United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-163WWW

October 2005

FIRE EFFECTS PLANNING 

FRAMEWORK

User’s Guide

Version 1.0

Anne E. Black

Tonja Opperman
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Black, A.; Opperman, T. 2005. Fire Effects Planning Framework: a user’s guide. Gen. Tech. Rep.GTR-RMRS163WWW. Fort Colins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

63 p.

Abstract

Each decision to suppress fire reinforces a feedback cycle in which fuels continue to accumulate, risk escalates, 

and the tendency to suppress fires grows (Miller and others, 2003). Existing decision-support tools focus primarily on the negative consequences of fire. This guide outlines a framework managers can use to (1) identify key areas of fire risk and (2) systematically determine where and under what fire weather conditions fire will benefit ecological conditions and management targets while reducing fuels. The Fire Effects Planning Framework (FEPF) 

sequentially links state-of-the-art, publicly available analysis tools, data, and knowledge to generate GIS-based 

planning information for a variety of scales. Primary funding for this effort was provided by the Joint Fire Science 

Program and the National Fire Plan.

Keywords: fire, prescribed fire, wildland fire use, planning, fire effects, fuel treatment priorities, benefits, resource effects, FEPF, GIS, FLAMMAP, SIMPPLLE .

Authors

Anne E. Black is a post-doctoral ecologist with the Rocky Mountain Research Station at the Aldo Leopold 

Wilderness Research Institute in Missoula, MT. She completed a B.S. degree in resource conservation at the 

University of Montana’s School of Forestry, a Master’s degree in Environmental Studies from Yale University, and 

a Ph.D. in ecology from University of Idaho.

Tonja Opperman is a fire ecologist with the Bitterroot National Forest in Hamilton, MT. She earned a B.S. degree in Forestry from Michigan Technological University and a Master’s degree in Forest Science from Yale 

University.

Acknowledgements

To develop and test this framework, we worked closely with fire and resource managers from a number of different agencies. Key among them are staff from the Bitterroot National Forest, Beaverhead-Deerlodge National 

Forest, and Yosemite National Park. The guidebook has benefited from the critical reviews of Larry Bradshaw, 

Deb Tirmenstein and Mark Finney. Jimmie Chew provided advice during analysis. This document was produced 

as part of a Joint Fire Science Project: “Wildland Fuels: planning and evaluating benefits and risks.”
Fire Effects Planning Framework: Users Guide 

Table of Contents

I. The Fire Effects Planning Framework..................................................... 1 

 Summary 1 

Management questions addressed by FEPF 3 

Model considerations 5 

Software, skills and data requirements 7 

 

II. Guide to FEPF using demonstrated models.............................................9 

How to use this guide 9 

Chapter 1. Developing rulesets to link fire behavior to fire effects...............10 

Map ecosystem targets or processes 11 

Define fire effects on ecosystem targets or processes 13 

Define risk and benefit 13 

Chapter 2. Creating map libraries for analysis of..................................16 

A. Current conditions using a stand-based, deterministic model 16 

1. Generating the landscape input file 

2. Generating fire weather input files 

3. Creating a fire behavior library 

4. Creating a fire effects library 

B. Current conditions using a landscape-level, stochastic model 25 

1. Parameterizing the model 

2. Creating fire type and probability maps

3. Creating fire effects maps

C. Future conditions using a stand-based, deterministic model 35 

1. Creating a future vegetation cover 

2. Creating a future fuels cover 

3. Identifying change 

D. Future conditions using a landscape-level, stochastic model 38 

1. Identifying and prioritizing stands 

2. Creating treatment schedules 

3. Creating fire type and probability maps

4. Comparing fire type and probability maps 

 

Chapter 3. Using map libraries..........................................................48 

Support long-range management plan development 48 

Support identification of fuel treatment priorities 50 

Support fire stewardship activities 51 

III. Forms............................................................................................54
List of Figures 

Figure 1. Basic elements of the Fire Effects Planning Framework process..............................2 

Figure 2. Applications of Fire Effects Planning Framework output..........................................4 

Figure 3. Crosswalk needed to determine fire effects........................................................11 

Figure 4. Illustration of concatenation field and join tables...........................................................12 

Figure 5. Overview of A current conditions using a stand-based deterministic model...............17 

Figure 6. Generating weather and fuel moisture files for FLAMMAP............................................20 

Figure 7. Run FLAMMAP............................................................................................23 

Figure 8. Fire Effects Library using FLAMMAP ............................................................... 24 

Figure 9. Overview of B current conditions using a landscape-level, stochastic model..............25 

Figure 10. SIMPPLLE’s Fire Occurrence Input for entering fire history..................................27 

Figure 11. SIMPPLLE’s Fire suppression logic screens.....................................................28 

Figure 12. SIMPPLLE’s Weather ending event screens.....................................................30 

Figure 13. SIMPPLLE’s Type of fire logic screen.............................................................31 

Figure 14. Calculating probable fire type in SIMPPLLE......................................................34 

Figure 15. Overview of C future conditions using a stand-based, deterministic model..............35 

Figure 16. Illustration of the crosswalk between future vegetative conditions and

 FARSITE fuel layers....................................................................................37 

Figure 17. Overview of D future conditions using a landscape-level, stochastic model.............38 

Figure 18. Potential treatment priorities identified by proximity to the wildland-urban 

 interface, stands ‘at risk’ of uncharacteristic fire and sensitive wildlife 

 habitat......................................................................................................40 

Figure 19. SIMPPLLE’s Treatment Logic screen..............................................................42 

Figure 20. SIMPPLLE’s Treatment Scheduler screen........................................................43 

Figure 21. Example map of change in probability of Stand Replacing Fire.............................45 

Figure 22. Example map of change in most probable fire type using SIMPPLLE output............47 

Figure 23. Using SIMPPLLE to compare probable fire effects under different 

alternatives...............................................................................................49 

Figure 24. Illustration of how to query a fire behavior library to determine treatment 

priorities...................................................................................................51 

Figure 25. Illustration of potential use for WFIP/WFSA analysis.........................................52
I. The Fire Effects Planning Framework 

 

Summary 

Each decision to suppress fire reinforces a feedback cycle in which fuels continue to 

accumulate, risk escalates, and the tendency to suppress fires grows (Miller and others 

2004). To make good decisions regarding fuels and fire, managers need to assess the 

benefits, risks, and consequences of fire and fire suppression. Without information on the

benefits of fire, justifying wildland fire as a management strategy may be unpractical. 

The need for information is immediate, but existing decision-support tools focus 

primarily on the negative consequences of fire. 

The challenge, then, is to create and institutionalize a more balanced analysis of fire (fire 

stewardship), considering both ecological and social benefits and risks. The goal can be 

facilitated by using tools that managers already have and working within existing 

planning and activity frameworks (e.g., using fire management and prediction tools to 

inform resource planning). Information on benefits must be available before major 

planning efforts (long-range planning, annual Fire Management Plan development, 

incident management). Additionally, information must be expressed in units that directly 

translate into those currently used to describe both land and fire management plans. 

These needs determined the focus of the Fire Effects Planning Framework (FEPF): 

•

•

•

•

 

to allow functional integration of fire and resource tasks; 

to express fire effects in terms meaningful to both fire and resource 

staff; and 

to enable immediate use by relying on existing tools and knowledge. 

The FEPF allows managers to systematically determine (map and quantify) where and 

under what fire weather conditions fire is likely to create benefits or pose threats to 

important ecological conditions or management targets. FEPF1

 is not a stand-alone tool; 

it is more of a conceptual model or ‘meta-model’ that sequentially links state-of-the-art, 

publicly available analysis tools, data, and knowledge to generate information for a

variety of planning scales from long-range to site-specific. The key is to develop this 

information in the off-season and have it available in digital and/or hard copy form for

decision-makers during the fire season. 

The process outlined by FEPF is straightforward (Figure 1):

Map existing conditions of each planning target (fish and wildlife, 

vegetative condition, fuels, firefighter safety, and so forth); 

1

 FEPF does not provide special software; rather it outlines an analysis process using existing software to support strategic and tactical 

fire planning. 

Fire Effects Planning Framework RMRS-GTR-163WWW 1
• Model fire; 

•

•

•

Identify how various fire behaviors (for example, surface vs. crown 

fire) are likely to affect targeted resources (causing a move towards or 

away from desired condition) and capture this in database 

‘crosswalks’; 

Use these crosswalks to build GIS map libraries that display expected 

effects of fire on social and ecological values; and 

Use the resulting map libraries to: 

 Assist in long-range planning, for instance to help analyze 

alternative management strategies; 

 Assist in mid-range planning such as developing Fire Management 

Plans to identify potential Wildland Fire Use zones and 

prescriptions for the go/no-go decision; 

 Assist incident support, for instance to identify where fire is likely 

to provide benefits or pose risks to planning targets; 

 Quantify the cumulative effect of a fire season on long-range 

planning targets; 

 Identify treatment priorities for the next season; and 

 Determine feasibility of wildland fire use (WFU), prescribed fire 

(Rx), mechanical treatment, or suppression. 

 Figure 1 - Basic elements of the Fire Effects Planning Framework process.

One can operationalize FEPF in many ways and build numerous tools into the 

framework. In this guide we describe the framework and then illustrate the process using 

examples of both stand-based and landscape-level models. We take advantage of models 

2 RMRS-GTR-163WWW Fire Effects Planning Framework
currently in use by regional fire and resource planners (FLAMMAP and SIMPPLLE, 

respectively). We do not claim that these are the ‘best’ tools for determining benefits and 

risk; they are simply two models available now. Where tools are unavailable, others may 

be substituted. Readers from these areas may still benefit from the overview and 

procedural descriptions included in this guide. This guide is not intended to replace the 

user’s guides or courses supporting individual programs that FEPF draws upon; instead, 

we offer it as a means to integrate existing programs to provide information none provide 

alone. 

As with any analysis it is important to keep in mind key limitations of the models used. 

All models are simplifications of reality; none provides an infallible or complete picture 

of the real world. Fire effects as predicted by the tools used here reflect current

knowledge of fire effects predominantly on above-ground biomass. They also assume

that fuels are homogeneous within a mapped unit – be that a polygon or a pixel. At this 

time, we – as a management and scientific community – lack the ability to quantify,

predict and spatially display ground2

 fuels, ground fire behavior, or ground fire effects3. 

Thus, it is not possible in 2005 to accurately and consistently predict effects on soils at a 

landscape level, or to quantitatively predict whether a ‘surface’ fire is likely to result in 

stand replacement of fire tolerant species due to ground fuel accumulation and

consumption. (As new information and new models become available, they can be

readily incorporated into FEPF.) 

Management Questions Addressed by FEPF 

FEPF produces information on the relative risks and benefits of fire under a variety of 

different conditions. Benefits and risks may be monetary or non-monetary. Monetary 

benefits are most likely to result from reduced cost of future management or fire 

suppression efforts than from the sale of a commodity. We focus on non-monetary 

benefits and risks. We define benefit as the number of acres that will be moved towards 

or into a more desirable condition based on reference to the area’s targets found in longrange plans, Fire Management Plans, monitoring plans, and other targets such as 

management indicator species. Risks are defined as undesirable effects resulting from

movement away from target conditions. 

FEPF’s output units are the same as those used by resource managers to track ecosystem 

targets and by fire managers to evaluate fire behavior (for example, habitat or fuels). 

Resulting spatially explicit map libraries can support fire management decisions at many 

strategic levels (Figure 2): 

Fire Effects Planning Framework RMRS-GTR-163WWW 3

 

2 Ground fire is defined as fire in the upper, organic layers of the soil horizon, as distinct from surface fire which burns fuels on top of 

the soil and in the grass and shrub layer (see DeBano or Agee). 3

 However, FOFEM5.0 begins to address this important issue. 
Figure 2 – Applications of Fire Effects Planning Framework output. 

Planning (long-term, broad-scale): prediction, quantification, and mapping of 

ability to meet future planning targets under proposed and alternative fire 

management strategies. Includes monitoring and reporting of progress. 

•

•

•

 How are proposed fire management tactics (suppression, prescribed 

fire, wildland fire use) likely to influence our ability to meet proposed

resource targets in specific areas?

Fire management plan (mid-term, broad-scale): delineation of wildland fire 

use (WFU) fire management units derived from benefit/risk maps.

 Under particular weather conditions (for example, 80th percentile 

ERC), where are there opportunities for WFU or prescribed fire? 

Fuels treatment (short to mid-term, fine-scale): identification and

prioritization of areas (a) where treatment by fire (Rx or WFU) would assist in 

meeting planning targets, and (b) where mechanical treatment is preferred

from a fire behavior or ecological perspective.

 Where are fuel conditions such that mechanical treatments should be 

used before reintroduction/application of fire?

• Incident Support (short-term, fine-scale): identification and quantification of 

ecological benefits, such as changes in habitat or fuel profiles, for 

4 RMRS-GTR-163WWW Fire Effects Planning Framework
development of Wildland Fire Implementation Planning (WFIP) Stage I, II, or 

III, and Wildland Fire Situation Analyses (WFSA); identification of areas

where light-handed suppression techniques may be most appropriate from 

ecological and cost-containment perspectives. 

 Where are opportunities for accomplishing resource objectives, 

lowering costs of fire management, or creating fuel breaks for 

populated areas or areas of potentially severe fire behavior? 

Model Considerations 

Ideally, FEPF would rest upon a model, or suite of models, that provide quantitative 

measures of the contagious processes involved in succession and disturbance (fire, 

disease, fuels, vegetation growth, and management) across the entire western United

States. Unfortunately, although a rich variety of models is currently available, none can 

‘do it all’. Because users must choose from among existing models to populate FEPF, 

users should understand something about the strengths and weaknesses of models. Our 

intent in this section is not to provide a comprehensive review of models – that is covered 

elsewhere (see for instance Barrett 2001, Lee and others 2003) – but to raise a couple of 

basic differentiations users should consider before initiating analysis: contagion and 

variability. 

In general, the choice of model to incorporate into FEPF is between a deterministic, 

stand-based model and a stochastic, landscape-based model. Landscape-level models (or

landscape dynamic simulation models, LDSMs) explicitly incorporate spread4

 into the

determination of stand conditions. Most LDSMs are stochastic, producing multiple

outcomes for a given set of inputs by varying the inputs according to some distribution. 

The distribution is often an estimation of natural variability. The advantage is that

incorporation of both experimental and experiential knowledge allows construction of 

complex and often comprehensive system models. A key disadvantage is that it is not 

possible to calculate significance or error measures for those parts of the model based on 

expert opinion, and it is difficult to evaluate how closely predictions will emulate the 

actual future. 

Alternatively, stand-level models do not account for spread between stands but can often 

be mapped spatially. Most stand-level models are deterministic. They produce a single 

outcome for a given set of inputs. Their advantage is that the significance and error 

associated with the experimental data are known. Their main weakness is that they must

often greatly simplify the ecological processes of interest, incorporating only those pieces 

that can be measured and controlled in an experiment. For both types of models, it is 

difficult to disentangle error from variability. Further, when working with any type of 

model, one must remember to treat results only as indicators of reality. 

Fire Effects Planning Framework RMRS-GTR-163WWW 5

 

4

 Spread, or contagion, refers to whether what is adjacent has an impact on behavior or processes within each polygon (stand) or pixel.
To determine which type of model best suits the user’s needs for FEPF, consider these

 questions: 

•

•

•

•

 

Where are you most comfortable accepting error/variability: 

 incorporated into the outcome → use a stochastic model, or 

 excluded from the outcome → use a deterministic model? 

Do you desire a quantitative measure 

 of error → possible for deterministic models, or 

 of variability → possible for stochastic models?

Is your primary interest 

 to develop information about the current situation → either, or 

 to compare results of alternative management strategies → stochastic?

How important is spread: 

 can you accept stand-based predictions (no spread), or 

 do you need to consider the influence of spread? 

We describe how to use FEPF with two models: a deterministic, stand-based model 

(FLAMMAP, Finney, in press), and a stochastic, landscape model (SIMPPLLE, Chew and 

others 2004, Chew 1995). Though both are spatial, they have very different architectures. 

Briefly, FLAMMAP is a non-contagious, deterministic program based on empiricallyderived fire process/behavior equations (for example, Rothermel’s [1972] and Albini’s 

[1976] fire behavior equations).While it maps fire behavior across an entire landscape, 

calculations are performed on each pixel independently. FLAMMAP uses quantified fuels

information for a single point in time. To enable consideration of future fire behavior, a 

vegetation simulator must be used to create future fuels data. Because FLAMMAP is based 

on process equations, it is easily transported to any situation in which base data on 

vegetation and fuels are available. SIMPPLLE is a stochastic, contagious vegetation 

dynamics simulator developed from both empirical and knowledge-based sources. It 

incorporates significant process variability (climate, fire weather, suppression efficiency, 

fire start location) and calculates fire effects by considering biophysical and vegetative 

conditions in both the ‘initiating’ and ‘receiving’ polygons/pixels. SIMPPLLE does not 

generate or track quantitative fuels information, but because it incorporates fire effects, it 

can be used to model either the current or future situations. Because SIMPPLLE 

incorporates significant local information about a number of complex ecosystem 

processes (insects, disease, fire) which science has yet to define or describe 

mathematically at the landscape scale, SIMPPLLE must be parameterized locally 

(generally by forest or region, BLM Resource Area, or planning unit)5

. 

We do not recommend use or avoidance of any particular model; nor will following this 

protocol provide a black-box that will give you the ‘right’ answer. Following the 

Framework will provide you with information relevant to your area and be useful to both

fire and resource management to aid in decision-making. 

5

 SIMPPLLE datasets have been created and parameterized for a number of National Forests and BLM Resource Areas around the 

west, predominantly in the Northern Rockies, but including southern California, the Kenai and Michigan’s Upper Peninsula (Chew 

and others 2004).

6 RMRS-GTR-163WWW Fire Effects Planning Framework
Software, Skills and Data Requirements 

Software 

FEPF requires several existing software programs. For all versions of the analysis, a

spatial mapping program – such as ARCVIEW or ARCGIS – is required. 

To develop map libraries of fire behavior and fire effects programs : 

• FIREFAMILYPLUS – to generate weather stream information for FLAMMAP;

•

•

•

FLAMMAP – to generate fire behavior maps; and

FARSITE – to generate weather files from the FIREFAMILYPLUS analysis. 

To generate information on potential future landscapes, or to consider current conditions 

from a landscape perspective, you will also need a landscape dynamic simulation model 

(LDSM). For this document, we used 

SIMPPLLE, but are currently working with FVS-FFE as well. Other LDSMs, 

such as LANDSUM or RMLANDS, may also be used. 

To generate other fire effects, we used FOFEM for emissions and first order fire effects 

such as mineral soil exposed or soil heating. One can also build in sedimentation or runoff models. Disturbed-WEPP is an existing stand-based model. 

With the exception of the ESRI products (ARCVIEW, ARCGIS), all may be downloaded from 

the Internet at no cost (visit www.frames.gov, SIMPPLLE site). ESRI products are 

proprietary and should be obtained from your IT specialist. Contact the SIMPPLLE

developers group (Chew and others 2004) to determine whether SIMPLLE is available for 

your area. 

Skills, expertise and time 

While this guide is intended for both expert and casual users of the identified software 

programs, a basic understanding of the tools is assumed. Although the fire-related tools 

are easier to use “out of the box” than ESRI products (the ARC suite), consultation with 

fire experts is recommended to ensure proper identification of key parameters and model

specification. Information in the detailed TASK sections provide tips on how to 

accomplish each ACTION and assists in methodological consistency and documentation. 

The most time-consuming aspect of FEPF – and the one requiring coordination among 

the most people – is likely to be specification of fire effects. In the absence of 

comprehensive knowledge of fire effects based on experimental results, much of the 

information to build the fire behavior – fire effects crosswalks – will need to be inferred

from available models, literature, and local knowledge. These crosswalks should be 

developed with or reviewed by relevant experts. 

Fire Effects Planning Framework RMRS-GTR-163WWW 7
Data 

FEPF input data are those required and/or developed during land management planning 

activities: GIS data on vegetation, fuels, fire weather, fire occurrence, and hydrology, and 

resource targets for fisheries, wildlife, recreation and silvics, and so forth. The models 

we’ve used to operationalize FEPF will require manipulation of these general datasets as 

outlined below. 

To develop planning target maps, you will need to identify characteristics of the target 

that can be linked to GIS map attributes (of vegetative, soils, and/or aquatic condition). 

To develop fire behavior maps with FLAMMAP you will need: 

Daily fire weather data. Daily weather data is processed through 

FIREFAMILYPLUS to identify fire behavior parameters, such as Energy Release 

Component (ERC) values, at threshold fire weather conditions (for example, 

80th%, 90th%, 97th%, or 99th%). This information is then used to produce wind 

and weather files for FLAMMAP. 

•

•

•

•

•

•

Digital DEM and fuels data. These include the necessary GIS grids to create a 

FLAMMAP landscape file (.lcp): fire behavior fuel model, canopy fuels data 

(stand height, crown closure, crown base height, crown bulk density); and 

digital elevation models for calculating separate grids of aspect, slope, and 

elevation. 

To develop probability of fire, of fire type, and of return interval, using SIMPPLLE, 

you will need, in addition to a modified existing vegetation cover and a series of 

parameter files for ecosystem processes (fire, insects/disease, succession): 

Historic fire starts. This cover is used to determine appropriate number of 

starts per simulation time step. 

Fire Management Units (or fire management zones, whichever is used for 

tactical decisions). This cover is used in combination with the historic start 

information to determine how to distribute starts spatially. Though a fire 

management zone map is most often used, one could just as easily use an 

ecologically based map such as Potential Vegetation Type to distribute fire 

starts. This cover can also be used to calculate suppression costs.

Land use. The FMU/FMZ or Land Use cover is used to specify type of fire

management strategy (for example, Wildland Fire Use) and fire suppression 

efficiency rates. 

To develop a map library of fire effects, you will need, in addition to the vegetation

cover: 

Ancillary resource data. Additional data are used to predict probable locations 

of management indicators or resource targets. Examples might be aspect, 

elevation, soils, or aquatic data. 

8 RMRS-GTR-163WWW Fire Effects Planning Framework
II. Guide to FEPF Using Demonstrated Models 

How to Use This Guide 

This guide is broken into two main sections: an overview of FEPF (above), and a guide to 

FEPF. The following guide is broken into three chapters covering: (1) development of 

crosswalks, (2) development of map libraries, and (3) use of map libraries. Chapter 2 

contains four non-sequential sub-sections outlining development of map libraries for

current and future conditions using both a stand- and a landscape-based model. Which 

sub-section you choose in Chapter 2 depends on the type of analysis you wish to perform 

and the model assumptions you are willing to accept: 

• current condition analysis using a stand-based, deterministic model; → 2A

•

•

•

current condition analysis using a landscape-level, stochastic model; → 2B

future condition analysis using a stand-based, deterministic model; → 2C

future condition analysis using a landscape-level, stochastic model → 2D

Within each chapter, we describe the Framework using three headings with an increasing 

order of detail - ACTION, DISCUSSION, TASKS - to assist users of various skill 

levels. This guide also provides a number of FORMS to assist in planning and tracking 

analyses. EXAMPLES are offered to illustrate the ACTION.

ACTION briefly identifies the task and outcome of the section. Users who have already 

generated this information or know how to use the programs utilized in the step can skip 

ahead to the next ACTION. Such users may still be interested in reading the discussion 

section to ensure that their existing data, or alternative method, include the information 

necessary in future steps. Information included under this section should also be helpful 

in determining how outcomes might differ if alternative processes are used. 

DISCUSSION provides background on the goal of the action and identifies some of 

the alternatives and key assumptions. 

TASKS provide a more detailed step-by-step guide to generate the outcome using 

specified computer models or programs. This subsection is intended as a mechanical 

guide to supplement the existing user’s guide for each program; it does not substitute for 

consultation with the relevant expert or for training on the programs. Generation of each 

outcome may require consultation with local experts to determine parameters of interest,

thresholds, and data sources. 

The FORMS subsection offers forms we have developed for collecting or evaluating 

input data. 

Fire Effects Planning Framework RMRS-GTR-163WWW 9
We supplement the guide with sidebars using examples from our work on the Bitterroot 

National Forest. 

Chapter 1. Developing rule-sets (crosswalks) to link fire behavior to 

fire effects for each management target 

FEPF uses effects of fire on ecosystem targets (for example, management goals or 

Desired Future Conditions/processes) as the criteria for evaluating the desirability of fire.

This is calculated in a spatial context to allow easy quantification of benefits and risks 

and because many targets contain spatial criteria. Thus, it is important to be able to link 

some critical aspect of the target or ecological process to a map-attribute affected by fire: 

vegetation, soils, water. 

Linkage of fire behavior to fire effects rests on the development of rule-sets, or 

crosswalks. We found it helpful to break these crosswalks down into logical steps, first

assessing effects to vegetation, then basing species effects assessments on changes in 

vegetation, and finally determining whether these changes are a benefit – movement

towards the desired condition, or a risk – movement away from target conditions. 

Following this logic, you will need to develop several crosswalks, which may be 

combined into a single crosswalk upon completion (Figure 3): 

(1) An initial crosswalk to identify how to map management targets spatially 

using available data (for example, use a species-habitat relationship to map a 

species). 

(2) Another to identify how fire of various intensities/severities affects the 

primary dataset (generally vegetation for terrestrial species, soils for 

sedimentation/erosion processes, and/or aquatic for aquatic species) – firstorder. 

(3) In some cases, you can use the initial crosswalk (1) to determine fire effect, 

but for many species, you will need a third crosswalk to translate the firstorder effects into second order habitat effects. 

(4) A fourth crosswalk may be necessary to specify how changes in habitat will 

affect the target. 

(5) A final crosswalk containing the rules used to determine whether fire will 

confer a benefit or a risk. 

These steps should be repeated for each target. Sources of information for determining 

most likely fire effects can be obtained from local expert knowledge, quantified from

previous fires, or predicted from other computer tools such as FOFEM or SIMPPLLE. 

Confidence in the final maps, and in the plans based on these maps, will increase if

appropriate planning and resource staff is included in this step of the process. 

10 RMRS-GTR-163WWW Fire Effects Planning Framework
 Figure 3 – Crosswalk needed to determine fire effects. 

Map Ecosystem Targets or Processes 

DISCUSSION: To ensure easy updating and defensible cross-walks (science-based

and transparent), base the mapping criteria on parameters and attributes contained in preexisting digital vegetation (GIS) data. For instance, if you have an understory-nesting 

bird species and a vegetation cover that maps only cover type and ‘Habitat Type’, 

consider using the ‘Habitat Type’ attribute if you can point to peer-reviewed literature 

that identifies the particular understory conditions required by the bird species as being 

present in specific habitat types. Clearly identify the relevant ecological and scientific

basis of the link between the target and map attributes and store this in a new text (string) 

attribute. 

TASKS

1. Create a ‘join’ field in your GIS vegetation layer and create a ‘primary 

ecosystem component’ field (Figure 4). Calculate this field as a concatenation 

of the individual vegetation attributes (for example, cover type, size class,

density, height). This field should be calculated in the baseline dataset and in 

any future simulation sets. This will be one of your primary ‘join’ fields. Once 

created, changes in either fire behavior or vegetation structure are easily remapped into new effects maps using this crosswalk. For targets that respond 

Fire Effects Planning Framework RMRS-GTR-163WWW 11
most closely to fire-related changes in soils or water, complete this step for 

those baseline datasets as well. 

1.1. Count the number of classes of each attribute and determine the 

appropriate multiple of 10 sufficient to enable concatenation into a unique

descriptor. For example, in our dataset, we had 12 classes of cover type, 8 

structure classes, 4 densities and 3 stand height classes. We created a 

numeric value as follows: new value = (cover type*10000 + structure*100 

+ density*10). (We left the ones position open to multiply this unique 

descriptor “<FLAMMAP grid output variable>*1” in a later step.) Capture 

the translation of text to numeric values in text files (.txt). 

1.2. Create the new field and populate it. 

Figure 4 - Illustration of concatenation field and join tables. 

12 RMRS-GTR-163WWW Fire Effects Planning Framework
Define Fire Effects on Ecosystem Targets or Processes 

DISCUSSION: This is generally a two step process, first to identify how fire is likely 

to affect the vegetation (or soils, water), then how these changes will affect the target of

interest. 

STEP 1. Define how various fire behaviors are likely to affect the primary 

ecosystem component ________________________________________ 

TASKS

1. Create a .txt file (or table) identifying how the various fire behavior classes you 

will use are likely to affect the primary ecosystem component. For example, if 

using the crown fire potential option in FLAMMAP, is a surface fire likely to be 

light, moderate, or stand-replacing in a stand of sapling sub-alpine fir? 

STEP 2. Define how the resulting changes in the primary ecosystem 

component is likely to affect each target __________________________ 

TASKS

1. Create a .txt file (or table) identifying how change in the primary ecosystem

component is likely to affect each target. For example, is moderate severity 

fire in a stand of sapling sub-alpine fir likely increase or decrease habitat 

quality for the target species?

2. (If necessary) specify additional spatial and/or temporal criteria crosswalks. 

Define Risk and Benefit 

Benefits and risks may be identified for a single time-step (FLAMMAP, SIMPPLLE), 

under particular weather conditions (FLAMMAP), or compared across different 

management scenarios (SIMPPLLE). 

ACTION: Determine whether changes in each target are a benefit or a 

risk. This determination will depend upon your targets and the basis for those targets.

For instance, if your goal is to manage a resource within a certain ‘range of 

variability’, then you may need only 2 classes – a ‘desirable’ or ‘characteristic’ class 

that is interpreted as a benefit, and an ‘undesirable’ or ‘uncharacteristic’ class that is a 

risk. If there is a greater range of effects – or additional criteria, such as patch size, 

you may need additional classes to adequately assess risk or benefit.

Fire Effects Planning Framework RMRS-GTR-163WWW 13
TASKS 

1. Create a .txt file (or table) identifying whether the change in the target assists in 

meeting long-term planning targets (benefit) or inhibits attainment (risk). For 

example, is a decrease in habitat quality for the target species acceptable?

14 RMRS-GTR-163WWW Fire Effects Planning Framework
SIDEBAR 1: Determining fire effects on lynx on the Bitterroot National Forest 

Step 1. Map existing lynx habitat.

Based on the Lynx Conservation Assessment Strategy and discussions with biologists 

on the Bitterroot National Forest, we defined critical habitat as lynx foraging habitat. 

Foraging habitat in this area consists of 15-45 year old stands of trees above 6200’ 

elevation (Ruediger and others, 2000). Since we did not have stand age in our 

vegetation data, but did have structure, we used stand dynamics information to 

determine that sapling and pole size classes meet the age criteria. We combined these 

criteria to map existing lynx foraging habitat. 

Step 2. Create a crosswalk between fire behavior and vegetation effects.

We knew we would use Crown Fire Potential as our measure of fire behavior for 

modeling effects on lynx. FLAMMAP identifies four classes in its prediction of crown 

fire potential (no fire, surface fire, passive crown fire, and active crown fire). This 

crosswalk consists of three columns: the primary ecosystem component (our combined 

numeric value describing all four vegetation characteristics), the four fire types, and a 

final column to hold our vegetation effect. For instance, we focused on fire types 

resulting in stand replacement within lynx foraging habitat. 

Step 3. Create a crosswalk between vegetation effects and lynx foraging 

habitat. 

Lynx foraging habitat develops 15-39 years post-fire in conifer habitats at the proper 

elevations. This simple crosswalk identified which of the combinations in Step 2 met 

this criteria.

Step 4. Identify fire and habitat effects desirable and undesirable for 

lynx. 

Stand replacing fire in current foraging habitat removes lynx habitat for up to 15 years. 

Stands older than 45 years no longer provide foraging habitat and in these areas a stand 

replacing fire will create future habitat. If the key concern is current foraging habitat, 

then a stand-replacing fire in existing habitat is undesirable and the creation of future 

habitat may be neutral. If the concern is to maximize habitat in the future, then fire may 

be a benefit in both. An additional ‘rule’ or crosswalk might consider either the spatial 

arrangement of foraging habitat or the proportion of habitat desired in each Lynx 

Assessment Unit. In this last case, the target might be a certain proportion of area in 

habitat and the measure of risk or benefit would be whether fire is likely to move the 

unit towards or away from that target. 

Fire Effects Planning Framework RMRS-GTR-163WWW 15
Chapter 2. Creating map libraries for analysis of ... 

This chapter is broken into four non-sequential subsections. Choose whichever 

subsection corresponds to the time-scale of primary interest and the model type you’re 

using: 

A. Current conditions analysis using a stand-based, deterministic model; 

B. Current condition analysis using a landscape-level, stochastic model; 

C. Future condition analysis using a stand-based, deterministic model; or 

D. Future condition analysis using a landscape-level, stochastic model. 

A 

Current Conditions Using a Stand-based, Deterministic Model 

This section outlines the development of map libraries of fire behavior using a standbased, deterministic model (Figure 5). The goal is to predict fire behavior under a suite 

of fire weather conditions meaningful for fire management. You will need to identify and 

use a fire behavior metric (for example, Energy Release Component) and percentile 

weather thresholds (such as 80%, 90%, 99%) used by local fire officials for strategic and 

tactical decision-making. Hopefully the parameters chosen are the same as those in your 

Fire Management Plan (FMP). FLAMMAP provides the opportunity to predict a number of 

fire behavior parameters: crown fire activity, flame length, fire line intensity, heat per 

unit area, and so forth. We chose to use Crown Fire Activity (CFR) because effects on 

our ecological targets can be tied to changes in vegetation. For watershed effects one may 

want to choose heat per unit area (HPA) or other output that can be used to determine 

effects on soil or aquatic systems more directly. 

We use FLAMMAP (FLAMMAP2 , http://fire.org) as our primary model, FIREFAMILYPLUS to 

summarize and identify threshold weather conditions, FARSITE to generate the wind and 

weather files, and either FARSITE or FLAMMAP to create the landscape file required by 

FLAMMAP. 

Step 1. Generating the landscape input file _________________________ 

 

ACTION: Use FLAMMAP (or FARSITE) to generate the landscape file (.lcp). 

16 RMRS-GTR-163WWW Fire Effects Planning Framework
DISCUSSION: Due to the memory requirements of FLAMMAP, it may be necessary to 

break large geographic areas into smaller sections, generate separate FLAMMAP output for 

each, then merge the sections in ARC. 

All input GIS layers must have the same extent. Clipping all with the same boundary 

achieves this. For this protocol, you must also have the four canopy fuels data layers 

(stand height, crown base height, canopy cover, crown bulk density). 

TASKS 

1. Create a landscape file (.lcp). 

Figure 5 - Overview of A current conditions using a stand-based deterministic model.

Step 2. Generating fire weather input files _____________________ 

ACTION: Use historical weather data (in FIREFAMILYPLUS and/or Excel) to 

generate weather input files. 

DISCUSSION: To define the typical threshold conditions relevant to any fire year, 

you will need to determine the appropriate temporal range of fire weather analysis. 

FLAMMAP uses daily weather data, so the question becomes over what range should 

threshold values be calculated – weeks, months, years? You may want to consider 

making some preliminary runs using different analyses to determine the appropriate 

temporal frame for your analyses. In our studies, we compared FLAMMAP outcomes using 

Fire Effects Planning Framework RMRS-GTR-163WWW 17
a variety of different criteria: separate analyses by month, a single analysis for the entire 

fire season, and separate analyses for wet/cool, hot/dry, and normal years. For the 

Bitterroot National Forest, the outcomes were not highly variable; therefore we chose to 

calculate threshold weather conditions based on the entire fire season. 

For each landscape modeled, consult with local fire experts to determine the most 

appropriate weather stations to use and the areas over which they apply. For instance, on 

the Bitterroot National Forest, we determined that fire weather and behavior across the 

forest was sufficiently different to warrant using different weather stations to model 

different areas – a SIG file combining all weather stations for application across the entire 

forest was inappropriate. 

 2.1 Identify threshold weather conditions 

TASKS 

1. Use FIREFAMILY PLUS (FF+) to complete the Data Form found at the end of 

this section. The Data Form captures key fire weather threshold values for 

running different fire scenarios in FLAMMAP. Choose a single weather station or 

SIG (special interest group) file, and specify dates to use in the analysis (years, 

months, days). 

1.1 Identify fire weather thresholds to analyze. We chose values associated 

with the 80th, 90th, and 99th percentile weather conditions associated with

the Energy Release Component (ERC) since these are identified as 

decision thresholds in the Fire Management Plan. On the Data Form

under Fire Parameter Values, use one box for each parameter. For 

example, the first box might be for ERC percentile values, the second for 

wind speed values, and the third for 10-hour total live fuel moisture 

(TLFM) values. Proceed with the steps below to fill the percentile values

for each of the boxes. 

1.2 Identify percentile ERC values. In FF+ choose WeatherÆSeason 

ReportsÆSeverity SummaryÆERCÆcritical percentile of 80ÆGreater 

ThanÆRun. Read the ERC output value at the top of the output, 

“...percentile values greater than x” and record this number on the Data 

Form under Fire Parameter Values, 80th percentile. There is no need to 

save anything else in the output. Repeat this task for 90th and 99th

percentiles and record on the form. At the end of task 1.2 you should have 

three numbers recorded in one of the Data Form boxes under the Fire 

Parameter Values heading. 

1.3 Identify percentile wind speed. In FF+ choose WeatherÆSeason 

ReportsÆSeverity SummaryÆWindspeedÆ critical percentile of 

80ÆGreater ThanÆRun. Read the wind speed value at the top of the 

output, “...percentile values greater than x” and record this number for 

80th percentile; repeat for 90th and 99th percentile wind speeds. At the end 

of task 1.3 you should have a number recorded for each percentile of 

interest in one of the Data Form boxes under the Fire Parameter Values 

heading. Repeat step 1.3 if you want to obtain 1-hour and 10-hour fuel 

18 RMRS-GTR-163WWW Fire Effects Planning Framework
moisture values independently from ERC (since there is likely little 

correlation between ERC thresholds and fine fuel moistures). 

1.4 Generate fuel moisture and weather values. In FF+ choose 

WeatherÆSeason ReportsÆDaily Listing. In the dialogue box, select 

formats MM/DD/YYYY and HH:MM. Leave all other defaults in the top

half of the box. Under “Available Variables” choose all the variables you 

may want to use in your analysis. We chose ERC, wind speed, all fuel 

moisture parameters (1,10, 100, 1000), precipitation, minimum and 

maximum temperature, minimum and maximum RH, herbaceous and 

woody fuel moistures, state of the weather, and wind direction. Press OK 

at bottom of dialogue box. Save output as *.txt file, and during the next 

step be sure not to delete any data in this file as it is used differently in two 

independent tasks. 

1.5 Calculate percentile values for each fuel moisture and weather value. In 

MS Excel, open the *.txt, separate date into three columns, and determine 

average values for different weather parameters by percentile to use in the 

FLAMMAP simulation. Do this by opening the *.txt document as a fixed 

width file in Excel, adjusting columns, and sorting by ERC. Using the 

ERC threshold values from task 1.2, determine averages for weather 

values. For example, if 80th percentile threshold is 27, and 90th percentile 

threshold is 32, take all the ERC values in the Excel spreadsheet from 27 

to 31 and average these values to get final parameters for min/max 

temperature, min/max RH, and fuel moistures of choice. Because ERC 

does not consider wind, we did not use ERC thresholds to calculate these 

variables. Repeat for all percentiles. 

1.6 To determine values for wind speed, the maximum value is recorded on 

the Data Form by taking the same *.txt file and sorting it by wind speed. 

Using the threshold values determined in task 1.3, again parse out the data 

into sections representing the 80th, 90th, and 99th percentiles. Here, rather 

than use the average wind speed calculated in these data sets, use the 

maximum value. Consult with local fire behavior experts to determine if 

these results are reasonable. Because weather stations only record one 

wind speed per hour, they can under-characterize winds meaningful to fire 

behavior (NWS website). We decided to multiply the 90th percentile 

maximum value by 1.5 and the 99th percentile maximum value by 2.5 to 

create wind speeds for the final simulation. Repeat for other parameters of 

interest that are independent of ERC, such as fine fuel values. 

2.2 Use threshold weather values to develop FLAMMAP input files 

TASKS 

1. In FARSITE, choose Input|Define Weather/wind types. An input 

window will appear (Figure 6). (Alternatively, you can use the Custom editors, 

changing values found under New WTR and New WND files to those of your 

FireFamilyPlus analysis.) 

Fire Effects Planning Framework RMRS-GTR-163WWW 19
(a) 

(b) 

Figure 6 - Generating weather and fuel moisture files for FlamMap. 

2. Enter variables relating to each of the adjective ratings (High, 

Moderate, Low) of interest. 

2.1 Select Weather Type (for example, Moderate) 

2.2 Input Elevation from weather station, or average of weather stations used 

on weather form. 

2.3 Set Rain Amt. at 0 in. 

2.4 Set T Hi and T Lo and H Hi and H Lo for this Weather Type and the 

hours when this hi/lo occurs. 

20 RMRS-GTR-163WWW Fire Effects Planning Framework
2.5 Click on radio button to indicate coarseness of defined winds (we used 4 

hours), then click on Wind button to define winds. In this screen, input 

Wind speed for this “weather type” at the 1200 hr and 1600 hr. Use 0 

cloud cover and 0 wind direction (if uphill winds being modeled). Since 

wind speeds at hours outside of burn period will affect fuel moistures 

during the conditioning period, input an appropriate number for these 

hours. We used 2 mph for Moderate weather types during 2000, 2400, 

0400, and 0800 hours. We used 4 mph for High and 6 mph for Extreme. 

This is a judgment call. 

2.6 Repeat steps a-e for all other Weather Types of interest. 

2.7 Save As *.wwd under the Input directory in FLAMMAP where you are 

storing the files for running this geographic area in FLAMMAP. 

3. Use the newly created *.wwd file to generate *.wtr and *.wnd files

in FARSITE : 

3.1 In FARSITE, choose Input|Generate from types (*.wtr/*.wnd). An input 

screen will appear. 

3.2 Click on Use WWDefs button and retrieve *.wwd file. 

3.3 Set Month and Day to set the conditioning period. This should be a full 

seven days before your intended fire start date. Our fire start date was 

August 1, so we set start date as July 26 and end date as August 1. 

3.4 Click on Edit Table to auto-fill dates from above. 

3.5 Next, fill in the W-type column by clicking on the small square buttons 

near bottom of input window and the NexT button to enter the adjective 

rating for these wtr/wnd files. For example, we need files created for the 

“moderate” weather scenario; therefore we click on the “M” and “next” 

for each day in this conditioning period. Make sure you hit “next” after 

each entry—especially the final entry. 

3.6 The final step is to click on Generate WTR and WND file. Enter a 

filename that will allow you to differentiate this weather condition from

others because this step is repeated for each “weather type.”

3.7 You should get a FARSITE message indicating that both a .wtr and .wnd 

file were created. 

3.8 Repeat steps a-g for all Weather Types that must be generated from this 

wwd file. 

3.9 Repeat steps a-h for additional *.wwd files corresponding to other 

geographic areas if you have parsed out your landscape. 

4. Generate a new initial fuel moisture file (*.fms) file using FARSITE or by 

editing a default moisture file. 

4.1 In FARSITE, go to InputÆProject Inputs and click on the “Æ “ to the right 

of Moistures (*.fms). 

Fire Effects Planning Framework RMRS-GTR-163WWW 21
4.2When the dialogue box appears, click the New .FMS File button. A new 

file with fuel models 1-50 and default fuel moistures is displayed in the 

text box. 

4.3 Edit these data based on moisture percentiles on your Data Form. Make 

sure to include fuel moistures for all fuel models in your .lcp. 

4.4 click Save *.fms file and save in the directory where your FLAMMAP data 

are stored. 

4.5 Repeat steps a-d for each weather scenario (80th, 90th, and 99th

percentiles) and each subunit as needed. 

Step 3. Creating a fire behavior library ___________________________ 

ACTION: Create fire behavior maps for each fire weather threshold. 

DISCUSSION: In addition to the input files, you will need to decide and manually set 

for each FLAMMAP run: wind direction, wind speed, time of burn, fuel conditioning 

period, and live fuel moisture content. These values will determine conditions for the run, 

whereas the .fms, .wnd, and .wtr files are used to condition the fuels. 

In our demonstrations, we specified Wind Blowing Uphill. Based on discussions with 

Mark Finney (who has found that wind speeds across the western United States – one of 

the major determinants of crown fire activity – are often 2.5 times those recorded in daily 

weather streams), we multiplied our 90thpercentile winds by 1.5 and 99th percentile winds 

by 2. We generally specified a 2 or 4 p.m. (1400 or 1600) burn to capture the most active 

time of day for fire. We adjusted Foliar moisture content from 120 % at our low end 80th 

% ERC to 80 % at the high end 99th percentile ERC. Whatever time of day you choose 

for your analysis, the total length of the fuel conditioning period should be at least seven 

days (Figure 7). 

Users may also want to consider generating additional CFR maps for each fire behavior 

threshold by varying the input parameters (uphill as well as directional winds, for 

instance). Combining all output files for a given weather threshold will create a 

probability map. 

Repeat this step for every subunit in your analysis area. 

TASKS 

1. Load the FARSITE landscape file (.lcp) into FLAMMAP. 

2. Choose Analysis Area - New Run – Inputs 

2.1 Load fuel moisture (<%>.fms) files, weather(<%>.wtr), and wind 

(<%>.wnd) for the relevant threshold condition. 

2.2 Set Winds Blowing Uphill. 

2.3 Set Wind Speed (20’ windspeed) for the particular percentile class. 

22 RMRS-GTR-163WWW Fire Effects Planning Framework
 OR set Wind Direction and Azimuth if specifying wind direction other 

than uphill. 

3. Establish fuel conditioning period.

4. Establish time of burn. Setting the Fuel Moisture Conditioning Period, 

End Day, Time according to times set in your .wtr and .wnd files. 

5. Choose Outputs and check appropriate output grid boxes. Run the 

simulation. 

Figure 7 - Run FlamMap. 

6. Export the outputs. Right click on Run Name, right click on the appropriate 

output grid, Save as .asc file. If running ROS, be sure to export with 2 decimal 

places and in m/min. 

Fire Effects Planning Framework RMRS-GTR-163WWW 23
7. Import the .asc grids into GIS, merge together, and re-project to match resource 

base data (if necessary). 

Step 4. Creating a fire effects library ___________________________ 

ACTION: Use the fire effects crosswalk to link fire behavior to fire 

effects. 

DISCUSSION: This step will result in a number of fire effects maps which can be 

printed out and/or saved on a CD and made available to management units and included 

in fire data packages for incident support (Figure 8). 

TASKS 

1. Merge the FLAMMAP output grid to your baseline GIS data (vegetation, 

soils and/or aquatics), preserving all attributes of each layer. (This requires that 

your baseline data be in GRID format.) 

 1.1. Add new field, calculate the concatenated field for your baseline data (see 

Mapping Ecosystem Targets, discussed previously). 

2. Link fire effects crosswalk to merged GIS data. Use the concatenated 

field as the ‘join’ item. Sort and check to make sure you have all possible 

combinations in your crosswalk (if you find blanks, then you are missing those 

combinations). Adjust your crosswalk as necessary (see Figure 4). 

3. Create fire effects library. Save each output of fire effects as a separate GIS 

layer. 

 

Figure 8 - Fire Effects Library using FlamMap.

24 RMRS-GTR-163WWW Fire Effects Planning Framework
B 

Current Conditions Using a Landscape-level, Stochastic Model 

 

In this section we illustrate how a stochastic, landscape dynamics simulator program can 

be used to identify current fire behavior and likely effects on key resources (Figure 9). 

The goal is to identify probabilities of fire and probable fire type under a variety of 

weather and climatic conditions. We use SIMPPLLE a landscape-level, stochastic model 

that predicts and tracks vegetative succession and disturbances (fire, insects, disease, 

management) at a landscape scale. SIMPPLLE‘s rule-based architecture allows users to 

adjust many successional and disturbance parameters (type of fire, likelihood of wind 

driven event, and so forth). Before running a simulation, users should always evaluate the 

default values and inquire about revised files for their area. 

We used SIMPPLLE Version 2.2 (based on irregular polygons) and 2.3 (in its ‘pseudogrid’, or regular polygon, mode) and conducted analysis in both ArcView 3.2 (with 

Spatial Analyst) and ArcGIS 8.1. 

Figure 9- Overview of B current conditions using a landscape-level, stochastic model. 

Fire Effects Planning Framework RMRS-GTR-163WWW 25
Step 1. Parameterizing the model ________________________________ 

ACTION: Evaluate and update, if necessary, SIMPPLLE parameter defaults. 

DISCUSSION: SIMPPLLE uses vector coverages to distribute ecological processes (fire 

and treatments) and track changes within and across simulations. While it refers to these 

as ownership, Fire Management Zone (FMZ), and an optional Special Area, these can 

actually refer to any geographic subdivision or reporting unit the user decides. Please 

refer to the SIMPPLLE user’s guide for instructions on how to change attribute fields and 

create SIMPPLLE coverages. 

SIMPPLLE uses graphical interface screens to assist users in editing the various logic 

screens. Because these screens can only be viewed separately, we have created an Excel 

spreadsheet to capture the logic comprehensively. Initially, these must be filled out 

manually, but once created, they provide a quick easy-reference for defaults, as well as 

reviewing and tracking changes. All defaults for the Bitterroot National Forest (11/2003) 

are shown comprehensively in run_template.xls (see Forms and CD at end of document). 

1.1 Create an historical fire start cover 

DISCUSSION: SIMPPLLE distributes fire starts by converting the number of starts 

per FMZ (annual or decadal) into a per-acre basis, multiplying this by the total 

acreage in that FMZ and randomly distributing the resulting starts within the assigned 

FMZ. Historical start data is entered into the FMZ Editor. The fire size designations 

on this screen assist in a cost estimation, not in determining fire growth or ultimate 

fire size. Users may also choose to confine fire and/or treatments to particular areas 

(by ownership or management strategy, for example, wilderness or wildland-urban 

interface) by eliminating starts in a geographic area. Historical fire data may be 

obtained from KCFAST on the web. 

TASKS 

1. Overlay historical fire starts with your Fire Management 

Zones/Units (FMZ). Sum the number of lightning starts (human, too, if 

appropriate) by FMZ. Sum by decade if your SIMPPLLE time step will be 10 

years; sum by year if running the program on an annual basis. Calculate the 

acreage of each FMZ. 

2. Populate the FMZ Editor screen with the fire start information. 

Select System Knowledge→ Vegetative Process→Fire Occurrence Input and 

enter your fire start information by Fire Management Zone (Figure 10).

3. Save file and track file name for future reference (see the fire occurrence 

workshop in run_template.xls at the end of this document for an example). 

26 RMRS-GTR-163WWW Fire Effects Planning Framework
Figure 10 - SIMPPLLE’s Fire Occurrence Input for entering fire history. 

1.2 Set fire management and fire weather logic 

DISCUSSION: SIMPPLLE offers users a number of opportunities to adjust both fire 

suppression effort and efficiency rates. Specific file names (and the manner in which 

the file affects fire management) include: 

• Fire suppression for Class A fires (effort and efficiency), 

•

•

•

•

•

Fire suppression for beyond Class A (effort), 

Weather ending events for fires < 0.25 acres (efficiency), 

Weather ending events for fires > 0.25 acres (probability), 

Extreme fire probability (probability), and 

Regional climate (weather) 

To identify the consequences of changing or establishing a Wildland Fire Use (WFU) 

zone, these are the files you need to adjust (see SIMPPLLE user’s guide to check field 

type). 

The qualitative weather settings, which cannot be adjusted by the user, are based on 

weather and climate conditions over the past 50 years – generally the time period over 

which our knowledge of ecosystem processes has been developed. 

TASKS 

1. Evaluate and adjust Fire Suppression for Class A fires data, as 

necessary. This screen allows one to model various suppression efficiency 

rates, or to consider the effect of not suppressing any Wilderness fires. To do 

so, set efficiency to 0 for Wilderness. 

Fire Effects Planning Framework RMRS-GTR-163WWW 27
1.1.Choose System Knowledge→Vegetative Process→Fire Suppression 

Logic→ Class A (Figure 11). Settings are read as efficiency levels. For 

example in the screen shown below, 75 % of the class A fires in non-forest 

types in Wilderness are successfully suppressed under Normal and Wetter 

climatic conditions, but none are caught at the Class A level under Drier 

conditions. 

 (a) Class A fires. 

 (b) Beyond class A. 

Figure 11- SIMPPLLE’s Fire suppression logic screens.

28 RMRS-GTR-163WWW Fire Effects Planning Framework
2. Evaluate and adjust Fire Suppression Beyond Class A data as necessary. 

This file offers another place to limit fire suppression. If you choose to 

apply a no suppression tactic in roadless and Wilderness areas, check the 

appropriate boxes under Road Status, and uncheck appropriate boxes under 

Ownership. 

2.1. Choose System Knowledge→Vegetative Process→Fire Suppression 

Logic→Beyond Class A. 

3. Evaluate and adjust Weather Ending Events ---Fires < 0.25 data, as 

necessary. This screen allows the user to adjust fire ending events for the

normal climatic condition and to model changes due to climate shifts 

(Figure 12). 

3.1. Choose System Knowledge→Vegetative Process→Weather Ending 

Events→ Fires Less than .25 Acres. Settings are read as probability of 

fire events ending fire under various climatic conditions. In Figure 12 

(a), 25 % of fires under 1⁄4 acre in Engelmann spruce are put out by 

weather under normal climatic conditions; 50 % under wetter 

conditions, and none under drier conditions. These probabilities can be 

altered by the user. In order to change the habitat types or species for 

which these apply, one must work with the SIMPPLLE team to change 

hard-wired inputs.

4. Evaluate and adjust Weather Ending Events ---Fires > 0.25 data, as

necessary. Settings in this file adjust the probability that a weather event 

will extinguish fires of various sizes greater than 1⁄4 acre. 

4.1. Choose System Knowledge→Vegetative Process→Weather Ending 

Events→ Fires greater than .25 Acres. File is read as probability. In 

the example shown in Figure 12 (b), there is no possibility that fires 

less than 500 acres and a 75 % probability that a fire of greater than 

50,000 acres will eventually be put out by a weather event. 

Fire Effects Planning Framework RMRS-GTR-163WWW 29
(a) Fires < 0.25 acres. 

(b) Fires > 0.25 acres. 

Figure 12- SIMPPLLE’s Weather ending event screens. 

5. Evaluate and adjust Extreme Fire Spread Probability data, as

necessary. This file allows the user to control the dry, windy conditions 

under which a fire may escape or become large. 

5.1. Choose System Knowledge→Vegetative Process→Extreme Fire Spread 

Probability. The Probability due to weather event is the probability that

weather drives the fire while Fire event acres for 100 percent 

probability sets the fire acreage at which the incident may begin to drive 

internal weather condition. These settings should be based upon 

knowledge of frequency of weather-driven extreme fire behavior and 

local conditions. For example, setting probability due to weather event 

at 100 % and Fire event acres for 100 percent probability at 0 will force 

all fires to act with extreme behavior. Alternatively, setting the 

probability due to weather event at 100 %, but specifying a much larger 

acreage, say 10,000 acres for Fire event acres for 100 percent 

probability specifies that only fires greater than 10,000 acres will exhibit 

extreme behavior due to plume-domination. 

30 RMRS-GTR-163WWW Fire Effects Planning Framework
6. Use the Regional Climate file to adjust for wet/dry cycles. This file 

allows the user to determine the climatic regime for each step in a SIMPPLLE 

run. Choices are wetter, normal, and drier. Default is normal. Variations in 

annual moisture, such as to model drought cycles (wetter, drier, and normal) 

are first specified in the Regional climate file. Fire suppression for Class A 

fires, Weather ending events for fires < 0.25 acres and Type of Fire Logic

tables can then all be adjusted to reflect different responses to different 

regional climatic conditions.

6.1. Choose System Knowledge→Regional Climate. 

1.3 Set fire behavior logic 

DISCUSSION: SIMPPLLE offers users the ability to specify and adjust fire spread. 

Fire spread is discussed in terms of fire type (light, mixed, or stand-replacing fire). 

Fire spread is influenced by the relative position (adjacency) and type of fire in a 

neighboring polygon, as well as the receiving stand’s density, size structure, and past 

processes (fire, insects, disease, treatments, and so forth). The user may specify the 

type of fire in the receiving stand under average or extreme weather conditions. 

Values for fire spread are stored in files with the following extensions: firespread and 

fire logic (Figure 13). 

Figure 13- SIMPPLLE’s Type of fire logic screen. 

TASKS 

1. Evaluate fire spread logic. Choose System Knowledge→Vegetative 

Process→Fire Spread Logic. Make adjustments as needed. Save file. 

1.1 Populate run_template.xls.fire spread logic to facilitate review and to 

track changes. 

Fire Effects Planning Framework RMRS-GTR-163WWW 31
2. Evaluate Type of Fire Logic. Choose System Knowledge→Vegetative 

Process→Type of Fire Logic. Make adjustments as needed. Save file. 

2.1 Populate run_template.xls.type of fire logic to facilitate review and to 

track changes. 

Step 2. Creating fire type and probability maps _____________________ 

ACTION: Run 30 iterations of SIMPPLLE (decadal time step) for historic and 

current conditions and generate probability maps. 

DISCUSSION: SIMPPLLE developers recommend running 30 iterations of each 

simulation to capture the full range of ecosystem process variability. Output files (.txt) 

provide the probability that any given stand (polygon) will burn with a particular severity. 

This information is used to create maps of most probable fire type, probability of burning, 

probable fire frequency (on a per pixel or polygon-basis), and probable fire return interval 

or departure from historic fire regime map. Which type of summary you choose depends 

on what you are trying to determine and whether you want to work with probabilities of 

conditions or actual conditions. 

Once run, users must choose between evaluating a single iteration (which will provide a 

single version) or evaluating the total probability summed across all iterations. One 

option is to graph a vegetation attribute such as Species across all simulations, then 

choose a single iteration that illustrates the conditions you are interested in: average, 

high, low. Another is to translate the final probabilities into a single value based on 

highest probability. 

SIMPPLLE creates multiple .txt files for every iteration; some are quite large. It is 

advisable to create a new directory for each analysis and save the runs to this folder. 

After the simulation is complete, save the probability, spread, update, and GIS files. 

Refer to the SIMPPLLE user’s manual for additional background and instructions. 

2.1 Create an historic fire regime map 

DISCUSSION: To create an historical coverage, unburnable cover types such as 

urban and agriculture - as well as any cover types and pathways that incorporate 

introduced species - must be set to a probable historical class and/or pathway. 

TASKS 

1. Reclassify urban and agriculture cover types (Species) to an 

historic cover type. Reset, or reclass, any introduced/exotic/weed cover 

types and pathways to historical conditions. 

2. Run 30 iterations of a 50 decade simulation (decadal time steps), 

specifying historic pathways and no fire suppression. Save in a new folder. 

32 RMRS-GTR-163WWW Fire Effects Planning Framework
3. Load the .area file for last decade of last simulation and run 30 

iterations of a single decade using historic pathways and no fire 

suppression. Save to a subfolder of historic folder. 

4.Determine probability of burn and most probable fire type 

(Figure 14).

4.1. In a database manager open the -n-process.txt file for the single 

decade simulation. Strip and save the SLINK and fire attribute fields in 

a new .txt file. NOTE: EXCEL will only accept 65,000+ records. It 

truncates larger files, but will perform analyses. Use another program

(Arc, or SPSS) to work with large files. 

4.2. Add a field and sum probabilities for Light, Mixed and Stand 

Replacing fire. This is the probability of fire. 

4.3. Add another field and calculate the maximum of Light, Mixed, and 

Stand Replacing fire. This is the most probable fire type. 

4.4. Save file as .txt or .dbf and link table to coverage using Stand-Id field 

in GIS. Sum acres, sort by Special Area and sum acres, map, and so 

forth. 

5. Link the new .txt file to the GIS coverage to create probability 

maps. Add the .txt file to your Arc project and join to the base vegetation 

coverage using the SLINK as the join field. 

2.2. Create a current conditions map 

TASKS 

1. Determine probability of burn and most probable fire type 

1.1 In a database manager open the -n-process.txt file for the simulation. 

Strip and save SLINK and fire fields in a new .txt file. NOTE: EXCEL

will only accept 65,000+ records. It truncates larger files, but will 

perform analyses. Use another program – ARC, or SPSS – to work with 

large files. 

1.2. Add a field and sum probabilities for Light, Mixed and Stand 

Replacing fire. This is the total probability of fire field. 

1.3 Add an additional field and calculate the maximum of Light, Mixed 

and Stand Replacing fire. This is the most likely fire type (or probable 

burn type) field. 

1.4 Save file as .txt or .dbf. 

Fire Effects Planning Framework RMRS-GTR-163WWW 33
Figure 14- Calculating probable fire type in SIMPPLLE.

Step 3. Creating fire effects maps _______________________________ 

ACTION: Apply fire effects cross-walk to map probabilities. 

TASKS 

1. Add the .txt or .dbf created above and use the SLINK field to join the table to 

the vegetation coverage. 

2. Create separate GIS layers for the various probability maps you would like. 

Post these on an intranet website and add information about these maps to 

data provided to incident support teams. 

34 RMRS-GTR-163WWW Fire Effects Planning Framework
C 

Future Conditions Using a Stand-based, Deterministic Model 

Predicting future fire behavior using a stand-based, deterministic model requires 

development of fuels data for some future vegetative condition (Figure 15). To 

accomplish this task, three decisions must be made: 

What base data and software will be used to create and compare current and 

future fire behavior predictions?

•

•

•

How will future vegetation attributes be assigned to probability data? 

What algorithm will be used to create fuels data from vegetation attributes? 

Figure 15 - Overview of C future conditions using a stand-based, deterministic model.

Step 1. Creating a future vegetation map __________________________ 

DISCUSSION: One can develop a map of future vegetation conditions by either 

selectively changing the fuels condition of specific areas, or creating a entirely new 

vegetation map using an LSDM. A key consideration, though, is that if your goal is to 

Fire Effects Planning Framework RMRS-GTR-163WWW 35
compare the existing condition to a potential future, then use the same base data source 

and the same algorithm to specify fuels from vegetative conditions. 

In our work on the Bitterroot National Forest, we used SIMPPLLE to generate the future 

vegetative conditions. However, comparing a FLAMMAP run based on fuels derived from

a 2002/2003 satellite image to a prediction of future vegetation based on significantly 

different vegetation data would not yield the necessary ‘apples to apples’ comparison. 

Thus, we used SIMPPLLE to generate both the current and future vegetation coverages. 

Since SIMPPLLE is a stochastic model, summary data for a multi-iteration simulation are 

expressed as probabilities. To obtain a single condition for each key attribute (cover type, 

density, structure) one can either use output from a single iteration (representing the mean 

or an extreme condition) or translate the summary probability files into most likely 

vegetation condition. 

TASKS 

See next section (D) for directions to create a future vegetation simulation using 

SIMPPLLE. 

Step 2. Creating a future fuels map ______________________________ 

DISCUSSION: We adapted an algorithm created by the Fire Behavior Modeling 

Institute (Fire Lab) to develop 2003 FARSITE fuels data for use with SIMPPLLE, then used 

this new algorithm to develop both current and future FARSITE data. This crosswalk uses 

information on habitat type, canopy cover, size class, and cover type to generate the five 

FARSITE fuels grids (Figure 16). 

TASKS 

1. Develop a crosswalk between vegetation attributes and FARSITE fuels 

data (stand height, canopy bulk density, crown base height, crown 

closure, fuel model). 

1.1. Create a .dbf (or .txt) identifying the five FARSITE fuels values for each 

unique combination of vegetation cover type, structure, and density 

variables. Use the same ‘primary ecosystem component’ variable 

discussed in Chapter 1. Join this new table to the GIS cover of future 

vegetation and create separate grids of each FARSITE fuels field. 

36 RMRS-GTR-163WWW Fire Effects Planning Framework
Figure 16 - Illustration of the crosswalk between future vegetative conditions and FARSITE fuel 

layers. 

Step 3. Identifying change____ ______________________________ 

ACTION: Use the fire behavior-fire effects crosswalks to identify 

changes.

DISCUSSION: Although the results of such an analysis may have questionable 

cell to ground validity (depending upon the degree of ground-truthing), the process 

does yield an ‘apples to apples’ comparison useful for strategic fire planning. For 

instance, this type of analysis can identify whether future conditions as a result of 

particular management strategies are likely to be generally ‘better’ or ‘worse’ than the

current situation. 

TASKS 

Follow the steps outlined in Section A Step 4, above.

Fire Effects Planning Framework RMRS-GTR-163WWW 37
D 

Future Conditions Using a Stochastic, Landscape-level Model 

Landscape dynamics models offer the opportunity for ‘gaming’: the comparison of 

outcomes under different management strategies. Being stochastic in nature, it is 

necessary to run multiple iterations to capture the full range of ecosystem variability. If

the real range of variability has been captured, the actual future should fall somewhere 

within this range of variation described by the multiple iterations. 

To use a stochastic landscape dynamics model for planning, you must make a decision 

about how to view and use simulation outcomes. You can identify a specific iteration (an 

average or an extreme) to choose or use the final probabilities. For our purposes, we need 

to be able to identify and extract a single condition for each vegetation attribute at each 

target time period. Thus, the choice is between using a single iteration and translating the 

final probabilities into the most likely condition. 

In this section we describe how to use SIMPPLLE to identify stands for treatment, enter 

these into SIMPPLLE as either specific units or random units, and re-calculate fire effects 

(Figure 17). 

Figure 17 - Overview of D future conditions using a landscape-level, stochastic model. 

38 RMRS-GTR-163WWW Fire Effects Planning Framework
Step 1. Identifying and prioritizing stands _________________________ 

DISCUSSION: Data used to address prioritization are the output from the effects 

determination (see Section A Step 4 or Section B Step 3, above). Treatment options vary 

by fire effects. Where fire is likely to result in characteristic effects under low to normal 

weather conditions, prescribed fire or even Wildland Fire Use may be an option. 

Mechanical treatment is necessary prior to reintroduction of fire in areas where fire 

effects are likely to be uncharacteristic under any weather conditions. 

There are several approaches to prioritization. In this example, we prioritize stands based 

on a comparison between likely within-stand effects of fire and desired stand conditions. 

It is possible to use this concept to consider ecological effects within a landscape context. 

Since SIMPPLLE does not at this time model the physical process of fire, it does not 

provide information on rate of spread or other quantitative fire behavior parameters. 

Thus, if the goal is to identify stands that are the greatest contributors to spread of fire 

across the landscape, another program such as those based on the FARSITE /FLAMMAP

family (Finney, in development) should be used. 

Here, we prioritize stands based on effects that are likely to be undesirable. In a planning 

process, this would be integrated with other criteria and a final set of stands identified. 

We then use a simulation model to test the results of treating stands identified and 

determining the future conditions – fire behavior, cost, and so forth. 

SIDEBAR 2: Identifying priority treatment areas 

We used three criteria to identify priority treatment areas. 

(1) First, we determined and mapped ‘at risk’ stands. Primary cover types of concern 

are ponderosa pine (Pinus ponderosa), Douglas-fir (Pseudotsuga menziesii) and western larch 

(Larix occidentalis). Uncharacteristic fire types in these cover types we defined as crown fire 

in stands with larger diameter trees. For this example we used SIMPPLLE to identify ‘at risk’ 

stands, but one could use FLAMMAP as well. 

(2) Next, we buffered the Bitterroot National Forest’s urban interface fire 

management zone by one mile and selected ‘at risk’ stands within this zone. 

(3) Finally, we identified areas of high erosion hazard within watersheds currently 

containing westslope cutthroat trout, a critical wildlife species on the Forest. 

Our illustrations show the convergence of high crown fire danger and WUI in one 

map (Figure 18a), and high crown fire danger and wildlife in another (Figure 18b). 

Most likely, you will apply several criteria then use a weighting or ranking system to 

combine the criteria maps to arrive at a final solution. 

Fire Effects Planning Framework RMRS-GTR-163WWW 39
(a) At risk stands within 1 mile of the WUI. 

 (b) Areas of high erosion potential in critical watersheds. 

Figure 18 - Potential treatment priorities identified by proximity to the wildland-urban interface,

stands ‘at risk’ of uncharacteristic fire, and sensitive wildlife habitat. 

40 RMRS-GTR-163WWW Fire Effects Planning Framework
Step 2. Creating treatment schedules ____________________________ 

DISCUSSION: Scheduling fuels activities can be accomplished in SIMPPLLE by either 

identifying the specific stands to treat (Option 1), or allowing the program to randomly 

select stands meeting certain criteria (Option 2). There are a number of ways to identify 

stands to treat. One is to use either FLAMMAP or SIMPPLLE to identify highest priority 

locations by spatial location or stand criteria. Note that random treatments will change

within-stand conditions, but may or may not affect landscape-scale processes such as 

species movement, migration, or fire movement. 

We began by identifying stand structure conditions meeting our cover type criteria 

(dense, multi-story stands), then selecting and exporting these records from our GIS 

vegetation cover. We used this list to create an input file for SIMPPLLE. 

Option 1: Identifying specific treatment units 

TASKS 

1. Generate a .txt file of stand-id’s to be treated, by decade (by year 

if running SIMPPLLE in annual mode). Stand-id’s must be those used in the 

SIMPPLLE coverage. Specific stands may be identified using any number 

of criteria. One way is to use the stands identified in previous analysis 

(either FLAMMAP or SIMPPLLE) in which fire poses a ‘risk’. 

1.1. In Arc, export the .dbf associated with selected stands to EXCEL. 

1.2. Eliminate all fields except for the stand-id. 

1.3. Save as .txt file. 

1.4. In a text editor, open and add treatment name on the first line of the 

file exactly as shown in SIMPPLLE ’s System Knowledge→ Vegetative 

Treatments→ Treatment Logic→Current Treatment screen.

2. Load the .txt file into SIMPPLLE. In SIMPPLLE, open System

Knowledge→ Vegetative Treatment→ Treatment Schedule→ Load Unit Id 

File.

3. Determine upper acreage limit for each simulation step (decade or

year) and enter in Treatment Schedule. Track files in run_template.xls. 

4. Evaluate, and adjust if necessary, default treatment effects. 

These determine stand condition following treatment. 

4.1. Open System Knowledge→ Vegetative Treatments→ Treatment Logic 

(Figure 19).

4.2. Choose a Current Treatment and activate one of the vegetation 

classes listed under Feasibility.

4.3. Choose Change and evaluate the logic. It is helpful to view PseudoCode Text to help understand how to read the screen. 

Fire Effects Planning Framework RMRS-GTR-163WWW 41
Figure 19 - SIMPPLLE’s Treatment Logic screen. 

Option 2: Randomly selecting treatment units 

TASKS 

1. Identify the stand conditions for treatment. Conditions may include 

habitat type, species, size class, and density. The Treatment Scheduler is 

used to identify stand types for treatment. Treatment Logic is used to 

determine the effects of treatment. 

1.1. Open System Knowledge→ Vegetative Treatment→ Treatment 

Schedule (Figure 20). Choose File→ New Treatment. Choose from

among the possible choices for each time step. 

1.2. Identify the Desired acres to treat in each time step. 

1.3. If applicable, select Special Area and/or Road Status. This will be

necessary if one does not want to treat roadless, wilderness, or 

private lands, or alternatively, if one wishes to confine treatments to 

a particular type of area, such as the Wildland-Urban Interface. 

1.4. Evaluate, and adjust if necessary, defaults in Treatment Logic. 

42 RMRS-GTR-163WWW Fire Effects Planning Framework
Figure 20 - SIMPPLLE’s Treatment Scheduler screen. 

Step 3. Creating fire type and probability maps ___________________________ 

TASKS 

Follow directions under Section B Step 2, above, to create fire type and 

probability maps. 

Fire Effects Planning Framework RMRS-GTR-163WWW 43
Step 4. Comparing fire type and probability maps ____________________ 

DISCUSSION: Completing these tasks allows users to perform an effects analysis by 

analyzing consequences of alternative management scenarios. This is one of many ways 

to conduct change detection. We conducted the same analysis using grids and vectors. 

Here we describe the vector steps; these can be conducted at the same time. 

TASKS 

1. Compare fire probabilities. 

1.1. Join the fire portions of the –n-process.txt files to the SIMPPLLE

coverage (or use SIMPPLLE ’s ArcView extension to do this for you). 

1.1.a Edit the –n-process.txt files to eliminate all but the fire fields. 

Rename the fields to reflect the appropriate simulation. 1.2. Add 

two new fields, one for each simulation. 

1.2.a. Sum Light, Mixed, and Stand Replacing fire probabilities for 

each simulation. 

1.2.b. Add another new field and subtract one total probability from

the other (for example, subtracting historic from current results 

in negatives for decreases and positive number for increases in 

probability).

1.2.c. Save as .txt file, load back into ARCVIEW, and join to SIMPPLLE

coverage. 

1.3. Map change in probability of fire severity class. It is easiest to hide 

fields not in use (Figure 21). 

44 RMRS-GTR-163WWW Fire Effects Planning Framework
Figure 21 - Example map of change in probability of Stand Replacing Fire.

Fire Effects Planning Framework RMRS-GTR-163WWW 45
2. Compare most likely fire type. 

2.1. Join the fire portions of the –n-process.txt files to the SIMPPLLE

coverage (or use SIMPPLLE ’s ARCVIEW extension to do this for you). 

We found it least confusing to edit the .txt files first, eliminating all 

but the relevant fields and renaming fields to reflect the different 

simulations before joining the file to the SIMPPLLE coverage. 

2.1.a. Add a new field to identify change in fire type and develop a 

classification of change. We found three general types of change: 

no change, more/less severe, more/less variable. 

2.1.b. Develop a rule-set for classifying change. 

2.1.c. Subtract one severity class from the other (for example, 

subtracting historic from current results in negatives for decreases 

and positive number for increases in probability). It is easiest to 

hide fields not in use. 

2.2. Map change in probability of fire severity class (Figure 22). 

46 RMRS-GTR-163WWW Fire Effects Planning Framework
Figure 22 - Example map of change in most probable fire type using SIMPPLLE output. 

Fire Effects Planning Framework RMRS-GTR-163WWW 47
Chapter 3. Using map libraries 

FEPF map libraries support decisions at all stages of fire management planning. This sets 

the stage for identifying opportunities and risks in the coming season(s). Here, we outline 

potential analyses, then provide examples of decision support documents to: 

 Support long-range management plan development; 

 Support identification of fuels treatment priorities; and 

 Support fire stewardship activities. 

Support Long-range Management Plan Development 

FEPF can facilitate fire management by assisting in establishing the range of acceptable 

appropriate management responses codified in the Land/Resource Management plan. 

Potential Analyses 

1. Support for long-range plan development 

1.1. Run analyses for alternatives to determine feasibility, consequences, and 

opportunities for fire and fuels management. Compare fire probability and 

most likely fire type across time or management alternatives. 

2. Support for plan implementation 

2.1. Run a comparison between the current situation and ecological targets.

2.2. Identify alternative treatment strategies and conduct runs to determine 

feasibility, consequences, and opportunities for meeting targets. 

At the broad-scale, FEPF generates maps and criteria for determining feasibility, 

consequences, and opportunities for fire and fuels management. In our demonstrations 

with the Bitterroot National Forest, we used two combinations of SIMMPLLE and FLAMMAP

to compare the landscape level effects of treating stands ‘at risk’. A key management concern 

for the Bitterroot National Forest is the restoration of fire to fire-adapted cover types. We

identified these cover types as stands of fire tolerant species of ponderosa pine, Douglas-fir, 

and western larch. Stands ‘at risk’ are those with sufficient stem density to carry a standreplacing crown fire under even moderate fire weather conditions. For both analyses, we used 

SIMPPLLE and FLAMMAP runs on the current landscape condition to identify early seral stands 

(though not necessarily young stands) currently ‘at risk’ from fire. 

These analyses help managers and the public understand the opportunities, consequences, 

and feasibility of various land, fuel, and fire management strategies. 

Using SIMPPLLE to compare probable burn type We programmed SIMPPLLE to treat the 

identified stands with a combination mechanical treatment followed by a broadcast 

48 RMRS-GTR-163WWW Fire Effects Planning Framework
underburn (ecosystem management thin and underburn). Treatments were intended to 

restore the stands to more historically natural conditions that support a surface fire but not 

a crown fire. 

We then ran a single decade SIMPPLLE simulation on the existing and the treated 

landscapes, using 30 iterations for each to capture ecosystem variability. We calculated 

the most probable fire type (light, mixed, or stand-replacing fire) for each stand in each 

simulation from the SIMPPLLE output files. Most probable burn type maps were 

translated into fire effects maps using a rule-based crosswalk. These final effects maps 

identify where fire is likely to produce uncharacteristic (risk) or characteristic (benefit or 

opportunity) effects (Figure 23). If we were to do this for several treatment strategies, 

we could then quantify the difference between the alternatives and identify how each 

would affect the Forest’s ability to meet its target. 

Figure 23 - Using SIMPPLLE to compare probable fire effects under different Alternatives. 

Using FLAMMAP to compare fire type Alternatively, if one desires to use a quantitative 

measure of fire behavior, it is possible to use SIMPPLLE to generate the future landscape, 

apply a crosswalk from SIMPPLLE vegetation composition and structure to fuel model 

and canopy fuels for both the existing and future landscapes, and use FLAMMAP to 

predict fire behavior parameters for both situations. This combination can produce 

comparisons of fire behavior parameters, such as flame length or rate of spread, as well 

as fire type (surface, passive crown fire, active crown fire). 

Fire Effects Planning Framework RMRS-GTR-163WWW 49
Support Identification of Fuels Treatment Priorities 

Potential Analyses 

1. Query across the threshold fire effects maps to identify areas that 

are consistently: 

a) characteristic or desirable. These areas are candidates for reintroduction of 

fire. 

b) uncharacteristic or undesirable. These areas (or areas with their attributes)

are candidates for mechanical treatment. 

c) variable. Fire behavior and/or effects vary in character and/or desirability 

across the fire weather spectrum. These areas may be candidates for 

prescribed fire. 

This analysis should be considered a preliminary classification. Field assessment 

should always follow to check validity of the model runs.

On the Bitterroot National Forest, we focused this example at a finer scale, on the 

Bitterroot Front, using our Fire Behavior Library (output from FLAMMAP) to identify 

potential treatment units. The purpose of this simulation was to demonstrate the 

feasibility and potential utility of FEPF, not to provide actual data. 

We combined the fire effects grids for lynx (for example, all percentile weather conditions) to 

develop a map indicating where fire under all situations creates benefits, and where fire under 

all situations creates risks (Figure 24). Areas of consistent benefit are candidates for fire use; 

areas of consistent risk are candidates for mechanical treatments prior to reintroduction of fire. 

Areas with variable effects can be further analyzed for either prescribed fire or mechanical 

treatments. 

Information on the consistent potential for desirable or undesirable fire effects under 

various weather scenarios can be used to prioritize areas for different types of treatment 

(wildland fire use, mechanical, or prescribed fire). Treatment unit boundaries or 

ecological units (for example lynx analysis units, or LAUs) can be added to the GIS and 

analyzed using the desirable/undesirable frequency data. This GIS table can be sorted 

and exported to essentially create a prioritized treatment list. 

50 RMRS-GTR-163WWW Fire Effects Planning Framework
Figure 24 - Illustration of how to query a fire behavior library to determine treatment 

priorities. 

Support Fire Stewardship Activities 

Fire Plan Development 

Map layers can be used during creation or revision of Fire Management Plans to: 

identify resources values, objectives/desired conditions/standards and guides, and 

constraints for each management/response unit; 

•

•

•

pre-plan Fire Management or Maximum Manageable Areas; and 

aid in determining appropriate prescriptions, boundaries, and priorities for 

management-ignited prescribed fires. 

Either SIMPPLLE or FLAMMAP can be used for this purpose. 

Potential Analyses 

1. Use the difference between ecological targets and existing condition to identify 

alternative fire/fuel management strategies –such as expanded WFU zones. 

2. Test these potential strategies by running multiple simulations and checking the 

results against ecological (and social) targets. 

Fire Effects Planning Framework RMRS-GTR-163WWW 51
In one exercise, we identified potential WFU zones by calculating the percentage of each 

subwatershed (6th HUC) in (a) low severity fire under moderate fire weather conditions 

and (b) active crown fire under severe fire weather conditions from FLAMMAP output for 

the Bitterroot River valley. Subwatersheds with a high percentage of lands in a low 

severity class and low percentage in high severity we classed as candidates for Wildland 

Fire Use zones (Figure 25). Areas with high proportions of negative effects we classed as 

candidates for mechanical treatment and/or suppression. Areas falling in the middle can 

either be conditional WFU zones and/or used to define appropriate conditions for 

prescribed burns. These subwatersheds could be used to summarize benefits and risks 

from the map library, with the resulting maps and analysis included in the Fire 

Management Plan. 

Figure 25 - Illustration of potential use for WFIP/WFSA analysis. 

52 RMRS-GTR-163WWW Fire Effects Planning Framework
Fire Season Planning

Areas identified during fuels treatment prioritization efforts can be rolled into annual 

activity plans, particularly areas where Wildland Fire Use and prescribed fire are the most 

ecologically, economically and socially feasible options. When considered in light of 

other resources values, values at risk, and potential for a natural ignition, these maps can 

be used to prioritize annual management activities. 

Potential Analyses 

1. Determine areas where natural fires, under various fire weather conditions, might 

be managed for Wildland Fire Use. Determine areas where fire, under various fire 

weather conditions, poses unacceptable risks. 

2. Use this information to develop spring-fall fuels management activities. 

Incident Management and Cost Containment 

FEPF output can be used at each planning step mandated in the federal Wildland and 

Prescribed Fire Management Policy - – Implementation Procedures Reference Guide, 

published by the NIFC (National Interagency Fire Center) in August 1998. 

Potential Analyses 

Map libraries of fire effects provide important information for incident support. 

1.Overlay predicted fire perimeters on predicted effects maps for the threshold 

conditions closest to existing conditions to determine spatial location of benefits 

and risks. 

2. Identify and quantify non-monetary benefits and changes to management targets 

using the GIS database and integrate this information into the various fire

management decision documents. 

The fire effects map library can be used to develop tactical plans, taking advantage of 

where fire may be used to achieve resource benefits. Because fire use is generally much 

cheaper than aggressive suppression efforts, taking advantage of information contained in 

map libraries can contribute to cost containment. The Appropriate Management Response 

includes the full range of management options from full, aggressive suppression to 

Wildland Fire Use. FEPF can provide spatial and tabular summaries of opportunities 

under a gamut of fire weather threshold conditions. Information on potential benefits 

under threshold conditions closest to the expected weather can be weighed along with 

effects on other values and values at risk during the initial “go/no go” decision stage. If 

Wildland Fire Use is the chosen strategy, development of WFIP Stage II and III reports 

are informed through the FEPF process as well. 

Finally, pre-developed map layers identifying areas of ecological benefit and risk can be 

useful in developing and prioritizing fire suppression activities. Spatial identification of 

potential benefits can help management teams target areas to contain or confine strategies 

instead of employing the generally more resource intensive control tactics. Map layers 

may also be indispensable for teams unfamiliar with the fire area or ecology.

Fire Effects Planning Framework RMRS-GTR-163WWW 53
III. Forms 

NOTE: these forms are available as two Excel files, but are provided here for illustration 

and hard copy use. 

1. Weather Form (for capturing critical weather data from 

FIREFAMILYPLUS) 

2. Tracking sheet for FLAMMAP runs. 

3. Run_template.xls (for assessing and capturing adjustments to

critical SIMPPLLE logic tables). 

3.1 System Knowledge 

3.2 Simulations 

3.3 Import/export 

3.4 Reports 

3.5 Knowledge files 

a. Fire occurrence (FMZ) 

b. Class A 

c. beyondclassA 

d. firesuppweathera 

e. firesuppweather 

f. extreme fire prob 

g. regional climate 

h. Type of Fire Logic 

 RMRS-GTR-163WWW Fire Effects Planning Framework 

 54
Form 2. FlamMap 

FlamMap FlamMap FlamMapINPUTS INPUTS INPUTSRun Name___________________ Run Name___________________ Run Name______________________________*.lcp ___________*.lcp ___________*.lcpFuel Moisture Files Fuel Moisture Files Fuel Moisture Files_____________*.fms _____________*.fms _____________*.fms____________________*.fmd ____________________*.fmd ____________________*.fmdWinds Winds WindsUphill___ 20' wind=__________________ Uphill___ 20' wind=__________________ Uphill___ 20' wind=__________________

Direction_____ Azimuth=__________________ Direction_____ Azimuth=__________________ Direction_____ Azimuth=__________________Canopy Canopy CanopyLiveFM%__________________ LiveFM%__________________ LiveFM%__________________Fuel Moisture Settings Fuel Moisture Settings Fuel Moisture SettingsFixed_____ Fixed_____ Fixed_____Conditioning_____ Conditioning_____ Conditioning_____Conditioning Period Start Date/Time Conditioning Period Start Date/Time Conditioning Period Start Date/Time

End Date/Time End Date/Time End Date/Time

OUTPUTS OUTPUTS OUTPUTSThreading: Multiple Single Threading: Multiple Single Threading: Multiple SingleOutput Grids Output Grids Output Grids____________________*.fli ____________________*.fli ____________________*.fli____________________*.ros ____________________*.ros ____________________*.ros____________________*.fml ____________________*.fml ____________________*.fml____________________*.hpa ____________________*.hpa ____________________*.hpa____________________*.cfr ____________________*.cfr ____________________*.cfr____________________*.rhm ____________________*.rhm ____________________*.rhmSpread Direction: Relative Absolute (degrees_____) Spread Direction: Relative Absolute (degrees_____) Spread Direction: Relative Absolute (degrees_____)
Form 2. FlamMap 

FlamMap FlamMap FlamMapINPUTS INPUTS INPUTSRun Name___________________ Run Name___________________ Run Name______________________________*.lcp ___________*.lcp ___________*.lcpFuel Moisture Files Fuel Moisture Files Fuel Moisture Files_____________*.fms _____________*.fms _____________*.fms____________________*.fmd ____________________*.fmd ____________________*.fmdWinds Winds WindsUphill___ 20' wind=__________________ Uphill___ 20' wind=__________________ Uphill___ 20' wind=__________________

Direction_____ Azimuth=__________________ Direction_____ Azimuth=__________________ Direction_____ Azimuth=__________________Canopy Canopy CanopyLiveFM%__________________ LiveFM%__________________ LiveFM%__________________Fuel Moisture Settings Fuel Moisture Settings Fuel Moisture SettingsFixed_____ Fixed_____ Fixed_____Conditioning_____ Conditioning_____ Conditioning_____Conditioning Period Start Date/Time Conditioning Period Start Date/Time Conditioning Period Start Date/Time

End Date/Time End Date/Time End Date/Time

OUTPUTS OUTPUTS OUTPUTSThreading: Multiple Single Threading: Multiple Single Threading: Multiple SingleOutput Grids Output Grids Output Grids____________________*.fli ____________________*.fli ____________________*.fli____________________*.ros ____________________*.ros ____________________*.ros____________________*.fml ____________________*.fml ____________________*.fml____________________*.hpa ____________________*.hpa ____________________*.hpa____________________*.cfr ____________________*.cfr ____________________*.cfr____________________*.rhm ____________________*.rhm ____________________*.rhmSpread Direction: Relative Absolute (degrees_____) Spread Direction: Relative Absolute (degrees_____) Spread Direction: Relative Absolute (degrees_____)
FORM 3.1 System Knowledge

Work Area:Team:

INPUT OPTIONS File extension Storage file name Parameters Source Data

ZoneAreaS

ystem Knowledge

Pathways

Vegetative .pathwayAquatic n/a at this time

Vegetation processes

Lockin processes .processFire spread .firespreadExtreme fire spread probFire suppression cost .fmzFire occurrence input .fmzFire suppression logic

Class A .firesupression

Beyond Class A .firesuppbeyonda

Type of fire logic .firetypeWeather ending events

< 0.25 acre .firesuppweathera> 0.25 acre .firesuppweather 

WSBW logic n/a at this timeInsect/Disease logic n/a at this timeInsect/disease occurrence pro

b .probability

Use regen pulse (Y/N)Regen n/a at this timeWeed spread n/a at this timeConifer encroachment n/a at this time

Aquatic processes n/a at this timeVegetative Treatments

Treatment schedule .treatment

Treatment logic .treatmentlogic

Aquatic treatmentsRegional Climate .climateSystem Knowledge .sysknowledge SIMPPLLE_forms.xls
Form 3.2 Simulations

Work Area:Team:ZoneArea

SIMPPLLE simulation .area file/landscape file Storage area Timestep (1,10) # steps # iterationsName
Form 3.3 Import/Export

Work Area:Team:

OPTIONS File extension Storage file name Parameters Source Data

ZoneArea

Import

run neighbors .nbrcreate new area .areaload attributes .attributes

Export

GIS Simulation Files

spread

accumulation

probability by decade

probability of reburn

Attributes .attributes

Form 3.4 Reports

Work Area:Team:ZoneArea
Form 3.5a Fire Occurrence

FMZ

all 1 2 3 4

Acres Acres Acres Acres Acres Lightning Human $/Acre Lightning Human $/Acre Lightning Human $/Acre Lightning Human $/Acre Lightning Human $/Acre

0.00 - 0.250.26 - 9.9910.00 - 99.99100.0 - 299.99300.00 - 999.991000.00+SIMPPLLE file name:Source files:
Form 3.5c - 3.5g Fire Information

Form 3.5c Beyond Class A FireProcess Ownership Roads Status

(choose to suppress) (Choose to limit suppression)

x NF Wilderness

SRF x NF Other open-roaded closed-roaded none

x Other open-roaded closed-roaded nonex NF Wilderness

MSF x NF Other open-roaded closed-roaded none

x Other open-roaded closed-roaded nonex NF Wilderness

LSF x NF Other open-roaded closed-roaded none

x Other open-roaded closed-roaded none

SIMPPLLE file name: DEFAULTForm 3.5d Fire Suppression Weather a Form 3.f Extreme Fire Probabilit

y

species wetter normal drier Defaulta 50 25 0 Probability due to weather event 0 b 25 0 0 Fire event acres for 100% probability 1000 

SIMPPLLE file name: defaultsForm 3.5e Fire Suppression Weather Form 3.5g Regional Climate

Size Event Probability timestep0.25 - 500 0 1 2 3 45501 - 1000 10 Temp1001 - 5000 25 Moisture5001 - 10000 3510001 - 50000 5050000+ 75SIMPPLLE file name: default
Form 3.5h Type of Fire Logic

Size/Structure Resistance Size Class Density Past Treatment Past Process Regional Climate

defaults for bitterroot - june 2003 wetter normal drier

low all 1,2,3,4 SRF SRF SRF

Non-Forest moderate 1,2 MSF MSF MSF

3,4 SRF SRF SRF

scatt, clump,op 1,2 LSF LSF LSF

scat, clump ope3,4 LSF MSF SRF

high uniform, closed 1,2,3,4 MSF SRF SRF

open shrub type1,2,3,4 LSF MSF MSF

1,2 EMBroad, PCT LSF LSF MSF

Single Story ss 3,4 all others MSF SRF SRF

1,2,3,4 no match MSF SRF SRF

1,2 EMBroad, PCT LSF LSF MSF

low pole 3,4 all others MSF SRF SRF

1,2,3,4 no match MSF SRF SRF

1,2 EMU, EMTU LSF, MSF LSF LSF MSF

m, l, vl 1,2,3,4 all others

root, s-wsbw, ppmpb, s-lp-mpb LSF MSF SRF

1,2,3,4 no match MSF SRF SRF

1,2 EMBroad, PCT LSF LSF MSF

ss 3,4 all others LSF MSF SRF

1,2,3,4 no match MSF MSF MSF

 1,2 EMBroad, PCT LSF LSF MSF

moderate pole 3,4 all others LSF MSF SRF

1,2,3,4 no match LSF MSF MSF

1,2 EMU, EMTU LSF, MSF LSF LSF LSF

m, l, vl 1,2,3,4 all others

root, s-wsbw, ppmpb, s-lp-mpb MSF MSF SRF

1,2,3,4 no match LSF MSF MSF

1,2 EMB, PCT LSF LSF LSF

3,4 EMB, PCT MSF MSF MSF

ss 3,4 all others MSF MSF MSF

 1,2 no match LSF LSF LSF

high 3,4 no match LSF MSF SRF

1,2 EMB, PCT LSF LSF LSF

pole 3,4 EMB, PCT MSF MSF MSF

3,4 all others MSF MSF MSF

1,2 no match LSF LSF LSF

3,4 no match LSF MSF SRF

1,2,3,4 EMB, TMTU LSF LSF LSF

m, l, vl 1,2 all others MSF MSF MSF

3,4 all others SRF MSF SRF

1,2,3,4 no match MSF LSF LSF

Multi-story 1,2 EMU, EMTU LSF LSF MSF

low all 1,2,3,4 all others root disease MSF MSF SRF

1,2,3,4 no match MSF SRF SRF

1,2 EMU, EMTU LSF LSF LSF

moderate all 1,2,3,4 all others root disease LSF MSF SRF

1,2,3,4 no match LSF MSF MSF

1,2 EMU, EMTU LSF LSF LSF

high 1,2 all others root disease LSF MSF MSF

3,4 all others root disease LSF MSF SRF

1,2,3,4 no match LSF MSF MSF

SIMPPLLE file name: revised default - new_bttr.firetype
References 

Albini, F.A. 1976. Estimating wildfire behavior and effects. GTR-INT-30. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Research Station. 

Barrett, T.M. 2001. Models of vegetative change for landscape planning: a comparison of FETM, 

LANDSUM, SIMPPLLE, and VDDT. RMRS-GTR-76WWW. Fort Collins, CO: U.S. 

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

Bradshaw, L., McCormick, E. 2000. FireFamilyPlus user’s guide, version 2.0. RMRS-GTR67WWW. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 

Chew, J.D. 1995. Development of a system for simulating vegetative patterns and processes at landscape

scales. PhD Dissertation. University of Montana, Missoula. 

Chew, J.D., Stalling, C., Moeller, K. 2004. Integrating knowledge for simulating vegetation change 

at landscape scales. Western Journal of Applied Forestry. 19(Part 2):102-108. 

DeBano, L.F., Neary, D.G., Ffolliott, P.F. 1998. Fire's effects on ecosystems. John Wiley & Sons, 

Inc. 333 p. 

Finney, M.A. 1994. Modeling the spread and behavior of prescribed natural fires. In: Proceedings 

of the 12th conference on fire and forest meteorology; 1993 October 26-28; Jekyll Is. 

GA. Society of American Publishers:138-143. 

Finney, M.A. [in preparation]. FlamMap. Missoula, MT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station, Fire Sciences Laboratory. 

Lee, B., Meneghin, M., Turner, M., Hoekstra, T. 2003. An evaluation of landscape dynamic 

simulation models. Internal review by the Inventory and Monitoring Institute. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 

Miller, C., Landres, P.B., Alaback, P.B. 2000. Evaluating the risks and benefits of wildland fire at 

landscape scales. In: Neuenschwander, L.F., Ryan K.C. (tech. eds.):Joint Fire 

Science Conference and Workshop: proceedings; 1999 June 15-17; Boise, ID.

Moscow, ID:University of Idaho:78-87. 

Rothermel, R.C. 1972. A mathematical model for predicting fire spread in wildland fuels. Research 

Paper INT-115. Ogden, UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station. 

Ruediger, B., Claar, J., Gniadek, S., Holt, B., Lewis, L., Mighton, S., Naney, B., Patton, G., Rinaldi, 

T., Trick, J., Vandehey, A., Wahl, F., Warren, N., Wenger, D., Williamson, A. 2000. 

Canada lynx conservation assessment and strategy. U.S.Department of Agriculture 

Forest Service, U.S. Departments of Interior: Fish and Wildlife Service, Bureau of Land 

Management, National Park Service, Missoula, MT. 

Fire Effects Planning Framework RMRS-GTR-163WWW 63
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