reduction program.
[D] These regions each cover several states and have a large variety of
vegetation; therefore, no one vegetation type is predominant, according
to agency officials.
[End of table]
To learn about the project selection processes used by local units, we
selected a nonprobability sample of 20 local units in eight states to
interview.[Footnote 39] The sample included 8 national forests, 5 BLM
district or field offices, 2 BIA agencies, 2 national wildlife refuges,
and 3 national parks. Table 6 lists the units we visited. The local
units selected for interviews represented diversity with respect to
geographic location and predominant vegetation type. In addition, we
selected units that represented diversity with respect to their
proximity to communities and development, including units that were
located in counties that were predominantly rural or urban.
Table 6: Field Units GAO Visited:
Agency and unit:
Forest Service: Angeles National Forest;
State: California;
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Check].
Agency and unit:
Forest Service: Arapaho-Roosevelt National Forests and Pawnee National
Grassland;
State: Colorado;
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
Forest Service: Bitterroot National Forest[A];
State: Montana and Idaho;
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
Forest Service: Boise National Forest;
State: Idaho;
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
Forest Service: Chattahoochee-Oconee National Forests;
State: Georgia;
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
Forest Service: Medicine Bow-Routt National Forests and Thunder Basin
National Grassland;
State: Wyoming and Colorado;
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
Forest Service: National Forests in Florida (Ocala National Forest);
State: Florida;
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
Forest Service: San Bernardino National Forest;
State: California;
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Check].
Agency and unit:
BIA: Crow Agency;
State: Montana;
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Check];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
BIA: Zuni Agency;
State: New Mexico;
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
BLM: Albuquerque District Office;
State: New Mexico;
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
BLM: Billings Field Office;
State: Montana;
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Check];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
BLM: Little Snake Field Office;
State: Colorado;
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Check];
Predominant vegetation type: Shrub: [Check].
Agency and unit:
BLM: Twin Falls District Office;
State: Idaho;
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Check];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
BLM: White River Field Office;
State: Colorado;
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Check].
Agency and unit:
FWS: Merritt Island National Wildlife Refuge;
State: Florida;
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Check].
Agency and unit:
FWS: Rocky Mountain Arsenal National Wildlife Refuge;
State: Colorado;
Predominant vegetation type: Forest: [Empty];
Predominant vegetation type: Grass: [Check];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
NPS: Cape Canaveral National Seashore;
State: Florida;
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
NPS: Rocky Mountain National Park;
State: Colorado;
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty].
Agency and unit:
NPS: Sequoia and Kings Canyon National Parks[A];
State: California;
Predominant vegetation type: Forest: [Check];
Predominant vegetation type: Grass: [Empty];
Predominant vegetation type: Shrub: [Empty].
Source: GAO analysis of Forest Service and Interior data.
[A] We met with officials from these field units at off-site locations,
in order to facilitate cost-effective travel logistics.
[End of table]
During all of these visits, we collected documents and interviewed
staff; during some of these visits, we also observed fuel reduction
treatments. Because we conducted in-depth analyses of only a few
selected units, we cannot generalize our findings beyond the local
units and officials we contacted.
Potential Improvements to Agency Processes to Better Ensure They
Contribute to Reducing Risk:
To identify potential improvements to the agencies' processes for
allocating fuel reduction funds and selecting treatments, we analyzed
the information we collected through our site visits, structured
interviews, agency documentation, and discussions with other agency
officials. To identify the overall goals of the fuel reduction program,
and the extent to which earlier assessments of the program identified
shortcomings in the agencies' ability to meet these goals, we also
evaluated (1) agency policy documents, including strategy documents,
program guidance, and related documents discussing the program's
objectives; (2) legislative direction associated with the fuel
reduction program, including laws, congressional committee report
language, and other direction; and (3) previous reviews of the fuel
reduction program by GAO, the Inspectors General, and others.
In our interviews with agency officials, we asked about the factors
they considered when allocating funds and selecting projects--including
the influence of specific factors, such as acreage targets and risk
assessments--as well as factors that prevented high-priority work from
being accomplished. We also asked about regional and local definitions
of the wildland-urban interface. We assigned the allocation processes
used by the agencies' headquarters, regional offices, and local units
to one of two categories: quantitative or judgmental. We also verified
the factors used in allocation processes with agency officials.
To determine the extent to which the locations of wildland-urban
interface treatments, population centers, and federal lands coincided,
we analyzed fuel reduction data from a Forest Service and Interior
database--the National Fire Plan Operations and Reporting System
(NFPORS)--as well as population data from the U.S. Census Bureau, and
federal lands data from the U.S. Geological Survey's National Atlas Web
site [hyperlink,http://www.NationalAtlas.gov]. Using the agency data on
fuel reduction treatments, we used geographic information system (GIS)
tools to map the location and size of wildland-urban interface
treatments completed in 2005 and 2006. We also applied GIS tools to
Census data to map population density in three categories: (1) fewer
than 28 people per square mile, on average; (2) 28 to 249 people per
square mile, on average; and (3) 250 or more people per square mile, on
average.[Footnote 40] We used these categories because they reflect the
definition of wildland-urban interface published in the January 4, 2001
Federal Register and used by the agencies.[Footnote 41] We also mapped
the location of federal lands using the data from the U.S. Geological
Survey. In addition, we created maps of two counties--one urban and one
rural--showing the locations of wildland-urban interface treatments
completed in 2005 and 2006, population density, and federal lands. We
also contacted officials in those two counties to discuss the location
of specific wildland-urban interface projects and their rationale for
selecting those projects.
To determine the reliability of the agencies' fuel reduction data, we
reviewed related documentation, such as the NFPORS database users'
manual; interviewed knowledgeable agency officials, including database
administrators; discussed data input and verification procedures with
regional and field staff; and conducted electronic data testing. We
found that these fuel reduction data were sufficiently reliable for the
purposes of this review. We obtained the federal lands data prepared by
[hyperlink,http://www.NationalAtlas.gov] and reviewed the documentation
provided on the limitations of the file. From this review, we
determined that the federal lands data were sufficiently reliable for
our purposes. To measure population density, we used Census ZIP Code
Tabulation Area data from the 2000 U.S. Census and the geographic
boundary for those areas. We reviewed documentation provided on the
limitations of these files and compared their consistency with other
Census sources. From this review, we determined that the population
density data were sufficiently reliable for our purposes.
We conducted our work from August 2006 to September 2007 in accordance
with generally accepted government auditing standards.
[End of section]
Appendix II: Forest Service and Interior Fuel Reduction Funding
Allocations, Fiscal Years 2005, 2006, and 2007:
This appendix provides information on fuel reduction funding
appropriations and allocations to the Forest Service, the Department of
the Interior (Interior) and its agencies, and their regions for 2005,
2006, and 2007.[Footnote 42] Interior allocates separate fuel reduction
funds to its four agencies for treatments within and outside of the
wildland-urban interface (WUI), while the Forest Service allocates one
single source of fuel funding to its regions. Therefore, information on
allocation amounts to WUI and non-WUI areas are included for Interior
but not for the Forest Service. Table 7 provides total appropriations
and allocations to the Forest Service and Interior agencies--Bureau of
Indian Affairs (BIA), Bureau of Land Management (BLM), National Park
Service (NPS), and Fish and Wildlife Service (FWS)--for 2005, 2006, and
2007. As shown in table 7 and figure 11, of the approximately $500
million appropriated to the Forest Service and Interior for fuel
reduction in 2007, the Forest Service received about 61 percent of the
total; BLM received about 19 percent of the total; and the remaining 20
percent was allocated to BIA, NPS, and FWS.
Table 7: Total Appropriations to Forest Service, and Allocations to
Interior Agencies, Fiscal Years 2005, 2006, and 2007:
Agency: BLM;
2005; Total allocation: $91,386,000;
Percentage of total allocation: 18.8.
Agency: BLM;
2006; Total allocation: 96,299,000;
Percentage of total allocation: 19.9.
Agency: BLM;
2007; Total allocation: 93,389,000;
Percentage of total allocation: 18.8.
Agency: BIA;
2005; Total allocation: 42,488,000;
Percentage of total allocation: 8.7.
Agency: BIA;
2006; Total allocation: 43,237,000;
Percentage of total allocation: 8.9.
Agency: BIA;
2007; Total allocation: 40,664,000;
Percentage of total allocation: 8.2.
Agency: NPS;
2005; Total allocation: 33,040,000;
Percentage of total allocation: 6.8.
Agency: NPS;
2006; Total allocation: 33,299,000;
Percentage of total allocation: 6.9.
Agency: NPS;
2007; Total allocation: 31,396,000;
Percentage of total allocation: 6.3.
Agency: FWS;
2005; Total allocation: 27,527,000;
Percentage of total allocation: 5.7.
Agency: FWS;
2006; Total allocation: 32,162,000;
Percentage of total allocation: 6.6.
Agency: FWS;
2007; Total allocation: 30,666,000;
Percentage of total allocation: 6.2.
Agency: Subtotal--Interior agencies;
2005; Total allocation: 194,441,000;
Percentage of total allocation: 39.9.
Agency: Subtotal--Interior agencies;
2006; Total allocation: 204,997,000;
Percentage of total allocation: 42.3.
Agency: Subtotal--Interior agencies;
2007; Total allocation: 196,115,000;
Percentage of total allocation: 39.4.
Agency: Forest Service[A];
2005; Total allocation: 292,389,000;
Percentage of total allocation: 60.1.
Agency: Forest Service[A];
2006; Total allocation: 280,119,000;
Percentage of total allocation: 57.7.
Agency: Forest Service[A];
2007; Total allocation: 301,258,000;
Percentage of total allocation: 60.6.
Agency: Total;
2005; Total allocation: 486,830,000;
Percentage of total allocation: 100.
Agency: Total;
2006; Total allocation: 485,116,000;
Percentage of total allocation: 100.
Agency: Total;
2007; Total allocation: 497,373,000;
Percentage of total allocation: 100.
Source: GAO analysis of Forest Service and Interior data.
Notes: Interior allocated additional amounts of $6,968,000 in 2005;
$5,115,000 in 2006; and $3,672,000 in 2007 to the Office of Wildland
Fire Coordination, which is responsible for the coordination,
integration, and oversight of wildland fire management programs within
Interior. Total allocations do not include carryover from the previous
fiscal year. Numbers may not total due to rounding.
[A] Forest Service figures represent appropriations.
[End of table]
Figure 11: Agency Funding Levels as a Percentage of Total Fuel
Reduction Funding, Fiscal Year 2007:
[See PDF for image]
This figure is a pie chart depicting funding levels for various
agencies:
Forest Service: 61%;
BLM (Interior Agency): 19 %;
BIA (Interior Agency): 8%;
NPS (Interior Agency): 6%;
FWS (Interior Agency): 6%.
Total: $497,373,000.
Source: GAO analysis of Forest Service and Interior data.
Notes: Interior allocated an additional $3,672,000 to the Office of
Wildland Fire Coordination. Total allocations do not include carryover
from the previous fiscal year.
[End of figure]
Table 8 shows the Forest Service's total allocations to its nine
regions and its headquarters for 2005, 2006, and 2007. In 2007, of the
total funding allocated to the Forest Service for fuel reduction, about
68 percent was allocated to the regions. Approximately 32 percent was
allocated to the Forest Service's headquarters, research stations, and
general cost pools that are used for expenses not charged to a single
program, including indirect, support, and common services charges. The
Pacific Southwest region received the most funding for 2005, 2006, and
2007; the Southwestern region received the second-most funding during
that time period.
Table 8: Forest Service Allocations to Regions and Headquarters, Fiscal
Years 2005, 2006, and 2007:
Region: Pacific Southwest;
2005; Total allocation: $66,656,000;
Percentage of Forest Service's total allocation: 22.8.
2006; Total allocation: 41,944,000;
Percentage of Forest Service's total allocation: 15.0.
2007; Total allocation: 43,737,000;
Percentage of Forest Service's total allocation: 14.5.
Region: Southwestern;
2005; Total allocation: 30,638,000;
Percentage of Forest Service's total allocation: 10.5.
2006; Total allocation: 36,891,000;
Percentage of Forest Service's total allocation: 13.2.
2007; Total allocation: 37,341,000;
Percentage of Forest Service's total allocation: 12.4.
Region: Southern;
2005; Total allocation: 25,478,000;
Percentage of Forest Service's total allocation: 8.7.
2006; Total allocation: 26,368,000;
Percentage of Forest Service's total allocation: 9.4.
2007; Total allocation: 29,092,000;
Percentage of Forest Service's total allocation: 9.7.
Region: Pacific Northwest;
2005; Total allocation: 24,622,000;
Percentage of Forest Service's total allocation: 8.4.
2006; Total allocation: 23,179,000;
Percentage of Forest Service's total allocation: 8.3.
2007; Total allocation: 25,794,000;
Percentage of Forest Service's total allocation: 8.6.
Region: Rocky Mountain;
2005; Total allocation: 21,032,000;
Percentage of Forest Service's total allocation: 7.2.
2006; Total allocation: 23,706,000;
Percentage of Forest Service's total allocation: 8.5.
2007; Total allocation: 25,445,000;
Percentage of Forest Service's total allocation: 8.4.
Region: Intermountain;
2005; Total allocation: 13,673,000;
Percentage of Forest Service's total allocation: 4.7.
2006; Total allocation: 15,881,000;
Percentage of Forest Service's total allocation: 5.7.
2007; Total allocation: 16,165,000;
Percentage of Forest Service's total allocation: 5.4.
Region: Northern;
2005; Total allocation: 11,875,000;
Percentage of Forest Service's total allocation: 4.1.
2006; Total allocation: 12,006,000;
Percentage of Forest Service's total allocation: 4.3.
2007; Total allocation: 15,782,000;
Percentage of Forest Service's total allocation: 5.2.
Region: Eastern;
2005; Total allocation: 8,633,000;
Percentage of Forest Service's total allocation: 3.0.
2006; Total allocation: 8,631,000;
Percentage of Forest Service's total allocation: 3.1.
2007; Total allocation: 9,718,000;
Percentage of Forest Service's total allocation: 3.2.
Region: Alaska;
2005; Total allocation: 1,834,000;
Percentage of Forest Service's total allocation: 0.6.
2006; Total allocation: 853,000;
Percentage of Forest Service's total allocation: 0.3.
2007; Total allocation: 805,000;
Percentage of Forest Service's total allocation: 0.3.
Subtotal, regions;
2005; Total allocation: 204,441,000;
Percentage of Forest Service's total allocation: 69.9.
2006; Total allocation: 189,459,000;
Percentage of Forest Service's total allocation: 67.6.
2007; Total allocation: 203,879,000;
Percentage of Forest Service's total allocation: 67.7.
Region: Headquarters, Research stations, and cost pools;
2005; Total allocation: 87,948,000;
Percentage of Forest Service's total allocation: 30.1.
2006; Total allocation: 90,659,000;
Percentage of Forest Service's total allocation: 32.4.
2007; Total allocation: 97,379,000;
Percentage of Forest Service's total allocation: 32.3.
Region: Total;
2005; Total allocation: 292,389,000;
Percentage of Forest Service's total allocation: 100.
2006; Total allocation: 280,119,000;
Percentage of Forest Service's total allocation: 100.
2007; Total allocation: 301,258,000;
Percentage of Forest Service's total allocation: 100.
Source: GAO analysis of Forest Service data.
Notes: Total allocations do not include carryover from the previous
fiscal year. Numbers may not total due to rounding.
[End of table]
Table 9 shows Interior's allocations to BLM, BIA, NPS, and FWS--
including WUI and non-WUI allocations--for 2005, 2006, and 2007. In
2007, about 65 percent of Interior's total allocation was to WUI areas
and 35 percent was to non-WUI areas. In 2007, BLM received the largest
percentage of Interior's fuel reduction funding allocation--almost 48
percent.
Table 9: Interior Allocations to BLM, BIA, FWS, and NPS, Including WUI
and Non-WUI Allocations, Fiscal Years 2005, 2006, and 2007:
Agency: BLM;
2005; Total allocation: $91,386,000;
Percentage of Interior's total allocation to the agencies: 47.0;
Total WUI allocation: $64,437,000;
Total non-WUI allocation: $26,949,000.
2006; Total allocation: 96,299,000;
Percentage of Interior's total allocation to the agencies: 47.0;
Total WUI allocation: 66,245,000;
Total non-WUI allocation: 30,054,000.
2007; Total allocation: 93,389,000;
Percentage of Interior's total allocation to the agencies: 47.6;
Total WUI allocation: 66,590,000;
Total non-WUI allocation: 26,799,000.
Agency: BIA;
2005; Total allocation: 42,488,000;
Percentage of Interior's total allocation to the agencies: 21.9;
Total WUI allocation: 27,299,000;
Total non-WUI allocation: 15,189,000.
2006; Total allocation: 43,237,000;
Percentage of Interior's total allocation to the agencies: 21.1;
Total WUI allocation: 27,494,000;
Total non-WUI allocation: 15,743,000.
2007; Total allocation: 40,664,000;
Percentage of Interior's total allocation to the agencies: 20.7;
Total WUI allocation: 26,681,000;
Total non-WUI allocation: 13,983,000.
Agency: NPS;
2005; Total allocation: 33,040,000;
Percentage of Interior's total allocation to the agencies: 17.0;
Total WUI allocation: 15,320,000;
Total non-WUI allocation: 17,720,000.
2006; Total allocation: 33,299,000;
Percentage of Interior's total allocation to the agencies: 16.2;
Total WUI allocation: 14,948,000;
Total non-WUI allocation: 18,351,000.
2007; Total allocation: 31,396,000;
Percentage of Interior's total allocation to the agencies: 16.0;
Total WUI allocation: 14,583,000;
Total non-WUI allocation: 16,813,000.
Agency: FWS;
2005; Total allocation: 27,527,000;
Percentage of Interior's total allocation to the agencies: 14.2;
Total WUI allocation: 15,583,000;
Total non-WUI allocation: 11,944,000.
2006; Total allocation: 32,162,000;
Percentage of Interior's total allocation to the agencies: 15.7;
Total WUI allocation: 19,772,000;
Total non-WUI allocation: 12,390,000.
2007; Total allocation: 30,666,000;
Percentage of Interior's total allocation to the agencies: 15.6;
Total WUI allocation: 18,922,000;
Total non-WUI allocation: 11,744,000.
Agency: Total--Interior agencies;
2005; Total allocation: $194,441,000;
Percentage of Interior's total allocation to the agencies: 100;
Total WUI allocation: $122,639,000;
Total non-WUI allocation: $71,802,000.
2006; Total allocation: $204,997,000;
Percentage of Interior's total allocation to the agencies: 100;
Total WUI allocation: $128,459,000;
Total non-WUI allocation: $76,538,000.
2007; Total allocation: $196,115,000;
Percentage of Interior's total allocation to the agencies: 100;
Total WUI allocation: $126,776,000;
Total non-WUI allocation: $69,339,000.
Source: GAO analysis of Interior data.
Notes: Interior allocated an additional $6,968,000 in 2005, $5,115,000
in 2006, and $3,672,000 in 2007 to the Office of Wildland Fire
Coordination.
Total allocations do not include carryover from the previous fiscal
year.
[End of table]
Table 10 shows BLM's allocations to its 12 state offices and
headquarters for 2005, 2006, and 2007. In 2007, about 71 percent of
BLM's total fuel reduction funding was allocated to WUI areas, while
about 29 percent was allocated to non-WUI areas. In 2005, 2006, and
2007, the Oregon/Washington state office received the most funding,
followed by the Idaho and Utah state offices. These three offices
accounted for about 50 percent of BLM's total annual fuel reduction
allocation.
Table 10: BLM Allocations to State Offices and Headquarters, Fiscal
Years 2005, 2006, and 2007:
State office: Oregon/Washington;
2005: Total allocation: $26,177,000;
Percentage of BLM's total allocation: 27.6;
Total WUI allocation: $19,027,000;
Total non-WUI allocation: $7,150,000.
2006: Total allocation: 24,596,000;
Percentage of BLM's total allocation: 24.1;
Total WUI allocation: 17,966,000;
Total non-WUI allocation: 6,630,000.
2007: Total allocation: 24,878,000;
Percentage of BLM's total allocation: 24.8;
Total WUI allocation: 18,542,000;
Total non-WUI allocation: 6,336,000.
State office: Idaho;
2005: Total allocation: 14,536,000;
Percentage of BLM's total allocation: 15.3;
Total WUI allocation: 10,130,000;
Total non-WUI allocation: 4,406,000.
2006: Total allocation: 14,787,000;
Percentage of BLM's total allocation: 14.5;
Total WUI allocation: 10,033,000;
Total non-WUI allocation: 4,754,000.
2007: Total allocation: 14,598,000;
Percentage of BLM's total allocation: 14.6;
Total WUI allocation: 10,338,000;
Total non-WUI allocation: 4,260,000.
State office: Utah;
2005: Total allocation: 8,557,000;
Percentage of BLM's total allocation: 9.0;
Total WUI allocation: 5,479,000;
Total non-WUI allocation: 3,078,000.
2006: Total allocation: 7,968,000;
Percentage of BLM's total allocation: 7.8;
Total WUI allocation: 5,225,000;
Total non-WUI allocation: 2,743,000.
2007: Total allocation: 10,078,000;
Percentage of BLM's total allocation: 10.1;
Total WUI allocation: 6,164,000;
Total non-WUI allocation: 3,914,000.
State office: California;
2005: Total allocation: 7,257,000;
Percentage of BLM's total allocation: 7.6;
Total WUI allocation: 6,096,000;
Total non-WUI allocation: 1,161,000.
2006: Total allocation: 6,364,000;
Percentage of BLM's total allocation: 6.2;
Total WUI allocation: 5,382,000;
Total non-WUI allocation: 982,000.
2007: Total allocation: 7,322,000;
Percentage of BLM's total allocation: 7.3;
Total WUI allocation: 6,294,000;
Total non-WUI allocation: 1,028,000.
State office: Nevada;
2005: Total allocation: 6,663,000;
Percentage of BLM's total allocation: 7.0;
Total WUI allocation: 4,572,000;
Total non-WUI allocation: 2,091,000.
2006: Total allocation: 5,794,000;
Percentage of BLM's total allocation: 5.7;
Total WUI allocation: 3,881,000;
Total non-WUI allocation: 1,913,000.
2007: Total allocation: 6,414,000;
Percentage of BLM's total allocation: 6.4;
Total WUI allocation: 4,317,000;
Total non-WUI allocation: 2,097,000.
State office: Colorado;
2005: Total allocation: 6,480,000;
Percentage of BLM's total allocation: 6.8;
Total WUI allocation: 4,891,000;
Total non-WUI allocation: 1,589,000.
2006: Total allocation: 6,068,000;
Percentage of BLM's total allocation: 5.9;
Total WUI allocation: 4,589,000;
Total non-WUI allocation: 1,479,000.
2007: Total allocation: 6,843,000;
Percentage of BLM's total allocation: 6.8;
Total WUI allocation: 5,285,000;
Total non-WUI allocation: 1,558,000.
State office: New Mexico;
2005: Total allocation: 5,676,000;
Percentage of BLM's total allocation: 6.0;
Total WUI allocation: 2,930,000;
Total non-WUI allocation: 2,746,000.
2006: Total allocation: 6,167,000;
Percentage of BLM's total allocation: 6.0;
Total WUI allocation: 3,347,000;
Total non-WUI allocation: 2,820,000.
2007: Total allocation: 6,412,000;
Percentage of BLM's total allocation: 6.4;
Total WUI allocation: 3,630,000;
Total non-WUI allocation: 2,782,000.
State office: Montana;
2005: Total allocation: 5,338,000;
Percentage of BLM's total allocation: 5.6;
Total WUI allocation: 4,248,000;
Total non-WUI allocation: 1,090,000.
2006: Total allocation: 4,871,000;
Percentage of BLM's total allocation: 4.8;
Total WUI allocation: 4,000,000;
Total non-WUI allocation: 871,000.
2007: Total allocation: 5,461,000;
Percentage of BLM's total allocation: 5.5;
Total WUI allocation: 4,366,000;
Total non-WUI allocation: 1,095,000.
State office: Arizona;
2005: Total allocation: 4,219,000;
Percentage of BLM's total allocation: 4.4;
Total WUI allocation: 2,509,000;
Total non-WUI allocation: 1,710,000.
2006: Total allocation: 3,787,000;
Percentage of BLM's total allocation: 3.7;
Total WUI allocation: 2,396,000;
Total non-WUI allocation: 1,391,000.
2007: Total allocation: 4,355,000;
Percentage of BLM's total allocation: 4.3;
Total WUI allocation: 2,608,000;
Total non-WUI allocation: 1,747,000.
State office: Wyoming;
2005: Total allocation: 3,143,000;
Percentage of BLM's total allocation: 3.3;
Total WUI allocation: 1,830,000;
Total non-WUI allocation: 1,313,000.
2006: Total allocation: 2,898,000;
Percentage of BLM's total allocation: 2.8;
Total WUI allocation: 1,786,000;
Total non-WUI allocation: 1,112,000.
2007: Total allocation: 3,684,000;
Percentage of BLM's total allocation: 3.7;
Total WUI allocation: 2,185,000;
Total non-WUI allocation: 1,499,000.
State office: Alaska;
2005: Total allocation: 785,000;
Percentage of BLM's total allocation: 0.8;
Total WUI allocation: 365,000;
Total non-WUI allocation: 420,000.
2006: Total allocation: 1,044,000;
Percentage of BLM's total allocation: 1.0;
Total WUI allocation: 502,000;
Total non-WUI allocation: 542,000.
2007: Total allocation: 1,556,000;
Percentage of BLM's total allocation: 1.6;
Total WUI allocation: 786,000;
Total non-WUI allocation: 770,000.
State office: Eastern states;
2005: Total allocation: 98,000;
Percentage of BLM's total allocation: 0.1;
Total WUI allocation: 78,000;
Total non-WUI allocation: 20,000.
2006: Total allocation: 83,000;
Percentage of BLM's total allocation: 0.1;
Total WUI allocation: 58,000;
Total non-WUI allocation: 25,000.
2007: Total allocation: 126,000;
Percentage of BLM's total allocation: 0.1;
Total WUI allocation: 90,000;
Total non-WUI allocation: 36,000.
State office: Subtotal, state offices;
2005: Total allocation: 88,929,000;
Percentage of BLM's total allocation: 93.7;
Total WUI allocation: 62,155,000;
Total non-WUI allocation: 26,774,000.
2006: Total allocation: 84,427,000;
Percentage of BLM's total allocation: 82.6;
Total WUI allocation: 59,165,000;
Total non-WUI allocation: 25,262,000.
2007: Total allocation: 91,727,000;
Percentage of BLM's total allocation: 91.5;
Total WUI allocation: 64,605,000;
Total non-WUI allocation: 27,122,000.
State office: Headquarters;
2005: Total allocation: 5,979,000;
Percentage of BLM's total allocation: 6.3;
Total WUI allocation: 4,527,000;
Total non-WUI allocation: 1,452,000.
2006: Total allocation: 17,822,000;
Percentage of BLM's total allocation: 17.4;
Total WUI allocation: 12,029,000;
Total non-WUI allocation: 5,793,000.
2007: Total allocation: 8,473,000;
Percentage of BLM's total allocation: 8.5;
Total WUI allocation: 6,315,000;
Total non-WUI allocation: 2,158,000.
State office: Total;
2005: Total allocation: 94,908,000;
Percentage of BLM's total allocation: 100;
Total WUI allocation: 66,682,000;
Total non-WUI allocation: 28,226,000.
2006: Total allocation: 102,249,000;
Percentage of BLM's total allocation: 100;
Total WUI allocation: 71,194,000;
Total non-WUI allocation: 31,055,000.
2007: Total allocation: 100,200,000;
Percentage of BLM's total allocation: 100;
Total WUI allocation: 70,920,000;
Total non-WUI allocation: 29,280,000.
Source: GAO analysis of BLM data.
Notes: Total allocations include the allocation for the current year
plus carryover from the previous fiscal year.
Numbers may not total due to rounding.
[End of table]
Table 11 shows BIA's allocations for 2005, 2006, and 2007 to its 12
regions and the National Interagency Fire Center. In 2007, about 67
percent of BIA's total fuel reduction funding was allocated to WUI
areas, while about 33 percent was allocated to non-WUI areas. In 2005,
2006, and 2007, the Northwest region received the most funding of the
BIA regions, followed by the Southwest region. These two regions
accounted for about 50 percent of BIA's total fuel reduction allocation
in 2007.
Table 11: BIA Allocations to Regions and the National Interagency Fire
Center, Fiscal Years 2005, 2006, and 2007:
Region: Northwest;
2005: Total allocation: $12,204,855;
Percentage of BIA's total allocation: 28.1;
Total WUI allocation: $7,823,617;
Total non-WUI allocation: $4,381,238.
2006: Total allocation: 11,387,925;
Percentage of BIA's total allocation: 26.4;
Total WUI allocation: 8,012,751;
Total non-WUI allocation: 3,375,174.
2007: Total allocation: 11,835,643;
Percentage of BIA's total allocation: 29.6;
Total WUI allocation: 8,465,745;
Total non-WUI allocation: 3,369,898.
Region: Southwest;
2005: Total allocation: 7,651,861;
Percentage of BIA's total allocation: 17.6;
Total WUI allocation: 3,518,105;
Total non-WUI allocation: 4,133,756.
2006: Total allocation: 9,175,694;
Percentage of BIA's total allocation: 21.3;
Total WUI allocation: 4,115,843;
Total non-WUI allocation: 5,059,851.
2007: Total allocation: 8,366,522;
Percentage of BIA's total allocation: 21.0;
Total WUI allocation: 5,128,157;
Total non-WUI allocation: 3,238,365.
Region: Western;
2005: Total allocation: 4,497,336;
Percentage of BIA's total allocation: 10.4;
Total WUI allocation: 2,912,773;
Total non-WUI allocation: 1,584,563.
2006: Total allocation: 4,020,742;
Percentage of BIA's total allocation: 9.3;
Total WUI allocation: 2,218,682;
Total non-WUI allocation: 1,802,060.
2007: Total allocation: 3,366,120;
Percentage of BIA's total allocation: 8.4;
Total WUI allocation: 2,287,498;
Total non-WUI allocation: 1,078,622.
Region: Pacific;
2005: Total allocation: 3,846,125;
Percentage of BIA's total allocation: 8.9;
Total WUI allocation: 2,863,206;
Total non-WUI allocation: 982,919.
2006: Total allocation: 3,096,619;
Percentage of BIA's total allocation: 7.2;
Total WUI allocation: 2,546,681;
Total non-WUI allocation: 549,938.
2007: Total allocation: 2,401,976;
Percentage of BIA's total allocation: 6.0;
Total WUI allocation: 1,453,604;
Total non-WUI allocation: 948,372.
Region: Great Plains;
2005: Total allocation: 2,464,878;
Percentage of BIA's total allocation: 5.7;
Total WUI allocation: 1,461,855;
Total non-WUI allocation: 1,003,023.
2006: Total allocation: 3,196,997;
Percentage of BIA's total allocation: 7.4;
Total WUI allocation: 1,742,168;
Total non-WUI allocation: 1,454,829.
2007: Total allocation: 2,281,299;
Percentage of BIA's total allocation: 5.7;
Total WUI allocation: 1,061,894;
Total non-WUI allocation: 1,219,405.
Region: Alaska;
2005: Total allocation: 2,417,117;
Percentage of BIA's total allocation: 5.6;
Total WUI allocation: 2,247,117;
Total non-WUI allocation: 170,000.
2006: Total allocation: 2,256,079;
Percentage of BIA's total allocation: 5.2;
Total WUI allocation: 2,163,458;
Total non-WUI allocation: 92,621.
2007: Total allocation: 1,780,638;
Percentage of BIA's total allocation: 4.5;
Total WUI allocation: 1,538,479;
Total non-WUI allocation: 242,159.
Region: Midwest;
2005: Total allocation: 1,876,013;
Percentage of BIA's total allocation: 4.3;
Total WUI allocation: 1,280,813;
Total non-WUI allocation: 595,200.
2006: Total allocation: 2,736,830;
Percentage of BIA's total allocation: 6.3;
Total WUI allocation: 1,988,630;
Total non-WUI allocation: 748,200.
2007: Total allocation: 2,913,975;
Percentage of BIA's total allocation: 7.3;
Total WUI allocation: 2,497,657;
Total non-WUI allocation: 416,318.
Region: Rocky Mountain;
2005: Total allocation: 1,699,576;
Percentage of BIA's total allocation: 3.9;
Total WUI allocation: 1,004,728;
Total non-WUI allocation: 694,848.
2006: Total allocation: 1,769,815;
Percentage of BIA's total allocation: 4.1;
Total WUI allocation: 1,015,550;
Total non-WUI allocation: 754,265.
2007: Total allocation: 1,795,609;
Percentage of BIA's total allocation: 4.5;
Total WUI allocation: 1,066,157;
Total non-WUI allocation: 729,452.
Region: Navajo;
2005: Total allocation: 1,258,919;
Percentage of BIA's total allocation: 2.9;
Total WUI allocation: 342,421;
Total non-WUI allocation: 916,498.
2006: Total allocation: 1,335,559;
Percentage of BIA's total allocation: 3.1;
Total WUI allocation: 443,588;
Total non-WUI allocation: 891,971.
2007: Total allocation: 933,183;
Percentage of BIA's total allocation: 2.3;
Total WUI allocation: 357,126;
Total non-WUI allocation: 576,057.
Region: Southern Plains;
2005: Total allocation: 572,247;
Percentage of BIA's total allocation: 1.3;
Total WUI allocation: 27,552;
Total non-WUI allocation: 544,695.
2006: Total allocation: 568,471;
Percentage of BIA's total allocation: 1.3;
Total WUI allocation: 30,174;
Total non-WUI allocation: 538,297.
2007: Total allocation: 489,663;
Percentage of BIA's total allocation: 1.2;
Total WUI allocation: 21,867;
Total non-WUI allocation: 467,796.
Region: Eastern;
2005: Total allocation: 503,175;
Percentage of BIA's total allocation: 1.2;
Total WUI allocation: 425,375;
Total non-WUI allocation: 77,800.
2006: Total allocation: 215,825;
Percentage of BIA's total allocation: 0.5;
Total WUI allocation: 115,732;
Total non-WUI allocation: 100,093.
2007: Total allocation: 433,609;
Percentage of BIA's total allocation: 1.1;
Total WUI allocation: 272,952;
Total non-WUI allocation: 160,657.
Region: Eastern Oklahoma;
2005: Total allocation: 284,557;
Percentage of BIA's total allocation: 0.7;
Total WUI allocation: 216,900;
Total non-WUI allocation: 67,657.
2006: Total allocation: 224,465;
Percentage of BIA's total allocation: 0.5;
Total WUI allocation: 189,709;
Total non-WUI allocation: 34,756.
2007: Total allocation: 240,074;
Percentage of BIA's total allocation: 0.6;
Total WUI allocation: 164,172;
Total non-WUI allocation: 75,902.
Region: Subtotal, regions;
2005: Total allocation: 39,276,659;
Percentage of BIA's total allocation: 90.4;
Total WUI allocation: 24,124,462;
Total non-WUI allocation: 15,152,197.
2006: Total allocation: 39,985,021;
Percentage of BIA's total allocation: 92.7;
Total WUI allocation: 24,582,966;
Total non-WUI allocation: 15,402,055.
2007: Total allocation: 36,838,311;
Percentage of BIA's total allocation: 92.3;
Total WUI allocation: 24,315,308;
Total non-WUI allocation: 12,523,003.
Region: National Interagency Fire Center;
2005: Total allocation: 4,172,185;
Percentage of BIA's total allocation: 9.6;
Total WUI allocation: 3,621,793;
Total non-WUI allocation: 550,392.
2006: Total allocation: 3,156,566;
Percentage of BIA's total allocation: 7.3;
Total WUI allocation: 2,720,552;
Total non-WUI allocation: 436,014.
2007: Total allocation: 3,092,337;
Percentage of BIA's total allocation: 7.7;
Total WUI allocation: 2,608,684;
Total non-WUI allocation: 483,653.
Region: Total;
2005: Total allocation: 43,448,844;
Percentage of BIA's total allocation: 100;
Total WUI allocation: 27,746,255;
Total non-WUI allocation: 15,702,589.
2006: Total allocation: 43,141,587;
Percentage of BIA's total allocation: 100;
Total WUI allocation: 27,303,518;
Total non-WUI allocation: 15,838,069.
2007: Total allocation: 39,930,648;
Percentage of BIA's total allocation: 100;
Total WUI allocation: 26,923,992;
Total non-WUI allocation: 13,006,656.
Source: GAO analysis of BIA data.
Notes: Total allocations include the allocation for the current year
plus carryover from the previous fiscal year.
Numbers may not total due to rounding.
[End of table]
Table 12 shows NPS's allocations to its seven regions and the
Washington Office for 2005, 2006, and 2007. In 2007, about 46 percent
of NPS's total fuel reduction funding was allocated to WUI areas, while
about 54 percent was allocated to non-WUI areas. NPS was the only
Interior agency that allocated more funds to non-WUI areas than to WUI
areas. In 2005, 2006, and 2007, the Pacific West region received the
most funding; followed by the Intermountain region. These two regions
accounted for about 60 percent of NPS's total annual fuel reduction
allocation in 2007.
Table 12: NPS Allocations to Regions and the Washington Office, Fiscal
Years 2005, 2006, and 2007:
Region: Pacific West;
2005: Total allocation: $12,550,296;
Percentage of NPS's total allocation: 35.5;
Total WUI allocation: $7,317,240;
Total non-WUI allocation: $5,233,056.
2006: Total allocation: 10,478,930;
Percentage of NPS's total allocation: 32.4;
Total WUI allocation: 6,231,822;
Total non-WUI allocation: 4,247,108.
2007: Total allocation: 10,693,592;
Percentage of NPS's total allocation: 31.6;
Total WUI allocation: 6,401,068;
Total non-WUI allocation: 4,292,524.
Region: Intermountain;
2005: Total allocation: 10,070,518;
Percentage of NPS's total allocation: 28.5;
Total WUI allocation: 4,268,238;
Total non-WUI allocation: 5,802,280.
2006: Total allocation: 8,578,937;
Percentage of NPS's total allocation: 26.5;
Total WUI allocation: 3,138,962;
Total non-WUI allocation: 5,439,975.
2007; Total allocation: 9,398,600;
Percentage of NPS's total allocation: 27.8;
Total WUI allocation: 4,006,559;
Total non-WUI allocation: 5,392,041.
Region: Southeast;
2005: Total allocation: 4,279,340;
Percentage of NPS's total allocation: 12.1;
Total WUI allocation: 1,973,600;
Total non-WUI allocation: 2,305,740.
2006: Total allocation: 4,047,002;
Percentage of NPS's total allocation: 12.5;
Total WUI allocation: 1,648,782;
Total non-WUI allocation: 2,398,220.
2007: Total allocation: 4,604,308;
Percentage of NPS's total allocation: 13.6;
Total WUI allocation: 1,843,531;
Total non-WUI allocation: 2,760,777.
Region: Midwest;
2005: Total allocation: 3,104,724;
Percentage of NPS's total allocation: 8.8;
Total WUI allocation: 627,690;
Total non-WUI allocation: 2,477,034.
2006: Total allocation: 3,341,288;
Percentage of NPS's total allocation: 10.3;
Total WUI allocation: 634,129;
Total non-WUI allocation: 2,707,159.
2007; Total allocation: 3,469,731;
Percentage of NPS's total allocation: 10.3;
Total WUI allocation: 644,967;
Total non-WUI allocation: 2,824,764.
Region: Northeast;
2005: Total allocation: 898,084;
Percentage of NPS's total allocation: 2.5;
Total WUI allocation: 521,020;
Total non-WUI allocation: 377,064.
2006: Total allocation: 879,070;
Percentage of NPS's total allocation: 2.7;
Total WUI allocation: 406,936;
Total non-WUI allocation: 472,134.
2007; Total allocation: 1,201,497;
Percentage of NPS's total allocation: 3.6;
Total WUI allocation: 757,338;
Total non-WUI allocation: 444,159.
Region: Alaska;
2005: Total allocation: 560,582;
Percentage of NPS's total allocation: 1.6;
Total WUI allocation: 0;
Total non-WUI allocation: 560,582.
2006: Total allocation: 813,140;
Percentage of NPS's total allocation: 2.5;
Total WUI allocation: 0;
Total non-WUI allocation: 813,140.
2007: Total allocation: 739,037;
Percentage of NPS's total allocation: 2.2;
Total WUI allocation: 0;
Total non-WUI allocation: 739,037.
Region: National Capital;
2005: Total allocation: 142,043;
Percentage of NPS's total allocation: 0.4;
Total WUI allocation: 122,320;
Total non-WUI allocation: 19,723.
2006: Total allocation: 99,851;
Percentage of NPS's total allocation: 0.3;
Total WUI allocation: 99,851;
Total non-WUI allocation: 0.
2007: Total allocation: 103,631;
Percentage of NPS's total allocation: 0.3;
Total WUI allocation: 103,631;
Total non-WUI allocation: 0.
Region: Subtotal, regions;
2005: Total allocation: 31,605,587;
Percentage of NPS's total allocation: 89.5;
Total WUI allocation: 14,830,108;
Total non-WUI allocation: 16,775,479.
2006: Total allocation: 28,238,218;
Percentage of NPS's total allocation: 87.4;
Total WUI allocation: 12,160,482;
Total non-WUI allocation: 16,077,736.
2007: Total allocation: 30,210,396;
Percentage of NPS's total allocation: 89.3;
Total WUI allocation: 13,757,094;
Total non-WUI allocation: 16,453,302.
Region: Washington Office;
2005: Total allocation: 3,705,107;
Percentage of NPS's total allocation: 10.5;
Total WUI allocation: 1,572,455;
Total non-WUI allocation: 2,132,652.
2006: Total allocation: 4,080,417;
Percentage of NPS's total allocation: 12.6;
Total WUI allocation: 1,904,137;
Total non-WUI allocation: 2,176,280.
2007: Total allocation: 3,605,604;
Percentage of NPS's total allocation: 10.7;
Total WUI allocation: 1,804,906;
Total non-WUI allocation: 1,800,698.
Region: Total;
2005: Total allocation: 35,310,694;
Percentage of NPS's total allocation: 100;
Total WUI allocation: $16,402,563;
Total non-WUI allocation: 18,908,131.
2006: Total allocation: 32,318,635;
Percentage of NPS's total allocation: 100;
Total WUI allocation: 14,064,619;
Total non-WUI allocation: 18,254,016.
2007: Total allocation: 33,816,000;
Percentage of NPS's total allocation: 100;
Total WUI allocation: 15,562,000;
Total non-WUI allocation: 18,254,000.
Source: GAO analysis of NPS data.
Notes: Total allocations include the allocation for the current year
plus carryover from the previous fiscal year.
Numbers may not total due to rounding.
[End of table]
Table 13 shows FWS's allocations in 2005, 2006, and 2007 to its seven
regions, the California-Nevada Operations office, and
headquarters.[Footnote 43] In 2007, about 61 percent of FWS's total
fuel reduction funding was allocated to WUI areas, while about 39
percent was allocated to non-WUI areas. In 2005, 2006, and 2007, the
Southeast region received the most funding, followed by the Great Lakes-
Big Rivers region.
Table 13: FWS Allocations to Regions and Headquarters, Fiscal Years
2005, 2006, and 2007:
Region: Southeast;
2005: Total allocation: $7,005,484;
Percentage of FWS's total allocation: 24.7; T
Total WUI allocation: $3,715,115;
Total non-WUI allocation: $3,290,369.
2006: Total allocation: 7,966,857;
Percentage of FWS's total allocation: 23.8;
Total WUI allocation: 4,780,616;
Total non-WUI allocation: 3,186,241.
2007: Total allocation: 7,543,624;
Percentage of FWS's total allocation: 24.2;
Total WUI allocation: 4,481,330;
Total non-WUI allocation: 3,062,294.
Region: Great Lakes-Big Rivers;
2005: Total allocation: 4,867,717;
Percentage of FWS's total allocation: 17.1;
Total WUI allocation: 2,111,460;
Total non-WUI allocation: 2,756,257.
2006: Total allocation: 5,438,168;
Percentage of FWS's total allocation: 16.2;
Total WUI allocation: 2,658,862;
Total non-WUI allocation: 2,779,306.
2007: Total allocation: 5,336,376;
Percentage of FWS's total allocation: 17.1;
Total WUI allocation: 2,604,636;
Total non-WUI allocation: 2,731,740.
Region: Mountain-Prairie;
2005: Total allocation: 3,376,546;
Percentage of FWS's total allocation: 11.9;
Total WUI allocation: 1,281,699;
Total non-WUI allocation: 2,094,847.
2006: Total allocation: 3,776,901;
Percentage of FWS's total allocation: 11.3;
Total WUI allocation: 1,658,254;
Total non-WUI allocation: 2,118,647.
2007: Total allocation: 3,690,940;
Percentage of FWS's total allocation: 11.8;
Total WUI allocation: 1,566,284;
Total non-WUI allocation: 2,124,656.
Region: Southwest;
2005: Total allocation: 3,363,824;
Percentage of FWS's total allocation: 11.9;
Total WUI allocation: 2,139,589;
Total non-WUI allocation: 1,224,235.
2006: Total allocation: 3,903,921;
Percentage of FWS's total allocation: 11.7;
Total WUI allocation: 2,499,122;
Total non-WUI allocation: 1,404,799.
2007: Total allocation: 3,721,205;
Percentage of FWS's total allocation: 11.9;
Total WUI allocation: 2,434,533;
Total non-WUI allocation: 1,286,672.
Region: Pacific;
2005: Total allocation: 2,556,707;
Percentage of FWS's total allocation: 9.0;
Total WUI allocation: 1,504,681;
Total non-WUI allocation: 1,052,026.
2006: Total allocation: 2,853,522;
Percentage of FWS's total allocation: 8.5;
Total WUI allocation: 1,864,739;
Total non-WUI allocation: 988,783.
2007: Total allocation: 2,566,156;
Percentage of FWS's total allocation: 8.2;
Total WUI allocation: 1,736,153;
Total non-WUI allocation: 830,003.
Region: Northeast;
2005: Total allocation: 2,201,297;
Percentage of FWS's total allocation: 7.8;
Total WUI allocation: 1,751,683;
Total non-WUI allocation: 449,614.
2006: Total allocation: 2,597,811;
Percentage of FWS's total allocation: 7.8;
Total WUI allocation: 2,106,088;
Total non-WUI allocation: 491,723.
2007: Total allocation: 2,416,798;
Percentage of FWS's total allocation: 7.7;
Total WUI allocation: 1,977,669;
Total non-WUI allocation: 439,129.
Region: California-Nevada[A];
2005: Total allocation: 1,712,138;
Percentage of FWS's total allocation: 6.0;
Total WUI allocation: 986,919;
Total non-WUI allocation: 725,219.
2006: Total allocation: 2,542,027;
Percentage of FWS's total allocation: 7.6;
Total WUI allocation: 1,960,241;
Total non-WUI allocation: 581,786.
2007: Total allocation: 2,254,492;
Percentage of FWS's total allocation: 7.2;
Total WUI allocation: 1,460,866;
Total non-WUI allocation: 793,626.
Region: Alaska;
2005: Total allocation: 1,000,439;
Percentage of FWS's total allocation: 3.5;
Total WUI allocation: 815,607;
Total non-WUI allocation: 184,832.
2006: Total allocation: 1,271,882;
Percentage of FWS's total allocation: 3.8;
Total WUI allocation: 1,050,646;
Total non-WUI allocation: 221,236.
2007: Total allocation: 1,224,552;
Percentage of FWS's total allocation: 3.9;
Total WUI allocation: 1,074,688;
Total non-WUI allocation: 149,864.
Region: Subtotal, regions;
2005: Total allocation: 26,084,152;
Percentage of FWS's total allocation: 91.9;
Total WUI allocation: 14,306,753;
Total non-WUI allocation: 11,777,399.
2006: Total allocation: 30,351,089;
Percentage of FWS's total allocation: 90.6;
Total WUI allocation: 18,578,568;
Total non-WUI allocation: 11,772,521.
2007: Total allocation: 28,754,143;
Percentage of FWS's total allocation: 92.1;
Total WUI allocation: 17,336,159;
Total non-WUI allocation: 11,417,984.
Region: Washington Office;
2005: Total allocation: 2,302,509;
Percentage of FWS's total allocation: 8.1;
Total WUI allocation: 1,571,629;
Total non-WUI allocation: 730,880.
2006: Total allocation: 3,153,074;
Percentage of FWS's total allocation: 9.4;
Total WUI allocation: 2,037,897;
Total non-WUI allocation: 1,115,177.
2007: Total allocation: 2,450,015;
Percentage of FWS's total allocation: 7.9;
Total WUI allocation: 1,723,281;
Total non-WUI allocation: 726,734.
Region: Total;
2005: Total allocation: 28,386,661;
Percentage of FWS's total allocation: 100;
Total WUI allocation: 15,878,382;
Total non-WUI allocation: 12,508,279.
2006: Total allocation: 33,504,163;
Percentage of FWS's total allocation: 100;
Total WUI allocation: 20,616,465;
Total non-WUI allocation: 12,887,698.
2007: Total allocation: 31,204,158;
Percentage of FWS's total allocation: 100;
Total WUI allocation: 19,059,440;
Total non-WUI allocation: 12,144,718.
Source: GAO analysis of FWS data.
Notes: Total allocations include the allocation for the current year
plus carryover from the previous fiscal year.
Numbers may not total due to rounding.
[A] While FWS has seven regions, it has an eighth office--the
California-Nevada Operations office--that, although officially part of
the Pacific region, manages its own fuel reduction program.
[End of section]
[End of table]
Appendix III: Summary of Fuel Treatment Accomplishments for the Forest
Service and Interior, Fiscal Years 2005 and 2006:
The tables in this appendix summarize the fuel reduction
accomplishments of the Forest Service and the four Interior agencies we
reviewed--Bureau of Land Management (BLM), Bureau of Indian Affairs
(BIA), National Park Service (NPS), and Fish and Wildlife Service
(FWS)--for 2005 and 2006,[Footnote 44] the most recent years for which
complete data were available. National and regional office data are
presented for each agency.[Footnote 45]
Table 14 provides nationwide information for each of the five agencies,
including total acres treated; acres treated in the wildland-urban
interface (WUI) and in non-WUI areas; and acres treated with prescribed
fire, mechanical methods, and other treatment methods such as
herbicides and grazing. The Forest Service treated more acres than the
four Interior agencies combined--almost 1.7 million acres in 2005 and
more than 1.5 million acres in 2006. Within Interior, BLM and FWS
treated the most acres, with BLM treating more than 500,000 acres in
2005 and almost 430,000 acres in 2006, and FWS treating almost 420,000
acres in 2005 and more than 370,000 acres in 2006. In each year, about
60 percent of the total acres treated by the five agencies were in the
WUI, and about 40 percent of total acres treated were outside of the
WUI. The majority of acres were treated with prescribed fire--almost 75
percent in 2005 and almost 65 percent in 2006.
Table 14: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
Accomplishments for Interior and Forest Service:
Agency: BLM;
2005: Treated acres: 506,168;
WUI acres: 253,001;
Non-WUI acres: 253,167;
Acres treated using prescribed fire: 194,553;
Acres treated using mechanical means: 211,852;
Acres treated using other means[A]: 99,763.
2006: Treated acres: 427,912;
WUI acres: 230,932;
Non-WUI acres: 196,980;
Acres treated using prescribed fire: 107,443;
Acres treated using mechanical means: 206,123;
Acres treated using other means[A]: 114,346.
Agency: BIA;
2005: Treated acres: 193,617;
WUI acres: 71,983;
Non-WUI acres: 121,634;
Acres treated using prescribed fire: 96,881;
Acres treated using mechanical means: 94,168;
Acres treated using other means[A]: 2,568.
2006: Treated acres: 187,653;
WUI acres: 89,961; Non-WUI acres: 97,692;
Acres treated using prescribed fire: 78,304;
Acres treated using mechanical means: 106,204;
Acres treated using other means[A]: 3,145.
Agency: NPS;
2005: Treated acres: 153,972;
WUI acres: 58,873;
Non-WUI acres: 95,099;
Acres treated using prescribed fire: 139,455;
Acres treated using mechanical means: 13,036;
Acres treated using other means[A]: 1,481.
2006: Treated acres: 116,635;
WUI acres: 38,558;
Non-WUI acres: 78,077;
Acres treated using prescribed fire: 102,765;
Acres treated using mechanical means: 11,532;
Acres treated using other means[A]: 2,338.
Agency: FWS;
2005: Treated acres: 415,646;
WUI acres: 158,711;
Non-WUI acres: 256,935;
Acres treated using prescribed fire: 389,686;
Acres treated using mechanical means: 21,734;
Acres treated using other means[A]: 4,226.
2006: Treated acres: 373,933;
WUI acres: 173,113;
Non-WUI acres: 200,820;
Acres treated using prescribed fire: 333,038;
Acres treated using mechanical means: 32,118;
Acres treated using other means[A]: 8,777.
Agency: Interior subtotal;
2005: Treated acres: 1,269,403;
WUI acres: 542,568;
Non-WUI acres: 726,835;
Acres treated using prescribed fire: 820,575;
Acres treated using mechanical means: 340,790;
Acres treated using other means[A]: 108,038.
2006: Treated acres: 1,106,133;
WUI acres: 532,564;
Non-WUI acres: 573,569;
Acres treated using prescribed fire: 621,550;
Acres treated using mechanical means: 355,977;
Acres treated using other means[A]: 128,606.
Agency: Forest Service;
2005: Treated acres: 1,672,909;
WUI acres: 1,198,663;
Non-WUI acres: 474,246;
Acres treated using prescribed fire: 1,366,988;
Acres treated using mechanical means: 303,002;
Acres treated using other means[A]: 2,919.
2006: Treated acres: 1,503,475;
WUI acres: 1,090,721;
Non-WUI acres: 412,754;
Acres treated using prescribed fire: 1,061,277;
Acres treated using mechanical means: 433,077;
Acres treated using other means[A]: 9,121.
Agency: Total Forest Service and Interior;
2005: Treated acres: 2,942,312;
WUI acres: 1,741,231;
Non-WUI acres: 1,201,081;
Acres treated using prescribed fire: 2,187,563;
Acres treated using mechanical means: 643,792;
Acres treated using other means[A]: 110,957.
2006: Treated acres: 2,609,608;
WUI acres: 1,623,285;
Non-WUI acres: 986,323;
Acres treated using prescribed fire: 1,682,827;
Acres treated using mechanical means: 789,054;
Acres treated using other means[A]: 137,727.
Source: GAO analysis of Interior and Forest Service data.
[A] "Other" category includes treatments such as herbicides and grazing.
[End of table]
Table 15 summarizes 2005 and 2006 fuel treatment information for the
Forest Service regions. In both years, the Southern region treated
substantially more acres than the other regions, treating more than
half of the Forest Service's total treated acres. The Forest Service
treated more than twice as many acres in the WUI as in non-WUI areas,
and treated most acres with prescribed fire.
Table 15: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
Accomplishments for Forest Service Regions:
Region: Pacific Southwest;
2005: Treated acres: 100,540;
WUI acres: 59,194;
Non-WUI acres: 41,346;
Acres treated using prescribed fire: 41,565;
Acres treated using mechanical means: 58,785;
Acres treated using other means[A]: 190.
2006; Treated acres: 95,729;
WUI acres: 62,229;
Non-WUI acres: 33,500;
Acres treated using prescribed fire: 36,779;
Acres treated using mechanical means: 50,423;
Acres treated using other means[A]: 8,527.
Region: Southwestern;
2005: Treated acres: 164,506;
WUI acres: 69,929;
Non-WUI acres: 94,577;
Acres treated using prescribed fire: 118,326;
Acres treated using mechanical means: 46,180;
Acres treated using other means[A]: 0.
2006: Treated acres: 180,616;
WUI acres: 84,973;
Non-WUI acres: 95,643;
Acres treated using prescribed fire: 134,289;
Acres treated using mechanical means: 46,327;
Acres treated using other means[A]: 0.
Region: Southern;
2005: Treated acres: 976,176;
WUI acres: 803,654;
Non-WUI acres: 172,522;
Acres treated using prescribed fire: 969,528;
Acres treated using mechanical means: 6,616;
Acres treated using other means[A]: 32.
2006: Treated acres: 776,145;
WUI acres: 674,189;
Non-WUI acres: 101,956;
Acres treated using prescribed fire: 625,605;
Acres treated using mechanical means: 150,540;
Acres treated using other means[A]: 0.
Region: Pacific Northwest;
2005: Treated acres: 139,470;
WUI acres: 79,975;
Non-WUI acres: 59,495;
Acres treated using prescribed fire: 65,955;
Acres treated using mechanical means: 73,515;
Acres treated using other means[A]: 0.
2006: Treated acres: 133,528;
WUI acres: 60,904;
Non-WUI acres: 72,624;
Acres treated using prescribed fire: 73,068;
Acres treated using mechanical means: 60,432;
Acres treated using other means[A]: 28.
Region: Rocky Mountain;
2005: Treated acres: 93,969;
WUI acres: 65,862;
Non-WUI acres: 28,107;
Acres treated using prescribed fire: 51,353;
Acres treated using mechanical means: 39,919;
Acres treated using other means[A]: 2,697.
2006: Treated acres: 102,953;
WUI acres: 77,650;
Non-WUI acres: 25,303;
Acres treated using prescribed fire: 49,313;
Acres treated using mechanical means: 53,394;
Acres treated using other means[A]: 246.
Region: Intermountain;
2005: Treated acres: 74,676;
WUI acres: 34,163;
Non-WUI acres: 40,513;
Acres treated using prescribed fire: 47,077;
Acres treated using mechanical means: 27,599;
Acres treated using other means[A]: 0.
2006: Treated acres: 87,957;
WUI acres: 33,995;
Non-WUI acres: 53,962;
Acres treated using prescribed fire: 58,075;
Acres treated using mechanical means: 29,562;
Acres treated using other means[A]: 320.
Region: Northern;
2005: Treated acres: 70,594;
WUI acres: 43,824;
Non-WUI acres: 26,770;
Acres treated using prescribed fire: 39,726;
Acres treated using mechanical means: 30,868;
Acres treated using other means[A]: 0.
2006: Treated acres: 68,639;
WUI acres: 46,892;
Non-WUI acres: 21,747;
Acres treated using prescribed fire: 43,480;
Acres treated using mechanical means: 25,159;
Acres treated using other means[A]: 0.
Region: Eastern;
2005: Treated acres: 51,472;
WUI acres: 40,556;
Non-WUI acres: 10,916;
Acres treated using prescribed fire: 33,089;
Acres treated using mechanical means: 18,383;
Acres treated using other means[A]: 0.
2006: Treated acres: 57,221;
WUI acres: 49,202;
Non-WUI acres: 8,019;
Acres treated using prescribed fire: 40,242;
Acres treated using mechanical means: 16,979;
Acres treated using other means[A]: 0.
Region: Alaska;
2005: Treated acres: 1,506;
WUI acres: 1,506;
Non-WUI acres: 0;
Acres treated using prescribed fire: 369;
Acres treated using mechanical means: 1,137;
Acres treated using other means[A]: 0.
2006: Treated acres: 687;
WUI acres: 687;
Non-WUI acres: 0;
Acres treated using prescribed fire: 426;
Acres treated using mechanical means: 261;
Acres treated using other means[A]: 0.
Region: Total;
2005: Treated acres: 1,672,909;
WUI acres: 1,198,663;
Non-WUI acres: 474,246;
Acres treated using prescribed fire: 1,366,988;
Acres treated using mechanical means: 303,002;
Acres treated using other means[A]: 2,919.
2006: Treated acres: 1,503,475;
WUI acres: 1,090,721;
Non-WUI acres: 412,754;
Acres treated using prescribed fire: 1,061,277;
Acres treated using mechanical means: 433,077;
Acres treated using other means[A]: 9,121.
Source: GAO analysis of Forest Service data.
[A] "Other" category includes treatments such as herbicides and grazing.
[End of table]
Table 16 summarizes 2005 and 2006 fuel treatment information for the
BLM state offices. In both years, the Oregon/Washington and Idaho state
offices treated the most acres, followed by the New Mexico state
office. BLM treated about the same number of acres in the WUI and in
non-WUI areas in 2005, and treated about 34,000 more acres in the WUI
than in non-WUI areas in 2006. Unlike the Forest Service, BLM treated
more acres mechanically than with prescribed fire, and also treated a
substantial number of acres using other treatment methods, such as
herbicides or grazing.
Table 16: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
Accomplishments for BLM State Offices:
State office: Oregon/Washington;
2005: Treated acres: 108,909;
WUI acres: 71,218;
Non-WUI acres: 37,691;
Acres treated using prescribed fire: 47,273;
Acres treated using mechanical means: 61,636;
Acres treated using other means[A]: 0.
2006: Treated acres: 92,918;
WUI acres: 69,521;
Non-WUI acres: 23,397;
Acres treated using prescribed fire: 30,138;
Acres treated using mechanical means: 62,762;
Acres treated using other means[A]: 18.
State office: Idaho;
2005: Treated acres: 112,254;
WUI acres: 54,460;
Non-WUI acres: 57,794;
Acres treated using prescribed fire: 13,321;
Acres treated using mechanical means: 46,231;
Acres treated using other means[A]: 52,702.
2006: Treated acres: 113,778;
WUI acres: 69,648;
Non-WUI acres: 44,130;
Acres treated using prescribed fire: 12,307;
Acres treated using mechanical means: 47,614;
Acres treated using other means[A]: 53,857.
State office: Utah;
2005: Treated acres: 40,706;
WUI acres: 26,616;
Non-WUI acres: 14,090;
Acres treated using prescribed fire: 6,140;
Acres treated using mechanical means: 33,966;
Acres treated using other means[A]: 600.
2006: Treated acres: 40,535;
WUI acres: 28,906;
Non-WUI acres: 11,629;
Acres treated using prescribed fire: 3,900;
Acres treated using mechanical means: 36,601;
Acres treated using other means[A]: 34.
State office: California;
2005: Treated acres: 24,191;
WUI acres: 21,439;
Non-WUI acres: 2,752;
Acres treated using prescribed fire: 2,342;
Acres treated using mechanical means: 19,349;
Acres treated using other means[A]: 2,500.
2006: Treated acres: 19,389;
WUI acres: 16,231;
Non-WUI acres: 3,158;
Acres treated using prescribed fire: 4,187;
Acres treated using mechanical means: 11,422;
Acres treated using other means[A]: 3,780.
State office: Nevada;
2005: Treated acres: 28,427;
WUI acres: 15,190;
Non-WUI acres: 13,237;
Acres treated using prescribed fire: 10,391;
Acres treated using mechanical means: 16,272;
Acres treated using other means[A]: 1,764.
2006: Treated acres: 35,465;
WUI acres: 9,655;
Non-WUI acres: 25,810;
Acres treated using prescribed fire: 15,242;
Acres treated using mechanical means: 15,014;
Acres treated using other means[A]: 5,209.
State office: Colorado;
2005: Treated acres: 20,417;
WUI acres: 13,616;
Non-WUI acres: 6,801;
Acres treated using prescribed fire: 6,950;
Acres treated using mechanical means: 13,012;
Acres treated using other means[A]: 455.
2006: Treated acres: 17,870;
WUI acres: 10,132;
Non-WUI acres: 7,738;
Acres treated using prescribed fire: 4,906;
Acres treated using mechanical means: 12,774;
Acres treated using other means[A]: 190.
State office: New Mexico;
2005: Treated acres: 48,107;
WUI acres: 3,397;
Non-WUI acres: 44,710;
Acres treated using prescribed fire: 21,297;
Acres treated using mechanical means: 7,704;
Acres treated using other means[A]: 19,106.
2006: Treated acres: 53,329;
WUI acres: 4,390;
Non-WUI acres: 48,939;
Acres treated using prescribed fire: 11,682;
Acres treated using mechanical means: 5,643;
Acres treated using other means[A]: 36,004.
State office: Montana;
2005: Treated acres: 10,867;
WUI acres: 6,124;
Non-WUI acres: 4,743;
Acres treated using prescribed fire: 4,577;
Acres treated using mechanical means: 5,340;
Acres treated using other means[A]: 950.
2006: Treated acres: 12,446;
WUI acres: 7,530;
Non-WUI acres: 4,916;
Acres treated using prescribed fire: 5,910;
Acres treated using mechanical means: 6,426;
Acres treated using other means[A]: 110.
State office: Arizona;
2005: Treated acres: 35,424;
WUI acres: 17,078;
Non-WUI acres: 18,346;
Acres treated using prescribed fire: 17,297;
Acres treated using mechanical means: 4,391;
Acres treated using other means[A]: 13,736.
2006: Treated acres: 19,557;
WUI acres: 7,424;
Non-WUI acres: 12,133;
Acres treated using prescribed fire: 5,845;
Acres treated using mechanical means: 3,625;
Acres treated using other means[A]: 10,087.
State office: Wyoming;
2005: Treated acres: 30,839;
WUI acres: 1,976;
Non-WUI acres: 28,863;
Acres treated using prescribed fire: 19,885;
Acres treated using mechanical means: 3,004;
Acres treated using other means[A]: 7,950.
2006: Treated acres: 18,662;
WUI acres: 4,507;
Non-WUI acres: 14,155;
Acres treated using prescribed fire: 9,816;
Acres treated using mechanical means: 3,789;
Acres treated using other means[A]: 5,057.
State office: Alaska;
2005: Treated acres: 45,707;
WUI acres: 21,847;
Non-WUI acres: 23,860;
Acres treated using prescribed fire: 45,080;
Acres treated using mechanical means: 627;
Acres treated using other means[A]: 0.
2006: Treated acres: 3,963;
WUI acres: 2,988;
Non-WUI acres: 975;
Acres treated using prescribed fire: 3,510;
Acres treated using mechanical means: 453;
Acres treated using other means[A]: 0.
State office: Eastern states;
2005: Treated acres: 320;
WUI acres: 40;
Non-WUI acres: 280;
Acres treated using prescribed fire: 0;
Acres treated using mechanical means: 320;
Acres treated using other means[A]: 0.
2006: Treated acres: 0;
WUI acres: 0;
Non-WUI acres: 0;
Acres treated using prescribed fire: 0;
Acres treated using mechanical means: 0;
Acres treated using other means[A]: 0.
Total:
2005: Treated acres: 506,168;
WUI acres: 253,001;
Non-WUI acres: 253,167;
Acres treated using prescribed fire: 194,553;
Acres treated using mechanical means: 211,852;
Acres treated using other means[A]: 99,763.
2006: Treated acres: 427,912;
WUI acres: 230,932;
Non-WUI acres: 196,980;
Acres treated using prescribed fire: 107,443;
Acres treated using mechanical means: 206,123;
Acres treated using other means[A]: 114,346.
Source: GAO analysis of Interior data.
[A] "Other" category includes treatments such as herbicides and grazing.
[End of table]
Table 17 summarizes 2005 and 2006 fuel treatment information for the
BIA regions. The Northwest and Western regions treated the most acres
in 2005, with each region treating more than 38,000 acres. In 2006, the
Northwest and Southwest regions treated the most acres, with each
region treating more than 45,000 acres. The agency treated more acres
in non-WUI areas than in WUI areas in 2005 and 2006.
Table 17: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
Accomplishments for BIA Regions:
Region: Northwest;
2005: Treated acres: 38,284;
WUI acres: 17,297;
Non-WUI acres: 20,987;
Acres treated using prescribed fire: 15,589;
Acres treated using mechanical means: 21,488;
Acres treated using other means[A]: 1,207.
2006: Treated acres: 48,733;
WUI acres: 25,354;
Non-WUI acres: 23,379;
Acres treated using prescribed fire: 14,687;
Acres treated using mechanical means: 32,590;
Acres treated using other means[A]: 1,456.
Region: Southwest;
2005: Treated acres: 28,212;
WUI acres: 11,839;
Non-WUI acres: 16,373;
Acres treated using prescribed fire: 2,678;
Acres treated using mechanical means: 24,433;
Acres treated using other means[A]: 1,101.
2006: Treated acres: 45,132;
WUI acres: 20,558;
Non-WUI acres: 24,574;
Acres treated using prescribed fire: 8,096;
Acres treated using mechanical means: 36,049;
Acres treated using other means[A]: 987.
Region: Western;
2005: Treated acres: 38,753;
WUI acres: 16,210;
Non-WUI acres: 22,543;
Acres treated using prescribed fire: 13,813;
Acres treated using mechanical means: 24,880;
Acres treated using other means[A]: 60.
2006: Treated acres: 22,167;
WUI acres: 10,360;
Non-WUI acres: 11,807;
Acres treated using prescribed fire: 7,892;
Acres treated using mechanical means: 14,275;
Acres treated using other means[A]: 0.
Region: Pacific;
2005: Treated acres: 2,584;
WUI acres: 1,817;
Non-WUI acres: 767;
Acres treated using prescribed fire: 180;
Acres treated using mechanical means: 2,404;
Acres treated using other means[A]: 0.
2006: Treated acres: 4,431;
WUI acres: 3,179;
Non-WUI acres: 1,252;
Acres treated using prescribed fire: 331;
Acres treated using mechanical means: 4,100;
Acres treated using other means[A]: 0.
Region: Great Plains;
2005: Treated acres: 14,386;
WUI acres: 6,986;
Non-WUI acres: 7,400;
Acres treated using prescribed fire: 7,595;
Acres treated using mechanical means: 6,591;
Acres treated using other means[A]: 200.
2006: Treated acres: 13,234;
WUI acres: 4,828;
Non-WUI acres: 8,406;
Acres treated using prescribed fire: 6,217;
Acres treated using mechanical means: 6,508;
Acres treated using other means[A]: 509.
Region: Alaska;
2005: Treated acres: 1,253;
WUI acres: 1,253;
Non-WUI acres: 0;
Acres treated using prescribed fire: 167;
Acres treated using mechanical means: 1,086;
Acres treated using other means[A]: 0.
2006: Treated acres: 2,222;
WUI acres: 1,497;
Non-WUI acres: 725;
Acres treated using prescribed fire: 563;
Acres treated using mechanical means: 1,659;
Acres treated using other means[A]: 0.
Region: Midwest;
2005: Treated acres: 21,356;
WUI acres: 6,478;
Non-WUI acres: 14,878;
Acres treated using prescribed fire: 17,792;
Acres treated using mechanical means: 3,564;
Acres treated using other means[A]: 0.
2006: Treated acres: 18,559;
WUI acres: 16,401;
Non-WUI acres: 2,158;
Acres treated using prescribed fire: 15,585;
Acres treated using mechanical means: 2,974;
Acres treated using other means[A]: 0.
Region: Rocky Mountain;
2005: Treated acres: 11,347;
WUI acres: 2,856;
Non-WUI acres: 8,491;
Acres treated using prescribed fire: 6,616;
Acres treated using mechanical means: 4,731;
Acres treated using other means[A]: 0.
2006: Treated acres: 7,400;
WUI acres: 3,634;
Non-WUI acres: 3,766;
Acres treated using prescribed fire: 4,177;
Acres treated using mechanical means: 3,223;
Acres treated using other means[A]: 0.
Region: Navajo;
2005: Treated acres: 14,274;
WUI acres: 956;
Non-WUI acres: 13,318;
Acres treated using prescribed fire: 13,318;
Acres treated using mechanical means: 956;
Acres treated using other means[A]: 0.
2006: Treated acres: 11,065;
WUI acres: 470;
Non-WUI acres: 10,595;
Acres treated using prescribed fire: 10,595;
Acres treated using mechanical means: 470;
Acres treated using other means[A]: 0.
Region: Southern Plains;
2005: Treated acres: 12,322;
WUI acres: 434;
Non-WUI acres: 11,888;
Acres treated using prescribed fire: 8,401;
Acres treated using mechanical means: 3,921;
Acres treated using other means[A]: 0.
2006: Treated acres: 8,796;
WUI acres: 672;
Non-WUI acres: 8,124;
Acres treated using prescribed fire: 4,770;
Acres treated using mechanical means: 3,833;
Acres treated using other means[A]: 193.
Region: Eastern;
2005: Treated acres: 7,788;
WUI acres: 5,616;
Non-WUI acres: 2,172;
Acres treated using prescribed fire: 7,718;
Acres treated using mechanical means: 70;
Acres treated using other means[A]: 0.
2006: Treated acres: 4,607;
WUI acres: 2,547;
Non-WUI acres: 2,060;
Acres treated using prescribed fire: 4,099;
Acres treated using mechanical means: 508;
Acres treated using other means[A]: 0.
Region: Eastern Oklahoma;
2005: Treated acres: 3,058;
WUI acres: 241;
Non-WUI acres: 2,817;
Acres treated using prescribed fire: 3,014;
Acres treated using mechanical means: 44;
Acres treated using other means[A]: 0.
2006: Treated acres: 1,307;
WUI acres: 461;
Non-WUI acres: 846;
Acres treated using prescribed fire: 1,292;
Acres treated using mechanical means: 15;
Acres treated using other means[A]: 0.
Total:
2005: Treated acres: 193,617;
WUI acres: 71,983;
Non-WUI acres: 121,634;
Acres treated using prescribed fire: 96,881;
Acres treated using mechanical means: 94,168;
Acres treated using other means[A]: 2,568.
2006: Treated acres: 187,653;
WUI acres: 89,961;
Non-WUI acres: 97,692;
Acres treated using prescribed fire: 78,304;
Acres treated using mechanical means: 106,204;
Acres treated using other means[A]: 3,145.
Source: GAO analysis of Interior data.
[A] "Other" category includes treatments such as herbicides and grazing.
[End of table]
Table 18 summarizes 2005 and 2006 fuel treatment information for the
NPS regions. In 2005 and 2006, the Southeast region treated the most
acres, followed by the Intermountain region. NPS treated more acres in
non-WUI areas than the WUI, and treated the vast majority of acres
(more than 90 percent in 2005 and about 88 percent in 2006) using
prescribed fire.
Table 18: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
Accomplishments for NPS Regions:
Region: Pacific West;
2005: Treated acres: 25,949;
WUI acres: 9,432;
Non-WUI acres: 16,517;
Acres treated using prescribed fire: 18,922;
Acres treated using mechanical means: 5,681;
Acres treated using other means[A]: 1,346.
2006: Treated acres: 22,433;
WUI acres: 9,042;
Non-WUI acres: 13,391;
Acres treated using prescribed fire: 16,220;
Acres treated using mechanical means: 4,101;
Acres treated using other means[A]: 2,112.
Region: Intermountain;
2005: Treated acres: 43,823;
WUI acres: 28,585;
Non-WUI acres: 15,238;
Acres treated using prescribed fire: 38,874;
Acres treated using mechanical means: 4,844;
Acres treated using other means[A]: 105.
2006: Treated acres: 25,350;
WUI acres: 14,447;
Non-WUI acres: 10,903;
Acres treated using prescribed fire: 19,397;
Acres treated using mechanical means: 5,727;
Acres treated using other means[A]: 226.
Region: Southeast;
2005: Treated acres: 63,602;
WUI acres: 17,963;
Non-WUI acres: 45,639;
Acres treated using prescribed fire: 62,491;
Acres treated using mechanical means: 1,081;
Acres treated using other means[A]: 30.
2006: Treated acres: 45,471;
WUI acres: 9,413;
Non-WUI acres: 36,058;
Acres treated using prescribed fire: 44,641;
Acres treated using mechanical means: 830;
Acres treated using other means[A]: 0.
Region: Midwest;
2005: Treated acres: 20,082;
WUI acres: 2,433;
Non-WUI acres: 17,649;
Acres treated using prescribed fire: 18,971;
Acres treated using mechanical means: 1,111;
Acres treated using other means[A]: 0.
2006: Treated acres: 22,872;
WUI acres: 5,432;
Non-WUI acres: 17,440;
Acres treated using prescribed fire: 22,150;
Acres treated using mechanical means: 722;
Acres treated using other means[A]: 0.
Region: Northeast;
2005: Treated acres: 453;
WUI acres: 417;
Non-WUI acres: 36;
Acres treated using prescribed fire: 188;
Acres treated using mechanical means: 265;
Acres treated using other means[A]: 0.
2006: Treated acres: 486;
WUI acres: 224;
Non-WUI acres: 262;
Acres treated using prescribed fire: 348;
Acres treated using mechanical means: 138;
Acres treated using other means[A]: 0.
Region: Alaska;
2005: Treated acres: 29;
WUI acres: 9;
Non-WUI acres: 20;
Acres treated using prescribed fire: 0;
Acres treated using mechanical means: 29;
Acres treated using other means[A]: 0.
2006: Treated acres: 23;
WUI acres: 0;
Non-WUI acres: 23;
Acres treated using prescribed fire: 9;
Acres treated using mechanical means: 14;
Acres treated using other means[A]: 0.
Region: National Capital;
2005: Treated acres: 34;
WUI acres: 34;
Non-WUI acres: 0;
Acres treated using prescribed fire: 9;
Acres treated using mechanical means: 25;
Acres treated using other means[A]: 0.
2006: Treated acres: 0;
WUI acres: 0;
Non-WUI acres: 0;
Acres treated using prescribed fire: 0;
Acres treated using mechanical means: 0;
Acres treated using other means[A]: 0.
Total:
2005: Treated acres: 153,972;
WUI acres: 58,873;
Non-WUI acres: 95,099;
Acres treated using prescribed fire: 139,455;
Acres treated using mechanical means: 13,036;
Acres treated using other means[A]: 1,481.
2006: Treated acres: 116,635;
WUI acres: 38,558;
Non-WUI acres: 78,077;
Acres treated using prescribed fire: 102,765;
Acres treated using mechanical means: 11,532;
Acres treated using other means[A]: 2,338.
Source: GAO analysis of Interior data.
[A] "Other" category includes treatments such as herbicides and grazing.
[End of table]
Table 19 summarizes 2005 and 2006 fuel treatment information for the
FWS regions. In both years, the Southeast region treated substantially
more acres than the other regions--about 35 percent of total acres
treated in 2005 and about 30 percent in 2006--followed by the Southwest
and Great Lakes-Big Rivers regions. Like NPS, FWS treated most acres
outside of the WUI, and treated the vast majority of acres (about 94
percent in 2005 and about 89 percent in 2006) using prescribed fire.
Table 19: Summary of Fiscal Years 2005 and 2006 Fuel Reduction
Accomplishments for FWS Regions:
Region: Southeast;
2005: Treated acres: 144,902;
WUI acres: 83,218;
Non-WUI acres: 61,684;
Acres treated using prescribed fire: 141,616;
Acres treated using mechanical means: 3,202;
Acres treated using other means[A]: 84.
2006: Treated acres: 114,212;
WUI acres: 75,024;
Non-WUI acres: 39,188;
Acres treated using prescribed fire: 106,864;
Acres treated using mechanical means: 7,348;
Acres treated using other means[A]: 0.
Region: Great Lakes-Big Rivers;
2005: Treated acres: 73,550;
WUI acres: 27,719;
Non-WUI acres: 45,831;
Acres treated using prescribed fire: 70,880;
Acres treated using mechanical means: 2,204;
Acres treated using other means[A]: 466.
2006: Treated acres: 70,756;
WUI acres: 27,499;
Non-WUI acres: 43,257;
Acres treated using prescribed fire: 68,854;
Acres treated using mechanical means: 1,548;
Acres treated using other means[A]: 354.
Region: Mountain-Prairie;
2005: Treated acres: 42,252;
WUI acres: 7,994;
Non-WUI acres: 34,258;
Acres treated using prescribed fire: 42,032;
Acres treated using mechanical means: 220;
Acres treated using other means[A]: 0.
2006: Treated acres: 39,095;
WUI acres: 10,100;
Non-WUI acres: 28,995;
Acres treated using prescribed fire: 38,862;
Acres treated using mechanical means: 233;
Acres treated using other means[A]: 0.
Region: Southwest;
2005: Treated acres: 76,495;
WUI acres: 21,786;
Non-WUI acres: 54,709;
Acres treated using prescribed fire: 71,820;
Acres treated using mechanical means: 4,424;
Acres treated using other means[A]: 251.
2006: Treated acres: 56,607;
WUI acres: 17,036;
Non-WUI acres: 39,571;
Acres treated using prescribed fire: 54,792;
Acres treated using mechanical means: 1,800;
Acres treated using other means[A]: 15.
Region: Pacific;
2005: Treated acres: 13,865;
WUI acres: 6,039;
Non-WUI acres: 7,826;
Acres treated using prescribed fire: 7,703;
Acres treated using mechanical means: 6,063;
Acres treated using other means[A]: 99.
2006: Treated acres: 23,996;
WUI acres: 15,704;
Non-WUI acres: 8,292;
Acres treated using prescribed fire: 11,225;
Acres treated using mechanical means: 11,683;
Acres treated using other means[A]: 1,088.
Region: Northeast;
2005: Treated acres: 18,596;
WUI acres: 6,609;
Non-WUI acres: 11,987;
Acres treated using prescribed fire: 13,166;
Acres treated using mechanical means: 2,104;
Acres treated using other means[A]: 3,326.
2006: Treated acres: 16,515;
WUI acres: 8,791;
Non-WUI acres: 7,724;
Acres treated using prescribed fire: 13,007;
Acres treated using mechanical means: 1,794;
Acres treated using other means[A]: 1,714.
Region: California-Nevada[B];
2005: Treated acres: 45,216;
WUI acres: 4,595;
Non-WUI acres: 40,621;
Acres treated using prescribed fire: 42,176;
Acres treated using mechanical means: 3,040;
Acres treated using other means[A]: 0.
2006: Treated acres: 43,023;
WUI acres: 18,864;
Non-WUI acres: 24,159;
Acres treated using prescribed fire: 29,771;
Acres treated using mechanical means: 7,646;
Acres treated using other means[A]: 5,606.
Region: Alaska;
2005: Treated acres: 770;
WUI acres: 751;
Non-WUI acres: 19;
Acres treated using prescribed fire: 293;
Acres treated using mechanical means: 477;
Acres treated using other means[A]: 0.
2006: Treated acres: 9,729;
WUI acres: 95;
Non-WUI acres: 9,634;
Acres treated using prescribed fire: 9,663;
Acres treated using mechanical means: 66;
Acres treated using other means[A]: 0.
Total:
2005: Treated acres: 415,646;
WUI acres: 158,711;
Non-WUI acres: 256,935;
Acres treated using prescribed fire: 389,686;
Acres treated using mechanical means: 21,734;
Acres treated using other means[A]: 4,226.
2006: Treated acres: 373,933;
WUI acres: 173,113;
Non-WUI acres: 200,820;
Acres treated using prescribed fire: 333,038;
Acres treated using mechanical means: 32,118;
Acres treated using other means[A]: 8,777.
Source: GAO analysis of Interior data.
[A] "Other" category includes treatments such as herbicides and grazing.
[B] While FWS has only seven regions, it has an eighth office--the
California-Nevada Operations office--that, although technically part of
the Pacific region, manages its own fuel reduction program.
[End of table]
[End of section]
Appendix IV: Comments from the Department of the Interior and the
Forest Service:
Washington:
The Department Of Agriculture:
The Department Of The Interior:
September 21, 2007:
Robin M. Nazzaro, Director:
Natural Resources and Environment:
U.S. Government Accountability Office:
441 G. Street N.W.:
Washington, DC 20548:
Dear Ms. Nazzaro:
We appreciate the opportunity to review and comment on the draft
Government Accountability Office (GAO) report, GAO-07-1168, "Wildland
Fire Management: Better Information and a Systematic Process Could
Improve Agencies' Approach to Allocating Fuel Reduction Funds and
Selecting Projects." The Forest Service and the Department of the
Interior generally agree with the findings of this report and believe
that it provides an accurate, balanced assessment of the complex set of
issues involved in setting priorities and allocating funds at all
levels of our organizations.
We also agree with the GAO's findings related to the agencies' efforts
to improve hazardous fuels project selection and fund allocation. The
agencies are working together to develop and implement risk informed
hazardous fuels fund allocation processes that address consistent
criteria, but recognize the different legislated missions of each
agency. The Forest Service allocates a portion of its fuel reduction
funds according to congressional direction, which has an inevitable
effect on regional allocations, regardless of consistency with
criteria. As GAO has suggested, these processes are based not only on
output from a systematic model, but other relevant information
necessary to fully inform decision makers.
The Forest Service and the Department of the Interior recognize the
importance of collaboration and would like the GAO report to
prominently recognize the importance of including state, tribal, and
local concerns in the prioritization process. We have been and continue
to work on recommendations in the report.
We look forward to working with GAO on future reviews. If you have any
additional questions or concerns, please contact Sandy T. Coleman,
Forest Service Assistant Director for GAO/OIG Audit Liaison Staff, at
703-605-4699, or Deborah Williams, DOI/GAO Liaison at 202-208-3963.
Signed by:
Mark Rey:
Under Secretary:
Natural Resources and the Environment:
U.S. Department of Agriculture:
James E. Cason:
Associate Deputy Secretary:
U.S. Department of the Interior:
[End of section]
Appendix V: GAO Contact and Staff Acknowledgments:
GAO Contact:
Robin Nazzaro, (202) 512-3841 or nazzaror@gao.gov:
Staff Acknowledgments:
In addition to the individual named above, Steve Gaty, Assistant
Director; Christy Feehan; Rich Johnson; Ches Joy; Amanda Miller; John
Mingus; Lesley Rinner; and Carol Shulman made key contributions to this
report. Elizabeth Curda, Mehrzad Nadji, Jackie Nowicki, and Jena
Sinkfield also made important contributions to the report.
[End of section]
Related GAO Products:
Wildland Fire Management: A Cohesive Strategy and Clear Cost-
Containment Goals Are Needed for Federal Agencies to Manage Wildland
Fire Activities Effectively. GAO-07-1017T. Washington, D.C.: June 19,
2007.
Wildland Fire Management: Lack of a Cohesive Strategy Hinders Agencies'
Cost-Containment Efforts. GAO-07-427T. Washington, D.C.: January 30,
2007.
Wildland Fire Management: Update on Federal Agency Efforts to Develop a
Cohesive Strategy to Address Wildland Fire Threats. GAO-06-671R.
Washington, D.C.: May 1, 2006.
Wildland Fire Management: Timely Identification of Long-Term Options
and Funding Needs Is Critical. GAO-05-923T. Washington, D.C.: July 14,
2005.
Wildland Fire Management: Forest Service and Interior Need to Specify
Steps and a Schedule for Identifying Long-Term Options and Their Costs.
GAO-05-353T. Washington, D.C.: February 17, 2005.
Wildland Fire Management: Important Progress Has Been Made, but
Challenges Remain to Completing a Cohesive Strategy. GAO-05-147.
Washington, D.C.: January 14, 2005.
Wildland Fires: Forest Service and BLM Need Better Information and a
Systematic Approach for Assessing the Risks of Environmental Effects.
GAO-04-705. Washington, D.C.: June 24, 2004.
Wildland Fire Management: Additional Actions Required to Better
Identify and Prioritize Lands Needing Fuels Reduction. GAO-03-805.
Washington, D.C.: August 15, 2003.
Wildland Fire Management: Reducing the Threat of Wildland Fires
Requires Sustained and Coordinated Effort. GAO-02-843T. Washington,
D.C.: June 13, 2002.
Severe Wildland Fires: Leadership and Accountability Needed to Reduce
Risks to Communities and Resources. GAO-02-259. Washington, D.C.:
January 31, 2002.
The National Fire Plan: Federal Agencies Are Not Organized to
Effectively and Efficiently Implement the Plan. GAO-01-1022T.
Washington, D.C.: July 31, 2001.
Reducing Wildfire Threats: Funds Should Be Targeted to the Highest Risk
Areas. GAO/T-RCED-00-296. Washington, D.C.: September 13, 2000.
Western National Forests: A Cohesive Strategy Is Needed to Address
Catastrophic Wildfire Threats. GAO/RCED-99-65. Washington, D.C.: April
2, 1999.
Western National Forests: Catastrophic Wildfires Threaten Resources and
Communities. GAO/T-RCED-98-273. Washington, D.C.: September 28, 1998.
[End of section]
Footnotes:
[1] The National Fire Plan comprises multiple documents, including (1)
a September 2000 report from the Secretaries of Agriculture and of the
Interior to the President in response to the wildland fires of 2000,
(2) congressional direction accompanying substantial new appropriations
in fiscal year 2001, and (3) several strategies to implement all or
parts of the plan. For a description of these documents and their
contents, goals, and relationships to one another, see Severe Wildland
Fires: Leadership and Accountability Needed to Reduce Risks to
Communities and Resources, GAO-02-259 (Washington, D.C.: Jan. 31,
2002).
[2] Pub. L. No. 108-148 (2003).
[3] HFRA defines "federal land" to include land administered by the
Forest Service and BLM. Consequently, HFRA fuel reduction project
authorities are available only to the Forest Service and BLM, and its
fuel reduction project requirements apply only to these agencies as
well. In some cases, BIA, FWS, and NPS have chosen to comply with some
of the requirements.
[4] Years cited in this report refer to fiscal years except where
otherwise specified.
[5] GAO, Western National Forests: A Cohesive Strategy Is Needed to
Address Catastrophic Wildfire Threats, GAO/RCED-99-65 (Washington,
D.C.: Apr. 2, 1999); Reducing Wildfire Threats: Funds Should Be
Targeted to the Highest Risk Areas, GAO/T-RCED-00-296 (Washington,
D.C.: Sept. 13, 2000); GAO-02-259; Wildland Fire Management: Additional
Actions Required to Better Identify and Prioritize Lands Needing Fuels
Reduction, GAO-03-805 (Washington, D.C.: Aug. 15, 2003).
[6] GAO, Wildland Fires: Forest Service and BLM Need Better Information
and a Systematic Approach for Assessing the Risks of Environmental
Effects, GAO-04-705 (Washington, D.C.: June 24, 2004); Wildland Fire
Management: Important Progress Has Been Made, but Challenges Remain to
Completing a Cohesive Strategy, GAO-05-147 (Washington, D.C.: Jan. 14,
2005); Wildland Fire Management: Update on Federal Agency Efforts to
Develop a Cohesive Strategy to Address Wildland Fire Threats, GAO-06-
671R (Washington, D.C.: May 1, 2006).
[7] The Western Governors' Association is an independent, nonpartisan
organization of governors representing 19 western states. The governors
use the association to develop and advocate policies that reflect
regional interests.
[8] The agencies treated an additional 1.8 million acres from 2004 to
August 2007 through other land management activities.
[9] The agencies also conduct some treatments using other methods, such
as applying herbicides and allowing animals to graze on the land.
[10] Interagency policy directs land managers to select firefighting
strategies in accordance with local federal units' land and fire
management plans. If a plan has not been developed and approved, the
policy directs land managers to suppress the fire. Thus, under the
policy, the areas where wildland fire use is allowed must be defined in
a fire management plan, along with prescribed weather and other
conditions. The fires are monitored, and if weather conditions change
in a way that would potentially allow the fires to escape from the
designated areas, the fires are suppressed.
[11] This requirement applies only to projects conducted using HFRA
authorities. Agency officials told us they do not have reliable data on
the portion of their fuel reduction projects that used HFRA
authorities.
[12] Our review was limited to fuel reduction work activities using
federal funds appropriated specifically for this purpose. As a result,
fuel reduction work funded by other agency programs or outside
organizations is beyond the scope of this review.
[13] GAO/RCED-99-65.
[14] GAO, Wildland Fire Management: Reducing the Threat of Wildland
Fires Requires Sustained and Coordinated Effort, GAO-02-843T
(Washington, D.C.: June 13, 2002); GAO-05-147; GAO-06-671R; Wildland
Fire Management: Lack of a Cohesive Strategy Hinders Agencies' Cost-
Containment Efforts, GAO-07-427T (Washington, D.C.: Jan. 30, 2007).
[15] GAO/T-RCED-00-296; GAO-02-259.
[16] GAO-02-259; GAO-03-805.
[17] This approach, outlined by the National Academy of Public
Administration, uses risk as a specific term referring to the
probability of an event, as well as an umbrella term that encompasses
all three of these elements. See National Academy of Public
Administration, Managing Wildland Fire: Enhancing Capacity to Implement
the Federal Interagency Policy (Washington, D.C.: December 2001).
[18] For more information on the hazard-risk-value framework, see GAO-
04-705.
[19] GAO, The National Fire Plan: Federal Agencies Are Not Organized to
Effectively and Efficiently Implement the Plan, GAO-01-1022T
(Washington, D. C.: July 31, 2001).
[20] GAO-02-259.
[21] GAO-02-843T.
[22] The Forest Service's system for setting priorities for reducing
hazardous fuels and allocating resources excluded the Alaska region
(and did not give it a priority score) because the Alaska region's
program accounts for less than 1 percent of the agency's fuel reduction
funds.
[23] See GAO-03-805; U.S. Department of Agriculture, Office of
Inspector General, Audit Report: Implementation of the Healthy Forests
Initiative, 08601-6-AT (Washington, D.C.: September 2006); Office of
Management and Budget, Program Assessment Rating Tool: Review of U.S.
Department of Agriculture's Wildland Fire Management Program
(Washington, D.C.: 2006); U.S. House of Representatives, Committee on
Appropriations, Department of the Interior, Environment, and Related
Agencies Appropriations Bill of 2007, House Report 109-465, 109th
Cong., 2nd Sess. (Washington, D.C.: May 15, 2006); U.S. Senate,
Committee on Appropriations, Department of the Interior, Environment,
and Related Agencies Appropriation Bill of 2007, Senate Report 109-275,
109th Cong., 2nd Sess. (Washington, D.C.: June 29, 2006).
[24] The process the officials used to weight the factors is called the
analytical hierarchy process, which is a systematic process often used
in private industry to make complex decisions involving multiple
criteria, such as investment decisions. We did not assess the
appropriateness of the factors selected or the weights assigned, nor
did we evaluate the model's accuracy in applying these factors to
determine priority scores.
[25] GAO has previously noted the importance of the integrity,
credibility, and quality of data that underlie budget decisions and,
thus, the value in implementing changes gradually when the quality of
such data is in question. See, for example, GAO, Performance Budgeting:
Opportunities and Challenges, GAO-02-1106T (Washington, D.C.: Sept. 19,
2002).
[26] To ensure that the elements without data had no effect on priority
scores, all of the regions were assigned the same score for each of
these elements.
[27] The funds Interior allocated using the model represented 5 percent
of project funds--that is, funds expected to be spent on individual
projects--rather than 5 percent of the total allocation, which would
include program management expenses such as salaries, facility costs,
and so forth. NPS did not receive any of the 5 percent of 2007 funds
that were allocated using the new model because Interior allocated the
funds late in the fiscal year and NPS had already met its 2007 acreage
targets.
[28] Weights are not included in the list because Interior had not yet
finalized them at the time of our review.
[29] While agency officials told us the new model will be coordinated
with FPA, they did not provide details on how this coordination will
occur.
[30] While there are 12 BLM state offices--11 in the West and 1 in the
East--the vast majority of BLM-managed land is in the West, and the
Eastern States office receives only about 0.1 percent of BLM's total
fuel reduction funding. Further, this funding is allocated to just one
field unit. As a result, the Eastern States office is not included in
our description of BLM state office allocation processes.
[31] Regions that accomplished 90 percent or more of the previous
year's acreage target could request up to 120 percent of the prior
year's funding amount, while regions that accomplished less than 90
percent could request only up to 105 percent of the previous year's
amount.
[32] U.S. Department of the Interior and USDA Forest Service,
"Protecting People and Natural Resources: A Cohesive Fuels Treatment
Strategy," February 2006. Note that, although the document is referred
to as a cohesive strategy, previous GAO reports concluded that it does
not contain all the elements GAO called for in its earlier
recommendations for such a strategy. See, for example, GAO-06-671R.
[33] The agencies expect that nationally consistent data will be
available through LANDFIRE, a geospatial data and modeling system
currently being implemented. LANDFIRE data are complete for some of the
country, with data for the remainder of the country expected to be
completed by 2009.
[34] In 2001, a Federal Register notice was published with a list of
wildland-urban interface communities identified by states as being "in
the vicinity of federal lands" and "at high risk from wildfire."
However, the states and tribes used inconsistent approaches to identify
these communities at risk. To standardize these approaches, the
National Association of State Foresters was tasked, in the 10-Year
Implementation Plan, with developing a definition for community at
risk, and a process for states and tribes to follow to identify and
prioritize the communities. Accordingly, in 2003, the National
Association of State Foresters finalized its guidance, defining
community as "a group of people living in the same locality and under
the same government," and specifying that a community was to be
considered at risk from wildland fire if it was located within the
wildland-urban interface as defined in the 2001 Federal Register, which
stated that, "the urban-wildland interface community exists where
humans and their development meet or intermix with wildland fuel."
[35] We conducted our analysis using census data on the average
population per square mile across areas defined by ZIP codes. However,
especially in larger ZIP codes, there may be smaller pockets where the
population density is higher or lower than the average used in our
analysis.
[36] However, in a fire behavior assessment of the June 2007 Angora
Fire in California, Forest Service officials stated that a large number
of houses ignited because of embers from other burning houses, rather
than from wildland fuel--suggesting that even homes that are not
immediately adjacent to federal lands could be at risk from wildland
fire.
[37] Years cited in this appendix refer to fiscal years except where
otherwise specified.
[38] BIA, FWS, and NPS have regional offices, while BLM has state
offices. For the purposes of this appendix, we refer to all of these as
regional offices when we discuss the Interior agencies collectively.
[39] Results from nonprobability samples cannot be used to make
inferences about a population, because in a nonprobability sample, some
elements of the population being studied have no chance or an unknown
chance of being selected as part of the sample.
[40] We conducted our analysis using U.S. Census data on the average
population per square mile across areas defined by ZIP codes. However,
especially in larger ZIP codes, there may be small pockets where the
population density is higher or lower than the average used in our
analysis.
[41] In the 2001 Federal Register, the agencies provide three
categories of wildland-urban interface communities. The first is
"interface community," which exists where structures directly abut
wildland fuel; an alternative definition of the interface community
specifies a population density of 250 or more people per square mile.
The second is "intermix community," which exists where structures are
scattered throughout a wildland area; an alternative definition of
intermix community specifies a population density of between 28-250
people per square mile. The third is "occluded community," where
structures, often within a city, abut an island of wildland fuel (e.g.,
park or open space).
[42] Years cited in this appendix refer to fiscal years except where
otherwise specified. BIA, FWS, and NPS have regional offices, while BLM
has state offices. For the purposes of this appendix, we refer to all
of these as regional offices when we discuss the Interior agencies
collectively.
[43] While FWS has seven regions, it has an eighth office--the
California-Nevada Operations Office--that, although officially part of
the Pacific region, manages its own fuel reduction program.
[44] Years cited in this appendix refer to fiscal years except where
otherwise specified.
[45] BIA, FWS, and NPS have regional offices, while BLM has state
offices. For the purposes of this appendix, we refer to all of these as
regional offices when we discuss the Interior agencies collectively.
[End of section]
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