United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical Report 

RMRS-GTR-236WWW

April 2010

Retrospective Fire Modeling: 

Quantifying the Impacts of 

Fire Suppression

Brett H. Davis, Carol Miller, and Sean A. Parks
Davis, Brett H.; Miller, Carol; and Parks, Sean A. 2010. Retrospective fire modeling: 

Quantifying the impacts of fire suppression. Gen. Tech. Rep. RMRS-GTR236WWW. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 40 p.

Abstract

Land management agencies need to understand and monitor the consequences 

of their fire suppression decisions. We developed a framework for retrospective 

fire behavior modeling and impact assessment to determine where ignitions would 

have spread had they not been suppressed and to assess the cumulative effects 

that would have resulted. This document is a general guidebook for applying 

this methodology and is for land managers interested in quantifying the impacts 

of fire suppression. Using this methodology will help land managers track the 

cumulative effects of suppression, frame future suppression decisions and costbenefit analyses in the context of past experiences, and communicate tradeoffs to 

the public, non-government organizations, land management agencies, and other 

interested parties.

Keywords: fire behavior prediction, fire effects, fire modeling, retrospective, 

impact assessment

Authors

Brett Davis is a GIS Specialist and Fire Modeler with the Aldo Leopold Wilderness 

Research Institute, Rocky Mountain Research Station, USDA Forest Service. 

His professional interests include GIS analysis and automation, fire modeling, 

wildland fire use, and the relationship between wilderness health and wildland 

fire. Brett received his M.S. degree in Forestry (with an emphasis on GIS, remote 

sensing and fire) from Colorado State University and his B.A. degree in Biology 

and Psychology from the University of Colorado at Boulder.

Dr. Carol Miller is a Research Ecologist with RMRS, Aldo Leopold Wilderness 

Research Institute. Her research seeks to develop the knowledge necessary to 

improve the stewardship of fire as a natural process in wilderness and similarly 

protected lands.

Sean Parks is a GIS Specialist with the Aldo Leopold Wilderness Research 

Institute. He received a B.S. degree in Environmental Biology and an M.A. degree 

in Geography, both from University of California, Davis.

You may order additional copies of this publication by sending your 

mailing information in label form through one of the following media. 

Please specify the publication title and series number.

Fort Collins Service Center

Telephone (970) 498-1392

FAX (970) 498-1122

E-mail rschneider@fs.fed.us

Web site http://www.fs.fed.us/rm/publications

Mailing address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526

Rocky Mountain Research Station

240 W. Prospect Road

Fort Collins, Colorado 80526
Acknowledgments

This project was supported by the Joint Fire Science Program and the Rocky 

Mountain Research Station. The authors would like to thank the following people for 

their invaluable contributions to this project: Dave Bartlett, Mike Beasley, Jen Beck, 

Tony Caprio, Corky Conover, Karen Folger, Andi Thode, Jan van Wagtendonk, and 

Kent van Wagtendonk. We would also like to thank our reviewers Tobin Kelley and 

Kent van Wagtendonk. Laurie Kurth and Chuck McHugh’s invaluable comments 

led to an improved revision.

Contents

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

Setting Up the Project.......................................................................................2

Define Project Goals.........................................................................................2

Define Project Parameters................................................................................2

Choose Metric(s) to Quantify and Compare Alternative Landscapes ...............2

Model Selection ................................................................................................4

Data Acquisition and Preparation .....................................................................7

Modeling and Analysis.........................................................................................21

Defining Timeline ............................................................................................21

Revisiting Ignition Selection............................................................................22

Modeling Fire Growth and Behavior ...............................................................23

Updating Model Inputs....................................................................................27

Repeating Modeling Cycle..............................................................................31

Comparing Alternative Landscapes ................................................................32

Summary ...............................................................................................................38

References ............................................................................................................38

USDA Forest Service RMRS-GTR-236WWW. 2010. 1

Introduction

When wildfires are suppressed, opportunities are foregone to create fuel 

breaks, reduce fire regime departures, and decrease future extreme fire behavior 

by modifying fuels. To our knowledge, no one has yet attempted to systematically quantify these foregone opportunities. This general technical report describes 

a methodology to measure the cumulative impacts of suppression over time by 

modeling the spread of ignitions that were suppressed. We illustrate a set of 

analysis steps to simulate where ignitions would have spread had they not been 

suppressed and to assess the cumulative effects that would have resulted from 

those fires. The quantification of these effects will help land managers improve 

the prioritization and planning of fuels treatments and help inform decisions 

about the suppression of future ignitions.

In its simplest application, the methodology compares two landscapes: the 

realized landscape vs. a hypothetical landscape. As used throughout this guidebook, a “landscape” refers mainly to the biophysical characteristics of the study 

area such as vegetation and fuel conditions and potential fire behavior. The 

realized landscape is the landscape that resulted due to the fire management 

strategies actually implemented; this is typically the current landscape. The hypothetical landscape is the landscape that would have resulted if different fire 

management strategies had been chosen (e.g., if one or more suppressed ignitions had been allowed to burn freely). While the examples in this guidebook 

compare only two landscapes, any number of landscapes could be compared. 

A case study examines what conditions might have resulted if lightning-ignited fires were not suppressed in the South Fork Merced watershed of Yosemite 

National Park.

The retrospective modeling process requires modeling the spread of ignitions 

that were suppressed, updating the fuels data to reflect that modeled fire, and 

repeating this process to account for all the ignitions of interest throughout the 

simulation period; this results in the hypothetical landscape. Once the modeling cycles are complete, the final step involves assessing the impacts of fire 

suppression by comparing the hypothetical and realized landscapes using various metrics depending on need and purpose. For example, the hypothetical and 

realized landscapes might be compared in terms of potential fire behavior (i.e., 

flame length or crowning potential).

This document is a guidebook in that it provides a moderate level of detail 

for implementing the methodology and uses a case study to illustrate some procedures. However, it does not provide step-by-step instructions. Furthermore, 

inputs and parameters used in the case study are for illustration and should not 

be applied uncritically to other situations. Occasionally, specific tips on how 

best to accomplish the required steps are offered, but this guidebook is not intended to be a tutorial for specific modeling software, nor is it a text on fire 

behavior, ecology, or management.

To implement the methodology here, the user must have some basic skill 

sets. The most important skills include basic Geographic Information System 

(GIS) data manipulation and analysis, experience with fire growth modeling 

software such as FARSITE (Finney 1998), and familiarity with fire management 

terminology. Other useful skills include familiarity with other fire modeling 

software such as FlamMap (Finney 2006) and FireFamilyPlus (FFP; Bradshaw 

and McCormick 2000), and knowledge of fuels characterization, fire weather 

analysis, fire behavior, fire ecology, and fire management.
2 USDA Forest Service RMRS-GTR-236WWW. 2010.

The first section of this guidebook describes how to set up the project, followed by a section that explains how to conduct the data preparation, modeling 

and analysis. Below is a checklist of steps recommended to implement a retrospective fire modeling analysis and impact assessment.

Setting Up the Project

Define Project Goals

First and foremost, it is important to define the reasons for running a retrospective fire modeling analysis and how the impacts of suppression are going 

to be measured. The purpose of a retrospective analysis is to answer “what if” 

questions. For example, what if a particular set of ignitions had been allowed to 

burn? How would those fires have affected fuel loading, landscape condition, 

and potential fire behavior? Fire suppression impacts can be measured by comparing the resulting hypothetical and realized landscape conditions. Impacts of 

suppression might be measured in terms of fire regime departure, potential fire 

behavior, future ignition occurrence, and/or smoke emissions.

Define Project Parameters

Next, a few broad project parameters must be defined. These include defining 

the study area (park, forest, watershed, etc.). Second, determining the time span 

of interest (e.g., 11 years from 1994-2004), taking into consideration the availability of quality input data, computer processing capabilities, and the amount 

of time available to perform the analysis. The third step is defining the length of 

the fire season. These general parameters can be determined by consulting with 

local fire managers such as the Fire Management Officer (FMO) or by analyzing 

historical fire weather and fire occurrence data. A final consideration is that all 

spatial data must have a common projection, cell size, and extent.

EXAMPLE

Project parameters

Study area: South Fork Merced watershed, Yosemite National Park

Simulation length: 11 years, 1994-2004

Fire season definition: June-October

Spatial data properties: UTM projection; 90 m cell size

Choose Metric(s) to Quantify and Compare Alternative Landscapes

As previously mentioned, quantifiable metrics are needed to assess the 

impacts of differing fire management strategies. Below are some suggested metrics. Other metrics may be developed and used depending on the goals of the 

project, geographic location, and personal preference.

Fire Regime Departure

Measures of fire regime departure compare the frequency of fire in the distant 

past (typically pre-European “settlement”) to the frequency of fire in the recent 
USDA Forest Service RMRS-GTR-236WWW. 2010. 3

past (since effective fire suppression began). They are used to gauge the deviation of current fire patterns from historical norms.

Fire Return Interval Departure (FRID): FRID data are developed by determining how many years have passed since an area last burned and dividing 

that number by the characteristic Fire Return Interval (FRI) of the underlying 

vegetation. FRI represents a time interval between successive fire events. It is 

used to gauge how often a particular vegetation type would burn under natural conditions. FRI is based on fire history studies and stratified by vegetation 

types. For example, the mean FRI for giant sequoia forests has been calculated 

to be 10 years (Caprio and Lineback 2002). FRID, on the other hand, represents 

the number of FRIs that have been missed at a particular location. Therefore, a 

giant sequoia forest that has not experienced a fire for 50 years has a Fire Return 

Interval Departure of 5 (50 divided by 10 = 5).

Fire Regime Condition Class (FRCC): FRCC is a standardized metric for 

determining the degree of departure from reference condition vegetation, fuels, and disturbance regimes (Schmidt and others 2002). FRCC is divided into 

three departure classes: low, moderate, and high. A low departure represents 

areas within their natural range of variability (sensu Landres and others 1999) 

while moderate and high departures describe areas outside their natural range 

of variability.

In the case study, FRID is used instead of FRCC, because it is considered a 

finer measure of departure from natural conditions and because the fire managers in the case study area prefer this metric. They use it to estimate the degree 

to which an area’s vegetation has been modified relative to the vegetation 

and structure that may have occurred had fires been allowed to burn naturally 

(Caprio and others 2002).

Fire Behavior

A number of fire behavior characteristics can be used to measure the impacts 

of suppression.

Crown fire occurrence: Categorical measure predicting whether a fire is a 

surface fire, passive crown fire (torching), or an active crown fire.

Fireline intensity: The rate of heat energy released per unit length of flaming 

fire front. Fireline intensity is usually expressed in BTU/s/ft or kW/m.

Flame length: Distance from the tip of the flame to the middle of the flaming 

zone at the base of the fire. Flame length is measured on an angle when wind 

and/or slope are tilting the flame.

Rate of spread: The relative activity of a fire in extending its horizontal dimensions. Depending on the intended use of the information, rate of spread can 

be expressed as the rate of increase of the total perimeter of the fire, as the rate 

of forward spread of the fire front, or as the rate of increase in area.

In the case study, flame length is used, because it is a good indicator of fire 

intensity. Flame length can also be used as a proxy for firefighter safety and to 

determine attack strategies (National Interagency Fire Center 2006).

Smoke Emissions

Smoke emissions contain a variety of gases and particulate matter. PM 2.5 

(Particulate Matter smaller than 2.5 microns) and PM 10 emissions are the most 

commonly tracked components of wildfire smoke because of their potential 

impacts on human health. Simulated smoke emissions can be quantified on a 

per-fire modeling year basis and totaled at the end of the last simulation year in 
4 USDA Forest Service RMRS-GTR-236WWW. 2010.

the retrospective analysis. Alternatively, potential smoke emissions—the emissions that would result if the entire landscape burned—can be estimated and 

used to compare landscapes created by alternative management strategies.

Model Selection

Any model is merely an attempt to represent reality and will never be completely accurate. George Box (1979) put it well when he said, “All models are 

wrong, but some are useful.” The methodology described in this guidebook is no 

exception. It is subject to all the underlying assumptions and limitations of the 

different models used. It is the responsibility of users to familiarize themselves 

with these assumptions and limitations to ensure the correct interpretation of 

the results.

There are many models needed to effectively carry out a retrospective fire 

modeling analysis. A weather analysis tool is required to create the weather information necessary to model fires and to determine fire-ending weather events. 

A fire growth model is used to predict the spread and final perimeter of each 

ignition. A fuel succession model is necessary to update the fuels layer(s) after 

a fire. A fire behavior model is used to compare the hypothetical and realized 

landscapes. Finally, a smoke emissions model can be used to compare potential emissions on hypothetical and realized landscapes. This guide focuses on 

the models used in the case study. However, alternative models are sometimes 

suggested, and users may choose any model they deem appropriate. General 

guidance on many of the following models can be found in Guidance on Spatial 

Wildland Fire Analysis: Models, Tools and Techniques (Stratton 2006), available at http://www.fs.fed.us/rm/pubs/rmrs_gtr183.pdf.

Weather Analysis

Historical weather data need to be reviewed, analyzed, and manipulated for 

use in the fire growth and fire behavior models and to determine fire-ending 

weather events, such as significant precipitation. This process may be as simple 

as extracting input weather and wind data for use in a fire growth simulation 

model, or more complex, such as analyzing historical fire weather patterns to 

aid in the selection of historical ignitions. FireFamilyPlus (FFP), a fire climatology and occurrence analysis program, is well suited to perform either of these 

tasks. FFP can be used in retrospective fire modeling to define fire seasons, calculate fire weather index percentiles, calculate daily values of relevant weather 

indices, identify significant precipitation events, and export required weather 

and wind inputs for a fire growth or behavior model. Its calculations can be used 

to help select which ignitions to model and at what times they might be expected 

to spread. Many of these functions could also be performed using spreadsheet 

and/or text editing programs, but a program like FFP greatly simplifies the task.

Fire Growth

A fire growth model is the heart of the retrospective fire modeling process and 

is needed to predict the spread and final perimeter of each ignition. FARSITE 

is the most commonly used decision support system in the United States for 

modeling fire growth. It uses spatial information on topography and fuels, along 

with weather and wind data, to simulate the spread and behavior of wildland 

fires (Finney 1998). FARSITE is actually an assemblage of several underlying 

models including surface fire spread (Albini 1976, Rothermel 1972), spotting 
USDA Forest Service RMRS-GTR-236WWW. 2010. 5

(Albini 1979), crown fire spread (Rothermel 1991, Van Wagner 1977, 1993), 

fuel moisture dynamics (Nelson 2000), and others. As such, all the assumptions and limitations of these models apply to FARSITE as well. In addition 

to fire growth modeling, FARSITE can be used to model fire behavior (e.g., 

flame length) and smoke production. Its spatial input data layers are organized 

into a single “landscape” (LCP) file, which can also be used as an input to the 

fire behavior modeling tool FlamMap. Throughout the remainder of this guide, 

FARSITE and FlamMap’s landscape files will be referred to as LCP files to 

reduce confusion with the hypothetical and realized landscapes. LCP files require spatial data describing surface fuel models, topography, and canopy cover. 

Optional LCP file data include stand height, crown base height, crown bulk 

density, duff loading, and coarse woody debris. These optional inputs are necessary for crown fire and/or emissions modeling. FARSITE has been widely used 

for projections of active wildfire spread and behavior and for planning purposes. It is particularly well suited for asking multiple “what if” questions and 

comparing the results. FARSITE was used as the fire growth model in the case 

study. One alternative to FARSITE is the Canadian wildland fire growth model 

Prometheus (Tymstra and others 2009).

Fuel Succession

A fuel succession model is necessary to update the fuels layer(s) after each 

fire. Fuel loadings will change through time with disturbance and vegetative 

accumulation. Multi-year retrospective fire modeling requires that fuel data be 

updated to reflect these dynamics. After each fire, fuels data must be updated to 

account for the changes caused by the fire; similarly, if the time span of a project is long enough for fuel accumulation to occur, fuels data must be updated 

to account for those changes as well. Therefore, it is necessary to model fuel 

succession.

For the case study, an expert-opinion based fuel succession model was developed in collaboration with scientists and managers from Yosemite and 

Sequoia-Kings Canyon National Parks and the United States Geological Survey 

(USGS) for use in the Sierra Nevada (Davis and others 2009). This model was 

used for the case study, but a number of existing models could be used alone, or 

in conjunction with each other, to obtain similar results. For example, FOFEM 

(First Order Fire Effects Model; Reinhardt and others 1997), FVS-FFE (Forest 

Vegetation Simulator—Fire and Fuels Extension; Reinhardt and Crookston 

2003), and/or simplistic crosswalks based on local/expert opinion. Local experts should be consulted whether a new fuel succession model is created or 

existing models are used.

The fuel succession model used in the case study is a state and transition 

model describing transitions between fire behavior fuel models (Scott and 

Burgan 2005). It is represented by diagrams of succession describing fuel accumulation and disturbance by fire (Fig. 1) and predicts fuel model transitions in 

both the absence of fire and after a fire of a particular severity. These transitions 

are based on estimated fuel accumulation rates and how the underlying vegetation would be expected to respond after fires of different severities.

To illustrate how this fuel succession model operates, consider a Timber Litter 

3 (TL3; moderate load conifer litter) fuel model (Fig. 1). If an area of TL3 fuels 

experiences a low severity fire, it temporarily transitions to an unburnable state 

and then back to a TL3 model after a 20-year recovery (i.e., fuel accumulation) 
6 USDA Forest Service RMRS-GTR-236WWW. 2010.

period. After a moderate severity fire, TL3 transitions to unburnable, then to a 

Timber Litter 1 (TL1; low load compact conifer litter) fuel model after 10 years. 

After a high severity fire, it transitions to unburnable, then to a Shrub 1 (SH1; 

low load dry climate shrub) fuel model after 20 years. Finally, if it remains unburned for 25 years, it transitions to a Timber Litter 4 (TL4; small downed logs) 

model. During the recovery period (i.e., the time between the fire and the transition when the area is in an “unburnable” state), the burned area is considered 

to have too little fuel to carry another fire. The fuel models and transition times 

were selected based on the characteristics of the underlying vegetation.

For more detailed information on how this fuel succession model works see 

Davis and others (2009).

Fire Behavior

To compare the hypothetical and realized landscapes with each other, it is useful to estimate the potential fire behavior for the entire landscape given a set of 

static environmental conditions. Although the fire growth simulator FARSITE 

can be used to predict fire behavior, it does so only within the perimeter of each 

modeled fire, not the entire landscape. In the case study, the spatial fire behavior modeling tool FlamMap was used for this purpose. FlamMap requires an 

TL3

Unburned:

25 yrs → TL4

Low:

20 yrs → TL3

Moderate:

10 yrs → TL1

High:

20 yrs → SH1

Unburned:

25 yrs → TL7

Low:

20 yrs → TL3

Moderate:

10 yrs → TL1

High:

20 yrs → SH1

Unburned:

25 yrs → TL3

Low:

10 yrs → TL1

Moderate:

15 yrs → TL1

Unburned:

10 yrs → SH2

High:

20 yrs → SH1

Unburned:

TL7

Low:

15 yrs → TL4

Moderate:

10 yrs → TL1

High:

20 yrs → SH1

Unburned:

30 yrs → TU5

High:

20 yrs → SH1

Unburned:

20 yrs → TL3

High:

20 yrs → SH1

Figure 1. Fuel succession 

diagram for moderate 

load of conifer litter 

(TL3). SH = shrub.
USDA Forest Service RMRS-GTR-236WWW. 2010. 7

LCP file, fuel moisture and wind data (Finney 2006). FlamMap can calculate 

independent potential fire behavior for each pixel on a landscape for a single 

point-in-time. FlamMap predicts numerous fire behavior characteristics (e.g., 

flame length, rate of spread, crowning index) that can be used to compare the 

hypothetical and realized landscapes. BehavePlus (Andrews and others 2005) 

is another tool that potentially could be used to make comparative fire behavior 

and risk predictions.

Smoke Emissions

A smoke emissions model can be used to compare emissions from the hypothetical and realized landscapes. Emissions can be estimated for each simulation 

year during the process and compiled at the end of the last simulation year. 

Additionally, estimating an entire landscape’s potential emissions, based on estimating the potential emissions from all burnable pixels, can provide useful 

comparative information. In the case study, this comparative application was 

used to estimate emissions for the final landscapes resulting from both the hypothetical and realized scenarios. The case study used the GIS-based Emissions 

Estimation System (EES) (Clinton and others 2003) to quantify wildland fire 

emissions. EES uses FOFEM fuel consumption and emission estimation algorithms in a spatially explicit manner. It can be used in retrospective fire modeling 

to calculate PM 2.5 and PM 10 emissions for any modeled fire’s final perimeter. 

In addition, it can be used to predict potential emissions, as described above, for 

entire landscapes. Currently, EES only contains data for modeling emissions in 

California, but it could potentially be adapted to work in other areas. Alternative 

tools for estimating emissions include FARSITE and FOFEM. FARSITE can be 

used to estimate emissions during the fire growth modeling process, provided 

the user has the necessary duff and coarse woody debris data.

Data Acquisition and Preparation

Project parameters (study area location, simulation time span, and length of 

fire season) will dictate what and how much data are necessary. The basic data 

required for a retrospective fire-modeling project include:

• Boundary delineating the study area

• Historical ignition point location and timing within the study area

• Historical weather data from Remote Automated Weather Stations (RAWS) 

or similar weather stations covering the simulation period. Hourly data are 

preferable but where they are unavailable, daily data can be supplemented.

• Surface and canopy fuels data that reflect conditions at the beginning of the 

simulation period as nearly as possible

• Topographic data (elevation, slope, and aspect)

• Digital fire atlas data (polygons)

• Optional: Fire severity data for real fires that occurred during the simulation 

period

• Optional: Fire Return Interval (FRI) data if FRID is chosen as an impact 

measure

• Optional: Spatial duff and coarse woody debris data if emissions are being 

modeled using FARSITE
8 USDA Forest Service RMRS-GTR-236WWW. 2010.

• Other data may be required depending on the models, outputs, and 

methodologies selected.

TIP

Data Acquisition

If possible, attempt to obtain data that extends beyond the study area to 

reduce edge effects.

Each of these datasets is described below with suggestions on where to acquire 

them.

Study Area Boundary

The study area boundary (Fig. 2) should be obtained from local management 

staff and typically needs no particular manipulation other than to ensure that it is 

in the desired geographic projection. This boundary is used to define the analysis area and to manipulate (e.g., clip) ignition and fuels data layers.

Figure 2. South Fork Merced study area, Yosemite NP.
USDA Forest Service RMRS-GTR-236WWW. 2010. 9

Historical Weather Data

Successful retrospective fire modeling depends on high quality weather information. Weather data are very important inputs for fire growth and behavior 

modeling. Inaccurate or incomplete weather data will lead to inaccurate fire 

growth and behavior predictions. Time and effort spent on acquiring and preparing these data will ultimately pay off in increased confidence in the outputs.

Acquiring historical weather data: Remote Automated Weather Station 

(RAWS) data are the most commonly used weather data in the United States 

for fire modeling. When possible, hourly, rather than daily, weather data should 

be used. Some hourly (.fw9 format) and daily (.fwx format) RAWS data for the 

entire United States can be acquired from the Western Regional Climate Center 

(WRCC) website (http://www.wrcc.dri.edu/fpa/). FFP requires the weather data 

itself (.fwx or .fw9) and a station catalog file (.txt). Catalog files are available 

from the National Wildfire Coordinating Group’s (NWCG) website (http://fam.

nwcg.gov/fam-web/) under the “Fire and Weather data” link (FFP 4.02 has catalog information pre-loaded for most RAWS stations). Information should be 

verified that it is up-to-date as there are often delays in archiving these data. 

Daily RAWS data (but not hourly) can also be acquired from NWCG’s website. 

Information about RAWS and other types of weather stations (including locations) can be found at http://www.raws.dri.edu/index.html.

Selecting a weather station should be done in consultation with a local expert 

with fire management and modeling experience. To be representative of the 

study area as a whole, the station should be as near the average elevation and 

the center of the study area as possible. Another consideration is the influence of 

surrounding topography on wind direction and speed. Wind direction and speed 

should be as representative of the whole study area as possible and not overly 

influenced by topography (e.g., being in a wind shadow). If necessary, more 

than one weather station might better represent conditions in the study area. For 

example, one may be used for weather data (precipitation, temperature, humidity) and another for wind (speed and azimuth). Wind rose plots are helpful for 

evaluating the representativeness of weather stations.

The station(s) selected should also have complete and accurate records. 

RAWS data are particularly prone to missing and erroneous data. The longer 

the time period covered, the more likely these data will contain errors. FFP can 

be used to locate erroneous and missing data (see “Preparing weather data” below). There should be no large gaps in the hourly/daily records for the months 

of the fire season. Although finding stations that fit all these strict requirements 

may be difficult, it is important to adhere to them as closely as possible. Once 

the station(s) have been selected, download the weather (*.fwx or *.fw9) and 

catalog files (*.txt) if necessary.

EXAMPLE

Weather Station Selection

Weather data: Mariposa Grove RAWS (#44113), good hourly data but in a 

sheltered location

Wind data: Metcalf Gap RAWS (#44209), outside of the study area but in an 

unsheltered location on a ridge west of the study area

Both chosen in consultation with an experienced FARSITE user at Yosemite 

National Park
10 USDA Forest Service RMRS-GTR-236WWW. 2010.

Preparing weather data: Much of the weather data analysis described 

in this guidebook requires the use of FireFamilyPlus (FFP). If a program 

other than FFP is being used to analyze and prepare the weather data, the 

general concepts described below can be followed to replicate the analysis. 

Preparing weather data requires several steps: importing and reviewing for 

missing data and errors, calculating a threshold value for fire growth (op-

tional), calculating daily values for critical variables, and creating weather 

and wind streams for use in fire growth and behavior models.

Importing and reviewing weather station data: The first step is to im-

port the weather and catalog files into FFP. If these files were downloaded 

from different sources, it may be necessary to reconcile the station IDs so 

they will be correctly associated in FFP.

TIP

Reviewing Weather Data

Look at FFP’s import error log for information on erroneous values. 

Inspect the imported data by listing weather observations in FFP

(Weather > View Observations > All). Sort the resultant table by each 

weather variable to look for erroneous or missing data. Obvious data 

errors will need to be removed or replaced with average values, 

previous values, values from another nearby weather station, etc. 

The number of records reviewed can be reduced by setting the “Data 

Years” and “Time of Year” filters before viewing “all observations.” 

They can be reduced further if the first ignition selected for modeling 

in a particular year occurs substantially after the beginning of the fire 

season (see the “Preparing ignition data” section below).

It may be necessary to carry out more detailed quality control mea-

sures. For periods outside of the fire spread modeling dates (used for fire 

weather index calculations, etc.), a few errors can probably be ignored if 

the dataset is large enough. But, for those days when fire spread will ac-

tually be modeled, it is important to have weather data that are complete 

and as accurate as possible. This can be accomplished by editing the data 

within FFP or by importing the historical weather file into a spreadsheet 

program, such as Microsoft Excel, which facilitates the replacement of 

missing and erroneous data with averages or previous data. Unfortunately, 

the data cannot be saved back into a fw* format from Excel and therefore 

will need to be saved as one of the various text formats and manipulated 

with a text editor to return it to its original fw* format. Format details can 

be found at http://fam.nwcg.gov/fam-web/.

Calculating fire weather index percentiles (optional): At the discretion 

of the user, modeled fire growth can be limited to the days and times of 

day when fire is most likely to have substantial spread. This approach is 

highly recommended for use with FARSITE to reign in its propensity to 

over predict fire spread and to vastly shorten the time it takes to run a 

simulation (see “Modeling Fire Growth and Behavior” section below). A 

daily “burn period” is often used in FARSITE simulations to pause the 

simulation during periods of low fire activity, such as on cool nights when 

relative humidity increases. Historical fire spread can be used to identify 

conditions under which significant fire growth is likely. A “spread thresh-

old” can be defined in terms of a fire weather index such as Energy Release 

Component (ERC) or Spread Component (SC), or fuel moisture. For ex-

ample, if analysis of historical fires showed significant fire growth when 

ERC values exceeded 45, fire growth would only be simulated on days 
USDA Forest Service RMRS-GTR-236WWW. 2010. 11

when the ERC ≥ 45. This threshold can also be expressed in terms of a percentile (e.g., 90th percentile ERC). Spread thresholds can also be used to adjust the 

length of the burning period. For example, when a fire index is extremely high, 

fires might grow significantly during the night and the burning period may need 

to be increased.

EXAMPLE

Setting Fire Growth Thresholds

In the case study, historical fire spread was not analyzed to determine 

thresholds. Insead, local managers defined the spread threshold as the 

90th percentile ERC for the 1994-2004 seasons. While fire may spread 

at lower ERCs, the majority of spread occurs under extreme conditions. 

Setting the threshold greatly reduced modeling time. In addition, the daily 

burning period was increased from 9 hours (0900-1800) to 24 hours if ERC 

crossed the 98th percentile threshold, again based on local knowledge.

TIP

Calculating ERC

When using ERC to define a threshold for fire growth, the weather record 

should be buffered by approximately 40 days prior to the start of the 

season to ensure proper calculation of the 1000-hour fuel moistures, which 

are an important component for calculating ERC.

Set FFP’s “Annual Filter” to the length of the fire season (e.g., June-October; 

Fig. 3) including as many years of high-quality data as are available but taking 

care to avoid climate trends that may be unrepresentative (e.g., Heinsch et al. 

2009). Daily weather data (.fwx) are used to calculate these percentiles.

Figure 3. FFP “Working Set” screen.
12 USDA Forest Service RMRS-GTR-236WWW. 2010.

EXAMPLE

Calculating Fire Weather Index Percentiles

1) Set “Data Years” on FFP’s “Working Set” screen to the years for 

which quality, representative data exist.

2) Set “Annual Filter” to the length of the fire season (e.g., June 

1-October 31).

3) Use Weather > Climatology, select Stats graph, and enter percentile.

4) Save or print the graphs (Fig. 4) for future reference.

The threshold value of the 90th percentile ERC used in the case study 

equates to an ERC value of 45 (Fig. 4). If a different dataset is used to calculate the fire weather index percentile than the dataset used to define the 

fire growth weather variables, be sure to switch back to the correct dataset 

before proceeding with the next step.

Calculating fire-ending events: Next, it is necessary to define and 

identify fire-ending events. For the case study a fire-ending event in the 

Yosemite area was defined as 0.5 inches of rain within a three-day period. 

Significant precipitation events such as this can be identified using FFP’s 

“Event Locator” (Fig. 5). The exact definition of a fire-ending precipitation event will depend on the geographic area and fuel type and should be 

defined by a local fire behavior expert. Precipitation events that fall below 

the level judged capable of extinguishing a fire are commonly referred 

to as fire slowing events. The slowing effect these events have will be 

reflected by the impact increased fuel moistures have on fire growth modeling. They can be further accounted for when a fire weather index is used 

as a spread threshold.

Figure 4. FFP percentile ERC graphs.
USDA Forest Service RMRS-GTR-236WWW. 2010. 13

EXAMPLE

Identifying Fire-Ending Events

1) Set “Data Years” on FFP’s “Working Set” screen to the length of the 

simulation (e.g., 1994-2004).

2) Set “Annual Filter” to the length of the fire season (e.g., June 1-October 31).

3) Use Weather > Event Locator, input the appropriate values for the fire 

ending event (fig. 5).

4) Save or print the results for future reference.

Creating a list of relevant daily weather values: To help determine the exact 

dates for which fire growth will be simulated, it helps to have a list of those daily 

weather values that can affect the simulation. Relevant weather attributes include any index for which a threshold has been defined and any other variables 

of interest (e.g., precipitation, spread component). While it is more convenient 

to define a set date for the end of the fire season when analyzing historical data 

and calculating fire weather index percentiles, there are other options which 

may be more appropriate for the years in which fire spread will be modeled. For 

example, the end of the fire season is commonly defined as the point at which 

ERC falls below a certain level and doesn’t recover. No matter how the end of 

the fire season is determined, a “pre-fire season” buffer of weather records will 

allow for a more accurate calculation of 1000-hr fuel moisture and therefore an 

index like ERC.

EXAMPLE

Generating a Daily Listing of Weather Values in FFP

This list can be developed by using FFP’s Weather > Season Reports > Daily Listing, selecting the 

appropriate variables and saving the result as a text file for future reference.

DATE ERC SC RAIN 1000 hr FM DATE ERC SC RAIN 1000 hr FM

19940601 29 9 0.00 20 19941025 45 10 0.00 8

19940602 31 12 0.00 19 19941026 46 10 0.00 8

19940603 33 10 0.00 19 19941027 46 11 0.00 8

19940604 30 10 0.00 19 19941028 48 13 0.00 7

19940605 29 9 0.00 18 19941029 12 2 0.40 9

. . . 19941030 8 1 0.20 9

 19941031 1 1 0.10 9

Figure 5. FFP’s Event Locator.
14 USDA Forest Service RMRS-GTR-236WWW. 2010.

In the example above, ERC is used as the threshold index for fire growth and 

rain amount defines fire-ending events. Spread Component (SC) and 1000-hour 

fuel moistures were included for general interest.

Preparing weather and wind files: FFP can be used to generate weather (.wtr) 

and wind (.wnd) input files and to determine dead fuel moistures for the beginning of each fire season. Although FFP can generate live woody and herbaceous 

fuel moistures, it is recommended that live fuel moistures be determined in 

some other way (L. Kurth, USDA Forest Service, Rocky Mountain Research 

Station, personal communication). One weather file, one wind file, and at least 

one set of fuel moistures are required for each simulation year. As FARSITE 

does not change live fuel moistures with time, more than one set of moistures 

may be necessary to reflect seasonal changes.

EXAMPLE

To generate weather and wind files from hourly data in FFP, set the “Data 

Years” and “Time of Year” filters to the appropriate durations. Then use 

Weather > Hourly Data Analysis > FarSite Exports to generate the files. 

Repeat for each simulation year. To generate dead fuel moisture for input 

into FARSITE, calculate the 1-h, 10-h, and 100-h fuel moistures for the day 

of each year’s first ignition with Weather > Season Reports > Daily Listing, 

selecting the appropriate variables (fuel moistures) and making a note of 

the values for the appropriate days.

TIP

Generating FARSITE Inputs

FARSITE inputs are not needed for the entire fire season if there are time 

periods when fire growth won’t be simulated. Days can be excluded, for 

example, if the first ignition doesn’t occur until a month into the fire season 

or if a fire-ending event occurs before the end of the fire season.

If FARSITE is used, fuel moisture files (.fms) for each year are necessary. 

Follow FARSITE’s help file to correctly format these files.

The preceding steps should yield the following weather information:

1. Daily weather values for each fire season (e.g., *.wtr file)

2. Hourly (or daily) wind values for each year’s season (e.g., *.wnd file)

3. Starting fuel moistures (1-h, 10-h, 100-h Live Herbaceous, and Live Woody) 

for the first day of each fire season or the first day of the simulation. (e.g., 

values used to create, with a text editor, the *.fms file)

4. Daily listing of fire weather index values, rain amounts and other values of 

interest

5. List of fire-ending (precipitation) events

Historical Ignition Data

Ignition data (location and date) should be as spatially and temporally accurate as possible. Dates and locations of suppressed ignitions help determine 

whether or not the ignition would have become an established wildfire, and 

hence need to be modeled. Cause, final fire size, and management response are 

also highly relevant pieces of information.
USDA Forest Service RMRS-GTR-236WWW. 2010. 15

Acquiring ignition data: Historical ignition data for USDA Forest Service 

land reside in the National Interagency Fire Management Integrated Database 

(NIFMID). Historical ignition data for the Department of the Interior lands (NPS, 

BLM, FWS and BIA) reside in the Wildland Fire Management Information system (WFMI). Ignition data from both of these databases can be obtained from 

the National Wildfire Coordinating Group’s (NWCG) website (http://fam.nwcg.

gov/fam-web/ under “Fire and Weather data” link on the left).

These data have numerous, well-documented shortcomings, many of which are 

outlined in the Program for Climate, Ecosystem and Fire Application’s (CEFA) 

“Coarse Assessment of Federal Wildland Fire Occurrence Data” (Brown and 

others 2002; http://www.cefa.dri.edu/Publications/fireoccurrencereport.pdf). In 

addition to this report, CEFA has developed a database of federal ignition points 

in which some of the grossest errors have been corrected or flagged. To obtain 

these data, use the contacts listed in the CEFA report. Another potential source 

for these data may be the land management agencies themselves who may have 

performed some quality control on their historical ignition data.

Preparing ignition data: Ignition location and attribute data (e.g., start 

date, cause, management response, and final fire size) are necessary to determine which ignitions were suppressed and whether they had the potential for 

significant growth. Ignitions of interest may be defined by attributes (e.g., lightning-caused), location, timing or any other selection criteria deemed important.

EXAMPLE

Initial Ignition Selection

Attributes: Caused by lightning

Location: South Fork Merced watershed, Yosemite NP

Time period: 1994-2004

TIP

If the fire occurrence (ignition point) data don’t contain all the attribute 

information necessary to select potential simulation ignitions, it may be 

possible to supplement it with attribute information contained in a digital fire 

atlas (polygon) dataset.

Identifying ignitions that may have exhibited significant spread had they not 

been suppressed can be complicated. Many of the ignitions found in the fire 

occurrence files wouldn’t have become established wildfires due to fuel discontinuity, high fuel moistures, subsequent weather conditions (e.g., significant 

rain), or other spread inhibiting environmental conditions. Therefore, it is generally necessary to refine the set of potential simulation ignitions with additional 

selection criteria. Many criteria can be used, singly or in conjunction with one 

another, to make these decisions. These include basic weather conditions, fuel 

moistures, fire weather indices (e.g., ERC or SC), final fire size, underlying 

fuel models, surrounding fuel continuity, elevation, and expert opinion. In the 

case study, ERC was used to help identify which ignitions to model but SC or 

Burning Index (BI) could have been used instead. The best approach is to determine the weather conditions, fire weather indices, underlying fuel model, final 

fire size, and so on, for each ignition and consult with local fire management 

experts to define the criteria under which an ignition is most likely to spread.
16 USDA Forest Service RMRS-GTR-236WWW. 2010.

EXAMPLE

Ignition Selection Criteria

In the case study, a combination of ERC, the underlying fuel model, expert 

opinion, and attribute information, such as final fire size, were used to make 

the ignition selection. Specifically, fuel models were divided in terms of rate of 

spread into “fast” and “slow” categories. For example, a fully cured tall grass 

fuel model was considered “fast,” whereas a very low load conifer litter fuel 

model was considered “slow.” Some fuel types were considered slow early in 

the fire season and fast later in the fire season once their fuels had cured. The 

use of these criteria identified 10 ignitions out of 34 candidates (Fig. 6).

Criteria for inclusion as an ignition likely to spread:

1) Occurred in a “fast” fuel model and ERC > 15th percentile

2) Occurred in a “slow” fuel model and ERC > 50th percentile

3) ERC exceeds the threshold value of 90th percentile at some point between 

the ignition date and the end of the fire season

4) Exceptions: The above are general criteria. Any ignition that had other 

attributes (e.g., final fire size) indicating the potential for significant spread 

was considered a candidate as well.

Figure 6. Potential simulation ignitions, South Fork Merced watershed.
USDA Forest Service RMRS-GTR-236WWW. 2010. 17

Once the ignitions with potential for spread have been selected, their coordinates need to be manipulated into a file format that is compatible with the 

fire growth model. It is necessary to create a separate coordinate file for each 

individual ignition. FARSITE accepts multiple types of ignition files including 

ASCII text and shapefiles. ASCII files are recommended due to their small size 

and simplicity. See the FARSITE help file for formatting information.

Fuels Data

Fuels data represent the medium through which the modeled fires will spread. 

They have a significant impact on fire behavior and effects. Therefore, care 

should be given to their acquisition and development. See Stratton (2009) for a 

thorough discussion of considerations.

Acquiring fuels data: The FARSITE and FlamMap models require the construction of a LCP (landscape) file. Required fuels inputs for a LCP file include 

surface fuel models and canopy cover. Modeling crown fire requires the following additional inputs: canopy height, canopy base height, and canopy bulk 

density. Modeling emissions with FARSITE requires duff and coarse woody 

debris layers be included as well. Fuels data are best obtained from the agency 

responsible for the study site. If they are unavailable from the responsible agency, they may be available from the LANDFIRE project (Rollins 2009; http://

www.landfire.gov/dataproduct_overview.php). Precompiled LCP files can be 

downloaded from LANDFIRE, complete with topography and surface and 

canopy fuels. If fuels data are unavailable or not of sufficient quality, it may be 

necessary to develop the data by crosswalking any available vegetation data. 

For the case study, the latest vegetation data were obtained from the land management agency and, in cooperation with managers and scientists, a crosswalk 

was developed from vegetation type to a set of surface fuel models (Scott and 

Burgan 2005; Fig. 7).

Preparing fuels data: Some of the most important inputs for fire growth modeling describe surface and canopy fuels. It is important to determine when the 

fuels data were developed and if they have been updated or manipulated. These 

data are generally derived from the underlying vegetation data, which usually 

are at least partly based on remotely sensed data such as satellite or aerial imagery. It is desirable to use fuels data (and any other time sensitive data) that most 

accurately reflect the starting year of the simulation. If the year of the imagery 

capture differs from the desired starting year, modifying the input data to reflect 

an earlier or later date (e.g., updating fuels to reflect disturbances or successional changes that occurred between the chosen start date and the imagery capture 

date) will improve the accuracy of the data. Similar modifications may need to 

be made to canopy cover and the other crown fuels data.

EXAMPLE

Surface Fuel Development and Manipulation

The imagery used to develop vegetation data for Yosemite National 

Park was captured in 1997. The proposed start date for the case study 

retrospective fire simulation was 1994. To better reflect fuel conditions in 

1994 any fires that occurred between 1994 and 1996 were “unburned.” 

This was accomplished by replacing the vegetation classification for those 

areas that burned between 1994 and 1996 with data from the park’s 

previous vegetation coverage. The resulting vegetation data were then 

crosswalked to surface fuels (Fig. 7).
18 USDA Forest Service RMRS-GTR-236WWW. 2010.

Topographic Data

Elevation, slope, and aspect data are required elements of a LCP file and 

can be obtained from various sources, including the responsible agency and the 

National Elevation Dataset (http://seamless.usgs.gov/index.php). The elevation 

dataset can be used to create the slope and aspect data using a GIS program such 

as ArcGIS (ESRI 2005).

To build a LCP file, all spatial inputs (fuels, elevation, etc.) must be converted 

to ASCII format. Conversion methodology will vary depending on the format 

of the original data. For example, if the fuels data reside in an ArcInfo grid, the 

“gridascii” command in GRID can be used to convert the grid into an ASCII 

file, as can the “raster to ASCII” tool in ArcGIS. The original files must have a 

common projection, format, cell size, and origin (the same minimum X and Y 

coordinates) before conversion to ASCII files.

Fire Atlas Data

Historical fire atlas data are used for many purposes in retrospective fire 

modeling including updating fuels data, modifying fire spread, and as input to 

metrics used to measure the impact of alternative management strategies.

Figure 7. Surface fuel models, South Fork Merced watershed.

a
USDA Forest Service RMRS-GTR-236WWW. 2010. 19

Acquiring fire atlas data: Fire atlas data (aka fire history data) are generally polygon spatial data representing the final perimeters and other attributes 

(e.g., dates) of historical fires. They are usually available from the management 

agency responsible for the study area. When calculating a fire regime departure 

measure such as FRID, it is important to obtain as much historical fire data as 

possible, as these data are integral to calculating these measures. For example, 

in the case study analysis in the South Fork Merced watershed, approximately 

550 fire perimeters from 1930 through 2004 were obtained (Fig. 8). Because the 

majority of these fires were less than one acre, they will not be readily apparent 

in figure 8. This is a very extensive dataset with a long period of record. Such 

extensive data may not be available for all areas. It is important to state that 

any fire atlas used for the purpose of retrospective fire modeling should include 

prescribed fires as well as wildland fires. This is important because, from the 

landscape’s perspective, there is little difference whether the fires were planned 

or not.

Preparing fire atlas data: Fire atlas data are used in retrospective fire modeling for three purposes: to update fuels layers between simulation years, create 

barriers to fire spread within FARSITE, and calculate the fire regime departures (FRID, FRCC) that enable comparisons between alternative landscapes. 

Multiple sub-atlases must be derived from the complete fire atlas for these purposes; these sub-atlases are listed below.

Figure 8. 1930-2004 fire atlas for the South Fork Merced watershed.
20 USDA Forest Service RMRS-GTR-236WWW. 2010.

1. Historical fire atlas: This is an atlas containing the perimeters of the earliest 

fires available through the last year of the retrospective simulation. The 

historical fire atlas contains fires that contributed to the realized landscape.

2. Truncated historical fire atlas: This is an atlas that contains fires from the 

earliest year available up to the first year of the simulation. Modeled fire 

perimeters and the appropriate real fire perimeters will be added to this atlas 

after all simulations are complete to create a hypothetical fire atlas. This subatlas will contain fires that contributed to the hypothetical landscape.

3. Individual real fires: Each real fire that occurred during the simulation period 

must be extracted into its own, separate file. These files will be used to 

modify the landscape during fire growth modeling (i.e., to create barriers 

to fire spread), update fuels after each simulation year and rebuild an atlas 

containing both simulated and real fires.

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Fire Atlas Manipulation

Some fire atlases include very small fires. To expedite the retrospective 

modeling process, we recommend setting a minimum threshold, between 

1 and 10 acres, and eliminating fires smaller than this threshold. Eliminating 

these small fires will reduce the time it takes to prepare the data and perform 

the analysis without having a large impact on the results. When using 

FARSITE as the fire growth model, a “shapefile” is the recommended format 

for the fire atlases.

Example

Fire Atlas Preparation

Historical fire atlas: contains all real fires (1930-2004, 555 perimeters)

Truncated historical fire atlas: contains real fires only up until simulation start 

year (1930-1993, 439 perimeters)

Individual real fires: one shapefile per fire 1994-2004 (min. 1 acre)

Optional Data

Wildland fire severity data: Wherever available, severity data for real fires 

should be used to help update fuels between simulation years. The local management agency is the best starting point for finding these data. Another good 

option is the Monitoring Trends in Burn Severity project (http://mtbs.gov), 

which has burn severity data for most large fires (>1000 acres) and some smaller 

fires dating back to 1984. For those wildland fires for which there are no data, 

assumptions about the severity must be made. In the case study, a moderate severity was assumed for existing atlas fires when severity data were unavailable.

Fire Return Intervals (FRI): The calculation of Fire Return Interval Departure 

(FRID) requires FRI and fire atlas data (discussed above). FRI data may be obtained from the management agency, or it may be necessary to extract it from 

the scientific literature. Contacting a fire ecologist familiar with the study area 

may be the best way to discover what data are available. FRI data may be formatted as a table of return intervals by vegetation type or as spatial data. Tabular 

FRI data can generally be used to create spatial FRI data by crosswalking vegetation GIS data (Fig. 9). This spatial (raster) FRI data will be important to the 

calculation of FRID when comparing alternative landscapes.
USDA Forest Service RMRS-GTR-236WWW. 2010. 21

Other data: Other data may be required depending on the models, outputs, 

and methodologies selected. For example, duff and coarse woody debris data 

are required to model emissions with FARSITE, and the optional crown fire 

inputs are required to model crown fire.

Modeling and Analysis

The retrospective fire modeling process has a few generic steps: defining 

parameters and preparing input data (as described above), modeling fire spread 

and behavior, updating fuels layers based on modeled and real fire and fuel 

accumulation, repeating the process, and comparing the resultant alternative 

landscapes. In order to generate a clear picture of the sequential steps it is helpful to draw up a timeline of events.

Defining Timeline

For each year of the simulation, a timeline of all events during the fire season that can affect the simulation of fire growth must be created. These events 

Figure 9. Fire Return Intervals for South Fork Merced watershed.
22 USDA Forest Service RMRS-GTR-236WWW. 2010.

include ignition dates, weather events, occurrence of real fires and the crossing of thresholds defining when fire growth should be simulated, paused, or 

stopped. The timeline can be developed using the daily listing of fire weather 

index values, rain amounts, dates of ignitions, and any other information that 

may affect fire growth simulation. This timeline can be referred to during the 

fire growth modeling process. Note that the thresholds used in the case study 

were based on local manager opinion and should not be applied uncritically to 

a different study area.

EXAMPLE

Simulation Timeline

Date Event Action

June 27 Calder ignition Import ignition

July 2 Grove wildfire Import perimeter as barrier

July 25 ERC above 90th % Start simulation

August 1 Bug ignition Pause, import ignition, restart

August 4 ERC above 98th % Pause, change burning period*, restart

August 8 ERC below 98th % Pause, change burning period*, restart

August 12 ERC below 90th % Pause

August 25 Jones ignition Import ignition

September 3 ERC above 90th % Restart

September 6 Axe wildfire Pause, import as barrier, restart

September 9 ERC below 90th % Pause

October 3 ERC above 90th % Restart

October 15 Fire-ending rain event End simulation

*In the case study the length of the daily burning period changed when ERC 

exceeded the 98th percentile (see FARSITE help file for definition of burning period).

In the above example, fire growth is simulated only on those days when the 

ERC is above the 90th percentile. This translates into 36 simulation days divided 

into three simulation “periods” (July 25-August 12, September 3-September 9 

and October 3-October 15). The criteria that determine when fire growth is simulated are defined by the user (as described in the weather data section), as is 

the definition of a fire-ending precipitation event. Because modeled fires and 

fuel succession can impact events in a particular simulation year’s timeline, it 

shouldn’t be developed until after the previous year has been modeled and fuel 

succession is applied.

Revisiting Ignition Selection

The timeline created in the previous step can inform which ignitions need 

to be simulated for the retrospective fire analysis. In addition to the selection 

criteria described in the “Preparing ignition data” section above, ignitions can 

be eliminated during the modeling process for other reasons. They can be eliminated if a modeled fire from earlier in the same simulation year burns over their 

location before they occurred, or if a previous year’s modeled fire burns over 

their location and the fuels haven’t recovered yet. Similarly, real fires can be 

eliminated in the same fashion. In other words, some of the ignitions originally 

selected for modeling and the ignition points for “real” fires may fall on areas 

that became unburnable due to a previously modeled fire. These ignitions can 

be left out of the analysis. It is for this reason that each year’s timeline should 

be prepared only after the previous year’s simulation and post-season fuel succession modeling is complete. The ignitions and real fires that can be eliminated 

from the analysis can be determined by identifying the fuel conditions at the 

time and place of their occurrence.
USDA Forest Service RMRS-GTR-236WWW. 2010. 23

EXAMPLE

Ignition Elimination

In the case study, five of the ignitions selected for modeling were ignored 

because the fuels at their locations hadn’t recovered from previously 

simulated fires. In addition, 26 real wildfires were left out of the analysis for 

the same reason.

Modeling Fire Growth and Behavior

Once the ignitions have been refined to include only those that likely would 

have become established wildfires, they are used in the fire growth model as 

starting points for predicting fire spread and the final fire perimeter. The following instructions and descriptions are based on the use of FARSITE as the 

fire growth model. When using a program other than FARSITE to model fire 

growth, the following procedures can be used as a conceptual guide.

Modeling fire growth using FARSITE is deceptively simple to implement. 

With the assistance of the FARSITE tutorial and help file, anyone can run the 

model, but it takes training and experience to understand the underlying assumptions and limitations and to use it most effectively. Before this tool is used 

for retrospective fire modeling, we highly recommend appropriate training (e.g., 

taking the S-495 “Geospatial Fire Analysis, Interpretation and Application” 

course) and experience, or at least access to someone who has these. While 

information specific to using FARSITE for retrospective modeling is covered in this guide, instruction on the correct general operation of FARSITE is 

beyond the scope of this document and will not be presented. Details on setting up a FARSITE run can be found in the FARSITE tutorial and help files. 

General guidelines on LCP (landscape file) critiques, FARSITE parameters, and 

FARSITE calibration can be found in Stratton (2009). The following instructions assume a basic familiarity with FARSITE and fire growth modeling.

TIP

FARSITE File Management

Set up a file structure for the retrospective analysis. Create a separate 

folder to hold the input data for each simulation year. Inputs include the 

landscape file (.lcp), ignition point files, real fire perimeters, and files for 

weather (.wtr), wind (.wnd), fuel moisture (.fms), burn period (.bpd), rate 

of spread adjustment (.adj), and optionally fuel model conversion (.cnv), 

custom fuel model (.fmd), and coarse woody profile (.cwd). This is also a 

good place to save the project and bookmark files. Set up a similar file 

structure for outputs.

A note on calibrating FARSITE: Calibrating FARSITE to known fire spread 

and behavior will greatly increase confidence in its outputs. Calibration for 

historical ignitions is a more complicated task than calibrating for an actual 

and ongoing fire event because most historical ignitions were successfully suppressed by initial attack before growing to any substantial size and thereby 

providing information on natural fire spread. Historical fires that weren’t suppressed or for which the suppression tactics are well known are particularly 

valuable for calibrating FARSITE parameters. When progression data are available for historical fires, be sure to consider that they may not necessarily be 

representative of the ignition(s) you are modeling due to differences in location, 

fuels, and weather conditions. Broad guidelines for model calibration can be 
24 USDA Forest Service RMRS-GTR-236WWW. 2010.

found in Stratton (2006, 2009). If sufficient fire spread data aren’t available, 

consult with someone who has experience modeling fire in or near the study area 

for guidance on the adjustments for localized calibration. Especially useful are 

“starting point” rate of spread adjustments and burn periods (see “Calculating 

fire weather index percentiles” under the “Preparing weather data” section 

above for an explanation of burn periods) under varying fire weather conditions. 

These are generally based on the local fire modeler’s experience. If threshold 

conditions (e.g., a minimum ERC) are being used to determine when to simulate 

fire growth, calibrations should be made with these conditions in mind.

TIP

Using Custom Fuel Models Instead of FARSITE Adjustment Files

The use of the adjustment (.adj) file is the quickest and crudest way of 

calibrating FARSITE, but the adjustment file only affects rate of spread and 

not other fire behavior characteristics. If other fire behavior characteristics, 

such as flame length, are of concern, custom fuel models and/or 

conversion files can be used to adjust modeled fire behavior (for example, 

use the “tuning” feature of FARSITE’s custom fuel model editor). When a 

fuel model is “tuned” to change rate of spread, the adjustment is reflected 

in other fire behavior characteristics as well. The resultant custom fuel 

model file (.fmd) can be used in conjunction with a fuel model conversion 

file (.cnv) to adjust fire behavior without the need to incorporate the custom 

fuel model into the fuels data layer. See FARSITE’s help file for more 

information on creating custom fuel model, conversion and adjustment 

files.

Build and Load the LCP File

LCP files can be created using FARSITE’s landscape utility (Input > 

Landscape Utilities > Generate Landscape File). Creation of fuels and topography inputs is described above. Select the correct units for each input and set the 

latitude for the study area.

Project Inputs

Load the project inputs (Input > Project Inputs) (Fig. 10). The LCP, adjustment, fuel moisture, weather, wind and burn period files are minimally necessary 

inputs (the burn period file is not required but is almost certainly appropriate; see 

the FARSITE help file for explanations of why burn period files are important).

Project Parameters

Parameters should be defined based on general FARSITE knowledge, calibration results, local fire growth simulation knowledge, and output desires. Set 

the project parameters for the simulation (Model > Parameters, Model > Fire 

Behavior Options, Simulate > Options, and Simulate > Duration) (Fig. 11). 

Select whether or not crown fire and spotting will be simulated (Model > Fire 

Behavior Options). In the case study, crown fire was simulated, but spotting 

was not (Fig. 12). Simulation duration should be set from the beginning of the 

burn period on the first day of fire growth to the beginning of the burn period on 

the day after the final day of fire growth. Refer to the timeline and burn period 

times to define the simulation duration. If emissions are being modeled using 

FARSITE, set the post-frontal combustion parameters (Model > Post-Frontal 

Combustion). Other model parameters (e.g., fire acceleration, dead fuel moisture) can be set at the user’s discretion.
USDA Forest Service RMRS-GTR-236WWW. 2010. 25

Figure 10. FARSITE project 

inputs.

Figure 11. FARSITE model parameters. Figure 12. FARSITE fire behavior options.
26 USDA Forest Service RMRS-GTR-236WWW. 2010.

TIP

FARSITE Parameters

For longer simulations, it may be necessary to use coarser model 

parameters to ensure reasonable simulation times. This is a tradeoff with 

output precision. A one day visible time step is probably the most useful for 

long duration retrospective simulations.

Import initial ignitions (Simulate > Modify Map > Import Ignitions) and 

barriers (Simulate > Modify Map > Import Barriers) after “initiating” the simulation. The initial ignitions are those that occur prior to the first day of fire 

growth simulation. Later ignitions are imported at the appropriate time during 

the simulation. Now is a good time to save the project (Input > Save Project) 

and a bookmark (Input > Bookmarks).

Selecting Outputs

Prior to starting a simulation, select outputs (Output > Export and Outputs) 

and choose any outputs of interest or those necessary for future modeling or 

analysis. At a minimum, select a fire perimeter output. For the fire perimeter outputs, enabling “visible steps only,” “save perimeters as polygons,” and “exclude 

barriers” is recommended to facilitate the analysis of results. Raster outputs are 

chosen at the user’s discretion. If a subsequent analysis of fuel succession requires some measure of fire severity (e.g., crown fire occurrence, flame length), 

be sure to include it among the raster outputs.

Smoke emission estimates can be obtained by enabling an output table 

(Output > Data Tables > Post-frontal Combustion). The table for each emission 

of interest (e.g., PM 2.5) can be saved as a text file for later manipulation and 

analysis after the FARSITE run is complete. Change emission type and save 

emission files by right-clicking within the table.

Running the Model

Due to the need to modify parameters and import ignitions and barriers to fire 

spread (e.g., areas that have recently burned and have not accumulated enough 

fuel to burn again) during the course of the simulation, it is necessary to step 

through the simulation day-by-day using FARSITE’s “Step Through” function 

(Simulate > Step Through). “Step Through” runs the simulation one visible time 

step at a time. In addition, there may be times when the simulation needs to be 

put “on-hold.” To pause fire growth simulation for extended periods of time 

(i.e., multiple days) in FARSITE the simplest way should be to set start and 

end times in the burn period file to 00:00 on days when there is no fire growth. 

Unfortunately, FARSITE still simulates fire growth for one time step on each of 

those days. To ensure no fire growth at all occurs on those days, some trickery 

is is required. This trickery involves stepping FARSITE through each day of the 

simulation until the beginning of the “on-hold” period. A new adjustment file 

(.adj) containing zeros for all fuel models must be imported so that each day 

of the “on-hold” period can be stepped through until it is time to resume the 

simulation. Zeros in the adjustment file prevent FARSITE from simulating fire 

spread. After the “on-hold” period is over, the original adjustment file must be 

reloaded and the simulation resumed. There can be many pause and resume actions in a single simulation year.
USDA Forest Service RMRS-GTR-236WWW. 2010. 27

While other pauses of fire growth may be required (e.g., to modify parameters, import ignitions, or barriers to fire spread), such pauses can be handled 

with the step-through method (without importing the “zero .adj file”)

TIP

Minimizing Active Simulation Periods

If you are using fire weather thresholds to define when a fire will or will not 

grow, you can minimize the number of active simulation periods by defining 

the threshold as “crossed” only when the condition persists for multiple 

days. For example, in the case study, a pause was not implemented unless 

ERC dropped and remained below the 90th percentile threshold for two 

consecutive days and a resumption of fire growth was not enacted unless 

ERC remained above the 90th percentile for the same period of time.

Run the FARSITE simulation using the step-through option making the appropriate imports and parameter adjustments as necessary until the end date is 

reached (Fig. 13). If smoke emissions were modeled, save the files (right-click 

within the emissions table) for the emissions components of interest (e.g., PM 

2.5, PM 10, or CO2). These files can then be manipulated and analyzed using a 

spreadsheet (e.g., Excel) or other program. With the appropriate manipulation, 

they can be attached to FARSITE’s fire growth vector file as attributes. After 

the smoke emissions output tables are saved, “terminate” the simulation and 

review all FARSITE outputs to make sure the results seem reasonable (Fig. 14). 

Manipulate the FARSITE outputs into the forms and formats necessary to implement the selected method of fuel succession.

Updating Model Inputs

Surface Fuels

As described above, fuel succession can be simulated in a number of different ways. This section describes the methods used in the case study, but the 

general ideas are applicable with other methods as well. Drivers of the fuel 

succession model used in the case study include pre-fire fuel model, fire severity data (modeled and real), time since last fire, state transitions for each fuel 

type for each fire severity class, fuel accumulation rates, and post-disturbance 

recovery rates (Fig. 1).

For the modeled fires in the case study, the approximation of severity was 

driven by two factors. The first was the FARSITE output “Crown Fire Activity,” 

a categorical output divided up into surface fire, passive crown fire (torching) 

and active crown fire. These data were used as a proxy for fire severity where 

surface = low severity, passive = moderate severity, and active = high severity. 

Crown fire activity was chosen as the proxy because it has the capacity to approximate what would be seen from an overhead, remotely sensed perspective. 

The use of remote sensing (e.g., satellite imagery) is the most common way that 

real-world severity data are obtained. It is generally dependent on comparing 

before and after images and determining the amount of change between the two 

(Key & Benson 2006, Thode 2005). The second, and probably more important 

factor, is that the fuel succession model only allows for certain severities in each 

fuel type. This was based on the assumption that certain fuel types would only 

burn with particular severities. This addresses a number of problems, including 

severities in fuel types without canopy (e.g. grasses and shrubs), where using 
28 USDA Forest Service RMRS-GTR-236WWW. 2010. Figure 13. FARSITE.
USDA Forest Service RMRS-GTR-236WWW. 2010. 29

crown fire activity as a proxy for fire severity would always result in low severities (surface fire).

Some examples include the Grass 2 (GR2; Low load, dry climate grass) fuel 

model that, from a remote sensing perspective, would appear to have burned 

with high severity due to large differences in the pre- and post-fire images. 

Without analyst intervention, these areas may be classified as such. Using crown 

fire activity as a proxy will result in the area always being classified as a low 

severity (surface) fire which is considered to be a more accurate classification. 

Another example would be Timber Litter 1 (TL1; Low load, compact conifer 

litter), which is restricted to burning in low or moderate severity fires, because 

the amount of surface fuels and the structure of the canopy of the underlying 

vegetation types are not considered sufficient to support a high severity (active 

crown) fire.

The fuel succession model is run using both the modeled fires and the real 

fires that occurred during the simulation year as inputs. Real fires include any 

fire that actually occurred whether it was a wildfire or a prescribed fire. The 

locations of modeled fires should be compared to the locations of any real fires 

that occurred that year. If there is overlap, it should be determined whether the 

modeled fire arrived at the real fire’s ignition location before or after that ignition occurred. If it arrived before, the real fire can be removed from the fuel 

Figure 14. Simulated fire growth.
30 USDA Forest Service RMRS-GTR-236WWW. 2010.

update process and the results analyses because it would have been eliminated 

due to fuel consumption at its point of ignition. When evaluating the inclusion 

of prescribed fires in the fuel succession process, the ignition locations viability 

shouldn’t be the deciding factor. Instead, the amount of the prescribed fire’s 

area burned by a prior modeled fire should be taken into account. Usually it will 

come down to the user’s judgment whether or not to include the prescribed fire 

in the succession process.

EXAMPLE

Fuel Succession

The surface fuel succession model used in the case study is implemented 

using an Arc Macro Language (AML) script within ArcGIS Workstation. 

Inputs include the pre-fire fuel model, fire severity data (modeled and 

real), and crosswalk parameters including time since last fire, succession 

steps for the four potential fire conditions (no fire, low, moderate and high 

severities), rates of fuel accumulation, and post-fire fuel recovery.

The outputs include next year’s surface fuel model (Fig. 15) and all the 

inputs necessary to simulate the subsequent year’s succession with the 

exception of the fire severity grid.

Figure 15. Surface fuels after the modeled 1994 fire season.
USDA Forest Service RMRS-GTR-236WWW. 2010. 31

Canopy Fuels

Canopy fuel updates should be performed where and when the canopy may 

have changed due to modeled or real fire. The user must decide when and how 

to update the canopy fuels. Changes can be made using a simple rule set in a 

GIS. For example, a crosswalk can be developed using FARSITE’s crown fire 

activity output and assumptions about how crown fire activity affects canopy 

attributes.

EXAMPLE

Canopy Fuel Crosswalk

No fire: No change

Surface fire: No change

Passive crown fire: 25% reduction in canopy cover and 25% reduction in 

crown bulk density.

Active crown fire: 95% reduction in canopy cover and 100% reduction in 

crown bulk density

Alternatively, canopy fuel updates can be modeled using various fuel consumption and fuel accumulation models such as FOFEM and FVS-FFE.

Duff and Coarse Woody Debris

Updating duff and coarse woody debris can be done in much the same manner 

as updating canopy fuels. Simplistic user defined crosswalks or more involved 

fuel consumption and accumulation models can be used.

Update Fire Growth and Behavior Model Inputs

All FARSITE inputs affected by fuel succession need to be updated in the 

LCP file before simulating the subsequent year of fires in the retrospective 

analysis. New raster fuels data need to be exported into an ASCII format; this 

should be completed after modeling is complete for each year in the simulation.

TIP

Updating the FARSITE Project

The easiest way to create a new FARSITE LCP file is to update the 

previous one and save it with a new name. However, using the same 

approach with the FARSITE project file is not recommended due to the 

risk of forgetting to change a parameter from the previous year’s value and 

invalidating the results. Instead, the user should update the previous year’s 

LCP file with the new fuels and save it with a new file name. Then start a 

new FARSITE project, load the new LCP file, and populate all parameters, 

options, and other inputs as outlined in the “Fire growth” section above.

Before importing the next modeling year’s initial ignitions and barriers to fire 

spread, overlay them on the latest fuel model using a GIS. Some of the selected 

ignitions and the ignition points for other “real” fires may now fall on areas that 

are currently unburnable due to a previous year’s modeled fire. Those that do 

can be left out of the analysis.

Repeating Modeling Cycle

Once the new project (for the new modeling year) is parameterized and 

saved, run FARSITE in the same manner as the previous year. Continue this 

cycle of fire growth simulation and fuel succession through the final fire year in 
32 USDA Forest Service RMRS-GTR-236WWW. 2010.

the retrospective simulation period. Update the last LCP file with the results of 

the final fuel succession modeling before comparing the alternative landscapes.

Comparing Alternative Landscapes

Fire suppression impacts can be measured by comparing the hypothetical 

landscape (in the case study this is the landscape that would have resulted had 

the suppressed ignitions been allowed to burn) to the realized landscape (the 

landscape that resulted as a consequence of fire suppression). The impacts quantified for the case study include FRID and flame length.

Fire Regime Departure

Fire Return Interval Departure (FRID): FRID data are developed by determining how many years have passed since an area last burned and dividing 

that number by the characteristic Fire Return Interval (FRI) of the underlying 

vegetation. Time since last burn is calculated using a fire atlas. To compare the 

alternative landscapes there are two atlases necessary: the hypothetical atlas and 

the realized atlas.

Building the hypothetical fire atlas: The first step in calculating FRID is to 

build a new fire atlas to represent the alternative fire management strategy. This 

fire atlas should incorporate the modeled fire perimeters and those of real fires 

that weren’t eliminated by the modeled fires. This is accomplished by using a 

GIS to merge these modeled and real fire perimeters with the “truncated historical fire atlas” (which contains fires from the earliest year available through 

the year prior to the first simulation; see the “Preparing fire atlas data” section above). Once the additional fire perimeters are appended, it is important 

to populate at least the year attribute of the resulting fire atlas to facilitate the 

FRID calculation.

Calculating a hypothetical FRID: The “hypothetical fire atlas” and the “realized fire atlas” can then be used to create a “last burned” raster layer for each 

alternative landscape. Creating a last burned dataset can be accomplished in a 

number of ways using a GIS and should be performed by someone with GIS 

analysis experience. Most methods will involve ensuring that the fire perimeters 

are layered in the vector coverage in the correct order (oldest on the bottom, 

most recent on the top), converting the shapefile to a raster and subtracting the 

result from the year for which FRID is to be calculated (in this case the last year 

of the simulation).

FRID is calculated by dividing the “last burned” raster by the FRI raster (see 

“Fire Return Intervals” under the “Optional Data” section above). The process 

should be repeated for both the hypothetical atlas and the realized fire atlas to 

facilitate comparisons between the FRID of the hypothetical landscape (Fig. 16) 

and the FRID of the realized landscape (Fig. 17). In the case study, the FRID of 

realized landscape was subtracted from the FRID of the hypothetical landscape, 

resulting in a map showing the difference in FRID between the two alternative 

landscapes (Fig. 18).
USDA Forest Service RMRS-GTR-236WWW. 2010. 33

Figure 16. FRID 

resulting from 

the alternative 

fire management 

strategy.

Figure 17. FRID 

resulting from 

the actual fire 

management 

strategy.
34 USDA Forest Service RMRS-GTR-236WWW. 2010.

Potential Fire Behavior

Similar to the FRID analysis, the impact of suppression on potential fire behavior can be quantified by comparing the hypothetical and realized landscapes. For 

the case study, flame length was calculated for both landscapes using FlamMap. 

As outlined previously, FlamMap uses a LCP file, as does FARSITE, and requires fuel moisture, wind speed, and wind direction inputs. For the case study, 

fuel moistures and wind are held constant, and FlamMap calculates potential fire 

behavior for each location on the landscape for these static conditions. These 

predictions are static in that they are made without respect to fire spread across 

the landscape or changes in fuel moistures or wind. FlamMap should run by 

someone with experience with the software. The FlamMap tutorial can be used 

to gain familiarity with its configuration, assumptions, and limitations.

FlamMap inputs: The analyst will need two LCP files to compare the two 

alternative landscapes. Also required are wind speed and direction and fuel moisture files. It is common to use wind and fuel moisture values that reflect the upper 

percentiles of fire behavior indices such as ERC or SC to accentuate FlamMap 

outputs. Fuel moisture and wind data can be calculated for particular fire weather 

index percentiles using FFP. In the case study, the fuel moisture values (.fms file) 

were calculated based on the 98th percentile ERC; the wind values were based on 

98th percentile wind speed and the most common wind direction observed during the fire season was also used. The 98th percentile ERC and the 98th percentile 

wind speed will rarely co-occur and in this case study, were arbitrarily selected.

Figure 18. Median 

FRID difference.
USDA Forest Service RMRS-GTR-236WWW. 2010. 35

For the hypothetical landscape, the final retrospective fire modeling LCP file 

was used. This file incorporated fuel succession after the last year of fire simulation. For the realized landscape, the fuel succession model was run for the same 

years as the modeled landscape using only “real” fire severities. Canopy fuels 

for the realized landscape were updated in the same manner as described in the 

“Canopy fuels updates” section above.

Running FlamMap: Repeat the following steps for both the hypothetical and 

realized landscapes: Load the LCP file (Theme > FARSITE Landscape File) 

into FlamMap. Set up a new run, import the fuel moisture file and set the wind 

speed and direction (azimuth) (Fig. 19). Import a custom fuel model file if necessary. Select the desired fire behavior outputs (“Fire Behavior Outputs” tab of 

the run window). Verify the units for each output (Options > Preferences > Edit 

Preferences). Run the program after the inputs and outputs are set. When the run 

is complete, save the results. These should be saved as GRID ASCII files for 

input into a GIS. Loading them into a GIS allows for further analysis (e.g., differencing the two scenarios) and the creation of maps illustrating analysis results 

(Figs. 20 and 21).

Figure 19. FlamMap inputs.
36 USDA Forest Service RMRS-GTR-236WWW. 2010.

Figure 20. 2005 

Potential flame 

length on 

the realized 

landscape.

Figure 21. 2005 

Potential flame 

length on the 

hypothetical 

landscape.
USDA Forest Service RMRS-GTR-236WWW. 2010. 37

Potential Smoke Emissions

The concept of potential emissions was described in the “Model Selection” 

section above. In this case study, potential PM 2.5 and PM 10 emissions were 

calculated using the Emission Estimation System (EES) for both the hypothetical and realized landscapes. In addition, the fuel succession model was run from 

the end year of the simulation analysis, 2004, through 2015 (assuming no fires 

after the end of the simulation in 2004) to see how potential emissions might rebound over time as fuel accumulation and post-fire fuel recovery occur (Figs. 22 

and 23). For the case study smoke emissions were analyzed beyond 2004 to estimate the time period during which the landscape would have reduced potential 

emissions due to the hypothetical fire management strategies.

Figure 23. Potential 

PM 10 emissions.

Figure 22. Potential 

PM 2.5 emissions.
38 USDA Forest Service RMRS-GTR-236WWW. 2010.

Summary

The suppression of naturally occurring wildfires has an untold impact on fire 

dependent landscapes. The ability to quantify the impact of wildfire suppression 

can provide valuable insight to land managers. While the decision to allow an 

ignition to burn provides immediate feedback to land managers, in terms of fire 

behavior and effects, the impacts of the decision to suppress an ignition are not 

as immediate or clear.

This guidebook demonstrates a methodology to measure suppression impacts. 

The information developed through the retrospective fire modeling methodology can help managers make more informed decisions on whether to suppress 

an ignition. Retrospective fire modeling can be used to track the cumulative 

effects of fire suppression on indicators of the ecological health such as FRID 

or FRCC. It can also provide a means to compare the potential behavior of 

future fires. Simulating where suppressed ignitions might have spread can help 

managers prioritize future fuel treatments. For example, treatment preference 

might be given to areas where opportunities for fuel reduction through fire use 

have been foregone due to suppression. The analysis can reveal the managerial 

and ecological benefits of fire, such as the creation of fire breaks, reduction of 

fuel loadings, decline in the number of future ignitions requiring initial attack, 

improvement in fire regime departures, and a decrease in the potential severity of future fire behavior. Results can also be used to communicate tradeoffs 

inherent in allowing or suppressing fire on the landscape to the public and other 

governmental agencies such as air quality districts. The ability to measure suppression impacts provides a more complete picture of the consequences of the 

“go/no go” decision.

Using these methods to track the cumulative effects of fire suppression may 

initially be time intensive, but once the tools and methodologies have been 

chosen and the initial analyses are complete, it can easily be appended for future fires/ignitions and quickly updated on an annual basis. We believe that the 

advantages of the insights gained far outweigh the initial development costs. 

Happy modeling!

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