United States Department of Agriculture / Forest Service

Rocky Mountain Research Station

General Technical Report RMRS-GTR-315

October 2013

A Wildfire Risk Assessment Framework 

for Land and Resource Management

Joe H. Scott

Matthew P. Thompson

David E. Calkin
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Scott, Joe H.; Thompson, Matthew P.; Calkin, David E. 2013. A wildfire risk assessment 

framework for land and resource management. Gen. Tech. Rep. RMRS-GTR-315. U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 83 p. 

Abstract ______________________________________________

Wildfires can result in significant, long-lasting impacts to ecological, social, and economic 

systems. It is necessary, therefore, to identify and understand the risks posed by wildland fire, 

and to develop cost-effective mitigation strategies accordingly. This report presents a general 

framework with which to assess wildfire risk and explore mitigation options, and illustrates 

a process for implementing the framework. Two key strengths of the framework are its flexibility—allowing for a multitude of data sources, modeling techniques, and approaches to 

measuring risk—and its scalability, with potential application for project, forest, regional, and 

national planning. The specific risk assessment process we introduce is premised on three 

modeling approaches to characterize wildfire likelihood and intensity, fire effects, and the relative importance of highly valued resources and assets that could be impacted by wildfire. The 

spatial scope of the process is landscape-scale, and the temporal scope is short-term (that is, 

the temporal dynamics of succession and disturbance are not simulated). We highlight key 

information needs, provide guidance for use of fire simulation models and risk geo-processing 

tools, and demonstrate recent applications of the framework across planning scales. The aim 

of this report is to provide fire and land managers with a helpful set of guiding principles and 

tools for assessing and mitigating wildfire risk. 

Keywords: hazard, exposure analysis, burn probability, fire intensity, effects analysis, landscape scale, geospatial

Authors_______________________________________________

Joe H. Scott, Pyrologix LLC, Missoula, Montana

Matthew P. Thompson, USDA Forest Service, Rocky Mountain Research Station, Missoula, 

Montana

David E. Calkin, USDA Forest Service, Rocky Mountain Research Station, Missoula, Montana
Foreword ___________________________

This report envisions increasing adoption of risk assessment 

in wildfire management and planning, and seeks to facilitate 

this process by comprehensively describing a wildfire risk 

assessment framework as well as a toolkit for implementing 

the framework. Similarly, this report is premised on increasing 

sophistication within the fire modeling community, and the use 

of fire modeling outputs by managers to help support land and 

resource decisions. Embracing risk management principles is 

consistent with Federal wildfire policy as well as other significant 

efforts such as the National Cohesive Wildland Fire Management 

Strategy and the Collaborative Forest Landscape Restoration 

Program. 

The recent emergence of risk-based decision support tools, in 

particular the Wildland Fire Decision Support System, suggest 

a trend towards reliance on quantitative, geospatial information 

regarding the likely occurrence, spread, and consequences 

of wildfire. The use of burn probability modeling techniques is 

a key component that can capture spatial variation in wildfire 

likelihood and intensity as a function of ignitions patterns, fire 

weather, topography, and fuel conditions. In turn, this information can help managers assess the likely exposure of highly 

valued resources and assets to wildfire, as well as the likely 

effects of being exposed to wildfire.

To be clear, risk assessments are not in and of themselves 

decision making tools, but rather provide information that can 

be useful in a broader decision making process. Assessment 

i

results can be applied across the wildfire management spectrum, 

depending upon the spatial and temporal scope of analysis. 

That is, managers may be interested in assessing the risks associated with an upcoming fire season, with a given escaped 

large wildfire, or with post-fire consequences and burned area 

emergency response. The framework and toolkit described in 

this report are most relevant to pre-fire planning, and can help 

inform decisions relating to ignition prevention, fuels management, and response planning. Applying the assessment 

framework can provide managers with a “snapshot” of current 

landscape conditions and associated risks. Periodic assessment over time can provide critical information for monitoring 

trends in risk and evaluating the performance of previous risk 

mitigation investments.

Who Can Benefit from Reading This Report?

We hope that fire managers, geospatial fire analysts, and 

resource specialists will all find value in this report. Managers 

and decision makers will be most interested in the overview of 

the assessment framework (sections 1 and 2), as well as the 

concluding sections describing the value of risk assessment and 

its role for informing decisions (sections 7 and 8). Fire analysts 

and resource specialists will also benefit from reading those 

sections; in addition, they will find details on how they may 

be called upon to help implement the framework and support 

decision processes.

Table of Contents

Foreword......................................................................................................... i

1. Introduction............................................................................................... 1

2. Assessing Risk: From Concept to Analytical Approach ............................... 5

Concepts of Hazard and Risk ........................................................................ 5

Wildfire Likelihood and Intensity.................................................................. 6

Exposure Analysis.......................................................................................... 8

Effects Analysis.............................................................................................. 9

Relative Importance and Relative Extent....................................................... 9

Primary Modeling Steps.............................................................................. 10

Preparing to Implement the Framework...................................................... 13

Addressing Uncertainties............................................................................ 15

3. Wildfire Simulation.................................................................................. 16

Fire Modeling Landscape ............................................................................ 18

Historical Weather Analysis........................................................................ 20

Historical Fire Occurrence ......................................................................... 21

Near-Maximum Wildfire Behavior .............................................................. 22

Wildfire Likelihood ..................................................................................... 25

Conditional Wildfire Intensity..................................................................... 27

4. HVRA Identification and Characterization .............................................. 28

HVRA Identification.................................................................................... 29

Spatial Extent (Mapping)............................................................................. 30

Response to Wildfire ................................................................................... 31

Relative Importance.................................................................................... 33

5. Exposure Analysis..................................................................................... 35

6. Effects Analysis......................................................................................... 37

7. Interpreted Example: Bridger-Teton National Forest ................................ 39

Wildfire Simulation..................................................................................... 39

HVRA Characterization .............................................................................. 56

Exposure Analysis........................................................................................ 60

Effects Analysis............................................................................................ 61

8. Putting it All Together: Understanding and Managing Risk....................... 70

9. Discussion and Conclusions..................................................................... 73

Acknowledgments....................................................................................... 74

References .................................................................................................. 74

iii

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 1

1. Introduction __________________________________________________________

Wildfires—unplanned wildland fires—can result in significant, long-lasting impacts 

to ecological, social, and economic systems; therefore, it is necessary to identify and 

quantify the risks posed by wildfire, and to subsequently develop cost-effective mitigation strategies. To do so, fire and fuel managers require information on where fires are 

likely to occur, the intensity at which they might occur, and with what impacts to highly 

valued resources and assets (HVRAs; that is, the things we care about). Managers need 

to assess wildfire risk.

Risk assessment is a mature scientific approach to quantifying risk, and serves as a 

decision support tool that can inform strategic, operational, and tactical decision making. Analyzing risks helps managers make decisions where outcomes are inherently 

uncertain (Morgan and Henrion 1990). Recent developments in technology and decision 

support systems have improved the ability to assess, monitor, and respond to wildfire 

risk. Many in the fire management community are likely familiar with the Wildland Fire 

Decision Support System (WFDSS), which is premised on risk management principles 

(Noonan-Wright and others 2011). In the absence of wildfire risk assessments, decisions 

and management are likely to be less effective (Bar Massada and others 2009). Assessments of current conditions help land and resource managers (1) better understand how 

risks are distributed across their landscapes, (2) identify which HVRAs face the greatest 

expected loss (or benefit), and (3) inform decisions relating to preparedness planning 

and fuel treatment design. Further, evaluating how wildfire risk to HVRAs may change 

in response to alternative management scenarios through comparative risk assessment 

is a crucial component of risk-informed decision making (Calkin and others 2011a).

Fundamentally, wildfire risk analysis is about seeking answers to several important 

questions:

•	 How large are fires likely to grow?

•	 Which HVRAs have the greatest exposure to wildfire hazard?

•	 What are the likely effects to HVRAs of fire at different intensity levels?

•	 Where might fires cause harm/damage, and where might they lead to benefits?

•	 How is wildfire risk distributed across the landscape?

•	 Which areas are most likely to experience loss, how much loss, and to what 

HVRAs?

A Wildfire Risk Assessment Framework for Land 

and Resource Management

Joe H. Scott

Matthew P. Thompson

David E. Calkin

Unless there is an understanding of the type, likelihood, and magnitude of 

ecological changes that result from fire (either catastrophic fires or the controlled 

burns used as forest management tools), or conversely from the lack of fire, it 

will not be possible to quantify the relative risks and benefits associated with 

various fire management alternatives.

Fairbrother and Turnley (2005, p. 32)
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Quantifying wildfire risk facilitates analysis of tradeoffs across HVRAs, and enables 

cost-effectiveness analysis as a basis for evaluating risk mitigation options (Thompson 

and Calkin 2011). A critical aspect of risk assessment is the use of probabilistic information to capture uncertainty surrounding the occurrence or intensity of hazardous events. 

Although we do not know with certainty where or when wildfires will occur, we can 

assemble information from climate and weather patterns, historical fire occurrence, 

fuel conditions, etc., to make an informed estimate of the likelihood of a given area 

experiencing a wildfire, or experiencing a wildfire of a given intensity.

The fundamental components for quantifying wildfire risk—likelihood, intensity, 

and susceptibility to effects (Scott 2006; Thompson and Calkin 2011; Miller and Ager 

2012)—can be visualized as a Wildfire Risk Triangle (Figure 1). In this conceptual 

framework, fire intensity is a predictor of fire effects. Depending upon the HVRA and 

fire intensity level in question, fire effects could be negative or positive. Fire intensity 

is a robust fire characteristic that integrates two important fire characteristics—fuel 

consumption and spread rate. Other fire characteristics could be used in place of or in 

addition to fire intensity, but in this report we focus on the relationship between fire 

intensity and effects. Assessing wildfire risk requires quantifying potential wildfire 

intensity, quantifying its likelihood, and estimating the exposure and susceptibility of 

HVRAs to wildfire. These fundamental building blocks can be quantified and modeled 

in a variety of ways, and, in that sense, the risk assessment framework is quite flexible. 

The methods we promote and illustrate here are consistent with the scientific foundation 

for the Cohesive Strategy (Calkin and others 2011a), and have been increasingly applied 

for fire and land management planning applications at a variety of scales (Thompson 

and others 2011, 2013a).

The risk assessment framework is implemented in a geospatial context (Figure 2) that 

explicitly considers the location of HVRAs with respect to the three components of the 

wildfire risk triangle. The spatial interaction of wildfire likelihood and intensity with 

HVRAs is a key driver of wildfire risk (also known as exposure analysis). Further, the 

susceptibility or response of an HVRA to wildfire (termed “response function” in the 

figure) characterizes the likely fire-related losses and benefits to each HVRA included 

in the assessment.

Figure 1—The three building blocks of assessing 

wildfire risk are likelihood, intensity, and effects 

(Miller and Ager 2012; Thompson and Calkin 

2011; Scott 2006). Implicit in this triangle is the 

assessment of risk to a particular HVRA, or a suite 

of HVRAs, and their spatial intersection with wildfire 

likelihood and intensity.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 3

Risk assessments in and of themselves do not necessarily reveal appropriate mitigation 

strategies. Other factors to consider are relevant laws and regulations, strategic objectives, broader land and resource management plans (LRMPs), treatment opportunities, 

and likely effectiveness and negative consequences of various treatment alternatives. 

However, assessments of wildfire risk are critical for informing the development and 

implementation of cost effective risk mitigation efforts, and comparative risk assessment 

can be used as a basis to evaluate different treatment alternatives. That is, quantitative 

wildfire risk assessment serves as the yardstick by which to measure the effectiveness of 

mitigation alternatives. Designing efficient fire management strategies involves asking:

•	 Where can wildfire risk be best mitigated?

•	 What treatments and management activities are feasible?

•	 Where can different treatments be implemented, and to what extent?

•	 How will treatments affect various risk factors (likelihood and intensity)?

•	 How will treatments affect potential impacts to HVRAs?

•	 What combinations of activities can most cost-effectively mitigate wildfire risk?

Figure 2—Geospatial context of wildfire risk assessment framework, explicitly recognizing the three components of the “risk triangle” 

in relation to the locations of HVRAs across the assessment landscape.
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

The aim of this report is to provide fire and land managers with a helpful set of guiding principles and tools for assessing and mitigating wildfire risk. Key objectives of this 

report include ensuring that readers can:

•	 Understand basic concepts of wildfire hazard, exposure, and effects, and their 

relation to fire management;

•	 Define basic wildfire risk terms and concepts, and understand the major components of quantitative wildfire risk assessment;

•	 Interpret fire modeling outputs, principally burn probabilities and fireline intensities;

•	 Provide support to a wildfire risk assessment on their landscapes of interest; and

•	 Identify how to use wildfire hazard, exposure, and risk information in fire management plans and in fuel management prioritization.

Wildfire Hazard and Risk: Special Considerations

Wildfire can be Beneficial 

When we think of the terms hazard and risk we generally think of the potential for loss. 

That is true for wildfire hazard and risk, but, in contrast with most hazardous natural 

phenomena, wildfires can also lead to substantial ecological benefits. Thus, in this report 

the notions of hazard and risk are expanded to recognize the potential for fire-related 

benefits as well as losses. As a result, the effects of wildfire are not quantified in terms 

of loss, but rather net value change (NVC), considering the relative benefits and losses 

across fire intensity levels (Finney 2005). Unless otherwise noted, losses are identified 

as negative value change, and benefits as positive value change.

Wildfire is Spatial

Wildfire hazard is driven by complex interactions between ignitions, fuel, topography, and 

weather. Spatial variability in socioeconomic and biophysical characteristics influences 

spatial patterns in the frequency of natural and human-caused ignitions. Spatial variability 

in fuel conditions and terrain influences fire intensity and rate of spread. Fire spread direction (heading, flanking, backing, and points in between) also significantly influences fire 

intensity, and is itself influenced by fire spread characteristics of the broader landscape. 

Incorporating fire spread potential into the hazard assessment is especially important for 

large regions of the western United States where area burned is largely driven by spread 

from remote ignitions. 

Wildfire Risk is Spatial

Wildfire risk is jointly determined by wildfire likelihood and intensity, HVRA exposure 

to wildfire, and the effects of wildfire on HVRAs. As described above, wildfire likelihood 

and intensity are both inherently spatial. Additionally, spatial variability in the location 

of HVRAs results in spatial heterogeneity in HVRA exposure to wildfire. And further, 

spatial variability in environmental characteristics can influence the magnitude and extent 

of potential for fire-related losses and benefits. Thus, all components of wildfire risk are 

inherently spatial.

Wildfire Management is Spatial

Spatial variability in expected losses and benefits can influence spatial variability in fire 

management objectives and priorities. This spatial information on wildfire risk can inform 

development of fire management plans and responses to wildfire. It can also inform design 

of fuel treatment, ignition prevention, or other risk mitigation strategies.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 5

In the subsequent sections we present a framework that quantifies wildfire risk, in a 

spatial context, that accommodates multiple HVRAs simultaneously. We then describe a 

proven process for implementing this framework, including a description of the primary 

modeling and analytical components, to help resource managers apply these concepts 

and tools to manage fire on their landscapes. Following this introduction we delve into 

the details of the risk modeling process, with specific guidance and instruction provided 

for fire and fuel modelers, decision analysts, resource specialists, and others supporting 

the risk assessment process. We then describe how information on wildfire hazard and 

risk can be incorporated into risk mitigation strategies, including pre-fire planning, fuel 

treatment design, and incident response. Multiple illustrations and real-world applications are highlighted, along with recommendations for future opportunities. Lastly, we 

conclude by describing the pros and cons of adopting and implementing the wildfire 

risk assessment framework.

2. Assessing Risk: From Concept to Analytical Approach _______________________

Concepts of Hazard and Risk

It is important to begin with a common understanding of terms and concepts used in 

wildfire hazard and risk assessment. The terms hazard and risk are related, but not synonymous. Hazard is a physical situation with the potential to cause damage to HVRAs 

(Scott 2007), resulting in loss (of value). In the wildfire context, the concept of wildfire 

hazard must be expanded to include the potential for beneficial changes to the HVRA 

that partially or wholly offset any damage. Characterization of hazard typically relates to 

physical properties of the natural phenomenon itself, for instance the height of sea-level 

rise associated with a coastal storm surge, or the speed of sustained wind in a tropical 

cyclone. In the wildfire context, we use measures of fire intensity—fireline intensity 

and flame length—as measures of wildfire hazard, although other measures such as fuel 

consumption or other fire characteristics may also be useful.

Assessment of wildfire risk further incorporates the likelihood that an HVRA will 

experience an event and the HVRA’s susceptibility if it does (that is, consequences to 

the HVRAs resulting from exposure to varying intensity levels). The susceptibility of 

a home to coastal storm surge depends on construction materials and design. Thus, the 

amount of damage will vary for homes of different materials and design, even for the 

same storm surge height. Similarly, wildfire risk to HVRAs will vary with fire intensity 

level, their occurrence probabilities, and HVRA susceptibility. Thus, “risk” is conceptualized jointly as the likelihood, intensity, and susceptibility to effects of wildfires on 

HVRAs (see Figure 1). Fire intensity is the primary wildfire characteristic related to 

potential fire effects—typically, the greater the intensity the greater the loss, but this is 

not always the case.

Estimating risk entails estimating the exposure of HVRAs to a hazardous phenomenon, 

and the effects on the HVRA from that exposure (Thompson and Calkin 2011). Exposure 

analysis explores the potential spatial interactions of HVRAs with risk factors—fire 

likelihood and fire intensity—without considering how these factors affect HVRA 

value. In contrast, effects analysis explores the response of HVRAs to varying levels 

of these risk factors. Fire effects are often expressed as a percentage loss of value for 

a given intensity level. The effects of fire are driven by internal factors related to the 

HVRA itself as well as external factors related to the broader environment. For instance, 

post-fire impacts to water quality may be dictated not only by the intensity of fire but 

also the vegetation type and factors influencing erosive potential (soil type, slope steepness, precipitation patterns, etc.).
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Expected net value change is a risk-neutral measure of the wildfire risk to resources 

and assets, and forms the basis for the quantitative wildfire risk assessment process. 

Equation 1 shows how the probability of fire burning in different fire intensity levels 

(BPi

, where i refers to the fire intensity class) and fire effects for those same classes, 

expressed as net value change (NVCi

) are combined to arrive at a quantification of risk 

in terms of expected net value change E(NVC). For a given HVRA, the equation sums 

losses and benefits (expressed together as NVC) for all n fire intensity levels multiplied 

by the probability of the area burning at a given intensity level (Finney 2005). Wildfire 

may present risk to any number of HVRAs that may be present at one location. In a 

later section we modify this equation to incorporate multiple HVRAs and to account 

for differences in relative importance across HVRAs. Although it is recognized that the 

flow of benefits and losses from a given fire may change over time, for the purposes of 

calculating risk with this equation a fixed period of time should be used.

E(NNNNNN) = BBBB! ∗ NNNNNN!

!

!!!

[1]

There are four interrelated components to a comprehensive wildfire hazard and risk 

assessment (Figure 3). The wildfire simulation component uses tabular and geospatial 

input data regarding fuel, topography, weather and ignitions to produce geospatial outputs 

regarding burn probability and fire intensity. The HVRA characterization component 

identifies the resources and assets to include in the assessment, their locations on the 

landscape, their susceptibility to wildfire, and their relative importance. The exposure 

analysis component combines the fire simulation results with data regarding HVRA 

locations to produce tabular and graphical results depicting the wildfire simulation results 

where the HVRAs occur. Finally, the effects analysis component is similar to exposure 

analysis but also integrates the importance and susceptibility of HVRAs. Effects 

analysis is the implementation of equation 1, and produces the most comprehensive 

characterization of the potential for wildfire to cause a change in value, positive or 

negative, to an HVRA. The ability to characterize risk with a common measure E(NVC) 

facilitates integration of risk across multiple HVRAs, and allows for cost-effectiveness 

analysis as a basis for evaluating potential risk mitigation options.

Wildfire Likelihood and Intensity

Wildfire hazard is a physical situation with potential for wildfire to cause beneficial 

or negative impacts to HVRAs. The hazard that wildfire presents can be characterized 

rather simplistically as the occurrence of wildfire itself, but a more useful characterization also quantifies the potential intensity (or probability distribution of intensity) of a 

wildfire if it does occur. Within the wildfire risk framework it is further necessary to 

quantify the likelihood of a wildfire occurring (in some fields, this is called the hazard 

occurrence probability), be it the overall wildfire likelihood or the likelihood of wildfire 

occurring at a given fire intensity level.

Strictly speaking, fire intensity level is independent of likelihood (Miller and Ager 

2012); they are separate measures. For our purposes, we will describe wildfire hazard 

in terms of both likelihood and intensity. This choice is driven by three key considerations. First, modern wildfire simulation models co-estimate likelihood and intensity, 

which are driven by a similar set of environmental factors. Second, as will be described 

below, a useful way to characterize hazard in a given location is often according to a 

probability distribution over wildfire intensity levels (Ager and others 2012; Scott and 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 7

Figure 3—Process flowchart illustrating the relationships among the four components of the risk assessment process.

others 2012b). Further, and more intuitively, considering both likelihood and intensity as 

integrated measures of hazard fits with the concept of hazard as a situation with potential 

for damage. Consider two locations capable of producing identical wildfire intensities, 

but with different probabilities: one location is much more likely to experience wildfire 

than the other (due to higher ignition likelihood, for example). The location with higher 

likelihood to experience that wildfire intensity has a greater potential to cause damage, 

and therefore could be considered to be more hazardous. Taken together, wildfire likelihood and intensity are excellent quantitative measures of wildfire hazard.

The factors affecting wildfire intensity include the elements of the fire behavior triangle—fuel, weather, and topography—as well as spread direction (heading, flanking, 

backing, etc.). At a basic level, wildfire intensity can be assessed for a point, stand, or 

landscape without consideration of fire spread by assuming that a fire occurs at the given 
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

location(s) under specific weather, fuel moisture, and fire spread parameters (heading, 

flanking, or backing). Typically this is assessed as the near-maximum potential (for 

example, heading fire under 97th percentile fuel moisture and wind conditions). There 

may be a very low probability of a wildfire occurring under these conditions in any 

particular area, but nevertheless this level of assessment provides useful information 

about the potential wildfire behavior that different areas of a landscape are capable of 

producing. At the landscape scale, this type of wildfire hazard assessment may also be 

used to identify where on a landscape there is the potential to meet or exceed specific 

wildfire behavior thresholds, thus aiding the identification and prioritization of management opportunities.

In a complete assessment of wildfire hazard, wildfire occurrence and spread are 

simulated in order to characterize how temporal variability in weather and spatial variability in fuel, topography and ignition density influence wildfire likelihood across a 

landscape. In such cases, the hazard assessment includes modeling of burn probability, 

which quantifies the likelihood that a wildfire will burn a given point—typically a single 

grid cell (pixel)—during a specified period of time. Burn probability assessments can 

quantify the likelihood of wildfire of any intensity occurring or the likelihood of wildfire 

occurring at different fire intensity classes. Burn probability for fire management planning applications is often reported on an annual basis—the probability of burning during 

a single fire season. A distinguishing factor of modeling annual burn probability is the 

additional simulation of ignition probability and fire duration, in order to account for 

the relative frequencies and spatial patterns of historical ignitions. Alternatively, some 

planning applications report the burn probability conditional on a fire occurring during 

a specified “problem fire” weather scenario. Wildfire incident management applications, 

by contrast, express burn probabilities for a single fire over a matter of days or weeks.

Although some approaches to characterizing wildfire hazard do not include likelihood, burn probability modeling plays a major role in characterizing the potential for 

wildfire to cause effects, especially where analysts are interested in modeling fire spread 

with variable combinations of ignition location and weather conditions. Some wildfire 

modeling systems output fire behavior metrics in terms of probabilities, and therefore 

quantification of wildfire hazard will essentially have a probabilistic component. Typical outputs of probabilistic hazard assessment include summaries and maps of overall 

burn probability, burn probability by fire intensity level, mean wildfire intensity or 

flame length (that is, averaged over all simulations, incorporating non-heading spread 

direction and a range of simulated weather conditions), and the expected value of wildfire intensity/flame length calculated as the sum-product of burn probability and mean 

wildfire intensity/flame length.

Exposure Analysis

After characterizing wildfire hazard, the next critical step in assessing wildfire 

risk is exposure analysis. Wildfire exposure analysis refers to assessing the wildfire 

intensity and burn probability in locations where HVRAs are present. Thus this 

analytical step is premised on the ability to consistently map all HVRAs over the 

entire spatial extent of analysis. Exposure can be quantified in many ways, including 

summary statistics such as expected area burned or mean burn probability or mean 

fire intensity across mapped HVRA pixels. Wildfire exposure assessment has broad 

applicability in forest planning. Readers may be familiar with Rapid Assessment 

of Values at Risk (RAVAR) maps (Calkin and others 2011b) that assess exposure 

from an ongoing wildfire incident. Exposure analysis can also be used in National 

Environmental Policy Act (NEPA) assessments to compare differences in exposure of 

HVRAs under multiple alternatives or in National Forest Management Act (NFMA) 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 9

and Forest Plan revision projects to identify HVRAs that are most likely to interact 

with wildfire and where on the landscape those wildfires are most likely to occur. 

Other potential applications include revising fire management plans, and assessing 

the likelihood of remote ignitions impacting specific HVRAs under different fire 

management scenarios (Scott And Others 2012a).

Effects Analysis

The next step in the assessment process is to identify how HVRAs are affected by 

wildfire. Predicting and quantifying fire effects can be very complex and challenging, 

especially for many ecological and other non-market HVRAs (Venn and Calkin 2011). 

The impacts of fire vary across spatial and temporal scales, and future disturbances or 

other uncertain processes influence those impacts. Predicting fire effects is challenged 

by limited or inadequate empirical observations, a lack of predictive models, and gaps 

in core fire science and fire effects science (Hyde and others 2013). Nevertheless estimating HVRA response to wildfire is a crucial step for quantitative risk assessment and 

for prioritizing mitigation efforts (Fairbrother and Turnley 2005).

Analyzing the susceptibility of HVRAs to varying levels of fire intensity relies on a 

combination of fire effects modeling and expert judgment. Despite availability of models 

for first-order fire effects (for example, tree mortality, soil heating, fuel consumption, and 

smoke emissions), some level of inference is still necessary to characterize the secondorder effects (for example, sedimentation, habitat loss) in which managers are typically 

more interested (Reinhardt and Dickinson 2010). In some cases fire effects models may 

exist for a particular HVRA, perhaps a habitat suitability model that can account for 

fire. In most circumstances proxies or reliance on expert judgment may be required.

Fire effects analysis captures both fire-related benefits and losses, and is quantified in 

terms of value change, expressed in relative terms on a percentage basis (for example, 

complete loss = –100 percent). In this framework, expert-defined “response functions” 

translate fire effects into value change, based upon fire intensity and potentially other 

environmental characteristics. These response functions output a common measure of 

wildfire risk across market and non-market HVRAs.

Relative Importance and Relative Extent

The risk framework accommodates a variety of HVRAs using a common measure 

(NVC); when integrating the risk of wildfire to multiple coincident HVRAs it is important 

to incorporate the relative importance (RI) of HVRAs. That is, a weighted summation 

of risk [E(wNVC)] across HVRAs might be more representative of the broader social 

consequences of losses and benefits, and should reflect fire management objectives and 

priorities. For instance, the same degree of E(NVC) may be weighed differently across 

human communities, municipal watersheds, recreational areas, and wildlife habitat. If 

we were able to readily make value comparisons across market and non-market HVRAs 

in terms of monetary value (for example, dollars) then the articulation of RI would 

be straightforward. However a number of challenges preclude the use of non-market 

valuation and the definition of response functions in terms of monetary value for most 

practical applications (Venn and Calkin 2011).

Fortunately, multi-criteria decision analysis techniques can be employed to help 

balance and quantify tradeoffs and to articulate preferences and RI of HVRAs (Kiker 

and others 2005; Ananda and Herath 2009).The establishment of RI weights across 

HVRAs enables the weighted integration of risk across multiple HVRAs, allows for 

simpler mapping and visualization, and can facilitate prioritization decisions. Equation 

2 illustrates the calculation of E(wNVC), which extends Equation 1 to integrate any 

number (j) of HVRAs. That is, E(wNVC) is complementary to, and not a substitute for, 
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

the single-HVRA E(NVC) values. Thus, articulating RI scores serves only to further 

enrich the results of assessing risks.

E wwwwwwww = BBBB! ∗ NNNNNN!" ∗

RRRR!

RRRR! ! !

[2}

The relative importance of different HVRAs is partitioned spatially according to the 

HVRA’s relative extent (RE). Relative extent can be measured in any unit of measure 

(for example, number of hectares or grid cells). An HVRA that is extensively mapped 

will receive a lower relative importance value per unit area, whereas an HVRA that 

is rare on the landscape will receive a higher relative importance value per unit area.

Primary Modeling Steps

In order to quantify and characterize risks across any landscape, it is critical to have 

input from local staff, in particular line officers and resource specialists. Figure 4 outlines the primary steps of our analysis process to estimate wildfire risk. Step 1 (wildfire 

simulation) entails obtaining and updating fuel data layers, analyzing historical fire 

weather and fire occurrence, and outputs spatially resolved estimates of fire likelihood 

and intensity. Step 2 entails the elicitation of expert judgment from resource specialists 

regarding which HVRAs to assess and how fire may affect them. Step 3 uses multicriteria decision analysis to establish relative importance weights across HVRAs.

Figure 4—Conceptual flowchart for integrated wildfire risk assessment process, with three primary 

analytical components identified (Thompson and others 2013a).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 11

Quantifying Wildfire Risk

Risk is a composite measure of expected consequences, calculated from the probability of 

a set of possible outcomes and their associated consequences. Wildfire risk is a composite 

measure of expected consequences of wildfire, calculated from the probability of fire occurring at various fire intensity levels and the losses and benefits to HVRAs associated 

with each fire intensity level. A comprehensive depiction of risk describes the full range 

of fire intensity probabilities and associated outcomes. Risk can also be expressed on a 

probabilistic basis for particular outcomes, for instance a 15 percent chance of fire-related 

losses exceeding a certain threshold. More common is to use a probabilistic expectation of 

net value change across all possible fire intensity levels (see Equation 1). An expectation 

is the sum-product of fire intensity burn probabilities and net value change. This approach 

does have potential pitfalls: variability around the expectation is not captured, and lowlikelihood, high-magnitude events are equated with high-likelihood, low-magnitude events 

(Hanewinkel and others 2010). However, reducing risk to a single index nonetheless aids 

comparison of risk across complex landscapes where the sheer numbers to consider can 

be overwhelming. 

Landscape-scale risk calculations will include the aggregation of risk to multiple HVRAs 

in multiple locations, and will incorporate relative importance and relative extent weights. 

Here we begin with a simple illustration of expected value. Consider a given landscape 

pixel housing an HVRA that we know will experience total loss if exposed to fire of any 

intensity. That is, the response of this HVRA to fire is –100 percent (see sections 4 and 6). 

If the probability of the pixel burning is 0.05, the expected net value change for that pixel 

is –100 * 0.05 = –5.00 percent. Now assume that fire effects do vary with fire intensity, 

with only –20 percent loss for low intensity fire (probability = 0.04), and –100 percent 

loss for high intensity fire (probability = 0.01). In this case the expected net value change 

for the pixel is –20 * 0.04 ±100 * 0.01 = –1.80 percent. Calculations proceed similarly 

across additional fire intensity levels and responses can be positive in addition to negative.

In an expanded example, consider two geographically distinct landscape pixels that each 

house two HVRAs: critical infrastructure, and fire-dependent habitat. The table below 

displays the response functions for each HVRA according to fire intensity level (flame 

length category). Whereas fire is universally bad for critical infrastructure, low to moderate 

intensity fire can incur a benefit for the fire-dependent habitat. In both cases, as intensity 

increases so does the potential for loss.

Fire Intensity Level 1 2 3 4 5 6

Flame Length Range (feet) 0 – 2 2 – 4 4 – 6 6 – 8 8 – 12 12+

Critical Infrastructure –50 –60 –70 –80 –90 –100

Habitat 60 40 20 –20 –40 –

On the first landscape pixel (pixel A), there is an annual burn probability of 0.0155, and the 

distribution of burn probabilities by fire intensity level is shifted towards lower intensities 

(the fire intensity probabilities sum to 0.0155). The second landscape pixel (pixel B) has 

the same annual burn probability of 0.0155, but the distribution across fire intensity levels 

is shifted towards higher fire intensity levels. (Continued on next page)
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

The expected net value change for critical infrastructure can be calculated as: (–50 * 0.005) 

+ (–60 * 0.004) + (–70 * 0.003) + (–80 * 0.002) + (–90 * 0.001) + (–100 * 0.0005) = –1.00. 

For the fire-dependent habitat, by contrast, the expected net value change is positive at 

0.41. This result is due to the combined effects of beneficial effects of low intensity fire 

and a greater likelihood of the area burning with low intensity.

On landscape B the expected net value change is –1.21 for critical infrastructure, and –0.21 

for the fire-dependent habitat. Because of the higher likelihood of higher intensity fire, 

a net loss is anticipated for the fire-dependent habitat. Thus wildfire hazard and HVRA 

response to fire jointly affect the spatial distribution of risk across landscapes.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 13

Preparing to Implement the Framework

Broadly speaking, risk assessment is about more than the mere quantification of 

risk. The assessment process is comprised of three primary steps: problem formulation, problem analysis, and risk characterization. Problem formulation is a crucial first 

step; each analysis must be crafted to the specific management context and to address 

specific management objectives. Formulating the problem includes defining the spatial 

and temporal scope of analysis, the HVRAs to be included, the assessment objectives, 

and the intended use of the assessment results. The goal of the analysis phase is to estimate the likelihood of HVRA exposure to varying levels of wildfire intensity, and to 

predict HVRA responses resulting from exposure to varying levels of wildfire intensity. 

Lastly, risk characterization summarizes risks posed to various HVRAs, interprets the 

risk estimates, and evaluates uncertainties. Figure 5 summarizes this entire process.

The first stage in a risk assessment is to clearly formulate the problem to be solved, 

which in many cases may relate to a statement of purpose and need. Another important 

step is to identify the spatial and temporal scale of analysis. For the framework presented 

here, the spatial scale of analysis is landscape (in other words, not stand based or tree 

based), with the ultimate scope dictated by the size of the landscape under consideration. 

The framework itself is theoretically amenable to landscapes of any size, contingent upon 

availability and sufficiency of geospatial data and processing capacity. Geographically, 

the process we emphasize in this report is more amenable to areas where fire spread is 

Figure 5—The primary steps of the wildfire risk assessment. First the 

problem is formulated, including definition of the assessment objectives, 

the planning context, the HVRAs to be included, and the spatial and 

temporal scope of analysis. The primary analytical components identify 

and quantify hazards, HVRA exposure, and fire effects. Lastly, risk 

characterization summarizes risks posed to various HVRAs.
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

the predominant source of fire occurrence and “large” fires account for the majority of 

acres burned. The temporal scale for the process is over the short-term, including only 

the more immediate post-fire impacts; the wildfire modeling approach generates burn 

probabilities given current conditions, without modeling the long-term dynamics of 

post-fire recovery, succession, or disturbance. As projections go further into the future, 

the degree of uncertainty associated with predicting fire effects will tend to increase.

Setting the stage for this type of risk assessment will not come without effort. The time 

spent upfront organizing and clarifying the assessment process is important, however, 

and should pay dividends in the long run through a more streamlined analysis process 

and, ideally, through improved decision making. Fire and land managers and decision 

makers looking to implement this process should start by considering (1) the state of 

geospatial data, (2) the time and resources available for the assessment, (3) the role for 

leadership, (4) the role for resource specialists and other staff, and (5) the number and 

extent of HVRAs to be considered.

State of Geospatial Data—Current and accurate data is necessary for essentially 

every component of modeling and evaluating wildfire risk. Compiling a usable, accurate, valid, and complete geospatial dataset is one of the biggest hurdles of the entire 

risk assessment process. Historical fire weather and fire occurrence data are required 

to parameterize fire models. Spatial information on vegetative conditions is necessary 

to define the “fuelscape” over which simulated fires will grow. The location of HVRAs 

with respect to wildfire hazard will determine their exposure, and other environmental 

characteristics could codetermine fire effects.

Time and Resources Available for the Assessment—Typically, implementing the risk 

assessment framework requires a series of workshops to calibrate fuels, critique fire 

modeling outputs, define response functions, and define relative importance weights. 

These workshops will involve different levels of commitment from different types of 

individuals. The workshops themselves can take place over the course of a few days, 

although a significantly greater level of commitment is necessary prior to the workshops. A primary resource-intensive component is the compilation and stewardship of 

geospatial data. Modeling wildfire occurrence and behavior is another intensive activity 

that may require additional support depending on local fire modeling expertise. Agencyprovided support could stem from the Fire Modeling Institute (www.firelab.org/fmi), 

the National Fire Decision Support Center, or the Enterprise Program, although external 

options could also be appropriate.

Role for Leadership—In effect, the information used within a risk assessment and 

broader risk management process can be divided into two types: values-based and 

science-based. A clear delineation of these types of information helps provide transparency and rigor to the decision process (Gregory and Long 2009). It is the responsibility 

of leadership to provide values-based information in the form of management objectives 

and priorities and in the context of fire protection and restoration. This value-based 

information does not, of course, reflect the personal values of any individual line officer or resource manager, but rather reflects the values of society at large, as expressed 

through laws, regulations, agency mission, stakeholder participation, etc. This includes 

identifying the objectives of the assessment, identifying which HVRAs should be included in the assessment, and defining the relative importance weights, if desired, to 

differentiate risk scores across HVRAs.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 15

Role for Resource Specialists and Other Staff—It is the role of specialists and 

staff to provide science-based information. This can come in many forms, for instance 

provision of geospatial data regarding fuels or HVRA locations. Principally, resource 

specialist expertise is sought for the purposes of defining HVRA-specific response 

functions. Ideally the disciplinary backgrounds represented are well aligned with the 

HVRAs being assessed, for example, wildlife biology for critical habitat; soil science 

and hydrology for watershed response, etc.

Number and Extent of HVRAs to be Considered—On any landscape there are likely 

to be multiple resources and assets that could be impacted by fire. These could include 

the wildland urban interface (WUI), critical infrastructure, municipal watersheds, sites 

of cultural or historical importance, habitat for a variety of aquatic and terrestrial species, 

recreational sites, experimental forests, timber resources, etc. For this type of mid-scale 

assessment, however, it is typically sufficient to identify a handful (3-8) of the most 

important (that is, the most highly valued) resources and assets. Which HVRAs are of 

the greatest concern, or figure prominently in land and fire management plans? This 

“big picture” perspective enables an understanding of major patterns and likely impacts 

of risk, and can set the stage for further more refined analysis if necessary.

Addressing Uncertainties

Estimating wildfire risk is no easy task, and requires confronting multiple sources of 

uncertainty. First, and perhaps most obviously, we cannot with perfect accuracy predict 

the location and timing of fire occurrence, nor can we predict the weather driving fire 

behavior after an ignition (variability as a source of uncertainty). Second, although we 

know that not all fire is universally bad or universally good, it is difficult to project the 

relative fire-related losses and/or benefits to many HVRAs (limited knowledge as a 

source of uncertainty). Further, comparing benefits and losses across at-risk HVRAs can 

seem like an apples and oranges comparison, so to speak, especially when considering 

non-market HVRAs and trying to balance societal values. It can be difficult to balance 

priorities across HVRAs and to establish relative importance weights (unknown or illformed preferences as a source of uncertainty).

The risk assessment process outlined in this report is specifically tailored to address 

each of these sources of uncertainty. Different decision support tools and approaches 

are more appropriate for different manifestations of uncertainty. Table 1 outlines how 

the specific modeling approaches in this framework—probabilistic wildfire modeling, 

expert-based fire effects modeling, and multi-criteria decision analysis—relate to key 

components of risk and corresponding uncertainties.

Table 1—Three key components of the wildfire risk assessment framework, their respective 

predominant uncertainty type, and the corresponding methodology to appropriately 

manage uncertainty (modified from Thompson and Calkin 2011).

Wildland fire context Type of uncertainty Methodology

Fire likelihood and intensity Variability Probabilistic modeling

Fire effects Knowledge Expert judgment

Relative importance of HVRAs Preference Multi-criteria decision analysis
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

3. Wildfire Simulation ____________________________________________________

Wildfire hazard is a physical situation with potential for wildfire to cause harm to 

persons or damage to resources or assets. Wildfire intensity is the primary wildfire 

characteristic related to the potential for harm or damage—typically, the greater the 

intensity, the greater the potential for harm or damage. The primary factors affecting 

wildfire intensity—fuel, weather, and topography—therefore also affect wildfire hazard.

The likelihood of experiencing a wildfire is included in this section as a measure of 

wildfire hazard. Taken together, wildfire likelihood and intensity are excellent quantitative measures of wildfire hazard; both are also used in the quantitative risk assessment 

framework described in an earlier section. In this section we focus on analytical methods, modeling tools and model results useful for characterizing wildfire likelihood and 

behavior, primarily wildfire intensity. Although wildfire intensity for any discrete point 

on the landscape can be simulated independent of its surroundings, wildfire likelihood 

depends on geospatial context across a broad area. Therefore, we focus on geospatial 

assessment of wildfire hazard across a landscape rather than at a discrete point.

As mentioned above, the three main factors of the fire behavior triangle—fuel, 

weather, and topography—affect wildfire intensity. A fourth factor also strongly affects 

wildfire intensity: relative spread direction (heading, flanking, backing, and all points 

in between). For the same fire environment, wildfire intensity is greatest at the head 

and declines quickly along the flanks and rear where the flame front is oriented across 

or against the heading direction. Measures of conditional fire intensity described in this 

section incorporate the effect of spread direction, while near-maximum potential wildfire 

intensity does not—it is calculated in the heading direction only. Additionally, ignition 

frequency and density across a landscape influence wildfire likelihood.

Of the fire modeling systems capable of producing wildfire likelihood (Table 2), 

FSim—the large-fire simulator (Finney and others 2011b)—is particularly well-suited 

for a comprehensive hazard and risk assessment because it implicitly simulates all significant wildfires (the large ones that account for approximately 95 percent of the area 

burned), it incorporates the effects of fire suppression on wildfire containment, it addresses variability of fuel moisture, wind speed and wind direction as it historically varies 

throughout the fire season, and it simulates wildfire occurrence on an annualized basis.

Table 2—Review of the characteristics of three widely -used fire modeling systems that model natural variability with 

probabilities.

Characteristic FSPro FlamMap5 FSim

Planning context and Suppression strategy Fuel treatment planning, Fire management plan development,

decisions supported development “problem fire” analysis preparedness and response planning,

fuel treatment planning

Duration Days to weeks One to a few burning periods Entire fire season

Fires considered Individual escaped fire Problem fire ignitions All large fire ignitions

Simulation type One fire, many weather Problem fires, extreme All fires, all weather scenarios

scenarios weather scenarios

Type of burn Conditional on current fire Conditional on specified Annual (full fire season)

probability location and specified time weather scenario

period

Source of Variation Wind speed, wind direction, Ignition locations, fuelscape Wind speed, wind direction, fuel

fuel moisture content, moisture content, fuelscape, ignition

fuelscape location, ignition probability, 

containment probability, fire 

 duration
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 17

FlamMap5 (Finney 2006) produces outputs similar to FSim, but the results are 

‘conditional’ in that they pertain to the condition that an escaped fire has occurred. The 

more limiting factor, however, is that FlamMap5 typically does not simulate the very 

largest fires that can occur on a landscape, and it is those very large fires that tend to 

contribute to wildfire likelihood. FlamMap5 is an excellent choice of modeling system 

if the resources available for the hazard and risk assessment preclude the use of FSim.

We mention FSPro (Finney and others 2011a)—for ‘fire spread probability’—primarily for completeness. Many of the landscape-scale concepts we discuss in this report 

apply to the incident level as well. At the incident level, FSPro is the most appropriate 

modeling system for estimating wildfire likelihood over the expected life of the incident 

(days to weeks). FSPro does not currently produce conditional fire intensity outputs. 

If it were modified to do so, the same risk calculations described here could be applied 

to FSPro results to estimate incident-level hazard and risk, potentially providing even 

more detailed decision information than currently available.

Three broad classes of input data are required when using a fire behavior modeling 

system for geospatial assessment of wildfire hazard: a fire modeling landscape, historical weather data, and historical fire occurrence data (Figure 6). The wildfire simulation component produces three broad measures of wildfire hazard: the near-maximum 

potential wildfire behavior that hypothetically could occur, the likelihood that wildfire 

will reach a given location (burn probability), and the mean wildfire intensity given that 

it does burn (conditional fire intensity). The development of these simulation inputs and 

interpretation of the outputs are described in the following sections.

Figure 6—The wildfire simulation component of the overall wildfire risk assessment process 

uses tabular and geospatial data regarding fuel, weather, topography and ignitions to produce 

geospatial data regarding the near-maximum fire behavior, conditional fire intensity, flamelength probability and burn probability. These outputs are inputs to the exposure analysis 

and effects analysis components.
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Fire Modeling Landscape

A fire modeling landscape is a raster-format geospatial characterization of the fuel, 

vegetation, and topography inputs needed for simulating the full range of wildfire behavior—from surface fire through active crown fire—based on separate models of surface 

fire spread (Rothermel 1972), crown fire spread (Rothermel 1991), and the transition 

between them (Van Wagner 1977). Those inputs include surface fuel characteristics 

(fire behavior fuel model), canopy fuel characteristics (canopy base height, canopy 

bulk density), forest vegetation (forest canopy cover and height), and topography (slope 

steepness, aspect, and elevation). Geospatial fire modeling systems require the fire modeling landscape data in the form of a fire modeling landscape file (LCP), the file format 

originally developed for FARSITE (Finney 1998) but now also used in FlamMap5 and 

FSim. The LCP file consists of several raster data layers—one for each characteristic. 

Each raster data layer is a grid of cells, or pixels, where each cell contains a numerical 

value that indicates something about the layer. For example, each cell in the fuel model 

layer contains a coded value indicating the fuel model assigned to that cell.

The fire modeling landscape must meet certain characteristics to be suitable for geospatial assessment of wildfire hazard. The fire modeling landscape must have complete, 

wall-to-wall coverage across all ownerships, especially if a fire growth modeling system 

is to be utilized, so that simulated fires can spread across all parts of the landscape. Not 

only must the data cover the entire landscape extent, but the methods used to produce 

them should be consistent. Many locally available datasets do not meet these criteria 

because they were developed for only a portion of the required landscape extent (clipped 

to an administrative boundary, for example). Mixing data sources in a single landscape 

is not recommended because doing so can potentially affect the results and conclusions 

of the assessment.

For an assessment that relies on fire growth simulation to estimate wildfire likelihood, the extent of the fire modeling landscape must be larger than the primary area of 

interest in order to account for the effect of distant fires reaching the area of interest. A 

buffer of 15-30 km (10-20 mi) is usually sufficient, but specific landscape conditions 

could require an even larger buffer. For example, large areas of grass or grass-shrub fuel 

in the buffer area could allow fires to spread more than 20 miles, especially if strong 

winds are possible.

The appropriate resolution (the dimensions of each grid cell or pixel) of the fire 

modeling landscape depends on the landscape extent and requirements of the fire modeling systems used. Generally, the larger the landscape extent, the larger the cell size 

will be. A landscape consisting of more than 50 million cells is unwieldy to use in any 

fire modeling system; 10 million cells is a reasonable upper limit on a fire modeling 

landscape. Note that even if the primary area of interest were a single point, the buffer 

area suggested above would encompass roughly 250,000 to 1 million acres. Most assessments will therefore require a cell size between 30 m and 270 m.

Much of the geospatial data for creating a fire modeling landscape is available at a 

30-m cell size, and must therefore be re-sampled to the coarser resolutions using a geographic information system (GIS). Several resampling methods are available in a GIS; 

the “nearest-neighbor” method is recommended because it ensures that widely scattered 

or narrow, linear elements of the fuelscape—like riparian stringers or roadways—

remain represented in the re-sampled layers in proportion to their initial representation. 

The “majority” resampling method tends to eliminate those features, homogenizing the 

landscape by making the most prevalent fuel types even more prevalent.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 19

An important characteristic of geospatial data is its projection, which is a set of 

parameters required for characterizing geospatial information on the Earth’s surface as 

a 2-dimensional map. The map projection of the fire modeling landscape data is critical for its subsequent use in any fire growth modeling system because those systems 

implicitly assume that grid north is true north. Some map projections are designed to 

minimize the distortion of area across a large landscape, but those map projections can 

result in true north being nearly 20 degrees different than grid north in some parts of the 

continental United States. A projected coordinate system specifies the map projection 

and other reference parameters needed to calculate geospatial relationships. For fire 

behavior modeling, the ideal (and most commonly used) projected coordinate system is 

Universal Transverse Mercator (UTM), which balances distortion in distance, direction, 

shape, and area within a UTM zone. The best-fit UTM zone is the zone that contains 

the center of the landscape.

Data Sources—A fire modeling landscape can be obtained from a variety of sources 

or generated using a variety of methods. The LANDFIRE project (www.landfire.gov) 

publishes all of the required fuel, vegetation and topography rasters, both as separate 

rasters and as a complete LCP file, at a 30-m cell size, for the continental United States, 

Alaska, and Hawaii. In addition to the required rasters, LANDFIRE also publishes the 

underlying vegetation rasters (existing vegetation type, cover, and height; biophysical 

setting), a disturbance raster (which indicates disturbance type, severity, and time since 

disturbance) and the fuel mapping rulesets used to generate the fuel and vegetation rasters of the LCP. These underlying rasters and rulesets can be used to critique, edit, and 

update the published rasters, resulting in a locally calibrated fire modeling landscape.

Locally produced vegetation data can sometimes be used to generate one or more rasters 

of the fire modeling landscape, but several challenges exist. First, such data are often 

not produced for all land ownerships within the landscape extent, so the requirement for 

completeness may not be met. Second, these data are often produced in polygon format, 

with relatively large land areas mapped to the same fuel or vegetation characteristic. 

While it is easy to convert the polygon data to the required raster format, the resulting 

layers frequently miss small areas of important fuel or vegetation conditions, especially 

when those conditions are barriers to fire spread like rock outcroppings or riparian areas, 

and tend to homogenize the fuelscape. Lastly, locally produced vegetation rasters often 

consist of only vegetation cover type and not the associated vegetation characteristics 

of vegetation cover and height, which are useful for distinguishing among fuel models 

and canopy characteristics.

Regardless of how the fire modeling landscape is acquired or generated, a thorough 

critique should be performed (Stratton 2006, 2009) to ensure that no errors in producing 

the landscape have occurred.

Tools—The computer tools required to acquire or develop a fire modeling landscape 

depend on the method. The required LCP characteristics described above suggest that 

LANDFIRE will be a major source of the required layers, so in this section we will 

focus on the tools needed to work with those layers. It is possible to download an LCP 

file directly from the LANDFIRE Data Distribution Site (http://landfire.cr.usgs.gov/

viewer/) without using GIS software. This downloaded LCP file can be critiqued and 

edited (using relatively rudimentary editing tools) using a combination of geospatial 

fire modeling systems (FlamMap5 and FARSITE). Far more powerful editing and 

analysis tools are available using a GIS such as ESRI’s ArcMap with the Spatial Analyst 

extension. A variety of custom toolbars have been developed specifically to work with 

fire modeling landscape and related rasters in ArcMap. The LANDFIRE Data Access 

Tool (LFDAT; www.niftt.gov) is an ArcMap Toolbar that works in conjunction with 
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

the LANDFIRE Data Distribution Site, allowing the user to specify the extent of the 

fire modeling landscape using existing geospatial datasets (such as a buffered boundary 

around the analysis area). LFDAT also includes several useful utilities, including one 

for creating an LCP file from the edited rasters and another for splitting an LCP into 

separate rasters.

LANDFIRE has made available the LANDFIRE Total Fuel Change (LFTFC; www.

niftt.gov) Toolbar, a powerful tool for editing a fuelscape. To use the LFTFC Toolbar, the 

user downloads the underlying vegetation layers (biophysical setting; existing vegetation 

type cover and height) and disturbance layer (indicating disturbance type, severity, and 

elapsed time since disturbance). The fuel mapping rules for each LANDFIRE mapping 

zone are contained in a database within the LFTFC Toolbar; the user can import and edit 

the fuel mapping rules or update the disturbance raster to reflect disturbance since the 

last version of LANDFIRE data. From this information, the LFTFC Toolbar generates 

the fuel and vegetation layers required for the LCP.

Finally, the ‘LCP critique’ function of FlamMap5 (www.firemodels.org) provides 

a basic assessment of LCP characteristics and the layers it contains. In addition to 

landscape-wide summaries, ‘LCP Critique’ summarizes the landscape characteristics 

where each of the surface fuel models exists.

Historical Weather Analysis

The acquisition and analysis of historical weather data representative of the fire modeling landscape is the next step in a geospatial assessment of wildfire hazard. Datasets 

archived by the Desert Research Institute (www.raws.dri.edu) from Remote Automated 

Weather Stations (RAWS) are the principal source of historical weather data for a wildfire 

hazard assessment. On many landscapes, a single RAWS can be selected to represent 

the entire landscape, but large landscapes may need to be broken into weather zones 

with a different RAWS assigned to each zone. The selected RAWS should be representative of the larger landscape (or zone), not just the local area around the RAWS. 

For example, a station located in the bottom of a deep valley is not a good candidate if 

its location results in primarily up- and down-valley wind directions, or if temperature 

and precipitation conditions in the surrounding mountains differ significantly from the 

valley bottom. Instead, a RAWS located on an upper slope or ridge may better represent 

the conditions under which wildfires burn.

Two types of weather data are needed: hourly wind data (speed and direction) and 

daily fuel moisture content (MC) data. Hourly wind data are typically available for a 

shorter historical period of time than daily fuel moisture observations. Ideally, a complete, 

year-long record of daily observations should be available for the selected station for a 

period of 20-30 years. A longer record is not necessarily desirable if weather conditions 

beyond that time no longer represent current conditions. Also, for maximum utility, the 

time period of these daily observations must coincide with the time period for historical fire occurrence. The longest available period of hourly wind data at a station can be 

used, regardless of coincident fire occurrence data, because the wind data are used only 

to populate monthly wind speed and direction distributions and not to correlate with 

fire occurrence. Wind speed measurements at RAWS stations correspond to a height of 

6 m (20 ft) above the ground (or vegetation, if present).

Archived weather data can be used to calculate several indices of fire danger. The 

Energy Release Component (ERC) of the National Fire Danger Rating System (NFDRS) 

is the fire danger index most commonly used in fire management planning.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 21

Adjustments—Hourly wind speed data consist of the mean speed during the 10-minute 

period at the end of the hour, called the 10-min average, and the maximum instantaneous 

wind speed occurring any time during the hour, called the gust speed. Analysis of wind 

speed data for use in fire modeling can use either the 10-minute average, the gust, or both 

types of wind speed measurement. However, it has been suggested that a third wind speed 

type—the 1-minute average wind speed—may be better suited to some fire modeling 

applications than those two recorded wind types (Stratton 2006). Crosby and Chandler 

(1966) provide a table of conversions between the 10-minute average and the probable 

maximum 1-minute average during those 10 minutes. Depending on the fire behavior 

modeling system being used, converting to the 1-minute average wind speed can either 

be done as a simple hand calculation or as an update query in the wind speed database.

Each weather station contains an indication of the fire danger rating fuel model assigned to the station. However, it has been found that fire danger rating fuel model G 

is well suited to the purpose of relating the effect of fuel moisture to fire occurrence 

(Andrews and others 2003), regardless of the fuel model assigned to the station, because 

fuel model G contains fuel load in all size classes, including 1000-h timelag fuel particles (Finney and others 2011b). Therefore, we suggest changing the fire danger rating 

fuel model to “G” in FireFamily Plus before continuing with the analysis. We refer to 

calculation of ERC for fuel model G as ERC-G.

Tools—The main software tool for analyzing weather data is FireFamily Plus 4.1 

(www.firemodels.org), but additional custom charts or tables can be generated in a 

spreadsheet or graphing program.

Historical Fire Occurrence

The acquisition and analysis of historical wildfire occurrence data within the fire 

modeling landscape is the next step in preparing for a wildfire hazard assessment. The 

minimum fire occurrence dataset consists of just three variables for each wildfire igniting 

within the fire modeling landscape: start (or discovery) date, start location, and final fire 

size. Because “large” fires burn the majority of land area (Strauss and others 1989)—

contributing most to wildfire hazard—the fire occurrence dataset needs to be accurate 

for large fires; the completeness and accuracy for small fires is much less important. 

There is no single definition of a large fire for use across the United States, but typically 

a size threshold of 100-300 acres is used. Small fires contained during initial attack are 

critically important for preparedness planning because they represent wildfires that did 

not burn much of the landscape. However, because those small fires do not contribute 

much to area burned, they are not important to consider when assessing wildfire likelihood assuming the historical level of preparedness and initial attack success continues.

Just like information about fuel characteristics, fire occurrence data must be complete 

for the entire landscape, regardless of jurisdictional boundaries. This requirement presents 

a challenge because fire occurrence data are compiled separately by each jurisdiction 

across a landscape, with little effort to coordinate. For example, the same wildfire event 

could be recorded in multiple fire occurrence databases if more than one jurisdiction 

responded to the event. The duplicates must be removed before continuing with the 

analysis. Wildfires originating on private land may not be recorded in any fire occurrence database, especially if they are contained during initial attack. Fortunately, the 

large fires we are most interested in tend to be well captured in the available databases.

Like weather data, fire occurrence data is desired for a period of 20-30 years. A 

shorter period of fire occurrence data may not capture the true nature of fire occurrence 

on the landscape, and a longer period may include the influence of a past climate that 

no longer reflects current conditions.
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Data Sources—Several sources of historical fire occurrence data are available. Local 

land management units (an individual national forest, for example) usually have excellent 

records for wildfires originating within their own jurisdictions. Fire occurrence data for 

a variety of jurisdictions are available from the National Fire and Aviation Management 

Web Applications website (https://fam.nwcg.gov/fam-web/). Such datasets may contain duplicate records if different agencies record the same wildfire in their respective 

datasets. This situation can occur when an agency responding to the incident records 

the wildfire even though it starts in another jurisdiction. Eliminating duplicate records 

is a challenging but necessary exercise when compiling datasets across jurisdictions. 

Ensuring that all wildfires occurring on the analysis landscape are recorded is another 

challenge, especially for wildfires occurring on land for which a Federal or state agency 

is not primarily responsible for suppression.

For use in its own analyses, the Fire Program Analysis (FPA) project has compiled 

and critiqued nationwide fire occurrence data (Short 2013). This dataset is an excellent 

starting point for the generation of historical fire occurrence data for a fire modeling 

landscape, especially if the primary interest is fires that escape initial attack and become 

“large.”

Tools—Several software tools are necessary or useful for compiling and summarizing 

fire occurrence data, regardless of the source. First, because the fire occurrence dataset 

must be attributed to a known portion of the landscape, it will be necessary to work 

with the dataset in a GIS. At a minimum, GIS is used to select the records that apply 

to the fire modeling landscape or other designated fire occurrence area. GIS may also 

be used in conjunction with the location and characteristics of those records to produce 

a raster of spatial ignition likelihood (density). FireFamily Plus is a standard software 

system for processing and analyzing fire occurrence data alongside historical fire 

weather data. FireFamily Plus can be used to generate specific inputs to fire behavior 

modeling systems such as FlamMap5 and FSim. Finally, spreadsheet software can be 

used for custom analysis of fire occurrence data. Pivot tables are especially useful for 

summarizing historical fire occurrence by size, date, etc.

Near-Maximum Wildfire Behavior

It is often desirable to assess the near-maximum wildfire behavior possible at each 

pixel on a landscape. The near-maximum wildfire behavior is an assessment of headfire 

behavior for a severe weather condition (though usually not the most severe possible). 

Several measures of wildfire behavior can be assessed in addition to wildfire intensity, 

including the type of fire. With effort, it is also possible to quantify indices of crown 

fire potential. In this section we will focus on the most common measures: type of fire, 

fireline intensity, and flame length.

The first step in assessing near-maximum wildfire behavior is quantifying the severe 

weather condition in terms of wind speed, wind direction, fuel moisture, and spread 

direction. Near-maximum wildfire behavior is always calculated in the direction of 

maximum spread, representing a headfire. However, no standard exists for determining 

the weather condition to use, except that it is typically near, but not at, the most extreme 

values observed during the historical period. The (year-round) 97th percentile value of 

the probable maximum 1-minute average wind speed is a reasonable wind speed to use, 

applied in the upslope direction on all pixels regardless of aspect. Likewise, the 97th

percentile dead fuel moisture contents are reasonable values to use for the near-maximum 

condition. Moisture content values for live herbaceous and live woody fuel particles 

must be determined from experience. A live herbaceous moisture content of 30-45 percent, representing fully to near-fully cured grass and herbaceous fuel, and a live woody 

moisture content of 60-90 percent, should work well for the near-maximum condition.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 23

Type of Fire—The type of fire expected for the weather scenario is the most basic 

characterization of potential wildfire behavior. Geospatial fire modeling systems inherently classify type of fire into four classes: non-burnable, surface fire, passive crown 

fire, and active crown fire. However, that classification includes two very different fire 

types within the surface fire class—fires burning in a surface fuelbed with no canopy 

into which fire can move, and fires burning beneath a forest canopy because the transition criteria is not met. An updated classification may prove useful in distinguishing 

potential fire behavior (Table 3). A non-forest fire is defined as one for which there is 

no overlying forest canopy into which a fire could possibly transition. Here, we suggest 

a simple test for canopy cover to identify these situations. This type of fire will apply 

mainly to grasslands and shrublands, but areas temporarily deforested—such as young 

clearcuts or stands that recently experienced stand-replacing fire—will also be included. 

The surface fire type is then reserved for situations in which there is an overlying canopy 

in which a crown fire could develop, if conditions were amenable. Distinguishing nonforest from surface fire is a very straightforward calculation in ArcMap.

Fireline Intensity—Fireline intensity (FLI) is the rate of heat release per unit length 

of flaming fire front (kW/m), regardless of flame front depth (Byram 1959). Fireline 

intensity, also known as Byram’s fire intensity or, in Canada, frontal fire intensity, is a 

fundamental fire characteristic containing “...about as much information about a fire’s 

behavior as can be crammed into one number” (Van Wagner 1977). Fireline intensity 

is calculated from basic fire behavior and fuel particle characteristics:

FFFFFF = HH ∗ WW! ∗ RR

60 [3]

where H is the fuel particle low heat of combustion (kJ/kg), Wf is the load (mass per 

unit area) of fuel consumed in the flaming fire front (kg/m2

), and R is the linear rate of 

spread of the flame front (m/min) in the direction perpendicular to the fire front.

Rate of spread for use in Equation 3 is the linear rate of advance of the fire front in 

the direction normal to the fire front (see Catchpole and others 1982). Rate of spread 

on an actively spreading fire is estimated by observing the position of the fire front at 

different points in time. Rate of spread of surface fires (Rothermel 1972; Albini 1976) 

and crown fires (Rothermel 1991; Cruz and others 2005) can also be simulated using 

mathematical models. The overall spread rate of a wildfire, whether surface or crown, is 

simulated by combining separate surface and crown fire models into a coherent system 

(Finney 1998; Scott and Reinhardt 2001).

Table 3—An updated five-category type-of-fire classification. The original ‘surface fire’ 

category is divided into two classes: non-forest and surface fire. A non-forest fire is a fire 

burning where crown fire is not possible due to the absence of an overlying forest canopy. 

A surface fire is one for which an overlying forest canopy is present but the crown fire 

initiation criterion was not met.

FlamMap5 type of fire Additional criteria Five-category type of fire classification

Non-burnable Non-burnable

Surface fire CCa

 = 0 Non-forest

CC > 0 Surface fire

Passive crown fire Passive crown fire

Active crown fire Active crown fire

a

CC = canopy cover
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Although more difficult to conceptualize than flame length, fireline intensity is a 

more scientifically robust measure of wildfire intensity, and does not require of the 

use of separate models for surface and crown fires. Classifying fireline intensity by its 

common logarithm has proven useful for mapping and summarizing fireline intensity 

results (Table 4). The relationship between fireline intensity and flame length is illustrated in Figure 7.

Table 4—Classification of fireline intensity based 

on the common logarithm of Byram’s fireline 

intensity when measured in kW/m.

 Fireline intensity 

 range (kW/m)

Fireline intensity 

class

≥ <

3.162 I–

I

3.162 10 I+

10 31.62 II–

II

31.62 100 II+

100 316.2 III–

III

316.2 1,000 III+

1,000 3,162 IV–

IV

3,162 10,000 IV+

10,000 31,620 V–

V

31,620 V+

Figure 7—Illustration of a classification of fireline intensity based on its common 

logarithm. The chart also shows the difference between the Byram (1959) and Thomas 

(1963) relationships between fireline intensity and flame length. In geospatial modeling 

systems, the Byram model is applied to surface fires and the Thomas model is applied 

to passive and active crown fires. Note the common logarithm scaling of both axes. The 

six standard fire intensity levels (FILs) are indicated for reference. FIL6, corresponding 

to flame lengths greater than 12 feet, has been split at 50 feet.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 25

Flame Length—Flame length is often the preferred measure of fire intensity for 

its easy conceptualization; it is estimated from the more scientifically robust fireline 

intensity measure described above. Two flame-length models are used, one for surface 

fires (Byram 1959) and another, predicting longer flames for a given fireline intensity, 

is used for passive and active crown fires (Thomas 1963). The difference is predicted 

flame length for a given fireline intensity as shown in Figure 7.

Like fireline intensity, flame length is a continuous variable that must be classified 

for mapping and other applications. The Fire Program Analysis program (FPA) classifies 

flame length into six standard fire intensity levels (FILs; Roose and others 2008). These 

FILs are reasonable classes for creating a map legend, but an additional class break at 15 

m (50 ft) may be useful for distinguishing high intensity crown fires in conifer forests 

from intense surface fires in shrub fuels (Table 5).

Tools—FlamMap5 is the standard software system for generating near-maximum 

fire behavior characteristics across a fire modeling landscape. Nonetheless, a GIS is 

required to generate meaningful summaries of those results.

Wildfire Likelihood

Wildfire likelihood at a point on the landscape is measured as the annual or conditional 

burn probability (BP). Annual BP is the probability that a wildfire will burn a given pixel 

during a single calendar year. Conditional BP is the probability that a wildfire occurring 

during a specified weather condition will burn a given pixel, given that a fire does occur 

in that weather condition somewhere in the landscape. Conditional burn probabilities are 

relative, but not annual. Both annual and conditional BP are estimated across landscapes 

as the relative frequency of burning using a stochastic (or Monte Carlo) wildfire simulation system, which simulates thousands of iterations and then integrates those results. 

FSim produces annual BP results, while FlamMap5 produces conditional BP results. In 

both simulation systems, thousands to tens of thousands of iterations are simulated. An 

FSim iteration is the simulation of fire occurrence and growth on the landscape for a 

one-year period. A FlamMap5 iteration is the occurrence of a wildfire under a designated 

weather scenario. BP at a pixel is calculated as the number of iterations that resulted in 

that pixel burning divided by the total number of iterations.

Table 5—Flame length range associated with 

the six standard fire intensity levels (FILs) as 

defined by Roose and others (2008) and used 

in FSim. FIL6 is not split in FSim results; the 

split shown here is used to classify nearmaximum flame length.

Fire intensity level Flame-length range

 - - m - - - - ft - -

FIL1 0-0.6 0-2

FIL2 0.6-1.2 2-4

FIL3 1.2-1.8 4-6

FIL4 1.8-2.4 6-8

FIL5 2.4-3.7 8-12

FIL6a 3.7-15 12-50

FIL6b 15+ 50+
26 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Measures of Likelihood—Burn probability is most commonly expressed as a decimal 

fraction, theoretically varying from 0 to 1. Annual BP is generally quite a low value—

very often less than 0.01—making its expression as a fraction difficult to visualize 

or interpret. An alternative is to express BP in terms of “odds” rather than a decimal 

fraction. The odds of a wildfire occurring is the ratio of the number of iterations that 

resulted in a pixel burning to the number that did not. For example, a BP of exactly 

0.01 means that 1 out of 100 iterations burned the pixel; in odds, that is 1:99 (read as 

99-to-1 against burning). Another alternative expression of BP is the ratio of the total 

number of iterations to the number that burned the pixel, expressed as 1-in-X. For a 

BP of 0.01, the result is 1 in 100. It is important to note that the annual likelihood of a 

wildfire occurring when expressed as 1-in-X trials are not necessarily equivalent to an 

estimate of the contemporary fire return interval at that point, because these simulations 

do not address post-fire fuel and vegetation dynamics. In reality, a wildfire occurring 

this year can affect the likelihood of a wildfire occurring in subsequent years, because 

the fuelscape is modified. If fires were independent from one year to the next, then the 

odds would indeed be an estimate of return interval. Because fires are not independent, 

however, we cannot draw that conclusion. So, an annual BP of 0.001 means that the 

pixel has a 1-in-1000 chance of burning during a fire season, but we cannot conclude 

that wildfire would occur once every 1000 years.

Annual BP varies over several orders of magnitude, so a linear classification with 

equal intervals does a poor job of classifying BP across a landscape. An approximately 

geometric classification is well-suited for annual BP (Table 6). Each class break shown 

in the table is a factor of 2 or 2.5 times more frequent than the previous class break.

When using a Monte Carlo wildfire occurrence simulation system to quantify BP, a 

burnable pixel may, on occasion, not burn in any iteration, resulting in a BP value of zero. 

Truly non-burnable pixels (open water, bare ground, etc.) also are assigned a BP value 

of zero. On a map, only pixels mapped as non-burnable should be considered to have a 

BP of exactly zero, even if no iterations resulted in a burnable pixel actually burning. It 

is best to include those “burnable but unburned” pixels in the least-frequent BP class.

This discussion of BP has referred to a wildfire of any intensity occurring. Both 

FSim and FlamMap5 can sub-divide the estimate of BP by wildfire intensity class. 

FSim currently partitions BP into the six standard FILs (see Table 5). There is no way 

Table 6—A scheme for classifying annual burn 

probability expressed as a decimal fraction, as 

1-in-X trials (where X is the inverse of BP as a 

decimal fraction), and as odds. Each class break is 

2 to 2.5 times more frequent than the next; every 

third break is a full order of magnitude (10 times).

Decimal fraction 1-in-X trials Odds

0.0001 1-in-10,000 1:9999

0.0002 1-in-5000 1:4999

0.0004 1-in-2500 1:2499

0.001 1-in-1000 1:999

0.002 1-in-500 1:499

0.004 1-in-250 1:249

0.01 1-in-100 1:99

0.02 1-in-50 1:49
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 27

to reliably infer the type of fire—surface, passive crown, or active crown fire—from 

these FILs, because fires of all types can produce any of those FILs depending on fuel, 

weather and topography.

Because flame length is the distinguishing characteristic, BP by FIL may also be 

called flame-length probabilities (FLP). In FSim, these FLPs are quantified as conditional probabilities, meaning that they characterize the probability distribution among 

the FILs given that a wildfire has burned that pixel. The FLP values at a pixel sum to 1, 

and therefore can be used directly as a weighting factor for each FIL, as will be shown 

in the following section on conditional wildfire intensity.

Tools—Two fire behavior modeling systems—FlamMap5 and FSim—can be used to 

estimate BP across a landscape. FlamMap5 simulates conditional burn probability—the 

probability that a pixel will burn given that a wildfire occurs somewhere in the fire modeling 

landscape. FlamMap’s burn probability modeling is typically used to simulate short-duration 

events occurring during severe weather conditions—the so-called ‘problem-fire’ scenario. 

FSim is a more comprehensive BP modeling system that produces annual BP results by 

simulating the ignition, growth and suppression of wildfires for tens of thousands of fire 

seasons. A GIS is essential for developing inputs and generating useful summaries of BP

simulations.

Conditional Wildfire Intensity

Conditional wildfire intensity is the average intensity of the simulated wildfires that 

burned the pixel; it inherently incorporates the effects of relative spread direction (heading, 

flanking, backing, etc.), as well as any effects of variation in wind and fuel moisture, on 

wildfire intensity. Simply, if a pixel burns 10 times during a simulation, then conditional 

wildfire intensity is the average wildfire intensity of those 10 wildfires. The total number of iterations does not matter. Conditional wildfire intensity differs from measures 

of near-maximum wildfire intensity (see previous section) in that it characterizes how 

variation in weather and spread direction influence the wildfire intensity that occurs, 

rather than the near-maximum intensity that could occur. FlamMap5 and FSim permit 

calculation of two measures of conditional wildfire intensity: mean fireline intensity 

(MFI) and conditional flame length (CFL). Despite their slightly different names and 

different calculation methods, both attempt to characterize the average wildfire intensity 

after incorporating variation in relative spread direction, and, when using FSim, variation in wind and fuel moisture.

Mean Fireline Intensity—Mean fireline intensity (MFI) is the arithmetic mean fireline intensity of the iterations that actually burned that pixel. MFI is calculated directly 

within FSim as the sum of fireline intensity values for each fire divided by the number 

of fires that burn the pixel. Although fireline intensity is a more scientifically robust 

measure of wildfire intensity, it is more difficult to visualize and interpret than flame 

length due to its complex units (kW/m) and very wide range of variability—more than 

four orders of magnitude—and therefore is rarely included in a wildfire hazard assessment. Scaling fireline intensity by its common logarithm makes the distribution of 

values much simpler to interpret. There are two ways to accomplish this scaling. First, 

the common logarithm of the raw fireline intensity results can be calculated in a GIS, 

and then a legend for the resulting raster can be created in any convenient way. Second, 

without taking the common logarithm, the map legend can be generated such that the 

results fall on a common logarithm scale (Table 4).
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Conditional Flame Length—Conditional flame length (CFL) is an estimate of the 

mean flame length (FL) of the iterations that burned the pixel. CFL is not calculated 

directly by FSim or FlamMap5 and must be estimated through an expected value calculation based on the FLP values. CFL is calculated as the sum-product of FLP and 

flame length across all of the FILs

CCCCCC = FFFFFF! ∗ FFFF!

!

!!!

[4]

where FLPi

 is the conditional probability of FILi and FLi is the flame length that characterizes FILi

.

The midpoint of each FIL characterizes flame length for FIL1 through FIL5. A reasonable value must be assigned to FIL6. Table 7 illustrates the calculation of CFL for 

a hypothetical pixel, assuming a ‘mid-point’ flame length of 7.62 m (25 ft) for FIL6.

Tools—Only two fire behavior modeling systems produce information regarding 

conditional wildfire intensity—FlamMap5 and FSim. The conditional wildfire intensity outputs generated by FlamMap5 apply to the problem-fire scenario—the typically 

severe weather conditions that lead to escaped wildfires on the landscape. Although 

FlamMap5 incorporates non-heading behavior into its simulations of conditional fire 

intensity, it does not incorporate the possible effects of portions of the landscape burning under less-than-severe conditions that can occur during long-duration wildfires. In 

contrast, FSim incorporates both non-heading behavior and non-severe weather into its 

simulations of conditional wildfire intensity.

Conditional flame length is not calculated by either fire behavior modeling system 

directly, but instead must be calculated using a GIS.

4. HVRA Identification and Characterization_________________________________

This section addresses a time-consuming but critical step in the overall risk assessment process—the identification and characterization of HVRAs in the study area. Three 

primary characteristics must be determined for each HVRA identified: spatial extent 

(mapping), response to wildfire (benefit or loss), and relative importance (Figure 8). 

Table 7—Illustration of the calculation of conditional flame 

length (CFL) from the conditional probability of each fire 

intensity level occurring and the midpoint flame length of 

each FIL. There is no midpoint of FIL6, so an arbitrary value 

must be used. Here, that value is 7.62 m (25 ft).

Fire intensity Flame-length Mid-point

level probability flame length

(i) (FLPi

) (FLi)

 - - m - - - - ft - -

FIL1 0.0 0.33 1

FIL2 0.1 0.91 3

FIL3 0.2 1.52 5

FIL4 0.4 2.13 7

FIL5 0.3 3.05 10

CFL 2.16a 7.1

a

 Example: CFL = (0.0×0.33) + (0.1×0.91) + ... + (0.0×7.62) = 2.16 m
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 29

The wildfire hazard results (Section 3) will be combined with the HVRA characteristics described in this section to perform an exposure analysis (Section 5) and an effects 

analysis (Section 6). This section provides details about the identification and characterization of HVRAs.

HVRA Identification

Identifying which HVRAs to include in the assessment is a critical step in the overall 

risk assessment process. A key criterion is to limit inclusion of resources and assets to 

those that are highly valued. We recognize mandates for multiple use management and 

the presence of competing landscape objectives, but nevertheless the scope of analysis 

may need to be reined in to keep the assessment and interpretation of results manageable. 

Further, as will become clear later, as more HVRAs are included in the assessment their 

overall contribution to the weighted risk score will diminish. It is of course possible to 

perform more refined analyses of specific resources and assets outside of this landscape 

assessment process.

 The HVRA identification process can proceed hierarchically, by first naming primary 

HVRA categories followed by articulation of a series of sub-HVRAs. For instance, 

habitat can be the primary HVRA, with sub-HVRAs defined as the habitat for various 

individual species or species groups. Critical infrastructure is another example, with 

sub-HVRAs of telecommunication sites, power lines, fire lookouts, etc. Often primary 

HVRAs will include the wildland urban interface (WUI), other built structures, and 

municipal watersheds. HVRAs may also be more ecologically oriented and relate to 

vegetation structure and assemblage. In most cases there will be a range of socioeconomic, natural, and cultural HVRAs included. Quantitatively, all calculations are done at 

the sub-HVRA level. The hierarchical structure is simply a convenient way to organize 

and summarize a long list of HVRAs.

Figure 8—HVRA characterization is a primary component of the overall 

risk assessment process that produces geospatial data regarding where 

highly valued resources and assets (HVRAs) are found on the landscape, 

and tabular data regarding how the HVRAs respond to wildfire as well as 

their importance relative to one another.
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Several guiding documents exist to assist in the selection of HVRAs for analysis. 

The appropriate Land and Resource Management Plan (LRMP; also called a Forest 

Plan) and Fire Management Plan (FMP)—both of which provide explicit direction on 

fire management objectives and incident responses—often list resources and assets to 

protect or enhance. Also, a Wildland Fire Response Guidebook (also called a Forest 

Response Guidebook) is another useful source of information about resources and assets 

to protect from wildfire. The process of selecting HVRAs should include consultation 

with geospatial analysts and resource specialists. Ultimately the selection of which 

HVRAs to include is determined or approved by the leadership team of the land management unit. Risk to an HVRA cannot be assessed if there is insufficient geospatial 

data representing the HVRA. Ideally there exists wall-to-wall coverage across the study 

area with well documented metadata for every HVRA, but that is not always the case. 

There are a number of geospatial data management issues to address when identifying 

and mapping HVRAs.

Spatial Extent (Mapping)

For use in wildfire risk calculations, spatial HVRA data, ultimately, needs to be in 

raster format. Ideally, the raster data should match the extent, cell size and coordinate 

system of the fire modeling landscape. This may require any of a number of geoprocessing tasks, such as: converting feature class data (points, lines, or polygons) to raster 

format, re-sampling existing raster-format data to a different cell size, or re-projecting 

to a different coordinate system. Due to limitations on the spatial accuracy in HVRA 

mapping and fire modeling, it may be necessary to include a small buffer around point 

and line features to ensure they are adequately represented in the assessment; it may be 

undesirable for a point HVRA (a communication site, for example) to be mapped to a 

single grid cell, especially if the accuracy of the point is small compared to the cell size. 

Including a buffer size will increase the exposure of the HVRA to wildfire as measured 

by expected area burned, but otherwise the buffer simply increases the sample size for 

estimating fire behavior and effects. However, the relative extent factor in the weighted 

risk calculations counterbalance any increases in mapped HVRA area, so adding a buffer does not bias the overall wildfire threat. The apportionment of relative importance 

according to the mapped extent of an HVRA not only provides a more equal footing 

for comparing risk across HVRAs with vastly different spatial extents, but it can also 

alleviate issues where some HVRAs are mapped much more generously than others, 

as will be discussed below.

Additional data challenges present themselves in wildfire risk assessments that span 

administrative and geographic boundaries, including differing data standards used to 

map HVRAs across adjacent jurisdictions, and distinguishing between data “gaps” and 

true HVRA absence when combining disparate data sets. Data integration and quality 

control are essential ‘behind the scenes’ components of the HVRA characterization 

process, typically the responsibility of GIS specialists and geospatial analysts in consultation with resource specialists.

The compilation of HVRA data entails collecting data from various sources. Local 

data sources are often the most up-to-date and reflect local knowledge of the landscape. 

A variety of regional or national data sources could prove useful, such as certain critical infrastructure layers within the WFDSS, the Forests-to-Faucets dataset for surface 

drinking water quality, and the SILVIS Lab (Radeloff and others 2005) and Residentially Developed Populated areas (RDPA; Haas and others 2013) datasets for the WUI. 

LANDFIRE data products are useful for characterizing and mapping vegetation structure 

and other biophysical variables. Due to the rapidly changing availability and quality of 

geospatial HVRA data, we will not attempt to catalog all potential sources for all HVRAs.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 31

The characterization of HVRA susceptibility to wildfire can sometimes be improved 

by incorporating additional environmental or landscape characteristics. For instance, 

in past assessments measures of erosion potential such as slope and soil type have 

been included as variables in response functions for municipal watersheds and aquatic 

habitat. Though the definition of response functions is a different component, it can be 

helpful to begin to think through these issues early in the process, and to consider what 

additional geospatial data may be required.

Although the risk calculations require HVRA data in raster format, any data originally available as a shapefile or feature class should be retained in that format for use 

in exposure analyses requiring an HVRA to be represented by vector data.

Response to Wildfire

Simulating the response of HVRAs to wildfire is a challenging task subject to substantial uncertainty. Two considerations when characterizing the response of an HVRA 

to wildfire are (1) the time horizon of the analysis and (2) the notion of HVRA-specific 

fire effects. The framework represents an assessment of current landscape conditions 

and wildfire risk. Successional processes, disturbances, climate conditions, and a host 

of other dynamic landscape processes that may take place in the future are not included 

in this modeling effort; projecting from this assessment to long-term (cumulative) fire 

effects may not be appropriate. This limitation complicates matters for assessment of 

ecological fire effects, but does not obviate the importance of including such ecological 

effects within risk calculations. Similarly, it is inappropriate to define response functions that incorporate future fire risk reduction through fuel treatment effects. Those 

consequences can be evaluated in future analyses with updated fuel conditions, or in 

a comparative framework to analyze fuel treatment scenarios. Rather, response functions are designed to target the susceptibility of individual HVRAs to fire effects—the 

bottom leg of the risk triangle—not likelihood or intensity (Figure 1). Simply put, this 

framework can be used to monitor, but not model, temporal trends in wildfire risk.

The response function framework we describe here requires quantifying the relationship between HVRA-value and wildfire intensity (measured by flame length). We relate 

HVRA response to fire intensity because it is the best fire characteristic available for 

relating to fire effects, and because it integrates two important fire characteristics—fuel 

consumption and spread rate. This approach quantifies net value change (NVC) to a 

given HVRA as the percentage change in the initial resource value resulting from a fire 

at a given intensity. Response functions address relative rather than absolute change in 

resource or asset value, and represent both beneficial and adverse effects to the HVRA. 

It is possible to incorporate additional variables into response functions, subject to the 

quality and availability of spatial data to map the additional variables. For instance, in 

previous analyses impacts to watersheds were differentiated according to factors influencing post-fire erosion risk (Thompson and others 2013a, b). More in-depth analysis 

could consider additional modeling to augment or substitute for response function 

definitions, for instance fire effects on tree mortality (Ager and others 2007, 2010) or 

post-fire debris flow modeling (Cannon and others 2010).

The approach we promote here is based on using the best judgment of experts as a 

substitute for mathematical fire effects models, which may be inadequate, difficult to 

parameterize, or nonexistent (Hyde and others 2013). Typically, this elicitation will occur 

in a workshop setting, and incorporates input and feedback from several individuals. 

Factors influencing implementation of expert-based approaches include identification 

of: the type of information to be elicited, the most appropriate experts, and the best 

way to encapsulate and elicit expert information. Further driving many applications are 

practical considerations relating to available resources and timelines. Below we outline 
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

an eight-step process for eliciting expert judgment, premised largely on frameworks 

presented by Knol and others (2010), Kuhnert and others (2010), and MacMillan and 

Marshall (2006). The process employs multiple experts, seeks expert consensus, and 

provides multiple opportunities to refine HVRA-specific response function definitions.

Step 1: Articulate the Research Question—Articulation of the research question will 

frame the study design, the collection and aggregation of relevant data, and ultimately 

the structure of the modeling approach. The research question in the wildfire context is 

how to characterize wildfire effects to HVRAs, and how to integrate that information 

with wildfire simulation modeling outputs in order to characterize wildfire risk. To align 

with the wildfire risk assessment framework of Finney (2005), characterization of fire 

effects should be both spatially explicit and quantitative.

Step 2: Identify and Characterize Relevant Uncertainties—Identifying and characterizing uncertainties affect choices about the type of information to elicit, the type of experts 

to engage, and the format of the elicitation. Formally identifying and characterizing the 

uncertainties can prove very useful for distinguishing between the types of uncertainties 

faced and for identifying appropriate approaches for managing uncertainty (Ascough and 

others 2008; Thompson and Calkin 2011). Limited understanding of fire effects can be 

a key source of knowledge uncertainty in the wildfire management context. This type of 

uncertainty can be best managed with expert input as opposed to, say, variability in fire 

occurrence and spread, which is better managed with probabilistic wildfire modeling.

Step 3: Resolve the Scope and Format of Elicitation—This step entails identification 

of the number of experts to engage and the nature of the engagement (interview, group 

workshop, survey distribution, etc.), while considering available resources and other 

constraints. Due to time constraints, a one- or two-day group workshop is appropriate. 

Establishment of expert sub-groups based upon alignment of relevant expertise with 

HVRA and sub-HVRA layers, where feasible, is recommended.

Step 4: Select the Expert(s)—Given the choice to employ multiple experts (Step 3), 

the task in this step becomes identification of the appropriate type and balance of experts, given availability and other constraints. Types of experts can include generalists, 

subject-matter experts, and process experts. In this context, generalists span a range of 

professions, including fire management officers, fuels specialists, fire planners, foresters, 

silviculturists, and district rangers. Specialists, by contrast, are HVRA-specific, including 

wildlife biologists, fisheries biologists, soil scientists, and forest hydrologists. Process 

experts have experience in risk assessment and group facilitation.

Step 5: Design the Expert Judgment Elicitation Protocol—Protocol design considers the intended use of the elicited information, how to best encapsulate that information, and how to best elicit that information. In this context, HVRA-specific response 

functions output net value change as a function of fire intensity and potentially other 

spatial variables. These response functions are then used to summarize risk across the 

landscape and across HVRAs (see Equations 1 and 2).

Step 6: Prepare the Elicitation Protocol—A key component to preparing for expert 

elicitation is the provision of background information to experts. This background information should outline the issue at hand, identify relevant uncertainties, and provide 

details about the elicitation procedure. Informative brochures that introduce and outline 

the process can be useful. It can also be useful to distribute surveys to experts ahead of 

time to inquire about factors affecting how each HVRA responded to wildfire, and to 

prime the pump, so to speak. One such survey used in the past queried experts regard-
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 33

ing three key factors: wildfire characteristics, HVRA characteristics, and landscape 

characteristics. The surveys served to introduce experts to the mental process of evaluating fire effects, and further to help identify potential variables to include in response 

function definitions.

Step 7: Elicit Expert Judgment—As previously described, the elicitation protocol 

presented here is based around a group workshop format, and involves multiple individuals that can vary by nature of expertise (generalist and subject-matter experts). The 

workshop is premised on consensus-based definition of quantitative HVRA-specific 

response functions. The workshops typically begin by reviewing the definition of HVRA 

layers, the initial feedback from survey responses, and the role of response functions in 

the risk assessment framework. Then the process of assigning response functions can 

proceed, and, if applicable, the group can be broken down according to HVRA subgroups. Including documentation for rationales behind response functions is a critical 

piece of information. This documentation can include references to published literature, 

other models, and observations, in addition to expert opinion.

Step 8: Provide Feedback and Refine—Post-elicitation feedback provides an opportunity for critique and evaluation from the larger group of experts, can encourage 

reflection of engaged experts, and allows for iterative revision or refinement of stated 

expert judgment. This process will begin by having experts present their response function 

definitions and justifications, followed by critique and debate, and where appropriate, 

modification of response function definitions.

There exist other approaches, which in some cases seek to explicitly quantify the level 

of agreement or disagreement between experts (Marcot and others 2012). There also 

exist a multitude of tools, datasets, and other sources that experts can turn to for help 

in thinking through response function definitions. Peterson and others (2007) provide 

an extensive list, including the Fire Effects Information System (FEIS), CONSUME, 

the First Order Effects Fire Model (FOFEM), the Fire and Fuels Extension–Forest 

Vegetation Simulator (FFE-FVS), and the Wildlife Habitat Response Model (WHRM). 

Selection of the appropriate framework for eliciting and encapsulating expert opinion, 

and more broadly for characterizing fire effects, will vary by planning context and assessment needs.

Relative Importance

Balancing competing or conflicting land and resource management objectives is 

a significant challenge to land and resource management planners. Likewise, it is 

also difficult to articulate quantitative weights establishing the relative importance of 

HVRAs. This step is not necessary when assessing wildfire risk to a single HVRA such 

as municipal watersheds or the WUI. It is only when attempting to combine the risk to 

multiple overlapping HVRAs, or when comparing risk among several HVRAs, that the 

issue of weighting arises. Even then, the task can be avoided altogether by assuming that 

each pixel of each HVRA is of equal value, or weight. With that assumption, however, 

over-mapping an HVRA will overstate its risk, and extensive HVRAs will always be 

shown to have greater effects than HVRAs that cover a small amount of land area. There 

are some strong tradeoffs to consider before avoiding the step of articulating relative 

importance. Without relative importance, how does one characterize risk in areas where 

multiple HVRAs overlap? How does one compare risks across different spatial areas 

that house different HVRAs? Using relative importance scores helps to address all of 

these questions, and allows for summarization and visualization of risks in a single 

metric. Further, if assessment results are to ultimately be used for planning mitigation 

treatments and strategies, then prioritization decisions that integrate all HVRAs will still 
34 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

ultimately need to be made. Articulating relative importance scores and how objectives 

are balanced makes this decision explicit rather than implicit, and increases the overall 

transparency of decision processes (Marcot and others 2012).

There are a number of places to turn for guidance in articulating relative importance 

scores, with which leadership will likely be well familiar. These include the USDA 

Strategic Plan, the USDA Forest Service Strategic Plan, national fire policy documents, 

regional restoration strategies, LRMPs, FMPs, and a host of other documents, regulations and laws. Principally LRMPs and FMPs will contain the most location-specific 

information on HVRAs and management objectives.

As with response function definition, a workshop is a recommended setting for 

eliciting relative importance. The purpose of this workshop is to establish quantitative 

weights that differentiate the importance of HVRAs and sub-HVRAs. Absent the ability to work closely with a resource economist (or team of economists) to monetize all 

HVRAs, the use of multi-criteria decision analysis is recommended. Below we present 

a type of multi-criteria decision analysis that is effectively the Simple Multi-Attribute 

Rating Technique, or SMART (Kajanus and others 2004), although a variety of other 

multi-criteria decision analysis techniques could be used (Ananda and Herath 2009). 

Relative importance weights are assigned according to a three-step process (below), 

which proceeds first across HVRA categories, and then hierarchically across sub-HVRAs 

within an HVRA category. Rank HVRAs and sub-HVRAs according to importance to 

the group conducting the assessment.

1. Provide qualitative justification for rankings, and their relation to existing guidance/doctrine/policy.

2. Assign top-ranked HVRA and sub-HVRA a score of 100; assign all other HVRAs 

and sub-HVRAs relative importance scores on scale of 0-100. Relative importance scores can also be converted into percentages of overall importance across 

HVRAs and across sub-HVRAs within a given HVRA category.

3. Review, critique, and refine scores (iterative for both HVRAs and sub-HVRAs).

Effects analysis is conducted at the pixel level, so the weighting for relative importance 

of each HVRA must, ultimately, also pertain to a unit area. However, it is easier to first 

elucidate the importance of an HVRA as a whole, as described above, and then divide 

by its extent. The relative importance score for a particular HVRA is spread evenly over 

all pixels of that HVRA for use in the effects analysis calculations. Thus, for HVRAs 

with a very broad extent, the relative importance per individual pixel might be very 

low; for less extensive HVRAs the relative importance per pixel might be very high.

Consider a stylized example landscape with two HVRAs (Communication sites and 

Watersheds). The Communication Sites HVRA, taken as a whole, has been assigned 

a relative importance of 50 percent of Watersheds HVRA, meaning that two-thirds of 

the overall importance (100/150) is allocated to watersheds and the remaining one-third 

to Communication sites (Table 8). The communication sites HVRA covers a relatively 

small land area compared to the watersheds HVRA, meaning that it has a smaller relative extent (RE). In our framework, the relative importance is distributed across each 

Table 8—Hypothetical example of the relative importance (RI) and relative extent (RE) calculations. The 

ratio of RI to RE is used as a weighting factor when combining HVRA results.

 Relative importance Share of importance Relative extent

HVRA (RI) (percent) (RE) RI/RE

Communication sites 50 33.3 2 25

Watersheds 100 66.7 100 1
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 35

HVRAs extent, so RI divided by RE is the appropriate weighting factor. Ultimately, 

the RI/RE in this example is greatest for the communication sites because they cover so 

little land area. In contrast, a unit area of watershed has just 1/25th the importance per 

unit area of the communication sites, despite the fact that watersheds, taken as a whole, 

were assigned twice the importance of the communication sites.

5. Exposure Analysis ______________________________________________________

Exposure analysis is the characterization of wildfire likelihood and intensity where 

HVRAs occur (Figure 9). Modeling of wildfire likelihood and intensity was covered in 

Section 3; identifying where HVRAs occur—their spatial extent—was covered in Section 4. Exposure analysis can be performed within a GIS using one of several geospatial 

techniques that identify or summarize the wildfire hazard characteristics of all pixels 

where an HVRA is mapped (a ‘Sample’ in ESRI’s ArcMap), or a calculation of descriptive statistics that summarize the hazard characteristics (‘Zonal Stats’ in ArcMap). The 

ArcFuels Toolbar for ArcMap (Vaillant and others 2013) also performs these functions.

In an exposure analysis, wildfire hazard can be characterized for an HVRA using 

several different summaries. First, a histogram or box plot of a wildfire hazard characteristic—BP, MFI and CFL are common measures in an exposure analysis—illustrates 

the distribution of these characteristics across the HVRA or within specific units of an 

HVRA. For example, Scott and others (2012b) compared box plots of BP, MFI and 

integrated wildfire hazard (the product of BP and MFI) for ten municipal watersheds 

on the Beaverhead-Deerlodge National Forest, Montana (Figure 10).

Figure 9—The exposure analysis component of the overall wildfire risk assessment process uses outputs 

from the wildfire simulation and HVRA characterization components to generate tabular and graphical summaries 

of wildfire characteristics where HVRAs occur, optionally summarized by reporting units. A reporting unit is an 

administrative unit (Forest or Ranger District, for example) or other geographic area such as a hydrologic unit.
36 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Figure 10—Box plots of BP, MFI, and integrated hazard (BP * MFI) for all 10 municipal 

watersheds, sorted by mean integrated hazard. Box plots indicate the quartiles (the box), 

10th/90th percentiles (whiskers), median (black line), mean (thick grey line), and individual 

values outside the 10th/90th percentiles (dots). The BP box plots also indicate the probability 

that a wildfire will reach any portion of the watershed (diamond) (reproduced from Scott 

and others 2012b).

Because integrated wildfire hazard is BP × MFI, it is amenable to being plotted on a 

single chart as a scatterplot, with reference lines delineating zones of equal integrated 

hazard. Because BP and MFI can vary over several orders of magnitude, it may be 

convenient to generate this plot on a log-log scale, as Scott and others (2012b) did for 

two contrasting watersheds (Figure 11). Such scatterplots are particularly useful for 

identifying HVRAs with a greater tendency to experience fire or to experience fire of 

a given intensity level (Ager and others 2012).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 37

Expected annual HVRA area burned is another characteristic that can be identified 

during an assessment of HVRA exposure to wildfire. Expected annual HVRA area burned 

is calculated as the product of HVRA-mean BP and burnable HVRA area; it represents 

the average amount of HVRA area burned in the wildfire simulations. Expected annual 

watershed area burned for the example of municipal watersheds is shown in Table 9.

6. Effects Analysis ________________________________________________________

Effects analysis integrates wildfire hazard (likelihood and intensity) and HVRA 

vulnerability (exposure and susceptibility), producing a comprehensive measure of 

wildfire risk (Figure 12). We quantify wildfire risk as the weighted expectation of net 

value change [E(wNVC)], where NVC is expressed in relative terms on a percentage 

basis, as defined by expert-based loss/benefit functions (for example, complete loss = 

-100 percent). The ArcFuels Toolbar for ArcMap (Vaillant and others 2013) provides 

some functionality for applying response functions.

A given pixel on the landscape can present risk to multiple HVRAs. Equation 1 

provided the formula for calculating E(NVC) for a single pixel of a single HVRA. To 

provide an integrated measure of wildfire risk across multiple HVRAs, a weighting 

scheme that includes the relative importance (RIj

) and relative extent (REj) of each 

HVRA must be incorporated; this formulation was presented in Equation 2. The overall relative importance score (RIj

), which applies to the HVRA as a whole, is divided 

by REj

 in order to allocate the importance across the area where the HVRA occurs. 

Incorporating the relative extent of HVRAs (for example, number of grid cells covered 

by the HVRA) distributes relative importance across HVRAs with a large number of 

mapped pixels HVRAs with few mapped pixels can have a high importance per pixel 

even if their overall importance is not high.

Figure 11—Wildfire hazard characteristics chart for two contrasting municipal 

watersheds. Gray dots represent the watershed with the lowest integrated 

hazard; black dots represent the watershed with the highest integrated hazard 

(reproduced from Scott and others 2012b).
38 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Table 9—Summary of pixel-based wildfire hazard characteristics within each of 

the ten municipal watersheds on the Beaverhead-Deerlodge National Forest. 

Expected annual area burned (column d) is the product of watershed-mean 

burn probability (column b) and the burnable watershed area (column c). 

Expected annual area burned as a fraction of the burnable watershed area is 

therefore equivalent to the watershed-mean burn probability (adapted from 

Scott and others (2012b).

(a) (b) (c) (d)

Watershed Mean burn Burnable watershed Expected annual

 probability area area burned

 ha ha/yr

A 0.0001628 10,093 1.64

B 0.0001634 3,115 0.51

C 0.0004873 521 0.25

D 0.0013794 722 1.00

E 0.0009767 2,494 2.44

F 0.0004720 5,930 2.80

G 0.0008151 1,580 1.29

H 0.0002570 1,264 0.32

I 0.0009899 1,303 1.29

J 0.0001347 1,330 0.18

Figure 12—The effects analysis component of the overall risk assessment process draws from outputs 

from wildfire simulation and HVRA characterization components to produce the most comprehensive 

measures of wildfire risk. Like the exposure analysis, the effects analysis results can be summarized by 

reporting units. A reporting unit is an administrative unit (forest or ranger district, for example) or other 

geographic area such as a hydrologic unit.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 39

7. Interpreted Example: Bridger-Teton National Forest_________________________

In this section we will illustrate each component of a comprehensive geospatial 

wildfire risk assessment conducted for the Bridger-Teton National Forest (BTNF), 

located in far western Wyoming. This example application originated from the Tetons 

Interagency Risk Assessment (TIARA), an interagency assessment of wildfire risk to 

HVRAs within Grand Teton National Park and the BTNF (Scott and others 2013). In 

that assessment, each agency first identified a suite of HVRAs to be analyzed, and then 

characterized the susceptibility and relative importance of those HVRAs according to 

the assessment process outlined by Thompson and others (2013a). For simplicity, this 

example focuses on the BTNF portion of the assessment.

A wide range of fuel types, biophysical settings, and vegetation types occur within the 

3.5 million acre BTNF. The valley-bottoms, at roughly 2,000 m elevation, are covered 

by grasslands and grass mixed with sagebrush (Artemisia tridentata). The highest peaks 

in the study area exceed 3,600 m; the terrain above 3,000 m typically does not support 

wildfire spread due to the prevalence of rock and persistent snow. The slopes between 

the valley-bottoms and the peaks are covered by coniferous forests, montane meadows, 

and stands dominated by quaking aspen (Populous tremuloides).

Wildfire Simulation

Fire Modeling Landscape—For the assessment, we used LFDAT to download and 

process LANDFIRE version 1.0.5 (Refresh 2001) datasets of fuel, vegetation, and topography. The landscape extent included a minimum buffer of 10 miles from the National 

Forest and National Park administrative boundaries (Figure 13). This buffer allows 

for the simulation of wildfire spread onto the study area from adjacent land without 

introducing an artificial “edge effect.” Using LFTFC, we worked with local resource 

specialists to critique and update vegetation characteristics and the associated surface 

fire behavior fuel model mapping rules. We also updated the fire modeling landscape 

to reflect wildfire and insect disturbance for the time period of 2001 through 2010. The 

end result is a fire modeling landscape current as of the 2011 fire season.

A summary of surface and canopy fuel characteristics was made by finding 

the unique combinations of fuel model, canopy base height, canopy bulk density, 

canopy cover, and vegetation system group physiology (hereafter called vegetation 

system). Vegetation system is an attribute of LANDFIRE’s Existing Vegetation Type 

dataset, and reflects the type of vegetation currently present at each landscape pixel. 

Pivot tables were used to summarize the distribution of land area covered by different surface fire behavior fuel models (Scott and Burgan 2005) among the various 

vegetation systems, in three separate tables: non-burnable areas (Table 10), which 

comprise 10 percent of the landscape; burnable areas with no overlying forest canopy 

(Table 11), which comprise 54 percent of the entire landscape; and burnable areas 

with a forest canopy present (Table 12), which comprise 36 percent of the entire 

landscape. The areas mapped to fuel model NB9 (bare ground) in the grassland and 

shrubland vegetation systems occur because the fuel conditions in those areas do 

not support fire spread; they were found primarily high in the mountains adjacent 

to non-vegetated systems.

Burnable areas with no forest canopy were identified as burnable pixels where canopy 

cover = 0. Fuel models GR1, GS2, and SH1 cover more than 80 percent of such areas; 

GS2 alone covers 41 percent of the burnable non-forested area (Table 11). Similarly, 

TU1 and TL3 cover 62 percent of the forested portion of the landscape, and TU1 alone 

covers 41 percent (Table 12). Less than 10 percent of the forested landscape is covered 

by TU5.
40 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Figure 13—Overview of the 5.8 million ha (14.4 million acre) Teton Interagency Risk Assessment 

landscape (rectangle in center of figure). The study area includes the Bridger-Teton National Forest 

(shown in crosshatch) and Grand Teton National Park, plus a minimum buffer of 16 km (10 mi).

Table 10—Distribution of land area (ha) covered by non-burnable fuel models (Scott and Burgan 2005) by vegetation system on 

the Teton Interagency Risk Assessment landscape. Ten percent of the entire landscape is non-burnable. Fuel model NB1 refers 

to developed urban areas that cannot support wildfire spread’ NB2 refers to permanent snowfields or glaciers; NB3 refers to 

non-burnable agricultural land; NB8 refers to open water; and NB9 refers to land with insufficient wildland fuel to support 

wildfire spread.

 Sparsely

Fuel model Agricultural Developed Non-vegetated vegetated Grassland Shrubland Total

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - hectares - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

NB1 Urban 16,226 16,226

NB2 Snow/ice 7,130 7,130

NB3 Agricultural 169,452 169,452

NB8 Open water 71,941 71,941

NB9 Bare ground 600 82,270 11,605 213,357 7,084 314,916

TOTAL 169,452 16,826 161,341 11,605 213,357 7,084 579,665
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 41 Table 11—Distribution of land area (ha) by fuel model (Scott and Burgan 2005) and vegetation system for areas with no overlying forest canopy cover on the Teton Interagency Risk Assessment fire modeling landscape (54 percent of the landscape). Fuel Exotic Exotic model Agricultural Developed Grassland herbaceous Shrubland tree-shrub Hard-wood Riparian Conifer Total - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - hectares - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - GR1 684 321,121 33,127 266,449 171 621,553 GR2 9,832 148,650 1,247 237 236 160,204 GR4 2 2 GS1 11,325 488 80,797 369 92,977 GS2 11 1,286,930 286 1,287,228 SH1 641,599 641,599 SH2 155,998 19 1,538 436 157,991 SH5 1,160 1,160 SH7 61 714 230 1,005 TU1 67,722 5 67,726 TL1 76,401 21 76,423 TL3 58,120 55 58,175 TL8 30 30 SUM 10,517 11,325 546,672 34,375 2,490,213 1,874 19 69,260 1,817 3,166,071
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Table 12—Distribution of land area (ha) by fuel model (Scott and Burgan 2005) and vegetation type for areas 

with an overlying forest canopy (36 percent of the Teton Interagency Risk Assessment landscape).

FBFM Developed Conifer-hardwood Hardwood Riparian Conifer Grand total

GR1 102 4 165,154 165,260

GR2 116 17 133

GS1 1,620 166 321 947

GS2 130 40,672 50,743 91,544

SH1 117 117

SH2 21,634 16,381 38,015

SH7 151 151

TU1 890 67,983 195,167 38,435 549,505 851,981

TU2 2,437 154,358 156,795

TU5 11,807 155,928 167,735

TL1 195 640 3,990 4,825

TL3 4 425,871 425,875

TL4 645 102,191 102,835

TL5 63,396 63,396

TL6 1,751 1,751

SUM 2,641 105,025 196,460 79,229 1,688,005 2,071,360

Summary statistics were calculated for canopy base height and canopy bulk density 

in the forested areas of the landscape. Canopy base height averaged 2.0 m across the 

forested landscape, but averaged 1.4 m in the conifer vegetation types and 1.5 m in the 

conifer-hardwood types (Table 13). Canopy bulk density averaged 0.06 kg/m3

 across the 

forested landscape, but was slightly higher in the conifer types; the maximum canopy 

bulk density (0.45 kg/m3

) also occurred in the conifer vegetation types (Table 13).

The fuel and vegetation rasters were generated using LFTFC, then a fire modeling 

landscape file (LCP) was generated from these fuel and vegetation layers, plus slope, 

aspect, and elevation rasters, using LFDAT.

Historical Weather—In this section we illustrate a few basic summaries of the historical weather information gathered for the TIARA: (1) percentile values of ERC-G

and 1-hr moisture content (MC), (2) Seasonal ERC-G chart, and (3) 6-m (20-ft) wind 

speed and direction.

Table 13—Summary statistics for canopy fuel characteristics (canopy base height and canopy bulk density) in forested 

areas of the landscape.

- - - - - - - - - - - - - - - - - - - - - - - - - Cover type - - - - - - - - - - - - - - - - - - - - - - - - - - -

 Conifer/ Land-scape

 Statistic Developed hard-wood Hard-wood Riparian Conifer mean

canopy Min 2.0 0.4 1.6 0.1 0.0 0.0

base Mean 3.1 1.5 7.2 2.8 1.4 2.0

height Max 4.9 10.0 10.0 10.0 10.0 10.0

(m)

canopy Min 0.01 0.01 0.01 0.01 0.01 0.01

bulk density Mean 0.06 0.06 0.01 0.05 0.07 0.06

(kg/m3

) Max 0.08 0.30 0.01 0.34 0.45 0.45
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 43

After evaluating several weather stations, we selected the Raspberry RAWS as most 

representative of the entire landscape because it was somewhat centrally located, its 

ridgetop location was not subject to valley-influenced wind directions, and its elevation 

was more representative of the landscape than most other RAWS, which tended to be 

located in the valley bottoms. All of the summaries presented in this section were made 

from information contained in the so-called ‘FRISK file’ (for ‘fire risk’) generated by 

the FlamMap Fire Risk Export feature of FireFamily Plus. The fire danger fuel model 

at Raspberry RAWS was already set to fuel model G, so no adjustment was necessary. 

Before generating the FRISK file we replaced the 10-minute average wind speed in 

the database with the probable maximum 1-minute average with an update query using 

database management software. From the FRISK file we identified a few percentile 

values of ERC-G and dead 1-h fuel moisture content (Table 14).

These percentile values are useful both on their own and for generating the remaining weather summaries. In the near-maximum fire behavior section below it will be 

necessary to know the 97th percentile dead fuel moisture content values for all three size 

classes of dead surface fuel. From the same FRISK file, the 97th percentile 10-h timelag 

moisture content value was determined to be 

4 percent, and the 100-h timelag moisture 

content was 8 percent.

Figure 14 depicts the seasonal trend in 

ERC-G at the weather station. Both the 

daily mean value and the mean plus two 

standard deviations are shown, with the 

80th, 90th and 97th percentile ERC-G values 

indicated. Note that the mean ERC-G value 

barely reaches the 80th percentile value 

even at its highest.

Table 14—Percentile values of ERC-G and 

dead 1-h timelag moisture content as 

determined from the FRISK file for the 

Raspberry RAWS, 1999-2010.

 Dead 1-h timelag 

 moisture content 

Percentile ERC-G (percent)

80th 49 5.0

90th 56 4.2

97th 69 2.9

Figure 14—Seasonal trend in daily mean ERC-G and mean plus two standard deviations 

for the Raspberry RAWS, as calculated in FireFamily Plus and summarized in the FRISK 

file (data years 1990 – 2010). For reference, the 80th, 90th, and 97th percentile values 

of ERC-G (see Table 14) are shown.
44 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

The next summary from the FRISK file is the joint distribution of wind speed and 

direction. Such joint distributions are most often displayed as a wind rose (Figure 15). 

The wind rose is an excellent chart for showing the dominance of wind direction, but 

can be challenging to use for judging the relative frequency of wind speed.

Mathematical models capable of adapting the landscape-average wind speed and 

direction (or the wind speed and direction at a specific point on the landscape) to wind 

speed and direction across an entire landscape based on topography and vegetation 

cover—Wind Wizard and Wind Ninja—are now available. The use of such terrainadapted winds has proven useful for simulating fire growth using the FARSITE and 

FlamMap5 systems, as well as within WFDSS. However, their application in wildfire 

hazard assessment is challenging. Two wildfire hazard modeling applications could 

potentially use terrain-adapted winds: modeling burn probability and conditional fire 

intensity using Monte Carlo simulation, and modeling the near-maximum potential 

wildfire behavior. The FSim fire occurrence and growth simulation system could theoretically use terrain-adapted winds, but it currently does not have that capability—it 

would need more than 40 wind simulations, one for each combination of wind speed 

and direction. Using terrain-adapted winds for modeling near-maximum fire potential 

is problematic because, unless all directions are simulated, the selected wind direction 

may result in far lower than maximum wind speeds on certain aspects

For simulating the near-maximum fire behavior it will be necessary to estimate the 

97th percentile 6-m (20-ft) wind speed (probable maximum 1-minute average) occurring during the typical burning period of the typical fire season. This was estimated by 

Figure 15—Wind rose constructed from the joint distribution wind speed 

and direction during the calendar month of August, 1200 – 2000 hours, 

at the Raspberry RAWS (data years 1990 – 2010). The wind rose clearly 

shows the relative frequency of wind directions, but does not indicate the 

relative frequency of wind speeds well. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 45

generating a daily listing of the probable maximum 1-min average wind speed for every 

day of the hourly data record (1990-2010 in this case). In Excel, the dataset was filtered 

to include only the months May – October and only the hours between noon and 8 pm. 

The 97th percentile value was calculated from this filtered dataset. The result was 32 

km/h (20 mi/hr). This wind speed was exceeded only 3 percent of the time during the 

historical period (within the fire season months and burn period hours).

Historical Fire Occurrence—Three summaries illustrate historical fire occurrence 

within the TIARA fire modeling landscape. The first fire occurrence summary is a set 

of charts showing the acres burned by wildfires of different sizes. Figure 16 plots the 

cumulative land area burned over the historical period (normalized so that the total land 

area burned = 100) against the cumulative number of fires during the period, sorted 

from smallest to largest. Line coloring indicates fire size class. Size class A and B 

fires (fires up to 4 ha final size) account for almost 90 percent of all fires, but less than 

1 percent of the land area burned. The large black dots on the chart indicate the land 

area burned by fires of the 90th, 95th, 98th, and 99th percentiles by size; those results are 

also tabulated in Table 15. A pair of histograms—one for number of fires and another 

for acres burned—also shows the asymmetrical distribution of the number of fires and 

area burned by fires of different size classes (Figure 17).

Figure 16—Cumulative number of historical fires on the X-axis (sorted from 

smallest to largest and expressed as a percentage of the total number of historical 

fires) versus historical cumulative acres burned (as a percentage of total) on the 

Y-axis (log scale) by fire size class. Black dots indicate the 90th, 95th, 97th and 

99th percentile fires. The smallest 90 percent of the historical fires (those less 

than 6 ha [15 ac]) accounted for less than 1 percent of the acres burned, and the 

smallest 99 percent of fires (those less than 1214 ha [3000 ac]) accounted for 

just 31 percent of the total acres burned. Class A and B = fires less than 4 ha final 

size; Class C = 4 – 40 ha; Class D = 40 – 121 ha; Class E = 121 – 404 ha; Class 

F = 404 – 2023 ha; Class G = fires larger than 2023 ha.
46 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Table 15—Fraction of historical acres burned by wildfires larger than the given percentile 

size. These results indicate the strongly non-linear influence of large fires on area 

burned (and therefore burn probability).

 Percentage of Percentage of

historical fires historical acres

Percentile value larger than burned by fires

(percent) Percentile fire size this size larger than this size

 - ha - - ac -

99 1214 3000 1 69

98 554 1370 2 85

95 64 159 5 97

90 6 15 10 99

Figure 17—The historical relative distribution of the number of fires (left panel) 

and total acres burned (right panel) by fire size class. (A/B = fires less than 4 ha 

[10 ac]; C = 4 – 40 ha [10 – 100 ac]; D = 40 – 121 ha [100 - 300 ac]; E = 121 

– 405 ha [300 – 1000 ac]; F = 405 – 2023 ha [1000 – 5000 ac]; G = more than 

2023 ha [5000 ac]). The asymmetry between the number of fires and the area 

they burn suggests that smaller fires need not be considered when simulating 

burn probability because they contribute so little to it. Burn probability simulations 

typically focus on fires in size class D or E and larger.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 47

These charts illustrate the asymmetry between the number of wildfires and their 

contribution to acres burned. There are many more small fires than large fires, but they 

do not contribute much to the total area burned. From this chart we can tabulate the 

cumulative acres burned by wildfires of various percentile sizes (Table 15). The main 

purpose of this summary is to confirm that large fires do most of the “work” (burn acres), 

and to judge what size threshold may be most applicable on the landscape.

A third chart illustrates the seasonality of fire occurrence on a landscape by plotting 

the final fire size (common logarithm scale) against the date of fire start. If desired, 

the points can reflect different fire cause types, different regions of the landscape, or 

different vegetation types (Figure 18). This chart is useful for determining the earliest 

start dates of fires of different size classes, and for identifying differences in fire occurrence across a landscape. This is useful information when setting up a wildfire hazard 

simulation with FSim.

Figure 18—A chart showing the historical occurrence of wildfire in relation to their start date, final 

size and biophysical setting group on the Tetons Interagency Risk Assessment landscape. Wildfires 

occurring in the lower-elevation valley-bottoms dominated by sagebrush-grass and pinon-juniper 

vegetation types tend to start both earlier and later in the season, but do not account for as many 

acres burned as on areas dominated by timber-based fuel complexes.
48 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

The next step in the analysis of historical fire occurrence is the determination of coefficients for a logistic regression of fire occurrence to ERC-G using FireFamily Plus. 

After the historical weather and historical occurrence data are imported into FireFamily 

Plus, a simple tool determines these coefficients for any specified threshold fire size. The 

threshold fire size is the minimum fire size to consider in the analysis; fires smaller than 

the threshold are excluded. The regression coefficients determine the daily probability 

that at least one wildfire larger than the threshold (PLFD) will occur given the ERC-G

value for the day. The form of the regression equation is:

PP!"# = 1

1 + ee !!! !!∗!"# [5]

where a and b are coefficients determined for different minimum fire size thresholds 

(Table 16).

A separate analysis identifies the distribution of the daily number of fires exceeding the threshold given that at least one occurred. The mean of that distribution can be 

combined with the logistic regression model to determine the propensity for fires of 

different size classes to occur in relation to ERC-G (Table 16). The resulting probability 

of a large-fire day (LFD) as calculated from the regression is illustrated in Figure 19.

Table 16—Summary of coefficients for determining the probability of a 

large-fire day from a logistic regression equation for three large-fire size 

thresholds. More than one large fire can occur on a large-fire day, so 

the mean number of large fires per large-fire day is also indicated.

 Mean number of 

Threshold fire size Regression coefficient fires per LFD

- ha - - ac - a b

4 10 -4.2217 0.0388 1.13

40 100 -5.1242 0.0444 1.14

121 300 -5.5398 0.0462 1.14

Figure 19—Results of the logistic 

regression daily fire occurrence probability 

coefficients determined for historical fires 

occurring in the Teton Interagency Risk 

Assessment landscape in relation to 

ERC-G measured at the Raspberry RAWS. 

This chart indicates the probability of at 

least one fire start that eventually exceeds 

the size thresholds. More than one large 

fire starts on many such large-fire days 

(see Table 16); this chart does not take 

into account the mean number of large 

fires per large-fire day.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 49

The final piece of fire occurrence information needed for a geospatial wildfire hazard 

assessment is a raster indicating the spatial likelihood of large-fire occurrence, based on 

the locations of historical wildfires on the landscape. For the TIARA, a logistic regression model was built to predict the spatial large-fire ignition likelihood (0 to 1) based on 

vegetation type, topographical variables, and distance to a road or trail (Scott and others 

2012a). When large fires occur on this landscape, they tend to be located in the forested 

northwestern portion of the study area (Figure 20). The low-elevation grasslands do not 

produce many large fires, nor do the high-elevation areas of the major mountain ranges.

Near-Maximum Wildfire Behavior—A combination of fire behavior modeling and 

GIS software was used to simulate several measures of the near-maximum wildfire 

behavior (97th percentile wind speed and dead fuel moisture content). As described in 

Section 3, the 97th percentile probable maximum 1-minute average 6-m (20-ft) wind 

speed at the selected RAWS was 32 km/h (20 mi/hr); this wind speed value was 

applied in the upslope direction for all simulations of near-maximum wildfire behavior. 

Figure 20—Relative large-fire ignition density across the Teton Interagency Risk Assessment landscape. 

Dark red areas have highest ignition density; dark blue have the lowest ignition density. White indicates zero 

ignition probability due to non-burnable fuel condition.
50 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Also noted in Section 3, the 97th percentile 1-h timelag moisture content was determined 

from the FRISK file to be 3 percent, the 10-h timelag moisture content value was 4 

percent, and the 100-h timelag moisture content was 8 percent. Live herbaceous fuel 

was assumed to be 45 percent, corresponding to fuel that is almost fully cured, and the 

live woody moisture content was assumed to be 75 percent. These moisture content 

values were applied to the entire landscape, regardless of aspect, elevation or canopy 

cover. FlamMap5 was used to simulate type of fire (with an adjustment for ‘non-forest’ 

fire), fireline intensity, and flame length for these weather conditions, with crown fire 

occurrence simulated with the ‘Scott and Reinhardt 2001’ option.

Non-forest fire dominates the landscape (Figure 21). More than half of the forested 

areas are simulated as passive crown fire for the near-maximum fire weather condition; 

only on a very small fraction is fully active crown fire possible. Passive crown fire 

encompasses a broad range of fire behavior, from single-tree torching to almost fully 

active crowning, so even passive crown fires can generate very high fireline intensity 

and flame length values. The majority of the area where passive crown fire is predicted 

would be likely to result in high-severity effects on the overstory vegetation. Nonburnable portions of the landscape include lakes, agricultural areas and bare ground at 

high elevation.

Figure 21—Type of fire classification for the near-maximum condition on the Teton Interagency Risk Assessment 

landscape. Non-forest fire dominates the landscape. Where conifer crown fires are possible, passive crown 

fire is most likely; only a small amount of active crown fire is possible. Inset histogram indicates the relative 

distribution of fire type across the entire landscape.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 51

Near-maximum fireline intensity spans the entire spectrum from class I where spread 

is slow in light fuel to class V where active crown fire and high-grade passive crown fire 

is simulated (Figure 22). The modal intensity class is IV-, corresponding to the large, 

non-forest areas of the landscape mapped to fuel models GS2 and GR2.

Near-maximum flame length (Figure 23) exhibits a pattern similar to that of fireline 

intensity. A small portion of the landscape is mapped as capable of producing flame 

lengths in excess of 50 feet. The 8–12 foot flame-length class is the most prevalent, 

again corresponding primarily to the non-forest portions of the landscape mapped to 

GS2 and GR2.

Wildfire Likelihood—FSim was used to estimate annual BP across the TIARA landscape. The bulk of the area is in the two classes representing a BP of 0.001 to 0.004 

(Figure 24). These high-likelihood areas of the landscape correspond to land covered by 

grass and grass-shrub surface fuel, which exhibit high spread rates. The low-likelihood 

areas of the landscape correspond to areas with low spread rates, large non-burnable 

areas, and low historical ignition probability (see Figure 20).

Figure 22—Near-maximum fireline intensity (kW/m) for the Teton Interagency Risk Assessment 

landscape, displayed on a logarithmic scale. The inset histogram indicates a preponderance of the class 

IV- (Table 4), corresponding to the large area of grass-shrub fuel type surrounding the south and east flanks 

of the mountain ranges. 
52 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Figure 23—Near-maximum flame length for the Teton Interagency Landscape. Flame length classes are 

the six standard Fire Intensity Levels, plus an additional class break at 15 m (50 ft). The inset histogram 

indicates a preponderance of area in the 2.4 – 3.7 m (8-12 ft) flame-length class, corresponding to the large 

area of grass-shrub fuel type surrounding the south and east flanks of the mountain ranges. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 53

Conditional Wildfire Intensity—FSim was also used to estimate two measures of 

conditional wildfire intensity: mean fireline intensity (MFI) and conditional flame length 

(CFL). MFI is a direct output of FSim; CFL is calculated as the expected value of flame 

length based on flame length probabilities. Both measures incorporate the effects of 

variability in fuel moisture, wind speed and wind direction, as well as the effects of 

spread direction. Unlike measures of potential fire intensity, conditional wildfire intensity does not assume a headfire, but instead uses simulation modeling that implicitly 

incorporates the effect of non-heading spread on wildfire intensity. For these reasons, 

MFI (Figure 25) is typically lower than the near-maximum fire intensity (Figure 22), 

and CFL (Figure 26) is lower than near-maximum flame length (Figure 23).

Figure 24—Annual burn probability across the Teton Interagency Risk Assessment landscape. Non-burnable 

areas, consisting mainly of bare ground at high-elevation and lakes, are shown in white. The inset histogram 

illustrates the distribution of likelihood across the classes.
54 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Figure 25—Mean fireline intensity (scaled by its common logarithm) across the Teton Interagency Risk 

Assessment landscape. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 55

Figure 26—Conditional flame length (CFL) across the Teton Interagency Risk Assessment landscape. CFL 

is an estimate of the characteristic flame length at each pixel, given the fuel characteristics at the pixel in 

conjunction with the distribution of fire weather. CFL includes the effect of relative spread direction (heading, 

flanking, backing, etc.) on flame length.
56 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

HVRA Characterization

HVRA Identification—Initially, BTNF fire managers and resource specialists, 

joined by Wyoming Game and Fish biologists, gathered together to create an initial 

list of possible HVRAs. Forest Leadership identified certain resources and assets as 

‘highly valued’ based on their importance in driving fire management decision making. 

For example, infrastructure was identified as an HVRA because depicting the potential 

for loss to infrastructure helps identify considerations for managing a natural ignition. 

Similarly, aspen stands (specifically those determined to be a high priority for restoration) were also identified as a highly valued resource. Capturing the potential for wildfire 

benefit to these stands may help identify prescribed fire treatments or craft objectives 

for managing a wildfire. Recognizing the basic land management direction to steward 

the fire-adapted ecosystems present on their landscapes and the desire to have vegetation in its historical mix of structure and composition, the BTNF staff determined that 

they needed an HVRA that would characterize the ‘diverse and resilient vegetation’ of 

fire-adapted ecosystems.

The list of individual HVRAs identified for an analysis area can be quite long, suggesting a two-level hierarchical structure to organize HVRAs (Thompson and others 

2013a). The primary HVRA represents a group of similar HVRAs. The individual 

HVRAs within a primary HVRA are called sub-HVRAs. HVRAs and sub-HVRAs can 

be assigned different response functions and different relative importance values. In 

some cases, an additional variable—called a covariate—may have been identified by 

resource specialists as an important HVRA characteristic affecting the HVRA’s response 

to wildfire. A covariate (soil erosion class, for example) results in a different response 

function, but the relative importance is the same as the HVRA or sub-HVRA to which 

it belongs. On the BTNF, the Forest ultimately identified seven primary HVRAs to be 

analyzed in this assessment (Table 17).

The Investments HVRA represents infrastructure within and adjacent to the BTNF. 

This HVRA includes investments made by the Forest Service as well as those made by 

cooperating agencies and holders of special use permits. The Investments HVRA consists 

of nine sub-HVRAs mapped on and within 1 mile of the Forest boundary. Data on the 

Wyoming Game and Fish elk feed grounds were provided by the Wyoming Game and 

Fish Department. Oil and gas development area data were provided by the Wyoming 

Oil and Gas Commission. Power line data were provided by Lower Valley Energy. Remaining sub-HVRA data were derived from the Forest’s corporate geospatial data set.

The wildland urban interface HVRA represents areas on the Forest closest to private 

land. The HVRA has two sub-HVRAs: the WUI defense zone and the portion of the 

Protection Fire Management Unit (FMU) outside the WUI defense zone. The WUI 

defense zone is characterized as NFS land within a 0.25 mile buffer of private land. 

This sub-HVRA represents the area of highest interest for fuel reduction projects. The 

Protection FMU outside the WUI defense zone captures the balance of the Forest lands 

that predominantly receive a suppression response to wildfires due to their proximity 

to values at risk. The Protection FMU is characterized in the Forest’s Fire Management 

Plan (USDA Forest Service 2012). The sub-HVRAs were derived from the Forest’s 

corporate geospatial dataset.

The critical fish and wildlife habitat HVRA represents habitat components of four 

wildlife and three fish species. Sage-grouse is a candidate species for listing as endangered. Pronghorn antelope, bighorn sheep, and moose are all species whose habitat 

needs are of concern to the Wyoming Game and Fish Department. The fish species—

Northern leatherside chub, and Bonneville and Colorado River cutthroat trout—are 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 57

Table 17—Highly valued resources and assets (HVRAs) and sub-HVRAs identified in 

the BTNF risk assessment.

Primary HVRA Sub-HVRAs

Investments Feed grounds

Special use permit areas

Trailheads/boating sites

Campgrounds/picnic areas

Cabins/guard stations/lookouts

Oil and gas development areas

Communication sites

Power lines

White bark pine plus trees

WUI WUI defense zone

Protection FMU

Critical fish and wildlife habitat Pronghorn migration routes

Bighorn migration routes

Moose thermal cover

Sage grouse core areas

Sage grouse near leks

Trout and chub streams

Priority vegetation Whitebark pine A

Whitebark pine B

Whitebark pine C

Whitebark pine D

Sensitive plants

Feedground Aspen

Aspen (high priority)

Watershed Municipal watershed (DFC4)

Diverse and resilient vegetation Subalpine dry-mesic spruce-fir

Montane sagebrush steppe

Subalpine parkland

Subalpine wet-mesic spruce-fir

Aspen forest and woodland

Montane Douglas-fir

Timber base Desired Future Condition 1B

Desired Future Condition 10

proposed candidates for listing. This sub-HVRA was mapped as 6th level Hydrologic 

Unit Code (HUC) watershed boundaries occupied by those species. The 6th level HUC 

was used because only fire over the entire watershed would impact the fishes’ habitat. 

Pronghorn antelope migration routes are split into conifer and non-conifer covariates. 

Geospatial data for all sub-HVRAs, with the exception of fisheries, were obtained from 

the Wyoming Game and Fish Department. The fisheries sub-HVRA was derived from 

existing USFS data.
58 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

The priority vegetation HVRA represents Threatened, Endangered, Sensitive, and 

Proposed Candidate vegetation on the Forest that is sensitive to wildfire (positive or 

negative). The priority vegetation HVRA also represents aspen stands that are particularly 

sensitive to wildfire (positive or negative). Aspen was identified as a species of particular 

interest given its decline throughout the region and its critical role as wildlife habitat. 

The HVRA is split into seven sub-HVRAs. Whitebark pine data were obtained from 

the Greater Yellowstone Coordinating Committee (GYCC) Whitebark sub-committee. 

The whitebark pine stands were then split into four sub-HVRAs (A-D) based on the 

extent of canopy damage and anticipated fire effects. This characterization was provided 

by the GYCC Whitebark sub-committee co-chair. The sensitive plants sub-HVRA is 

further split into two covariates representing those species that have a positive response 

to fire and those that have a negative response. Data for these plants were provided by 

the BTNF botanist, based on the Wyoming Natural Diversity Database. Aspen adjacent 

to elk feed grounds may be damaged post-fire due to intensive herbivory by elk. These 

aspen stands at risk were identified by the Wyoming Game and Fish department, based 

on the Forest’s 2007 vegetation map (USDA Forest Service 2007). The high restoration 

priority aspen is based on the Campbell and Bartos (2001) aspen risk key used in the 

Grey’s River Aspen Assessment (Loosen and others 2009). These high restoration priority 

aspen stands are limited to those identified during the Greys River Aspen Assessment.

The municipal watersheds HVRA represents areas identified by the Desired Future 

Condition (DFC) of the same name in the Forest Plan (USDA Forest Service 1990). 

Data for the municipal watersheds HVRA were obtained from the Forest’s corporate 

geospatial dataset.

The diverse and resilient vegetation HVRA (DRV) represents the combination of 

vegetation communities and their distribution of successional states. This study assumes 

that the reference condition distribution of successional states present in fire-adapted 

vegetation communities equates to diverse and resilient vegetation communities. The 

Forest wanted to capture diverse and resilient vegetation as an HVRA in order to capture 

both its importance as the cornerstone of successful land management and as a proxy 

for fire’s role in fire-adapted ecosystems. Six sub-HVRAs are characterized as the biophysical settings shown in Table 17. Each sub-HVRA was further split into covariates 

that represent whether the current percentage of the individual succession classes are in 

deficit, similar, or in surplus when compared to the mean percentage under the historical 

range of variation. Reference conditions were acquired from LANDFIRE vegetation 

dynamics models. The sub-HVRAs and covariates were derived from LANDFIRE 

geospatial data using the Fire Regime Condition Class mapping tool (Helmbrecht and 

others 2013).

The timber base HVRA represents two DFC areas identified in the 1990 Forest Plan 

(USDA Forest Service 1990) where commercial timber harvest activities may take place. 

DFC 1B refers to areas of substantial commodity resource development and DFC 10 

refers to areas of simultaneous development of resources and other opportunities. The 

sub-HVRAs were derived from the Forest’s corporate geospatial dataset.

Response to Wildfire—The analysis quantifies wildfire response as the expected 

value of net response (Finney 2005). This approach has previously been applied to 

national, regional, and forest-level assessments of wildfire response (Scott and Helmbrecht 2010; Thompson and others 2011; Helmbrecht and others 2012; Thompson and 

others 2013a, b). The analysis relies on local resource specialists to produce a tabular 

response function for each HVRA occurring in the analysis area. A response function 

is a tabulation of the relative change in value of an HVRA if it were to burn in each of 

six fire intensity levels (FILs), represented as flame length classes (Table 5). A positive 

value in a response function indicates a benefit, or increase in value; a negative value 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 59

indicates a loss, or decrease in value. Response function values ranged from -100 (greatest possible loss of resource value) to +100 (greatest increase in value). Covariates are 

other environmental variables (with supporting geospatial data) that could affect HVRA 

response to fire. Response functions for the BTNF HVRAs (Table 18) were generated 

during a 2-day workshop. Resource and fire management specialists were present and 

participated in assigning the response functions.

Relative Importance—In order to integrate HVRAs with differing units of measure 

(for example, habitat vs. timber), relative importance (RI) values were assigned to each 

HVRA by Forest line officers. Relative importance values were developed by first ranking the HVRAs, then assigning an RI value to each. The most important HVRA was 

assigned RI = 100. Each remaining HVRA was then assigned an RI value indicating its 

importance relative to that most-important HVRA.

The RI values apply to the overall HVRA on the Forest as a whole, not a unit area 

of HVRA. The calculations need to take into account the relative extent of each HVRA 

to avoid overemphasizing HVRAs that cover many acres. This was accomplished by 

normalizing the calculations by the relative extent (RE) of each HVRA on the forest. 

Relative extent refers to the number of pixels mapped to each HVRA. In using this 

method, the relative importance of each HVRA is spread out over the HVRA’s extent. 

An HVRA with few pixels can have a high importance per pixel; an HVRA with a great 

many pixels has a low importance per pixel.

Each HVRA and sub-HVRA was assigned a value of Relative Importance in order to 

permit weighting the HVRAs together. On the BTNF, the WUI and Investments HVRAs 

were assigned a relative importance of 100, the highest possible value (Table 19). Habitat, 

Priority Vegetation, and Municipal Watersheds were assigned 70 to 75 percent of the 

maximum importance. Diverse and Resilient Vegetation (DRV) was given 50 percent, 

and the timber resource just 15 percent of maximum importance. These RI values are 

divided by the extent of each HVRA, in terms of the number of pixels, to produce the 

final weighting factor for each HVRA—relative importance per unit relative extent.

Table 18—Response functions for selected HVRAs on the Bridger-Teton National Forest HVRAs. Please see Scott and others 

(2013) for a complete listing of response function values for the Critical Fish and Wildlife Habitat, Diverse Resilient 

Vegetation, and Priority Vegetation HVRAs.

HVRA Name Sub-HVRA Name FIL 1 FIL 2 FIL 3 FIL 4 FIL 5 FIL 6

Investments Game and Fish feedgrounds -50 -70 -90 -100 -100 -100

Special use permit areas -50 -70 -90 -100 -100 -100

Trailheads/boating sites 0 -10 -20 -30 -40 -50

Campgrounds/picnic areas 0 -10 -20 -55 -75 -75

Cabins/guard stations -50 -70 -90 -100 -100 -100

Oil and gas development -10 -20 -40 -80 -100 -100

Communication sites 0 -30 -60 -80 -100 -100

Power lines -10 -20 -40 -80 -100 -100

Whitebark pine plus trees -10 -70 -100 -100 -100 -100

Wildland urban Interface WUI defense zone 0 -50 -75 -100 -100 -100

Protection FMU 10 0 -25 -50 -50 -50

Watershed Municipal Watershed (DFC 4) 20 0 -20 -50 -75 -100

Timber base Desired future condition 1B 20 -20 -50 -80 -100 -100

Desired future condition 10 50 25 10 0 -25 -50
60 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Exposure Analysis

The exposure of HVRAs to wildfire on the BTNF was accomplished by characterizing, 

where each HVRA occurs, the near-maximum fire intensity and type of fire, annual burn 

probability, and conditional fire intensity. Detailed results for the WUI HVRAs (both the 

defense and protection zones) are shown in Figure 27. Each small black dot represents 

the combination of BP and near-maximum FLI at a single pixel; a systematic sample 

(0.1 percent, or 1 out of 1000) of the total number of WUI pixels is shown. The large 

gray dots represent the average BP and FLI in each zone. The distribution of BP and 

FLI are similar in the two zones; on average, the protection zone is exposed to slightly 

Table 19—Relative importance (RI), relative extent (RE) and relative importance per unit relative extent (RI/RE) for primary 

and sub-HVRAs on the Bridger-Teton National Forest. RI/RE are expressed on a logarithmic scale; smallest RI/RE = 1.0.

Primary HVRA RI Sub-HVRAs RI RE (ha) RI/RE log10

Investments 100 Feed grounds 8.1 6 6.3

Special use permit areas 13.5 3,152 3.7

Trailheads/boating sites 4.7 34 5.2

Campgrounds/picnic areas 11.5 175 4.9

Cabins/guard stations/lookouts 13.5 9 6.3

Oil and gas development areas 13.5 5,251 3.5

Communication sites 13.5 2 7.0

Power lines 13.5 4,081 3.6

White bark pine plus trees 8.1 4 6.4

WUI 100 WUI defense zone 58.8 52,223 3.1

Protection FMU 41.2 79,963 2.8

Critical fish and wildlife habitat 75 Pronghorn migration routes 5.1 17,655 2.5

Bighorn migration routes 10.2 22,680 2.7

Moose thermal cover 11.9 14,951 3.0

Sage grouse core areas 17 2,203 4.0

Sage grouse near leks 13.6 8,028 3.3

Trout and chub streams 8.5 476,520 1.3

Priority vegetation 75 Whitebark pine A 22.6 57,244 2.7

Whitebark pine B 4.5 80,100 1.8

Whitebark pine C 18.1 62,013 2.6

Whitebark pine D 0.0 4,967 --

Sensitive plants 6.8 131,690 1.8

Feedground Aspen 3.8 3,487 3.1

Aspen (high priority) 19.2 11,518 3.3

Watershed 70 Municipal watershed (DFC4) 70.0 22,200 3.6

Diverse and resilient vegetation 50 Subalpine dry-mesic spruce-fir 4.1 511,033 1.0

Montane sagebrush steppe 13.5 146,293 2.1

Subalpine parkland 9.5 156,142 1.9

Subalpine wet-mesic spruce-fir 2.7 128,038 1.4

Aspen forest and woodland 13.5 132,215 2.1

Montane Douglas-fir 6.8 70,279 2.1

Timber base 15 Desired Future Condition 1B 8.8 73,991 2.2

Desired Future Condition 10 6.2 311,849 1.4
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 61

Figure 27—Exposure of Wildland Urban Interface (WUI) defense zone and protection zone to wildfire on the 

Bridger-Teton National Forest. The gray dot on each panel shows the mean burn probability and mean fireline 

intensity. Y-axis is the near-maximum fireline intensity (97th percentile condition).

higher near-maximum FLI values (Table 20) and slightly lower BP values (Table 21) 

than the defense zone. Although the near-maximum FLI is greater in the protection 

zone, the conditional mean FLI is greater in the defense zone (Table 21), indicating a 

difference in fuel structure between these two zones.

The mean exposure of each HVRA to wildfire is depicted in Figure 28 and Figure 29. Focusing on the Investments HVRA, the Wyoming Fish and Game feedgrounds 

are exposed to the highest BP and FLI, whereas the communication sites are exposed 

to the lowest BP and FLI values.

These results address only the likelihood of experiencing a fire and the conditional 

mean or near-maximum fire intensity and type of fire. They do not consider the effect 

of various intensity levels on the HVRA, nor do they incorporate the importance of the 

different HVRAs relative to one another. Those results are addressed in the next section.

Effects Analysis

Effects analysis results are integrated across all HVRAs and described for the whole 

BTNF using the weighted sum described in Equation 2. All HVRAs were clipped to 

the Forest boundary except investments, which were clipped to 1 mile of the Forest 

boundary. Results for the investments HVRA that occur outside the Forest boundary 

are labeled “non-Forest” in the ranger district summary below.

The most general indicator of potential for wildfire effects at a grid cell is the number 

of overlapping HVRAs at that cell—the more overlapping HVRAs present, the greater 

the potential for effects. Because this assessment includes the DRV HVRA, which 

covers nearly all of the landscape, only 8 percent of the landscape is not covered by 

any HVRA (Figure 30). Sixty-five percent of the landscape is covered by just one or 

two HVRAs, whereas four or more HVRAs overlap on seven percent of the landscape. 

Because the wildfire risk measure, E(wNVC), sums the risk associated with overlapping 

HVRAs, the effects of multiple HVRAs can be additive, if their effects are uniformly 

positive or negative, or offsetting, if some HVRAs exhibit a positive E(NVC) whereas 

others are negative.
62 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Table 20—Exposure of highly valued resources and assets to wildfire on the Bridger-Teton National Forest based on nearmaximum potential wildfire behavior.

Mean 

Type of firea intensity

Primary HVRA Sub-HVRA NB NF S P A FLI FL

Percentage of sub-HVRA kW/m m

Investments Feed grounds 0 71 0 29 0 4,777 5.2

Special use permit areas 6 34 31 28 2 2,387 3.1

Trailheads/boating sites 5 62 17 17 0 1,005 1.8

Campgrounds/picnic areas 5 38 26 29 1 1,991 2.6

Cabins/guard stations/lookouts 0 73 9 18 0 1,726 2.6

Oil and gas development areas 0 47 19 30 3 3,310 4.1

Communication sites 50 50 0 0 0 75 0.4

Power lines 12 63 17 8 0 1,272 1.9

White bark pine plus trees 0 20 20 60 0 925 2.0

WUI WUI defense zone 2 59 23 15 0 1,796 2.5

Protection FMU 3 38 29 28 2 3,005 3.7

Critical fish and wildlife Pronghorn migration routes 4 68 13 16 0 1,817 2.6

habitat Bighorn migration routes 25 37 16 21 1 1,775 2.4

Moose thermal cover 0 0 28 71 1 5,346 6.9

Sage grouse core areas 10 66 16 7 0 1,460 2.0

Sage grouse near leks 0 93 4 3 0 1,512 2.1

Trout and chub streams 11 33 22 33 1 2,308 3.1

Priority vegetation Whitebark pine A 7 18 35 39 1 1,609 2.4

Whitebark pine B 5 12 37 45 1 1,705 2.6

Whitebark pine C 5 16 37 42 1 1,771 2.6

Whitebark pine D 4 31 29 36 0 871 1.7

Sensitive plants 10 36 24 30 1 1,789 2.6

Feedground Aspen 0 55 28 17 0 2,005 2.7

Aspen (high priority) 0 20 39 39 2 3,718 4.5

Watershed Municipal watershed (DFC4) 13 27 23 23 3 3,750 4.4

Diverse and resilient Subalpine dry-mesic spruce-fir 1 18 23 56 2 3,523 4.7

vegetation Montane sagebrush steppe 0 100 0 0 0 1,503 2.0

Subalpine parkland 5 27 29 38 1 1,255 2.0

Subalpine wet-mesic spruce-fir 0 14 15 69 2 5,467 6.9

Aspen forest and woodland 0 12 73 15 0 1,019 1.7

Montane Douglas-fir 0 13 61 24 2 2,825 3.4

Timber base Desired Future Condition 1B 0 30 19 50 1 3,356 4.6

Desired Future Condition 10 1 36 24 39 1 3,120 4.1

a NB = non-burnable; NF = non-forest; S = surface; P = passive crown; A = active crown
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 63

Table 21—Exposure of highly valued resources and assets to wildfire on the Bridger-Teton National Forest based on results from 

FSim.

Mean fireline Conditional 

Primary Sub-HVRA Burn probability intensity flame length

fraction kW/m m

Investments Feed grounds 0.00565 873 1.2

Special use permit areas 0.00284 297 0.8

Trailheads/boating sites 0.00243 326 0.8

Campgrounds/picnic areas 0.00315 350 0.9

Cabins/guard stations/lookouts 0.00380 354 0.9

Oil and gas development areas 0.00253 367 0.9

Communication sites 0.00148 37 0.2

Power lines 0.00433 539 1.0

White bark pine plus trees 0.00300 165 0.8

WUI WUI defense zone 0.00553 543 1.1

Protection FMU 0.00428 391 0.9

Critical fish and wildlife habitat Pronghorn migration routes 0.00592 592 1.1

Bighorn migration routes 0.00241 238 0.6

Moose thermal cover 0.00169 119 0.6

Sage grouse core areas 0.00556 639 1.1

Sage grouse near leks 0.00754 794 1.3

Trout and chub streams 0.00217 238 0.7

Priority vegetation Whitebark pine A 0.00124 158 0.6

Whitebark pine B 0.00111 138 0.6

Whitebark pine C 0.00130 141 0.6

Whitebark pine D 0.00212 270 0.8

Sensitive plants 0.00225 237 0.7

Feedground Aspen 0.00637 492 1.1

Aspen (high priority) 0.00353 253 0.8

Watershed Municipal watershed (DFC4) 0.00270 333 0.7

Diverse and resilient vegetation Subalpine dry-mesic spruce-fir 0.00223 209 0.7

Montane sagebrush steppe 0.00495 656 1.1

Subalpine parkland 0.00131 171 0.6

Subalpine wet-mesic spruce-fir 0.00285 266 0.8

Aspen forest and woodland 0.00361 171 0.8

Montane Douglas-fir 0.00343 266 0.8

Timber base Desired Future Condition 1B 0.00264 245 0.8

Desired Future Condition 10 0.00366 314 0.8
64 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Figure 28—Exposure of the 

Diverse Resilient Vegetation, 

Wildlife Habitat and Priority 

Vegetation HVRAs to wildfire on 

the Bridger-Teton National Forest. 

Y-axis is the near-maximum 

fireline intensity (97th percentile 

condition).

Figure 29—Exposure of the 

Investments, WUI, Timber and 

Watershed HVRAs to wildfire 

on the Bridger-Teton National 

Forest. Y-axis is the nearmaximum fireline intensity (97th 

percentile condition). 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 65

Figure 30—Distribution of the number of overlapping HVRAs across the 

Bridger-Teton National Forest.

E(wNVC) was calculated using Equation 2 and summed across all HVRAs. The 

results are spatially displayed, with a logarithmic scale (log10) used for the results (Figure 31). Each class of threat or benefit is an order of magnitude (10 times) greater than 

the previous. E(wNVC) combines information about wildfire hazard (burn probability 

by flame-length class) with information about the susceptibility, importance and extent 

of HVRAs. Positive values indicate that wildfire is expected to have a net positive effect; negative values indicate that wildfire is expected to have a net negative effect. The 

magnitude of the response values indicates the strength of the effect, whether positive 

or negative. These results show only the net effect; whether any offsetting effects are 

present are not indicated here.

The sum and mean E(wNVC) values—integrated wildfire risk—are shown for each 

HVRA in Table 22. The WUI defense zone has the greatest cumulative wildfire risk; 

the WUI protection zone accounts for just 7 percent as much risk. Sage grouse habitat 

represents the next-most threatened HVRAs, with core areas accounting for about half 

of the WUI defense zone risk and habitat near leks accounting for about 40 percent as 

much. All of the WUI and Investments HVRAs exhibit a net negative expected response 

to wildfire; none of these HVRAs had positive response function values for any FIL. 

In contrast, the expected wildfire “risk” to the diverse resilient vegetation HVRAs is 

uniformly positive, indicating a net benefit of wildfire on restoring or maintaining vegetation structure in all biophysical settings on the BTNF.
66 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Figure 31—Weighted E(NVC) summed across all HVRAs on the BTNF. Values are displayed on a logarithmic scale 

(log10). Each category is an order of magnitude (10 times) greater than the previous.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 67

Table 22—Weighted expected net value change [E(wNVC)] by HVRA on the Bridger–Teton National Forest. The sum of E(wNVC) 

is calculated by summing the E(wNVC) values for all grid cells of each sub–HVRA, and expressed as a percentage of the 

sub–HVRA with the greatest magnitude of NVC (whether positive or negative). For example, the WUI Protection FMU has 7.1 

percent of the total risk of the WUI defense zone, which had the greatest magnitude of expected NVC. The mean E(wNVC) 

is the arithmetic mean of E(wNVC) values for all grid cells of a sub–HVRA, and is expressed on a logarithmic scale; a unit 

difference in values corresponds to a 10–fold difference in risk per unit area.

Primary Sub–HVRA Sum of E(wNVC) Mean of E(wNVC)

pct of max log10

Investments Feed grounds –18.5 –6.9

Special use permit areas –13.2 –4.0

Trailheads/boating sites –1.6 –5.1

Campgrounds/picnic areas –2.4 –4.6

Cabins/guard stations/lookouts –18.3 –6.7

Oil and gas development areas –4.4 –3.3

Communication sites –0.8 –6.1

Power lines –9.9 –3.8

White bark pine plus trees –6.5 –6.6

WUI WUI defense zone –100.0 –3.7

Protection FMU –7.1 –2.4

Critical fish and wildlife habitat Pronghorn migration routes 6.9 3.0

Bighorn migration routes 15.0 3.2

Moose thermal cover 3.1 2.7

Sage grouse core areas –55.0 –4.8

Sage grouse near leks –38.2 –4.1

Trout and chub streams 2.6 1.2

Priority vegetation Whitebark pine A –4.7 –2.3

Whitebark pine B –0.8 –1.4

Whitebark pine C –3.9 –2.2

Whitebark pine D 0.0 –––

Sensitive plants –0.1 –0.2

Feedground Aspen –1.3 –3.0

Aspen (high priority) 11.8 3.4

Watershed Municipal watershed (DFC4) –0.4 –1.7

Diverse and resilient vegetation Subalpine dry–mesic spruce–fir 1.0 0.7

Montane sagebrush steppe 1.3 2.3

Subalpine parkland 1.3 1.3

Subalpine wet–mesic spruce–fir 0.8 1.2

Aspen forest and woodland 3.3 1.8

Montane Douglas–fir 5.4 2.3

Timber base Desired Future Condition 1B –1.8 –1.8

Desired Future Condition 10 3.2 1.4
68 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Dividing the cumulative risk to an HVRA by the land area it covers produces the 

mean risk per unit area. The resulting values vary over several orders of magnitude, so 

we present the results on a logarithmic scale (Table 22). Taken as a whole, the Investments HVRAs have the greatest risk per unit area, followed by the sage grouse HVRAs. 

The WUI defense zone, despite its status as the most-threatened HVRA, has less risk 

per unit area than the Investments, primarily because it covers so much more land area. 

The concentration of risk is an important factor when considering risk mitigation activities, such as fuel treatment. Where risk is concentrated, less land area may need to be 

treated in order to mitigate that risk. Where risk is distributed more thinly, such as in 

the municipal watershed HVRA, more treatment effort may be necessary to accomplish 

the same level of mitigation.

The cumulative risk results can also be summarized by any geographic unit, such as 

ranger districts, fire management unit, watersheds, etc. Wildfire hazard and response 

characteristics were summarized among ranger districts of the BTNF (Table 23). HVRA 

area is the land area covered by at least one HVRA; a pixel with overlapping HVRAs is 

counted only once. Expected annual area burned is the land area covered by at least one 

HVRA that is expected to burn annually; it is calculated as the product of HVRA area 

and mean BP. Mean BP is the arithmetic mean BP of the pixels covered by at least one 

HVRA. Finally, the last three columns in this table represent the cumulative weighted 

wildfire response results. The ‘Benefit’ column is the weighted positive value change 

among the ranger districts, without considering any offsetting negative effects. The 

‘threat’ column is the weighted negative value change, without considering any offsetting positive effects. The values in these two columns are scaled such that the ranger 

district with the greatest positive or negative NVC is scaled to 100 or –100, respectively. 

The final column represents the sum of E(wNVC) in each district.

These three basic measures—benefit, threat, and net response—can be summarized 

to illustrate how the overall response is distributed among HVRAs. The net response 

results for each HVRA (and sub-HVRA) are sorted by decreasing threat and displayed 

on a bar chart (Figure 32). Negative response is shown as a red bar; positive response 

is shown as a green bar. On the BTNF, the WUI defense zone is the HVRA with the 

greatest weighted net threat, so it is given a value of -100. The next most-threatened 

HVRAs are sage grouse core areas and sage grouse habitat near leks, which exhibit 55 

percent and 38 percent of the threat represented by the WUI defense zone. As an example of the effect of offsetting threats and benefits, note that power lines and the WUI 

Table 23—Wildfire hazard and cumulative weighted wildfire response across all HVRAs by BTNF ranger district.

Expected annual

HVRA area burned Mean burn prob. Benefit Threat

BTNF Ranger District area (ha) (ha) (fraction) (+VC) (–VC) Risk (NVC)

Big Piney RD 423,360 1,435 0.0034 9 –78 –69

Buffalo RD 334,786 643 0.0019 5 –12 –7

Greys River RD 329,569 1,042 0.0032 20 –21 –1

Jackson RD 422,881 1,414 0.0033 24 –100 –76

Kemmerer RD 317,642 1,238 0.0039 5 –12 –6

Pinedale RD 757,325 1,337 0.0018 8 –33 –26

non–Forest 4,227 15 0.0035 0 –11 –11
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 69

Figure 32—Weighted net response over all highly valued resources and assets (HVRAs) on the BTNF. HVRAs are 

listed in order from greatest expected negative net response at the top to greatest net benefit at the bottom. 

protection zone have equal threat values (as represented on the x-axis), but the WUI 

protection zone has a different net response value (7 percent vs. 9 percent as represented 

in parentheses on the y-axis). This is because the response function for the protection 

zone indicates a small benefit of burning at low flame lengths, indicated by the small 

blue bar. The municipal watershed HVRA exhibits both positive and negative effects (at 

different flame lengths), but these effects are almost completely offsetting. Twelve of the 

22 HVRAs exhibit a net positive response to wildfire, including all of the biophysical 

settings in the diverse and resilient vegetation HVRA. Note that the overall net positive 

effects are generally smaller in magnitude than the negative effects.
70 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

8. Putting it All Together: Understanding and Managing Risk ___________________

At this point, the major components of a spatial wildfire risk assessment framework 

have been discussed. Wildfire risk assessment is premised on the analysis of exposure 

and effects, and on the estimation of wildfire likelihood, wildfire intensity, and HVRA 

response to wildfire. The assessment process we illustrated relies on burn probability 

modeling, the use of expert judgment to characterize HVRA-specific fire effects, and 

if necessary the articulation of relative importance weights across different HVRAs. 

These processes entail geospatial calculations that are performed on a pixel-by-pixel 

basis and summarized across the assessment landscape. Combining fire modeling outputs 

with HVRA-specific information (location, response, and relative importance) yields 

valuable information regarding expected net value change to individual HVRAs as well 

as integrated, weighted risk scores across all HVRAs of interest (Figure 33). Risk calculations can be displayed in graphical and tabular formats to help fire managers better 

understand how risks are distributed across the landscapes they manage.

The basic building block of the entire assessment process is burn probability modeling 

and the use of fire modeling outputs. Understanding the likely patterns of wildfire and 

its interactions with HVRAs (in other words, exposure analysis) is a critical first step 

to developing risk mitigation strategies. Integrating fire behavior models and geospatial 

analysis, therefore, can help directly inform risk mitigation and fuel management planning 

(Ager and others 2011). Further incorporating HVRA-specific response functions can 

capture differential responses to fire across HVRAs, both in terms of varying magnitudes 

of likely loss, as well as potential fire-related benefits for many ecological HVRAs. The 

additional integration of relative importance weights allows for comparison of landscape 

areas with a common measure, and can help inform prioritization of high risk areas.

Figure 33—Components of the risk assessment framework are combined to yield HVRAspecific risk scores as well as integrated, weighted risk scores across multiple HVRAs.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 71

Figure 34 presents a conceptual model for analyzing interactions between wildfire 

management actions, wildfire likelihood and intensity, and the consequences of wildfire 

(including both benefits and losses). Use of this model can help explore how alternative land and fire management actions may reduce risks. These actions could include 

pre-fire investments in ignition prevention programs, response capacity, and hazardous 

fuels reduction, as well as changes in strategic incident response. Reducing HVRA 

exposure is also possible for a limited set of HVRAs such as human development or 

newly constructed infrastructure, and then only by not building the asset in a hazardous 

location in the first place. Controlling exposure is generally not possible for natural and 

cultural resources. This basic analytical framework can be brought to bear for strategic 

identification and comparison of wildfire risk mitigation options.

Further critical for examination and prioritization of risk mitigation strategies is the 

consideration of wildfire management objectives and management opportunities (Figure 35). The former will dictate the degree to which wildfire management emphasizes 

restoration or protection of HVRAs, and will likely exert significant influence on the 

design of both fuel treatment and suppression strategies. The latter relates to spatial and 

temporal constraints such as prescribed burning windows and land designations, operational constraints such as limitations for mechanical equipment, financial constraints 

relating to management costs, and could include broader sociopolitical concerns such as 

public support, all of which will vary with the nature and scope of the planning context.

Figure 34—Conceptual model of wildfire management actions and their 

relation to primary factors driving wildfire risk. Boxes in light gray are 

management actions, and boxes in dark grey are assessment outputs. Figure 

modified from Calkin and others (2011a).
72 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Figure 35—Combining risk assessment, management objectives, and management opportunities can 

lead to development of risk mitigation strategies. Here three examples of different planning contexts 

are illustrated within the dashed-line box.

Principles of Wildfire Risk Assessment

•	 States scope of assessment and HVRAs

•	 Incorporates spatial information on wildfire likelihood, 

intensity, and effects

•	 Discloses data sources, methods, logic, and rationale

•	 Articulates major assumptions and uncertainties

•	 Separates science-based from value-based and policy-based 

judgments

•	 Expresses results clearly and concisely
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 73

9. Discussion and Conclusions

Assessing wildfire risk is a crucial component of wildfire management and risk mitigation planning. A spatial, quantitative characterization of wildfire risk allows for identification of areas on the landscape where aggressive treatment might be cost-effective, or 

alternatively where fire may play a benign or even beneficial role and could be encouraged rather than suppressed. This information could prove useful for the preparation of 

fire management plans, the delineation of FMUs, and development of corresponding 

fire response objectives. The definition of HVRA response as a function of fire behavior 

facilitates the design of fuel treatments to ameliorate undesirable or encourage desirable 

fire intensities. Including landscape variables into the response functions (in addition to 

the baseline fire intensity) can improve response function definitions.

Wildfire risk assessment results provide a snapshot of current landscape conditions 

and associated risks. Periodic assessment over time can provide critical information for 

monitoring trends in risk and evaluating the performance of previous risk mitigation 

investments. It is also possible to modify assessment inputs (for example, fuel conditions) to reassess risks in a comparative risk assessment framework to evaluate the likely 

cost-effectiveness of future mitigation investments.

Two great strengths of the risk assessment framework described herein are its flexibility and its scalability. The framework is quite flexible in the sense that the set of 

HVRAs to be included, the response function definitions, and additional environmental 

variables influencing HVRA response can vary widely from analysis to analysis. Further, although we illustrated a specific assessment process here, the general framework 

incorporating fire likelihood, intensity, and effects can be implemented in a variety of 

ways. The framework is scalable in the sense that the basic framework can be applied 

at project level all the way to national scale planning.

Key factors influencing the success of risk assessment efforts are the level of resources 

committed and the sufficiency and availability of scale-appropriate geospatial data. A 

fundamental component of the assessment framework is geospatial analysis and, in 

our experience, a necessary first step is to establish clear and consistent definitions of 

geospatial data (fire and fuels data in addition to HVRA data). Only then can the assessment proceed to wildfire modeling, HVRA fire effects analysis, and HVRA relative 

importance articulation. It can be helpful to devote time for pre-workshop training to 

familiarize participants with the processes of defining response functions and relative 

importance weights.

It is important to explicitly recognize key sources of uncertainty and to clearly document assessment data and processes. The predominant role of fire modeling in the assessment process points to a need for careful calibration and critique of fire modeling 

inputs (for example, fire behavior fuel models and canopy characteristics) and outputs 

(for example, burn probabilities). The proposed reliance on formality and documentation in the elicitation of expert input for both response functions and relative importance 

weights enables transparency and future external review of risk assessment results. Further, the explicit separation of fire effects from management priorities avoids potential 

pitfalls of conflating science-based and values-based information, or from “gaming” 

the assessment to achieve desired results.

In summary, the risk assessment framework and toolkit presented in this report 

provides for a systematic, transparent, and understandable approach to evaluating and 

mitigating the likely consequences of wildfire. The framework has already been applied 

in a variety of locations, at a variety of scales, and in a variety of planning contexts. We 

anticipate further adoption of the framework as fire managers become comfortable with 

risk management and as modelers and analysts become comfortable with burn probability 

modeling techniques. Research and development will continue to refine our abilities to 
74 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

understand and quantify wildfire risk. Implementing this risk assessment framework in 

the meantime is another step in the direction of risk-informed fire management to best 

achieve land and resource objectives.

Acknowledgments

Funding for the production of this report was provided by the National Fire Decision 

Support Center; initial funding for development of some risk assessment concepts presented here was provided by the Western Wildland Environmental Threat Assessment 

Center. Alan Ager and Mark Finney were both integrally involved in the development 

and advancement of wildfire exposure and risk concepts, without which this report would 

not be possible. We thank Julie Gilbertson-Day (Rocky Mountain Research Station), 

Don Helmbrecht (TEAMS Enterprise Unit), Eva Karau (Fire Modeling Institute) and 

Rick Stratton (Pacific Northwest Region) for their helpful review comments. We thank 

Martha Williamson (Bridger-Teton National Forest) for her assistance in implementing 

the example risk assessment used in this report.

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USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 77

Appendix A—Acronyms and Variables

Acronyms

BTNF—Bridger-Teton National Forest

DFC—desired future condition

DRV—Diverse, resilient vegetation

ERC—Energy Release Component of the NFDRS

FIL—fire intensity level

FMP—Fire Management Plan

FMU—Fire Management Unit

FPA—Fire Program Analysis

GIS—geographic information system

GYCC—Greater Yellowstone Coordinating Committee

HUC—hydrologic unit code

HVRA—highly valued resource or asset

LCP—fire modeling landscape file

LRMP—Land and Resource Management Plan (Forest plan)

NEPA—National Environmental Policy Act

NFDRS—National Fire Danger Rating System

NFMA—National Forest Management Act

NAD—North American Datum

RAWS—Remote Automated Weather Station

RAVAR—Rapid Assessment of Values at Risk

RDPA—Residentially Developed Populated Areas

TIARA—Tetons Interagency Risk Assessment

WFDSS—Wildland Fire Decision Support System

WUI—wildland urban interface

Variables

BP—burn probability

CFL—conditional flame length

E(NVC)—expected value of NVC

E(wNVC)—expected value of weighted NVC

ERC—Energy Release Component of the NFDRS

ERC-G—ERC calculated using fuel model ‘G’

FL—flame length

FLI—fireline intensity

FLP—flame-length probability

H—fuel particle low heat of combustion

MC—moisture content

MFI—mean fireline intensity

NVC—net value change

PLFD—probability of a large-fire day

R—rate of spread

RE—relative extent

RI—relative importance

Wf —load of fuel consumed in the flaming fire front
78 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Appendix B—Glossary

It will be helpful to establish a common set of definitions and a common understanding of the various components of wildfire risk. 

Asset

An asset is a man-made thing—a building, communication tower, road, etc.—of use 

or value to its owner. By contrast, resources are naturally occurring—wildlife habitat, 

forage, timber, etc. Assets and resources can be damaged by wildfire, resulting in reduction in value, or loss. Some resources increase in value after fire (a benefit of fire), 

but assets generally do not. 

Benefit

An increase in the value of a resource or asset (although assets generally do not 

benefit from fire). The benefit to a resource may partially or wholly offset loss due to 

resource damage. The net effect of benefit and loss is called net value change (NVC), 

but has also been called net loss or net benefit.

Burn probability (BP)

The probability that a wildfire will burn a given point or area during a specified period of time. Burn probability for wildfire management planning applications is often 

reported on an annual basis—the probability of burning at any time during a single 

calendar year. Some planning applications report the conditional burn probability given 

that a fire occurs during a specified “problem fire” weather scenario. Wildfire incident 

management applications express burn probability for a much shorter time frame, typically one to four weeks. For practical purposes, wildfire simulation systems treat the 

burning of each pixel, the smallest landscape unit, as a point. 

Conditional wildfire intensity

The typical wildfire intensity produced by the fire environment at a point, incorporating 

non-heading spread directions and the full range of weather scenarios. Two measures of 

wildfire intensity are in common use—flame length and fireline intensity. Flame length 

is commonly used in contemporary wildfire hazard and threat assessments. When using 

a Monte Carlo wildfire simulation system, conditional flame length (CFL) is the mean 

flame length of the iterations that burned a particular landscape pixel. The FSIM wildfire 

simulation system also produces an output raster for mean fireline intensity—the mean 

fireline intensity of the iterations that burned each landscape pixel. Conditional wildfire 

intensity refers to the contemporary, not historical, typical wildfire intensity. See also: 

conditional flame length (CFL), mean fireline intensity (MFI).

Conditional flame length (CFL)

The mean flame length at a point, quantified as the mean flame length simulated 

with a Monte Carlo fire occurrence simulator. Conditional flame length is one of two 

common measures of conditional wildfire intensity (the other is mean fireline intensity). 

Conditional burn probability

Burn probability given a specific set of defining criteria. The specific criteria can be 

a weather scenario and a fixed, usually short period of active fire spread. Conditional 

burn probability is calculated for use in hazard and threat assessments that use FlamMap5 rather than FSIM or FSPro. The flame length probabilities reported by FSim and 

FlamMap5 are conditional.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 79

Damage

An adverse physical change of an asset or resource. Damage is physical change, not 

the effect of that change on value (that is loss). The consumption of a building, death of 

desirable trees in a forest stand, and degradation of air quality are example of damage a 

wildfire can cause. Damage can be direct or induced. Examples of direct wildfire damage include tree mortality and consumption of buildings. Examples of induced wildfire 

damages include reduction of forest productivity due to soil erosion and sedimentation 

of a reservoir.

Effects

The anticipated benefits and losses to HVRAs, typically quantified as a function of 

fire intensity.

Effects analysis

The analysis of likely HVRA response to wildfire (benefits and losses), typically 

quantified as a function of fire intensity. This analysis can incorporate preexisting 

HVRA-specific models, or, as implemented here, can rely on expert-based response 

functions. See also: effects.

Expected loss

See expected net value change.

Expected net value change

Expected net value change, or E(NVC), is calculated as the sum-product of burn 

probability and value change (to one or more resources or assets) over a range of wildfire intensity classes (usually flame length). Expected net value change is a risk-neutral 

measure of the wildfire risk to resources and assets, and forms the basis for the quantitative wildfire risk assessment process described in this report. If no beneficial effects 

are under consideration, expected net value change can simply be called expected loss. 

The terms value change, response and net response are functional synonyms for net 

value change; all refer to the net effects of positive and negative changes on the value 

of a resource or asset.

Expected value

Expected value is the probability-weighted average outcome, a good measure of the 

central tendency of outcomes. For example, if a system or simulation has a 90 percent 

probability of producing an outcome of 0, a 9 percent chance of an outcome of 10, and 

a one percent chance of an outcome of 1000, then the expected value is 10.9, as shown 

in the table below. Notice that 10.9 is not among the possible outcomes.

probability outcome Expected value

0.90 0 0

0.09 10 0.9

0.01 1000 10

1.00 10.9
80 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Exposure

The spatial coincidence of wildfire likelihood and intensity with the location of an 

HVRA. For example, a building (asset) in a flammable forest (hazard) is exposed to 

wildfire. Exposure can be intentional or incidental. A valuable but flammable forest is 

incidentally exposed to damage from wildfire, because the forest cannot be physically 

separated from the hazard. The construction of a residential building in the same flammable forest is intentional exposure to the same hazard. 

Exposure analysis

An assessment of wildfire hazard—likelihood and intensity—where resources and 

assets are located.

Fire modeling landscape

A raster-format geospatial characterization of fuel (fire behavior fuel model, canopy 

base height, and canopy bulk density), vegetation (canopy cover and stand height) and 

topography (slope, aspect and elevation) needed to simulate potential fire behavior 

and fire growth across a landscape. For use in FlamMap5 and FSim, the fire modeling 

landscape must be in the form of an LCP file (Finney 1998).

Fire occurrence

An instance of a wildfire event; a wildfire incident. Fire occurrence is defined by 

the characteristics of historical wildfires occurring in a specified area during a specified period of time: frequency, density, start location, start date, fuel type, final size, 

management objective, and so on.

Fireline intensity (FLI)

The rate of heat release per unit length of flaming fire front, calculated as the product 

of heat content, fuel consumption during flaming front passage, and rate of spread. 

Frequency

The number of occurrences per unit time. 

Fuelscape

A raster-format geospatial characterization of ground, surface and canopy fuel across 

a landscape, typically consisting of one or more fuel characteristics data layers. For fire 

behavior modeling, a fuelscape consists of geospatial data layers representing surface 

fuel model, canopy base height and canopy bulk density. Other geospatial data layers 

required for geospatial fire modeling include topography characteristics (slope, aspect, 

elevation) and vegetation characteristics (forest canopy cover and height).

Grid cell

A grid cell—also called a pixel—is the smallest addressable unit in a raster dataset.

Harm

Injury to a person. Harm is analogous to damage. Damage occurs to anthropogenic 

or natural objects—assets or resources—whereas harm occurs to persons.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 81

Hazard

A physical situation with potential for harm to persons or damage to resources and assets. Wildfire hazard can be described qualitatively as a fire environment—fuel, weather, 

topography, and ignitions—with potential for causing harm or damage, or quantitatively 

by two characteristics: (1) the probability of a fire occurring at a specific point during 

a specified time period, and (2) the expected distribution of intensity given that the 

event does occur. Wildfire hazard at a given location on the landscape is quantified as: 

(1) burn probability and (2) conditional wildfire intensity given that a fire does occur. 

Those two characteristics can be combined into a single spatially resolved measure of 

wildfire hazard: integrated wildfire hazard. It is important to note that since the definition of risk in the wildfire context is expanded to include beneficial as well as negative 

effects, the consideration of wildfire likelihood and intensity (in other words, hazard) 

should be expanded as well.

HVRA

Highly Valued Resource or Asset. Some resources have only modest value and may 

not be analyzed in an assessment of risk to HVRAs. Likewise, low-value assets like 

outbuildings are often left un-analyzed so that efforts can be focused on the more highly 

valued resources and assets (HVRAs). 

Ignition density

Number of ignitions per unit area.

Ignition density grid

Raster-format geospatial data representing the relative number of ignitions per 

unit area. 

Ignition frequency

Number of ignitions per unit time.

Ignition probability

The probability of an ignition occurring during the specified time period, usually one 

day or one year, expressed as a fraction (0-1) or a percentage (0-100).

Integrated wildfire hazard

Integrated wildfire hazard combines two important measures of wildfire—burn 

probability and conditional wildfire intensity—into a single characteristic that can be 

mapped. Integrated wildfire hazard is the product of burn probability and conditional 

wildfire intensity, where intensity is expressed either as the expected flame length or as 

the expected fireline intensity, depending upon which is used to characterize wildfire 

intensity. 

Likelihood

Non-technical synonym for probability. 

Loss

The reduction in value of a resource or asset. See net value change
82 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013

Net value change

The net effect of both damaging and beneficial effects on the value of a resource or 

asset, whether it increases or decreases. Negative numbers for net value change indicate 

a net loss; positive numbers indicate a net benefit.

Pixel

A pixel—for picture element—is also called a grid cell or landscape element. It is 

the smallest addressable unit in a raster dataset.

Probability

The likelihood that an event will occur during a specified period of time, typically 

defined as the relative frequency of an event; the ratio of the number of cases that represent the event to the total number of cases. 

Resource

A resource is something found in nature and necessary or useful to people—wildlife habitat, forage, timber, etc. By contrast, assets are man-made things—buildings, 

communication towers, roads, etc.—of value to its owner. Assets and resources can be 

damaged by wildfire, resulting in loss of value. Some resources increase in value after 

fire (a benefit of fire), but assets generally do not.

Risk

Generally, risk is the the potential for realization of adverse or beneficial consequences to HVRAs. Although there exists no single, best measure of risk, in this risk 

assessment framework we quantify the potential for effects as the expected value of 

the probability of an event occurring multiplied by the magnitude of the effect, given 

that and event has occurred.

Risk assessment

An appraisal of the interaction of hazard, exposure, and effects to a given set of 

HVRAs in a given area. Components of wildfire hazard include the likelihood of burning and distribution of wildfire intensity given that a burn occurs; both are a function 

of the fire environment: fuel, weather, and topography. Components of effects include 

intrinsic HVRA factors as well as broader environmental factors.

Risk analysis 

A detailed examination including risk assessment, risk evaluation, and risk management alternatives, performed to understand the nature of unwanted, negative consequences to human life, health, property, or the environment; an analytical process to 

provide information regarding undesirable events; the process of quantification of the 

probabilities and expected consequences for identified risks.

Risk estimation

The scientific determination of the characteristics of risks, usually in as quantitative 

a way as possible. These include the magnitude, spatial scale, duration and intensity of 

adverse consequences and their associated probabilities as well as a description of the 

cause and effect links. (source: Society for Risk Analysis)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-315. 2013 83

Risk management

“Risk Management is the identification, assessment, and prioritization of risks followed by coordinated and economical application of resources to minimize, monitor, 

and control the probability and/or impact of unfortunate events” (Hubbard 2009, p. 10) 

Susceptibility

The propensity of an asset or resource to experience an effect as a result of burning 

at a given level of wildfire intensity. An asset or resource that is easily damaged by a 

low-intensity wildfire is susceptible, whereas one that is difficult to damage even with 

a high-intensity wildfire is resistant. Modifications to a building (changing to a fireresistant roof covering, screening vents, etc.) make it less susceptible to fire damage. 

The term susceptibility is used for the propensity to experience either an increase or 

decrease in value.

Threat

The expected value of loss; nearly synonymous with risk but specifically excludes 

any potential for beneficial fire effects.

Uncertainty

Imperfect information or a lack of knowledge. Uncertainty can manifest in many 

forms, and in risk analyses often relates to understanding of the probabilities of events. 

Uncertainty can also relate more to knowledge gaps, linguistic confusion, or unknown 

preferences.

Value

The worth or importance of an asset or resource.

Value change

The change in value of a resource or asset arising from an event such as wildfire. 

Some resources or assets experience offsetting beneficial and adverse effects, so the 

term net value change is often used to acknowledge that these offsetting effects have 

been accounted.

Wildfire intensity

The rate of energy release of a wildfire at a point on a fire perimeter, typically measured as flame length (FL) or fireline intensity (FLI). 


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