United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-116

September 2003

The Fire and Fuels

Extension to the Forest

Vegetation Simulator

Technical Editors:

Elizabeth D. Reinhardt

Nicholas L. Crookston

Wildfire only 

 With prescribed fire 
Abstract _______________________________________

Reinhardt, Elizabeth; Crookston, Nicholas L. (Technical Editors). 2003. The Fire and Fuels

Extension to the Forest Vegetation Simulator. Gen. Tech. Rep. RMRS-GTR-116.

Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research

Station. 209 p.

The Fire and Fuels Extension (FFE) to the Forest Vegetation Simulator (FVS) simulates fuel

dynamics and potential fire behaviour over time, in the context of stand development and

management. Existing models of fire behavior and fire effects were added to FVS to form this

extension. New submodels representing snag and fuel dynamics were created to complete the

linkages.

This report contains four chapters. Chapter 1 states the purpose and chronicles some

applications of the model. Chapter 2 details the model’s content, documents links to the

supporting science, and provides annotated examples of the outputs. Chapter 3 is a user’s guide

that presents options and examples of command usage. Chapter 4 describes how the model was

customized for use in different regions.

Fuel managers and silviculturists charged with managing fire-prone forests can use the FFEFVS and this document to better understand and display the consequences of alternative

management actions.

Keywords: FVS, FFE, forest fire, stand dynamics, FOFEM, BEHAVE, NEXUS, snags, coarse

woody debris

The Authors ____________________________________

Elizabeth D. Reinhardt is a Research Forester in the Prescribed Fire and Fire Effects

Project at the Rocky Mountain Research Station, Missoula Fire Sciences Laboratory,

Missoula, MT. She has degrees in English (A.B., Harvard University), and forestry (M.S.

1982, and Ph.D., 1991, University of Montana). Her research has included studies of

tree mortality, fuel consumption, modeling fire effects, and canopy fuels. For this

publication, she was both an author and technical editor of the entire volume.

Nicholas L. Crookstonis an Operations Research Analyst at the Forest Service Rocky

Mountain Research Station, Forestry Sciences Laboratory, Moscow, ID. His work

involves development and applications of the Forest Vegetation Simulator, its extensions, and user interface. For this publication, he was both an author and lead project

manager and technical editor.

Sarah J. Beukema is Senior Systems Ecologist at ESSA Technologies, Vancouver,

Canada. Some of her research interests include modeling fire dynamics, the simulation

of pest and disease dynamics, and successional dynamics. She is also the principal

developer of the Vegetation Dynamics Tool (VDDT) and a codeveloper of the Tool for

Exploratory Landscape Scenario Analyses (TELSA). Ms. Beukema obtained a M.Sc.

degree in Zoology and Applied Mathematics from the University of British Columbia

Werner A. Kurz is a Senior Research Scientist with Natural Resources Canada at the

Canadian Forest Service in Victoria, BC, Canada. His research interests are stand,

landscape, and regional models of ecosystem carbon dynamics. With a group of

scientists, he is currently developing a National Forest Carbon Accounting Framework

for Canada. Dr. Kurz obtained his Ph.D. degree in forest ecology from the University of

British Columbia in 1989 and worked for a research and consulting company until

joining the Canadian Forest Service in 2001. He is an adjunct professor at the University

of British Columbia and at Simon Fraser University.
Julee A. Greenough is Systems Ecologist at ESSA Technologies, Vancouver,

Canada. Her research interests include the integration of timber and nontimber values

into the landscape planning process. She is the principal developer of the Prognosis

Environmental Indicators model; a habitat model that integrates customized Geographic Information System (GIS) components, the FVS Parallel Processing Extension, the Fuel and Fire Effects extension, the Western Root Disease extension, and

habitat-projection models for a number of animal species. Dr. Greenough received her

Ph.D. degree in theoretical biology from the University of Oxford.

Donald C.E. Robinson is Systems Ecologist at ESSA Technologies, Vancouver,

Canada. His research interests include modeling fire dynamics, the simulation of pest

and disease dynamics, and modeling forest dynamics at the individual, stand, and

landscape level. He is principal developer of a spatial statistical model for the spread

of dwarf mistletoe. He also continues to assist in the introduction of FVS as a forest

management tool in Canada. Mr. Robinson obtained his M.Sc. degree in Zoology from

the University of British Columbia.

Duncan C. Lutes is a Research Forester with Systems for Environmental Management, stationed at the Missoula Fire Sciences Lab in Missoula, MT. He has a B.S. and

M.S. in Forestry from the University of Montana. His background is in fuels, principally

coarse woody debris, primarily studying spatial and temporal distributions. He has been

involved in the development of the FOFEM model and is currently program lead for the

FIREMON fire effects monitoring project. He has been involved in the development of

several Fire and Fuels Extension variants.

The use of trade or firm names in this publication is for reader information and does not

imply endorsement by the U.S. Department of Agriculture of any product or service
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 number.

Telephone (970) 498-1392

FAX (970) 498-1396

E-mail rschneider@fs.fed.us

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

Mailing Address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526
The Fire and Fuels Extension to the

Forest Vegetation Simulator

Technical Editors:

Elizabeth D. Reinhardt

Nicholas L. Crookston

Model Availability and Support

USDA Forest Service, Forest Management Service Center, Fort Collins,

CO, provides technical support and software distribution for the Fire and

Fuels Extension to the Forest Vegetation Simulator (FFE-FVS). The

Center may be reached through: Web site http://www.fs.fed.us/fmsc;

phone (970) 295-5770; e-mail wo_ftcol_fvs@fs.fed.us; or address Forest

Management Service Center, Natural Resources Research Center, 2150A

Center Avenue, Suite 341A, Fort Collins, CO 80526-1891 USA.
Acknowledgments _______________________________________________

This work was a collaborative effort between the Fire Effects Research Work Unit of the USDA Forest Service, Rocky

Mountain Research Station, Fire Sciences Laboratory in Missoula, MT, and of the Forest Ecosystem Processes

Research Work Unit, Forestry Sciences Laboratory, Moscow, ID. ESSA Technologies, Ltd, of Vancouver British

Columbia, provided crucial professional services. National Forest Systems and the Joint Fire Sciences Program of the

Departments of the Interior and Agriculture supported the work.

Collectively, the authors of the papers published in this volume thank the following people. Albert R. Stage encouraged

the project team members throughout the project, attended workshops, and reviewed manuscripts. Jim Brown and

Stage co-led this work at the beginning, found resources, and mentored our work. To a large degree the lifetime of work

from Brown and Stage is merged in this project. The team received extremely helpful reviews on the Model Description

from Jane Kappler-Smith, Paul Langowski, and Al Stage. David Atkins, Jamie Barbour, and Paul Stancheff reviewed

the Preface. The User’s Guide was reviewed by Renee Lundberg and Glenn Christensen, and the FFE Variants chapter

was reviewed by Stephanie Rebain, Elizabeth Reinhardt and Nick Crookston. Casey Teske evaluated the model

behavior. Stephanie Rebain found and fixed many problems, and greatly improved the model’s performance.

The following people participated in one or more workshops, held to specify the components of the FFE, to calibrate

it for specific regions, or both. The free flow of information and ideas at these workshops was key to the success and

adaptation of this model.

Asherin, Lance

Atkins, David

Bahro, Berni

Barber, Klaus

Becker, Rolan

Beighley, Mark

Brown, Jim

Burgan, Bob

Bushnell, Wayne

Carter, Allen

Chappell, Linda

Close, Kelly

Cutler, Kristi

Dallison, Dave

David, Lance

Dixon, Gary

Ferguson, Dennis

Foran, Joe

Gleason, Paul

Greenhalgh, Kevin

Greer, Kendrick

Gregg, Tommy

Hardy, Colin C.

Hermit, Ray

Higgins, Bruce

Holthausen, Dick

Husari, Susan

Kolu, Maxine

Krivacek, Janet

Johnson, William

Landram, Michael

Langowski, Paul

Larsen, Mike

Lasko, Rich

Laudenslayer, Bill

Long, Donald

Lutes, Duncan

Maffei, Helen

McGaughey, Bob

Mincemoyer, Scott

Newton, Rick

Omi, Phil

Ortega, Aaron

Ottmar, Roger

Reeberg, Paul

Rich, Tim

Russel, Jim

Ryan, Kevin

Sandberg, David V. “Sam”

Sapsis, Dave

Schmidt, Marcus

Schulte, Darrell

Shepperd, Wayne

Shoun, Dan

Siemers, Roger

Smith, Eric

Snell, Ken

Soper, Kim

Stage, Albert R.

Stiller, Elizabeth “Bitsy”

Teck, Richard

Thompson, Gary

Till, Ken

Tiné, Paul

Truman, Russ

van Wagtendonk, Jan

VanderLinden, Larry

Vickery, Chuck

Weltch, Mike

Wiitala, Marc

Wilson, Larry

Wine, Dale

Winkler, Fred

Woods, Mark
Contents_______________________________________

Page

Chapter 1 Purpose and Applications...................................................................... 1

1.1 Introduction ................................................................................................ 1

1.2 An Example ............................................................................................... 2

1.3 Applications ............................................................................................... 8

1.4 Conclusions ............................................................................................... 9

Chapter 2 Fire and Fuels Extension: Model Description ...................................... 11

2.1 Introduction .............................................................................................. 11

2.2 Model Structure ....................................................................................... 12

2.3 Snag Submodel ....................................................................................... 15

2.4 Fuel Submodel ........................................................................................ 24

2.5 Fire Submodel ......................................................................................... 43

2.6 Discussion ............................................................................................... 59

Chapter 3 User’s Guide........................................................................................ 61

3.1 Introduction .............................................................................................. 61

3.2 Simple Run .............................................................................................. 62

3.3 Initializing the Model ................................................................................ 64

3.4 Fires ......................................................................................................... 65

3.5 Adjusting Snag Parameters ..................................................................... 74

3.6 Adjusting Fuel Parameters ...................................................................... 78

3.7 Management ............................................................................................ 80

3.8 Output Keywords ..................................................................................... 86

3.9 Using the FVS Event Monitor .................................................................. 89

Chapter 4 Variant Descriptions ............................................................................ 93

4.1 Introduction .............................................................................................. 93

4.2 Northern Idaho (NI).................................................................................. 95

4.3 Eastern Montana (EM) .......................................................................... 104

4.4 Southern Oregon/Northern California (SO) ........................................... 114

4.5 Central Rockies (CR) ............................................................................. 127

4.6 Utah (UT) ............................................................................................... 139

4.7 Western Sierras (WS) ............................................................................ 149

4.8 Eastern Cascades (EC) ......................................................................... 159

4.9 Central Idaho (CI) .................................................................................. 174

4.10 Tetons (TT) .......................................................................................... 185

4.11 Blue Mountains (BM) ........................................................................... 193

References ......................................................................................................... 203

Keyword Index ................................................................................................... 207

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 1

1.1 Introduction ___________________________________________________

Fire is now represented in the Forest Vegetation Simulator’s (FVS)

predictions of forest stand dynamics. At long last! Al Stage (1973) recognized the importance of including disturbance agents in stand projections

when he included mountain pine beetle-caused mortality of lodgepole pine

in the first release of the FVS parent model, the Prognosis Model for Stand

Development.

Furthermore, long-term stand dynamics are now included in simulations

of fires and fire effects. Fuel managers have a tool, the Fire and Fuels

Extension to FVS (FFE-FVS), to evaluate the effectiveness of proposed fire

and fuel management treatments in the context of potential fire effects on

short- and long-term stand dynamics, important to silviculture, wildlife

habitat, and fuel hazard.

Adding fire to FVS was accomplished by programming an extension to FVS

largely based on existing models of fire behavior (including crowning) and

fire effects. New dynamic models that represent snag dynamics and down

Nicholas L. Crookston

Chapter 1

Purpose and

Applications

Abstract—The Fire and Fuels Extension (FFE) to the Forest Vegetation Simulator (FVS)

simulates fuel dynamics and potential fire behavior over time, in the context of stand development and management. This chapter provides an introduction to the model by illustrating its

purpose and chronicling some of the applications it has supported.

Keywords: FVS, FFE, forest fire, stand dynamics, FOFEM, BEHAVE, NEXUS, snags, coarse

woody debris
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

wood decomposition were constructed to complete the system. The details of

these components and their scientific support are the subject of chapter 2,

“Model Description.” Chapter 3 “User’s Guide,” presents options and examples of command usage. Chapter 4, “FFE Variants,” summarizes the

changes made to customize the model for different geographic regions.

FFE-FVS is based on a huge legacy of research, generally dating to the

middle of the 20th century. Contemporary contributors include many who

attended meetings and workshops where there was a free flow of knowledge,

data, and inspiration. Their names are listed in the acknowledgments.

Other papers have been published that introduced the model at various

meetings and symposia. Beukema and others (1997) reported the first

introduction to the FFE-FVS at the FVS conference held in Ft. Collins, CO,

in February 1997. An updated introduction was presented at the Joint Fire

Science Conference and Workshop held in Boise, ID, in June 1999 (Beukema

and others 2000).

The need for this work and the way that this model fits into the firemodeling toolbox was the subject of a meeting held in Seattle, WA, in

February 1999 (Kurz and Beukema 1999). That meeting led to the development of the research program subsequently funded by the interagency Joint

Fire Science Program. Crookston and others (1999) presented a summary of

the findings from that meeting and highlighted the workshop methods.

What follows in this paper is an example that demonstrates the kinds of

outputs the model produces and the dynamic interactions between the fire,

fuel, and tree growth components. Following the example is a summary of

some of the applications recorded to date. These document the range of the

model’s applicability from the stand to regional levels and include the use of

the model in conjunction with other FVS extensions that represent insects

and diseases.

1.2 An Example ___________________________________________________

The main use of the FFE-FVS is to support fuel management and postfire

treatment decisions in the context of other vegetation management concerns,

including wildlife habitat, insect and pathogen hazards, and timber production. FFE-FVS displays measures of fire hazard as they change during the

course of stand development and in response to management actions and

other disturbances.

The following example displays a few of the many FFE-FVS outputs. It is

taken from a Forest Inventory Analysis (FIA) plot on the Flathead National

Forest in western Montana. The forest type is Douglas-fir, although the

potential type is classified as subalpine fir. While there is little species

diversity, there is a great deal of variation in tree size, ranging from seedlings

to trees over 30 inches in diameter.

Two simulation scenarios are offered. The first, named Wildfire only, includes

a simulated wildfire in the year 2065 and was run with no other management

actions. The second is like the first except that a series of prescribed fires was

simulated prior to the wildfire and is therefore named With prescribed fire. A

series of figures show the results of running these two scenarios. The variables

were chosen to illustrate the relevance of the model outputs to various disciplines

and to demonstrate the dynamic interactions between fire, fuel, and tree

dynamics. There are many more variables that could be displayed, and many

more scenarios on many more stands could be run.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 3

1.2.1 Output for Everyone: Stand Visualization

The Stand Visualization System (SVS, McGaughey 1997) can create

images like the ones illustrated in figure 1.1. The images (reproduced in color

on the cover) show how the fire behavior differs during the wildfire under the

two scenarios. In the Wildfire only case, the fire is burning in the crown, while

the With prescribed fire case exhibits some torching. Images like these can be

made for each time period of a simulation and viewed on computers as a timelapse sequence showing the dynamic changes that take place in a stand. The

software needed to construct these sequences is freely available and includes

linkages to FVS.

Figure 1.1—Stand Visualization System (McGaughey 1997) images show how the fire behavior is

different during the 2065 wildfire under the two scenarios. In the Wildfire only case (top), the fire is

burning in the crown, while in the With prescribed fire case only a surface fire is burning (bottom).

Wildfire only 

 With prescribed fire 
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

1.2.2 Outputs for Fire and Fuel Managers

The potential flame length indicates the expected fire intensity if a fire

were to burn. It is computed over the duration of the simulation period using

the same logic as used to simulate a fire except that no fire effects are

included. Figure 1.2 illustrates that the Wildfire only case provides a rather

static potential flame length until the year 2060 when it increases dramatically. This is due to a reduction in canopy base height and other factors that

result in the FFE predicting that fuels would support an active crown fire.

Consequently, the wildfire simulated in year 2065 is classified as a crown fire

and results in 100 percent tree mortality. Following the fire, the potential

flame length dips sharply due to fuel consumption, and then increases

because of the increase of dead surface fuels that accumulate immediately

after the fire as a result of fire-caused tree mortality. In the With prescribed

fire scenario, a pattern of reduction and increase in potential flame length

follows the prescribed fires.

Figure 1.3 shows changes in crowning index, the wind speed necessary to

sustain crown fire. The series of prescribed fires in the With prescribed fire

scenario increased the crowning index from 15 to 20 miles per hour until after

the severe fire simulated at 2065. The huge increase in the crowning index

under the Wildfire only scenario is due to the lack of overstory trees in which

the fire can burn.

0

10

20

30

40

50

2000 2020 2040 2060 2080 2100

Year

Potential severe fire flame length (feet)

Wild fire only

(max = 75) 

With prescribed fire 

Figure 1.2—The potential fire flame

length for severe burning conditions

is illustrated for both scenarios. The

With prescribed fire scenario has a

much lower potential flame length in

this example until the end of the

simulation when it jumps up to 50

feet.

Figure 1.3—Crowning index is the

wind speed necessary to cause a

fire that is torching trees to become

a running crown fire.

0

20

40

60

80

100

2000 2020 2040 2060 2080 2100

Year

Crowning index (miles/hour)

Wildfire only

With prescribed fire 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 5

1.2.3 Outputs for Silviculturists and Fuel and Wildlife Managers

The surface fuel load in tons per acre is an indicator for fuel managers

because generally, the more there is, the greater the fuel hazard. Figure 1.4

shows total weight of woody fuels summed over all size classes. To wildlife

and vegetation managers, this fuel is considered coarse woody debris, and

that is often a valuable resource. The Wildfire only scenario shows consistently high fuel loads while the With prescribed fire scenario shows that

surface fuels are reduced by the prescribed fires. In general, however, the

reductions are short lived as the trees killed by the prescribed fires create

surface dead material soon after each prescribed fire.

Snags are less important to fire behavior than down fuel yet can be

important to wildlife habitat management (fig. 1.5). The Wildfire only

scenario shows a slow, steady, increase in snag numbers with a peak after the

wild fire. The With prescribed fire scenario shows an early increase and a

smaller spike after the fires of 2065.

0

2

4

6

8

10

12

2000 2020 2040 2060 2080 2100

Year

Surface dead fuel (tons/acre)

Wildfire only 

With prescribed fire 

0

20

40

60

80

2000 2020 2040 2060 2080 2100

Year

Snags (>12 inches dbh) per acre

Wildfire only 

With prescribed fire 

Figure 1.4—Surface fuel loads

are of interest to fuel managers.

To wildlife and vegetation managers this variable measures

coarse woody debris. For the

Wildfire onlyscenario, the model

predicts surface fuel decomposition exceeds accumulation

after the initial accumulation.

Figure 1.5—The number of

large snags per acre for the two

scenarios.
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

1.2.4 Outputs For Wildlife Managers and Silviculturists

Percent canopy cover for each of the scenarios is shown in figure 1.6.

Wildlife habitat managers and silviculturists use this variable to evaluate

management alternatives. Thomas and others (1979) say that 70 percent

canopy cover is an important level with respect to deer and elk habitat needs.

While neither of the scenarios demonstrate 70 percent cover, it is clear that

the Wildfire only scenario shows high cover values for the simulated period

up to the wildfire of 2065. In contrast, the With prescribed fire scenario shows

reduced canopy cover, leaving the stand relatively open for most of the

simulation period.

Fire is a major disturbance agent and can change the successional

pathways of forest stands. FVS classifies the successional stage at each time

step into one of the classes shown in figure 1.7 (O’Hara and others 1998,

Crookston and Stage 1999). In both scenarios, fire acted to modify succession

on this stand. A third scenario, without any fire, showed that the stand would

be classified an old forest in 250 years.

0

20

40

60

2000 2020 2040 2060 2080 2100

Year

Canopy cover (%)

Wildfire only 

With prescribed fire 

2000

2015

2030

2045

2060

2075

2090

With prescribe...

Wildfire

only

Year

Stand initiation

Old forest, multistrata

Old forest, single stratum

Young forest, multistrata

Understory reinitiation

Stem exclusion

No trees

Figure 1.6—Canopy cover is a

key variable used in habitat assessments. The prescribed fires

caused a steady reduction of this

variable while the Wildfire only

scenario provided significant

cover until the wildfire of 2065.

Figure 1.7—Stand structure is

classified by FVS (Crookston

and Stage 1999) and plotted

here for each scenario. After

the 2065 wildfire, the With prescribed fire scenario maintains

later successional stages compared to Wildfire only.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 7

1.2.5 Outputs for Silviculturists, Wildlife Managers, and Foresters

Top height (fig. 1.8) and volume (fig. 1.9) are key indicators for silviculturists and foresters. The simulations show that the average height of the

largest trees is not greatly affected under the With prescribed fire scenario.

The sequence of prescribed fires protects this vertical component of the stand

from destruction by the wildfire of 2065. On the other hand, the prescribed

fires cause a great deal of mortality and reduction in stocking resulting in a

great loss in timber production. A plot of cubic volume over time (fig. 1.9)

shows the model’s ability to integrate growth, mortality, and fire processes

showing how these processes affect productivity. There is no doubt that the

Wildfire only scenario leads to the destruction of the timber in this stand in

the 2065 wildfire while the With prescribed fire scenario left the stand

capable of escaping the complete loss of timber.

0

1000

2000

3000

4000

5000

6000

7000

2000 2020 2040 2060 2080 2100

Year

Total volume (ft 3/acre)

Wildfire only 

With prescribed fire 

Figure 1.9—The series of prescribed fires seriously reduced

timber production as seen by

this graph of cubic volume over

time; this trend is similar when

board foot volume over time is

plotted.

Figure 1.8—Top height is the

average height of the largest 40

trees per acre. The scenarios

provide similar top heights until

the wildfire.

0

20

40

60

80

100

120

2000 2020 2040 2060 2080 2100

Year

Top height (feet)

Wildfire only 

With prescribed fire 
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

1.2.6 Summary of the Example

Structure, function, and composition of forest stands can be assessed for

each management alternative using FFE-FVS. The base FVS model and the

FFE calculate many variables besides those shown in figures 1.1 through 1.9.

The dynamic interactions between the model components are evident.

Different, perhaps better, management options could be run as well. The

FFE-FVS system provides several options to manage the trees, snags, and

simulate fuel treatments. The “User’s Guide” (chapter 3) lists them all.

1.3 Applications ___________________________________________________

The FFE-FVS has proven useful in several situations. The first involved

the evaluation of fuel treatments in an urban forest interface zone near Coeur

D’Alene, ID. Simulations demonstrated to the National Forest managers and

interested members of the public that post thinning fuel treatments were

needed in addition to proposed thinning to meet fire hazard reduction goals.

The simulation period was a few decades, and the analysis was done at the

stand level.

Later, FFE-FVS was used to evaluate alternatives for managing forests

and fuels in the wake of a Douglas-fir beetle out break on the Idaho

Panhandle National Forests. The model was used to show the changes in

potential flame length (using such figures as fig. 1.2) given different infestation and management scenarios, over a 150-year simulation period. The

analysis was done in support of an environmental impact statement prepared while deciding what actions should be taken in response to the

outbreak (IPNF 1999). How the outbreak affected long-term fuel loading and

subsequent fire intensity was a key question. The results of the analysis were

used to support a related environmental assessment (IPNF 2001).

During the summer 2000 fire season, FFE-FVS was used to confirm

satellite-based data to predict future fire perimeter. Fire managers were

using the spatially explicit model, FARSITE (Finney 1998), to predict fire

spread. The FFE-FVS choice of fire behavior fuel model and estimates of

canopy base height and bulk density were used to provide inputs to FARSITE.

This application had a large spatial scope and 1-year time horizon. Since

2000, FFE-FVS has often been used as a step in generating fuel maps for use

in FARSITE and, more recently, FlamMap (Hayes and others, in review).

The Northern Region of the Forest Service (Atkins and Lundberg 2002)

used FFE-FVS to characterize forest structure, fuel loads, potential fire

hazard, and forest health conditions in Montana. The analysis units are the

Forest Inventory Analysis (FIA) plots on public and private lands. The work

will be extended to Utah.

Christensen and others (2002) used the FFE-FVS to determine the

effectiveness of several stand treatment options designed to reduce fire

hazard both now and into the future. Long-term effects are reported in terms

of the stocking, size, and species mix of stands and the size and species mix

of trees and logs that might be removed for wood products.

FFE-FVS, coupled with SVS, was used to build the “Living with Fire”

educational computer game intended for use by the general public. The game

Web site is: http://www.fs.fed.us/rm/fire_game.

FFE-FVS is part of Prognosis EI (Greenough and others 1999), a detailed

watershed-level environmental indicators model developed and used in
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 9

British Columbia. It is capable of representing several disturbance agents

besides fire, represents the dynamic interactions of agents in space within

the landscape, and directly outputs or links to scores of indicators measuring

stand structural attributes, species-specific wildlife habitat quality for birds,

bats, ungulates, and bears, patch size, old growth, 23 measures of water

quality, visual quality, and timber. It is based on the Parallel Processing

Extension of FVS (Crookston and Stage 1991), western root disease model

(Frankel 1998), and is linked to a geographic information system. Its spatial

scope is several thousand stands, and its time scope is over one generation

of trees, about 300 years.

1.4 Conclusions ___________________________________________________

FFE-FVS is a rich model that provides outputs of interest to several

disciplines, has been successfully used in a number of applications, and can

be linked to other models and tools. The science on which it is based and its

limitations are the subjects of the next paper in this volume. A user’s guide

follows outlining how to apply it to your needs. Differences between regional

variants are outlined in the fourth paper.
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 11

Chapter 2

Fire and Fuels

Extension: Model

Description

Sarah J. Beukema

Elizabeth D. Reinhardt

Julee A. Greenough

Donald C. E. Robinson

Werner A. Kurz

Abstract—The Fire and Fuels Extension to the Forest Vegetation Simulator is a model that

simulates fuel dynamics and potential fire behavior over time, in the context of stand development

and management. Existing models are used to represent forest stand development (the Forest

Vegetation Simulator, Wykoff and others 1982), fire behavior (Rothermel 1972, Van Wagner 1977,

and Scott and Reinhardt 2001), and fire effects (Reinhardt and others 1997). These models are

linked together with newly developed models of snag and fuel dynamics. Users can simulate fuel

treatments including prescribed fire, thinning, and mechanical treatments. Wildland fires can also

be modeled. Model output includes predicted fuel loadings over time, and measures of fire hazard

including potential flame length, canopy base height and canopy bulk density, torching and

crowning indices and potential stand mortality over the simulation period. If a prescribed fire or

wildland fire is simulated, output also includes predicted fire behavior, fuel consumption, smoke

production, and tree mortality.

Keywords: fire behavior, fire effects, stand dynamics, silviculture, fuel treatment, prescribed fire,

potential wildfire behavior, fuel dynamics

2.1 Introduction ___________________________________________________

The Forest Vegetation Simulator (FVS) (Stage 1973; Wykoff and others

1982) is used by forest managers throughout the United States and Canada

to predict stand dynamics and the effects of various management actions on

future forest conditions. It is an individual tree, distance-independent

growth and yield model. The role of fire in ecosystem dynamics has not

previously been explicitly represented in FVS. Other models have been
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

developed to represent fuel dynamics with and without fire (Keane and

others 1989), fire behavior (Albini 1976a,b; Rothermel 1972), and fire effects

(Reinhardt and others 1997). These models, however, do not address the

dynamics of vegetation management.

We developed the Fire and Fuels Extension to FVS (FFE-FVS) by integrating FVS with elements from existing models of fire behavior and fire effects.

FFE-FVS predicts changes in stand and fuel characteristics over time and

the behavior and impacts of fire. The model is not intended to predict the

probability of fire or the spread of fire between stands.

The FVS simulates tree growth, tree mortality and regeneration, and the

impacts of a wide range of silvicultural treatments. The Fire and Fuels

Extension simulates fuel accumulation from stand dynamics and management activities, and the removal of fuel through decay, mechanical treatments and prescribed or wildfires. Various types of fuel are represented,

including canopy fuel and surface fuel in several diameter classes. Fire

behavior and fire effects such as fuel consumption, tree mortality, and smoke

production are modeled. Model output describes fuel characteristics, stand

structure, snags, and potential fire behavior over time and provides a basis

for comparing proposed fuel treatments.

Where possible, FFE-FVS uses existing models and algorithms to simulate fires. To predict fire intensity, it uses Rothermel’s fire behavior model as

implemented by Albini (1976a) in FIREMOD and subsequently by Andrews

(1986) in Behave. The onset of crowning is predicted using approaches

developed by Van Wagner (1977) and Scott and Reinhardt (2001). The model

uses methods from FOFEM (Reinhardt and others 1997) for predicting tree

mortality, fuel consumption and smoke production. Methods for simulating

fuel accumulation and decay and snag dynamics were developed for FFEFVS using information described in detail in sections 2.3 and 2.4 of this

chapter.

The model does not simulate fire spread or the probability of fire. It

calculates potential fire intensity over time, under user-defined conditions,

as a measure of the fire hazard of stand and fuel conditions. It also allows the

user to schedule or simulate a fire or series of fires at given points in time or

when certain stand conditions are reached. When a fire is simulated, the

model computes its intensity, its effects on different stand components, and

the associated emissions.

This chapter describes the model processes and assumptions in detail for the

northern Idaho variant. Details about other variants are given in chapter 4.

Examples of FFE-FVS output in this chapter use the same example stand

as does chapter 1: a Douglas-fir stand in western Montana that is burned

with a wildfire in 2065.

2.2 Model Structure ________________________________________________

The Fire and Fuels Extension includes three major submodels:

1. A snag model for tracking and simulating decay and fall down of

standing dead trees.

2. A fuel model that simulates the accumulation (through litterfall and

other sources) and decomposition of surface fuel, tracks canopy fuel

characteristics, and selects fire behavior fuel models.

3. A fire model that simulates fire intensity and fire effects on trees,

snags, and fuel as well as smoke production and mineral soil exposure.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 13

As with all of FVS, users interact with the FFE using keywords specific to

FVS and to FFE. Once the FFE is invoked, the snag and fuel components are

automatically present. Users can simulate fires or request fuel treatments

using keywords. Many FFE-specific characteristics are linked to the Event

Monitor (Crookston 1990). This allows users to request the simulation of

events or management actions, such as fuel treatment, if certain stand or fuel

conditions are predicted by the model.

FVS passes control to the FFE in every growth cycle (fig. 2.1). The FFE

operates on an annual time step within the FVS cycle (normally representing

5- or 10-year time steps). All simulation results relevant to FVS, such as fire

effects on tree mortality, are passed back to FVS at the end of the cycle. Figure

2.2 illustrates the general scheme of the FFE-FVS. FVS uses a tree list to

represent a stand. For each tree in the list, FVS stores several attributes

including dbh, height, crown length, and the number of trees per acre

represented by the sample tree. Similarly, the FFE tracks snags using a snag

list, which carries attributes specific to snags (see section 2.3.1). Snags are

created through mortality and gradually break apart and fall, thus contributing to the surface fuel.

Fuel is tracked in a number of fuel pools (section 2.4.1) representing the

quantity of fuel in different size classes. Fuel pools can be initialized by the

user, or the FFE will estimate initial loadings from the tree list and habitat

Figure 2.1—Order of calculations in the FFE, including sections of FVS. Italicized activities are part of FVS.

Read all keywords and the tree list (sample-based collection of individual trees that represent a stand).

Establish initial snag and fuel conditions

For Each Growth Cycle

Apply stand management including the creation of snags and fuels

Estimate normal growth and mortality for each sample tree

For each year in the Growth Cycle

Print snag information

 Update condition of snags

Apply snag management

Apply fuel treatments

Compute potential fire intensity

Simulate fires

Update mortality predictions and add newly created snags to snag pools

Print potential fire and actual fire information

Update and print fuel pools

End annual loop

Call Establishment Model

Update Stand Attributes (and apply the possibly altered mortality rates)

Add new mortality to snag pools

Display Indicators

End Growth Cycle Loop
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

type. The fuel pools are updated on an annual timestep by simulating input

and decomposition, as well as movement from one pool to another. The

simulated fuel loadings, along with the habitat type and management

history of the stand, are used to select one or more fire behavior fuel models

(Anderson 1982) that most closely represent fuel conditions (section 2.4.8).

These fire behavior fuel models are used to predict fire behavior rather than

the simulated fuel loadings because of the extreme sensitivity of the fire

behavior model to fuel parameters we cannot easily track in the FFE, in

particular surface area to volume ratio and fuel bed depth.

Surface fire intensity is predicted using Rothermel’s model (Rothermel

1972; Albini 1976a) for each of the selected fire behavior fuel models. The

predicted fire behavior for the models is then combined in a weighted average

(section 2.5.4). The weighted average and canopy fuel characteristics are

used to determine whether crown fire occurs. Fire intensity, expressed as

flame length, and degree of crowning (surface, passive or active) are used as

indicators of the fire hazard of the fuel and stand conditions. They are also

used to calculate the effects of a simulated fire (that is, fuel consumption,

smoke production, tree mortality, mineral soil exposure, and so forth; see

section 2.5.5).

fuel 

loads

debris

< > 3

tree

mortality

Snag

Model

snag

information

fuel 

consumption

tree data and

harvest data

Fuel Model

canopy closure, 

habitat type

User Inputs/Model Defaults

Harvest and slash

management information

Harvest and slash

management information

Moisture, wind, 

temperature, slope

select Fire Behavior

Fuel Model(s)

characteristics

of small fuels

Fire Model (fire intensity and fire effects)

smoke production, mineral soil exposure, etc.

tree information ( ),

tree mortality ( )

FVS

Figure 2.2—Scheme of the FFE-FVS Model. The boxes in this figure show the major

submodels of the FFE. Arrows indicate the flow of information between submodels. See text

for further explanation.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 15

2.3 Snag Submodel ________________________________________________

2.3.1 Overview

The snag submodel tracks the breakage, decay and fall-down of the boles

of standing dead trees. The term “snag” throughout is used only to refer to

standing dead trees; once they have fallen, they are modeled as surface fuel.

The foliage and branches of snags also fall and contribute to surface fuel, as

described in section 2.4.4.

Snags are represented in the model using a snag list. Each list element,

called a snag record, represents a group or class of snags. These are snags of

the same species, that died in the same simulation cycle or year, and that are

in the same diameter and height class. The snags in each record are described

by the following characteristics:

• Diameter class—Snags are grouped into 2-inch diameter classes, based

on their dbh at the time of death. The largest class represents all snags

with a dbh of 36 inches or more.

• Species—Tree species.

• Height at death—Average height of the trees in that record at the time

of death (for the initially hard and initially soft snags separately; see

item 6 below). If the height of otherwise similar trees differs by more

than 20 feet, two records are created (section 2.3.3). This allows the

model to follow these height differences in the simulation of snag

dynamics.

• Current height—Average current height of the snags in the record,

again for initially hard and initially soft snags separately. The height

will decrease over time as the snags start to break apart (section 2.3.4).

• Years since death—Number of years since the death of the tree (that is,

the time since the snag was created).

• Decay status—Decay status: hard or soft. Soft snags are more decayed

and are assumed to have 80 percent of the wood density of hard snags.

• Density—Number of stems per acre represented by this record. This

will decrease as snags of this record start to fall down (section 2.3.6).

Only four of the characteristics will change over time (current height,

years since death, decay status, and density). The simulated change in height

as snags age allows the corresponding reduction in volume to be calculated

(using the diameter at time of death).

2.3.2 Initialization

Snags can be initialized in the model using two options. Snags can be

included in the input FVS tree list along with live trees by recording the

species, dbh, and height information and a code indicating that the tree is

dead. At present, all trees initialized in this manner are assumed to have died

5 years before the inventory year. The model does not use the FVS tree list

codes that describe snag age. By default, these snags are hard, but the

SNAGPSFT keyword can be used to change this assumption.

Snag records can also be created using the SNAGINIT keyword. Each of

the snag characteristics described above, except decay status, can be defined

using this keyword. These snags are also assumed to be hard, unless the user

has changed the default using the SNAGPSFT keyword.
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

During a model simulation, snags may be created through FVS-predicted

natural mortality (every simulation cycle), fire-caused mortality (in the year

of fire) (see section 2.5.5), and some management actions (see section 2.3.7).

2.3.3 Creation and Maintenance of Snag Records

The model uses snag records to represent groups of snags that die in the

same simulation cycle or year, belong to the same species, dbh class, and are

within a 20-foot height range. When new snags are created, the model

determines the height range of snags of the same species and dbh class. If

height varies by more than 20 feet, two records are created for snags of that

species and dbh class. Thus, some of the variability in initial snag heights is

maintained in the model. In all cases, the density-weighted average height

and average dbh of all the snags in each record are used as the attributes.

Snag records are eliminated once all snags in the record have fallen

(section 2.3.6), when the record contains fewer than 0.0002 snags per acre

(equivalent to 1/100th of one snag in a 50 acre stand), or when the current

height of the snags in the record is less than 1.5 feet. Any remaining snag

material in these records is added to the surface fuel with the other fallen

snags.

Currently the number of snag records in the FFE is limited to 2,000. If a

new snag record is needed and all of the snag records are already in use, then

the model must search for a snag record to overwrite. The model first

searches the snag records created in all previous years to determine which

contains the fewest snags. If this record contains fewer snags than the new

record would have if all the new snags were in the same height group, then

the existing snags are knocked over and the record is used by the new snags.

If not, then the model determines which snag record already created this year

has the fewest snags. Again, if this record contains fewer snags than the new

record would have, the snags are felled and the new snags are used instead.

If at this point no record has been found for the new snags, then these snags

are placed on the ground. The activity summary will report whenever snags

are moved to the surface fuel pools in this manner.

2.3.4 Height Loss

As snags age, their tops break off and fall to the ground, decreasing the

snag height. In the model, this process slows with time, as the remaining top

of the tree becomes wider at each successive breakpoint. We assume breakage occurs at a faster rate until half of the initial height has been lost, then

occurs at a slower rate. All species use the same pattern of breakage, but the

rates differ between them (fig. 2.3). In addition, initially soft snags lose

height twice as quickly as initially hard snags. This difference in height loss

is under user control.

The basic equations for snag breakage are:

HTt = HT0(1 – 0.0228mx)t if(1–0.0228mx)t > 0.5

HTt = 0.5HT0(1 – 0.01mx)t –y if(1–0.0228mx)t ≤ 0.5

where:

t = number of years since death;

y = number of years after death when half of the initial height has been

lost;
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 17

HTt = height of the snag at t years after death;

HT0 = height of the snag at death;

m = multiplier used to change the base rate for different species; and

x = multiplier used to accelerate the rate of breakage of initially soft

snags (default values are x=1 for initially hard snags, and x=2 for

initially soft snags).

These equations are defined such that, with m=1, snags lose 2.28 percent

of their current height each year until they have lost 50 percent of their

original height in about 30 years. After that, the remaining breakage occurs

at a rate of 1 percent per year. The switch from the faster rate to the slower

rate occurs when 50 percent of the initial height of the snag has been lost

(table 2.1).

Snags are considered surface fuel if they are less than 1.5 feet in height.

At this point, the amount of material represented by the remaining bole is

transferred to the appropriate surface fuel pools and the record is eliminated

from the snag list.

Using the SNAGBRK keyword, users can control the breakage rates for

each species by defining the time it takes for a given amount to break. The

model translates these times into the parameter m.

Figure 2.3—Comparison between patterns of height loss

for initially hard or soft snags

with the three different sets of

default rates.

Table 2.1—Comparison between height loss for different species. The “Years to 50 percent height loss” is the number of years after

death required for 50 percent of the original height to be lost. This is the time at which the simulated breakage rate

switches from the faster rate (for example, 2.28 percent) to the slower rate (for example, 1 percent). The “Multiplier” is

the value used by default on the initially defined percentages. The “% of height after 100 years” gives the percent of the

initial height that is still remaining on standing snags after 100 years.

Years to 50% height loss % of Height after 100 years

Species Multiplier Hard Soft Hard Soft

Base Ponderosa pine, Other 1.0 30 14 25 21

Faster Grand fir, Western hemlock, Cedar,

Lodgepole pine, Spruce, Subalpine fir 1.1 27 13 22 19

Slower White pine, Larch, Douglas-fir 0.9 33 16 27 2
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

2.3.5 Decay

Decay is the process by which snags become softer. In the snag model, there

are only two stages of decay: hard and soft. Newly created snags are classified

as “hard” in the model, unless otherwise specified by the user. Over time,

these snags decay until eventually they are considered “soft”. Soft snags

experience more rapid height loss in the model (section 2.3.4). Debris

originating from soft snags decays faster than debris from hard snags

(section 2.4.5).

All hard snags, assuming that they remain standing, will eventually

become soft snags. The rate of this decay depends on the diameter of the tree

at the time of death and its species. The basic decay rate is based on a linear

approximation of some rates for Douglas-fir (Bruce Marcot, USFS, Portland,

OR, unpubl. data, 1995), and has the form:

DecayTime = m(1.24dbh + 13.82)

where:

DecayTime = number of years it takes for a hard snag to become soft (that

is, the time from death to transition to soft);

dbh = dbh (in inches) of the snag at the time of death; and

m = multiplier used to scale the equation to increase or decrease

the decay rate for different species.

The default decay rate of each species is assigned using a scaling multiplier

of 0.9, 1.0, or 1.1 (fig. 2.4). The scaling value, m, used for each species can be

changed using the SNAGDCAY keyword.

2.3.6 Falldown

Standing snags will eventually fall. In the model, fall rates vary based on

species, size, and whether the snag was present during a fire. With one

exception, the rates do not depend on snag age or decay status. As with the

breakage and decay rates, a basic set of rates is defined. These rates are based

Figure 2.4—Number of years

until decay for the different

default rates and a range of

dbh. A multiplier less than 1

decreases the amount of time

until decay (that is, the snag

decays faster) while multipliers greater than 1 increase the

amount of time before decay

(that is, the snag decays

slower).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 19

on a linear approximation of data for ponderosa pine snags (Bruce Marcot,

USFS, Portland, OR, unpubl. data, 1995), with a modification to ensure that

some large snags remain standing for 100 years.

For all snags less than 18 inches, and for all but the last 5 percent of snags

over 18 inches, the number of snags in a record that fall each year is

calculated as:

R = –0.001679d + 0.064311

F = mRN0

where:

R = rate of fall (fig. 2.5); for records with a dbh greater than 32.3 inches,

this rate is set to 0.01;

d = initial dbh of the snag, in inches;

N0 = initial density (stems/acre) of snags in the record;

m = multiplier that can be used to change the rate of fall; and

F = density of snags (stems/acre) that fall each year from that record.

For the last 5 percent of snags over 18 inches, the number of snags falling

each year is:

F

A T = N −

0 05

0

.

where:

F = density of snags (stems/acre) that fall each year from that record;

A = maximum number of years that snags will remain standing (that

is, the time when all snags will have fallen);

T = time when 95 percent of the snags had fallen; and

N0 = initial density of snags (stems per acre) in the record.

This is the only exception to the rule that the fall rates do not depend on

age. This equation ensures that some large snags persist throughout the

period of time A, but that none persist beyond this time. By default,

Figure 2.5—The rate of fall of small snags and the first 95

percent of large snags.
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 2.2—Default snag fall rate modifiers for different species. Ponderosa pine is the base

species. Species that are assumed to fall faster have a higher multiplier and a shorter

maximum persistence time. The opposite is true for the species with slower falling

snags. Species “other” was assigned the base rate values because it is not known

which species will be included in “other.”

Maximum Persistence

Species Multiplier (m) Time (years, A)

Base Ponderosa pine, Other 1.0 100

Faster Grand fir, Western hemlock, Cedar,

Lodgepole pine, Spruce, Subalpine fir 1.1 90

Slower White pine, Larch, Douglas-fir 0.9 110

ponderosa pine snags fall at the rate calculated with m=1 and with a

maximum persistence time of 100 years for snags over 18 inches. All other

species are assumed to fall either 10 percent faster or 10 percent slower.

Similarly, the maximum persistence time for snags over 18 inches is also

assumed to be either 10 percent longer, or 10 percent shorter (table 2.2).

Figure 2.6 compares the effect of the three fall rates for large and small snags.

The user can specify both the normal fall-rate multiplier m and the persistence time A for each species using the keyword SNAGFALL.

Fires that exceed a threshold scorch height (by default 0 ft) increase the fall

rates of previously existing soft snags and small snags (fig. 2.7). After a fire,

all soft snags and 90 percent of hard snags smaller than 12 inches dbh will

fall within 7 years. Snags that would already fall in less than 7 years will still

fall at their “preburn” rate. Large, hard snags are unaffected by fires. These

parameters may all be controlled by the user using the keyword SNAGPBN.

Figure 2.6—Percent of large and small snags standing as a

function of years since death. The last 5 percent of large snags, or

those greater than 18" dbh, remain for a long period of time, while

small snags fall at a constant rate. Fall rates decrease with increasing dbh and differ between species (see table 2.2).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 21

2.3.7 Management

Snags can be created by simulating thinning using base FVS model

keywords and requesting that all (or a portion) of the thinned trees be left

standing. This request can be specified with the YARDLOSS keyword.

Snag removal is simulated using the FFE keyword SALVAGE. Users can

select snags to salvage based on time since death, size (dbh at death), and

decay status (hard/soft). Salvage operates after base model management

options and after the snag dynamics (falling, breakage, and so forth) have

been applied but before fires are simulated. Thus, if a user specifies that all

new snags be removed, any snags created using the YARDLOSS keyword in

the current year will be eligible for removal, but those created from a fire in

the current year have not yet been produced, and cannot be salvaged.

Note that the FFE SALVAGE keyword is different from the base model

SALVAGE keyword. The FFE keyword removes snags from the snag list

maintained by the FFE while the base model version acts on the main FVS

tree list.

The amount of the salvage is printed in two places: as the last field in the

activity summary (volume/acre removed), and in the column “standing removed” in the detailed fuel report (tons removed; section 2.4.10). The size and

species distribution of the salvage can also be inferred to some degree through

changes in snags reported in the detailed snag output report (section 2.3.8). No

more detailed breakdown of salvage amounts is currently available.

2.3.8 Output

Information about snags in the model can be important for determining

wildlife values or other nontimber indicators. Two snag output reports –

detailed or summary – can be produced by the model.

Detailed Snag Report: The detailed snag report produces information

about snags at user-requested intervals or years (SNAGOUT keyword). It is

printed at the beginning of the year, after base model harvesting (which could

create snags) but before any other FFE operations such as snag dynamics,

salvage logging, or fires. The report summarizes the snag records by species

Figure 2.7—Effect of fire on the fall rate of a sample snag record.

The example record contains 6.8 inch GF snags that were

created in 2006. The graph shows the density of the snags with

and without a fire in 2011.
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

into up to six user-defined diameter size classes. The report provides the

following information on these summary records (table 2.3):

Year The simulation year of the report.

Species The two-letter species code of the species being reported.

DBH cl A value from 1 to 6, indicating the user defined size class of

snags in this record.

Death dbh The average diameter (inches), at the time of death, of the

snags that are aggregated into this record.

Curr height (ft)

Hard The average height of currently hard snags aggregated into

this record.

Soft The average height of currently soft snags aggregated into

this record.

Curr volume (ft3/acre) This volume is estimated from the original height of

the snags, the current height, and the diameter at the time of

death.

Hard The volume of currently hard snags.

Soft The volume of currently soft snags.

Total Sum of hard and soft volumes.

Year died The year the record was created (the year that the tree died).

Density Number of snags/acre

Hard

Soft

Total

Table 2.3—Example detailed snag report. In this example, snags were only reported for the year 2008. The stand

contains hard Douglas-fir (DF), larch (L) and lodgepole pine (LP) snags that had died in 2004 and 1999,

as well as some soft lodgepole snags that had died in 1983.

ESTIMATED SNAG CHARACTERISTICS, STAND ID=300290024601

-------------------------------------------------------------------------

DEATH CURR HEIGHT CURR VOLUME (FT3) DENSITY (#/ACRE)

DBH DBH ----(FT)--- ----------------- YEAR --------------------

YEAR SP CL (IN) HARD SOFT HARD SOFT TOTAL DIED HARD SOFT TOTAL

-------------------------------------------------------------------------

2008 L 1 2.0 22.0 0.0000 2004 5.4 0.0 5.4

2008 DF 1 4.8 35.8 0.0 55 0 55 2004 17.7 0.0 17.7

2008 DF 2 13.5 72.3 0.0202 2004 0.1 0.0 0.1

2008 DF 3 21.0 79.3 0.0606 2004 0.1 0.0 0.1

2008 DF 6 38.1 92.5 0.0202 2004 0.0 0.0 0.0

2008 LP 1 6.4 64.1 0.0 25 0 25 2004 3.1 0.0 3.1

2008 L 1 1.8 18.6 0.0000 1999 6.4 0.0 6.4

2008 DF 1 4.4 29.5 0.0 53 0 53 1999 21.4 0.0 21.4

2008 DF 2 13.4 62.7 0.0202 1999 0.1 0.0 0.1

2008 DF 3 20.6 69.5 0.0 7 0 7 1999 0.1 0.0 0.1

2008 DF 6 37.9 82.2 0.0202 1999 0.0 0.0 0.0

2008 LP 1 6.1 54.5 0.0 28 0 28 1999 4.1 0.0 4.1

2008 LP 1 6.9 0.0 41.2 0 257 257 1983 0.0 33.8 33.8
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 23

The default size class boundaries for reporting are, in inches, at 12, 18, 24,

30, 36, and greater than 36 inches dbh at death. These values can be changed

with the SNAGCLS keyword. The report lists only species and size classes

that are present in the reporting year. Classes with low densities (less than

0.05 trees/acre) show densities of .0 in this report.

Each line in the report may represent more than one snag record because

for reporting purposes snags are grouped into larger diameter size classes.

Within each class, all reported values are averages of the characteristics of

each snag record. This averaging means that some reported values may

change between years in a counterintuitive fashion as records within the

class lose height or numbers at different rates. Table 2.4 shows a selection of

output for Douglas-fir snags that were created in 1996 and that are in the

first dbh class. Although the dbh of a particular snag record does not change

during the simulation, the average dbh and height in the reported class

increases over time because the smaller snag records included in the class fall

faster (and thus contribute less to the average) than the larger snag records

in the reporting class.

Snag records can be created at harvesting, after a fire, or from natural

mortality applied at cycle boundaries. They can only be removed through

falling or salvage. If the detailed snag report is printed every year (and the

cycle length is longer than a year), there will be slight regular changes from

height-loss and falling. Dramatic changes such as fire mortality or salvage

should be relatively easy to distinguish. Any newly created snags should

correspond to other reports: the fire mortality information (section 2.5.7) or

the distribution of harvested trees from the base model.

Summary Snag Report: The detailed snag report contains a large

amount of information typically required only for detailed analyses. A

summary snag report can be requested using the keyword SNAGSUM. It is

printed at the beginning of the last year of every cycle and contains the total

density of snags that are larger than the given diameter. In table 2.5 for

example, the first column lists all snags, the second column gives the density

of all snags greater than 12 inches diameter, and so forth. With the exception

of a distinction between hard and soft snags, this table contains no other

Table 2.4—Sample output from the detailed snag report showing a selection of reports about size class 1 Douglas-fir that died

in 1996. Death dbh is the average dbh of snags combined in a given record.

Dbh Death Height Total Volume Year Density

Year Sp. cls dbh hard soft hard soft total Dead hard soft Total

2009 DF 1 0.9 7.2 0.0 4.0 0 4.0 1996 19.51 0.00 19.15

2010 DF 1 1.0 7.2 0.0 3.5 0 3.5 1996 15.33 0.00 15.33

2011 DF 1 1.0 7.3 0.0 2.9 0 2.9 1996 11.15 0.00 11.15

2012 DF 1 1.2 19.1 4.7 2.3 0 2.3 1996 1.48 5.50 6.97

2013 DF 1 1.7 19.5 4.6 1.8 0 1.8 1996 0.97 1.82 2.79

2014 DF 1 4.6 21.3 0.0 1.3 0 1.3 1996 0.46 0.00 0.46

2015 DF 1 7.4 31.4 0.0 0.9 0 0.9 1996 0.12 0.00 0.12

2016 DF 1 10.5 46.2 0.0 0.6 0 0.6 1996 0.05 0.00 0.05
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

distinguishing information about species, heights, volumes, or age. The table

has the format:

Year

Density of hard snags

diameter size class 1

diameter size class 2

etc.

Density of soft snags

diameter size class 1

diameter size class 2

etc.

The sizes classes are the same ones that are used in the detailed snag

output report and can be defined by the user with SNAGCLS keyword. If the

detailed snag report and the summary snag report are both printed in the

same year, the total densities reported in both tables should be the same.

2.4 Fuel Submodel_________________________________________________

2.4.1 Overview

The fuel submodel accounts for the dynamics of all nonliving biomass

derived from aboveground sources in the stand. It receives input from live

trees (litterfall, crown lifting, and breakage), snags (either breaking up or

Table 2.5—Example output from the summary snag report. In this example, most snags are hard, but some

soft snags appear briefly in 2005 through 2010. These snags were previously hard (note the sharp

decline in hard snags between 2000 and 2005). Each column of each snag type also contains

the snags in the larger size classes to the right. Thus, in 2070, there are 163.2 snags/acre total

(greater than 0"), of which 4.1 snags/acre are ≥ 12". Throughout the simulation, most of the snags

are small (< 12”), however, the 2070 wildfire killed a number of larger trees. These do not show

up till 2075 because the snag report is written before fire is simulated.

-------------------------------------- SNAG SUMMARY REPORT ------------------------------

------------ HARD SNAGS/ACRE ------------ ------------ SOFT SNAGS/ACRE ------------

YEAR >=00" >=12" >=18" >=24" >=30" >=36" >=00" >=12" >=18" >=24" >=30" >=36"

---- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------

1993 270.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2000 215.5 0.3 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2005 80.4 0.5 0.3 0.0 0.0 0.0 72.8 0.0 0.0 0.0 0.0 0.0

2010 79.2 0.8 0.3 0.0 0.0 0.0 3.4 0.0 0.0 0.0 0.0 0.0

2015 74.9 1.1 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2020 149.3 1.3 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2025 126.2 1.7 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2030 109.5 2.1 0.5 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0

2035 119.5 2.6 0.5 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0

2040 173.1 2.7 0.5 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0

2045 184.2 2.9 0.5 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0

2050 155.8 3.1 0.5 0.2 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0

2055 138.4 3.4 0.5 0.2 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0

2060 169.0 3.5 0.6 0.2 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0

2065 175.5 3.7 0.6 0.3 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0

2070 163.2 4.1 0.7 0.3 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0

2075 228.5 77.4 10.8 4.9 0.7 0.7 0.0 0.0 0.0 0.0 0.0 0.0

2080 76.1 61.5 9.4 4.4 0.6 0.6 0.0 0.0 0.0 0.0 0.0 0.0

2085 55.1 45.7 8.0 3.9 0.6 0.6 0.0 0.0 0.0 0.0 0.0 0.0

2090 44.3 30.1 6.6 3.5 0.6 0.6 0.0 0.0 0.0 0.0 0.0 0.0

2095 54.1 14.6 5.2 3.0 0.5 0.5 0.0 0.0 0.0 0.0 0.0 0.0
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 25

falling over), and harvest activity, (fig. 2.8) and simulates decay over time

using a simple constant proportional loss model. Litter and six size classes

of woody fuel are modeled (table 2.6). The fuel submodel simulates decay

dynamics based on up to four species-dependent decay rates, and accounts for

differences attributable to the hard or soft condition of the input from snag

boles and snag material (table 2.6).

Some of the decaying material from the above classes moves into a duff

pool. Duff does not use different species-dependent decay rates and, like

litter, is not stratified as hard or soft. Thus, a single decay rate is used for all

duff material.

The fuel submodel also tracks a nominal measure of live herbs and shrubs

in the stand (see section 2.4.6).

Canopy fuel characteristics are tracked as indicators and for use in

predicting fire behavior (section 2.4.7).

Surface fuel loads are important indicators. They are also used in the FFE

to key to predefined fire behavior fuel models used for calculating fire

intensity (section 2.4.8).

Figure 2.8—Flow of material

within the fuel model. Material enters the various size

classes, decays and goes either to the duff pool or to air

(which is not tracked in this

model). Not all size classes

are shown in the figure. See

table 2.6 for a complete list.

Table 2.6—Fuel pools may be characterized by a combination of

the following attributes.

Fuel characteristics

Size class Decay rate Initial decay status

Litter Very fast Hard

diam <0.25" Fast Soft

0.25" ≤ diam < 1" Slow

1"≤ diam <3" Very slow

3"≤ diam < 6"

6" ≤ diam < 12"

diam ≥ 12"

Foliage

(snags, cut

trees, litterfall)

Branches

(snags, cut trees,

live crown breakage)

Boles

(snags, unmerchantable trees, tops)

Input

Output

Surface

Fuel

Pools

litter

duff

CO2

< 1” 1-3” ... 6-12” 12”
26 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

2.4.2 Initialization

Fuel loads can be initialized with the keyword FUELINIT. If the user does

not specify initial fuel loads, the model sets them based on the dominant cover

type in the stand and the percent cover (table 2.7; Jim Brown, pers. comm.

1995). When the model simulation is started from a tree list, the cover type

is set to the species with the highest total basal area. If there are no trees in

the stand, the cover type is defined as the major climax species in the stand’s

given habitat type (Cooper and others 1991; Pfister and others 1977), because

those were likely the tree species that created the existing fuel pools. The

rules and values used to determine default initial fuel loads by size class vary

greatly between FVS variants.

The amount and distribution of fuel in an actual forest stand is highly

dependent on the stand’s history. For example, a stand generated after

stand-replacing fire will have different fuel than one generated after a clear

cut. This variation is not captured by the model’s default initial values, so we

recommend initializing fuel loadings to appropriate values rather than using

model defaults whenever possible.

During a simulation, woody debris from each tree is assigned a fuel decay

rate class based on species (table 2.8). At initialization, once the total amount

of fuel in each size class has been established, it is apportioned between the

various decay rate classes using the relative amounts of basal area of each

tree species present in the stand. If there are no trees in the stand, all fuel

is placed into the decay class corresponding to the cover type determined

above. All initial fuel is assumed hard.

Table 2.7—Default initial fuel loadings (tons/acre), by size class, based on the cover type of the stand. If there are

trees present at the time of initialization, values in row “E” (for “Established”) will be used, while if there

are no trees (in other words, a bare ground simulation), the canopy cover is less than 10 percent, or

all trees are smaller than 1” dbh, the values in row “I” (for “Initiating”) will be used. The 0-1" pool is divided

equally between the smallest two classes (0-0.25" and 0.25-1") (Jim Brown, pers. comm., 1995).

Surface Fuel Size Class

0-1" 1-3" 3-6" 6-12" >12" Litter Duff

Western White pine E 1.0 0.8 10.0 10.0 10.0 0.8 30.0

I 0.6 0.4 6.0 6.0 6.0 0.4 12.0

Douglas-fir, E 0.9 0.8 3.5 3.5 0.0 0.6 10.0

Western larch I 0.5 0.5 1.4 1.4 0.0 0.3 5.0

Grand fir E 0.7 1.5 7.0 7.0 0.0 0.6 25.0

I 0.5 1.0 2.8 2.8 0.0 0.3 12.0

Western hemlock, E 2.2 2.6 15.0 20.0 15.0 1.0 35.0

Western redcedar I 1.6 1.8 6.0 8.0 6.0 0.5 12.0

Lodgepole pine E 0.9 0.6 7.0 8.0 0.0 0.6 15.0

I 0.6 0.4 2.8 3.2 0.0 0.3 7.0

Englemann spruce, E 1.1 1.1 10.0 10.0 0.0 0.6 30.0

Subalpine fir, I 0.7 0.8 4.0 4.0 0.0 0.3 12.0

Mountain hemlock

Ponderosa pine E 0.7 0.8 2.5 2.5 0.0 1.4 5.0

I 0.1 0.1 0.5 0.5 0.0 0.5 0.8
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 27

Table 2.8—Decay class and wood density of the tree

species found in the northern Idaho FVS

variant. The density values are for oven-dry

wood. See section 2.4.5 for the decay rates

associated with each decay rate class.

Decay Density

Species rate class lb/ft3

White pine 4 24.8

Larch 3 34.3

Douglas-fir 1 31.9

Grand fir 3 24.1

Western hemlock 2 29.5

Cedar 2 21.1

Lodgepole pine 4 26.4

Spruce 2 22.6

Subalpine fir 1 21.1

Ponderosa pine 4 26.4

Mountain hemlock & other 4 29.5

2.4.3 Estimation of Tree Material

The boles and crowns of both live trees and snags contribute to the surface

fuel pools in the FFE. Therefore, the estimation of the amount of bole and

crown material on each live tree has a large impact on fuel amounts and

dynamics. The remaining material in this section describes how these

amounts are calculated. Section 2.4.4 describes how the material moves from

the standing pools to the surface pools.

Estimation of Bole Material: The FFE uses an FVS routine to determine the volume of wood in each bole. In the case of live trees and hard snags,

the resulting bole volumes are converted to biomass using wood density

values given by Brown and others (1977) and shown in table 2.8. The boles

of soft snags are assumed to have only 80 percent of the density of hard snags.

All biomass is tracked and reported as dry weight.

When tree boles become surface fuel (as described below), the bole material

is partitioned among the size classes shown in table 2.6. The partitioning is

done by approximating bole shape as a cone of the specified total height and

diameter at breast height. Using this approximation, the length of the bole

at each diameter-class breakpoint is determined. These lengths are then

used, together with the base FVS model volume routine, to determine bole

volume between each breakpoint. No attempt is made to simulate the

physical fragmentation or actual piece lengths of boles. The material in each

portion of the bole is simply assigned to the appropriate size class at the time

the bole falls.

The material from each bole is also assigned a decay rate based on its tree

species, as shown in table 2.8. Model users may change the decay rate

assignments for each species with the keyword FUELPOOL. The model also

classifies the down material from each snag bole according to its decay status

at the time it falls (hard or soft).

Estimation of Tree Crown Components: The FFE estimates the

amount of crown material on each tree using the equations in Brown and

Johnston (1976). These equations estimate the total dry weight of live and
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

dead material in each crown, as well as the proportions of that material in

foliage, 0 to 0.25, 0.25 to 1, 1 to 3, and more than 3 inches diameter

branchwood.

According to Brown and Johnston’s equations, the total amount of crown

material and the partitioning of that material among size classes depends on

the following variables: tree species, dbh, height, crown ratio, and the tree’s

dominance position in the stand. The FFE classifies the dominance of trees

based on their height. Trees above the 70th percentile (that is, the tallest 30

percent) are considered dominant or codominant, while trees below the 40th

percentile are considered intermediate or suppressed. Between these values

a linear interpolation is used for estimating crown weight. The crowns of

trees classified as species 11 (“other”) are estimated from Brown and

Johnston’s equations for western hemlock.

When crown material becomes surface fuel in the FFE, all foliage is

classified as litter and the other crown components enter the appropriate fuel

pools based on size and species. As recommended by Jim Brown (USFS,

Missoula, MT, pers. comm., 1995), the branch material over 3 inches is all

classified as 3 to 6 inch fuel. Fallen crown material is also classified into

different pools based on the decay rate of the tree species from which it

originates, and whether it originates from a live tree/hard snag or a soft snag.

Except in the case of fire-scorched trees, the amount of crown material

associated with each live tree is calculated in every FVS cycle based on the

current attributes of the tree record. For one cycle after a tree has been

scorched by fire, the amount of crown material associated with that tree is

held static at the level remaining immediately after the fire (as described in

section 2.5.5).

2.4.4 Sources of Woody Fuel and Litter

Every year, some material is transferred from the crowns and boles of live

trees and snags into the appropriate fuel pools. This transfer is based on tree

growth and mortality, snag fall and breakage, fires, and management. The

the remaining material in this section describes each of these processes in

more detail.

Annual Litterfall: The FFE simulates annual foliage litterfall from each

live tree using data from Keane and others (1989) on foliage lifespan. The

model assumes that 100 percent of the current foliage will fall during the

specified leaf lifespan, so that the average proportion of foliage falling each

year can be approximated from the inverse of the leaf lifespan. This gives the

following equation for annual litterfall from each tree:

Litterfall Foliage Weight

Leaf Lifespan =

where:

Litterfall = weight of litter (lbs/year) to fall from this tree in each

year of the current FVS cycle;

Foliage Weight = current weight of foliage on this tree (lbs); and

Leaf Lifespan = expected foliage lifespan (years) for this tree species

(table 2.9).

In accounting for litterfall, the amount of foliage remaining on the tree is

not reduced as we assume that the dropped material is replaced by new

growth each year.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 29

Crown Lifting: The FFE simulates the die-back of lower branches as a

tree grows and the crown lifts. The model assumes that a portion of the woody

crown material that was present in the previous cycle has now died and will

fall during the current cycle. The amount is estimated from the ratio of the

change in height of the base of the crown to the previous total length of the

crown. As shown in figure 2.9, this is equivalent to assuming that the crown

is cylindrical in shape with crown material evenly distributed throughout

this space. In reality, crowns may be broader across the bottom with crown

material less dense in this space. Because these factors tend to cancel each

other out, the model’s simple approximation should not systematically bias

the timing of debris inputs.

The woody crown material that has died as a result of crown lifting is

assumed to fall at a constant rate during the current FVS cycle. In reality,

some material might not fall until later time periods, but there would also be

older material from earlier time periods falling in the current year; the two

effects would largely cancel each other out. Mathematically, the amount of

Table 2.9—Leaf lifespan data used in calculating annual

litterfall. Data shown are from Keane and others

(1989). Where this source did not provide data for

a species that occurs in the northern Idaho FVS

variant, data from another species were substituted

as shown in the table.

Species Leaf lifespan (years)

White pine data from ponderosa pine

Larch 1

Douglas-fir 5

Grand fir 7

Western hemlock data from Douglas-fir

Western redcedar data from Douglas-fir

Lodgepole pine 3

Spruce 6

Subalpine fir 7

Ponderosa pine 4

Mountain hemlock & other data from ponderosa pine

Figure 2.9—Simulation of crown lifting.

B0 is the height of the base of the crown

in the previous FVS cycle, B1 is the

height of the base of the crown in the

current cycle, and L0 is the length of the

crown in the previous cycle. The FFE

assumes that all crown material in the

space vacated by the lifting crown – the

cross-hatched area in the figure – has

died and will fall during the current cycle.
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

material of each size class (excluding foliage) that will fall due to crown lifting

in each year of the cycle is calculated as:

Annual Fall B B L

W

Cycle Length

i

oi

=

−



 1 0 0

where:

Annual Fall

i = weight of material (lb/acre/year) in size class 

ito fall from

this tree in each year of the current cycle;

B

0 = height of the base of the crown (ft) of this tree in the

previous FVS cycle;

B

1 = height of the base of the crown (ft) in the current cycle;

L

0

= length of the crown (ft) in the previous cycle;

W0i = weight of crown material (lb/acre) in size class 

i in the

previous cycle; and

Cycle Length= length of the current FVS cycle (years).

The crown material that falls due to crown lifting is assumed to be “hard”

when it is added to the surface fuel pools.

The current crown weight is not reduced as a result of these calculations.

Current crown weight is a function of the tree characteristics as described

above.

Crown lifting calculations also include the material that is removed from

the crown during pruning. FVS decreases the length of the crown by the

amount that was pruned off. The FFE tracks the change in crown length and

simulates the resulting litterfall. In reality, the pruned material would enter

the debris pool in 1 year, but in the FFE, since this material is indistinguishable from crown die-back, the material falls throughout the cycle.

Background Crown Breakage: Crown material on live trees may fall as

a result of normal background breakage due to snow, wind, disease, or falldown of adjacent stems. The FFE simulates this breakage by adding a small,

constant proportion of each crown component to the debris pools each year.

This proportion is set to 1 percent per year and is not under user control. The

material is all assumed to be hard when it enters the surface fuel pools.

Current crown weight is not reduced as a result of the loss of this material,

as it is assumed that new growth replaces it.

Snag Breakage and Crown Loss: The FFE models the breakage and

fall-down of snags as described in section 2.3. As each snag breaks or falls

naturally, the fallen bole is partitioned into the appropriate size classes and

decay rate classes as described in section 2.4.3. The material from each snag

is classified as “hard” or “soft” depending on the current decay status of the

snag.

Over time, the crowns of snags will also fall and contribute to surface fuel

pools. The rate at which this happens is estimated in the model from

available data on the amount of foliage, twigs, and limbs remaining 5 years

after death (table 2.10). The model uses the estimated time to 100 percent

loss to calculate a constant fall-down rate following the death of the snag. For

example, 100 percent loss in 10 years means that 10 percent of the material

will be scheduled to fall each year for the next 10 years. Two exceptions to this

rule occur. First, material will never fall over more than 20 years. Second, the

model calculates the time by which a snag will become soft, since soft snags

are assumed to have already lost all their branches. The model will cause the
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 31

Table 2.10—Rate of crown loss for snags of different species. Data on the amount of each crown component remaining 5 years after

death were estimated from a field handbook (Division of Forest Economics 1961). “-” indicates no data were available.

The estimated time to 100 percent loss was derived from the available data, with subsequent modifications as requested

during model review.

Estimated time to 100% loss (years)

Amount remaining 5 years after death (data) Twigs Branches Large Limbs

Snag species Foliage Twigs Branches Large limbs Foliage 0-1” 1-3” >3”

White pine 0% < 75% — “numerous” limbs gone 2 5 15 15

Ponderosa pine 0% < 50% < 50% “falling” 2 5 10 10

Spruce 0% < 30% < 50% “falling” 2 5 10 10

Douglas-fir 0% < 50% < 75% “falling” 2 5 15 15

Western hemlock — — — — 2 5 15 15

True firs 0% < 50% < 75% “falling” 2 5 15 15

Grand fir — — — — 2 5 15 15

Subalpine fir — — — — 2 5 15 15

Western larch — — — — 2 5 15 15

Lodgepole pine 0% < 75% < 75% — 2 5 15 15

Western redcedar 0% < 60% — “some” limbs falling 2 5 20 20

Other — — — — 2 5 15 15

material to fall over whichever time frame is shorter: the time to 100 percent

lost or the time to turn soft.

The fall of snag crown material is scheduled at the time each snag record

is created. That is, all of the snag crown material is put into the appropriate

pools (based on size and decay-rate class) and scheduled to be added to down

fuel pools over succeeding years – pools of “debris-in-waiting”. In this way,

the need to store explicit crown data for each snag record is avoided. When

snag records are created during model initialization, the FFE schedules only

the portion of crown material expected to fall after the start of the simulation.

When a salvage operation occurs in the stand, a proportion of the material

scheduled to fall in all future years is brought down early and added to fuel

pools in the year of the salvage. The proportion brought down is equal to the

proportion of total snag volume that was removed by the salvage operation.

Similarly, when crown fires occur, a proportion of this material is removed

(not added to down fuel) to simulate its consumption in the fire. The

proportion removed is set equal to the proportion of the stand in which crown

fire occurred, for foliage and one-half the 0 to 0.25 inch branchwood only.

Larger branches are not consumed.

All snag crown material is considered hard at the time that it falls because

snags are assumed to have lost all their branches by the time they become

soft.

Scorched Crowns: As described in section 2.5.5 on fire effects, the FFE

simulates the consumption by fire of a portion of small diameter crown

material below the scorch height on surviving trees. All crown material below

the scorch height that is not consumed in the fire is assumed to have been

killed and to fall over the following years in the same manner as is described

for snags in the previous section.

Slash: Harvest activity can result in an increase in surface fuel through

the creation of slash. Slash is created when crown material from harvested

trees is left in the stand, as well as when submerchantable or damaged trees

are felled and left in the stand. The YARDLOSS keyword allows model users
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

to specify a proportion of crown material from harvested trees to be left in the

stand. The keyword also allows users to specify a proportion of “harvested”

live trees to be left in the stand, and whether these stems are left as standing

snags or felled. By default, the FFE assumes that all harvested boles are

removed from the stand, but that the associated crown material is left in the

stand.

2.4.5 Decay Rates

Decay of surface fuel is simulated in annual time steps. By default, 2

percent of the decayed matter from each fuel pool is added to the duff

compartment while the remaining biomass is lost as CO2 and is not tracked

by the model. Pools decay according to the equation:

Fuelt+1 = Fuelt(1 – r)

where:

Fuel = weight of fuel in a given pool;

t = year (and t + 1 is the following year); and

r = decay rate from table 2.11.

The default decay rates for each size class are based on Abbott and Crossley

(1982; table 2.11). Users can change the decay rates using two different

keywords: FUELDCAY and FUELMULT. These change the decay rate for

a specific pool, or apply a multiplier to the rates in all pools. The default

amount of the lost material that becomes duff is the same for each size class

but can be controlled with the DUFFPROD keyword.

The FFE can accommodate up to 57 unique decay rates based on size class,

decay rate classes, and the hard/soft status of input debris (table 2.6). By

default far fewer decay rates are used, decay rate classes are not used, and

decay is usually based almost solely on the size class. The one exception is a

differential decay rate assigned to debris originating from soft snags. Because this material is less physically cohesive, it is considered more susceptible to decay, and each size class is assigned a rate 10 percent higher than

that shown in table 2.11 for the corresponding size class of hard material. The

hard/soft attribute applies only to woody fuel; litter and duff are always

classified as hard for the purpose of calculating decay.

Table 2.11—Default annual losses due to decay and the proportion of the loss that

becomes duff for each of the size class, litter and duff components.

These loss rates are for hard material; soft material in all size

classes, except litter and duff, decays 10 percent faster.

Size Class

Component

Annual Loss

Rate

Proportion of Loss

Becoming Duff

diam < 0.25"

0.25" ≤ diam < 1"

0.12

1" ≤ diam < 3" 0.09

3" ≤ diam < 6"

6" ≤ diam < 12"

diam ≥ 12"

0.015

Litter 0.5

0.02

Duff 0.002 0
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 33

2.4.6 Live Surface Fuel

FFE does not dynamically simulate amounts of live fuel such as herbs and

shrubs, but it does represent them with habitat-type specific values. The

total fuel load of these materials is felt to be roughly constant in a stand (after

canopy closure). Understory herbaceous vegetation is often stimulated by

fire. The rapid increase in herb biomass will compensate for the slower

recovery of shrub biomass. Fires may change the species composition of the

herbs and shrubs, but we assume that the approximate total loading of the

live fuel is unchanged.

The values used for the herbs and shrubs are determined from a combination of percent cover and the dominant species in the stand, as determined

by basal area. The actual values are based on those used in FOFEM

(Reinhardt and others 1997) and modified by Jim Brown (USFS, Missoula,

MT, pers. comm., 1995; table 2.12). If there are no trees at the beginning of

the simulation, the cover type is determined from the major climax species

in the stand’s habitat type (Cooper and others 1991; Pfister and others 1977),

as is done for the initial fuel levels. Otherwise, the assumed cover type is

either the current one calculated from the dominant basal area in the stand,

or the last one that was used in the stand if the stand was recently fully cut.

The values for herbs and shrubs are calculated annually but will only change

if the percent cover or species composition of the overstory changes (from a

fire, harvesting, planting, growth, or mortality).

Users cannot change the amount of live fuel or the rules by which the live

fuel loads are assigned. Some differences exist in the rules and default values

between the different FVS variants.

Table 2.12—Default fuel loadings (tons/acre) for herbs and shrubs based on cover type. If trees are

present the values in the two columns labeled “E” (for “Established”) will be used, while if

there are no trees (that is, a bare ground simulation), the cover is less than 10 percent, or

all trees are smaller than 1" dbh, the values in the two columns labeled “I” (for “Initiating”) will

be used.

Herbs Shrubs

E I E I

Western white pine,

Grand fir 0.15 0.3 0.1 2.0

Douglas-fir,

Western larch,

Western hemlock,

Western redcedar

0.2 0.4 0.2 2.0

Lodgepole pine 0.2 0.4 0.1 1.0

Englemann spruce,

Subalpine fir,

Mountain hemlock

0.15 0.3 0.2 2.0

Ponderosa pine 0.2 0.25 0.25 0.1
34 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

2.4.7 Canopy Fuels

Canopy fuel characteristics, including the stand-level canopy base height

and canopy bulk density, are calculated as described in Scott and Reinhardt

(2001). The model assumes that the amount of crown on each tree is evenly

distributed along the crown’s length. The model sums the total weight of

foliage and fine branchwood (one-half the 0 to 0.25 inch diameter branchwood)

from all trees in 1-foot height increments from the ground to the top of the

tallest tree. It then calculates the 13-foot running mean weight of crown in

each section (fig. 2.10).

Canopy bulk density is the highest average value. Canopy base height is

the lowest height at which a 3-foot running mean is greater than 30 lb/acre/

foot (.011kg/m3). Trees less than 6 feet tall are not included in this calculation

because they are considered part of the surface fuel complex. Trees over 6 feet

tall may contribute canopy fuels between the ground and 6 feet, however, so

it is possible to have canopy base heights of less than 6 feet.

2.4.8 Fire Behavior Fuel Models

Predicted fuel loads are important indicators of potential fire behavior and

effects. However, most applications of Rothermel’s fire behavior model (such

as Andrews 1986, Finney 1998) use predefined fire behavior fuel models

(Anderson 1982) rather than actual or estimated fuel loads. Thirteen of these

Figure 2.10—Canopy fuel characteristics are determined by examining the vertical distribution of canopy fuels. Canopy bulk

density is defined as the maximum of the 13-foot running mean of

1-foot deep layers. Canopy base height is defined as the lowest

height at which the canopy bulk density exceeds 0.011 kg/m3.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 35

models are in widespread use (table 2.13), and some regions have customized

additional models. Each fuel model is typically used to represent a range of

fuel conditions in which fire behavior may be expected to respond similarly

to changes in fuel moisture, wind, and slope. The models are named

descriptively (for example, timber litter and understory; medium logging

slash) and define values for a number of parameters that are difficult to

measure in the stand and that are not tracked in the FFE. These parameters

include fuel characteristics such as: surface-to-volume ratio, loading, depth,

moisture of extinction, heat of combustion, dry density, total mineral content, and silica-free mineral content (table 2.13). Rothermel’s fire behavior

model uses these parameters to calculate surface fire behavior. Users can

change the parameters of existing fuel models, or enter their own customized

fire model using the keyword DEFULMOD.

FFE simulates fuel loadings by size class over time but does not use these

loadings directly as inputs to the fire behavior model. Instead, FFE uses the

loadings and other stand characteristics to select one or more of the stylized

Table 2.13—Parameter values for the fire behavior fuel models (Anderson 1982). Fire Behavior Fuel Models 14, 25, and 26 are

customized fuel models.

Surf-vol ratio (1/ft) Loading (lb/ft2)

Name 0-0.25" 0.25-1" 1-3" Live 0-0.25" 0.25-1" 1-3" Live

Depth

(ft)

Moisture

of

Extinction

1 Short grass 3500 0.034 0 0 0 1 0.12

2 Timber (grass &

understory)

3000 0.092 0.046 0.023 0.023 1 0.15

3 Tall grass 1500 0.138 0 0 0 2.5 0.25

4 Chaparral 2000

109 30 1500

0.23 0.184 0.092 0.23 6 0.20

5 Brush 2000 109 0 1500 0.046 0.023 0 0.092 2 0.20

6 Dormant brush,

hardwood slash

1750 109 30 1550 0.069 0.115 0.092 0 2.5 0.25

7 Southern rough 1750 109 30 1550 0.052 0.086 0.069 0.017 2.5 0.40

8 Closed timber

litter

2000 0.069 0.046 0.115 0 0.2 0.3

9 Hardwood litter 2500 0.134 0.019 0.007 0 0.2 0.25

10 Timber (litter &

understory)

2000 0.138 0.092 0.23 0.092 1 0.25

11 Light logging

slash

1500 0.069 0.207 0.253 0 1 0.15

14 Light-medium

logging slash

1500 0.126 0.426 0.506 0 1.8 0.2

12 Medium logging

slash

1500 0.184 0.644 0.759 0 2.3 0.2

13 Heavy logging

slash

1500 0.322 1.058 1.288 0 3 0.25

25 Plantation older

than 25 years

2000 0.069 0.069 0.092 0.207 3.5 0.25

Fire Behavior Fuel Model

26 Modified FM 4 2000

109 30 1500

0.1242 0.1242 0.0828 0.1656 3.6 0.35
36 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

fuel models that best represent the fuel. The rules used to select the fire

behavior fuel models vary among the geographic variants of FFE-FVS.

FFE can use the fire behavior fuel models in two ways: static or dynamic.

The static option selects the single model that best represents current

conditions. Figure 2.11 shows an example of four possible fuel models for

natural fuel in closed stands with predominantly dead surface fuel as regions

on the graph of small versus large fuel. Within each of these regions, Fuel

Model 8, 10, 12 or 13 is used. For example, if the fuel loading is as shown by

the point in the figure, Fuel Model 12 is selected.

Once the fuel model is selected, its parameters are used to calculate fire

behavior. This approach, while useful in many applications, has a disadvantage when simulating changes in fuel and potential fire behavior over time.

Since there are a small number of fire behavior fuel models, as fuel changes

over time the same model may be selected and predicted fire behavior remain

constant. At the time step when another fuel model is selected there may be

dramatic changes in fire behavior with only a tiny change in simulated fuel

conditions. A more reasonable result is a gradual change in predicted fire

behavior corresponding to the gradual changes in fuel.

Because of this drawback, we developed the dynamic option for selecting

fuel models. The dynamic method selects two or more fuel models based on

fuel loads and other stand characteristics, calculates the resulting fire

behavior for each fuel model, and takes a weighted average of the results.

The selection of the fuel models and their weights depends on stand

conditions, including fuel loads. For example, fuel loads might place the

stand somewhere in the diagram in figure 2.12. The model computes the

Figure 2.11—Regions defined for each static fuel model (numbers

8, 10, 12, 13), based on large (> 3" diameter) and small (< 3"

diameter) woody fuel (ton/acre). The regions here correspond to

fuel models for natural fuel in closed stands for many habitat types,

with the model number shown in the region. Similar regions exist for

activity fuel and fuel from dry grassy or dry shrubby habitat types.

The combination of small and large fuel quantity indicated by the

point will result in the selection of Fuel Model 12.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 37

Figure 2.12—An example of dynamic fuel modeling, based on

large and small woody surface fuel. The lines here correspond to

fuel models for natural fuel in closed stands for many habitat

types, with the fuel model number shown on the line. Similar lines

exist for activity fuel and fuel from dry grassy or dry shrubby

habitat types. The point shown here will result in an interpolation

using Fuel Models 10 and 12, as described in the text.

shortest distance to each of the nearest neighboring fuel model lines. For

combinations of fuel found in the lower left corner or upper right corner in the

example, there will be only one nearest neighbor (Fuel Model 8 or 13

respectively, in figure 2.12). Typically though, there will be two neighbors.

Once the distance to each neighboring fuel model is known, the influence of

each fuel model is calculated by using the inverse of the distance from the fuel

model line to the current condition as a weight. In figure 2.12, the distance

from the sample point (small = 20; large = 10) to neighboring Fuel Model 12

is 8.94 units; the distance to Fuel Model 10 is 17.89 units. The resulting fire

behavior will be more like Fuel Model 12, which is nearer than Fuel Model

10, but the contributions of both models will be present. The weights of the

two models, W10 and W12 can be calculated in this example as:

W10

1 17 89

1 17 89 1 8 84 = 0 33+ = / .(/ . ) (/. ) .

W12

1 8 94

1 17 89 1 8 84 = 0 67+ = / .(/ . ) (/. ) .

where

W10 = weight for Fuel Model 10; and

W12 = weight for Fuel Model 12.

In this example, fire intensity will be computed as a weighted average of

the intensity predicted using Fuel Model 10 (33.3 percent) and Fuel Model 12

(66.7 percent).

In more complex examples, it is possible to define fuel model lines that are

not parallel (as in the example) or that are horizontal or vertical. In these
38 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

cases, the interpolation searches to the left and right of the sample point, and

then searches above and below the sample point. Based on these searches,

between one and four unique neighboring models may be found, and the same

weighting system will be used to compute the influence of the neighboring

models. In all current variants, however, the fuel model lines are oriented

similarly to the ones shown in this example.

Fuel loads provide the system for weighting when woody surface fuels

dominate the fuel complex. In situations where woody fuels are sparse and

litter, shrubs or herbaceous fuels dominate, a similar distance-based weighting system is used based most often on the amount of canopy coverage.

The benefit of the dynamic approach is that the calculated potential fire

intensity varies continuously as fuel conditions change in the stand. Figure

2.13 shows the flame length predicted by different small (0 to 3 inches) and

large (3 inches+) woody fuel loads using the static and dynamic approaches.

Users can choose whether to use the static or the dynamic standard fuel

models using the keyword STATFUEL. They cannot, however, change the

definition of the fuel regions or lines (that is, the fuel levels at which different

fuel models apply). These definitions are customized in the development of

regional variants of FFE.

The logic used by FFE to select fire behavior fuel model(s) varies between

FFE variants and is one of the main differences between FFE variants.

Complete selection logic for each variant is contained in chapter 4. Users can

also set fuel models in any year using the keyword FUELMODL.

Certain common features are present in all variants, however. In all cases,

different selection logic is used for natural and activity fuels (fuels resulting

from harvesting within the last 5 years), and for high and low woody fuel

loads. All variants use the same logic for activity fuels and when woody debris

is abundant. In these cases, the fuel model depends only on the amount of

Figure 2.13—Example of predicted flame length over a range of small and large woody fuel loads (tons/acre).

Predictions using the dynamic option are on the left, and predictions using the static option are on the right.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 39

small (less than 3 inches) and large (greater than 3 inches) fuel in the stand,

and whether the fuel is “natural” or “activity” (table 2.14).

At low natural fuel loads in the northern Idaho variant, the fuel model

depends on habitat type and crown closure. The habitat types are divided into

dry grassy types, dry shrubby types, and all other habitat types. For the dry

grassy and shrubby types, the fuel model choice is further defined by the

canopy closure of the stand (table 2.15). The rules for choosing a fire behavior

fuel model at low fuel loads vary widely between FFE variants.

2.4.9 Management

Management can affect surface and canopy fuel in different ways, either

directly or indirectly. FFE-FVS allows simulation of a full range of thinning

treatments, prescribed fire, and mechanical fuel treatments. When thinning

is simulated using base model keywords, FFE can simulate creation of

activity fuel (see section 2.4.4). Thinning also changes canopy fuel characteristics – amount of canopy fuel, canopy base height, and canopy bulk density.

Other surface fuel treatments are specific to the FFE and do not interact with

base model thinning keywords. These management options include treatments that affect fuel depth, reduce fuel loading, or reduce fuel size.

Table 2.14—Rules for determining the Fuel Model based on fuel

loadings. These rules are used if woody fuels are abundant

and for activity fuel. Table 2.15 shows the rules used if

woody fuels are sparse.

Fuel loading (t/acre) Fuel model

Small (<3") Large (3+”) Natural Activity

<6 ≤15 use other rules 11

>15 10 14

6-15 ≤20 10 14

>20 12 12

15-30 ≤25 12 12

>25 13 13

>30 ≥0 13 13

Table 2.15—Rules for determining the Fuel Model at low woody fuel loads. For stands that

are between the percent cover values listed below, the fire behavior will be

based on a combination of the two fuel models (unless the static option is

being used).

Habitat type Percent cover Fuel model

Ponderosa pine / bluebunch wheatgrass (130) <20 1

>60 9

Ponderosa pine / common snowberry (170) <20 2

Douglas-fir / common snowberry (310) >60 9

Other any 8
40 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Methods Affecting Fuel Depth: Several harvest methods can be simulated as well as different types of mechanical slash treatments. Harvesting

and mechanical fuel treatment methods have no effect on the volume

harvested by FVS or on the quantity of logging residue left on site. The

methods do affect the depth of the logging residue, and thus overall fuel

depth. In the FFE, fuel depth affects fire intensity but not fuel consumption.

Three harvesting options are available in the FFE: (1) ground based

(including cat skidding and line skidding); (2) high lead (including skyline);

and (3) precommercial or helicopter. Any other type of harvesting is assumed

to have no impact on fuel depth.

Two general types of slash treatments are also available: (1) trampling /

crushing / chopping / chipping; and (2) flailing / lopping. No other type of slash

treatment (excluding burns, which are discussed in section 2.5) impacts fuel

depth. The slash treatments specified here have no impact on the actual size

distribution of fuels. Treatments affecting size must be simulated differently

(see below).

The harvest method and slash treatment are used to determine a multiplier for fuel depth (table 2.16). If no activity is specified at the time of a

harvest, no multiplier will be applied (that is, fuel depth will not be changed).

The multiplier can be changed with the keyword FUELTRET.

Multipliers are applied for 5 years following a stand entry. After that time,

fuel from activities is assumed to have the same depth as natural fuel.

Methods Affecting Fuel Loads: Burning fuel to reduce fuel loadings is

a common practice. Broadcast burning, piling and burning fuel, and jackpot

burning are discussed in section 2.5.3.

Fuel loads can also be manually increased or decreased with the

FUELMOVE keyword to simulate treatments involving fuel removals or to

ensure that fuel levels are at some predetermined level.

Methods Affecting Fuel Size: Chipping or other treatments that reduce

fuel size can be simulated independently of any harvesting action in the

model. They move material from the larger fuel classes to the smaller fuel

classes, without reducing fuel loads or affecting fuel depth. These treatments

are scheduled using the FUELMOVE keyword.

2.4.10 Output

Using the keyword FUELOUT, the user can request the detailed fuel

report, a table describing fuel in specific years or at specific intervals. The

report contains information about surface fuel and standing dead and live

fuel, consumed fuel, and removed fuel (table 2.17). All values, including the

live components, are given in dry weight, tons per acre. All fuel is included

Table 2.16—Default fuel depth multipliers based on harvest type and slash

treatment method.

Slash treatment type

Harvest method None Trampling etc. Flailing etc.

Ground based 1.0 0.83 0.75

High lead 1.3 0.83 0.75

Precommercial 1.6 0.83 0.75
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 41

in this table, as are all removals except decay. The following is a short

description of the columns in the output table.

Year Year being reported

Surface fuel Reported in tons/acre

Dead

Litter

Duff

0 to 3 inches woody

Greater than 3 inches woody (This column is the sum of the

 three columns following)

3 to 6 inches woody

6 to 12 inches woody

Greater than 12 inches woody

Live

Herbs

Shrubs

Total Sum of all surface fuels (both Dead and Live)

Standing wood Reported in tons per acre

Dead

0 to 3 inches Small diameter snags and branches

Greater than 3 inches Larger diameter snags and branches

Table 2.17—Example detailed fuel report. This example reports every 5 years, but users can request any desired time interval. Notice

that a fire in 2065 consumed surface fuel and killed trees (moved standing live biomass to standing dead). Notice also

the sharp increase in surface woody fuels in 2070. This is a result of fire-killed trees breaking up and falling down. For

the 0-3” fuels this peak is short-lived because smaller materials fall quickly and begin to decompose. The >3” material

continues to accumulate over the remainder of the simulation period, as standing dead tree boles slowly fall over. By

comparing the columns showing surface fuel and standing dead, one can track the process of dead wood falling to the

forest floor.

 --------------------------------------------------------------------------------------------------------------------------

 ****** FIRE MODEL VERSION 1.0 ******

 ALL FUELS REPORT

 --------------------------------------------------------------------------------------------------------------------------

 ESTIMATED FUEL LOADINGS

 SURFACE FUEL (TONS/ACRE) STANDING WOOD (TONS/ACRE)

 ----------------------------------------------------------- -----------------------------------

 DEAD FUEL LIVE DEAD LIVE

 ----------------------------------------- ---------- SURF ----------- --------------- TOTAL TOTAL BIOMASS

 YEAR LITT. DUFF 0-3" >3" 3-6" 6-12" >12" HERB SHRUB TOTAL 0-3" >3" FOL 0-3" >3" TOTAL BIOMASS CONS REMOVED

 --------------------------------------------------------------------------------------------------------------------------

 1993 1.24 9.8 3.3 7.9 4.2 3.8 .0 .21 .28 22.8 .00 25.6 4.6 9.6 44 84 107 0 0

 1995 1.73 9.8 3.3 10.2 5.6 4.5 .0 .21 .28 25.5 .00 22.4 4.6 9.6 44 81 106 0 0

 2000 2.02 9.8 4.4 15.5 9.2 6.3 .0 .21 .30 32.3 .83 16.4 4.7 10.5 49 81 113 0 0

 2005 2.04 9.9 7.3 20.8 12.8 8.0 .0 .21 .31 40.6 .65 9.4 4.8 10.8 52 78 118 0 0

 2010 2.06 10.0 7.6 25.1 15.5 9.5 .1 .21 .32 45.3 .80 3.8 4.9 11.1 55 76 121 0 0

 2015 2.28 10.2 7.4 24.4 15.0 9.3 .1 .21 .32 44.8 .80 4.0 5.8 11.8 58 80 125 0 0

 2020 2.47 10.3 7.2 23.8 14.5 9.1 .2 .21 .32 44.4 .69 3.9 5.9 11.9 62 84 128 0 0

 2025 2.49 10.4 7.2 23.2 14.0 9.0 .2 .21 .32 43.9 .68 4.0 5.9 12.1 65 88 132 0 0

 2030 2.49 10.6 7.1 22.8 13.6 8.9 .3 .21 .32 43.4 .72 4.2 5.9 12.4 68 92 135 0 0

 2035 2.56 10.7 6.9 22.4 13.1 8.8 .4 .21 .32 43.1 .74 4.6 6.2 12.8 72 96 139 0 0

 2040 2.63 10.8 6.9 22.1 12.7 8.8 .6 .21 .31 42.9 .70 4.7 6.3 13.1 75 100 142 0 0

 2045 2.63 11.0 6.8 21.9 12.3 8.9 .7 .21 .31 42.7 .73 4.9 6.3 13.3 78 103 146 0 0

 2050 2.63 11.1 6.7 21.7 11.9 9.0 .8 .21 .30 42.7 .76 5.2 6.2 13.5 81 107 149 0 0

 2055 2.66 11.2 6.6 21.7 11.6 9.2 .9 .21 .30 42.7 .79 5.6 6.4 13.9 84 111 153 0 0

 2060 2.70 11.3 6.5 21.7 11.3 9.4 1.1 .21 .29 42.8 .74 5.6 6.5 14.2 87 114 157 0 0

 2065 .00 2.6 1.0 4.9 .5 3.6 .8 .21 .29 9.1 16.92 95.8 .0 .0 0 113 122 52 0

 2070 .28 2.7 11.2 39.1 9.5 24.9 4.7 .40 1.00 54.7 3.06 55.8 .0 .0 0 59 114 0 0

 2075 .03 2.9 8.3 50.9 11.6 31.1 8.2 .40 2.00 64.5 1.53 39.0 .0 .1 0 41 105 0 0

 2080 .07 3.0 6.1 56.1 12.1 32.6 11.5 .38 .92 66.6 .00 29.4 .2 .3 0 30 96 0 0

 2085 .13 3.1 3.6 60.7 12.1 34.1 14.5 .34 .72 68.6 .00 20.1 .3 .7 0 21 90 0 0

 2090 .25 3.2 2.2 64.3 11.6 35.4 17.3 .30 .54 70.8 .02 11.5 .6 1.7 0 14 85 0 0

 2095 .49 3.3 1.4 64.6 10.8 34.7 19.0 .25 .34 70.4 .04 6.2 1.2 2.5 3 13 83 0 0
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Live

Foliage

0 to 3 inches branch and stem wood

Greater than 3 inches branch and stem wood

Total Total standing wood (Dead and Live)

Total Biomass Total (tons per acre) of all standing wood and surface

fuels

Total Consumption Total amount of fuel (not including live trees) that was

consumed in a fire. In most years this column will be

zero, but if a fire was simulated this value is the same

as that reported in the fuel consumption report (section 2.5.7). Consumption will not be shown if it occurs

in a year when no output is requested.

Biomass Removed Amount of wood that was harvested (live or dead) in

tons per acre. This includes removals from standard

base model thinning as well as salvage. Removals

from pruning may not be present. Removals will not

be shown if they occur in years when no output is

requested.

This report provides a look at the dynamics of live and dead, standing and

surface biomass. Before simulating management, a user might run a nomanagement simulation to assess model performance (see if predicted fuel

loads are reasonable) and possibly calibrate the model by adjusting snag or

fuel keywords. Then, a number of management alternatives can be simulated and results compared in terms of predicted fuel loads over time. If

management objectives are expressed in terms of an acceptable range of

surface fuel loadings, prescriptions can be developed by repeatedly changing

treatment prescriptions and examining this report to see whether objectives

are met.

This report is printed at the end of the simulation year, after all other FFE

activities have occurred, but before the base model has applied the growth

and mortality estimates. This means that the change in any fuel pool could

be the result of many factors. For example, in the first year of the simulation,

the output report combines the results of the initial conditions with input

from any trees and snags, and removals due to decay. Thus, the fuel levels

reported in the table for the first year can be quite different from those that

were set by the user or by the default values.

Harvesting, fuel treatments, and fires can further confuse the interpretation or the output or the predictions of changes in fuel levels. The last two

columns showing the removals can give some indication of the level of impact

expected from harvest or fire. The fuel consumption report can also aid in

interpretation of the table because it shows, for each surface fuel class, how

those pools are affected by fire.

Note that the snag output reports are printed at the beginning of the

simulation year. Thus, the reported snag values in the snag reports and the

fuel reports could be different, especially if a fire or salvage occurred in that

time step. Also note that the snag and fuel reports use different units for

reporting purposes, further confounding any attempt at comparison between

the reports.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 43

The change in stand biomass from one year to another can be calculated

as:

TotBiot = TotBiot–1 – TotalConsumption – Removals – Decay + Growth

The growth and decay terms are not explicitly included in the table, so

their net contribution must be inferred. Decay is applied every year to all

surface fuel and dead standing fuel, while growth is added only at cycle

boundaries and only to standing live fuel. The equations for individual pools

are similar but include terms representing transfer to and from other pools.

Some changes in pool size can be tracked between different parts of the

table. Large standing live may become large standing dead. Standing dead

will become surface fuel, and some portion of the surface fuels will become

duff.

When no fires or other actions are simulated, the biggest changes in fuel

levels will occur on cycle boundaries. Within a cycle, only decay is simulated,

resulting in a slow decline in surface fuel levels and standing dead levels

(through height loss and fall-down). On cycle boundaries, growth and

mortality estimates are applied to the standing live component, resulting in

a relatively large change in standing live and an increase in standing dead

pools

2.5 Fire Submodel _________________________________________________

2.5.1 Overview

The FFE-FVS uses the fire submodel two ways. First, model users can

simulate the effect of a prescribed fire or wildfire at any point in time.

Repeated fires can be simulated, and fires can be set at random times or when

certain conditions are met. If a fire is simulated in this way, it modifies stand

and fuel conditions by killing trees and consuming fuel, and alters future

stand and fuel dynamics. The second method uses exactly the same calculations to examine potential fire behavior and effects without actually changing any of the stand conditions. In this case, managers can assess the

changing fuel conditions by examining predicted changes in fire characteristics and impacts. Typically one might look at potential fire behavior and

effects over the course of the simulation period, possibly also scheduling one

or more fires along the way.

The fire model receives input from other model components. Users provide

simulation instructions such as the year to simulate fire or calculate potential fire effects, and the conditions at the time of the burn. FVS provides

detailed information about all the trees in the stand. The fuel component of

the FFE provides information on surface fuel loadings and canopy fuel

characteristics. The fire model can then calculate the predicted effects of the

fire on fuel, live trees, and snags, as well as fire characteristics such as flame

length and whether the fire is a crown fire. This section describes the fire

model in more detail. First, fire conditions are discussed. Methods for

calculating fire behavior are then presented. We then discuss fire effects

including tree mortality, fuel consumption, and smoke production. Use of

potential fire calculations follows, and finally, fire related output tables are

summarized.
44 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

2.5.2 Burn Conditions

In nature, the conditions under which a fire burns have a large impact on

its behavior and effects. Users can specify fuel moisture content, wind speed,

and ambient air temperature using various keywords, or they can choose

from a set of predefined conditions using the SIMFIRE keyword. The model

does not simulate changes in any of these environmental variables over time,

so the values must be entered for each simulated fire. Other conditions, such

as slope and fuel loading, cannot be altered by the user at the time of a fire.

Slope is a constant that is established at the beginning of the simulation, and

fuel loading is simulated by the FFE (see section 2.4).

If a fire is scheduled with the SIMFIRE keyword, it will be simulated and

result in tree mortality and fuel consumption even if burn conditions are

marginal.

Fuel Moisture: The fuel moisture content (weight of water/dry weight of

fuel expressed as a percentage) is used to calculate fire intensity and fuel

consumption. Each fuel size class, except litter, must have an assigned

moisture value. Users can choose one of four predefined fuel moisture

combinations in the model (table 2.18), or specify the moisture conditions for

each of the classes using the MOISTURE keyword.

While all moisture levels affect fuel consumption, only the values for the

live and the small (less than 3 inches) fuel are used to calculate fire intensity.

In general, wetter fuel produces shorter flames (fig. 2.14) and results in less

fuel consumption, as is described further in section 2.5.5.

Wind Speed: Fire intensity increases with increasing wind speed. The

default wind speed is 20 miles/hour. Users can set the wind speed using the

SIMFIRE keyword.

Both the default wind speed and the value entered by the user describe the

wind speed at 20 feet above the ground level or the top of the vegetation if any.

This value is then converted to an expected mid-flame wind speed by

multiplying it by a correction factor based on the canopy closure in the stand

(fig. 2.15) (Albini and Baughman 1979).

Canopy closure is calculated from the total area occupied by crowns in the

stand using the assumption that crowns are randomly distributed within the

stand. It is determined by finding the width of the crown of each tree (Moeur

1981), calculating the associated area, and summing this area for all the trees

Table 2.18—Default percent fuel moisture for the four

predefined moisture conditions for each fuel

size class.

Moisture level

Very dry Dry Moist Wet

0-0.25" (1 hour) 4 8 12 16

0.25-1" (10 hour) 4 8 12 16

1-3" (100 hour) 5 10 14 18

>3" 10 15 25 50

Duff 15 50 125 200

Live 70 110 150 150
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 45

Figure 2.14—Example flame lengths for each of the predefined moisture conditions for four different combinations of

small (S, < 3 in) and large (L > 3 in) fuel amounts (in tons/acre).

Other factors being equal, the flame lengths decrease as

moisture levels increase.

Figure 2.15—The correction factors used to calculate mid-flame wind

speed from wind speed at 20 feet above the ground or the top of the

vegetation.

in the stand. Because all calculations in the stand are on a per acre basis, the

total area is also based on an acre. This total area is then used to calculate

percent cover (Crookston and Stage 1999):

PercentCover = 100(1–e–TotalCrownArea/43560)

0

0.1

0.2

0.3

0.4

0.5

Wind Correction Factor

0 20 40 60 80 100

Canopy Closure (%)

(5.0%,0.5)

(17.5%,0.3)

(37.5%,0.2)

(75.0%,0.1)

∆

∆

∆

∆

∆

x

x x

x

x

∆

∆

∆

∆

∆
46 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

where:

PercentCover = percent cover in the stand;

TotalCrownArea = total area (per acre) covered by crowns (measured in

square feet); and

43560 = number of square feet in an acre.

Air Temperature: The ambient air temperature (∞F) at the time of a fire

affects scorch height, and thus tree mortality. Scorch height increases

exponentially with temperature (fig. 2.16) (Van Wagner 1973). This is the

only use of temperature in this model. The indirect effects of air temperature

on fire behavior as fine fuel dries in response to heating are not simulated;

fuel moisture content is a required user input.

2.5.3 Controlling Fire Extent

Continuous Burns: In FFE, by default, fires impact the entire area of the

stand (that is, no unburned patches remain). Continuous fires (either

prescribed fires or wildfires) are scheduled using the SIMFIRE keyword.

Pile burns: Pile burns do not impact the entire area within the stand, but

burn concentrations of fuel. This kind of fuel treatment is requested with the

PILEBURN keyword. By default, in pile burning, 80 percent of the fuel from

70 percent of the stand is concentrated into piles that cover 10 percent of the

stand area (fig. 2.17, left). These piles are assumed to be far enough from trees

not to cause any mortality.

Jackpot burns are a special kind of pile burn. They are more widespread,

burning by default 30 percent of the stand (fig. 2.17, right). The model

assumes that the majority of the fuel is in the burned part of the stand (by

default, 65 percent of the stand’s fuel). Because there is less effort to avoid

Figure 2.16—The change in scorch height as temperature

changes. In this figure, the scorch heights are relative to the

scorch height at an air temperature of 70° (the default value).

Thus, at 105° the scorch height would be about two times (200

percent) higher than at 70°.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 47

Figure 2.17—Schematic diagram of the difference between

pile burning (left) and jackpot burning (right). Each diagram

represents a stand. The shaded areas are the areas from which

fuel is concentrated. The hatched area represents the area that

is then burned.

burning near trees, 50 percent of the trees in the burned area are assumed

to die as a result of the burning.

In both cases, 100 percent of the litter and duff in the burned area are

consumed, and all of the burned area will have the mineral soil exposed. In

addition, 100 percent of piled fuel less than 1 inch and 90 percent of the piled

fuel greater than that are burned. Smoke production is calculated assuming

that the burned fuel contains the preset “moist” moisture values.

Afterwards, any unburned, piled fuel is assumed to again have the

characteristics of unpiled fuels. Decay rates are unchanged by piling, and the

burned and unburned areas are not tracked separately in the remainder of

the simulation.

All parameters defining the area of the stand, the amount of fuels that

are treated, and the associated tree mortality can be changed using the

PILEBURN keyword.

2.5.4 Fire Behavior

Overview: FFE computes two indicators of fire behavior: flame length

and fire type: surface, passive crown fire, or active crown fire. In addition, two

indices of crown fire hazard are reported: the torching and crowning index.

Fire behavior in FFE-FVS is computed using methods developed by

Rothermel (1972), Albini (1976a), Scott and Reinhardt (2001) and Scott

(2001). Surface fuels are assessed to determine which fire behavior fuel

models best represent current conditions (section 5.4.2). The selected fuel

models, along with slope, user specified fuel moistures and 20-foot wind

speed, and canopy closure, are used to compute the intensity of a surface fire

(section 5.4.4). This computed intensity and the canopy base height determine the occurrence of torching. Active crowning is modeled if: (1) conditions

support torching, and (2) canopy bulk density is great enough to support

active crowning at the specified wind speed and fuel moisture conditions

(section 2.5.4). If torching or crowning occurs, intensity is recalculated to take

into account the contribution of canopy fuels and accelerated fire behavior.

Flame length is then computed from intensity.

Fire behavior is an important output of FFE. It also impacts subsequent

model behavior by causing tree mortality and thus impacting future stand

and fuel dynamics, and influencing fuel consumption, further impacting

future fuel dynamics.

Surface Fire Behavior: Surface fire intensity is calculated using

Rothermel’s 1972 fire behavior prediction model, as implemented in FIREMOD
48 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

(Albini 1976a). Fire intensity depends on static variables such as slope,

variables that depend on stand conditions such as fuel quantities (represented by fire behavior fuel models) and mid-flame wind speed, and environmental variables specified by the user, such as fuel moisture levels. Surface

fire intensity is used to calculate flame length and scorch height, which affect

tree mortality and growth. It is also used to determine the amount of

crowning in the stand.

Intensity of continuous fires is computed by the FFE. If users wish to

control predictions of fire intensity more closely, they can use the FLAMEADJ

keyword to specify either the flame length or a flame scaling factor. These

adjustments apply only to surface fires.

Crown Fire: Crown fires are typically faster moving than surface fires,

more difficult to suppress, and result in more tree mortality and smoke

production. FFE-FVS uses information about surface fuel and stand structure to predict whether a fire is likely to crown.

Two crown fire hazard indices are calculated in the model: torching index

and crowning index. Torching index is the 20-foot wind speed (in miles per

hour) at which a surface fire is expected to ignite the crown layer, while

crowning index is the 20-foot wind speed (in miles per hour) needed to

support an active or running crown fire. Torching index depends on surface

fuels, surface fuel moisture, canopy base height, slope steepness, and wind

reduction by the canopy. As surface fire intensity increases (with increasing

fuel loads, drier fuels, or steeper slopes), or canopy base height decreases, it

takes less wind to cause a surface fire to become a crown fire. Crowning index

depends on canopy bulk density, slope steepness, and surface fuel moisture

content. As a stand becomes denser, active crowning occurs at lower wind

speeds, and the stand is more vulnerable to crown fire. For both indices, lower

index numbers indicate that crown fire can be expected to occur at lower wind

speeds, so crown fire hazard is greater at lower index values. The complete

algorithms for determining torching and crowning index are described in

Scott and Reinhardt (2001).

Both torching and crowning index depend in part on surface fuel moisture,

therefore these conditions must be specified. Drier conditions produce lower

indices, indicating a more severe risk of crown fire. Temperature and wind

speed do not affect the indices.

Torching and crowning indices, together with the specified wind speed (set

using keywords SIMFIRE or POTFWIND), determine the amount of

crowning. Three outcomes are possible (table 2.19):

1. Surface fires – crowns do not burn (if the specified wind speed is less

than the torching index)

2. Active crown fires – the fire moves through the tree crowns, burning all

crowns in the stand (thus killing all trees); (specified wind speed is

greater than the torching and crowning index)

Table 2.19—Rules for determining the occurrence of crowning. Wind speed is the mid-flame wind speed

(in miles per hour) at the time of the fire.

Torching Index < Wind Speed Torching Index > Wind Speed

Crowning Index > Wind Speed PASSIVE SURFACE

Crowning Index < Wind Speed ACTIVE SURFACE
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 49

3. Passive crown fires – some crowns will burn as individual trees or

groups of trees torch (specified wind speed is greater than the torching

index but less than the crowning index)

Users can override this prediction by entering their own value for the

percent of the canopy that experiences crowning in a particular fire (using the

FLAMEADJ keyword).

If torching or crowning occurs, intensity and flame length are recalculated

using methods in Scott and Reinhardt (2001) and Scott (2001).

2.5.5 Fire Effects

When a fire is simulated, the FFE calculates several effects from the fire:

tree mortality, crown scorch, fuel reduction, mineral soil exposure, and

smoke production.

Effects on Trees: Fires can kill trees and can have a short-term effect on

tree growth for some of the surviving stems. Probability of tree mortality,

Pmort, is calculated based on scorch height, crown length, diameter, and

species (Ryan and Reinhardt 1988):

P

e mort e c b =+ − + − − −

1

1 1 941 6 313 1 0 000535 2 (. . ( ). )

b = vspd

c = 100s

2

2

l s

l

 −



 





where:

b = bark thickness (inches). This is not necessarily the same bark

thickness equation as the one used by the base FVS model.

vsp = species bark thickness parameters (table 2.20);

d = diameter (in inches) of the tree;

c = percent of the crown volume that is scorched;

s = length of the crown (feet) that is scorched; and

l = total length of the crown (feet).

The resulting mortality is shown in figure 2.18. This equation predicts

some mortality of thin-barked trees even if none of the crown is scorched

(c=0). The amount of mortality in this case is dependent just on species and

diameter. In all fires, at least 80 percent of the spruce of any size is killed.

When the scorch height is greater than the base of a tree’s crown, but the

tree is not killed, the crown is assumed partially killed. In these cases, the

crown ratio of the tree is reduced and the growth of that tree may be slowed

for the subsequent cycle. In the following cycle, FVS will recalculate the

crown ratio assuming the tree is healthy but taking into account that the fire

reduced the crown length.

Table 2.20—Parameter values vsp, used in the calculation of bark thickness.

WP L DF GF WH C LP S AF PP Oth

v 0.035 0.063 0.063 0.046 0.040 0.035 0.028 0.036 0.041 0.063 0.040
50 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Figure 2.18—Predicted tree mortality is based on bark thickness and the percent of the volume of the crown scorched.

Scorched foliage and branches are assumed to be killed and will fall to the

ground at the rates specified for the crowns of snags (section 2.4.4).

The portion of a crown that is within the flames of either a surface or crown

fire is killed. FFE assumes that 100 percent of the foliage and 50 percent of

the small branch wood (smaller than 0.25 inches) in the flames are consumed.

The remainder of the burned portion of a crown is assumed to be dead and

falls at the same rate as scorched canopy material.

More complicated effects of fire on trees may include increased susceptibility to insects and disease, decreased growth due to fine root mortality, or

increased growth due to enhanced nutrient availability. These effects are not

represented by the FFE-FVS. Fire may also indirectly result in increased

tree growth of residual trees due to decreased stand density, and this effect

is simulated by FVS.

Fuel Consumption: Fuel consumption algorithms in FFE are simplified

from those in FOFEM (Reinhardt and others 1997). The three main factors

affecting fuel consumption are: size, moisture content, and type (natural,

activity, or piled) (Brown and others 1985; Ottmar and others 1993). The

intensity of the fire does not directly affect surface fuel consumption in the

FFE.

The consumption of both activity and natural fuels greater than 3 inches

depends on moisture and the size class of the fuels. Dry large woody fuel is

more fully consumed than wet (fig. 2.19). The model assumes that at high

moistures, natural fuel is more fully consumed than activity fuel because, in

general, it is on the ground and partially rotted, while the activity fuel tends

to be sound, green, and not as close to the ground.

The consumption of small fuel less than 1 inch diameter is dependent on

the consumption of fuel that is 1 to 3 inches. The rationale is that if over 90

percent of the larger fuel is consumed, conditions must be right to burn all of

the smaller fuel. Otherwise only 90 percent of the smaller fuel is burned. The

consumption of 1 to 3 inches fuel depends on the moisture content of the
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 51

smaller fuel rather than its own moisture content (fig. 2.20). Naturally

occurring fuel in these size classes is consumed independently of moisture

levels (65 percent for the 1 to 3 inches fuel and 90 percent for fuel less than

1 inch).

Litter and live fuel consumption in FFE is independent of their moisture

content. The model assumes that 100 percent of litter and herbs and 60

percent of shrubs are consumed.

Figure 2.19—Predicted fuel consumption for large

woody fuel (> 3") for different size classes and moisture

contents. The points on the graph indicate the four

default fuel moisture conditions. The solid lines and

symbols are for the naturally occurring fuel, while the

dotted lines and open symbols are for fuel resulting

from management activities.

Figure 2.20—Predicted fuel consumption for 1 to 3 inches woody

fuel for different moisture levels. The points on the graph indicate

the values at the four default fuel moisture conditions. The solid

line is for the naturally occurring fuel, while the dotted line is for

fuel resulting from management activities. Notice that the consumption of larger fuel depends on the moisture values of the

smaller fuel.
52 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Mineral soil exposure is calculated by the model as a function of the

burning of the duff layers (Brown and others 1985), which in turn is a

function of the duff moisture content. If less than 10 percent of the duff layer

is burned, then there will be no mineral soil exposure. Mineral soil exposure

can have an impact on any automatic regeneration that is triggered in the

following FVS cycle.

Fuel that has been piled burns differently (in general more completely)

than unpiled fuel (section 2.5.3).

Smoke Production: Two categories of particulate matter emissions are

calculated: less than 2.5 microns in diameter and less than 10 microns in

diameter. Smoke production is calculated using a series of multipliers

(emission factors) applied to the amount of fuel consumed in each size class

(table 2.21; Reinhardt and others 1997). For duff and large woody fuel, these

multipliers vary with moisture content.

2.5.6 Potential Fires

In addition to simulating a fire and its effects at a specified point in time,

the FFE can also compute indicators of potential fire behavior and effects as

they change over the simulation period. These provide an important method

for assessing fuel and the associated fire hazard, as fuel and stand conditions

change over time and with management actions.

For given wind and moisture conditions, fire intensity changes with the

amount of fuel in the stand and with the likelihood of a full or partial crown

fire. The FFE calculates the potential surface fire flame length, degree of

crown fire activity (surface, passive crown fire, or active crown fire), tree

mortality (percent stand basal area, and percent volume), and smoke production (fig. 2.21). Each of these is calculated for two sets of conditions. By

default, the first set represents severe conditions that might represent

wildfires (dry, windy) and the second represents moderate conditions that

are more typical of prescribed fires. Conditions can be modified by the user.

Crown fires play an important role in the spread and impact of fires.

Information about canopy fuel and the wind speed necessary to induce

crowning under various scenarios is useful to fire and fuel managers. The

model therefore reports the canopy base height, the canopy bulk density, the

Table 2.21—Emission factors (lb emission per ton of fuel consumed) used to calculate smoke emissions.

Emission factors for some fuels vary with moisture content, others are constant.

Particulate Matter < 2.5

microns

Particulate Matter < 10

microns

wet moist dry wet moist Dry

Surface

woody

fuels

Litter 7.9 9.3

0-0.25" 7.9 9.3

0.25-1" 7.9 9.3

1-3" 11.9 14.0

3+" 22.5 18.3 16.2 26.6 21.6 19.1

Duff 23.9 25.8 25.8 28.2 30.4 30.4

Live Herbs & shrubs 21.3 25.1

Canopy fuels 21.3 25.1

Piled fuels 17.0 20.0
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 53

Figure 2.21—Relationship between duff consumption, mineral soil exposure, and duff

moisture levels. The points on

the graph indicate the four default fuel moisture conditions.

Duff consumption depends on

duff moisture and mineral soil

exposure is a function of duff

consumption.

torching index, and the crowning index. The predicted values change over

time and can be affected by management activities (fig. 2.22, 2.23).

All information about potential fires is calculated using the same methods that are used for calculating the simulated fires. Thus, if a fire with the

same wind, temperature, and moisture conditions is scheduled in the same

year that the potential information was calculated, the simulated flame

length and basal area mortality would be the same as shown in the

potential fire report.

Users can control the frequency with which this information is calculated

(POTFIRE keyword) and the wind, temperature, and moisture conditions

that are used for the potential severe and moderate calculations (POTFWIND,

POTFTEMP and POTFMOIS keywords).

2.5.7 Output

At the time of a fire, several output tables can be printed that give more

information about the effect of the fire on trees and fuel and confirm the

parameters that were set for the fires. Each of these tables is optional and

must be requested using a keyword. An additional output file that reports the

potential intensity and effect of fires under two sets of conditions can also be

produced.

Burn Conditions Report: At the time of a fire, the moisture conditions and wind speed play a role in the intensity and effect of the fire. The

burn conditions output table allows users to check the moisture conditions and slope (both originally set by the user) and to see the mid-flame

wind speed, flame length, and scorch heights that were calculated by the

model (table 2.22). The columns of the table are:

Year The year of the burn.

 Percent Moisture The percent fuel moisture.

1 hr 0 to 0.25 inch fuel

10 hr 0.25 to 1 inch fuel

100 hr 1 to 3 inches fuel

3+ 3 inches+ fuel

Duff duff

Live Live fuel
54 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Figure 2.23—Canopy base height

and bulk density under different management scenarios. A mixed-conifer stand is established at the

beginning of the simulation (thick

line). In one scenario this stand is

repeatedly thinned from below (thin

line). The canopy fuel is independent of whether the harvested material is removed or left in the stand.

Figure 2.22—Potential flame

length and mortality in a stand under three management scenarios.

The stand is established at the

beginning of the run. In one scenario no treatment was applied

(thick line). In two scenarios the

stand was thinned from below

(in 2040, 2055 and 2080). In one

scenario the harvested material

was left on the ground causing an

increase in surface fuels, and

thus in potential flame length and

mortality (dotted line) and in the

other scenario it was all removed

(thin line). Even when activity

fuels are removed, flame length

increases temporarily after thinning because reductions in canopy

closure cause an increase in

midflame windspeed.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 55

Mid-flame wind The mid-flame wind speed (mph) calculated based on

the user-defined 20-foot wind speed and the canopy

closure of the stand.

Slope The percent slope of the stand. This is part of the basic

stand information. It is included in this report because

it is one of the factors that determine flame length.

Flame length The flame length (ft) that will be used for calculating

tree mortality.

Scorch height The scorch height (ft), based on the flame length.

Fire Type Surface, passive or active crown fire.

Fuel model Fire behavior fuel models and their weights.

This output file can be requested using the keyword BURNREPT. Use

this output to make sure you are simulating the fire as you intended, and to

document simulated fire treatments.

Fuel Consumption Report: When fires occur, this table reports the

amount of fuel consumed in each size class, the mineral soil exposure, and the

amount of smoke produced (table 2.23). The columns of the table are:

Year The year of the fire.

Percent Mineral Soil Exposure

Fuel Consumed (tons per acre)

Litr Litter

Duff

0 to 3 inches Small woody fuel less than 3 inches diameter.

3 inches+ Large fuel. This is the sum of the next three columns in the

table.

3 to 6 inches

6 to 12 inches

12 inches+

Herbs and Shrubs

Crown The amount of crowns consumed through crown scorching or crown fires.

Total The total amount of fuel consumed. This column is the

sum of the litter, duff, small, and large fuel as well as

the herbs, shrubs, and crowns. It corresponds to the

column labeled “TOTAL CONS” in the all fuels report.

Percent Consume Percent of the available fuel in the following two

categories that was consumed.

Table 2.22—Example burn conditions report. This report shows that in the year 2065 the fuels were most like a fuel model 10, with

some characteristics of a fuel model 12. The simulated fire was an active crown fire.

--------------------------------------------------------------------------------------------------------

****** FIRE MODEL VERSION 1.0 ******

BURN CONDITIONS REPORT -- CONDITIONS AT THE TIME OF THE FIRE

--------------------------------------------------------------------------------------------------------

MIDFLAME FLAME SCORCH FUEL MODELS

-------- % MOISTURE -------- WIND SLOPE LENGTH HEIGHT -------------------------------

YEAR 1HR 10HR 100HR 3+ DUFF LIVE (MPH) (%) (FT) (FT) FIRE TYPE MOD %WT MOD %WT MOD %WT MOD %WT

--------------------------------------------------------------------------------------------------------

2065 4. 4. 5. 10. 15. 70. 2.9 20 75.3 374.7 ACTIVE 10 80 12 20
56 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Duff Percent duff and large woody fuel consumption can be

an indicator of fire severity.

3 inches+

Smoke Production The amount (tons/acre) of smoke produced in two

different categories.

Less than 2.5 Smoke particles less than 2.5 microns in diameter.

These tiny particles are considered respirable, and

thus have implications to human health.

Less than 10 Smoke particles less than 10 microns in diameter (this

value includes smoke less than 2.5 microns).

This table can be requested using the FUELREPT keyword.

Fuel consumption is an important indicator of fire severity. This report can

be used to document simulated fire effects.

Mortality Report: A complete report on the percent of trees by species

and size class that were killed by fires in a given year can be produced using

the MORTREPT keyword (table 2.24). This report is created only in years

a fire occurs. The table contains the following fields.

Year The year of the fire.

SP Two-letter species code. Only species in the current stand will be

listed.

Number Killed / Number before

In each column, the value to the right of the “/” are the number of trees that

were killed (trees per acre) and the value to the right of the “/” are the number

of trees in that size class and species that were present before the fire. This

information can be used to easily tell what sizes and species of trees were

killed as well as the relative susceptibility of different categories.

0.0 to 5.0

5.0 to 10.0

10.0 to 20.0

20.0 to 30.0

30.0 to 40.0

40.0 to 50.0

Greater than=50.0

Basal Area Total basal area killed, ft2/acre

Total Cu Ft Total volume killed, ft3/acre

Table 2.23—Example fuel consumption report. The total consumption reported here is the same as on the detailed fuel report

(table 4.9).

--------------------------------------------------------------------------------------------------------

****** FIRE MODEL VERSION 1.0 ******

FUEL CONSUMPTION & PHYSICAL EFFECTS REPORT

--------------------------------------------------------------------------------------------------------

PERCENT FUEL CONSUMED (TONS/ACRE) % SMOKE

MINERAL------------------------------------------------------------- TREES PRODUCTION

SOIL HERB& TOTAL %CONSUME WITH (TONS/ACRE)

YEAR EXPOSR LITR DUFF 0-3" 3"+ 3-6" 6-12" 12"+ SHRUB CRWNS CONS. DUFF 3"+ CRWNG <2.5 < 10

--------------------------------------------------------------------------------------------------------

2065 60 1.3 8.9 4.8 16.9 10.4 6.0 .5 .5 19.3 51.7 77 77 100 .43 .51
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 57

Table 2.24—Example report for fire-based mortality. In this simulation all trees were killed.

This report allows users to examine in detail the impact of a simulated fire

on a stand. It can be used to iteratively develop a burn prescription that

achieves desired levels of tree mortality. It can also be used to gain insight

into the expected effects of fire on a particular stand.

Potential Fire Report: The potential fire report gives information about the

potential impact of fires under two sets of conditions. By default, these

conditions represent extreme and moderate fire conditions, but users can

select any sets of conditions they choose (section 2.5.6). The report can be

produced at any interval using the POTFIRE keyword (table 2.25). The

columns of the report are:

Year The year of the fire.

Flame Length The potential flame length (ft)

Condition 1 under condition set 1 (severe conditions)

Condition 2 under condition set 2 (moderate conditions)

Type of Fire Surface, Passive or Active Crown Fire

Condition 1 under condition set 1

Condition 2 under condition set 2

Torching index The 20-ft wind speed (miles/hour) required to cause

torching of some trees under condition set 1.

Crown index The 20-ft wind speed (miles/hour) required to cause an

active crown fire under condition set 1.

Canopy base height The height (ft) of the base of the canopy.

Canopy bulk density The bulk density of the canopy (kg/m3).

Potential Mortality The potential tree mortality as measured by two

different indicators for both fire conditions:

Percent BA 1 percent of the basal area that would be killed under

condition set 1 or 2.

Percent BA 2

VOL 1 total volume (cubic feet) that would be killed under

condition set 1 or 2.

VOL 2

Potent Smoke The potential amount of smoke emissions (tons per

acre) less than 2.5 microns.

---------------------------------------------------------------------------------------------------------------------

****** FIRE MODEL VERSION 1.0 ******

MORTALITY REPORT

---------------------------------------------------------------------------------------------------------------------

NUMBER KILLED / NUMBER BEFORE (BY DIAMETER CLASS IN INCHES) BASAL TOTAL

YEAR SP .0- 5.0 5.0-10.0 10.0-20.0 20.0-30.0 30.0-40.0 40.0-50.0 >=50.0 AREA CU FT

---------------------------------------------------------------------------------------------------------------------

2065 L 1/ 1 .14 1

DF 165/ 165 79/ 79 127/ 127 6/ 6 0/ 0 0/ 0 175.93 5733

GF 42/ 42 .07 0

LP 6/ 6 12/ 12 3/ 3 7.03 320

S 7/ 7 .04 0

AF 139/ 139 .20 1

ALL 363/ 363 91/ 91 131/ 131 6/ 6 0/ 0 0/ 0 183.41 6058
58 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Condition 1 under condition set 1

Condition 2 under condition set 2

Fuel Models The fire behavior fuel models that are used in the

weighting scheme. Up to four such fuel models may be

shown, but normally only one or two are present. If the

static option is in effect, only one fuel model will be

shown.

Mod A fuel model

Percent wt The percent weighting for that model. These should

sum to 100, but may not due to rounding

Values of –1 are printed for canopy base height, torching index and

crowning index if canopy fuels are so sparse that the canopy base height is

undefined (section 2.4.7).

This report provides a way to assess stand and fuel conditions, as well as

proposed management, in terms of expected fire behavior and effects.

Examining the potential mortality columns, for example, gives insight into

the changing vulnerability of a stand to stand-replacement fire over time.

Comparing this report from a no-management simulation and simulations

with a variety of treatment alternatives provides a way of assessing treatments in terms of their impact on fire hazard. For example, a goal of

management might be to reduce the likelihood of crown fire. Crown fire

potential depends on both surface and canopy fuels. A number of treatments

might be simulated to compare the effectiveness of prescribed fire, surface

fuel management, and thinning in reducing the likelihood of crowning.

Table 2.25—Example potential fire report. Changes in fuels are reflected in the flame length (which also affects mortality) and type

of fire. Changes in stand structure are reflected in the canopy base height and canopy bulk density. Active crown fires

always have 100 percent mortality.

---------------------------------------------------------------------------------------------------------------------------------

****** FIRE MODEL VERSION 1.0 ******

POTENTIAL FIRE REPORT

---------------------------------------------------------------------------------------------------------------------------------

FIRE WIND TEMP ---- FUEL MOISTURE CONDITIONS (PERCENT) ------

CONDITION (MPH) (F) 0-0.25" 0.25-1" 1-3" 3"+ DUFF LIVE

SEVERE 20.0 70 4. 4. 5. 10. 15. 70.

MODERATE 6.0 70 12. 12. 14. 25. 125. 150.

---------------------------------------------------------------------------------------------------------------------------------

FLAME LENGTH TYPE OF FIRE TORCH CROWN CNPY CANPY POTENTIAL MORTALITY POTEN. SMOKE

------------ ---------------- INDEX INDEX BASE BULK ---------------------- ------------ FUEL MODELS

SEVERE MODER SEVERE MODERATE SEVERE SEVERE HT DENSTY SEV. MOD. SEV. MOD. SEV. MOD. -------------------------------

YEAR FT FT MI/HR MI/HR FT KG/M3 %BA %BA (TOT CU VOL) (T/A <2.5) MOD %WT MOD %WT MOD %WT MOD %WT

---------------------------------------------------------------------------------------------------------------------------------

1993 1.5 .7 SURFACE SURFACE 132.7 15.2 18 .168 34 34 996 996 .18 .11 8 86 10 14

1995 2.2 .9 SURFACE SURFACE 106.4 15.2 18 .168 34 34 996 996 .20 .13 8 70 10 30

2000 3.5 1.4 SURFACE SURFACE 79.3 16.5 19 .152 32 32 1025 1023 .25 .17 10 63 8 37

2005 5.5 2.4 SURFACE SURFACE 49.2 15.7 20 .161 35 30 1146 1046 .32 .22 10 80 12 20

2010 6.0 2.7 SURFACE SURFACE 51.4 15.3 22 .167 34 29 1184 1064 .37 .26 10 64 12 36

2015 5.9 2.6 SURFACE SURFACE 54.5 13.8 23 .190 33 28 1209 1079 .36 .25 10 67 12 33

2020 5.8 2.6 SURFACE SURFACE 57.7 13.4 24 .197 32 27 1236 1101 .36 .25 10 69 12 31

2025 5.8 2.5 SURFACE SURFACE 63.8 13.1 26 .204 30 26 1241 1113 .35 .24 10 72 12 28

2030 5.7 2.5 SURFACE SURFACE 69.9 12.9 28 .207 28 24 1223 1120 .34 .24 10 74 12 26

2035 5.7 2.5 SURFACE SURFACE 73.5 12.6 29 .213 25 23 1187 1123 .34 .23 10 76 12 24

2040 5.6 2.4 SURFACE SURFACE 79.8 12.4 31 .217 23 22 1157 1126 .33 .23 10 77 12 23

2045 5.6 2.4 SURFACE SURFACE 83.3 12.3 32 .219 22 21 1132 1124 .33 .23 10 78 12 22

2050 5.5 2.4 SURFACE SURFACE 89.9 12.1 34 .224 20 20 1119 1118 .33 .22 10 79 12 21

2055 5.5 2.4 SURFACE SURFACE 93.4 12.1 35 .222 19 19 1109 1109 .33 .22 10 80 12 20

2060 75.4 2.4 ACTIVE SURFACE .0 12.1 3 .224 100 19 5857 1105 .43 .32 10 80 12 20

2065 75.3 2.4 ACTIVE SURFACE 2.8 12.0 4 .226 100 18 6058 1097 .43 .32 10 80 12 20

2070 13.0 5.4 SURFACE SURFACE -1.0 -1.0 -1 .000 100 100 0 0 .37 .28 12 92 10 8

2075 14.1 5.9 SURFACE SURFACE -1.0 -1.0 -1 .000 100 100 0 0 .45 .34 12 81 13 19

2080 15.3 5.3 PASSIVE PASSIVE .0 89.5 1 .015 99 99 6 6 .47 .35 12 79 13 21

2085 15.7 4.9 PASSIVE PASSIVE .0 63.5 1 .024 99 99 32 32 .49 .36 12 78 13 22

2090 17.7 4.7 PASSIVE PASSIVE .0 50.4 1 .034 99 99 112 112 .50 .37 12 76 13 24

2095 26.1 4.5 PASSIVE PASSIVE .0 37.1 1 .052 99 99 285 285 .50 .38 12 80 13 20
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 59

The potential fire report is produced before the impacts of any fires are

simulated. Thus, the conditions of the stand are the same when calculating

the potential impacts and any actual impacts. If the environmental variables

(wind, temperature, and moisture) are the same for the potential fire as for

the simulated fire, the simulated fire should produce the impacts predicted

by the potential fire report.

2.6 Discussion ____________________________________________________

2.6.1 Model Contributions

FFE-FVS is a tool for managers. It has a broad geographic scope encompassing most of the Western United States. A broad range of management

actions – silvicultural as well as prescribed fire and mechanical fuel treatment – can be simulated. FFE-FVS provides an extensive set of outputs that

allow forest management decisions to be assessed in a temporal context: not

only short-term effects on fuels, stand dynamics, and potential fire behavior

are modeled, but also the way in which these interacting ecosystem components may be expected to change over time.

Perhaps the most important contribution of this model is to explicitly link

stand and fuel dynamics. A simulation tracks the biomass, growth, and

mortality of individual trees in a stand; litterfall from the living trees and

falldown of the snags determine surface woody fuel loads. Fire, if simulated,

impacts surface fuels directly by consuming them, indirectly, over time, as

fire-killed trees fall to the ground, and, even more indirectly, by impacting

future stand structure.

Recent research on potential for crown fire behavior is linked in this model

with dynamically computed canopy fuel characteristics. With or without

management, canopy fuels change over time. Because FVS already tracked

crown characteristics of the individual trees making up the stand over time

(including ingrowth), it provides a natural vehicle for assessing changing

crown fire hazard.

Many of the components of FFE-FVS have long histories in both scientific

and management communities. For example, Rothermel’s surface fire model,

included here, was first presented in 1972 and has been in widespread use

ever since. Both modelers and users have gained a good understanding of its

robustness as well as its limitations and are comfortable interpreting its

output. This is also true of the growth and yield algorithms that drive FVS.

2.6.2 Model Limitations

FFE-FVS has a number of weaknesses. One problem is that the base

model, FVS, simulates growth and mortality using cycles of typically 10

years. The FFE operates on a 1-year time step. Sometimes this can lead to

model behavior that is an artifact of cobbling together the two time steps and

is not an intended representation of a real phenomenon. Snag numbers, for

example, tend to exhibit a saw-toothed pattern, with sharp increases at cycle

boundaries when all the cycle’s natural mortality is added, and gradual

declines between, as snag fall-down occurs. Choosing short cycle lengths or

reporting indicators only at cycle boundaries can somewhat compensate for

this problem.
60 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Discontinuous behavior is particularly evident in indicators that depend

in part on canopy base height – canopy base height itself, torching index,

potential tree mortality, and fire type. In this case the underlying processes

probably are discontinuous – regeneration often occurs in pulses, a stand

suddenly passes a critical point after which vulnerability to torching sharply

increases or decreases. These intended discontinuities are probably exaggerated by the fact that in the model, all regeneration occurs on cycle boundaries,

as well as all natural tree mortality. Self-pruning and mortality of suppressed understory trees may cause the stand’s canopy base height to

increase sharply at a cycle boundary, or in-growth may cause the canopy base

height to fall abruptly.

Within a year, users cannot control the order of simulated management

actions.

Live fuels (herbaceous plants and shrubs) are poorly represented in FFEFVS. Their biomass and its contribution to fuel consumption and smoke are

only nominally represented as a fixed amount that depends on percent cover

and dominant tree species. Live fuels can contribute significantly to the

behavior of a fire. Their contribution to fire behavior is represented in the

selection of fire behavior fuel models. Canopy cover, overstory composition,

habitat type, and stand history influence selection of fire behavior fuel

models. Live fuels are not dynamically tracked and simulated in FFE-FVS.

Decomposition rates are not sensitive to aspect, elevation, or potential

vegetation type in FFE-FVS. Decomposition rates can be controlled by the

user, however, so it is possible for a knowledgeable user to “tune” the

decomposition algorithms and, thus, the fuel dynamics.

Fire conditions (fuel moisture and wind speed) must be selected by the

user. FFE contains no climatologic data and will not estimate site-specific

moistures. If you want to look at differences in fire dynamics between north

and south slopes, for example, you must be able to give the model different

fuel moistures for the different sites.

These limitations suggest opportunities for further research and model

development. In the meantime, we designed FFE so that its commands allow

users to apply any information they have to their specific situation. Users can

overcome many of these limitations and customize the model by careful use

of the keywords.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 61

Nicholas L. Crookston

Donald C.E. Robinson

Sarah J. Beukema

Chapter 3

User’s Guide

Abstract—The Fire and Fuels Extension (FFE) to the Forest Vegetation Simulator (FVS)

simulates fuel dynamics and potential fire behavior over time, in the context of stand development

and management. This chapter presents the model’s options, provides annotated examples,

describes the outputs, and describes how to use and apply the model.

Keywords: FVS, FFE, forest fire, stand dynamics, FOFEM, BEHAVE, snags, coarse woody debris

3.1 Introduction ___________________________________________________

The Fire and Fuels Extension to the Forest Vegetation Simulator (FFEFVS) is introduced in chapter 1, “Purpose and Applications,” and chapter 2,

“Model Description,” covers the model’s content. Features customized for

various geographic regions are described in chapter 4, “FFE Variants.” Here,

the subject is how to use the FFE-FVS. We start with the simplest form of a

run, show how to change the initial values, set fires, adjust the snag and fuel

parameters, specify management actions, and control the generation of

outputs. (See chapter 2 for a detailed explanation of the output tables.) This

current chapter also presents information on using the FFE-FVS with the

Event Monitor (Crookston 1990), a feature of FVS that allows for scheduling

activities predicated on conditional statements.

This chapter assumes that you already know how to use the Forest

Vegetation Simulator (Wykoff and others 1982), that you have the FFE-FVS

software installed on your computer, and that you know how to start the

program. Instructions for getting this program and accessing the background information you need are printed on the inside front cover of this

volume.
62 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

We assume that you will use a text editor to prepare and edit your keyword

files and that your tree data files are already prepared. If you are using

Suppose (Crookston 1997), you can readily apply what is presented here.

Suppose is kept up to date with respect to changes in keywords, the model,

and default parameter values.

3.2 Simple Run____________________________________________________

The example used in the preface paper is used to illustrate how to run the

FFE-FVS. We start with the basic keyword fire needed create a no fire and

no management run of FVS (fig. 3.1). The necessary tree data file is shown

in figure 3.2.

To make FFE run, only two keywords need to be added to those illustrated

in figure 3.1. FMIN signals the start of the FFE keywords, and END signals

the end. However, unless another keyword is added, the FFE will not provide

any output and nor simulate any fires or effects. In short, you need at least

one additional keyword.

FMIN Signals the start of the FFE keywords.

END Signals the end of the FFE keywords. All other FFE

keywords must appear between the FMIN-END pair. You

may code several FMIN-END pairs and you may have one

or many FFE keywords between each pair. The sequence

may appear anywhere in the keyword file prior to the

PROCESS keyword (line 16, fig. 3.1).

Several keywords are used to request output reports, set initial values,

simulate fuel and fire management actions and otherwise control the model.

Figure 3.3 shows how the keyword file from figure 3.1 is modified to request

an FFE run and, using POTFIRE keyword, the Potential Fire Report

output. This keyword is fully described later in section 3.8 Output. The

important feature of figure 3.3 is the set of keywords inserted between lines

11 and 12. Exactly the same method is used in all of the examples.

Figure 3.1—Keyword file used to make a no fire and no management run of FVS. The user’s guide examples are

illustrated by showing how this basic keyword file is modified to include FFE keywords. Note that not all of these

keywords are critical to the execution of a basic run; their use ensures that the simulation will produce better results.

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----

1 StdIdent

 2 300290024601 Stand 300290024601 at Flathead FIA Data

 3 Screen

 4 InvYear 1993

 5 StdInfo 110 692 84 243 20 53

 6 Design 0 1 999 1 0 6104

 7 Growth 1 10 1 10 10

 8 SiteCode 3 54

 9 TimeInt 5

 10 TimeInt 1 7

 11 NumCycle 21

 12 Open 2

 13 02900246.tre

 14 TreeData 2

 15 Close 2

 16 Process

 17 Stop
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 63

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----

1 101 14.851DF 84 0 52 0226 027 0 0 021 0 0

 2 102 42.861DF 44 0 38 02556127 0 0 021 0 0

 3 103 42.861DF 35 0 31 02556127 0 0 031 0 0

 4 104 42.861DF 30 0 23 01556127 0 0 031 0 0

 5 201 38.751DF 52 0 32 0527 0 0 0 0 021 0 0

 6 301 12.381DF 92 0 62 0426 027 1 0 021 0 0

 7 302 2.811DF 193 0 77 0426 82712 0 021 0 0

 8 303 23.341DF 67 0 48 0226 027 0 0 021 0 0

 9 304 22.746LP 96 96 63 00 2 32635275021 0 0

 10 401 36.286LP 76 76 63 00 2 3263527 021 0 0

 11 402 5.841DF 134 0 74 04 0 0 0 0 0 021 0 0

 12 403 19.661LP 73 0 64 02 0 0 0 0 0 021 0 0

 13 404 9.151DF 107 0 70 025572 0 0 0 021 0 0

 14 405 58.206LP 60 60 60 00 2 3 0 0 0 021 0 0

 15 406 56.316LP 61 61 53 00 2 3 0 0 0 021 0 0

 16 501 18.631DF 75 0 69 045577 0 0 0 021 0 0

 17 502 11.371DF 96 0 76 04 0 0 0 0 0 021 0 0

 18 503 26.401DF 63 0 50 015574 0 0 0 021 0 0

 19 504 8.061DF 114 0 73 02 0 0 0 0 0 021 0 0

 20 505 36.286LP 76 76 68 00 2 3 0 0 0 021 0 0

 21 506 60.196LP 59 59 59 00 2 3 0 0 0 021 0 0

 22 507 33.411LP 56 0 68 01 0 0 0 0 0 021 0 0

 23 508 42.861DF 14 0 19 015561 0 0 0 021 0 0

 24 601 37.301DF 53 0 41 03 0 0 0 0 0 021 0 0

 25 602 0.731DF 379 0 97 045574 0 0 0 021 0 0

 26 603 2.441DF 207 0 88 04 0 0 0 0 0 021 0 0

 27 701 9.501DF 105 0 49 035579 0 0 0 021 0 0

 28 702 18.141DF 76 0 46 03 0 0 0 0 0 021 0 0

 29 703 19.131DF 74 0 45 015561 0 0 0 021 0 0

 30 704 2.181DF 219 0 82 04 0 0 0 0 0 021 0 0

31 705 42.861L 18 0 22 015561 0 0 0 021 0 0

Figure 3.2—Tree data file, 02900246.tre, referred to in line 13 of figure 3.1. These tree data are used

with all of the examples.

Figure 3.3—The keyword file used to make a no fire and no management run of FVS (fig. 3.1) is modified

to run the FFE (line FFE 1)and to generate the Potential Fire report (line FFE 3; more information on this

keyword is presented in section 3.4.4).

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7

1 StdIdent

 2 300290024601 Stand 300290024601 at Flathead FIA Data

 3 Screen

 4 InvYear 1993

 5 StdInfo 110 692 84 243 20 53

 6 Design 0 1 999 1 0 6104

 7 Growth 1 10 1 10 10

 8 SiteCode 3 54

 9 TimeInt 5

 10 TimeInt 1 7

 11 NumCycle 21

FFE 1 Fmin

FFE 2 PotFire

FFE 3 End

 12 Open 2

 13 02900246.tre

 14 TreeData 2

 15 Close 2

 16 Process

 17 Stop
64 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

3.3 Initializing the Model ____________________________________________

The FFE dynamically tracks snags by species, 2-inch diameter class, and

hardness. Fuel is tracked by size class. While some default values are present

in the model to initialize fuel loads, your projection will be better if you enter

values appropriate for your stand.

You can initialize the number of snags using two different methods,

separately or in combination. First, trees that are recorded as recent

mortality in the FVS tree data file (fig. 3.2) are made into snags in the FFE.

Those snags are always considered hard unless the SNAGPSFT keyword is

used to change the proportion of snags that are soft at the time of death (see

section 3.5.3). In addition, you can specify the number of hard and soft snags

by species and diameter class using one or more SNAGINIT keywords.

SNAGINIT Add a snag to the snag list. Use as many of these keywords

as you need to enter the data that represent your stand.

Field 1: Species number or letter code; entry is required.

Field 2: Diameter at breast height (DBH) at the time of

death (inches); entry is required.

Field 3: Height at the time of death (feet); entry is required.

Field 4: Current height (feet); entry is required.

Field 5: Number of years the tree has been dead.

Field 6: Number of snags per acre with these characteristics; entry is required.

Initial dead fuel loads depend on the variant, the cover type of the stand,

and the percent cover of the stand. However, you can set the initial amount

of dead fuel using the FUELINIT keyword. Live fuel loads include the

weights of herbs, shrubs, and grasses, but you cannot adjust or initialize

those fuels.

FUELINIT Set the amount of dead fuel in each fuel size class. Values

left blank are replaced with variant-dependent defaults

shown in the section 2.4.2 of chapter 2, “Model Description,” and in the documentation for the individual variants.

Field 1: Initial fuel load for fuel 0 to 1 inch (tons per acre).

This loading gets divided equally between the 0 to

0.25 inch class and the 0.25 to 1 inch class.

Field 2: Initial fuel load for fuel 1 to 3 inches (tons per acre).

Field 3: Initial fuel load for fuel 3 to 6 inches (tons per acre).

Field 4: Initial fuel load for fuel 6 to 12 inches (tons per

acre).

Field 5: Initial fuel load for fuel greater than 12 inches

(tons per acre).

Field 6: Initial fuel load for litter (tons per acre).

Field 7: Initial fuel load for duff (tons per acre).

Figure 3.4 illustrates how to enter initial snag and fuel values. The

SNAGINIT keywords are used to create five Douglas-fir snags between 30

and 32 inches, 10 grand fir snags between 20 and 25 inches, and three

lodgepole pine snags of 15 inches. The trees that created these snags died 12

years prior to the inventory year of the stand, and their heights are all lower

than they were originally. In the example, each species is grouped using a
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 65

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

FFE 1 Fmin

FFE 2 PotFire

FFE 3 SnagInit DF 31 82 52 12 5

FFE 4 SnagInit GF 23 72 58 12 10

FFE 5 SnagInit LP 15 65 43 12 3

FFE 6 FuelInit 3.5 5.2 8.5

FFE 7 End

 12-17 See figure 3.1

Figure 3.4—The keyword file presented in figure 3.1 further modified to initialize snags (in addition to those that could

be in the sample tree data file) and dead fuel loads.

single keyword that defines the average characteristics. Alternatively, the

exact characteristics of each of the 18 snags could have been entered using

the SNAGINIT keyword 18 times. Because snags end up grouped into

classes with an average diameter and height (see chapter 2, “Model Description”), it is unlikely that the extra effort would produce substantially

different results. Initial fuel loads are set to: 3.5 tons per acre in the 3 to 6 inch

category, 5.2 tons per acre in the 6 to 12 inch category, 2.1 tons per acre of

fuels 12 to 15 inches, and some downed large logs that were about 6.4 tons

per acre. Note that the “8.5” in Field 5 is the sum of all the large material

because the FFE does not track these separately. The model uses default

values for size classes that are not entered.

3.4 Fires _________________________________________________________

3.4.1 Introduction

FFE-FVS can simulate fire and its effects three ways: simulated fires, pile

burns, and potential fires. Simulated fires compute fire effects and apply

them to the stand, creating snags and changing the state of the fuel and trees

for the next time step. These fires simulate forest fires regardless of the

method of ignition or policy regarding suppression.

A second way is to simulate burning piles of fuel. The model predicts the

smoke produced and the reduction of fuel and allows you to specify the

percentage of trees killed during the treatment. This method is a simple way

to simulate a fuels treatment.

In the third way, the model simulates potential fires. All the calculations

for simulated fires are done except that the effects are not applied to the fuels

and stand; changes in those values are computed as if no fire occurred. When

the Potential Fire Report is being generated, as done using the keywords

shown in figure 3.1, information about potential fires is frequently computed,

perhaps every year of projected time. In contrast, information about simulated fires is only computed when the simulated fires are scheduled to burn.

When you simulate fires or use the model to compute potential fires, you

can set the environmental conditions and several other parameters that

control how the model simulates the fires. Keywords used to control the

simulated fires are presented next (section 3.4.2), followed by pile burning
66 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

(section 3.4.3), potential fires (section 3.4.4), and last, are keywords used to

control the fuel model selection that is common to both simulated and

potential fires (section 3.4.5).

3.4.2 Simulated Fires

A fire is simulated in each year the SIMFIRE keyword is scheduled, since

the FFE-FVS model does not predict when a fire will occur. This keyword

actually serves two purposes. The main purpose is implied by its name, which

is to signal when a fire is simulated. The second purpose is to specify some

of the environmental conditions in place at the time of the fire. The environmental conditions at the time of a fire play a big role in the intensity of the

fire, and therefore in the mortality of the trees and the consumption of fuel.

Some of the environmental conditions can be set using the SIMFIRE

keyword. Fuel moisture conditions are set using the MOISTURE keyword.

Note that fuel consumption is dependent on fuel moisture, not on the flame

length, wind speed, or temperature. Drier fuel burns more completely and

more mineral soil may be exposed. Dry duff produces more smoke than wet

duff, while the reverse is true for large fuels. Smoke production from all other

fuels is not dependent on moisture. The flame length is also dependent on the

moisture of small (less than 3 inch) fuel.

A keyword called DROUGHT also exists (see section 3.4.5) but it has no

impact on the moisture content of fuels or on the fire conditions. In some

variants, it affects the choice of fire behavior fuel model, which will affect fire

intensity and mortality.

The last keyword in this group is called FLAMEADJ. It can be used to

modify the flame lengths predicted by the model directly, in turn affecting the

predicted fire effects. It is designed to provide a way to simply tell the FFE

what flame lengths to use or how to modify those that the model predicts. Its

purpose is to ensure that the FFE uses flame lengths that you expect. The

FLAMEADJ keyword only applies to surface fires.

SIMFIRE Signal that a fire and its effects should be simulated and

specify some of the environmental conditions for the fire.

Use one SIMFIRE keyword for each fire you wish to

simulate.

Field 1: The FVS cycle number or the calendar year; default is 1.

Field 2: Wind speed in miles per hour 20 feet above ground;

default is 20.

Field 3: Nominal moisture levels as shown in table 3.1. If

the MOISTURE keyword is used the value in this

field is ignored; the default is 1=very dry.

Field 4: Temperature (oF); default is 70.

MOISTURE Set the moisture content for each fuel size class. These

moisture values apply to simulated fires scheduled for the

same calendar year. If this keyword is used for any size

class, it must be used for all size classes because there are

no default moisture conditions.

Field 1: The FVS cycle number or the calendar year; default is 1.

Field 2: Moisture value for 1 hour fuel (0 to 0.25 inch).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 67

Field 3: Percent moisture for 10 hour fuel (0.25 to 1 inch).

Field 4: Percent moisture for 100 hour fuel (1 to 3 inches).

Field 5: Percent moisture for 3 inches+ fuel.

Field 6: Percent moisture for duff.

Field 7: Percent moisture for live fuel.

FLAMEADJ Modify or set the flame length for a fire simulated using the

SIMFIRE keyword scheduled for the same year.

Field 1: The FVS cycle number or the calendar year; default is 1.

Field 2: Flame length multiplier. The default is 1.0, which

is suggested for free-burning fires. We suggest

using a value of 0.3 to simulate a throttle-back fire

and 2.0 to simulate a mass-ignition fire.

Field 3: Enter a flame length to be used in place of a

computed length. The default is for the model to

compute the length and is signified by leaving the

field blank or coding -1.

Field 4: Percent of crowns that burn (crowning). If blank or

-1, the model computes the percent crowning.

Figure 3.5 shows the keywords for simulating two fires, a prescribed burn

in 2002 and a wildfire in 2003. The prescribed fire is the first instance of the

SIMFIRE keyword. Field 1 is used to specify the year and field 5 is used to

signal that the default wind speed for prescribed fires be used. The other

fields are left blank. The MOISTURE keyword is used to define the moisture

values for the prescribed fire, indicated by using the same year in field 1 as

Table 3.1—Percent fuel moisture for the four nominal levels defined for field 3 of the SIMFIRE keyword.

Fuel size class

Field 3 Name of 0-2.5 inch 0.25-1 inch 1-3 inch

value moisture level (1 hour) (10 hour) (100 hour) >3 inch Duff Live

1 Very dry 4 4 5 10 15 70

2 Dry 8 8 10 15 50 100

3 Moist 12 12 14 25 125 150

4 Wet 16 16 18 50 200 150

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

FFE 1 Fmin

FFE 2 SimFire 2002 1

FFE 3 Moisture 2002 12 12 14 150 25 150

FFE 4 SimFire 2003 60 2 75 0

FFE 5 End

 12-17 See figure 3.1

Figure 3.5—The keyword file needed to simulate two fires, a prescribed fire in 2002 with specific moisture conditions

and a wildfire in 2003 using more extreme conditions.
68 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

is used in field 1 of the first SIMFIRE keyword. The second use of the

SIMFIRE keyword simulates a wildfire scheduled for 2003 with winds of 60

miles per hour, temperature of 75 oF, and “dry” moisture conditions.

Figure 3.6 illustrates using the SIMFIRE and FLAMEADJ keywords to

simulate a low intensity fire. The SIMFIRE keyword is used with its default

values and it is followed by the FLAMEADJ keyword where the flame length

is set to 2.5 feet and the percent crowning is set to zero to eliminate any

possibility of a crown fire.

3.4.3 Burning Piles

The PILEBURN keyword is used to simulate burning piled fuel in the

stand. When used, it reduces fuels, estimates smoke production, and kills the

proportion of trees you specify. No other fire effects are simulated.

The keyword has several options as listed below. Default conditions for pile

burns and jackpot burns can be used simply by indicating either of these

types of fuel burns on the keyword. In place of selecting one of the default

types of burns, you can specify exact values for the parameters of the burn,

or do both.

PILEBURN Signal that a pile or other concentration of fuel is to be

burned.

Field 1: The FVS cycle number or the calendar year; default is 1.

Field 2: The index to the type of fuel burn where 1=pile

burn and 2=jackpot burn; the default is 1. These

values control the defaults for fields 3 through 5

on this keyword and otherwise have no special

significance.

Field 3: Percent of the stand’s area affected by the treatment; the default is 70 when field 2 is 1=pile burn,

and 100 for 2=jackpot burn.

Field 4: Percent of the fuel from the affected area in which

the fuel is concentrated (area which will be treated);

the default is 10 when field 2 is 1=pile burn, and 30

when it is 2=jackpot burn.

Field 5: Percent of the fuel from the affected area that is

concentrated in the treated area; the default is 80

when field 2 is 1=pile burn, and 60 when it is

2=jackpot burn.

Field 6: Percent mortality of trees in the stand caused by

this fuel treatment; default is 0.

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

FFE 1 Fmin

FFE 2 SimFire 2002

FFE 3 FlameAdj 2002 2.5 0

FFE 4 End

 12-17 See figure 3.1

Figure 3.6—The keyword file needed to simulate a low intensity fire with a specified flame length.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 69

The default conditions imply a pile burn and can be interpreted as: 80

percent of the fuels from 70 percent of the stand are concentrated into piles

that cover 10 percent of the stand’s area. When these piles burn, no trees die.

Since the FFE-FVS is a nonspatial model, the fuel is assumed to be evenly

distributed across the stand both before and after the treatment. Thus, these

percentages are simply used to determine how much of the fuel actually

burns, and how much mineral soil will be exposed after the burn. For

example, if there were 100 tons per acre of fuels in the stand excluding duff,

the result of applying the default treatment would be to burn 0.8*0.7*100 =

56 tons per acre. Then 10 percent of the duff would burn and 10 percent of the

mineral soil would be exposed. Because of differential consumption rates, if

the fuels include some that are larger than 1 inch, less than 56 tons per acre

of fuel will actually be consumed by fire.

Line FFE 2 of figure 3.7 illustrates an example with a fuel treatment in

year 2007. A total of 15 percent of the stand is actually burned, but it holds

75 percent of the fuel from 100 percent of the stand. Of the living trees, 1

percent are killed in this fuel treatment. Note that duff consumption only

occurs on the 15 percent of the stand that contains the burn.

Nonpiled fuel treatments may be specified using combinations of the

SIMFIRE, FLAMEADJ, and MOISTURE keywords. This approach implies that 100 percent of the fuels will be treated, but setting the moisture

values of the different fuel pools, the flame lengths, or both provides for better

control the consumption rates. For example, lines FFE 3 and 4 of figure 3.7

show how to use the SIMFIRE and FLAMEADJ keywords to simulate a fuel

treatment in year 2037. The SIMFIRE keyword is used to create a fire that

burns when there is no wind and with wet moisture conditions (field 3 has the

value 4). The FLAMEADJ keyword sets the flame length and percent

crowning both to zero, so no trees will be killed.

3.4.4 Potential Fires

As pointed out in section 3.4.1, the FFE can simulate potential fires, those

where most of the model predictions are computed and output without

actually applying any fire effects. The main output table for this option is the

Potential Fire Report, described in section 2.5.7 of chapter 2, “Model Description.” You control when calculations for the potential fires start and how

often they are output using the POTFIRE keyword. This keyword’s use is

shown in figure 3.3, and its full description is in section 3.8, with the other

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

FFE 1 Fmin

FFE 2 PileBurn 2007 100 15 75 1

FFE 3 SimFire 2037 0 4

FFE 4 FlameAdj 2037 0 0

FFE 5 End

 12-17 See figure 3.1

Figure 3.7—This keyword file will simulate burning piled fuel in 2007 and a carefully controlled simulated fire in 2037

representing a prescribed burn.
70 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

keywords that control output. The model estimates two sets of values for the

potential fires. One set uses moisture, temperature, and wind conditions that

are consistent with severe fire conditions often associated with wild fires, and

the other set corresponds to moderate conditions often associated with

prescribed fire situations where the suppression policy or required level of

action would be considered moderate or light.

You can specify the wind speed, temperature, and fuel moisture conditions for each of the two categories, known to the model as 1=severe and

2=moderate.

POTFWIND Set the wind speeds for the two categories of potential fire

severity.

Field 1: The wind speed for the severe category; default is

20 miles per hour.

Field 2: The wind speed for the moderate category; default

is 6 miles per hour.

POTFTEMP Set the temperature for the two categories of potential fire

severity.

Field 1: The temperature for the severe category; default is

70 oF.

Field 2: The temperature for the moderate category; default is 70 oF.

POTFMOIS Set the fuel moisture conditions for the two categories of

potential fire severity. The defaults for severe conditions

correspond to the values for very dry moistures shown in

table 3.1, and the defaults for moderate conditions correspond to the moist values.

Field 1: An index value that signals which of the two

categories of fire the values in fields 2 through 6

apply where 1=severe and 2=moderate; 1 is the

default.

Field 2: Percent moisture for 1-hour fuels (0 to 0.25 inch).

Field 3: Percent moisture for 10-hour fuels (0.25 to 1 inch).

Field 4: Percent moisture for 100-hour fuels (1 to 3 inches).

Field 5: Percent moisture for 3 inches+ fuels.

Field 6: Percent moisture for duff.

Field 7: Percent moisture for live fuels.

3.4.5 Fire Behavior Fuel Models

Calculations of flame length, for both the simulated fires and the potential

fires, are done using Rothermel’s 1972 fire behavior prediction system as

implemented in FIREMOD (Albini 1976). This model requires a number of

parameters that are not carried or readily calculated by the FFE. These

include fuel characteristics such as surface-to-volume ratio, depth, and

moisture of extinction, in addition to some parameters that are computed by

the FFE, namely, the fuel loads by fuel size class. Thirteen stylized fuel

models proposed by Anderson (1982) are used by the FFE in its adaptation

of FIREMOD. How these fuel models are used and how they have been

extended is covered in section 2.4.8 of chapter 2, “Model Description.”
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 71

Briefly, the FFE picks one or more fuel models, calculates the fire intensity

from each one, and then computes a weighted average flame length by

interpolating using factors such as fuel loading or canopy cover. This interpolation provides flame lengths that change more gradually as stand conditions

change than those that are computed without the interpolation logic.

By default, the model uses the interpolation method for calculating fire

intensity, which we call using dynamic fuel models. If you prefer, however,

you can tell the model to use only the fuel model it considers the best choice,

rather then using the interpolation approach. This is done using the

STATFUEL keyword.

Furthermore, you can define new fuel models, or change the parameters

of existing models using the DEFULMOD (DEFine FUeL MODels) keyword. The purpose of this keyword is to give you the maximum control over

the fire intensity calculations. The final keyword that controls the fire

intensity calculations is the FUELMODL keyword. With it, you specify

which fuel model(s) to use in any given year and specify the weights used in

the interpolation logic in cases where more than one model is provided.

In some habitat or cover types, in a few FVS variants, the selection of the

appropriate fuel model(s) depends on the weather conditions at the time of

the selection. The DROUGHT keyword can identify one or more years as

drought years, which may affect the fuel model selection and subsequent fire

intensity. Consult the documentation for your variant to see if drought

conditions are used. At the time the guide was written, the drought conditions were only used in the Utah and Central Rockies variants.

STATFUEL Signal that the dynamic interpolation logic not be used

throughout the simulation.

DEFULMOD Modify the parameters of an existing fuel model or define

the parameters of a new fuel model. Note that the defaults

for fields 3 through 12 are those defined for the fuel models

listed in table 3.2. Fields 8 through 12 are coded on a second

line; each value in fields that are 10 columns wide starting

in column 1. Even if these fields are not used, this additional line must be entered.

Field 1: The FVS cycle number or the calendar year when

the definition takes place; default is 1. Once in

effect, the changes stay until they are changed

again.

Field 2: Fuel model index number from table 3.2. Values

for the standard 13 fuel models of Anderson (1982)

are 1 through 13. Number 14 is a modified version

of fuel model 11. You can define new fuel models by

giving them values greater than the maximum

number listed in table 3.2, up to a maximum index

value of 30.

Field 3: Surface to volume ratio (1/ft) for 0 to 0.25 inch fuel.

Field 4: Surface to volume ratio for 0.25 to 1 inch fuel.

Field 5: Surface to volume ratio for 1 to 3 inches fuel.

Field 6: Surface to volume ratio for live fuel.

Field 7: Loading for 0 to 0.25 inch fuel.

Field 8: Loading for 0.25 to 1 inch fuel.

Field 9: Loading for 1 to 3 inches fuel.
72 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 3.2—Fire behavior fuel models used in the FFE-FVS. Values can be changed using the DEFULMOD keyword.

DEFULMOD keyword field numbers

2 3 4 5 6 7 8 9 10 11 12

Surface to volume ratio (1/ft) Loading (lb/ft2

)

Name 

Index 

Num

ber 0-0.25" 0.25-1" 1-3" Live 0-0.25" 0.25-1" 1-3" Live 

Depth 

(ft) 

Moisture of 

Extinction

Short grass 1 3500 0.034 0 0 0 1 0.12

Timber 

(grass & 

understory) 

2 3000 

109 30 1500 

0.092 0.046 0.023 0.023 1 0.15

Tall grass 3 1500 0.138 0 0 0 2.5 0.25

Chaparral 4 2000 0.23 0.184 0.092 0.23 6 0.20

Brush 5 2000 0 0.046 0.023 0 0.092 2 0.20

Dormant 

brush, 

hardwood 

slash 

6 1750 1550 0.069 0.115 0.092 0 2.5 0.25

Southern 

rough 

7 1750 1550 0.052 0.086 0.069 0.017 2.5 0.40

Closed 

timber litter 

8 2000 0.069 0.046 0.115 0 0.2 0.3

Hardwood 

litter 

9 2500 0.134 0.019 0.007 0 0.2 0.25

Timber 

(litter & 

understory 

10 2000 0.138 0.092 0.23 0.092 1 0.25

Light 

logging 

slash 

11 1500 0.069 0.207 0.253 0 1 0.15

Medium 

logging 

slash 

12 1500 0.184 0.644 0.759 0 2.3 0.2

Heavy 

logging 

slash 

13 1500 0.322 1.058 1.288 0 3 0.25

Lightmedium 

logging 

slash 

14 1500 

109 30 1500 

0.126 0.426 0.506 0 1.8 0.2

Field 10: Loading for Live fuel.

Field 11: Fuel depth (ft).

Field 12: Moisture of extinction.

FUELMODL Specify the fuel models and the weights used in place of the

fuel model selection described in chapter 2, “Model Description.” This keyword overrides the dynamic and static fuel

model selection during the years it is in effect. Code fields

8 and 9 on a second line, each value in fields that are 10

columns wide starting in column 1. If these fields are not

used, there must be a blank line after the keyword. The

weights are automatically scaled so that they sum to 1.

Field 1: The FVS cycle number or the calendar year the fuel

models specified start being used; default is 1.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 73

Field 2: Index to fuel model 1 (if left blank or zero, then the

automatic logic is used from this year onward).

Field 3: Weight given fuel model 1; default is 1.

Field 4: Index to fuel model 2.

Field 5: Weight given fuel model 2; default is 1 when field

4 contains an entry, zero otherwise.

Field 6: Index to fuel model 3.

Field 7: Weight given fuel model 3; default is 1 when field

6 contains an entry, zero otherwise.

Field 8: Index to fuel model 4.

Field 9: Weight given fuel model 4; default is 1 when field

8 contains an entry, zero otherwise.

DROUGHT Set drought years for the fuel model selection process.

Drought conditions are used in the automatic fuel model

selection in a few variants.

Field 1: The FVS cycle number or the calendar year when

the drought starts; default is 1.

Field 2: The duration in years; default is 1.

Figure 3.8 illustrates the used of FUELMODL and DEFULMOD keywords to simulate two fires – one in 2002 (lines FFE 2-4) and the other in 2037

(lines FFE 5-9). A line-by-line explanation follows:

Line FFE 1: The FMIN keyword signals that FFE keywords follow.

Line FFE 2: SIMFIRE is used to signal that a fire is simulated in 2002

using default moisture and weather conditions.

Lines FFE 3 and 4: The FUELMODL keyword is used to specify that two

fuel models will be used, model 8 receives 40 percent of the weight and model

10 receives 60 percent of the weight. Note that FFE scales the weights so that

they sum to 1. Rather than percentages, 0.4 and 0.6 could have been used, or

simply the numbers 4 and 6. Note that this keyword has nine fields and that

the last two are coded on a separate line. This additional line must be entered

even if the values are left blank.

Line FFE 5: A second SIMFIRE is used to simulate another fire, this one

in 2037.

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

FFE 1 Fmin

FFE 2 SimFire 2002

FFE 3 FuelModl 2002 8 40 10 60

FFE 4 

FFE 5 SimFire 2037

FFE 6 DeFulMod 2037 30 2000 109 30 1500 0.069 

FFE 7 0.046 0.115 0 0.2 0.3

FFE 8 Fuelmodl 2037 30 .4 10 .6

FFE 9 

FFE 10 End

 12-17 See figure 3.1

Figure 3.8—This keyword file shows a fire simulated in 2002 with a specific set of fire behavior fuel models and

another fire, set in 2037 that uses specific fuel model parameters.
74 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Lines FFE 6 and 7: In the year 2037 (see field 1) fuel model index 30 (see

field 2) is defined with the values coded in the remainder of the fields.

Lines FFE 8-9: This new fuel model, number 30, is given weight 0.4 and

used with the existing fuel model 10. Close inspection of the values used to

define fuel model 30 happen to be exactly those tabulated for model 8. The

practical consequence of this example is that the fire that burns in the year

2037 will have similar flame lengths as the one that burns in 2007. They will

be exactly the same if the stand canopy closure is identical. Canopy closure

affects midflame wind speed, and it in turn affects fire intensity.

Line FFE 10: The END keyword is used to signal that the keywords that

follow are base FVS keywords.

If POTFIRE keyword were added to the example shown in figure 3.8, the

values in the potential fire report would be affected by the FUELMODL

values shown for year 2002 and 2037. Furthermore, if the DEFULMOD

keyword were used to change the values of some or all of the default fuel

models, the values in the Potential Fire report may also change. Lastly, if the

STATFUEL keyword is entered, the static rather than dynamic fuel model

logic would be used for simulated fires.

3.5 Adjusting Snag Parameters ______________________________________

3.5.1 Introduction

You can modify how FFE calculates the creation, decay, breakage, falling,

and removal of snags using the keywords shown in table 3.3. Keywords that

control height loss, decay, and falling of snags are presented in this section.

Review section 2.3 of chapter 2, “Model Description,” for an overview of all

aspects of how the FFE models snags. Note that the length of time it takes

for a snag to lose, say, half of its height, or to fall down, depends on whether

it is hard or soft. The proportion of snags that are soft, rather than hard, when

they are created is controlled using the SNAGPSFT keyword presented in

section 3.5.3 on snag decay.

3.5.2 Height Loss

Height loss occurs in two stages. The first stage lasts until 50 percent of the

original height is lost. The second stage lasts for the remainder of the life of

the snag. Use the SNAGBRK to change the number of years it takes for hard

and soft snags of various species to lose 50 percent of the height they had at

the time the tree died and to set the number of additional years it takes for

the remaining 30 percent of the snag’s height to be lost. The FFE converts

Table 3.3—Summary of the keywords controlling snags.

Keyword Use Section

SALVAGE Remove snags as a management action 3.7.2

SNAGBRK Change the rate snag height declines 3.5.2, 3.5.3

SNAGDCAY Change the snag decay rate 3.5.3

SNAGFALL Change snag fall down rate 3.5.4

SNAGPBN Change snag fall down rate 3.5.4

SNAGINIT Set the initial number and size of snags 3.3

YARDLOSS Creates snags as part of FVS management actions 3.7.1
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 75

these values into snag breakage rates and uses them in the snag breakage

equations described in section 2.3 of chapter 2, “Model Description.” The snag

breakage rates for initially soft snags are generally faster than initially hard

snags.

SNAGBRK Control the snag height loss rates. The default values

depend on the species of the snag. See the individual

variant description for details.

Field 1: The tree species letter code or number for the FVS

variant you are using. Code a zero (“0”) or “All” for

all species; the default is 0.

Field 2: The number of years from when a hard snag is

created until 50 percent of the original height is

lost.

Field 3: The number of years from when a soft snag is

created until 50 percent of the original height is

lost.

Field 4: The number of years from when a hard snag is

created until the next 30 percent of the original

height is lost. The height loss rate implied by this

number is used until the snag falls down.

Field 5: The number of years from when a soft snag is

created until the next 30 percent of the original

height is lost. The height loss rate implied by this

number is used until the snag falls down.

3.5.3 Snag Decay

You can control the time it takes for a hard snag to become soft using the

SNAGDCAY keyword to set a multiplier that is used to adjust the base rate.

The formula used to compute the base rate is covered in section 2.3.5 of

chapter 2, “Model Description.” It is a linear function of species and size (dbh).

Inside the model, the only significant difference between a hard snag and one

that has become soft is that fuel from soft snags decays differently than that

from hard snags (but see section 3.5.2 for a difference for soft snags that were

never hard).

Table 3.4 shows a set of multipliers that imply different numbers of years

that snags of different sizes will take to make the transition from being hard

to being soft. For example, a multiplier of 1 means that a 10-inch tree will

take 27 years to become soft, while a 20-inch tree will take 39 years to become

soft. You can use the multipliers shown in the body of the table to pick

adjustment multipliers that meet your needs. Note that a single multiplier

is used for all sizes of a given species.

You control the proportion of newly created snags that are classified as soft

using the SNAGPSFT keyword.

SNAGDCAY Set a rate multiplier that modifies how fast hard snags

become soft.

Field 1: The tree species letter code or number for the FVS

variant you are using. Code a zero (“0”) or “All” for

all species; the default is 0.
76 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Field 2: The rate of decay adjustment multiplier; must be

positive; Higher values increase the amount of

time it takes for a hard snag to become soft. Default

is 1.0.

SNAGPSFT Set the proportion of snags listed soft when trees die. This

proportion applies to snags created from all sources, which include those

specified using the SNAGINIT keyword (section 3), the input sample tree

data file, mortality caused by fires and all other causes, and by stand

management. The snags that are initially soft can lose height at a different

rate than those snags that are initially hard.

Field 1: The tree species letter code or number for the FVS

variant you are using. Code a zero (“0”) or “All” for

all species; the default is 0.

Field 2: The proportion of snags that are soft when they are

created; range is 0 to 1; default is zero.

3.5.4 Snag Fall

The FFE computes that rate at which snags fall depending on whether the

snag was present at the time of a fire in addition to the snag’s size and species.

Furthermore, there is a built-in assumption that some of the large snags (a

few of those over 18 inches dbh) will stand for a long time. See section 2.3.6

of chapter 2, “Model Description,” for all the details.

The SNAGFALL keyword is used to specify a species-specific multiplier

of the base snag fall rate that is calculated by the FFE. Using this keyword,

you can also specify how long some of the large snags stand.

The default values, those that are obtained by entering a multiplier of 1,

are defined such that 95 percent of 10-inch snags will fall in 20 years, and they

will all be gone in 22 years. For a 20-inch snag, 95 percent will fall in 31 years,

and they will all fall in 100 years. Table 3.5 shows a set of multipliers that

imply different numbers of years that snags of different sizes will take to fall.

For example, to have 95 percent of 15-inch snags fall in 40 years, a multiplier

Table 3.4—Multipliers useful in the SNAGDCAY keyword that

result in different numbers of years that must pass

for a hard snag to become soft. Multipliers near

1.0 are shown in bold as a reference to show the

implications of the default decay rates.

Years to Snag dbh (inches)

 soft 10 15 20 25 30

10 0.38 0.31 0.26 0.22 0.20

20 0.76 0.62 0.52 0.45 0.39

30 1.14 0.93 0.78 0.67 0.59

40 1.53 1.23 1.04 0.89 0.78

50 1.91 1.54 1.29 1.12 0.98

60 2.29 1.85 1.55 1.34 1.18

70 2.67 2.16 1.81 1.56 1.37

80 3.05 2.47 2.07 1.78 1.57

90 3.43 2.78 2.33 2.01 1.76

100 3.81 3.08 2.59 2.23 1.96
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 77

Table 3.5—Multipliers useful in the SNAGFALL keyword that

result in different numbers of years that must pass

before 95 percent of the snags fall. Multipliers

near 1.0 are shown in bold as a reference to show

the implications of the default fall rates.

Years to Snag dbh (inches)

95% Fall 10 15 20 25 30

10 2.00 2.43 3.09 4.25 6.81

20 1.00 1.21 1.55 2.13 3.41

30 0.67 0.81 1.03 1.42 2.27

40 0.50 0.61 0.77 1.06 1.70

50 0.40 0.49 0.62 0.85 1.36

60 0.33 0.40 0.52 0.71 1.14

70 0.29 0.35 0.44 0.61 0.97

80 0.25 0.30 0.39 0.53 0.85

90 0.22 0.27 0.34 0.47 0.76

100 0.20 0.24 0.31 0.43 0.68

110 0.18 0.22 0.28 0.39 0.62

120 0.17 0.20 0.26 0.35 0.57

130 0.15 0.19 0.24 0.33 0.52

140 0.14 0.17 0.22 0.30 0.49

150 0.13 0.16 0.21 0.28 0.45

of 0.61 would be entered in Field 2 of the SNAGFALL keyword. Multipliers

for snags of other sizes or persistence times can be estimated through

interpolation. Note that the multiplier in field 2 of the keyword is used for all

sizes of snags. For example, if a multiplier of 0.5 were to be used, 95 percent

of 10-inch snags would fall in 40 years, while 30-inch snags would take 139

years for 95 percent of them to fall.

The SNAGPBN (SNAG Post BurN) keyword is used to set the snag fall

down rates for snags that exist when a fire burns. The basic assumption is

that soft snags and small snags fall faster than hard snags and large snags

if they are present when a fire burns. Using the defaults for this keyword

implies that all soft snags and 90 percent of snags less than 12 inches will fall

in 7 years after any fire. Note that the parameters are not species specific and

that the rates implied by the SNAGFALL keyword will take precedence if

they are faster than the postfire rates.

SNAGFALL Set a rate multiplier that modifies how soon snags fall and

set the length of time the some of the large snags stand.

Field 1: The tree species letter code or number for the FVS

variant you are using. Code a zero (“0”) or “All” for

all species; the default is 0.

Field 2: The rate of fall adjustment multiplier; must be

greater than or equal to 0.001; default is 1.0. This

affects all snags less than 18 inches dbh and the

first 95 percent of snags greater than 18 inches.

Values greater than 1.0 cause the snag to fall

faster.

Field 3: The snag age (number of years the tree is dead) by

which the last 5 percent of snags have fallen. This

only affects snags larger than 18 inches dbh.
78 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

SNAGPBN Control the fall rates for snags that are present during a

fire.

Field 1: Proportion of soft snags that will fall faster after a

fire; range is 0 to 1; default is 1.0.

Field 2: Proportion of small snags that will fall faster after

a fire; range is 0 to 1; default is 0.9.

Field 3: Number of years it will take for these snags to all

fall; default is 7.

Field 4: dbh (inches) that divides small snags from large

snags for this calculation; default is 12.

Field 5: Scorch height (feet) that must be exceeded for the

increased fall rates implied by this option to be

used by the FFE; default is 0.

3.5.5 Summary

Snag height loss and fall down have a combined impact on the overall

amount of volume represented by snags. The definition of one set of

parameters may make it less important to define the other. For example,

if multipliers are defined so that the snags all fall within 10 years, the

impact of the height loss is negligible. And if the height loss rates are very

high, it may be less important to precisely define the fall rate. Direct

creation and removal of snags is done using keywords found elsewhere in

the guide (table 3.3).

3.6 Adjusting Fuel Parameters _______________________________________

Fuels accumulate, they decompose, and if there is a fire, they burn. These

processes are covered in detail in section 2.4 of chapter 2, “Model Description.” This section covers those keywords that affect fuel decomposition.

Section 3.3 described how to set the initial amount of fuel using FUELINIT

keyword. The breakage of snags is a source of fuel; keywords that control

snags are presented in section 3.5. Management actions, like thinning, can

add to fuel. Parameters that control how much fuel is added are set using the

YARDLOSS keyword presented in section 3.7.1. Burning piled fuel as a

treatment is simulated using the PILEBURN keyword described in section

3.4.3. Fuel consumption from prescribed or wildfire is simulated using the

SIMFIRE keyword, described in section 3.4.2. The moisture of fuel influences consumption and can be modified using the MOISTURE keyword

presented in section 3.4.2.

3.6.1 Decay Rates

Fuels are tracked in several pools. A pool is like a table entry where the

rows are the six fuel size classes and the columns are four decay rate classes.

That makes 24 fuel pools.

You can change the decay rates associated these pools using the

FUELDCAY keyword. You can modify the decay rates for each rate class

rather than setting them directly using the FUELMULT keyword to specify

a multiplier of the model’s default rates.

FUELDCAY Set the decay rates for each the fuel pools. The default

values depend on the variant.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 79

Field 1: Decay class code, range 1 through 4. Code a 5 to set

the rates for all four decay classes at once; an entry

is required.

Field 2: Decay rate for the litter fuel size class.

Field 3: Decay rate for the duff fuel size class.

Field 4: Decay rate for the 0 to 0.25-inch fuel size class.

Field 5: Decay rate for the 0.25 to 1-inch fuel size class.

Field 6: Decay rate for the 1 to 3-inches fuel size class.

Field 7: Decay rate for the greater than 3 inches fuel size

class.

FUELMULT Specify multipliers for each decay rate class that applies to

the decay rates for all fuel size classes.

Field 1: Multiplier for decay rate class 1=very slow; default

is 1.

Field 2: Multiplier for decay rate class 2=slow; default is 1.

Field 3: Multiplier for decay rate class 3=fast; default is 1.

Field 4: Multiplier for decay rate class 4=very fast; default

is 1.

3.6.2 Assignment to Pools

The decay rate class of fuel is determined by the tree species from which

it originated. By default, in most variants, all of the biomass for all species

is added to the first decay rate class (see section 2.4.5 of chapter 2, “Model

Description”). You can change the assignment of a species to a different decay

rate class (or pool) using the FUELPOOL keyword. If different decay rates

are set for each decay class, then the assignment of the species to a class is

important.

FUELPOOL Specify the assignment of each species to a specific decay

rate class.

Field 1: Valid species letter codes or number. Use a “0” or

“ALL” to indicate all species; no default.

Field 2: Decay rate class number, 1 to 4; no default.

As the biomass in each pool decays, some portion becomes duff, while the

remainder is lost to the air. Because duff usually decays slowly, the amount

of decayed biomass that becomes duff plays an important role in the amount

of duff present in the stand over the long term. The decay rate of the duff pool

can be changed using the FUELDCAY keyword described above. You can

change the proportion of the decayed biomass that goes into the duff pool

using the DUFFPROD keyword. This keyword does not affect the decay rate

of the original pools, just the amount that moves from the original pools to the

duff pool. The portion that does not enter the duff pool is lost to the

atmosphere and is not tracked by the FFE.

DUFFPROD Set the proportion of the decayed material that becomes

duff, the remainder is lost.

Field 1: Decay class code, range 1 through 4. Code a 5 to set

the proportion for all four classes at once; there is

no default.

Field 2: Proportion of decayed litter; default is 0.02.
80 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Field 3: Proportion for the 0 to 0.25 inch fuel; default is

0.02.

Field 4: Proportion for the 0.25 to 1 inch fuel; default is

0.02.

Field 5: Proportion for the 1 to 3 inches fuel; default is 0.02.

Field 6: Proportion for the greater than 3 inches fuel;

default is 0.02.

Field 7: Proportion for all fuel size classes. Values coded in

this field automatically replace blanks in fields 2 to

6; default is 0.02.

Figure 3.9 illustrates some of the keywords presented this section. The

example assumes that fuel originating from all species is placed by default

in the first decay rate pool. In line FFE 2 of figure 3.9, the FUELMULT

keyword is used to modify the second decay rate class so that the rates are

twice as fast as those in the first pool and to modify rates in the third class

so that they are half as fast. In line FFE 3, the FUELPOOL keyword is used

to assign aspen (using species code AS) to the second class and, in line FFE

4, cedar (using species code WC) is assigned to the third. Line FFE 5 is shown

as a reminder that if you don’t ask for output, none is provided. In this case,

the FUELOUT keyword (described in section 3.8) is used to request that fuel

reports start in year 2000.

3.7 Management __________________________________________________

Various management options exist in both the FFE and the base FVS

model. Most of these options have a direct impact on snags or fuels by creating

or destroying them (table 3.6). These options will also have an indirect impact

on fuels or fire intensity. For example, removing snags through salvage

logging will directly decrease the number of snags. It will indirectly decrease

the amount of fuel because there will be less input coming from the snags. It

could also indirectly affect the fire intensity or smoke emissions because

these can be dependent on fuel loads. Only one option has an impact on fuel

depth, and thus directly on fire intensity.

All the management options with the exception of pile burning (section

3.4.3) are discussed in the remainder of this chapter. Two base model

keywords are discussed in this document because of their direct impact on

FFE snags and fuels.

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

FFE 1 Fmin

FFE 2 FuelMult 1 2 .5

FFE 3 FuelPool AS 2

FFE 4 FuelPool WC 3

FFE 5 FuelOut 2000

FFE 6 End

 12-17 See figure 3.1

Figure 3.9—The fuel decay rate for the second decay rate class is modified to be twice as fast and the rate for the third class

half as fast as the default rates. Then biomass from aspen (species code AS) trees is assigned to the second class and from

cedar (species code WC) is assigned the third.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 81

Table 3.6—Summary of management action keywords that affect snags, fuel, and fuel depth.

Keyword Use Model Section

FUELMOVE Change the distribution of fuel among fuel size classes FFE 3.7.4

PILEBURN Decrease the amount of fuel FFE 3.4.3

FUELTRET Change the fuel depth and therefore its bulk density FFE 3.7.4

PRUNE Increase the amount of fuel and the base crown height Base 3.7.2

SALVAGE Decrease the number of snags FFE 3.7.3

YARDLOSS Increase the number of snags and increase or decrease

the amount of biomass left after a logging operation. Base 3.7.2

3.7.1 Base Model Keywords

When management options such as thinning are done in the main FVS

model, the FFE assumes that the crowns from the cut trees are left in the

stand on the ground and the stems are removed. A base model keyword,

YARDLOSS, allows you to change these assumptions and to make some

others. The keyword can be used to create snags by “cutting” trees and

leaving them dead and standing in the stand. Alternatively, some of the trees

can be left in the stand on the ground. These two options are especially useful

when simulating a thinning in which some trees are left in the stand. The

keyword only applies to the thinning actions that immediately follow it in the

keyword file and are scheduled for the same year. You can use the YARDLOSS

keyword as often as necessary to achieve your needs. Whole tree yarding can

be represented using this keyword.

YARDLOSS Set the proportion of the harvested stems that are not

removed and, of those, set the portion that is left standing.

Also specify the proportion crown biomass that is removed

with removed stems.

Field 1: The FVS cycle number or the calendar year; default is 1.

Field 2: Proportion of harvested stems that are not removed from stand; default is 0. Setting this to 1.0

simulates “cut and leave”.

Field 3: Proportion of the trees that are left in the stand

that are down; default is 1.0. Biomass from trees

that are left in the stand and are down is added to

the fuel pools and trees that are not down become

snags. Biomass from snags is added to fuel pools as

the snags fall apart and fall down, just as when

they are created through mortality.

Field 4: Proportion of crowns remaining in stand from

removed stems; default is 1. Set this value to 0.0 to

simulate whole tree removal.

Figure 3.10 illustrates how to use the YARDLOSS keyword. In this

example, a stand is being thinned from below to remove a large number of the

small trees. At the same time, some large trees are killed but are all left in

the stand as snags. Line 12 signals that, for the next thinning option, all of
82 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

the trees will be left in the stand, and that 100 percent of those that are left

will be left down. As none of the trees will be removed, the value coded in the

fourth field is ignored. Line 13 shows the ThinBBA keyword used to cut all

of the trees less than 5 inches dbh with 100 percent efficiency. Line 14 shows

that the YARDLOSS keyword is used a second time to specify that all the

trees are not removed and are not felled. Therefore, all the trees in the

following harvest will become snags. Line 15 illustrates the ThinABA

keyword used to “cut” trees larger than 20 inches dbh, down to a residual of

200 ft2 of basal area, with a10 percent efficiency. Lines FFE 1 to 3 illustrate

a request for the fuel model output.

The second base model keyword, PRUNE, is used to shorten crowns.

Biomass from pruned branches is added to the appropriate fuel pools. The

indirect impact within the FFE is that pruning can change the crown base

height, weight, and density, thus affecting the chance of a crown fire

occurring. The options for this keyword are discussed more fully in other

documents.

3.7.2 Snag Management

Use the SALVAGE keyword to simulate the removal of snags. You can

specify a size range, age, and decay status of snags to be removed.

SALVAGE Schedule a snag removal operation.

Field 1: The FVS cycle number or the calendar year; default is 1.

Field 2: Minimum dbh (inches) to be removed; default is

10.

Field 3: Maximum dbh (inches) to be removed; default is

999.

Field 4: Maximum number of years the removed snags

have been dead; default is 5.

Field 5: Decay state to remove where: 0=both hard and

soft, 1=hard, and 2=soft snags; default is 1.

Field 6: Proportion of eligible snags to remove; default is

0.9.

Field 7: Proportion of affected snags to leave in the stand;

default is 0.0.

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

12 YardLoss 2000 1 1 1

13 ThinBBA 2000 0 1 5

14 YardLoss 2000 1 0 0

15 ThinABA 2000 200 .1 20 999

FFE 1 Fmin

FFE 2 FuelOut 2000

FFE 3 End

 See lines 12-17 in figure 3.1

Figure 3.10—The use of the FVS keyword YardLoss is shown in conjunction with two thinning options. Had the

YardLoss keywords been left out, the FFE would not account for the stem wood left in the stand from the precommercial

thinning (line 13) nor would account for the standing snags created in the thinning from above (line 15).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 83

A chaining operation can be simulated using the SALVAGE keyword in

conjunction with base FVS keywords (fig. 3.11). To achieve the effect of

chaining, the YARDLOSS and THINBBA keywords (lines 12 and 13) are

used to fell all the trees, leaving them in the stand. Secondly, the SALVAGE

keyword (line FFE 2) is used to fell all the snags, leaving them all in the stand.

As in previous examples, a fuels output report is requested on line FFE 3.

3.7.3 Fuel Management

You can manage fuels by changing fuel depth and by changing the amount

of fuel in each size class. The PILEBURN keyword, covered in section 3.4.3,

changes the amount of fuel. In this section, keywords used to change the

depth or amount of fuel are presented.

The practical methods of changing fuel depth include direct fuel treatments, such as lopping or trampling, or harvest methods that result in

different amounts or distributions of fuels. These might include groundbased skidding verses skyline or helicopter logging. You can change the sizeclass distribution of fuels by chipping or chunking large fuels so that they

become small. Or you can simply haul fuels away.

Use the FUELTRET keyword to modify the fuel depth and use the

FUELMOVE keyword to transfer fuels from one size class to another. The

FUELTRET keyword allows you to specify the name of a fuel treatment or

logging method; for each method the FFE supplies a multiplier (table 3.7) to

the fuel depth that simulates the treatment. If you prefer, you can specify the

multiplier yourself and ascribe any meaning you wish to its use.

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

12 YardLoss 2010 1 1 1

13 ThinBBA 2010 0

FFE 1 Fmin

FFE 2 Salvage 2010 0 999 999 1 1

FFE 3 FuelOut 2000

FFE 4 End

 See lines 12-17 in figure 3.1

Figure 3.11—Chaining is simulated by cutting all trees (line 13), leaving them in the stand (line 12), and cutting all snags

and leaving them in the stand as well (line FFE 2).

Table 3.7—Default fuel depth multipliers used to simulate various nominal fuel treatments

and harvest types. Specifying the multiplier on field 4 of the FUELTRET

keyword has the same effect in the FFE as specifying the nominal treatment.

Fuel Treatment (field 2)

1=lopping or 2=trampling, chopping,

 Harvest Method (field 3) 0=none flailing chipping, or crushing

1=ground-based, cat skidding or

line skidding 1.0 0.83 0.75

2=high lead or skyline, 1.3 0.83 0.75

3=precommercial or helicopter 1.6 0.83 0.75
84 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

The FUELMOVE keyword does not image any nominal treatments. You

specify an amount of fuel to move from one class to another. You may

alternatively specify the proportion to move or the residual to leave. You

ascribe the practical meaning to the use of this keyword.

FUELTRET Specify a fuel treatment or harvest method, or specify the

multiplier used to modify the fuel depth.

Field 1: The FVS cycle number or the calendar year; default is 1.

Field 2: Fuel treatment type: 0=none, 1=lopping or flailing, 2=trampling, chopping, chipping, or crushing;

default is 0.

Field 3: Harvest type: 1=ground-based, cat skidding or line

skidding, 2=high lead or skyline, 3=precommercial

or helicopter; default is 1.

Field 4: Multiplier used to increase or decrease fuel depth;

default depends on the values in field 2 and 3 (table

3.7) but if both are 0.0, the default for this field is

1.0.

Figure 3.12 shows how to specify two fuel treatments. Line FFE 2

illustrates that a lopping treatment is applied in 2010, thereby reducing the

fuel depth to 83 percent of its depth prior to the treatment. Line FFE 3 shows

another treatment that increases the fuel depth by 10 percent in 2030.

FUELMOVE Move fuel between size classes to simulate fuel treatments.

The amount of fuel to move can be specified in five ways (see

fields 3 through 7); if values are provided for more than one

method, the FFE will use the method that results in the

largest transfer. Setting the source pool to 0=none implies

that fuel is being imported from outside and setting the

destination pool to 0=none implies that fuel is being removed. The order that FUELMOVE keywords are entered

into the keyword file is important, especially if proportions

are used. FFE processes keywords in the scheduled order

and removes the fuel from the source pool at that time. The

fuel is not added to the destination pool until all keywords

for the year have been processed.

Field 1: The FVS cycle number or the calendar year; default is 1.

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

FFE 1 Fmin

FFE 2 FuelTret 2010 1

FFE 3 FuelTret 2030 1.1

FFE 4 FuelOut 2000

FFE 5 End

 See lines 12-17 in figure 3.1

Figure 3.12—This keyword file illustrates simulating fuel treatments, once with a nominal treatment that implies

a fuel depth multiplier (0.83 in this case, see table 3.7) and a second that shows how to set a specific multiplier.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 85

Field 2: Source fuel pool (0=none, 1=less than 0.25 inch,

2=0.25 to 1 inch, 3=1 to 3 inches, 4=3 to 6 inches,

5=6 to 12 inches, 6=greater than 12 inches, 7=litter, 8=duff); default is 6.

Field 3: Destination fuel pool; same codes used in field 2;

default is 8.

Field 4: Amount of fuel (tons per acre) to move from the

source pool; default is 0.

Field 5: Proportion of source fuel to move; default is 0.

Field 6: Residual fuel (tons per acre) to leave in the source

pool; default is 999.

Field 7: Final amount (tons per acre) of fuel in the target;

default is 0.

Figure 3.13 shows how to simulate a fuel treatment that chops large fuel

into smaller pieces, presumably to increase decay rates and reduce future fire

intensity. The treatment goal is to process 80 percent of the largest fuel

(greater than 12 inches) such that 60 percent of the treated large fuel is added

to the 1 to 3 inches class and the rest are added to the 0.25 to 1 inch class. The

6 to 12 inches fuel is similarly treated. Only a small amount of the 3 to 6 inches

fuel is treated, to reduce the amount in that size class to about 12 tons per

acre. A line-by-line description of the lines FFE 2-7, figure 3.13, follows with

notes on the reasoning behind their use. Note that the order of the keywords

is important.

Line FFE 2: 60 percent of the fuel in size class 6 is moved to size class 3.

The FFE will remove these fuels from the size class before processing the next

keyword, leaving the remaining 40 percent of the fuel in the size class.

Line FFE 3: 50 percent of the fuel remaining in size class 6 is moved to size

class 2. Thus, in total, 80 percent of the fuel in size class 6 is moved.

Lines FFE 4 and 5: Fuel in size class 5 is moved using the same logic used

to move fuel from size class 6 except that an amount of fuel is entered into

field 4 of line FFE 5. The FFE calculates whether the 8 tons per acre amount

specified in field 4 is more or less than 50 percent of the remaining fuel in the

class. The greater of the two amounts is moved.

Lines FFE 6 and 7: Enough fuel is moved from size class 4 to size class 3

to bring the residual down to 18 tons per acre. Then, enough is moved from

class 4 to 2 so that the residual is 12 tons per acre.

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

FFE 1 Fmin

FFE 2 FuelMove 2023 6 3 .6

FFE 3 FuelMove 2023 6 2 .5

FFE 4 FuelMove 2023 5 3 .6

FFE 5 FuelMove 2023 5 2 8 .5

FFE 6 FuelMove 2023 4 3 18

FFE 7 FuelMove 2023 4 2 12

FFE 8 FuelOut 2000

FFE 9 End

 See lines 12-17 in figure 3.1

Figure 3.13—This keyword file illustrates the use of the FUELMOVE keyword to simulate breaking large fuel into smaller

pieces (making chunks or chips).
86 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Figure 3.14 shows how to use FUELMOVE to remove fuel from a stand.

When removing fuel, the destination tagged as “0” in field 3, and when adding

fuel, the source field is tagged as a “0” in field 2. The command is ignored if

both the destination and the source fields are “0”. In the example, 85 percent

of the 6 to 12 inches fuel and 90 percent of fuel over 12 inches is removed, and

the rest is left in the stand. Note that the values are entered as proportions,

not as percentages.

3.8 Output Keywords ______________________________________________

The content of the output reports is presented in chapter 2, “Model

Description,” along with the descriptions of the model components to which

they apply. The keywords that control output generation are presented

below. Seven output tables are available from the FFE. Four provide

information about the current state of the stand in terms of levels of snags

and fuel, or the potential fire intensity and effects. The remaining three are

produced only after a fire and give summary information about the fire and

the impact of the fire. Four of the output files have a second keyword that can

be used to affect what is printed in the output file. For example, mortality and

snags are printed as size class summaries, and keywords affect the definition

of the size classes. Table 3.8 provides a list of the keywords that control or

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

FFE 1 Fmin

FFE 2 FuelMove 2023 5 0 .85

FFE 3 FuelMove 2023 6 0 .90

FFE 8 FuelOut 2000

FFE 9 End

 See lines 12-17 in figure 3.1

Figure 3.14—This keyword file illustrates the use of the FUELMOVE keyword to simulate the removal of large fuel from

the stand.

Table 3.8—List of output tables, the keywords that control them, and references to detailed output

descriptions. Details on the keywords are presented in section 3.8 in the order they are presented

below.

Keyword Model Description section were

Report name Name Use the report is described

Potential fire report POTFIRE Request report 2.5.7

Detailed fuel report FUELOUT Request report 2.4.10

Burn conditions report BURNREPT Request report 2.5.7

Fuel consumption report FUELREPT Request report 2.5.7

Detailed mortality report MORTREPT Request report 2.5.7

MORTCLAS Modify diameter class

boundaries See this user’s guide

Snag summary table SNAGSUM Request table 2.3.8

SNAGCLAS Modify diameter class

boundaries; values also

apply to the detailed

snag report. See this user’s guide

Detailed snag report SNAGOUT Request report 2.3.8
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 87

affect output format and includes a reference to the location in chapter 2,

“Model Description,” where the output is described.

All of the output tables except one are printed to the end of the main FVS

output file. Because of its potential size, the detailed snag output table is

printed to a separate file.

POTFIRE Request the potential fire report.

Field 1: The FVS cycle number or the calendar year when

the output starts; default is 1.

Field 2: Number of years to output; default is 200.

Field 3: Interval to output; default is 1 (every year).

FUELOUT Request the detailed fuels report.

Field 1: The FVS cycle number or the calendar year when

the output starts; default is 1.

Field 2: Number of years to output; default is 200.

Field 3: Interval to output; default is 1 (every year).

BURNREPT Request the burn conditions report output.

Field 1: The FVS cycle number or the calendar year when

the output starts; default is 1.

Field 2: Number of years to output; default is 200.

FUELREPT Request the fuel consumption report.

Field 1: The FVS cycle number or the calendar year when

the output starts; default is 1.

Field 2: Number of years to output; default is 200.

MORTREPT Request the detailed mortality report.

Field 1: The FVS cycle number or the calendar year when

the output starts; default is 1.

Field 2: Number of years to output; default is 200.

MORTCLAS Specify the class boundaries used in the detailed mortality

report. The classes must be specified in increasing order.

Field 1: Minimum dbh of size class 1; default is 0 inches.

Field 2: Minimum dbh of size class 2; default is 5 inches.

Field 3: Minimum dbh of size class 3; default is 10 inches.

Field 4: Minimum dbh of size class 4; default is 20 inches.

Field 5: Minimum dbh of size class 5; default is 30 inches.

Field 6: Minimum dbh of size class 6; default is 40 inches.

Field 7: Minimum dbh of size class 7; default is 50 inches.

SNAGSUM Request the snag summary report. Unlike the other reports, you cannot control when this report output starts

and ends.

Field 1: If a negative number is entered, no report is generated (useful only to turn off a previously requested report).

SNAGCLAS Set the snag class boundaries used to assign snags to class

in the snag summary report and for the detailed snag

report. Values must be specified in increasing order.

Field 1: Lower boundary of size class 1; default is 0 inches.
88 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Field 2: Lower boundary of size class 2; default is 12 inches.

Field 3: Lower boundary of size class 3; default is 18 inches.

Field 4: Lower boundary of size class 4; default is 24 inches.

Field 5: Lower boundary of size class 5; default is 30 inches.

Field 6: Lower boundary of size class 6; default is 36 inches.

SNAGOUT Request the detailed snag report.

Field 1: The FVS cycle number or the calendar year when

the output starts; default is 1.

Field 2: Number of years to output; default is 200.

Field 3: Interval to output; default is 5 years.

Field 4: Fortran data set reference number to which the

output file is written; default is 3.

Field 5: Enter a 0 if you want headings output for this

table, and enter a 1 if you want headings suppressed; default is zero.

SVIMAGES Set the number of frames, or images, showing the fire

progression when the base model SVS keyword is used.

This keyword is related to the use of the Stand Visualization System described the “Preface” and illustrated on

cover of the volume.

Field 1: The number if frames or images; default is 3.

Figure 3.15 illustrates asking for every kind of output in addition to

changing the default diameter class boundaries for the mortality report so

that they exactly match the default boundaries for the snag report. The

example starts with the keywords illustrated in figure 3.7. In that example,

no reports were requested, yet the example included a pile burn and a

simulated fire. Without those reports, evidence of the fires is limited to how

the fire affects base FVS outputs.

Line FFE 5: Request the potential fire report be generated starting in the

year 2000. The default interval of 1 year is used and the report is generated

for 200 years.

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

FFE 1 Fmin

FFE 2 PileBurn 2007 100 15 75 1

FFE 3 SimFire 2037 0 4

FFE 4 FlameAdj 2037 0 0

FFE 5 PotFire 2000

FFE 6 FuelOut 2000

FFE 7 BurnRept 2000

FFE 8 FuelRept 2000

FFE 9 MortRept 2000

FFE 10 MortClas 0 12 18 24 30 36 9999

FFE 11 SnagSum

FFE 12 SnagOut 2000

FFE 13 End

 12-17 See figure 3.1

Figure 3.15—The keyword file from figure 3.7 with keywords added to ensure that all possible output files will be

present.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 89

Lines FFE 6 through 9: Request the detailed fuels, burn conditions, fuel

consumption, and detailed mortality reports.

Line FFE 10: Set the class boundaries for the detailed mortality reports to

those used in the snag reports. The last field is coded with an 9999 to specify

a huge lower bound for the last class, so that there are really only six size

classes for reporting, which means that all trees over 36 inches are reported

in the sixth class.

Line FFE 11: Request the snag summary.

Line FFE 12: Request the detailed snag data be output to the data file

reference by the number 3, every 5 years, for 200 years, as the defaults

signify. The data file associated with the number 3 is automatically opened

by FVS for the tree list output. This example does not use that feature of FVS

so this file will contain only the snag data. If the tree list option were also

used, the file would contain both the tree list and snag data.

3.9 Using the FVS Event Monitor _____________________________________

The Event Monitor (Crookston 1990) is part of the FVS system. The FFE

interacts with the Event Monitor providing the ability to build powerful

keyword command files. Using the capabilities of the Event Monitor you can

specify logical expressions that are predicated on the value of state variables

that are automatically updated by FVS. Activities, such as thinnings, can be

set up so they are scheduled only if the logical expressions are true. You can

define new state variables as functions of those that are predefined plus new

ones that you defined earlier in the keyword set. You can also use variables

to define the parameter fields on keywords rather than specifying constant

values as shown in all the previous examples.

In this section, we build on the information provided by Crookston (1990)

on how to use the Event Monitor with the base FVS system. Therefore, the

following text assumes that you understand the information presented in

that document.

The FFE capitalizes on the Event Monitor by supporting the use of its

features. All FFE keywords that can be scheduled with a fixed year or cycle

in field 1 can alternatively be scheduled as part of an IF-THEN sequence. All

of these same keywords support the PARMS feature of the Event Monitor.

Lastly, automatic variables have been created whose values are defined

when the FFE is used. Note that the Event Monitor handles variables listed

as having arguments as if they were functions. The variables are as follows.

FIRE has the value 1 (yes) if a fire was simulated in the preceding FVS cycle

and has the value 0 (no) if not.

FIREYEAR is the year that the last fire is simulated; the value will be zero

if a fire has not been simulated during the run.

MINSOIL is the percent of mineral soil exposure from the most recent fire.

FUELLOAD(arg1, arg2) is the total tons per acre of fuel in the stand for

a range of fuel size classes. The lower limit of the range is defined using

arg1, and the upper limit is arg2. The value of arg1 and arg2 can be the

same. A coding system is used to specify the classes, where 1 is greater

than 0 to less than 0.25 inch, 2 is equal or greater than 0.25 to less than

1 inch, 3 is equal or greater than 1 to less than 3 inches, 4 is equal or

greater than 3 to less than 6 inches, 5 is equal or greater than 6 to less
90 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

than12 inches, 6 is equal or greater than 12 inches, 7=litter, and

8=duff.

CROWNIDX is the crowning index reported in the potential fire report.

CRBASEHT is the crown base height reported in the potential fire report.

CRBULKDN is the crown bulk density reported in the potential fire report.

POTFLEN(arg1)is the flame length reported in the potential fire report for

the severe fire conditions when arg1 is 1 and for the moderate fire

conditions when arg1 is 2.

SNAGS(arg1, arg2, ..., arg7) is the total number, volume, or basal area of

snags meeting the criteria specified using the arguments. Only the

first three arguments are required, so only code the others if you need

them. Note that the snag data are stored in 2 inch-wide dbh classes.

The definitions of the arguments are:

Arg1 defines the type of information returned where 1= snag density,

2 = basal area, and 3 = volume.

Arg2 defines the tree species where 0 = all species and other values are

the corresponding species codes for the variant. The numbers or

the short alpha codes may be used.

Arg3 defines the decay status where 0 = all, 1 = hard, and 2 = soft.

Arg4 is the lower limit dbh in inches (greater or equal); the default is

zero.

Arg5 is the upper limit dbh in inches (less than); the default is a large

number.

Arg6 is the lower limit height in feet (greater or equal); the default is

zero.

Arg7 is the upper limit height in feet (less than); the default is a large

number.

TORCHIDX is the torching index reported in the potential fire report.

Figure 3.16 illustrates a simple example for scheduling pile burn whenever the small (litter and less than 3 inches) fuel loads are greater than 8 tons

per acre. The COARSEWD function is used twice, once to sum up the first

three fuel classes, and once to return the litter, as described in the following

line-by-line account:

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

12 If 10

13 FuelLoad(1,3)+FuelLoad(7,7) GT 8

14 Then

FFE 1 Fmin

FFE 2 PileBurn 0

FFE 3 End

15 EndIF

See figure 3.1

Figure 3.16—This keyword file shows how to use the FVS Event Monitor to schedule a pile burn any time small

(litter and less than 3 inches) fuel loads are over 8 tons per acre.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 91

Figure 3.17—The FlameAdj keyword is used with the PARMS feature of the Event Monitor to dynamically compute

a flame length adjustment. This example is not an endorsement of the formula.

Line 12: An FVS IF keyword is used to signal that a logical expression is

being entered. The value of 10 in field 1 is a minimum waiting time between

events. In this case, the pile burn can not be scheduled any more often than

once each 10 years.

Line 13: The FUELLOAD function is used first to sum the first three fuel

classes and secondly to get the litter. The sum of these two values is compared

to 8.

Line 14: The THEN keyword signals that the expression has ended and

that activity keywords follow. Activities are keywords that have a cycle

number or year in the first field. They are scheduled when the event occurs,

which is when the expression is true. When used in an IF-THEN sequence,

the value coded in field 1 of activity keywords is added to the year the event

occurs, and the sum is the year in which the activity is scheduled. Note that

logical expressions that define events are only tested on FVS cycle boundaries; see the Event Monitor user’s guide for more information (Crookston

1990).

Line FFE 1: The FMIN keyword signals that the FFE keywords follow.

Line FFE 2: All the fields on the PILEBURN are left to their defaults

except field 1. The first field is set to 0 so the year the pile burn is scheduled

is the same year as the event occurs. Recall that the default value for field 1

is 1. If that number had been used, the PILEBURN would be scheduled for

the year following the year the event occurred.

Line FFE 3: End the FFE keywords.

Line 15: End the IF-THEN sequence.

The remainder of the file functions as shown in figure 3.3.

Figure 3.17 illustrates how to use the PARMS feature of the Event Monitor

with the FFE keywords. In this example, the FLAMEADJ keyword is used

to increase the computed flame length by an amount that is a function of the

slope and aspect of the stand. No adjustment is made for flat ground, or slopes

that face east or west. The flame length is increased by up to 50 percent on

south facing slopes and decreased by the same amount on north facing slopes.

The amount of the adjustment is computed using a cosine function. The lineby-line description of the keyword file follows:

Line FFE 1: Start the FFE keywords

Line FFE 2: Schedule a fire to burn in the year 2037.

Line FFE 3: Use the PARMS feature of the Event Monitor to compute the

flame length multiplier. The Event Monitor’s cos (cosine) function takes its

argument in radians, so the aspect is multiplied by π/180 ≈ 0.01745. The

Line 

Number 

Column ruler 

----+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8 

Keyword Field 1 Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 

1-11 See figure 3.1

FFE 1 Fmin

FFE 2 SimFire 2037

FFE 3 FlameAdj 2037 Parms(1-(.5*Slope*Cos(Aspect*0.01745)), -1, -1)

FFE 3 End

15 EndIF

See figure 3.1
92 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

cosine of aspect is multiplied by the slope so that places with little slope will

have a small adjustment, regardless of aspect, and further multiplied by 0.5.

The product is subtracted from 1 resulting in a multiplier of 0.5 for north

aspects and 1.5 for south aspects that have 100 percent slopes. As the slope

decreases to flat, the multiplier becomes close to 1.0; the same happens when

aspect is more east or west. The example is used to illustrate how to use the

PARMS feature and is not an endorsement of the formula. Note that the other

two parameters of this keyword must be supplied. In this case, –1 is used to

signal that the default values for those fields are used, which means that the

model is to compute the flame length and crowning percentages.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 93

Duncan C. Lutes

Donald C.E. Robinson

Chapter 4

Variant Descriptions

4.1 Introduction ___________________________________________________

The Fire and Fuels Extension (FFE) has been developed for a number of

Forest Vegetation Simulator (FVS) variants: Northern Idaho, Central Rockies,

Utah, Eastern Montana, Western Sierra, Blue Mountains, Eastern Cascades, Central Idaho, Tetons and Southern Oregon/Northern California.

Northern Idaho was the first variant developed and is considered the “base

variant” as described in the FFE Model Description and User’s Guide. The

“Model Description” chapter provides an in-depth look into the logic and

parameters of that variant. As new variants have been developed, logic and

parameter modifications were made to the NI variant in order to model fire

effects in the regions covered by the new variants. The modifications were

based on workshops and consultations with scientists and other fire experts

familiar with each variant’s region. Many revisions were based on “expert

knowledge” and unpublished information. References are included for modifications based on published information.

Abstract—The Fire and Fuels Extension (FFE) to the Forest Vegetation Simulator (FVS)

simulates fuel dynamics and potential fire behavior over time, in the context of stand development

and management. This report documents differences between geographic variants of the FFE. It

is a companion document to the FFE “Model Description” and “User’s Guide.” People who use FFE

variants can use this document to learn about the unique features of each geographic variant.

Keywords: FVS, FFE, forest fire, stand dynamics, snags, down woody debris
94 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

The user can modify many of the model processes, for instance snag

dynamics. Some of the keywords are identified in this document; however, all

of the user keywords are described in the “Model Description” chapter.

The purpose of this document is to describe the parameterization differences and, where applicable, logical modifications made to the NI variant in

order to make the FFE model fire effects appropriately in new variants of the

Fire and Fuels Extension to FVS.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 95

4.2 Northern Idaho (NI) _____________________________________________

4.2.1 Tree Species

The Northern Idaho variant models the 10 tree species shown in table 4.1.

One additional category, “other” is modeled using western hemlock.

4.2.2 Snags

The majority of the snag model logic is based on unpublished data provided

by Bruce Marcot (USFS, Portland, OR, unpublished data 1995). Snag fall

parameters were developed at the FFE design workshop. A complete description of the Snag Submodel is provided in section 2.3 of the FFE “Model

Description“ chapter.

Four variables are used to modify the Snag Submodel for the different

species in the NI-FFE variant:

• A multiplier to modify the species’ fall rate

• Amultiplier to modify the time required for snags to decay from a “hard”

to “soft” state

• The maximum number of years that snags will remain standing

• A multiplier to modify the species’ height loss rate

These variables are summarized in tables 4.2 and 4.3.

Snag bole volume is determined in using the base FVS model equations.

The coefficients shown in table 4.4 are used to convert volume to biomass.

Soft snags have 80 percent the density of hard snags.

Snag dynamics can be modified by the user using the SNAGBRK,

SNAGFALL, SNAGDCAY, and SNAGPBN keywords described in the FFE

“Model Description” chapter.

4.2.3 Fuels

Information on live fuels was developed using FOFEM 4.0 (Reinhardt and

others 1997) and FOFEM 5.0 (Reinhardt and others 2001) and in cooperation

with Jim Brown (USFS, Missoula, MT pers. comm. 1995). A complete

description of the Fuel Submodel is provided in section 2.4 of the FFE “Model

Description” chapter.

Table 4.1—Tree species simulated by the Northern Idaho variant.

Common name Scientific name Notes

western white pine Pinus monticola

western larch Larix occidentalis

Douglas-fir Pseudotsuga menziesii

grand fir Abies grandis

western hemlock Tsuga heterophylla

western redcedar Thuja plicata

lodgepole pine Pinus contorta

Engelmann spruce Picea engelmannii

subalpine fir Abies lasiocarpa

ponderosa pine Pinus ponderosa

other = mountain hemlock
96 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.2—Default snag fall, snag height loss and soft-snag characteristics for

20 inch DBH snags in the NI-FFE variant. These characteristics are

derived directly from the parameter values shown in table 4.3.

Species 95% fallen All down 50% height Hard-to-soft

- - - - - - - - - - - - - - - - - - -Years - - - - - - - - - - - - - - - - - - -

western white pine 34 110 33 42

western larch 34 110 33 42

Douglas-fir 34 110 33 42

grand fir 28 90 27 35

western hemlock 28 90 27 35

western redcedar 28 90 27 35

lodgepole pine 28 90 27 35

Engelmann spruce 28 90 27 35

subalpine fir 28 90 27 35

ponderosa pine 31 100 30 39

other 31 100 30 39

Table 4.3—Default snag fall, snag height loss and softsnag multipliers for the NI-FFE. These parameters result in the values shown in table 4.2.

(These three columns are the default values

used by the SNAGFALL, SNAGBRK, and

SNAGDCAY keywords, respectively.)

Snag Height

Species fall loss Hard-to-soft

western white pine 0.9 0.9 1.1

western larch 0.9 0.9 1.1

Douglas-fir 0.9 0.9 1.1

grand fir 1.1 1.1 0.9

western hemlock 1.1 1.1 0.9

western redcedar 1.1 1.1 0.9

lodgepole pine 1.1 1.1 0.9

Engelmann spruce 1.1 1.1 0.9

subalpine fir 1.1 1.1 0.9

ponderosa pine 1.0 1.0 1.0

other 1.0 1.0 1.0

Table 4.4—Wood density (ovendry lb/ft3

) used

in the NI-FFE variant.

Species Density (lb/ft3)

western white pine 24.8

western larch 34.3

Douglas-fir 31.9

grand fir 24.1

western hemlock 29.5

western redcedar 21.1

lodgepole pine 26.4

Engelmann spruce 22.6

subalpine fir 21.1

ponderosa pine 26.4

other 29.5
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 97

Fuels are divided into to four categories: live tree bole, live tree crown, live

herb and shrub, and down woody debris (DWD). Live herb and shrub fuel

load, and initial DWD are assigned based on the cover species with greatest

basal area. If there is no basal area in the first simulation cycle (a “bare

ground” stand), then the initial fuel loads are assigned by the vegetation code

provided with the STDINFO keyword. If the vegetation code is missing or

does not identify an overstory species, the model uses a ponderosa pine cover

type to assign the default fuels. If there is no basal area in other cycles of the

simulation (after a simulated clearcut, for example), herb and shrub fuel

biomass is assigned by the previous cover type.

Live Tree Bole: The fuel contribution of live trees is divided into two

components: bole and crown. Bole volume is transferred to the FFE after

being computed by the FVS model, then converted to biomass using ovendry

wood density calculated from table 4-3a and equation 3-5 of The Wood

Handbook (Forest Products Laboratory 1999) (table 4.4). The coefficient in

table 4.4 for Douglas-fir is based on Douglas-fir Interior north.

Tree Crown: As described in the section 2.2 of the FFE “Model Description” chapter, equations in Brown and Johnston (1976) provide estimates of

live and dead crown material for most species in the NI-FFE (table 4.5).

Mountain hemlock biomass is based on Gholz (1979), using western hemlock

equations from Brown and Johnston to partition the biomass and also to

provide estimates for trees less than 1 inch diameter.

Live leaf lifespan is used to simulate the contribution of needles and leaves

to annual litter fall. Dead foliage and branch materials also contribute to

litter fall, at the rates shown in table 4.6. Each year the inverse of the lifespan

is added to the litter pool from each biomass category. Leaf lifespan data are

from Keane and others (1989). Lifespans of western white pine and mountain

hemlock are mapped using ponderosa pine, and western hemlock and

western redcedar are based on Douglas-fir.

Live Herbs and Shrubs: Live herb and shrub fuels are modeled simply

by the FFE. Shrubs and herbs are assigned a biomass value based on total

tree canopy cover and dominant overstory species (table 4.7). When there are

Table 4.5—The crown biomass equations listed here determine the biomass

of foliage, branch and stem wood. Species mappings are done for

species for which equations are not available.

Species Species mapping and equation source

western white pine Brown and Johnston (1976)

western larch Brown and Johnston (1976)

Douglas-fir Brown and Johnston (1976)

grand fir Brown and Johnston (1976)

western hemlock Brown and Johnston (1976)

western redcedar Brown and Johnston (1976)

lodgepole pine Brown and Johnston (1976)

Engelmann spruce Brown and Johnston (1976)

subalpine fir Brown and Johnston (1976)

ponderosa pine Brown and Johnston (1976)

other Gholz (1979); Brown and Johnston (1976)
98 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

no trees, habitat type is used to infer the most likely dominant species of the

previous stand (“Model Description” chapter, section 2.4.2). When total tree

canopy cover is less than 10 percent, herb and shrub biomass is assigned an

“initiating” value (the ‘I’ rows from table 4.7). When canopy cover is greater

than 60 percent, biomass is assigned an “established” value (the ‘E’ rows).

Live fuel loads are linearly interpolated when canopy cover is between 10 and

60 percent. Data are taken from FOFEM 4.0 (Reinhardt and others 1997)

with modifications provided by Jim Brown (USFS Missoula, MT pers. comm.

1995).

Table 4.6—Life span of live and dead foliage (years) and dead branches

for species modeled in the NI-FFE variant.

Live Dead

Species Foliage Foliage <0.25” 0.25–1” >1”

western white pine 4 2 5 5 15

western larch 1 1 5 5 15

Douglas-fir 5 2 5 5 15

grand fir 7 2 5 5 15

western hemlock 5 2 5 5 15

western redcedar 5 2 5 5 20

lodgepole pine 3 2 5 5 15

Engelmann spruce 6 2 5 5 10

subalpine fir 7 2 5 5 15

ponderosa pine 4 2 5 5 10

other 4 2 5 5 15

Table 4.7—Values (dry weight, tons/acre) for live fuels used in the NI-FFE.

Biomass is linearly interpolated between the “initiating” (I) and

“established” (E) values when canopy cover is between 10 and 60

percent.

Species Herbs Shrubs Notes

western white pine E 0.15 0.10

I 0.30 2.00

western larch E 0.20 0.20

I 0.40 2.00

Douglas-fir E 0.20 0.20

I 0.40 2.00

grand fir E 0.15 0.10

I 0.30 2.00

western hemlock E 0.20 0.20 Use Douglas-fir

I 0.40 2.00

western redcedar E 0.20 0.20 Use Douglas-fir

I 0.40 2.00

lodgepole pine E 0.20 0.10

I 0.40 1.00

Engelmann spruce E 0.15 0.20

I 0.30 2.00

subalpine fir E 0.15 0.20

I 0.30 2.00

ponderosa pine E 0.20 0.25

I 0.25 0.10

other E 0.15 0.20 Use spruce-subalpine fir

I 0.30 2.00
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 99

Table 4.8—Canopy cover and cover type are used to assign default down woody debris (tons/acre) by size class

for established (E) and initiating (I) stands.

Size class (inches)

Species < 0.25 0.25 – 1 1 – 3 3 – 6 6 – 12 > 12 Litter Duff

western white pine E 1.0 1.0 1.6 10.0 10.0 10.0 0.8 30.0

I 0.6 0.6 0.8 6.0 6.0 6.0 0.4 12.0

western larch E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

Douglas-fir E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

grand fir E 0.7 0.7 3.0 7.0 7.0 0.0 0.6 25.0

I 0.5 0.5 2.0 2.8 2.8 0.0 0.3 12.0

western hemlock E 2.2 2.2 5.2 15.0 20.0 15.0 1.0 35.0

I 1.6 1.6 3.6 6.0 8.0 6.0 0.5 12.0

western redcedar E 2.2 2.2 5.2 15.0 20.0 15.0 1.0 35.0

I 1.6 1.6 3.6 6.0 8.0 6.0 0.5 12.0

lodgepole pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

Engelmann spruce E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

subalpine fir E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

ponderosa pine E 0.7 0.7 1.6 2.5 2.5 0.0 1.4 5.0

I 0.1 0.1 0.2 0.5 0.5 0.0 0.5 0.8

other E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

Dead Fuels: Initial default DWD pools are based on overstory species.

When there are no trees, habitat type is used to infer the most likely

dominant species of the previous stand (“Model Description” chapter, section

2.4.2). Default fuel loadings were provided by Jim Brown (USFS, Missoula,

MT pers. comm. 1995) (table 4.8). If tree canopy cover is less than 10 percent,

the DWD pools are assigned an “initiating” value; and if cover is greater than

60 percent they are assign the “established” value. Fuels are linearly

interpolated when canopy cover is between 10 and 60 percent. Initial fuel

loads can be modified using the FUELINIT keyword.

4.2.4 Bark Thickness

Bark thickness contributes to predicted tree mortality from simulated

fires. The bark thickness multipliers in table 4.9 are used to calculate single

bark thickness (“Model Description” chapter, section 2.5.5). The bark thickness equation used in the mortality equation is unrelated to the bark

thickness used in the base FVS model. Data are from FOFEM 5.0 (Reinhardt

and others 2001).

4.2.5 Decay Rate

Decay of down material is simulated by applying loss rates to biomass in

categories based on the original size-class of the branch and bole pieces as

described in section 2.4.5 of the “Model Description” chapter (table 4.10).

Default decay rates are based on Abbott and Crossley (1982). A portion of the

loss is added to the duff pool each year. Loss rates are for hard material; soft

material in all size classes, except litter and duff, decays 10 percent faster.
100 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

By default, the FFE decays all wood species at the rates shown in table

4.10. The decay rates of species groups may be modified by users, who can

provide rates to the four decay classes shown in table 4.11 using the

FUELDCAY keyword. Users can also reassign species to different classes

using the FUELPOOL keyword.

4.2.6 Moisture Content

Moisture content of the live and dead fuels is used to calculate fire intensity

and fuel consumption (“Model Description” chapter, section 2.5.2). Users can

choose from four predefined moisture groups (table 4.12) or they can specify

moisture conditions for each class using the MOISTURE keyword.

Table 4.9—Species specific constants

for determining single bark

thickness.

Species Multiplier (Vsp)

western white pine 0.035

western larch 0.063

Douglas-fir 0.063

grand fir 0.046

western hemlock 0.040

western redcedar 0.035

lodgepole pine 0.028

Engelmann spruce 0.036

subalpine fir 0.041

ponderosa pine 0.063

other 0.040

Table 4.10—Default annual loss rates are applied based on size class. A portion

of the loss is added to the duff pool

each year. Loss rates are for hard

material. If present, soft material in

all size classes except litter and

duff decays 10 percent faster.

Size class

(inches)

Annual loss rate

Proportion of loss

becoming duff

< 0.25

0.25 — 1

0.12

1 — 3 0.09

3 — 6

6 — 12

> 12

0.015

Litter 0.50

0.02

Duff 0.002 0.0
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 101

Table 4.11—Default wood decay classes

used in the NI-FFE variant.

Classes are from The Wood

Handbook (1999). (1 = exceptionally high; 2 = resistant or very resistant; 3 =

moderately resistant, and 4

= slightly or nonresistant.)

Species Decay class

western white pine 4

western larch 3

Douglas-fir 3

grand fir 4

western hemlock 4

western redcedar 2

lodgepole pine 4

Engelmann spruce 4

subalpine fir 4

ponderosa pine 4

other 4

Table 4.12—Moisture values, which alter fire intensity and consumption, have been predefined for four groups.

Moisture group

Size class Very dry Dry Moist Wet

0 – 0.25 in. (1-hr) 4 8 12 16

0.25 – 1.0 in. (10-hr) 4 8 12 16

1.0 – 3.0 in. (100-hr) 5 10 14 18

> 3.0 in. (1000+ -hr) 10 15 25 50

Duff 15 50 125 200

Live 70 110 150 150

4.2.7 Fire Behavior Fuel Models

Fire behavior fuel models (Anderson 1982) are used to estimate flame

length and fire effects stemming from flame length. Fuel models are determined using fuel load and stand attributes (“Model Description” chapter,

section 2.4.8) specific to each FFE variant. In addition, stand management

actions such as thinning and harvesting can abruptly increase fuel loads and

can trigger “Activity Fuels” conditions, resulting in the selection of alternative fuel models. At their discretion, FFE users have the option of:

1. Defining and using their own fuel models

2. Defining the choice of fuel models and weights

3. Allowing the FFE variant to determine a weighted set of fuel models

4. Allowing the FFE variant to determine a weighted set of fuel models,

then using the dominant model

This section explains the steps taken by the NI-FFE to follow the third and

fourth of these four options.
102 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

When the combination of large and small fuel lies in the lower left corner

of the graph shown in figure 4.1, one or more low-fuel fuel models become

candidate models. In other regions of the graph, other fuel models may also

be candidates. The habitat types shown in table 4.13 define which low fuel

model(s) will become candidates. According to the logic of this table, only a

single fuel model will be chosen for a given stand structure. Consequently, as

a stand undergoes structural changes due to management or maturation, the

selected fire model can jump from one model selection to another, which in

turn may cause abrupt changes in predicted fire behavior. To smooth out

changes resulting from changes in fuel model, the strict logic is augmented

by linear transitions between states that involve continuous variables (for

example, percent canopy cover, average height, snag density, and so forth).

If the STATFUEL keyword is selected, fuel model is determined by using

only the closest match fuel model identified by either figure 4.1 or table 4.13.

The FLAMEADJ keyword allows the user to scale the calculated flame

length or override the calculated flame length with a value they choose.

Figure 4.1—If large and small fuels map to the shaded area, candidate

fuel models are determined using the logic shown in table 4.13. Otherwise, flame length based on distance between the closest fuel models,

identified by the dashed lines, and on recent management (see “Model

Description” chapter, section 2.4.8 for further details).

10 20 30 40

10

20

30

40

0

3" + fuels (tons/acre)

0"-3" fuels (tons/acre)

FM10 - natural fuels

FM11 - activity fuels

FM12 - natural fuels

FM14 - activity fuels

FM13

Fuel model, 100% weight

low fuels

models

0
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 103

Table 4.13—When low fuel loads are present in the NI-FFE, fire behavior fuel models are determined

using one of three habitat groups: dry grassy, dry shrubby, and other. Fuel model is linearly

interpolated between the two low fuel models when canopy cover falls between 30 and 50

percent.

Canopy cover

< 30%

Canopy

Habitat type cover > 50%

number

Habitat type

name

FFE habitat

category Fuel model

130 PIPO/AGSP

140 PIPO/FEID

210 PSME/AGSP

220 PSME/FEID

230 PSME/FESC

Dry Grassy 1 9

161 PIPO/PUTR

170 PIPO/SYAL

171 PIPO/SYAL-SYAL

172 PIPO/SYAL-BERE

180 PIPO/PRVI

181 PIPO/PRVI-PRVI

182 PIPO/PRVI-SHCA

310 PSME/SYAL

311 PSME/SYAL-AGSP

312 PSME/SYAL-CARU

313 PSME/SYAL-SYAL

Dry Shrubby 2 9

All others Other 8 8
104 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

4.3 Eastern Montana (EM) ___________________________________________

4.3.1 Tree Species

The Eastern Montana variant models the seven tree species shown in

table 4.14. One additional category, “other” is modeled using western

juniper.

4.3.2 Snags

The majority of the snag model logic is based on unpublished data provided

by Bruce Marcot (USFS, Portland, OR, unpublished data 1995). Snag fall

parameters were developed at the FFE design workshop. A complete description of the Snag Submodel is provided in section 2.3 of the FFE “Model

Description” chapter.

Four variables are used to modify the Snag Submodel for the

different species in the EM-FFE variant:

• A multiplier to modify the species’ fall rate

• Amultiplier to modify the time required for snags to decay from a “hard”

to “soft” state

• The maximum number of years that snags will remain standing

• A multiplier to modify the species’ height loss rate

These variables are summarized in tables 4.15 and 4.16.

Table 4.14—Tree species simulated by the Eastern Montana variant.

Common name Scientific name Notes

whitebark pine Pinus albicaulis

western larch Larix occidentalis

Douglas-fir Pseudotsuga menziesii

lodgepole pine Pinus contorta

Engelmann spruce Picea engelmannii

subalpine fir Abies lasiocarpa

ponderosa pine Pinus ponderosa

other = western juniper

Table 4.15—Default snag fall, snag height loss and soft-snag characteristics for

20 inch DBH snags in the EM-FFE variant. These characteristics are

derived directly from the parameter values shown in table 4.16.

Species 95% fallen All down 50% height Hard-to-soft

- - - - - - - - - - - - - - - - - - -Year - - - - - - - - - - - - - - - - - - - -

whitebark pine 34 110 33 42

western larch 34 110 33 42

Douglas-fir 34 110 33 42

lodgepole pine 28 90 27 35

Engelmann spruce 28 90 27 35

subalpine fir 28 90 27 35

ponderosa pine 31 100 30 39

other 31 100 30 39
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 105

Table 4.16—Default snag fall, snag height loss and softsnag multipliers for the EM-FFE. These parameters result in the values shown in table

4.15. (These three columns are the default

values used by the SNAGFALL, SNAGBRK,

and SNAGDCAY keywords, respectively.)

Snag Height

Species fall loss Hard-to-soft

whitebark pine 0.9 0.9 1.1

western larch 0.9 0.9 1.1

Douglas-fir 0.9 0.9 1.1

lodgepole pine 1.1 1.1 0.9

Engelmann spruce 1.1 1.1 0.9

subalpine fir 1.1 1.1 0.9

ponderosa pine 1.0 1.0 1.0

other 1.0 1.0 1.0

Table 4.17—Wood density (ovendry lb/ft3

)

used in the EM-FFE variant.

Species Density (lb/ft3)

whitebark pine 24.8

western larch 34.3

Douglas-fir 31.9

lodgepole pine 26.4

Engelmann spruce 22.6

subalpine fir 21.1

ponderosa pine 26.4

other 34.9

Snag bole volume is determined in using the base FVS model equations.

The coefficients shown in table 4.17 are used to convert volume to biomass.

Soft snags have 80 percent the density of hard snags.

Snag dynamics can be modified by the user using the SNAGBRK,

SNAGFALL, SNAGDCAY and SNAGPBN keywords described in the FFE

“Model Description” chapter.

Additionally, the base fall rate diameter cutoff (diameter at which 5

percent of snags are assigned a slower fall rate) was changed from 18 inches

to 12 inches DBH and the fire fall rate cutoff (diameter at which 90 percent

of the smaller snags are assigned a faster fall rate after fire) was changed

from 12 inches to 10 inches DBH. Both of these changes were made to better

represent the smaller trees modeled in the EM variant.

4.3.3 Fuels

Information on live fuels was developed using FOFEM 4.0 (Reinhardt and

others 1997) and FOFEM 5.0 (Reinhardt and others 2001) and in cooperation

with Jim Brown (USFS, Missoula, MT pers. comm. 1995). A complete

description of the Fuel Submodel is provided in section 2.4 of the FFE “Model

Description” chapter.
106 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Fuels are divided into to four categories: live tree bole, live tree crown, live

herb and shrub, and down woody debris (DWD). Live herb and shrub fuel

load, and initial DWD are assigned based on the cover species with greatest

basal area. If there is no basal area in the first simulation cycle (a “bare

ground” stand) then the initial fuel loads are assigned by the vegetation code

provided with the STDINFO keyword. If the vegetation code is missing or

does not identify an overstory species, the model uses a ponderosa pine cover

type to assign the default fuels. If there is no basal area in other cycles of the

simulation (after a simulated clearcut, for example) herb and shrub fuel

biomass is assigned by the previous cover type.

Live Tree Bole: The fuel contribution of live trees is divided into two

components: bole and crown. Bole volume is transferred to the FFE after

being computed by the FVS model, then converted to biomass using ovendry

wood density calculated from table 4-3a and equation 3-5 of The Wood

Handbook (Forest Products Laboratory 1999). The coefficient in table 4.17

for whitebark pine is based on western white pine; Douglas-fir is based on

Douglas-fir Interior north.

Live Tree Crown: As described in the section 2.2 of the FFE “Model

Description” chapter, equations in Brown and Johnston (1976) provide

estimates of live and dead crown material for most species in the EM-FFE

(table 4.18). Western juniper (‘other’) equations are based on a single-stem

form.

Live leaf lifespan is used to simulate the contribution of needles and leaves

to annual litter fall. Dead foliage and branch materials also contribute to

litter fall, at the rates shown in table 4.19. Each year the inverse of the

lifespan is added to the litter pool from each biomass category. Leaf lifespan

data are from Keane and others (1989). Lifespan of western white pine is

assumed to be the same as ponderosa pine.

Live Herbs and Shrub: Live herb and shrub fuels are modeled simply

by the FFE. Shrubs and herbs are assigned a biomass value based on total

tree canopy cover and dominant overstory species (table 4.20). When there

are no trees, habitat type is used to infer the most likely dominant species of

the previous stand (“Model Description” chapter, section 2.4.2). When total

tree canopy cover is less than 10 percent, herb and shrub biomass is assigned

an “initiating” value (the ‘I’ rows from table 4.20). When canopy cover is

greater than 60 percent, biomass is assigned an “established” value (the ‘E’

Table 4.18—The crown biomass equations listed here determine the

biomass of foliage, branch, and stem wood.

Species Species mapping and equation source

whitebark pine Brown (1978)

western larch Brown and Johnston (1976)

Douglas-fir Brown and Johnston (1976)

lodgepole pine Brown and Johnston (1976)

Engelmann spruce Brown and Johnston (1976)

subalpine fir Brown and Johnston (1976)

ponderosa pine Brown and Johnston (1976)

other Chojnacky (1992), Grier and others (1992)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 107

Table 4.20—Values (dry weight, tons/acre) for live fuels used in the EM-FFE.

Biomass is linearly interpolated between the “initiating” (I) and “established” (E) values when canopy cover is between 10 and 60 percent

Species Herbs Shrubs Notes

whitebark pine E 0.20 0.05

I 0.40 0.50

western larch E 0.20 0.10

I 0.40 1.00

Douglas-fir E 0.20 0.10

I 0.40 1.00

lodgepole pine E 0.20 0.05

I 0.40 0.50

Engelmann spruce E 0.15 0.10

I 0.30 1.00

subalpine fir E 0.15 0.10

I 0.30 1.00

ponderosa pine E 0.20 0.12

I 0.25 0.05

other E 0.04 0.05 Ottmar and others (2000a)

I 0.13 1.63

Table 4.19—Life span of live and dead foliage (years) and dead branches

for species modeled in the EM-FFE variant.

Live Dead

Species Foliage Foliage <0.25” 0.25–1” >1”

whitebark pine 7 2 5 5 15

western larch 1 1 5 5 15

Douglas-fir 5 2 5 5 15

lodgepole pine 3 2 5 5 15

Engelmann spruce 6 2 5 5 10

subalpine fir 7 2 5 5 15

ponderosa pine 4 2 5 5 10

other 4 2 5 5 20

rows). Live fuel loads are linearly interpolated when canopy cover is between

10 and 60 percent. Data are taken from FOFEM 4.0 (Reinhardt and others

1997) with modifications provided by Jim Brown (USFS, Missoula, MT pers.

comm., 1995).

Dead Fuels: Initial default DWD pools are based on overstory species.

When there are no trees, habitat type is used to infer the most likely

dominant species of the previous stand (“Model Description” chapter, section

2.4.2). Default fuel loadings were provided by Jim Brown (USFS, Missoula,

MT pers. comm., 1995) (table 4.21). If tree canopy cover is less than 10

percent, the DWD pools are assigned an “initiating” value, and if cover is

greater than 60 percent they are assigned the “established” value. Fuels are

linearly interpolated when canopy cover is between 10 and 60 percent. Initial

fuel loads can be modified using the FUELINIT keyword.
108 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

4.3.4 Bark Thickness

Bark thickness contributes to predicted tree mortality from simulated

fires. The bark thickness multipliers in table 4.22 are used to calculate single

bark thickness (“Model Description” chapter, section 2.5.5). The bark thickness equation used in the mortality equation is unrelated to the bark

thickness used in the base FVS model. Data are from FOFEM 5.0 (Reinhardt

and others 2001).

4.3.5 Decay Rate

Decay of down material is simulated by applying loss rates to biomass in

categories based on the original size-class of the branch and bole pieces as

described in section 2.4.5 of the “Model Description” chapter (table 4.23).

Default decay rates are based on Abbott and Crossley (1982). A portion of the

loss is added to the duff pool each year. Loss rates are for hard material; soft

material in all size classes, except litter and duff, decays 10 percent faster.

By default, the FFE decays all wood species at the rates shown in table

4.23. The decay rates of species groups may be modified by users, who can

provide rates to the four decay classes shown in table 4.24 using the

FUELDCAY keyword. Users can also reassign species to different classes

using the FUELPOOL keyword.

Table 4.21—Canopy cover and cover type are used to assign default down woody debris (tons/acre) by size class

for established (E) and initiating (I) stands.

Size class (inches)

Species < 0.25 0.25 – 1 1 – 3 3 – 6 6 – 12 > 12 Litter Duff

whitebark pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

western larch E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

Douglas-fir E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

lodgepole pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

Engelmann spruce E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

subalpine fir E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

ponderosa pine E 0.7 0.7 1.6 2.5 2.5 0.0 1.4 5.0

I 0.1 0.1 0.2 0.5 0.5 0.0 0.5 0.8

other E 0.2 0.8 2.3 1.4 3.0 0.0 9.3 0.0

I 0.0 0.1 0.0 0.0 0.0 0.0 2.0 0.0

Table 4.22—Species specific constants

for determining single bark

thickness.

Species Multiplier (Vsp)

whitebark pine 0.030

western larch 0.063

Douglas-fir 0.063

lodgepole pine 0.028

Engelmann spruce 0.036

subalpine fir 0.041

ponderosa pine 0.063

other 0.025
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 109

Table 4.23—Default annual loss rates are applied based

on size class. A portion of the loss is added

to the duff pool each year. Loss rates are for

hard material. If present, soft material in all

size classes except litter and duff decays

10 percent faster.

4.3.6 Moisture Content

Moisture content of the live and dead fuels is used to calculate fire intensity

and fuel consumption (“Model Description” chapter, section 2.5.2). Users can

choose from four predefined moisture groups (table 4.25) or they can specify

moisture conditions for each class using the MOISTURE keyword.

4.3.7 Fire Behavior Fuel Models

Fire behavior fuel models (Anderson 1982) are used to estimate flame

length and fire effects stemming from flame length. Fuel models are determined using fuel load and stand attributes (“Model Description” chapter,

Table 4.24—Default wood decay classes used

in the EM-FFE variant. Classes

are from The Wood Handbook

(1999). (1 = exceptionally high;

2 = resistant or very resistant;

3 = moderately resistant, and

4 = slightly or nonresistant)

Species Decay class

whitebark pine 4

western larch 3

Douglas-fir 3

lodgepole pine 4

Engelmann spruce 4

subalpine fir 4

ponderosa pine 4

other 2

Size class

(inches)

Annual loss rate

Proportion of loss

becoming duff

< 0.25

0.25 — 1

0.12

1 — 3 0.09

3 — 6

6 — 12

> 12

0.015

Litter 0.50

0.02

Duff 0.002 0.0
110 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Figure 4.2—If large and small fuels map

to the shaded area, candidate fuel models are determined using the logic shown

in table 4.26. Otherwise, flame length is

based on the distance to the closest fuel

models, identified by the dashed lines,

and on recent management (see “Model

Description” chapter, section 2.4.8 for further details).

section 2.4.8) specific to each FFE variant. In addition, stand management

actions such as thinning and harvesting can abruptly increase fuel loads and

can trigger “Activity Fuels” conditions, resulting in the selection of alternative fuel models. At their discretion, FFE users have the option of:

1. Defining and using their own fuel models

2. Defining the choice of fuel models and weights

3. Allowing the FFE variant to determine a weighted set of fuel models

4. Allowing the FFE variant to determine a weighted set of fuel models,

then using the dominant model

This section explains the steps taken by the EM-FFE to follow the third and

fourth of these four options.

When the combination of large and small fuel lies in the lower left corner

of the graph shown in figure 4.2, one or more low-fuel fuel models become

candidate models. In other regions of the graph, other fuel models may also

be candidates. The habitat types shown in table 4.26 define which of eight

groups of low fuel model(s) will become candidates. According to the logic of

the table, only a single fuel model will be chosen for a given stand structure.

Consequently, as a stand undergoes structural changes due to management

or maturation, the selected fire model can jump from one model selection to

another, which in turn may cause abrupt changes in predicted fire behavior.

Table 4.25—Moisture values, which alter fire intensity and consumption, have been predefined for four groups.

Moisture group

Size class Very dry Dry Moist Wet

0 – 0.25 in. (1-hr) 4 8 12 16

0.25 – 1.0 in. (10-hr) 4 8 12 16

1.0 – 3.0 in. (100-hr) 5 10 14 18

> 3.0 in. (1000+ -hr) 10 15 25 50

Duff 15 50 125 200

Live 70 110 150 150

10 20 30 40

10

20

30

40

0

3" + fuels (tons/acre)

0"-3" fuels (tons/acre)

FM10 - natural fuels

FM11 - activity fuels

FM12 - natural fuels

FM14 - activity fuels

FM13

Fuel model, 100% weight

low fuels

models

0
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 111

Table 4.26—When low fuel loads are present in the EM-FFE, fire behavior fuel models are determined using

one of eight habitat categories: scree, dry grassy, grassy tall shrub, grassy shrub, long needle

shrubby, short needle shrubby, short needle grassy, and other. Fuel model is linearly interpolated between the two low fuel models when canopy cover falls between 30 and 50 percent.

Tree cover

< 30 percent

Tree cover

> 50 percent Habitat type

number

Habitat type

name FFE habitat category Fuel Model

10 scree Scree 8 8

66 Unknown Dry Grassy 1 2

70 PIFL/JUCO Grassy Tall Shrub 2 6

74 Unknown

79 Unknown

91 Unknown

92 Unknown

93 Unknown

95 Unknown

100 PIPO

110 PIPO-AND

120 Unknown

130 PIPO-AGSP

140 PIPO-FEID

141 PIPO-FEID-FEID

Dry Grassy 1 2

161 PIPO-PUTR-AGSP Grassy Shrub 1 9

170 PIPO-SYAL

171 PIPO-SYAL-SYAL

172 PIPO-SYAL-BERE

Long Needle Shrubby 2 9

180 PIPO-PRVI Grassy Tall Shrub 2 6

To smooth out changes resulting from changes in fuel model, the strict logic

is augmented by linear transitions between states that involve continuous

variables (for example, percent canopy cover, average height, snag density,

and so forth).

If the STATFUEL keyword is selected, fuel model is determined using

only the closest-match fuel model identified by either figure 4.2 or table 4.26.

The FLAMEADJ keyword allows the user to scale the calculated flame

length or override the calculated flame length with a value they choose.
112 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.26 (Con.)

Tree cover

< 30 percent

Tree cover

> 50 percent Habitat type

number

Habitat type

name FFE habitat category Fuel Model

181 PIPO-PRVI-PRVI

182 PIPO-PRVI-SHCA

200 PSME Short Needle Shrubby 2 8

210 PSME-AGSP

220 PSME-FEID

221 Unknown

230 PSME-FESC

Short Needle Grassy 1 8

250 PSME-VACA

260 PSME-PHMA

261 PSME-PHMA-PHMA

262 PSME-PHMA-CARU

280 PSME-VAGL

281 PSME-VAGL-VAGL

282 PSME-VAGL-ARUV

283 PSME-VAGL-XETE

290 PSME-LIBO

291 PSME-LIBO-SYAL

292 PSME-LIBO-CARU

293 PSME-LIBO-VAGL

310 PSME-SYAL

311 PSME-SYAL-AGSP

312 PSME-SYAL-CARU

313 PSME-SYAL-SYAL

315 Unknown

320 PSME-CARU

Short Needle Shrubby 2 8
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 113

Table 4.26 (Con.)

Tree cover

< 30 percent

Tree cover

> 50 percent Habitat type

number

Habitat type

name FFE habitat category Fuel Model

321 PSME-CARU-AGSP

322 PSME-CARU-ARUV

323 PSME-CARU-CARU

330 PSME-CAGE

331 Unknown

332 Unknown

340 PSME-SPBE

350 PSME-ARUV

360 PSME-JUCO

370 PSME-ARCO

371 Unknown

All others Other 5 8
114 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

4.4 Southern Oregon/Northern California (SO) __________________________

4.4.1 Tree Species

The Southern Oregon/Northern California (SORNEC) variant models the

10 tree species shown in table 4.27. White fir and grand fir are modeled

together as one species, as are red fir and subalpine fir. One additional

category, “other” is modeled using western juniper.

4.4.2 Snags

The majority of the snag model logic is based on unpublished data provided

by Bruce Marcot (USFS, Portland, OR, unpublished data 1995). Snag fall

parameters were originally developed at the SO-FFE workshop. Parameters

for California stands were revised at a California variants workshop

(Stephanie Rebain, personal communication, February 2003). A complete

description of the Snag Submodel is provided in section 2.3 of the FFE “Model

Description” chapter.

Four variables are used to modify the Snag Submodel for the different

species in the SO-FFE variant:

• A multiplier to modify the species’ fall rate

• Amultiplier to modify the time required for snags to decay from a “hard”

to “soft” state

• The maximum number of years that snags will remain standing

• A multiplier to modify the species’ height loss rate

These variables are summarized in tables 4.28 and 4.29.

Snag bole volume is determined in using the base FVS model equations.

The coefficients shown in table 4.30 are used to convert volume to biomass.

Soft snags have 80 percent the density of hard snags. Ponderosa pine trees

break extremely slowly in regions modeled by the SO-FFE, so the height loss

rate was changed to 0.3 from 1.0 (the value used by the NI-FFE).

Table 4.27—Tree species simulated by the Southern Oregon/Northern California

variant.

Common name Scientific name Notes

western white pine Pinus monticola

sugar pine Pinus lambertiana

Douglas-fir Pseudotsuga menziesii

white fir, grand fir Abies concolor, A. grandis

mountain hemlock Tsuga mertensiana

incense-cedar Calocedrus decurrens = Libocedrus decurrens

lodgepole pine Pinus contorta

Engelmann spruce Picea engelmannii

red fir, subalpine fir Abies magnifica, A. lasiocarpa

ponderosa pine Pinus ponderosa

other = western juniper
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 115

Table 4.28—Default snag fall, snag height loss and soft-snag characteristics for

20 inch DBH snags in the SO-FFE variant. These characteristics are

derived directly from the parameter values shown in table 4.29.

Species 95% fallen All down 50% height Hard-to-soft

- - - - - - - - - - - - - - - - - - Years - - - - - - - - - - - - - - - - - - - -

Oregon

western white pine 34 110 33 42

sugar pine 34 110 33 42

Douglas-fir 34 110 33 42

white fir, grand fir 28 90 27 35

mountain hemlock 28 90 27 35

incense-cedar 28 90 27 35

lodgepole pine 28 90 27 35

Engelmann spruce 28 90 27 35

red fir, subalpine fir 28 90 27 35

ponderosa pine 31 100 100 39

other 31 100 30 39

California

western white pine 25 100 20 —

sugar pine 25 100 20 —

Douglas-fir 35 100 20 —

white fir, grand fir 35 100 20 —

mountain hemlock 25 100 20 —

incense-cedar 45 100 20 —

lodgepole pine 25 100 20 —

Engelmann spruce 35 100 20 —

red fir, subalpine fir 35 100 20 —

ponderosa pine 25 100 20 —

other 45 150 20 —

4.4.3 Fuels

Information on live fuels was developed using FOFEM 4.0 (Reinhardt and

others 1997) and FOFEM 5.0 (Reinhardt and others 2001) and in cooperation

with Jim Brown (USFS, Missoula, MT pers. comm. 1995). A complete

description of the Fuel Submodel is provided in section 2.4 of the FFE “Model

Description” chapter.

Fuels are divided into to four categories: live tree bole, live tree crown, live

herb and shrub, and down woody debris (DWD). Live herb and shrub fuel

load, and initial DWD are assigned based on the cover species with greatest

basal area. If there is no basal area in the first simulation cycle (a “bare

ground” stand) then the initial fuel loads are assigned by the vegetation code

provided with the STDINFO keyword. If the vegetation code is missing or

does not identify an overstory species, the model uses a ponderosa pine cover

type to assign the default fuels. If there is no basal area in other cycles of the

simulation (after a simulated clearcut, for example) herb and shrub fuel

biomass is assigned by the previous cover type.

Live Tree Bole: The fuel contribution of live trees is divided into two

components: bole and crown. Bole volume is transferred to the FFE after

being computed by the FVS model, then converted to biomass using ovendry

wood density calculated from table 4-3a and equation 3-5 of The Wood
116 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.29—Default snag fall, snag height loss and soft-snag

multipliers for the SO-FFE. These parameters

result in the values shown in table 4.28. (These

three columns are the default values used by

the SNAGFALL, SNAGBRK, and SNAGDCAY

keywords, respectively.)

Snag Height

Species fall loss Hard-to-soft

Oregon

western white pine 0.9 0.9 1.1

sugar pine 0.9 0.9 1.1

Douglas-fir 0.9 0.9 1.1

white fir, grand fir 1.1 1.1 0.9

mountain hemlock 1.1 1.1 0.9

incense-cedar 1.1 1.1 0.9

lodgepole pine 1.1 1.1 0.9

Engelmann spruce 1.1 1.1 0.9

red fir, subalpine fir 1.1 1.1 0.9

ponderosa pine 1.0 0.3 1.0

other 1.0 1.0 1.0

California

western white pine 1.24 1.49 —

sugar pine 1.24 1.49 —

Douglas-fir 0.88 1.49 —

white fir, grand fir 0.88 1.49 —

mountain hemlock 1.24 1.49 —

incense-cedar 0.69 1.49 —

lodgepole pine 1.24 1.49 —

Engelmann spruce 0.88 1.49 —

red fir, subalpine fir 0.88 1.49 —

ponderosa pine 1.24 1.49 —

other 0.69 1.49 —

Table 4.30—Wood density (ovendry lb/ft3)

used in the SO-FFE variant.

Species Density (lb/ft3)

western white pine 24.8

sugar pine 23.3

Douglas-fir 32.7

white fir, grand fir 25.6

mountain hemlock 29.5

incense-cedar 24.1

lodgepole pine 26.4

Engelmann spruce 22.6

red fir, subalpine fir 24.8

ponderosa pine 26.4

other 34.9
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 117

Handbook (Forest Products Laboratory 1999). The coefficients in table 4.30

for white fir/grand fir are based on white fir; Douglas-fir is based on Douglasfir Interior west.

Tree Crown: As described in the section 2.2 of the FFE “Model Description” chapter, equations in Brown and Johnston (1976) provide estimates of

live and dead crown material for most species in the SO-FFE. Some species

mappings are used, as shown below in table 4.31. Mountain hemlock biomass

is based on Gholz (1979), using western hemlock equations from Brown and

Johnston to partition the biomass and also to provide estimates for trees less

than one inch diameter. Juniper (“other”) equations are based on a singlestem form.

Live leaf lifespan is used to simulate the contribution of needles and leaves

to annual litter fall. Dead foliage and branch materials also contribute to

litter fall, at the rates shown in table 4.32. Each year the inverse of the

lifespan is added to the litter pool from each biomass category. These data are

from the values provided at the SO-FFE workshop and California variants

model verification workshop (Stephanie Rebain, USFS, pers. comm. February 2003).

Live Herbs and Shrubs: Live herb and shrub fuels are modeled simply

by the FFE. Shrubs and herbs are assigned a biomass value based on

structural stage and cover type, using Fuel Characterization Classes (FCCs,

Ottmar and others 1996). In each time step, selection of the FCC begins with

the stand structure logic of Crookston and Stage (1999), embedded in FVS.

The resulting Crookston and Stage classification is then converted to Ottmar’s

classification system, using table 4.33. Cover type is then defined by the

species with the greatest basal area. When there are no trees, habitat type

is used to infer the most likely dominant species of the previous stand (“Model

Description” chapter, section 2.4.2). The FCC is then assigned using table

4.34. Finally, shrub and herb loads are assigned using table 4.35 and are set

to zero if the structural stage is undefined. The structural class rules used in

the SO-FFE variant were first developed for the Interior Columbia River

Basin Assessment (Hessburg and others 1999).

Table 4.31—The crown biomass equations listed here determine the biomass of foliage,

branch, and stem wood. Species mappings are done for species for which

equations are not available.

Species Species mapping and equation source

western white pine Brown and Johnston 1976

sugar pine western white pine (Brown and Johnston 1976)

Douglas-fir Brown and Johnston 1976

white fir, grand fir grand fir (Brown and Johnston 1976)

mountain hemlock Gholz (1979); western hemlock (Brown and Johnston 1976)

incense-cedar western redcedar (Brown and Johnston 1976)

lodgepole pine Brown and Johnston 1976

Engelmann spruce Brown and Johnston 1976

red fir, subalpine fir subalpine fir (Brown and Johnston 1976)

ponderosa pine Brown and Johnston 1976

other Chojnacky (1992), Grier and others (1992)
118 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.33—Stand structure classification is converted from the Crookston and Stage to

Ottmar system using these mappings and assumptions.

 Stand classification system

Crookston and Ottmar and

 Stage (1999) others (1996) Notes

0 1 Regenerating from bare ground

1 1 Stand initiation

2 2 Stem exclusion, open canopy: <60% canopy cover

2 3 Stem exclusion, closed canopy: >=60% canopy cover

3 4 Understory reinitiation

4 5 Young forest, single stratum

5 6 Old forest, single stratum

6 7 Old forest, multistrata

Table 4.32—Life span of live and dead foliage (years) and dead branches for

species modeled in the SO-FFE variant.Oregon

Live Dead

Species Foliage Foliage <0.25” 0.25–1” >1”

Oregon

western white pine 4 2 5 5 15

sugar pine 3 2 5 5 15

Douglas-fir 5 2 5 5 15

white fir, grand fir 7 2 5 5 15

Mountain hemlock 4 2 5 5 15

incense-cedar 5 1 5 5 20

Lodgepole pine 3 2 5 5 15

Engelmann spruce 6 2 5 5 10

red fir, subalpine fir 7 2 5 5 15

ponderosa pine 4 2 5 5 10

other 4 2 5 5 15

California

western white pine 4 3 10 15 15

sugar pine 3 3 10 15 15

Douglas-fir 5 3 10 15 15

white fir, grand fir 7 3 10 15 15

Mountain hemlock 4 3 10 15 15

incense-cedar 5 1 10 15 20

Lodgepole pine 3 3 10 15 15

Engelmann spruce 6 3 10 10 10

red fir, subalpine fir 7 3 10 15 15

ponderosa pine 4 3 10 10 10

other 4 3 10 15 15
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 119

Table 4.34—Cover type and structural stage class are used to determine the appropriate FCC, in order to estimate herb and

shrub load and the initial default down woody debris load.

FCCs for sugar pine are mapped using western white

pine. When a ponderosa pine stand is classed as regenerating from bare ground, it is assumed that it has been

recently logged and is assigned FCC-1 instead of FCC-4.

Structural stage §

Species 1 2 3 4, 5 6 7

western white pine 52 53 56 58 57 61

sugar pine 52 53 56 58 57 61

Douglas-fir 52 53 56 58 62 62

white fir, grand fir 52 53 56 58 62 62

mountain hemlock 52 53 56 58 62 62

incense-cedar 52 53 56 58 62 62

lodgepole pine 103 106 107 110 112 113

Engelmann spruce 52 53 56 59 61 62

red fir, subalpine fir 52 53 56 59 62 62

ponderosa pine 4, 1 4 4 8 11 10

other — — — 160 — —

§ 1 = stand initiation (si); 2 = stem exclusion, open canopy (cover <60%) (seoc);

3 = stem exclusion, closed canopy (canopy cover>60%) (secc); 4 = understory

re-initiation (ur); 5 = young forest, multi-story (yfms); 6 = old forest single-story

(ofss); 7 = old forest, multi-story (ofms).

Dead Fuels: Initial default values for the dead fuel components are

determined using Fuel Characterization Classes (FCCs; Ottmar and others

1996) using tables 4.33 and 4.34 and following the process just described in

the section on live herbs and shrubs. The FCC diameter breakpoints shown

in table 4.36 are different from those used by the FFE. Linear interpolation

is used to partition the FCC fuel loads into the FFE size classes. The SO-FFE

initial loads for litter are set to zero, since these data are absent from the FCC

system. Default initial fuel loads can be modified using the FUELINIT

keyword.

Table 4.35—Default live fuel loads (tons/acre) are determined for each FCC. The appropriate FCC is

assigned using table 4.34.

FCC Herb Shrub FCC Herb Shrub

1 0.3 0.4 61 0.3 0.4

4 0.5 0.5 62 0.8 0.5

8 0.0 0.0 103 0.3 0.4

10 0.5 2.5 106 0.5 0.5

11 0.5 0.5 107 0.5 0.5

52 0.5 0.5 110 0.5 0.5

53 0.5 0.5 112 0.3 0.4

56 0.5 0.5 113 0.5 0.5

57 0.3 0.4 160 0.7 3.3

58 0.3 0.4
120 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.36—Default dead fuel loads (tons/acre) are determined for each FCC used in the SO-FFE

variant. The appropriate FCC for each modeled stand is assigned using tables 4.33 and

4.34. Litter estimates are absent in the FCC, and set to zero.

Size class (inches)

FCC < 0.25 0.25 – 1 1 – 3 3 – 9 9 – 20 > 20 Litter Duff

1 0.5 0.8 1.7 1.9 3.0 0.0 – 2.3

4 0.1 1.5 2.2 1.1 1.8 3.3 – 6.0

8 0.1 1.6 4.2 2.1 2.9 4.7 – 9.8

10 0.2 1.2 2.3 2.3 2.4 2.0 – 12.8

11 0.0 1.5 4.9 10.1 6.2 4.0 – 12.8

52 0.6 2.3 1.9 2.0 0.0 0.0 – 2.3

53 0.5 1.3 3.0 4.5 1.5 0.0 – 2.3

56 0.5 1.3 3.0 4.5 1.5 0.0 – 9.1

57 0.4 0.6 1.1 8.8 7.2 5.0 – 9.1

58 0.7 1.1 1.5 3.1 4.7 0.0 – 15.9

61 0.5 1.2 1.2 2.5 5.2 2.0 – 20.4

62 0.5 2.6 4.3 7.0 10.5 3.0 – 20.4

103 0.5 0.8 1.7 0.9 0.0 0.0 – 2.3

106 0.3 0.7 4.0 0.8 0.0 0.0 – 3.8

107 0.4 1.2 7.4 2.1 0.0 0.0 – 3.8

110 0.7 2.3 5.9 5.1 2.0 0.0 – 4.5

112 0.2 0.9 1.7 1.3 3.0 0.0 – 6.0

113 0.2 1.1 3.4 14.8 3.5 0.0 – 6.0

160 0.2 0.4 0.8 0.0 0.0 0.0 – 2.3

4.4.4 Bark Thickness

Bark thickness contributes to predicted tree mortality from simulated

fires. The bark thickness multipliers in table 4.37 are used to calculate single

bark thickness as described in the “Model Description” chapter, section 2.5.5.

The bark thickness equation used in the mortality equation is unrelated to

the bark thickness used in the base FVS model. Data are from FOFEM 5.0

(Reinhardt and others 2001).

Table 4.37—Species specific constants for determining

single bark thickness.

Species Multiplier (Vsp)

western white pine 0.035

sugar pine 0.072

Douglas-fir 0.063

white fir, grand fir 0.048

mountain hemlock 0.040

incense-cedar 0.060

lodgepole pine 0.028

Engelmann spruce 0.036

red fir, subalpine fir 0.039

ponderosa pine 0.063

other 0.025
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 121

4.4.5 Decay Rate

Decay of down material is simulated by applying the loss rates shown in

table 4.38, as described in section 2.4.5 of the “Model Description” chapter.

Default decay rates are based on Abbott and Crossley (1982). Decay parameters were originally developed at the SO-FFE workshop. Parameters for

California stands were revised at a California variants workshop (Stephanie

Rebain, pers.comm, February 2003),

By default, the FFE decays all wood species at the rates shown in table

4.39. The decay rates of species groups may be modified by users, who can

Table 4.38—Default annual loss rates are applied based on size

class.

Size class

Annual loss

rate

Proportion of loss

becoming duff

Oregon

0 — 0.25 in.

0.25 — 1.0 in.

0.12

1.0 — 3.0 in. 0.09

3.0 — 6.0 in.

6.0 — 12.0 in.

> 12.0 in.

0.015

Litter 0.5

0.02

Duff 0.002 0.0

California

0 — 0.25 in.

0.25 — 1.0 in.

1.0 — 3.0 in.

0.025

3.0 — 6.0 in.

6.0 — 12.0 in.

> 12.0 in.

0.0125

Litter 0.5

0.02

Duff 0.002 0.0
122 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

provide rates to the four decay classes shown in table 4.39 using the

FUELDCAY keyword. Users can also reassign species to different classes

using the FUELPOOL keyword.

4.4.6 Moisture Content

Moisture content of the live and dead fuels is used to calculate fire

intensity and fuel consumption (“Model Description” chapter, section

2.5.2). Users can choose from four predefined moisture groups shown in

table 4.40, or they can specify moisture conditions for each class using the

MOISTURE keyword. The predefined moisture groups are the same as

those defined for the NI-FFE.

4.4.7 Fire Behavior Fuel Models

Fire behavior fuel models (Anderson 1982) are used to estimate flame

length and fire effects stemming from flame length. Fuel models are determined using fuel load and stand attributes (“Model Description” chapter,

section 2.4.8) specific to each FFE variant. In addition, stand management

actions such as thinning and harvesting can abruptly increase fuel loads and

Table 4.40—Moisture values, which alter fire intensity and consumption, have been predefined for four groups.

Moisture group

Size class Very dry Dry Moist Wet

0 – 0.25 in. (1 hr.) 3 8 12 12

0.25 – 1.0 in. (10 hr.) 4 8 12 12

1.0 – 3.0 in. (100 hr.) 5 10 14 14

> 3.0 in. (1000+ hr.) 10 15 25 25

Duff 15 50 125 125

Live 70 110 150 150

Table 4.39—Default wood decay classes

used in the WS-FFE variant.

Classes are from The Wood

Handbook (1999). (1 = exceptionally high; 2 = resistant or very resistant; 3 =

moderately resistant, and

4 = slightly or nonresistant.)

Species Decay class

western white pine 4

sugar pine 4

Douglas-fir 3

white fir, grand fir 4

mountain hemlock 4

incense-cedar 2

lodgepole pine 4

Engelmann spruce 4

red fir, subalpine fir 4

ponderosa pine 4

other 2
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 123

can trigger “Activity Fuels” conditions, resulting in the selection of alternative fuel models. At their discretion, FFE users have the option of:

1. Defining and using their own fuel models

2. Defining the choice of fuel models and weights

3. Allowing the FFE variant to determine a weighted set of fuel models

4. Allowing the FFE variant to determine a weighted set of fuel models,

then using the dominant model

This section explains the steps taken by the SO-FFE to follow the third and

fourth of these four options.

When the combination of large and small fuel lies in the lower left corner

of the graph shown in figure 4.3, one or more low-fuel fuel models become

candidate models. In other regions of the graph, other fuel models may also

be candidates. The logical flow shown in figure 4.4 defines which low-fuel

model(s) will become candidates. According to the logic of figure 4.4, only a

single fuel model will be chosen for a given stand structure. Consequently, as

a stand undergoes structural changes due to management or maturation, the

selected fire model can jump from one model selection to another, which in

turn may cause abrupt changes in predicted fire behavior. To smooth out

changes resulting from changes in fuel model, the strict logic is augmented

by linear transitions between states that involve continuous variables (for

example, percent canopy cover, average height, snag density, and so forth).

In addition, a fuzzy logic approach is used to incorporate weights based on the

dominant cover type.

If the STATFUEL keyword is selected, fuel model is determined by using

only the closest-match fuel model identified by either figure 4.3 or figure 4.4.

The FLAMEADJ keyword allows the user to scale the calculated flame

length or override the calculated flame length with a value they choose.

Figure 4.3—If large and small fuels

map to the shaded area, candidate fuel

models are determined using the logic

shown in figure 4.4. Otherwise, flame

length is based on the distance to the

closest fuel models, identified by the

dashed lines, and on recent management (see “Model Description” chapter, section 2.4.8 for further details).

10 20 30 40

10

20

30

40

0

3" + fuels (tons/acre)

0"-3" fuels (tons/acre)

FM10 - natural fuels

FM11 - activity fuels

FM12 - natural fuels

FM14 - activity fuels

FM13

Fuel model, 100% weight

low fuels

models

0
124 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Begin Begin

A B

Private Lands and Region 5 National Forests Region 6 National Forests

FM 2 FM 9

FM 5

FM 5

FM 8

FM 8

FM 1

FM 9

FM 8

FM 5

FM 8 FM 8

FM 9 FM 2

FM 10

FM 3

FM 5

FM 5

FM 9

FM 10

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES YES

YES

YES

YES YES YES

YES

YES

YES

YES

YES

YES YES

YES

YES

YES

YES

YES

YES

NO NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO NO

NO

NO

NO NO NO

NO

NO

NO

NO

NO

W. white

pine, sugar

pine, incense

cedar, or other

cover type

Dry

lodgepole

pine super

group

Wet

lodgepole

pine super

group

Juniper

shrubland

super

group

West

ponderosa

pine super

group

Dry

or wet mixed

conifer super

group 

Hemlock

super

group

Douglasfir, white/grand

fir, mountain hemlock

Engelmann spruce

or ponderosa

pine cover

type

Lodgepole pine or

red/subalpine

fir 

*> QMD of live trees

CC

<25%

Structural

class 0/1 and

crown base

height ≤3’

Structural

class 0/1 and

crown base

height ≤3’

CC

<60%

Structural

class 0/1

Structural

class ≥5 and

crown cover

<50%

CC

<40%

Top

height

 >35’ or crown

cover

>60% 

CCF

due to typic

species >95%

(or total

 CCF ~0)

Harvest

or fire ≤5

years

ago

Structural

class ≥2

# Large

snags*

>4 per acre 

Predicted

lodgepole pine

mortality >50%

(and total predicted

mortality

>0) 

FM 2

FM 5

FM 5

FM 1

FM 6

FM 8

FM 6

FM 8

FM 8

FM 6

FM8

FM 8

9

9

8

8

8

CC

>35%

Structural

class 0/1

CC

>80%

CC

>60%

CC

<60%

CC

>80%

Dry

ponderosa

pine super

group

CC

>40%

Figure 4.4—Logic for modeling fire at “low” fuel loads in USDA Forest Service Region 5 (a) and Region 6 (b) forests in the SO-FFE.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 125

4.4.8 Consumption

Consumption of natural fuels is modeled in the same way as in the NI-FFE

(“Model Description” chapter, section 2.5.5). Activity fuels, material created

from a stand entry in the previous 5 years, are modeled using equations from

Consume 1.0 (Ottmar and other 1993) with some modifications based on new

information.

1-hour and 10-hour fuels

100 percent consumption.

100-hour fuels

C M

F Slope Wind

= − −

+ +

−





 



 



























 0 9 0 0535 0 03 

05 1

20

60 4

2

12 10 .. .

ln( .

ln( )

where:

C is the percent consumption

F is the amount of 100-hour fuel present before the burn, in tons

per acre

M10 is the percent fuel moisture of the 10-hour fuels

Slope is the site slope, in percent

Wind is the wind speed at the time of the fire, in mph.

1000-hour+ fuels

The consumption of larger fuels depends on their moisture as well as the

moisture level of the 10-hour fuels, 1000-hour fuels, and the amount of

consumption of the 100-hour fuels.

First, a diameter reduction variable (DRED) is calculated based on fuel

moisture (M), as shown in table 4.41. Then, if the 10-hour fuel moisture is less

than 15 percent, the DRED value is further modified using table 4.42.

Finally, the percent consumption can be calculated as:

C a DRED = −  −

  1

5 2

2

.

where:

C is the percent consumption

DRED is the diameter reduction factor calculated above, and

a is 5.2 for 1000-hour fuels, and 13.7 for 10000-hour fuels

Duff—The consumption of duff depends on the moisture level of the duff

and consumption in some of the other fuel classes (table 4.43). Assumptions

were made about the duff moisture values at which each of the equations was

Table 4.41—The relationship between diameter reduction (DRED) and 1000-hour

moisture.

Condition Equation

M > 60% 1: DRED = –0.005 x M + 0.731

M > 44% and M ≤ 60% 2: DRED = –0.0178 x M + 1.489

M ≥ 44% and Consumption of 100hr ≤ 75% 3: DRED = –0.096 x M + 4.6495

M < 44% and Consumption of 100hr ≥ 85% 4: DRED = –0.125 x M + 6.27

M < 44% and Consumption of 100hr 75% – 85% Interpolate between eq. 3 and 4
126 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.43—Consumption equations for a range of duff

moisture levels.

Duff moisture Equation

≥ 200% R = 0.537 + (C1000 + C10000)

125% – 200% R = 0.323 + 1.034 + DRED

50% – 125% R = 1.323 + 1.034 + DRED

< 50% R = 2.323 + 1.034 + DRED

Table 4.42—The relationship between diameter reduction (DRED) and 1000-hour moisture, given

low 10-hour moisture.

1000-hr fuel moisture Equation

M≤ 40% DRED = DRED x (1 - 0.22)

M 40%-50% DRED = DRED x (1 - 0.11)

used, the quadratic mean diameter of the 100-hour fuels, the number of dry

months prior to the fire, and the bulk density.

where:

Ci is the consumption value of the i-th hour fuels.

DRED is the diameter reduction factor of the large fuels, as calculated

above.

R is the reduction factor of the duff.

Consumption, in tons per acre rather than percent, is then calculated as:

C = 12.1 x R x b

where:

C is the maximum tons per acre of duff consumed

R is calculated above, and

b is a multiplier which is:

0.50 – when duff depth is less than 1 inch;

0.75 – when duff depth is 2 or more inches, and

is interpolated when duff depth is 1 to 2 inches.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 127

4.5 Central Rockies (CR)____________________________________________

4.5.1 Tree Species

The Central Rockies variant models the 22 tree species shown in table

4.44. Two additional categories, “other softwood” and “other hardwood” are

modeled using pines and cottonwoods, respectively.

4.5.2 Snags

The majority of the snag model logic is based on unpublished data provided

by Bruce Marcot (USFS, Portland, OR, unpublished data 1995). Snag fall

parameters were developed at the FFE design workshop. A complete description of the Snag Submodel is provided in section 2.3 of the FFE “Model

Description” chapter.

Four variables are used to modify the Snag Submodel for the different

species in the CR-FFE variant:

• A multiplier to modify the species’ fall rate

• Amultiplier to modify the time required for snags to decay from a “hard”

to “soft” state

• The maximum number of years that snags will remain standing

• A multiplier to modify the species’ height loss rate

These variables are summarized in tables 4.45 and 4.46.

Height loss rate of quaking aspen and cottonwoods are insignificant in

comparison to their rapid snag fall rate and are not modeled. The fall rate of

these hardwoods is also halved in the 10 years following a burn. In the case

Table 4.44—Tree species simulated by the Central Rockies variant.

Common name Scientific name Notes

subalpine fir Abies lasiocarpa

corkbark fir Abies lasiocarpa arizonica

Douglas-fir Pseudotsuga menziesii

grand fir Abies grandis

white fir Abies concolor

mountain hemlock Tsuga mertensiana

western redcedar Thuja plicata

western larch Larix occidentalis

bristlecone pine Pinus aristata

limber pine Pinus flexilis

lodgepole pine Pinus contorta

pinyon pine Pinus edulis

ponderosa pine Pinus ponderosa

whitebark pine Pinus albicaulis

southwestern white pine Pinus strobiformis

Rocky Mountain juniper Juniperus scopulorum

blue spruce Picea pungens

Engelmann spruce Picea engelmannii

white spruce Picea glauca

quaking aspen Populus tremuloides

cottonwoods Populus spp.

oaks Quercus spp.

other softwoods = pines

other hardwoods = cottonwoods
128 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.45—Default snag fall, snag height loss and soft-snag characteristics for 20

inch DBH snags in the CR-FFE

Species 95% fallen All down 50% height Hard-to-soft

- - - - - - - - - - - - - - - - - - - -Year s- - - - - - - - - - - - - - - - - - -

subalpine fir 12 40 20 35

corkbark fir 12 40 20 35

Douglas-fir 97§ 100 33 42

grand fir 12 40 20 35

white fir 12 40 20 35

mountain hemlock 31 150 310 39

western redcedar 28 90 33 35

western larch 34 150 310 42

bristlecone pine – – 660 35

limber pine 31 150 310 35

lodgepole pine 31 150 660 35

pinyon pine 31 150 310 35

ponderosa pine 31 150 310 39

whitebark pine 31 150 310 35

southwestern white pine 31 150 310 39

Rocky Mountain juniper 31 150 310 35

blue spruce 97§ 100 660 35

Engelmann spruce 97§ 100 660 35

white spruce 97§ 100 660 35

quaking aspen 8 5 – 35

cottonwoods 8 5 – 35

oaks 12 40 20 35

other softwoods 31 150 660 35

other hardwoods 8 5 – 35

§ This value results from using 32% of the default rate for Douglas-fir and spruce snags >18”

DBH, as described in the text.

of Douglas-fir and spruce snags greater than 18 inches DBH, the fall rate is

reduced to 32 percent of the rate predicted by Marcot’s equation.

Snag bole volume is determined in using the base FVS model equations.

The coefficients shown in table 4.47 are used to convert volume to biomass.

Soft snags have 80 percent the density of hard snags.

Snag dynamics can be modified by the user using the SNAGBRK,

SNAGFALL, SNAGDCAY and SNAGPBN keywords described in the

model description.

4.5.3 Fuels

Information on live fuels was developed using FOFEM 4.0 (Reinhardt and

others 1997) and FOFEM 5.0 (Reinhardt and others 2001) and in cooperation

with Jim Brown (USFS, Missoula, MT pers. comm. 1995). A complete

description of the Fuel Submodel is provided in section 2.4 of the FFE “Model

Description” chapter.

Fuels are divided into to four categories: live tree bole, live tree crown, live

herb and shrub, and down woody debris (DWD). Live herb and shrub fuel

load, and initial DWD are assigned based on the cover species with greatest

basal area. If there is no basal area in the first simulation cycle (a “bare

ground” stand) then the initial fuel loads are assigned by the vegetation code

provided with the STDINFO keyword. If the vegetation code is missing or

does not identify an overstory species, the model uses a lodgepole pine cover
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 129

Table 4.46—Default snag fall, snag height loss and soft-snag

multipliers for the CR-FFE. These parameters

result in the values shown in table 4.45. (These

three columns are the default values used by the

SNAGFALL, SNAGBRK, and SNAGDCAY keywords, respectively.)

Snag Height

Species fall loss Hard-to-soft

subalpine fir 2.5 1.494 0.9

corkbark fir 2.5 1.494 0.9

Douglas-fir 1.0§ 0.9 1.1

grand fir 2.5 1.494 0.9

white fir 2.5 1.494 0.9

mountain hemlock 1.0 0.098 1.0

western redcedar 1.1 0.9 0.9

western larch 0.9 0.098 1.1

bristlecone pine — 0.046 0.9

limber pine 1.0 0.098 0.9

lodgepole pine 1.0 0.046 0.9

pinyon pine 1.0 0.098 0.9

ponderosa pine 1.0 0.098 1.0

whitebark pine 1.0 0.098 0.9

southwestern white pine 1.0 0.098 1.0

Rocky Mountain juniper 1.0 0.098 0.9

blue spruce 1.0§ 0.046 0.9

Engelmann spruce 1.0§ 0.046 0.9

white spruce 1.0§ 0.046 0.9

quaking aspen 4.0 — 0.9

cottonwoods 4.0 — 0.9

oaks 2.5 1.494 0.9

other softwoods 1.0 0.046 0.9

other hardwoods 4.0 — 0.9

§ This value apples to Douglas-fir and spruce snags <18” DBH; see text

for details.

type to assign the default fuels. If there is no basal area in other cycles of the

simulation (after a simulated clearcut, for example) herb and shrub fuel

biomass is assigned by the previous cover type.

Live Tree Bole: The fuel contribution of live trees is divided into two

components: bole and crown. Bole volume is transferred to the FFE after

being computed by the FVS model, then converted to biomass using ovendry

wood density calculated from table 4-3a and equation 3-5 of The Wood

Handbook (Forest Products Laboratory 1999). The coefficient in table 4.47

for Douglas-fir is based on ‘Douglas-fir south’.

Live Tree Crown: As described in the section 2 of the FFE “Model

Description” chapter, equations in Brown and Johnston (1976) provide

estimates of live and dead crown material for most species in the CR-FFE

(table 4.48). Mountain hemlock biomass is based on Gholz (1979), using

western hemlock equations from Brown and Johnston to partition the

biomass and also to provide estimates for trees less than 1 inch diameter.

Pinyon pine, juniper, and Gambel oak may have single or multiple stem

forms: single stem equations were used to compute biomass in all cases
130 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.47—Wood density (ovendry lb/ft3)

used in the CR-FFE variant

Species Density (lb/ft3)

subalpine fir 21.1

corkbark fir 21.1

Douglas-fir 30.3

grand fir 24.1

white fir 25.6

mountain hemlock 29.5

western redcedar 21.1

western larch 34.3

bristlecone pine 26.4

limber pine 24.8

lodgepole pine 26.4

pinyon pine 31.8

ponderosa pine 26.4

whitebark pine 24.8

southwestern white pine 24.8

Rocky Mountain juniper 34.9

blue spruce 25.6

Engelmann spruce 22.6

white spruce 25.6

quaking aspen 24.1

cottonwoods 21.1

oaks 39.6

other softwoods 26.4

other hardwoods 21.1

Table 4.48—The crown biomass equations listed here determine the biomass of foliage, branch and stem wood.

Species mappings are done for species for which equations are not available.

Species Species mapping and equation source

subalpine fir Brown and Johnston (1976)

corkbark fir subalpine fir: Brown and Johnston (1976)

Douglas-fir Brown and Johnston (1976)

grand fir Brown and Johnston (1976)

white fir Grand fir: Brown and Johnston (1976)

mountain hemlock Gholz (1979); Brown and Johnston (1976)

western redcedar Brown and Johnston (1976)

western larch Brown and Johnston (1976)

bristlecone pine pinyon pine: Chojnacky (1992), Grier and others (1992)

limber pine lodgepole pine: Brown and Johnston (1976)

lodgepole pine Brown and Johnston (1976)

pinyon pine Chojnacky (1992), Grier and others (1992)

ponderosa pine Brown and Johnston (1976)

whitebark pine Brown (1978)

southwestern white pine western white pine: Brown and Johnston (1976)

Rocky Mountain juniper Chojnacky (1992), Grier and others (1992)

blue spruce Engelmann spruce: Brown and Johnston (1976)

Engelmann spruce Brown and Johnston (1976)

white spruce Engelmann spruce: Brown and Johnston (1976)

quaking aspen Ruark (1987) <1” DBH, Standish and others (1985) >1” DBH

cottonwoods quaking aspen: Ruark (1987) <1” DBH, Standish and others (1985) >1” DBH

oaks Chojnacky (1992)

other softwoods lodgepole pine: Brown and Johnston (1976)

other hardwoods quaking aspen: Ruark (1987) <1” DBH, Standish and others (1985) >1” DBH
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 131

within the FFE. The FVS base model computes volume of these three species

based on firewood utilization with a minimum branch of diameter of 1.5

inches. Crown and bole dynamics compatibility was maintained by defining

tree crown as being made up of branches and twigs (including dead material)

less than 1.5 inches, and foliage.

Live leaf lifespan is used to simulate the contribution of needles and leaves

to annual litter fall. Dead foliage and branch materials also contribute to

litter fall, at the rates shown in table 4.49. Each year the inverse of the

lifespan is added to the litter pool from each biomass category. Leaf lifespan

data are from Keane and others (1989).

Live Herbs and Shrubs: Live herb and shrub fuels are modeled simply

by the FFE. Shrubs and herbs are assigned a biomass value based on total

tree canopy cover and dominant overstory species (table 4.50). When there

are no trees, habitat type is used to infer the most likely dominant species of

the previous stand (“Model Description” chapter, section 2.4.2). When total

tree canopy cover is less than 10 percent, herb and shrub biomass is assigned

an “initiating” value (the ‘I’ rows from table 4.50). When canopy cover is

greater than 60 percent, biomass is assigned an “established” value (the ‘E’

rows). Live fuel loads are linearly interpolated when canopy cover is between

10 and 60 percent. Data are taken from FOFEM 4.0 (Reinhardt and others

1997) with modifications provided by Jim Brown (USFS, Missoula, MT pers.

comm., 1995). Data on pinyon pine, Rocky Mountain juniper, quaking aspen,

and oaks were developed after examining live fuels reported in the Stereo

Photo Guides for Quantifying Natural Fuels (Ottmar and others 2000a,

Ottmar and others 2000b).

Table 4.49—Life span of live and dead foliage (years) and dead branches

for species modeled in the CR-FFE variant.

Live Dead

Species Foliage Foliage <0.25” 0.25–1” >1”

subalpine fir 7 2 10 15 15

corkbark fir 7 2 10 15 15

Douglas-fir 5 2 10 15 15

grand fir 7 2 10 15 15

white fir 7 2 10 15 15

mountain hemlock 4 2 10 10 10

western redcedar 5 2 10 15 20

western larch 1 1 10 15 15

bristlecone pine 3 2 10 15 20

limber pine 3 2 10 15 15

lodgepole pine 3 2 10 15 15

pinyon pine 3 2 10 15 15

ponderosa pine 4 2 10 10 10

whitebark pine 3 2 10 15 15

southwestern white pine 4 2 10 10 10

Rocky Mountain juniper 4 2 10 15 20

blue spruce 6 2 10 10 10

Engelmann spruce 6 2 10 10 10

white spruce 6 2 10 10 10

quaking aspen 1 1 10 10 10

cottonwoods 1 1 10 10 10

oaks 1 1 10 15 15

other softwoods 3 2 10 15 15

other hardwoods 1 1 10 10 10
132 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.50—Values (dry weight, tons/acre) for live fuels used in the CR-FFE. Biomass

is linearly interpolated between the “initiating” (I) and “established” (E)

values when canopy cover is between 10 and 60 percent.

Species Herbs Shrubs Comments

subalpine fir E 0.15 0.20

I 0.30 2.00

corkbark fir E 0.15 0.20 Use subalpine fir

I 0.30 2.00

Douglas-fir E 0.20 0.20

I 0.40 2.00

grand fir E 0.15 0.10

I 0.30 2.00

white fir E 0.15 0.10 Use subalpine fir

I 0.30 2.00

mountain hemlock E 0.15 0.20

I 0.30 2.00

western redcedar E 0.20 0.20

I 0.40 2.00

western larch E 0.20 0.20

I 0.40 2.00

bristlecone pine E 0.04 0.05 Use pinyon pine

I 0.13 1.63

limber pine E 0.20 0.10 Use lodgepole pine

I 0.40 1.00

lodgepole pine E 0.20 0.10

I 0.40 1.00

pinyon pine E 0.04 0.05 Ottmar and others (2000a)

I 0.13 1.63

ponderosa pine E 0.20 0.25

I 0.25 0.10

whitebark pine E 0.20 0.10 Use lodgepole pine

I 0.40 1.00

southwestern white pine E 0.15 0.10 Use western white pine

I 0.30 2.00

Rocky Mountain juniper E 0.04 0.05 Ottmar and others (2000a)

I 0.13 1.63

blue spruce E 0.15 0.20 Use Engelmann spruce

I 0.30 2.00

Engelmann spruce E 0.15 0.20

I 0.30 2.00

white spruce E 0.15 0.20 Use Engelmann spruce

I 0.30 2.00

quaking aspen E 0.25 0.25 Ottmar and others (2000a)

I 0.18 1.32

cottonwoods E 0.25 0.25 Ottmar and others (2000a)

I 0.18 1.32

oaks E 0.23 0.22 Use quaking aspen

I 0.55 0.35

other softwoods E 0.20 0.10 Use lodgepole pine

I 0.40 1.00

other hardwoods E 0.25 0.25 Use quaking aspen

I 0.18 1.32
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 133

Dead Fuels: Initial default DWD pools are based on overstory species.

When there are no trees, habitat type is used to infer the most likely

dominant species of the previous stand (“Model Description” chapter, section

2.4.2). Default fuel loadings were provided by Jim Brown (USFS, Missoula,

MT pers. comm., 1995) (table 4.51). If tree canopy cover is less than 10

Table 4.51—Canopy cover and cover type are used to assign default dead fuel loads (tons/acre) by size class for

established (E) and initiating (I) stands.

Size class (inches)

Species < 0.25 0.25 – 1 1 – 3 3 – 6 6 – 12 > 12 Litter Duff

subalpine fir E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

corkbark fir E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

Douglas-fir E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

grand fir E 0.7 0.7 3.0 7.0 7.0 0.0 0.6 25.0

I 0.5 0.5 2.0 2.8 2.8 0.0 0.3 12.0

white fir E 0.7 0.7 3.0 7.0 7.0 0.0 0.6 25.0

I 0.5 0.5 2.0 2.8 2.8 0.0 0.3 12.0

mountain hemlock E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

western redcedar E 1.6 1.6 5.2 15.0 20.0 15.0 1.0 35.0

I 1.6 1.6 3.6 6.0 8.0 6.0 0.5 12.0

western larch E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

bristlecone pine E 0.2 0.8 2.3 1.4 3.0 0.0 9.3 0.0

I 0.0 0.1 0.0 0.0 0.0 0.0 2.0 0.0

limber pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

lodgepole pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

pinyon pine E 0.2 0.8 2.3 1.4 3.0 0.0 9.3 0.0

I 0.0 0.1 0.0 0.0 0.0 0.0 2.0 0.0

ponderosa pine E 0.7 0.7 1.6 2.5 2.5 0.0 1.4 5.0

I 0.1 0.1 0.2 0.5 0.5 0.0 0.5 0.8

whitebark pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

southwestern white pine E 1.0 1.0 1.6 10.0 10.0 10.0 0.8 30.0

I 0.6 0.6 0.8 6.0 6.0 6.0 0.4 12.0

Rocky Mountain juniper E 0.2 0.8 2.3 1.4 3.0 0.0 9.3 0.0

I 0.0 0.1 0.0 0.0 0.0 0.0 2.0 0.0

blue spruce E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

Engelmann spruce E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

white spruce E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

quaking aspen E 0.2 0.6 2.4 3.6 5.6 0.0 1.4 16.8

I 0.1 0.4 5.0 2.2 2.3 0.0 0.8 5.6

cottonwoods E 0.2 0.6 2.4 3.6 5.6 0.0 1.4 16.8

I 0.1 0.4 5.0 2.2 2.3 0.0 0.8 5.6

oaks E 0.3 0.7 1.4 0.2 0.1 0.0 3.9 0.0

I 0.1 0.1 0.0 0.0 0.0 0.0 2.9 0.0

other softwoods E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

other hardwoods E 0.2 0.6 2.4 3.6 5.6 0.0 1.4 16.8

I 0.1 0.4 5.0 2.2 2.3 0.0 0.8 5.6
134 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

percent, the DWD pools are assigned an “initiating” value, and if cover is

greater than 60 percent they are assigned the “established” value. Fuels are

linearly interpolated when canopy cover is between 10 and 60 percent. Initial

fuel loads can be modified using the FUELINIT keyword.

4.5.4 Bark Thickness

Bark thickness contributes to predicted tree mortality from simulated

fires. The bark thickness multipliers in table 4.52 are used to calculate single

bark thickness (“Model Description” chapter, section 2.5.5). The bark thickness equation used in the mortality equation is unrelated to the bark

thickness used in the base FVS model. Data are from FOFEM 5.0 (Reinhardt

and others 2001). The pinyon pine coefficient is based on Pinus sp. And

corkbark fir is based on subalpine fir, both from FOFEM.

4.5.5 Decay Rate

Decay of down material is simulated by applying loss rates to biomass in

categories based on the original size-class of the branch and bole pieces as

described in section 2.4.5 of the “Model Description” chapter (table 4.53).

Workshop participants noted that material decays slower in the area covered

by the CR-FFE. This comment was supported by data in Brown and others

(1998). Decay rate for woody material was therefore reduced 55 percent from

the default decay rates based on Abbott and Crossley (1982). A portion of the

Table 4.52—Species specific constants

for determining single bark

thickness.

Species Multiplier (Vsp)

subalpine fir 0.041

corkbark fir 0.041

Douglas-fir 0.063

grand fir 0.046

white fir 0.048

mountain hemlock 0.040

western redcedar 0.035

western larch 0.063

bristlecone pine 0.030

limber pine 0.030

lodgepole pine 0.028

pinyon pine 0.030

ponderosa pine 0.063

whitebark pine 0.030

southwestern white pine 0.035

Rocky Mountain juniper 0.025

blue spruce 0.031

Engelmann spruce 0.036

white spruce 0.025

quaking aspen 0.044

cottonwoods 0.038

oaks 0.045

other softwoods 0.030

other hardwoods 0.038
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 135

loss is added to the duff pool each year. Loss rates are for hard material; soft

material in all size classes, except litter and duff, decays 10 percent faster.

By default, the FFE decays all wood species at the rates shown in table

4.53. The decay rates of species groups may be modified by users who can

provide rates to the four decay classes shown in table 4.54 using the

FUELDCAY keyword. Users can also reassign species to different classes

using the FUELPOOL keyword.

4.5.6 Moisture Content

Moisture content of the live and dead fuels is used to calculate fire intensity

and fuel consumption (“Model Description” chapter, section 2.5.2). Users can

choose from four predefined moisture groups (table 4.55) or they can specify

moisture conditions for each class using the MOISTURE keyword.

4.5.7 Fire Behavior Fuel Models

Fire behavior fuel models (Anderson 1982) are used to estimate flame

length and fire effects stemming from flame length. Fuel models are determined using fuel load and stand attributes (“Model Description” chapter,

section 2.4.8) specific to each FFE variant. In addition, stand management

actions such as thinning and harvesting can abruptly increase fuel loads and

can trigger “Activity Fuels” conditions, resulting in the selection of alternative fuel models. At their discretion, FFE users have the option of:

1. Defining and using their own fuel models

Table 4.53—Default annual loss rates are applied based on

size class. A portion of the loss is added to the

duff pool each year. Loss rates are for hard

material. The rates for woody material are 55

percent lower than the rates used in the NI-FFE

variant. If present, soft material in all size classes

except litter and duff decays 10 percent faster.

Size class

(inches)

Annual loss rate

Proportion of loss

becoming duff

< 0.25

0.25 — 1

0.054

1 — 3 0.041

3 — 6

6 — 12

> 12

0.0068

Litter 0.50

0.02

Duff 0.002 0.0
136 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.54—Default wood decay classes

used in the CR-FFE variant.

Classes are from The Wood

Handbook (1999). (1 = exceptionally high; 2 = resistant

or very resistant; 3 = moderately resistant, and 4 = slightly

or nonresistant.)

Species Decay class

subalpine fir 4

corkbark fir 4

Douglas-fir 3

grand fir 4

white fir 4

mountain hemlock 4

western redcedar 2

western larch 3

bristlecone pine 4

limber pine 4

lodgepole pine 4

pinyon pine 4

ponderosa pine 4

whitebark pine 4

southwestern white pine 4

Rocky Mountain juniper 2

blue spruce 4

Engelmann spruce 4

white spruce 4

quaking aspen 4

cottonwoods 4

oaks 2

other softwoods 4

other hardwoods 4

Table 4.55—Moisture values, which alter fire intensity and consumption, have been predefined for four groups. In

general they are drier than the default values used in

the NI-FFE.

Moisture group

Size class Very dry Dry Moist Wet

0 – 0.25 in. (1-hr) 4 5 8 10

0.25 – 1.0 in. (10-hr) 4 6 10 12

1.0 – 3.0 in. (100-hr) 5 8 12 15

> 3.0 in. (1000+ -hr) 10 15 16 18

Duff 15 50 125 200

Live 70 90 120 140
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 137

2. Defining the choice of fuel models and weights

3. Allowing the FFE variant to determine a weighted set of fuel models

4. Allowing the FFE variant to determine a weighted set of fuel models,

then using the dominant model

This section explains the steps taken by the CR-FFE to follow the third and

fourth of these four options

When the combination of large and small fuel lies in the lower left corner

of the graph shown in figure 4.5, one or more low-fuel fuel models become

candidate models. In other regions of the graph, other fuel models may also

be candidates. The logical flow shown in figure 4.6 defines which low fuel

model(s) will become candidates. According to the logic of figure 4.6, only a

single fuel model will be chosen for a given stand structure. Consequently, as

a stand undergoes structural changes due to management or maturation, the

selected fire model can jump from one model selection to another, which in

turn may cause abrupt changes in predicted fire behavior. To smooth out

changes resulting from changes in fuel model, the strict logic is augmented

by linear transitions between states that involve continuous variables (for

example, percent canopy cover, average height, snag density, and so forth).

The program logic shown in figure 4.6 also uses stand structure classes in

some decision rules. The CR-FFE uses the default structure class rules

documented in Crookston and Stage (1999) unless model users alter those

definitions using the STRCLS keyword.

If the STATFUEL keyword is selected, fuel model is determined by using

only the closest match fuel model identified by either figure 4.5 or figure 4.6.

The FLAMEADJ keyword allows the user to scale the calculated flame

length or override the calculated flame length with a value they choose.

10 20 30 40

10

20

30

40

0

3" + fuels (tons/acre)

0"-3" fuels (tons/acre)

FM10 - natural fuels

FM11 - activity fuels

FM12 - 

natural fuels

Fuel model, 100% weight

low fuels

models

0

Figure 4.5—If large and small fuels map to the shaded area, candidate fuel models are determined

using the logic shown in figure 4.6.

Otherwise, flame length is based

on the distance to the closest fuel

models, identified by the dashed

lines, and on recent management

(see “Model Description” chapter,

section 2.4.8 for further details).
138 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

FM 6

FM 2 FM 8

FM 9

FM 5

FM 5

YES

YES

YES

YES

YES YES

YES

NO

NO NO Top

height

>10’

Begin

FM 8

FM 5

FM 8

FM 10

FM 5

FM 2 FM 6

FM 2

FM 2

FM 5

FM 8

FM 8

FM 4

FM 8

FM 8

FM 5

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES YES

YES

YES

YES

YES

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Oak

brush cover type

Lodgepole

pine cover

type

Mixed

conifer cover type

Pinyon

juniper cover

type

White

spruce cover

type (in C.

Rockies) 

Spruce

fir cover

type

Midflame wind

speed

≤ 7mph

Lodgepole

pine site

index < 35

Stand

density > 1000

stems/

acre

% CC

< 50

Woody

debris

volume = 0

or mid-flame wind

speed ≤ 7 mph or crown cover < 20%

Structural

class ≥ 4 and

average DBH

≥ 12”

Avg. oak

height ≤2.0’

or total biomass

≤ 50% dead Stand

density ≤

1000 stems

per acre

In

Utah, with

pinyon juniper

BA > 0 and either [spruce

fir BA > 0 or white

fir density

>0] 

Structural

class ≤ 2

Within

a drought

period

Crown

cover

≤ 40%

Structural

class ≤ 2

Avg.

oak height

> 6’ and total

biomass > 50%

 dead Top

height > 10’

and mid-flame

wind speed

≤ 7 mph

FM4 5

FM 9

FM 8

* Trees or snags shorter than 1/2 the top height

END

FM 8

FM 2

Use rules for

mixed conifer

cover type

(non-existent state) END

Use rules

for pinyon juniper

cover type, but

assume woody

debris is

present 

Use rules for

oak brush

cover type

FM 2

FM 5

YES

YES YES

YES

YES

YES YES

YES

NO NO NO

NO

NO

NO

NO

Aspen

cover

type

Aspen

basal area

> 80% of

stand

BA

Live

conifer understory* BA

> 1 ft2

/ac

Live oak brush

 total BA

> 1 ft2

/ac

Live

pinyon juniper

 total BA

> 1 ft2/ac

Crown cover of over

10” conifers

> 40%

In

C. Rockies

using Southwest

mixed conifer

subvariant

* Trees shorter than 1/2 the top height

FM 6

FM 5

Continue

Continue

FM 2

FM 6

FM 2

FM 5

Use rules for oak

brush cover type

Use rules for

pinyon juniper cover

type but assume

woody debris

is present

FM 9

FM 5

YES

YES

YES YES

YES

YES

YES

YES

YES

YES

YES

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Ponderosa

pine cover

type

Crown

cover

≤ 60%

Understory* tree and

snag biomass < 0

Within

a drought

period

Mid- flame wind

speed

≤ 7 mph 

Midflame wind

speed

< 7 mph

Pinyon

juniper basal

area > 20% of

stand BA

Understory tree

biomass < snag

biomass

Oak

live BA in

understory > any

other

species

Pinyon

juniper live

BA in understory

> any other

species

Structural

class = 6

NO

Live coniferous

understory*

BA > 1 ft

2

/ac

FM 2

FM 8

FM 8

YES

NO

NO

Within a

drought

period

Total

ponderosa

pine BA > any

other

species

Within a

drought

period

Within a

drought

period

Live

conifer

 total BA

> 1 ft2

/ac

Figure 4.6—Logic for modeling fire at “low” fuel loads in the CR-FFE variant.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 139

4.6 Utah (UT) _____________________________________________________

4.6.1 Tree Species

The Utah variant models the 13 tree species shown in table 4.56. One

additional category, ”other” is modeled using whitebark pine.

4.6.2 Snags

The majority of the snag model logic is based on unpublished data provided

by Bruce Marcot (USFS, Portland, OR, unpublished data 1995). Snag fall

parameters were developed at the FFE design workshop. A complete description of the Snag Submodel is provided in section 2.3 of the FFE “Model

Description” chapter.

Four variables are used to modify the Snag Submodel for the different

species in the UT-FFE variant:

• A multiplier to modify the species’ fall rate

• A multiplier to modify the time required for snags to decay from a

“hard” to “soft” state

• The maximum number of years that snags will remain standing

• A multiplier to modify the species’ height loss rate

These variables are summarized in tables 4.57 and 4.58.

Height loss rate of quaking aspen is insignificant in comparison to its rapid

snag fall rate, and is not modeled. The fall rate of aspen is also halved in the

10 years following a burn. In the case of Douglas-fir and spruce snags greater

than 18 inches DBH, the fall rate is reduced to 32 percent of the rate predicted

by Marcot’s equation

Snag bole volume is determined in using the base FVS model equations.

The coefficients shown in table 4.59 are used to convert volume to biomass.

Soft snags have 80 percent the density of hard snags.

Snag dynamics can be modified by the user using the SNAGBRK,

SNAGFALL, SNAGDCAY and SNAGPBN keywords described in the FFE

“Model Description” chapter.

Table 4.56—Tree species simulated by the Utah variant.

Common name Scientific name Notes

whitebark pine Pinus albicaulis

limber pine Pinus flexilis

Douglas-fir Pseudotsuga menziesii

white fir Abies concolor

blue spruce Picea pungens

quaking aspen Populus tremuloides

lodgepole pine Pinus contorta

Engelmann spruce Picea engelmannii

subalpine fir Abies lasiocarpa

ponderosa pine Pinus ponderosa

pinyon pine Pinus edulis

western juniper Juniperus occidentalis

oak Quercus spp.

other = whitebark pine
140 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.57—Default snag fall, snag height loss and soft-snag characteristics for 20

inch DBH snags in the UT-FFE variant. These characteristics are

derived directly from the parameter values shown in table 4.58.

Species 95% fallen All down 50% height Hard-to-soft

- - - - - - - - - - - - - - - - - - - -Years- - - - - - - - - - - - - - - - - - - -

whitebark pine 31 150 310 35

limber pine 31 150 310 35

Douglas-fir 88§ 100 33 42

white fir 12 40 20 35

blue spruce 97§ 100 660 35

quaking aspen 8 5 — 35

lodgepole pine 31 150 660 35

Engelmann spruce 97§ 100 660 35

subalpine fir 12 40 20 35

ponderosa pine 31 150 310 39

pinyon pine 31 150 310 35

western juniper 31 150 310 35

Oak 12 40 20 35

other 31 150 310 35

§ This value results from using 32% of the default rate for Douglas-fir and spruce snags >18”

DBH, as described in the text.

Table 4.58—Default snag fall, snag height loss and softsnag multipliers for the UT-FFE. These parameters result in the values shown in table

4.57. (These three columns are the default

values used by the SNAGFALL, SNAGBRK,

and SNAGDCAY keywords, respectively.)

Snag Height

Species fall loss Hard-to-soft

whitebark pine 1.0 0.098 0.9

limber pine 1.0 0.098 0.9

Douglas-fir 1.1§ 0.9 1.1

white fir 2.5 1.494 0.9

blue spruce 1.0§ 0.046 0.9

quaking aspen 4.0 — 0.9

lodgepole pine 1.0 0.046 0.9

Engelmann spruce 1.0§ 0.046 0.9

subalpine fir 2.5 1.494 0.9

ponderosa pine 1.0 0.098 1.0

pinyon pine 1.0 0.098 0.9

western juniper 1.0 0.098 0.9

oak 2.5 1.494 0.9

other 1.0 0.098 0.9

§ This value apples to Douglas-fir and spruce snags <18” DBH;

see text for details.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 141

4.6.3 Fuels

Information on live fuels was developed using FOFEM 4.0 (Reinhardt and

others 1997) and FOFEM 5.0 (Reinhardt and others 2001) and in cooperation

with Jim Brown (USFS, Missoula, MT pers. comm. 1995). A complete

description of the Fuel Submodel is provided in section 2.4 of the FFE “Model

Description” chapter.

Fuels are divided into to four categories: live tree bole, live tree

crown, live herb and shrub, and down woody debris (DWD). Live herb and

shrub fuel load, and initial DWD are assigned based on the cover species with

greatest basal area. If there is no basal area in the first simulation cycle (a

“bare ground” stand) then the initial fuel loads are assigned by the vegetation

code provided with the STDINFO keyword. If the vegetation code is missing

or does not identify an overstory species, the model uses a ponderosa pine

cover type to assign the default fuels. If there is no basal area in other cycles

of the simulation (after a simulated clearcut, for example) herb and shrub

fuel biomass is assigned by the previous cover type.

Live Tree Bole: The fuel contribution of live trees is divided into two

components: bole and crown. Bole volume is transferred to the FFE after

being computed by the FVS model, then converted to biomass using ovendry

wood density calculated from table 4-3a and equation 3-5 of The Wood

Handbook (Forest Products Laboratory 1999). The coefficient in table 4.59

for Douglas-fir is based on Douglas-fir south.

Tree Crown: As described in the section 2.2 of the FFE “Model Description” chapter, equations in Brown and Johnston (1976) provide estimates of

live and dead crown material for most species in the UT-FFE (table 4.60).

Pinyon pine, juniper, and Gambel oak may have single or multiple stem

forms: single stem equations were used to compute biomass in all cases

within the FFE. The FVS base model computes volume of these three species

based on firewood utilization with a minimum branch of diameter of 1.5

inches. Crown and bole dynamics compatibility were maintained by defining

Table 4.59—Wood density (ovendry

lb/ft3

) used in the UT-FFE

variant.

Species Density (lb/ft3)

whitebark pine 24.8

limber pine 24.8

Douglas-fir 30.3

white fir 25.6

blue spruce 25.6

quaking aspen 24.1

lodgepole pine 26.4

Engelmann spruce 22.6

subalpine fir 21.1

ponderosa pine 26.4

pinyon pine 31.8

western juniper 34.9

oak 39.6

other 24.8
142 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.60—The crown biomass equations listed here determine the biomass of foliage, branch,

and stem wood. Species mappings are done for species for which equations are not

available.

Species Species mapping and equation source

whitebark pine Brown (1978)

limber pine lodgepole pine: Brown and Johnston (1976)

Douglas-fir Brown and Johnston (1976)

white fir grand fir: Brown and Johnston (1976)

blue spruce Engelmann spruce: Brown and Johnston (1976)

quaking aspen Ruark (1987) <1” DBH, Standish and others (1985) >1” DBH

lodgepole pine Brown and Johnston (1976)

Engelmann spruce Brown and Johnston (1976)

subalpine fir Brown and Johnston (1976)

ponderosa pine Brown and Johnston (1976)

pinyon pine Chojnacky (1992), Grier and others (1992)

western juniper Chojnacky (1992), Grier and others (1992)

oak Chojnacky (1992)

other whitebark pine; Brown (1978)

tree crown as being made up of branches and twigs (including dead material)

less than 1.5 inches, and foliage.

Live leaf lifespan is used to simulate the contribution of needles and leaves

to annual litter fall. Dead foliage and branch materials also contribute to

litter fall, at the rates shown in table 4.61. Each year the inverse of the

lifespan is added to the litter pool from each biomass category. Leaf lifespan

data are from Keane and others (1989).

Live Herbs and Shrubs: Live herb and shrub fuels are modeled simply

by the FFE. Shrubs and herbs are assigned a biomass value based on total

tree canopy cover and dominant overstory species (table 4.62). When there

are no trees, habitat type is used to infer the most likely dominant species of

Table 4.61—Life span of live and dead foliage (years) and dead branches

for species modeled in the UT-FFE variant.

Live Dead

Species Foliage Foliage <0.25” 0.25–1” >1”

whitebark pine 3 2 10 15 15

limber pine 3 2 10 15 15

Douglas-fir 5 2 10 15 15

white fir 7 2 10 15 15

blue spruce 6 2 10 10 10

quaking aspen 1 2 10 10 10

lodgepole pine 3 2 10 15 15

Engelmann spruce 6 2 10 10 10

subalpine fir 7 2 10 15 15

ponderosa pine 4 2 10 10 10

pinyon pine 3 2 10 15 15

western juniper 4 2 10 15 20

oak 1 1 10 15 15

other 3 2 10 15 15
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 143

the previous stand (“Model Description” chapter, section 2.4.2). When total

tree canopy cover is less than 10 percent, herb and shrub biomass is assigned

an “initiating” value (the ‘I’ rows from table 4.62). When canopy cover is

greater than 60 percent, biomass is assigned an “established” value (the ‘E’

rows). Live fuel loads are linearly interpolated when canopy cover is between

10 and 60 percent. Data are taken from FOFEM 4.0 (Reinhardt and others

1997) with modifications provided by Jim Brown (USFS, Missoula, MT pers.

comm., 1995). Data on pinyon pine, western juniper, quaking aspen, and

Gambel oak were developed after examining live fuels reported in the Stereo

Photo Guides for Quantifying Natural Fuels (Ottmar and others 2000a and

Ottmar and others 2000b).

Dead Fuels: Initial default DWD pools are based on overstory species.

When there are no trees, habitat type is used to infer the most likely

dominant species of the previous stand (“Model Description” chapter, section

2.4.2). Default fuel loadings were provided by Jim Brown (USFS, Missoula,

MT pers. comm., 1995) (table 4.63). If tree canopy cover is less than 10

percent, the DWD pools are assigned an “initiating” value and if cover is

greater than 60 percent they are assigned the “established” value. Fuels are

linearly interpolated when canopy cover is between 10 and 60 percent. Initial

fuel loads can be modified using the FUELINIT keyword.

Table 4.62—Values (dry weight, tons/acre) for live fuels used in the UT-FFE.

Biomass is linearly interpolated between the “initiating” (I) and “established” (E) values when canopy cover is between 10 and 60 percent.

Species Herbs Shrubs Notes

whitebark pine E 0.20 0.10 Use lodgepole pine

I 0.40 1.00

limber pine E 0.20 0.10 Use lodgepole pine

I 0.40 1.00

Douglas-fir E 0.20 0.20

I 0.40 2.00

white fir E 0.15 0.10

I 0.30 2.00

blue spruce E 0.15 0.20 Use Engelmann spruce

I 0.30 2.00

quaking aspen E 0.25 0.25 Ottmar and others (2000b)

I 0.18 1.32

lodgepole pine E 0.20 0.10

I 0.40 1.00

Engelmann spruce E 0.15 0.20

I 0.30 2.00

subalpine fir E 0.15 0.20

I 0.30 2.00

ponderosa pine E 0.20 0.25

I 0.25 0.10

pinyon pine E 0.04 0.05 Ottmar and others (2000b)

I 0.13 1.63

western juniper E 0.04 0.05 Ottmar and others (2000a)

I 0.13 1.63

oak E 0.23 0.22 Ottmar and others (2000a)

I 0.55 0.35

other E 0.20 0.10 Use whitebark pine

I 0.40 1.00
144 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.63—Canopy cover and cover type are used to assign default dead fuel loads (tons/acre) by size class for

established (E) and initiating (I) stands.

Size class (inches)

Species < 0.25 0.25 – 1 1 – 3 3 – 6 6 – 12 > 12 Litter Duff

whitebark pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

limber pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

Douglas-fir E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 15.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

white fir E 0.7 0.7 3.0 7.0 7.0 0.0 0.6 25.0

I 0.5 0.5 2.0 2.8 2.8 0.0 0.3 12.0

blue spruce E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

quaking aspen E 0.2 0.6 2.4 3.6 5.6 0.0 1.4 16.8

I 0.1 0.4 5.0 2.2 2.3 0.0 0.8 5.6

lodgepole pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

Engelmann spruce E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

subalpine fir E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

ponderosa pine E 0.7 0.7 1.6 2.5 2.5 0.0 1.4 5.0

I 0.1 0.1 0.2 0.5 0.5 0.0 0.5 0.8

pinyon pine E 0.2 0.8 2.3 1.4 3.0 0.0 9.3 0.0

I 0.0 0.1 0.0 0.0 0.0 0.0 2.0 0.0

western juniper E 0.2 0.8 2.3 1.4 3.0 0.0 9.3 0.0

I 0.0 0.1 0.0 0.0 0.0 0.0 2.0 0.0

oak E 0.3 0.7 1.4 0.2 0.1 0.0 3.9 0.0

I 0.1 0.1 0.0 0.0 0.0 0.0 2.9 0.0

other E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

4.6.4 Bark Thickness

Bark thickness contributes to predicted tree mortality from simulated

fires The bark thickness multipliers in table 4.64 are used to calculate single

bark thickness as described in the “Model Description” chapter, section 2.5.5.

The bark thickness equation used in the mortality equation is unrelated to

the bark thickness used in the base FVS model. Data are from FOFEM 5.0

(Reinhardt and others 2001). The pinyon pine coefficient is based on Pinus

spp from FOFEM.

4.6.5 Decay Rate

Decay of down material is simulated by applying loss rates to biomass in

categories based on the original size-class of the branch and bole pieces as

described in section 2.4.5 of the “Model Description” chapter (table 4.65).

Workshop participants noted that material decays slower in the area covered

by the UT-FFE. This comment was supported by data in Brown and others

(1998). Decay rate for woody material was therefore reduced 55 percent from

the default decay rates based on Abbott and Crossley (1982). A portion of the

loss is added to the duff pool each year. Loss rates are for hard material; soft

material in all size classes, except litter and duff, decays 10 percent faster.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 145

By default, the FFE decays all wood species at the rates shown in table

4.65. The decay rates of species groups may be modified by users, who can

provide rates to the four decay classes shown in table 4.66 using the

FUELDCAY keyword. Users can also reassign species to different classes

using the FUELPOOL keyword.

4.6.6 Moisture Content

Moisture content of the live and dead fuels is used to calculate fire intensity

and fuel consumption (“Model Description” chapter, section 2.5.2). Users can

choose from four predefined moisture groups (table 4.67) or they can specify

moisture conditions for each class using the MOISTURE keyword.

Table 4.64—Species specific constants

for determining single bark

thickness.

Species Multiplier (Vsp)

whitebark pine 0.030

limber pine 0.030

Douglas-fir 0.063

white fir 0.048

blue spruce 0.031

quaking aspen 0.044

lodgepole pine 0.028

Engelmann spruce 0.036

subalpine fir 0.041

ponderosa pine 0.063

pinyon pine 0.030

western juniper 0.025

oak 0.045

other 0.030

Table 4.65—Default annual loss rates are applied based on

size class. A portion of the loss is added to the

duff pool each year. Loss rates are for hard

material. The rates for woody material are 55

percent lower than the rates used in the NI-FFE

variant. If present, soft material in all size

classes except litter and duff decays 10 percent faster.

Size class

(inches) Annual loss rate Proportion of loss

becoming duff

< 0.25

0.25 — 1

0.054

1 — 3 0.041

3 — 6

6 — 12

> 12

0.0068

Litter 0.50

0.02

Duff 0.002 0.0
146 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

4.6.7 Fire Behavior Fuel Models

Fire behavior fuel models (Anderson 1982) are used to estimate flame

length and fire effects stemming from flame length. Fuel models are determined using fuel load and stand attributes (“Model Description” chapter,

section 2.4.8) specific to each FFE variant. In addition, stand management

actions such as thinning and harvesting can abruptly increase fuel loads and

can trigger “Activity Fuels” conditions, resulting in the selection of alternative fuel models. At their discretion, FFE users have the option of:

1. Defining and using their own fuel models

2. Defining the choice of fuel models and weights

3. Allowing the FFE variant to determine a weighted set of fuel models

4. Allowing the FFE variant to determine a weighted set of fuel models,

then using the dominant model

Table 4.66—Default wood decay classes

used in the UT-FFE variant.

Classes are from The Wood

Handbook (1999). (1 = exceptionally high; 2 = resistant

or very resistant; 3 = moderately resistant, and 4 = slightly

or nonresistant.)

Species Decay class

whitebark pine 4

limber pine 4

Douglas-fir 3

white fir 4

blue spruce 4

quaking aspen 4

lodgepole pine 4

Engelmann spruce 4

subalpine fir 4

ponderosa pine 4

pinyon pine 4

western juniper 2

oak 2

other 4

Table 4.67—Moisture values, which alter fire intensity and consumption,

have been predefined for four groups. In general they are

drier than the default values used in the NI-FFE.

Moisture group

Size class Very dry Dry Moist Wet

0 – 0.25 in. (1-hr) 4 5 8 10

0.25 – 1.0 in. (10-hr) 4 6 10 12

1.0 – 3.0 in. (100-hr) 5 8 12 15

> 3.0 in. (1000+ -hr) 10 15 16 18

Duff 15 50 125 200

Live 70 90 120 140
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 147

This section explains the steps taken by the UT-FFE to follow the third and

fourth of these four options

When the combination of large and small fuel lies in the lower left corner

of the graph shown in figure 4.7, one or more low-fuel fuel models become

candidate models. In other regions of the graph, other fuel models may also

be candidates. The logical flow shown in figure 4.8 defines which low fuel

model(s) will become candidates. According to the logic of figure 4.8, only a

single fuel model will be chosen for a given stand structure. Consequently, as

a stand undergoes structural changes due to management or maturation, the

selected fire model can jump from one model selection to another, which in

turn may cause abrupt changes in predicted fire behavior. To smooth out

changes resulting from changes in fuel model, the strict logic is augmented

by linear transitions between states that involve continuous variables (for

example, percent canopy cover, average height, snag density, and so forth).

The programme logic shown in figure 4.8 also uses stand structure classes

in some decision rules. The UT-FFE uses the default structure class rules

documented in Crookston and Stage (1999) unless model users alter those

definitions using the STRCLS keyword.

If the STATFUEL keyword is selected, fuel model is determined by using

only the closest match fuel model identified by either figure 4.7 or figure 4.8.

The FLAMEADJ keyword allows the user to scale the calculated flame

length or override the calculated flame length with a value they choose.

10 20 30 40

10

20

30

40

0

3" + fuels (tons/acre)

0"-3" fuels (tons/acre)

FM10 - natural fuels

FM11 - activity fuels

FM12

Fuel model, 100% weight

low fuels

models

0

Figure 4.7—If large and small fuels map to the shaded area, candidate fuel models are

determined using the logic shown in figure 4.8. Otherwise, flame length is based on the

distance to the closest fuel models, identified by the dashed lines, and on recent

management (see “Model Description” chapter, section 2.4.8 for further details).
148 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

FM 6

FM 2 FM 8

FM 9

FM 5

FM 5

YES

YES

YES

YES

YES YES

YES

NO

Within a NO NO drought

period

Top

height

>10’

Begin

FM 8

FM 5

FM 8

FM 10

FM 5

FM 2 FM 6

FM 2

FM 2

FM 5

FM 8

FM 8

FM 4

FM 8

FM 8

FM 5

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES YES

YES

YES

YES

YES

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Oak

brush cover

type

Lodgepole

pine cover

type

Mixed

conifer cover

type

Pinyon

juniper cover

type

White

spruce cover

type (in C.

Rockies) 

Spruce

fir cover

type

Midflame wind

speed

≤ 7mph

Lodgepole

pine site

index < 35

Stand density > 1000

stems/

acre

% CC

< 50

Woody

debris

volume = 0

or mid-flame wind

speed ≤ 7 mph or

crown cover

< 20%

Structural

class > 4 and

average DBH > 12”

Avg. oak

height ≤ 2.0’

or total biomass

≤ 50% dead Stand

density ≤

1000 stems

per acre

In

Utah, with

pinyon juniper

BA > 0 and either [spruce

fir BA > 0 or white

fir density

>0] 

Structural

class ≤ 2

Within

a drought

period

Crown

cover

≤ 40%

Structural

class ≤ 2

Avg.

oak height

> 6’ and total

biomass > 50%

dead Top

height > 10’

and mid-flame

wind speed

≤ 7 mph

FM4 5

FM 9

FM 8

* Trees or snags shorter than 1/2 the top height

END

FM 8

FM 2

Use rules for

mixed conifer 

cover type

(non-existent state)

END

Use rules

for pinyon juniper

cover type, but

assume woody

debris is

present 

Use rules for

oak brush

cover type

FM 2

FM 5

YES

YES YES

YES

YES

YES YES

YES

NO NO NO

NO

NO

NO

NO

Aspen

cover

type

Aspen

basal area

> 80% of

stand

BA

Live conifer understory* BA

> 1 ft2

/ac

Live

conifer

 total BA

> 1 ft2

/ac

Live

oak brush

 total BA

> 1 ft2

/ac

Live

pinyon juniper total BA

> 1 ft2

/ac

Crown

cover of over

10” conifers

> 40%

In

C. Rockies

using Southwest

mixed conifer

subvariant

* Trees shorter than 1/2 the top height

FM 6

FM 5

Continue

Continue

FM 2

FM 6

FM 2

FM 5

Use rules for oak

brush cover type

Use rules for

pinyon juniper cover

type but assume

woody debris

is present

FM 9

FM 5

YES

YES

YES YES

YES

YES

YES

YES

YES

YES

YES

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

Ponderosa

pine cover

type

Crown

cover

≤ 60%

Under- story* tree and

snag biomass < 0

Within

a drought

period

Midflame wind

speed

≤ 7 mph 

Midflame wind

speed

< 7 mph 

Pinyon

juniper basal

area > 20% of

stand BA

Understory tree

biomass < snag

biomass

Oak

live BA in

understory > any

other

species

Pinyon

juniper live

BA in understory

> any other

species

Structural

class = 6

NO

Live

coniferous

understory*

BA > 1 ft2/ac

FM 2

FM 8

FM 8

YES

NO

NO

Within a

drought

period

Total

ponderosa

pine BA > any

other

species

Within a

drought

period

Figure 4.8—Logic for modeling fire at “low” fuel loads in the UT-FFE variant.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 149

4.7 Western Sierras (WS) ___________________________________________

4.7.1 Tree Species

The Western Sierras variant models the 10 tree species shown in table

4.68. Two additional categories, “other hardwoods” and “other softwoods” are

modeled using California black oak and lodgepole pine, respectively.

4.7.2 Snags

The majority of the snag model logic is based on unpublished data provided

by Bruce Marcot (USFS, Portland, OR, unpublished data 1995). Snag fall

parameters were developed at the WS-FFE workshop. A complete description of the Snag Submodel is provided in section 2.3 of the FFE “Model

Description” chapter.

Three variables are used to modify the Snag Submodel for the different

species in the WS-FFE variant:

• A multiplier to modify the species’ fall rate

• The maximum number of years that snags will remain standing

• A multiplier to modify the species’ height loss rate

These variables are summarized in tables 4.69 and 4.70.

Unlike the some other FFE variants, snags in the WS-FFE do not decay

from a hard to soft state. Users can initialize soft snags using the SNAGINIT

keyword if they wish, but these initialized soft snags will eventually disappear as they are removed by snag fall. In addition, snags lose height only until

they are reduced to half the height of the original live tree. The maximum

standing lifetime for many snag species is set to 100 years (Mike Landram,

USFS, Vallejo, CA, pers. comm., 2000). Finally, the default coefficient for

snag height loss is changed from 0.0228 to 0.03406.

Snag bole volume is determined in using the base FVS model equations.

The coefficients shown in table 4.71 are used to convert volume to biomass.

Table 4.68—Tree species simulated by the Western Sierras variant.

Common name Scientific name Notes

sugar pine Pinus lambertiana

Douglas-fir Pseudotsuga menziesii

white fir Abies concolor

giant sequoia Sequoiadendron giganteum = Sequoia gigantea

incense-cedar Calocedrus decurrens = Libocedrus decurrens

California black oak Quercus kelloggii

Jeffrey pine Pinus jeffreyi

red fir Abies magnifica

ponderosa pine Pinus ponderosa

tanoak, giant chinkapin Lithocarpus densiflorus,

Castanopsis chrysophylla

other hardwood = California black oak

other softwood = lodgepole pine
150 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.69—Default snag fall, snag height loss and soft-snag characteristics for 20

inch DBH snags in the WS-FFE variant. These characteristics are

derived directly from the parameter values shown in table 4.70.

Species 95% fallen All down 50% height Hard-to-soft

- - - - - - - - - - - - - - - - - - - -Years - - - - - - - - - - - - - - - - - - - -

sugar pine 17 100 20 —

Douglas-fir 30 100 20 —

white fir 30 40 20 —

giant sequoia 30 150 20 —

incense-cedar 30 100 20 —

California black oak 30 100 20 —

Jeffrey pine 17 100 20 —

red fir 30 40 20 —

ponderosa pine 17 100 20 —

tanoak, giant chinkapin 30 100 20 —

other hardwood 30 100 20 —

other softwood 17 100 20 —

Table 4.70—Default snag fall, snag height loss and soft-snag

multipliers for the WS-FFE. These parameters result in the values shown in table 4.69. (These three

columns are the default values used by the

SNAGFALL, SNAGBRK, and SNAGDCAY keywords, respectively.)

Snag Height

Species fall loss Hard-to-soft

sugar pine 1.79 1.49 —

Douglas-fir 1.02 1.49 —

white fir 1.02 1.49 —

giant sequoia 1.02 1.49 —

Incense-cedar 1.02 1.49 —

California black oak 1.02 1.49 —

Jeffrey pine 1.79 1.49 —

red fir 1.02 1.49 —

ponderosa pine 1.79 1.49 —

tanoak, giant chinkapin 1.02 1.49 —

other hardwood 1.02 1.49 —

other softwood 1.79 1.49 —

Table 4.71—Wood density (ovendry lb/ft3

)

used in the WS-FFE variant.

Species Density (lb/ft3)

sugar pine 23.3

Douglas-fir 32.7

white fir 25.6

giant sequoia 23.3

incense-cedar 24.1

California black oak 41.0

Jeffrey pine 23.3

red fir 24.8

ponderosa pine 26.4

tanoak, giant chinkapin 42.7

other hardwood 41.0

other softwood 26.4
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 151

4.7.3 Fuels

Information on live fuels was developed using FOFEM 4.0 (Reinhardt and

others 1997) and FOFEM 5.0 (Reinhardt and others 2001) and in cooperation

with Jim Brown (USFS, Missoula, MT pers. comm. 1995). A complete

description of the Fuel Submodel is provided in section 2.4 of the FFE “Model

Description” chapter.

Fuels are divided into to four categories: live tree bole, live tree crown, live

herb and shrub, and down woody debris (DWD). Live herb and shrub fuel

load, and initial DWD are assigned based on the cover species with greatest

basal area. If there is no basal area in the first simulation cycle (a “bare

ground” stand) then the initial fuel loads are assigned by the vegetation code

provided with the STDINFO keyword. If the vegetation code is missing or

does not identify an overstory species, the model uses a ponderosa pine cover

type to assign the default fuels. If there is no basal area in other cycles of the

simulation (after a simulated clearcut, for example) herb and shrub fuel

biomass is assigned by the previous cover type.

Live Tree Bole: The fuel contribution of live trees is divided into two

components: bole and crown. Bole volume is transferred to the FFE after

being computed by the FVS model, then converted to biomass using ovendry

wood density calculated from table 4-3a and equation 3-5 of The Wood

Handbook (Forest Products Laboratory 1999). The coefficients in table 4.71

for giant sequoia are based on Redwood Young-growth; Douglas-fir is based

on Douglas-fir Interior west.

Tree Crown: As described in the section 2.2 of the FFE “Model Description” chapter, equations in Brown and Johnston (1976) provide estimates of

live and dead crown material for most species in the WS-FFE. Some species

mappings are used, as shown below in table 4.72. California black oak and

tanoak/giant chinkapin crown biomass equations are taken from new sources.

Live leaf lifespan is used to simulate the contribution of needles and leaves

to annual litter fall. Dead foliage and branch materials also contribute to

litter fall, at the rates shown in table 4.73. Each year the inverse of the

Table 4.72—The crown biomass equations listed here determine the biomass of foliage, branch,

and stem wood. Species mappings are done for species for which equations are not

available.

Species Species mapping and equation source

sugar pine western white pine (Brown and Johnston 1976)

Douglas-fir Brown and Johnston 1976

white fir grand fir (Brown and Johnston 1976)

giant sequoia western redcedar for biomass, western hemlock for partitioning

 (Mike Lander, pers. comm.; Brown and Johnston 1976)

incense-cedar western redcedar (Brown and Johnston 1976)

California black oak Snell and Little 1983; Snell 1979

Jeffrey pine western white pine (Brown and Johnston 1976)

red fir grand fir (Brown and Johnston 1976)

ponderosa pine Brown and Johnston 1976

tanoak, giant chinkapin Snell and Little 1983, Snell 1979

other conifers lodgepole pine (Brown and Johnston 1976)

other hardwoods California black oak (Snell and Little 1983, Snell 1979)
152 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.73—Life span of live and dead foliage (years) and dead branches for

species modeled in the WS-FFE variant.

Live Dead

Species Foliage Foliage <0.25” 0.25–1” >1”

sugar pine 3 3 10 15 15

Douglas-fir 5 3 10 15 15

white fir 7 3 10 15 15

giant sequoia 5 3 10 15 20

incense-cedar 5 1 10 15 20

California black oak 1 1 10 15 15

Jeffrey pine 3 3 10 15 15

red fir 7 3 10 15 15

ponderosa pine 3 3 10 10 10

tanoak, giant chinkapin 1 1 10 15 15

other conifers 3 3 10 15 15

other hardwoods 1 1 10 15 15

lifespan is added to the litter pool from each biomass category. These data are

from the values provided at the WS-FFE workshop.

Live Herbs and Shrubs: Live herb and shrub fuels are modeled simply

by the FFE. Shrubs and herbs are assigned a biomass value based on total

tree canopy cover and dominant overstory species (table 4.74). When there

are no trees, habitat type is used to infer the most likely dominant species of

Table 4.74—Values (dry weight, tons/acre) for live fuels used in the WS-FFE.

Biomass is linearly interpolated between the “initiating” (I) and “established” (E) values when canopy cover is between 10 and 60 percent.

Species Herbs Shrubs Notes

sugar pine E 0.20 0.10 Use lodgepole pine (NI-FFE)

I 0.40 1.00

Douglas-fir E 0.20 0.20

I 0.40 2.00

white fir E 0.15 0.10 Use grand fir (NI-FFE)

I 0.30 2.00

giant sequoia E 0.20 0.20 Use Douglas-fir

I 0.40 2.00

incense-cedar E 0.20 0.20 Use Douglas-fir

I 0.40 2.00

California black oak E 0.25 0.25 Chojnacky (1992)

I 0.18 1.32

Jeffrey pine E 0.20 0.10 Use lodgepole pine

I 0.40 1.00

red fir E 0.15 0.10 Use grand fir

I 0.30 2.00

ponderosa pine E 0.20 0.25

I 0.25 1.00

tanoak, giant chinkapin E 0.25 0.25 Chojnacky (1992)

I 0.18 2.00

other conifers E 0.20 0.10 Use lodgepole pine

I 0.40 1.00

other hardwoods E 0.25 0.25 Chojnacky (1992)

I 0.18 1.32
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 153

the previous stand (“Model Description” chapter, section 2.4.2). When total

tree canopy cover is less than 10 percent, herb and shrub biomass is assigned

an “initiating” value (the ‘I’ rows from table 4.74). When canopy cover is

greater than 60 percent, biomass is assigned an “established” value (the ‘E’

rows). Live fuel loads are linearly interpolated when canopy cover is between

10 and 60 percent. When more than one species is present, the final estimate

is computed by combining the interpolated estimates from the rows (table

4.74) representing the two dominant species. The two estimates are themselves weighted by the relative amount of the two dominant species. Data are

taken from FOFEM 4.0 (Reinhardt and others 1997) with modifications

provided by Jim Brown (USFS, Missoula, MT pers. comm., 1995). Hardwood

estimates are from Gambel oak stands reported by Chojnacky (1992).

Dead Fuels: Initial default DWD pools are based on overstory species.

When there are no trees, habitat type is used to infer the most likely

dominant species of the previous stand (“Model Description” chapter, section

2.4.2). Default fuel loadings were provided by Jim Brown (USFS, Missoula,

MT pers. comm., 1995) (table 4.75). If tree canopy cover is less than 10

percent, the DWD pools are assigned an “initiating” value, and if cover is

greater than 60 percent they are assigned the “established” value. Fuels are

linearly interpolated when canopy cover is between 10 and 60 percent. When

more than one species is present, the final estimate is computed by combining

the interpolated estimates from the rows (table 4.75) representing the two

dominant species. The two estimates are themselves weighted by the

relative amount of the two dominant species. Initial fuel loads can be

modified using the FUELINIT keyword.

Table 4.75—Canopy cover and cover type are used to assign default down woody debris (tons/acre) by size class

for established (E) and initiating (I) stands.

Size class (inches)

Species < 0.25 0.25 – 1 1 – 3 3 – 6 6 – 12 > 12 Litter Duff

sugar pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

Douglas-fir E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

white fir E 0.7 0.7 3.0 7.0 7.0 0.0 0.6 25.0

I 0.5 0.5 2.0 2.8 2.8 0.0 0.3 12.0

giant sequoia E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

incense-cedar E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

California black oak E 0.2 0.6 2.4 3.6 5.6 0.0 1.4 16.8

I 0.1 0.4 5.0 2.2 2.3 0.0 0.8 5.6

Jeffrey pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

red fir E 0.7 0.7 3.0 7.0 7.0 0.0 0.6 25.0

I 0.5 0.5 2.0 2.8 2.8 0.0 0.3 12.0

ponderosa pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

tanoak, giant chinkapin E 0.2 0.6 2.4 3.6 5.6 0.0 1.4 16.8

I 0.1 0.4 5.0 2.2 2.3 0.0 0.8 5.6

other conifers E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

other hardwoods E 0.2 0.6 2.4 3.6 5.6 0.0 1.4 16.8

I 0.1 0.4 5.0 2.2 2.3 0.0 0.8 5.6
154 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.76—Species specific constants

for determining single bark

thickness.

Species Multiplier (Vsp)

sugar pine 0.072

Douglas-fir 0.063

white fir 0.048

giant sequoia 0.081

incense-cedar 0.060

California black oak 0.030

Jeffrey pine 0.068

red fir 0.039

ponderosa pine 0.063

tanoak, giant chinkapin 0.052

other conifers 0.028

other hardwoods 0.030

4.7.4 Bark Thickness

Bark thickness contributes to predicted tree mortality from simulated

fires. The bark thickness multipliers in table 4.76 are used to calculate single

bark thickness as described in the “Model Description” chapter, section 2.5.5.

The bark thickness equation used in the mortality equation is unrelated to

the bark thickness used in the base FVS model. Data are from FOFEM 5.0

(Reinhardt and others 2001).

4.7.5 Decay Rate

Decay of down material is simulated by applying the loss rates shown in

table 4.77, as described in section 2.4.5 of the “Model Description” chapter.

Default decay rates are based on Abbott and Crossley (1982).

The default decay rates are modified by incorporating information from

the Dunning site class. The multipliers shown in table 4.78 modify the

default decay rates of table 4.77 by incorporating a measure of site quality

and moisture availability.

By default, the FFE decays all wood species at the rates shown in table

4.77. The decay rates of species groups may be modified by users, who can

provide rates to the four decay classes shown in table 4.79 using the

FUELDCAY keyword. Users can also reassign species to different classes

using the FUELPOOL keyword.

4.7.6 Moisture Content

Moisture content of the live and dead fuels is used to calculate fire intensity

and fuel consumption (“Model Description” chapter, section 2.5.2). Users can

choose from four predefined moisture groups shown in table 4.80, or they can

specify moisture conditions for each class using the MOISTURE keyword.

4.7.7 Fire Behavior Fuel Models

Fire behavior fuel models (Anderson 1982) are determined in two steps:

determination of cover classification and determination of dominant species.

The first step uses tree cover attributes classified by the California Wildlife
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 155

Table 4.78—The WS-FFE modifies default decay rate (table 4.77)

using Dunning Site Code to

improve simulated decomposition. Lower Dunning Site

Classes indicate moister

sites.

Dunning site class Multiplier

0 1.5

1 1.5

2 1.0

3 1.0

4 1.0

5 0.5

Table 4.77—Default annual loss rates are applied based on

size class. A portion of the loss is added to the

duff pool each year. Loss rates are for hard

material. If present, soft material in all size

classes except litter and duff decays 10 percent

faster.

Size class

(inches)

Annual loss rate

Proportion of loss

becoming duff

< 0.25

0.25 — 1

0.025

1 — 3

3 — 6

0.0125

6 — 12

> 12

0.02

Litter 0.65

0.02

Duff 0.002 0.0

Habitat Relationships (CWHR) system (Mayer and Laudenslayer 1988)

shown in table 4.81. The table classifies stands by their canopy cover and the

size of the larger trees in the stand, predicting CWHR size class and CWHR

density class (the third and fourth columns). The CWHR is a BASIClanguage function named “CWHRSizeDensity” that was provided at the WSFFE workshop. This function is incorporated into the WS-FFE with some

minor housekeeping modifications.
156 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.79—Default wood decay classes

used in the WS-FFE variant.

Classes are from The Wood

Handbook (1999). (1 = exceptionally high; 2 = resistant or

very resistant; 3 = moderately

resistant, and 4 = slightly or

nonresistant.)

Species Decay class

sugar pine 4

Douglas-fir 3

white fir 4

giant sequoia 2

Incense-cedar 2

California black oak 2

Jeffrey pine 4

red fir 4

ponderosa pine 4

tanoak, giant chinkapin 4

other hardwood 2

other softwood 4

Table 4.80—Moisture values, which alter fire intensity and consumption, have been predefined for four groups.

Moisture group

Size class Very dry Dry Moist Wet

0 – 0.25 in. (1 hr.) 3 8 12 12

0.25 – 1.0 in. (10 hr.) 4 8 12 12

1.0 – 3.0 in. (100 hr.) 5 10 14 14

> 3.0 in. (1000+ hr.) 10 15 25 25

Duff 15 50 125 125

Live 70 110 150 150

Table 4.81—California Wildlife Habitat Relationships, as defined by Mayer and Laudenslayer (1988).

Tree size Canopy CWHR CWHR Stand

(DBH in.)* cover (%) size class density class description

< 1 < 10 1 – Seedlings

1 - 6 10 – 24 2 S Sapling – sparse

1 - 6 25 – 39 2 P Sapling – open cover

1 - 6 40 – 59 2 M Sapling – moderate cover

1 - 6 > 60 2 D Sapling – dense cover

6 – 11 10 – 24 3 S Pole tree – sparse

6 – 11 25 – 39 3 P Pole tree – open cover

6 – 11 40 – 59 3 M Pole tree – moderate cover

6 – 11 > 60 3 D Pole tree – dense cover

11 – 24 10 – 24 4 S Small tree – sparse

11 – 24 25 – 39 4 P Small tree – open cover

11 – 24 40 – 59 4 M Small tree – moderate cover

11 – 24 > 60 4 D Small tree – dense cover

> 24 10 – 24 5 S Med/Lg tree – sparse

> 24 25 – 39 5 P Med/Lg tree – open cover

> 24 40 – 59 5 M Med/Lg tree – moderate cover

> 24 > 60 5 D Med/Lg tree – dense cover

> 24 > 60 6 – Multi-layer canopy, dense cover

* QMD of the 75 percent largest trees based on basal area.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 157

60

60

40

40

25

25

Overlap-adjusted Canopy Cover (%)

Unadjusted Canopy Cover (%)

S P

D

M

Figure 4.9—Two measures of canopy cover, unadjusted

and overlap-adjusted percent canopy cover, are used to

derive weighted estimates of the four CWHR density

classes (S = sparse, P = open, M = moderate, and D =

dense).

The WS-FFE modifies the internal CWHR logic slightly, making use of two

additional measures internal to the CWHR: unadjusted percent canopy cover

and overlap-adjusted percent canopy cover, respectively. The two kinds of

canopy estimates are used in combination with the CWHR logic to create

weights for the predicted CWHR density class. Each stand’s CWHR density

class becomes a combination of one or two adjacent classes. Figure 4.9 shows

how the two measures are used to weight the S, P, M or D classes at each

timestep of the simulation. When a point (defined by the two kinds of canopy

cover estimate) lies on a dashed line in the figure, that CWHR density class

is given a 100 percent weight. Otherwise, the distance from the point to the

nearest dashed lines is used to create weights for the nearest CWHR density

classes.

The second step determines the dominant species. A species is considered

dominant if it comprises more than 80 percent of the stand basal area. The

search starts with pine and moves down the column of forest types listed in

the leftmost column of table 4.82. If no species is dominant, then fir-mixed

conifer is the default cover type.

The rules governing table 4.82 select one or two candidate (usually low)

fuel models. These are used along with the high fuels models to select the

final set of weighted fuel models. The table has been modified from Landram’s

original table so that with the exception of the right-most column (mature

Size Class 6 stands), cells with fuel model 10 or 12 in the original table have

been replaced with fuel model 8. This change was made so that when

appropriate, the default FFE fuel model logic (described in section 2.4.8 and

fig. 2.12 of the FFE “Model Description” chapter) is not constrained in its

selection of candidate high fuel models: combinations of fuel models 10, 11,

12, and 12 may still be selected when fuel loads are high. Finally, in order

to give table 4.82 priority, fuel model 10 is removed from the list of candidate

models when fuel model 11 has been selected from the table.
158 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Fuel models 25 and 26 are custom fuel models developed in California and

are described fully in table 2.13 of the FFE “Model Description” chapter.

Model 25 is used to describe fire behavior in plantations greater than 25 years

old with shrub understory and low crown mass. Model 26 is used on sites

similar to those where Model 4 would be used but with lower fuelbed depth

and loading.

In some situations a thinning or disturbance may cause one of the selected

fuel models to switch from FM8 or FM9 to FM5 or FM26. When this happens,

the transition is modified to simulate a delay in brush ingrowth. In the case

where an FM8 or FM9 fuel model is predicted to change to FM5, the change

is made over 5 years, gradually shifting from FM8 or FM9 to FM5. In the case

where the fuel model is predicted to change to FM26, the model first changes

to FM5 over 5 years, and then changes to FM26 over the next 10 years,15

years after the initial disturbance.

Finally, flame length is calculated using the weights from above the

appropriate fuel models. The FLAMEADJ keyword allows users to scale the

calculated flame length or override the calculated flame length with a value

they choose.

Table 4.82—Fire behavior fuels models for the WS-FFE are determined using forest type and CWHR class, as described in the text.

The modeling logic allows one or more fuel models to be selected.

Size class 1 2 3 4 5 6

Density class S P M D S P M D S P M D S P M D

Forest type

Pine – east side 9 2 2 9 9 2 2 2 9 2 2 8 8 2 2 8 8 10

Pine – west side 9 5 5 9 9 26 26 25 9 26 26 8 8 26 26 8 8 10

Red fir 8 8 8 8 8 11 11 8 8 8 8 8 8 8 8 8 8 10

White fir – east side 8 8 8 8 8 11 11 11 8 8 8 8 8 8 8 8 8 10

White fir – west side 8 5 5 8 8 11 11 8 8 8 8 8 8 8 8 8 8 10

Douglas-fir 8 5 5 8 8 5 5 8 8 11 11 9 8 11 11 9 8 10

Giant sequoia 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 10

Jeffrey pine 9 9 9 9 9 2 2 2 9 2 2 2 9 2 2 2 9 10

Hardwoods 8 5 5 9 9 11 11 11 9 9 9 9 9 9 9 9 9 10

Lodgepole pine 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10

Pine mixed – conifer 9 5 5 9 9 26 26 25 9 26 26 8 8 26 26 8 8 10

Fir mixed – conifer 8 9 9 8 8 26 26 11 8 5 5 8 8 5 5 8 8 10

Other softwood 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 10
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 159

4.8 Eastern Cascades (EC) _____________________________________________________

4.8.1 Tree Species

The Eastern Cascades variant models the 10 tree species shown in table

4.83. One additional category, “other” is modeled using mountain hemlock.

4.8.2 Snags

The majority of the snag model logic is based on unpublished data provided

by Bruce Marcot (USFS, Portland, OR, unpublished data 1995). Snag fall

parameters were developed at the FFE design workshop. A complete description of the Snag Submodel is provided in section 2.3 of the FFE “Model

Description” chapter.

Four variables are used to modify the Snag Submodel for the different

species in the EC-FFE variant:

• A multiplier to modify the species’ fall rate

• Amultiplier to modify the time required for snags to decay from a “hard”

to “soft” state

• The maximum number of years that snags will remain standing

• A multiplier to modify the species’ height loss rate

These variables are summarized in tables 4.84 and 4.85.

Snag dynamics are similar to the NI-FFE variant, with the following

exceptions:

• Western larch, lodgepole pine, Engelmann spruce, subalpine fir, and

ponderosa pine snags experience no height loss, and their height loss

multiplier is set to zero.

• Western white pine and western redcedar lose 75 percent of their

original height, after which their height does not change.

• Larch and spruce snags greater than 18 inches dbh fall at a rate that

is 32 percent of the rate predicted by Marcot’s equation.

Snag bole volume is determined in using the base FVS model equations.

The coefficients shown in table 4.86 are used to convert volume to biomass.

Soft snags have 80 percent the density of hard snags.

Table 4.83—Tree species simulated by the Eastern Cascades variant.

Common name Scientific name Notes

western white pine Pinus monticola

western larch Larix occidentalis

Douglas-fir Pseudotsuga menziesii

Pacific silver fir Abies amabilis

western redcedar Thuja plicata

grand fir Abies grandis

lodgepole pine Pinus contorta

Engelmann spruce Picea engelmannii

subalpine fir Abies lasiocarpa

ponderosa pine Pinus ponderosa

other = mountain hemlock
160 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.85—Default snag fall, snag height loss and softsnag multipliers for the EC-FFE. These parameters result in the values shown in table

4.84. (These three columns are the default

values used by the SNAGFALL, SNAGBRK,

and SNAGDCAY keywords, respectively.)

Snag Height

Species fall loss Hard-to-soft

western white pine 0.9 0.4 1.1

western larch 1.0§ — 1.1

Douglas-fir 0.9 1.0 1.1

Pacific silver fir 0.9 1.5 0.9

western redcedar 0.3 0.3 0.9

grand fir 1.1 1.5 0.9

lodgepole pine 1.6 — 0.9

Engelmann spruce 1.2§ — 0.9

subalpine fir 0.8 — 0.9

ponderosa pine 1.0 — 1.0

other 0.9 1.5 1.0

§ This value apples to Douglas-fir and spruce snags <18” DBH;

see text for details.

Table 4.84—Default snag fall, snag height loss and soft-snag characteristics for

15 inch DBH snags in the EC-FFE variant. These characteristics are

derived directly from the parameter values shown in table 4.85.

Species 95% fallen All down 50% height Hard-to-soft

- - - - - - - - - - - - - - - - - - -Years - - - - - - - - - - - - - - - - - - -

western white pine 27 110 76 36

western larch 24 150 — 36

Douglas-fir 27 75 30 36

Pacific silver fir 27 30 20 29

western redcedar 81 300 101 29

grand fir 22 90 20 29

lodgepole pine 15 35 — 29

Engelmann spruce 20 100 — 29

subalpine fir 30 40 — 29

ponderosa pine 24 100 — 32

other 27 30 20 32

Table 4.86—Wood density (ovendry

lb/ft3) used in the EC-FFE

variant

Species Density (lb/ft3)

western white pine 24.8

western larch 34.3

Douglas-fir 32.7

Pacific silver fir 27.9

western redcedar 21.1

grand fir 24.1

lodgepole pine 26.4

Engelmann spruce 22.6

subalpine fir 21.1

ponderosa pine 26.4

other 29.5
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 161

Snag dynamics can be modified by the user using the SNAGBRK,

SNAGFALL, SNAGDCAY and SNAGPBN keywords described in the FFE

“Model Description” chapter.

4.8.3 Fuels

Information on live fuels was developed using FOFEM 4.0 (Reinhardt and

others 1997) and FOFEM 5.0 (Reinhardt and others 2001) and in cooperation

with Jim Brown (USFS, Missoula, MT pers. comm. 1995). A complete

description of the Fuel Submodel is provided in section 2.4 of the FFE “Model

Description” chapter.

Fuels are divided into to four categories: live tree bole, live tree crown, live

herb and shrub, and down woody debris (DWD). Live herb and shrub fuel

load, and initial DWD are assigned based on the cover species with greatest

basal area. If there is no basal area in the first simulation cycle (a “bare

ground” stand) then the initial fuel loads are assigned by the vegetation code

provided with the STDINFO keyword. If the vegetation code is missing or

does not identify an overstory species, the model uses a ponderosa pine cover

type to assign the default fuels. If there is no basal area in other cycles of the

simulation (after a simulated clearcut, for example) herb and shrub fuel

biomass is assigned by the previous cover type.

Live Tree Bole: The fuel contribution of live trees is divided into two

components: bole and crown. Bole volume is transferred to the FFE after

being computed by the FVS model, then converted to biomass using ovendry

wood density calculated from table 4-3a and equation 3-5 of The Wood

Handbook (Forest Products Laboratory 1999). The coefficient in table 4.86

for Douglas-fir is based on Douglas-fir south.

Tree Crown: As described in the section 2.2 of the FFE “Model Description” chapter, equations in Brown and Johnston (1976) provide estimates of

live and dead crown material for most species in the EC-FFE (table 4.87).

Mountain hemlock biomass is based on Gholz (1979), using western hemlock

equations from Brown and Johnston to partition the biomass and also to

provide estimates for trees less than 1 inch diameter.

Table 4.87—The crown biomass equations listed here determine the biomass of foliage, branch, and stem wood. Species mappings are

done for species for which equations are not available.

Species Species mapping and equation source

western white pine Brown and Johnston (1976)

western larch Brown and Johnston (1976)

Douglas-fir Brown and Johnston (1976)

Pacific silver fir grand fir; Brown and Johnston (1976)

western redcedar Brown and Johnston (1976)

grand fir Brown and Johnston (1976)

lodgepole pine Brown and Johnston (1976)

Engelmann spruce Brown and Johnston (1976)

subalpine fir Brown and Johnston (1976)

ponderosa pine Brown and Johnston (1976)

other Gholz (1979); Brown and Johnston (1976)
162 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Live leaf lifespan is used to simulate the contribution of needles and leaves

to annual litter fall. Dead foliage and branch materials also contribute to

litter fall, at the rates shown in table 4.88. Each year the inverse of the

lifespan is added to the litter pool from each biomass category. Leaf lifespan

data are from Keane and others (1989). Lifespans of western white pine and

mountain hemlock are mapped using ponderosa pine, and western hemlock

and western redcedar are based on Douglas-fir.

Live Herbs and Shrubs: Live herb and shrub fuels are modeled simply

by the FFE. Shrubs and herbs are assigned a biomass value based on total

tree canopy cover and dominant overstory species (table 4.89). When there

Table 4.89—Values (dry weight, tons/acre) for live fuels used in the EC-FFE.

Biomass is linearly interpolated between the “initiating” (I) and

“established” (E) values when canopy cover is between 10 and 60

percent.

Species Herbs Shrubs Notes

western white pine E 0.15 0.10

I 0.30 2.00

western larch E 0.20 0.20

I 0.40 2.00

Douglas-fir E 0.20 0.20

I 0.40 2.00

Pacific silver fir E 0.15 0.10 Use grand fir

I 0.30 2.00

western redcedar E 0.20 0.20

I 0.40 2.00

grand fir E 0.15 0.10

I 0.30 2.00

lodgepole pine E 0.20 0.10

I 0.40 1.00

Engelmann spruce E 0.15 0.20

I 0.30 2.00

subalpine fir E 0.15 0.20

I 0.30 2.00

ponderosa pine E 0.20 0.25

I 0.25 0.10

other E 0.15 0.20 Use spruce-subalpine fir

I 0.30 2.00

Table 4.88—Life span of live and dead foliage (years) and dead branches

for species modeled in the EC-FFE variant.

Live Dead

Species Foliage Foliage <0.25” 0.25–1” >1”

western white pine 4 2 5 5 15

western larch 1 1 5 5 15

Douglas-fir 5 2 5 5 15

Pacific silver fir 7 2 5 5 15

western redcedar 5 2 5 5 20

grand fir 7 2 5 5 15

lodgepole pine 3 2 5 5 15

Engelmann spruce 6 2 5 5 10

subalpine fir 7 2 5 5 15

ponderosa pine 4 2 5 5 10

other 4 2 5 5 10
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 163

are no trees, habitat type is used to infer the most likely dominant species of

the previous stand (“Model Description” chapter, section 2.4.2). When total

tree canopy cover is less than 10 percent, herb and shrub biomass is assigned

an “initiating” value (the ‘I’ rows from table 4.89). When canopy cover is

greater than 60 percent, biomass is assigned an “established” value (the ‘E’

rows). Live fuel loads are linearly interpolated when canopy cover is between

10 and 60 percent. Data are taken from FOFEM 4.0 (Reinhardt and others

1997) with modifications provided by Jim Brown (USFS, Missoula, MT pers.

comm., 1995).

Dead Fuels: Initial default DWD pools are based on overstory species.

When there are no trees, habitat type is used to infer the most likely

dominant species of the previous stand (“Model Description” chapter, section

2.4.2). Default fuel loadings were provided by Jim Brown (USFS, Missoula,

MT pers. comm., 1995) (table 4.90). If tree canopy cover is less than 10

percent, the DWD pools are assigned an “initiating” value, and if cover is

greater than 60 percent they are assigned the “established” value. Fuels are

linearly interpolated when canopy cover is between 10 and 60 percent. Initial

fuel loads can be modified using the FUELINIT keyword.

4.8.4 Bark Thickness

Bark thickness contributes to predicted tree mortality from simulated

fires. The bark thickness multipliers in table 4.91 are used to calculate single

bark thickness as described in the “Model Description” chapter, (section

2.5.5. The bark thickness equation used in the mortality equation is unrelated to the bark thickness used in the base FVS model. Data are from

FOFEM 5.0 (Reinhardt and others 2001).

Table 4.90—Canopy cover and cover type are used to assign default down woody debris (tons/acre) by size class

for established (E) and initiating (I) stands.

Size class (inches)

Species < 0.25 0.25 – 1 1 – 3 3 – 6 6 – 12 > 12 Litter Duff

western white pine E 1.0 1.0 1.6 10.0 10.0 10.0 0.8 30.0

I 0.6 0.6 0.8 6.0 6.0 6.0 0.4 12.0

western larch E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

Douglas-fir E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

Pacific silver fir E 0.7 0.7 3.0 7.0 7.0 0.0 0.6 25.0

I 0.5 0.5 2.0 2.8 2.8 0.0 0.3 12.0

western redcedar E 2.2 2.2 5.2 15.0 20.0 15.0 1.0 35.0

I 1.6 1.6 3.6 6.0 8.0 6.0 0.5 12.0

grand fir E 0.7 0.7 3.0 7.0 7.0 0.0 0.6 25.0

I 0.5 0.5 2.0 2.8 2.8 0.0 0.3 12.0

lodgepole pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

Engelmann spruce E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

subalpine fir E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

ponderosa pine E 0.7 0.7 1.6 2.5 2.5 0.0 1.4 5.0

I 0.1 0.1 0.2 0.5 0.5 0.0 0.5 0.8

other E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0
164 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.91—Species specific constants

for determining single bark

thickness.

Species Multiplier (Vsp)

western white pine 0.035

western larch 0.063

Douglas-fir 0.063

Pacific silver fir 0.047

western redcedar 0.035

grand fir 0.046

lodgepole pine 0.028

Engelmann spruce 0.036

subalpine fir 0.041

ponderosa pine 0.063

other 0.040

Table 4.92—Default annual loss rates on mesic sites

are applied based on size class. A portion of the loss is added to the duff pool

each year. Loss rates are for hard material. If present, soft material in all size

classes except litter and duff decays 10

percent faster.

4.8.5 Decay Rate

Decay of down material is simulated by applying loss rates to biomass in

categories based on the original size-class of the branch and bole pieces as

described in section 2.4.5 of the “Model Description” chapter. Default decay

rates on mesic sites (table 4.92) are based on Abbott and Crossley (1982). A

portion of the loss is added to the duff pool each year. Loss rates are for hard

material; soft material in all size classes, except litter and duff, decays 10

percent faster.

Decay rates on moist sites are one-third higher than the rates shown in

table 4.92; dry sites are one-third lower. The habitat code set by the

STDINFO keyword determines whether a stand is defined as a moist, mesic,

Size class

(inches)

Annual loss rate

Proportion of loss

becoming duff

< 0.25

0.25 — 1

0.12

1 — 3 0.09

3 — 6

6 — 12

> 12

0.015

Litter 0.50

0.02

Duff 0.002 0.0
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 165

or dry site, as shown in table 4.93. These assignments were provided by Tom

DeMeo of USFS, Portland, OR, and Terry Lillybridge of USFS, Wenatchee,

WA (pers. comm. 2001; based on Williams and others 1983, Williams and

others 1990, Williams and others 1995).

The decay rates of species groups may be modified by users, who can

provide rates to the four decay classes shown in table 4.94 using the

FUELDCAY keyword. Users can also reassign species to different classes

using the FUELPOOL keyword.

Table 4.93—Habitat type - moisture regime relationships for the EC-FFE variant. Moisture classes modify

default decay rates, as described in the text.

Habitat Habitat Habitat Habitat

type code Regime type code Regime type code Regime type code Regime

CAG112 Dry CDS716 Mesic CFS553 Moist CWF444 Mesic

CAS311 Mesic CDS811 Mesic CFS556 Moist CWF521 Moist

CCF211 Moist CDS813 Mesic CFS558 Moist CWF522 Moist

CCF212 Moist CDS814 Mesic CFS621 Moist CWF523 Moist

CCF221 Mesic CDS831 Moist CHC311 Moist CWF524 Moist

CCF222 Moist CDS832 Mesic CHF223 Moist CWG121 Dry

CCS211 Moist CDS833 Mesic CHF311 Moist CWG122 Dry

CCS311 Mesic CEF111 Mesic CHF312 Moist CWG123 Dry

CDF411 Dry CEF211 Moist CHF313 Moist CWG124 Dry

CDG123 Dry CEF222 Moist CHF422 Moist CWG125 Mesic

CDG131 Dry CEF421 Moist CHF521 Mesic CWS214 Moist

CDG132 Dry CEF422 Moist CHS142 Mesic CWS221 Moist

CDG134 Dry CEF423 Moist CHS143 Moist CWS222 Moist

CDG141 Dry CEF424 Moist CHS144 Moist CWS223 Moist

CDG311 Dry CEG121 Moist CHS225 Moist CWS224 Mesic

CDG321 Mesic CEG310 Mesic CHS226 Moist CWS225 Mesic

CDG322 Dry CEG311 Mesic CHS227 Moist CWS226 Mesic

CDG323 Dry CEM211 Moist CHS411 Moist CWS331 Dry

CDS231 Dry CES111 Moist CHS711 Moist CWS332 Moist

CDS241 Mesic CES113 Moist CLS521 Mesic CWS335 Dry

CDS411 Dry CES210 Mesic CMF131 Moist CWS336 Dry

CDS412 Dry CES211 Mesic CMF131 Moist CWS337 Dry

CDS631 Dry CES213 Mesic CMS121 Moist CWS338 Dry

CDS632 Dry CES312 Mesic CMS122 Moist CWS421 Dry

CDS633 Mesic CES313 Mesic CMS256 Moist CWS422 Moist

CDS636 Mesic CES342 Mesic CMS257 Moist CWS531 Dry

CDS637 Dry CES412 Mesic CMS258 Moist CWS532 Moist

CDS638 Dry CES413 Mesic CMS259 Moist CWS533 Dry

CDS639 Dry CES422 Moist CMS354 Moist CWS534 Dry

CDS640 Dry CES423 Moist CMS355 Moist CWS535 Mesic

CDS653 Dry CES424 Mesic CMS356 Moist CWS536 Moist

CDS654 Dry CES425 Mesic CPG141 Dry CWS537 Moist

CDS655 Dry CES426 Mesic CPG231 Dry CWS551 Moist

CDS661 Mesic CFF162 Moist CPH211 Dry CWS552 Mesic

CDS662 Dry CFF254 Moist CPH212 Dry CWS553 Moist

CDS673 Dry CFS232 Moist CPS241 Dry CWS554 Dry

CDS674 Dry CFS233 Moist CFS553 Moist CWS821 Moist

CDS675 Dry CFS234 Moist CWC511 Moist HQG111 Dry

CDS715 Dry CFS542 Moist CWF321 Moist HQS211 Dry
166 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

4.8.6 Moisture Content

Moisture content of the live and dead fuels is used to calculate fire intensity

and fuel consumption (“Model Description” chapter, section 2.5.2). Users can

choose from four predefined moisture groups (table 4.95) or they can specify

moisture conditions for each class using the MOISTURE keyword.

4.8.7 Fire Behavior Fuel Models

Fire behavior fuel models (Anderson 1982) are used to estimate flame

length and fire effects stemming from flame length. Fuel models are determined using fuel load and stand attributes (“Model Description” chapter,

section 2.4.8) specific to each FFE variant. In addition, stand management

actions such as thinning and harvesting can abruptly increase fuel loads and

can trigger “Activity Fuels” conditions, resulting in the selection of alternative fuel models. At their discretion, FFE users have the option of:

1. Defining and using their own fuel models

2. Defining the choice of fuel models and weights

3. Allowing the FFE variant to determine a weighted set of fuel models

Table 4.94—Default wood decay classes

used in the EC-FFE variant.

Classes are from The Wood

Handbook (1999). (1 = exceptionally high; 2 = resistant or very resistant; 3 =

moderately resistant, and

4 = slightly or nonresistant.)

Species Decay class

western white pine 4

western larch 3

Douglas-fir 3

Pacific silver fir 4

western redcedar 2

grand fir 4

lodgepole pine 4

Engelmann spruce 4

subalpine fir 4

ponderosa pine 4

other 4

Table 4.95—Moisture values, which alter fire intensity and consumption, have been predefined for four groups.

Moisture group

Size class Very dry Dry Moist Wet

0 – 0.25 in. (1-hr) 4 8 12 16

0.25 – 1.0 in. (10-hr) 4 8 12 16

1.0 – 3.0 in. (100-hr) 5 10 14 18

> 3.0 in. (1000+ -hr) 10 15 25 50

Duff 15 50 125 200

Live 70 110 150 150
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 167

4. Allowing the FFE variant to determine a weighted set of fuel models,

then using the dominant model

This section explains the steps taken by the EC-FFE to follow the third and

fourth of these four options.

The fuel model selection logic is based on information provided by Tom

Leuschen (USFS, Okanogan, WA pers. comm. 2001). The appropriate fuel

model is determined using combinations of categorical measures of cover

type, canopy closure (CC), size (QMD), and whether the canopy is composed

of a single stratum or is multistoried (Single). The FVS base model provides

measures of canopy closure and size, and the base model structural stage

logic from Crookston and Stage (1999) determines whether the canopy is

single- or multistoried.

There are 11 sets of logical rules, each based on cover type. As described

below, one of 15 cover types is used to select from among the 11 flowcharts

shown below. If one of these single species:

• Douglas-fir

• subalpine fir

• Pacific silver fir

• lodgepole pine

• ponderosa pine

• white pine

• Engelmann spruce

• western larch

• mountain hemlock

comprises more than half the stand basal area, then that species’ flowchart

will be used. Failing that, these combinations of two species are searched for:

• Douglas-fir/grand fir

• ponderosa pine/Douglas-fir

• lodgepole pine/western larch

and the corresponding cover type flowchart is used. If a cover type has not

been selected yet, these three cover types are searched in order:

• subalpine fir leading

• moist habitat mixed conifer

• dry habitat mixed conifer

Moist and dry habitats are based on the habitat code provided by the

STDINFO keyword, using the classification shown in table 4.93.

When the combination of large and small fuel lies in the lower left corner

of the graph shown in figure 4.10, one or more low-fuel fuel models become

candidate models. In other regions of the graph, other fuel models may also

be candidates. The cover types described above, along with the flow diagrams

in figure 4.11, define which low fuel model(s) will become candidates.

According to the logic of the figure, only a single fuel model will be chosen for

a given stand structure. Consequently, as a stand undergoes structural

changes due to management or maturation, the selected fire model can jump

from one model selection to another, which in turn may cause abrupt changes

in predicted fire behavior. To smooth out changes resulting from changes in
168 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

10 20 30 40

10

20

30

40

0

3" + fuels (tons/acre)

0"-3" fuels (tons/acre)

FM10 - natural fuels

FM11 - activity fuels

FM12 - natural fuels

FM13

Fuel model, 100% weight

low fuels

models

0

CC

<20%

CC

<50%

CC

<80%

Single Single FM5 FM8

FM 8

FM 1 FM 5

NO

Douglas-fir

NO NO

NO NO

YES YES YES

QMD

<3”

FM 8

FM 5

NO

YES YES YES

Figure 4.10—If large and small fuels map

to the shaded area, candidate fuel models

are determined using the logic shown in

figure 4.11. Otherwise, flame length is

based on the distance to the closest fuel

models, and on recent management (see

“Model Description” chapter, section 2.4.8

for further details).

Figure 4.11—Logic for modeling fire at “low” fuel loads in the EC-FFE variant.

fuel model, the strict logic is augmented by linear transitions between states

that involve continuous variables (for example, percent canopy cover, average height, snag density, and so forth).

If the STATFUEL keyword is selected, fuel model is determined by using

only the closest match fuel model identified by either figure 4.10 or figure

4.11. The FLAMEADJ keyword allows the user to scale the calculated flame

length or override the calculated flame length with a value they choose.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 169

CC

<20%

CC

<50%

CC

<80%

Single

Single QMD Single

<3”

QMD

<3”

QMD

<3”

FM 5 FM 8

FM 8

FM 8

FM 1 FM 1 FM 5

FM 5

FM 5

FM 8

NO

Lodgepole pine, white pine

NO NO

NO

NO NO

NO

NO NO

YES YES YES

YES

YES YES

YES

YES YES

CC

<20%

CC

<50%

CC

<80%

Single Single QMD

<3”

QMD

<3”

FM 5 FM 8

FM5

FM 8

FM 8

FM 5

FM 8

FM 1

NO

Subalpine fir, subalpine fir mixture

NO NO

NO NO NO

NO

YES YES YES

YES YES YES

YES
170 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

CC

<20%

Single Single

QMD

<3”

QMD

<20”

QMD

<3”

FM 1

FM 5

FM 5

FM 8

FM 5

FM 5 FM 8 FM 8

FM 8 NO NO NO

Mountain hemlock, Pacific silver fir, Douglas-fir/grand fir

NO NO

NO NO

NO

YES YES YES

YES YES

YES YES

YES

CC

<50%

CC

<80%

QMD

<3”

QMD

<8”

QMD

<20”

CC

<20%

CC

<20%

CC

<20%

CC

<20%

CC

<80%

Single Single

FM 8 FM 8 FM 8

FM 1 FM 5

FM 5 FM 1

FM 1 FM 5 FM 5

FM 8

NO

Engelmann spruce

NO NO

NO NO NO NO NO

NO NO

YES YES YES

YES YES YES YES YES

YES YES
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 171

CC

<20%

CC

<50%

CC

<80%

Single Single QMD

<3”

FM 2 FM 6 FM 6

FM 6 FM 9 FM 9

FM 9 NO NO NO

Ponderosa pine

NO NO NO

YES YES YES

YES YES YES

QMD

<3”

QMD

<8”

CC

<20%

CC

<20%

CC

<20%

CC

<20%

CC

<80%

Single

Single

Single

FM 8 FM 8 FM 8

FM 1 FM 5 FM 1

FM 5 FM 8 FM 5

FM 1 FM 5

FM 8

NO

Western larch

NO NO

NO NO NO NO NO

NO NO NO

YES YES YES

YES YES YES YES YES

YES YES YES

QMD

<20”
172 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

CC

<20%

CC

<50%

CC

<80%

Single Single Single Single

QMD

<3”

QMD

<3”

QMD

<3”

QMD

<20”

FM 2

FM 6 FM 8 FM 8 FM 8

FM6 FM 9 FM 9

FM 6 FM 6 FM 8 FM 8

NO NO NO

Ponderosa pine, Douglas-fir

NO NO NO NO

NO NO NO NO

YES YES YES

YES YES YES YES

YES YES YES YES

CC

<20%

CC

<50%

CC

<80%

Single

QMD

<8”

QMD

<3”

QMD

<20”

QMD

<20”

QMD

<3” Single

FM 1 FM 5

FM1 FM 5

FM 5

FM 5 FM 8

FM 5 FM 8 FM 8

FM 8 NO NO NO

Moist habitat mixture

NO

NO NO NO NO

NO NO

YES YES YES

YES

YES YES YES YES

YES YES
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 173

CC

<20%

CC

<50%

CC

<80%

Single Single QMD

<3”

QMD

<20”

Single

FM 6 FM 8

FM 8

FM 8

FM 8

FM 9

FM 2 FM 6 FM 6

NO NO NO

Dry habitat mixture

NO NO NO NO

NO

YES YES YES

YES YES YES YES

YES

CC

<20%

CC

<50%

CC

<80%

Single Single Single

QMD

<3”

QMD

<3”

FM 1

FM5 FM8

FM 5 FM 5

FM8 FM 8

FM 8

FM 5

NO NO NO

Lodgepole pine, western larch

NO NO NO NO

NO

YES YES YES

YES YES YES YES

YES
174 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

4.9 Central Idaho (CI)_______________________________________________

4.9.1 Tree Species

The Central Idaho variant models the 10 tree species shown in table 4.96.

One additional category, “other” is modeled using western hemlock.

4.9.2 Snags

The majority of the snag model logic is based on unpublished data provided

by Bruce Marcot (USFS, Portland, OR, unpublished data 1995). Snag fall

parameters were developed at the FFE design workshop. A complete description of the Snag Submodel is provided in section 2.3 of the FFE “Model

Description” chapter.

Four variables are used to modify the Snag Submodel for the different

species in the CI-FFE variant:

• A multiplier to modify the species’ fall rate

• Amultiplier to modify the time required for snags to decay from a “hard”

to “soft” state

• The maximum number of years that snags will remain standing

• A multiplier to modify the species’ height loss rate

These variables are summarized in tables 4.97 and 4.98.

Snag dynamics are similar to the NI-FFE variant, with the following

exceptions:

• Western larch, lodgepole pine, Engelmann spruce, subalpine fir and

ponderosa pine snags experience no height loss, and their height loss

multiplier is set to zero.

• Western white pine and western redcedar lose 75 percent of their

original height, after which their height does not change.

• Larch and spruce snags greater than 18 inches dbh fall at a rate that

is 32 percent of the rate predicted by Marcot’s equation.

Snag bole volume is determined in using the base FVS model equations.

The coefficients shown in table 4.99 are used to convert volume to biomass.

Soft snags have 80 percent the density of hard snags.

Table 4.96—Tree species simulated by the Central Idaho variant.

Common name Scientific name Notes

western white pine Pinus monticola

western larch Larix occidentalis

Douglas-fir Pseudotsuga menziesii

grand fir Abies grandis

western hemlock Tsuga heterophylla

western redcedar Thuja plicata

lodgepole pine Pinus contorta

Engelmann spruce Picea engelmannii

subalpine fir Abies lasiocarpa

ponderosa pine Pinus ponderosa

other = mountain hemlock
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 175

Table 4.97—Default snag fall, snag height loss and soft-snag characteristics for

20 inch DBH snags in the CI-FFE variant. These characteristics are

derived directly from the parameter values shown in table 4.98.

Species 95% fallen All down 50% height Hard-to-soft

- - - - - - - - - - - - - - - - - - -Years - - - - - - - - - - - - - - - - - - -

western white pine 34 110 76 42

western larch 97§ 150 — 42

Douglas-fir 34 75 30 42

grand fir 28 90 20 35

western hemlock 34 150 33 35

western redcedar 103 300 27 35

lodgepole pine 19 35 — 35

Engelmann spruce 81§ 100 — 35

subalpine fir 39 40 — 35

ponderosa pine 31 90 — 35

other 34 30 20 35

§ This value results from using 32% of the default rate for Douglas-fir and spruce snags

>18” DBH, as described in the text.

Table 4.98—Default snag fall, snag height loss and softsnag multipliers for the CI-FFE. These parameters result in the values shown in table 4.97.

(These three columns are the default values

used by the SNAGFALL, SNAGBRK, and

SNAGDCAY keywords, respectively.)

Snag Height

Species fall loss Hard-to-soft

western white pine 0.9 0.4 1.1

western larch 1.0§ — 1.1

Douglas-fir 0.9 1.0 1.1

grand fir 1.1 1.5 0.9

western hemlock 0.9 0.9 0.9

western redcedar 0.3 0.3 0.9

lodgepole pine 1.6 — 0.9

Engelmann spruce 1.2§ — 0.9

subalpine fir 0.8 — 0.9

ponderosa pine 0.8 — 0.9

other 0.9 1.5 0.9

§ This value apples to Douglas-fir and spruce snags <18” DBH;

see text for details.

Snag dynamics can be modified by the user using the SNAGBRK,

SNAGFALL, SNAGDCAY and SNAGPBN keywords described in the FFE

“Model Description” chapter.

4.9.3 Fuels

Information on live fuels was developed using FOFEM 4.0 (Reinhardt and

others 1997) and FOFEM 5.0 (Reinhardt and others 2001) and in cooperation

with Jim Brown (USFS, Missoula, MT pers. comm. 1995). A complete

description of the Fuel Submodel is provided in section 2.4 of the FFE “Model

Description” chapter.
176 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.99—Wood density (ovendry

lb/ft3

) used in the CI-FFE

variant.

Species Density (lb/ft3)

western white pine 24.8

western larch 34.3

Douglas-fir 31.9

grand fir 24.1

western hemlock 29.5

western redcedar 21.1

lodgepole pine 26.4

Engelmann spruce 22.6

subalpine fir 21.1

ponderosa pine 26.4

other 29.5

Fuels are divided into to four categories: live tree bole, live tree crown, live

herb and shrub, and down woody debris (DWD). Live herb and shrub fuel

load, and initial DWD are assigned based on the cover species with greatest

basal area. If there is no basal area in the first simulation cycle (a “bare

ground” stand) then the initial fuel loads are assigned by the vegetation code

provided with the STDINFO keyword. If the vegetation code is missing or

does not identify an overstory species, the model uses a Douglas-fir cover type

to assign the default fuels. If there is no basal area in other cycles of the

simulation (after a simulated clearcut, for example) herb and shrub fuel

biomass is assigned by the previous cover type.

Live Tree Bole: The fuel contribution of live trees is divided into two

components: bole and crown. Bole volume is transferred to the FFE after

being computed by the FVS model, then converted to biomass using ovendry

wood density calculated from table 4-3a and equation 3-5 of The Wood

Handbook (Forest Products Laboratory 1999). The coefficient in table 4.99

for Douglas-fir is based on Douglas-fir Interior north.

Tree Crown: As described in the section 2.2 of the FFE “Model Description” chapter, equations in Brown and Johnston (1976) provide estimates of

live and dead crown material for most species in the CI-FFE (table 4.100).

Table 4.100—The crown biomass equations listed here determine the

biomass of foliage, branch, and stem wood. Species mappings are done for species for which equations are not

available.

Species Species mapping and equation source

western white pine Brown and Johnston (1976)

western larch Brown and Johnston (1976)

Douglas-fir Brown and Johnston (1976)

grand fir Brown and Johnston (1976)

western hemlock Brown and Johnston (1976)

western redcedar Brown and Johnston (1976)

lodgepole pine Brown and Johnston (1976)

Engelmann spruce Brown and Johnston (1976)

subalpine fir Brown and Johnston (1976)

ponderosa pine Brown and Johnston (1976)

other Gholz (1979); Brown and Johnston (1976)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 177

Table 4.101—Life span of live and dead foliage (years) and dead branches

for species modeled in the CI-FFE variant.

Live Dead

Species Foliage Foliage <0.25” 0.25–1” >1”

western white pine 4 2 5 5 15

western larch 1 1 5 5 15

Douglas-fir 5 2 5 5 15

grand fir 7 2 5 5 15

western hemlock 5 2 5 5 15

western redcedar 5 2 5 5 20

lodgepole pine 3 2 5 5 15

Engelmann spruce 6 2 5 5 10

subalpine fir 7 2 5 5 15

ponderosa pine 4 2 5 5 10

other 4 2 5 5 15

Mountain hemlock biomass is based on Gholz (1979), using western hemlock

equations from Brown and Johnston to partition the biomass and also to

provide estimates for trees less than 1 inch diameter.

Live leaf lifespan is used to simulate the contribution of needles and leaves

to annual litter fall. Dead foliage and branch materials also contribute to

litter fall, at the rates shown in table 4.101. Each year the inverse of the

lifespan is added to the litter pool from each biomass category. Leaf lifespan

data are from Keane and others (1989). Lifespans of western white pine and

mountain hemlock are mapped using ponderosa pine, and western hemlock

and western redcedar are based on Douglas-fir.

Live Herbs and Shrubs: Live herb and shrub fuels are modeled simply

by the FFE. Shrubs and herbs are assigned a biomass value based on total

tree canopy cover and dominant overstory species (table 4.102). When there

are no trees, habitat type is used to infer the most likely dominant species of

the previous stand (“Model Description” chapter, section 2.4.2). When total

tree canopy cover is less than 10 percent, herb and shrub biomass is assigned

an “initiating” value (the ‘I’ rows from table 4.102). When canopy cover is

greater than 60 percent, biomass is assigned an “established” value (the ‘E’

rows). Live fuel loads are linearly interpolated when canopy cover is between

10 and 60 percent. Data are taken from FOFEM 4.0 (Reinhardt and others

1997) with modifications provided by Jim Brown (USFS, Missoula, MT pers.

comm., 1995).

Dead Fuels: Initial default DWD pools are based on overstory species.

When there are no trees, habitat type is used to infer the most likely

dominant species of the previous stand (“Model Description” chapter, section

2.4.2). Default fuel loadings were provided by Jim Brown (USFS, Missoula,

MT pers. comm., 1995) (table 4.103). If tree canopy cover is less than 10

percent, the DWD pools are assigned an “initiating” value, and if cover is

greater than 60 percent they are assigned the “established” value. Fuels are

linearly interpolated when canopy cover is between 10 and 60 percent. Initial

fuel loads can be modified using the FUELINIT keyword.
178 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.102—Values (dry weight, tons/acre) for live fuels used in the CI-FFE.

Biomass is linearly interpolated between the “initiating” (I) and

“established” (E) values when canopy cover is between 10 and 60

percent.

Species Herbs Shrubs Notes

western white pine E 0.15 0.10

I 0.30 2.00

western larch E 0.20 0.20

I 0.40 2.00

Douglas-fir E 0.20 0.20

I 0.40 2.00

grand fir E 0.15 0.10

I 0.30 2.00

western hemlock E 0.20 0.20 Use Douglas-fir

I 0.40 2.00

western redcedar E 0.20 0.20 Use Douglas-fir

I 0.40 2.00

lodgepole pine E 0.20 0.10

I 0.40 1.00

Engelmann spruce E 0.15 0.20

I 0.30 2.00

subalpine fir E 0.15 0.20

I 0.30 2.00

ponderosa pine E 0.20 0.25

I 0.25 0.10

other E 0.15 0.20 Use spruce-subalpine fir

I 0.30 2.00

Table 4.103—Canopy cover and cover type are used to assign default down woody debris (tons/acre) by size class

for established (E) and initiating (I) stands.

Size class (inches)

Species < 0.25 0.25 – 1 1 – 3 3 – 6 6 – 12 > 12 Litter Duff

western white pine E 1.0 1.0 1.6 10.0 10.0 10.0 0.8 30.0

I 0.6 0.6 0.8 6.0 6.0 6.0 0.4 12.0

western larch E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

Douglas-fir E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

grand fir E 0.7 0.7 3.0 7.0 7.0 0.0 0.6 25.0

I 0.5 0.5 2.0 2.8 2.8 0.0 0.3 12.0

western hemlock E 2.2 2.2 5.2 15.0 20.0 15.0 1.0 35.0

I 1.6 1.6 3.6 6.0 8.0 6.0 0.5 12.0

western redcedar E 2.2 2.2 5.2 15.0 20.0 15.0 1.0 35.0

I 1.6 1.6 3.6 6.0 8.0 6.0 0.5 12.0

lodgepole pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

Engelmann spruce E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

subalpine fir E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

ponderosa pine E 0.7 0.7 1.6 2.5 2.5 0.0 1.4 5.0

I 0.1 0.1 0.2 0.5 0.5 0.0 0.5 0.8

other E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 179

4.9.4 Bark Thickness

Bark thickness contributes to predicted tree mortality from simulated

fires The bark thickness multipliers in table 4.104 are used to calculate single

bark thickness as described in the “Model Description” chapter, section 2.5.5.

The bark thickness equation used in the mortality equation is unrelated to

the bark thickness used in the base FVS model. Data are from FOFEM 5.0

(Reinhardt and others 2001).

4.9.5 Decay Rate

Decay of down material is simulated by applying loss rates to biomass in

categories based on the original size-class of the branch and bole pieces as

described in section 2.4.5 of the “Model Description” chapter. (table 4.105)

Table 4.104—Species specific constants

for determining single

bark thickness.

Species Multiplier (Vsp)

western white pine 0.035

western larch 0.063

Douglas-fir 0.063

grand fir 0.046

western hemlock 0.040

western redcedar 0.035

lodgepole pine 0.028

Engelmann spruce 0.036

subalpine fir 0.041

ponderosa pine 0.063

other 0.040

Size class

(inches)

Annual loss rate

Proportion of loss

becoming duff

< 0.25

0.25 — 1

0.12

1 — 3 0.09

3 — 6

6 — 12

> 12

0.015

Litter 0.50

0.02

Duff 0.002 0.0

Table 4.105—Default annual loss rates on mesic

sites are applied based on size

class. A portion of the loss is added

to the duff pool each year. Loss

rates are for hard material. If

present, soft material in all size

classes except litter and duff decays 10 percent faster.
180 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Default decay rates on mesic sites are based on Abbott and Crossley (1982).

A portion of the loss is added to the duff pool each year. Loss rates are for hard

material; soft material in all size classes, except litter and duff, decays 10

percent faster.

Decay rates on moist sites are one-third higher than the rates shown in

table 4.105; dry sites are one-third lower. The habitat code set by the

STDINFO keyword determines whether a stand is defined as a moist, mesic

or dry site, as shown in table 4.106. These assignments were provided by

Kathy Geier-Hayes (USFS Boise, ID pers. comm., 2001).

The decay rates of species groups may be modified by users, who can

provide rates to the four decay classes shown in table 4.107 using the

FUELDCAY keyword. Users can also reassign species to different classes

using the FUELPOOL keyword.

4.9.6 Moisture Content

Moisture content of the live and dead fuels is used to calculate fire intensity

and fuel consumption (“Model Description” chapter, section 2.5.2). Users can

choose from four predefined moisture groups (table 4.108) or they can specify

moisture conditions for each class using the MOISTURE keyword.

Table 4.106—Habitat type - moisture regime relationships for the CI-FFE variant.

Habitat Habitat Habitat Habitat

type code Regime type code Regime type code Regime type code Regime

50 Dry 330 Dry 525 Mesic 691 Mesic

60 Dry 331 Dry 526 Mesic 692 Mesic

70 Dry 332 Dry 527 Mesic 694 Mesic

80 Dry 334 Dry 580 Dry 700 Mesic

100 Dry 340 Dry 585 Dry 705 Dry

120 Dry 341 Dry 590 Mesic 720 Mesic

130 Dry 343 Dry 591 Mesic 721 Mesic

140 Dry 344 Dry 592 Mesic 723 Mesic

160 Dry 360 Dry 593 Mesic 730 Mesic

161 Dry 370 Dry 600 Mesic 731 Dry

162 Dry 371 Dry 605 Moist 732 Mesic

170 Dry 372 Dry 620 Mesic 734 Mesic

190 Dry 375 Dry 621 Mesic 740 Mesic

195 Dry 380 Dry 625 Mesic 745 Dry

200 Mesic 385 Dry 635 Moist 750 Dry

210 Dry 390 Mesic 636 Moist 780 Dry

220 Dry 392 Dry 637 Moist 790 Dry

221 Dry 393 Mesic 638 Mesic 791 Dry

222 Dry 395 Dry 640 Dry 793 Dry

250 Dry 396 Dry 645 Mesic 810 Mesic

260 Mesic 397 Dry 650 Moist 830 Mesic

262 Mesic 398 Dry 651 Moist 831 Mesic

264 Dry 400 Mesic 652 Moist 833 Mesic

265 Dry 410 Moist 654 Mesic 850 Mesic

280 Mesic 440 Mesic 655 Moist 870 Mesic

290 Mesic 490 Moist 660 Mesic 900 Mesic

310 Dry 493 Dry 661 Mesic 905 Dry

313 Dry 500 Mesic 662 Mesic 920 Dry

315 Dry 505 Dry 663 Mesic 940 Mesic

320 Dry 510 Mesic 670 Mesic 955 Dry

323 Dry 511 Mesic 671 Mesic 999 Mesic

324 Dry 515 Mesic 672 Mesic

325 Dry 520 Mesic 690 Mesic
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 181

4.9.7 Fire Behavior Fuel Models

Fire behavior fuel models (Anderson 1982) are used to estimate flame length

and fire effects stemming from flame length. Fuel models are determined using

fuel load and stand attributes (“Model Description” chapter, section 2.4.8)

specific to each FFE variant. In addition, stand management actions such as

thinning and harvesting can abruptly increase fuel loads and can trigger

“Activity Fuels” conditions, resulting in the selection of alternative fuel

models. At their discretion, FFE users have the option of:

1. Defining and using their own fuel models

2. Defining the choice of fuel models and weights

3. Allowing the FFE variant to determine a weighted set of fuel models

4. Allowing the FFE variant to determine a weighted set of fuel models,

then using the dominant model

This section explains the steps taken by the CI-FFE to follow the third and

fourth of these four options.

Fuel Model selection logic is based on one of the 11 Potential Vegetation

Groups shown in table 4.109. The CI habitat code is mapped to one of these

groups using table 4.110. Site classification information in table 4.110 was

Table 4.107—Default wood decay classes

used in the CI-FFE variant.

Classes are from The Wood

Handbook (1999). (1 = exceptionally high; 2 = resistant or very resistant; 3 =

moderately resistant, and

4 = slightly or nonresistant.)

Species Decay class

western white pine 4

western larch 3

Douglas-fir 3

grand fir 4

western hemlock 4

western redcedar 2

lodgepole pine 4

Engelmann spruce 4

subalpine fir 4

ponderosa pine 4

other 4

Table 4.108—Moisture values, which alter fire intensity and consumption, have been predefined for four groups.

Moisture group

Size class Very dry Dry Moist Wet

0 – 0.25 in. (1-hr) 4 8 12 16

0.25 – 1.0 in. (10-hr) 4 8 12 16

1.0 – 3.0 in. (100-hr) 5 10 14 18

> 3.0 in. (1000+ -hr) 10 15 25 50

Duff 15 50 125 200

Live 70 110 150 150
182 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.109—Fuel model selection in the CI-FFE variant is

based in part on classifying each stand into one

of 11 site types.

Potential vegetation group Class

Dry ponderosa pine – xeric Douglas-fir 1

Warm/dry Douglas-fir – moist ponderosa pine 2

Cool moist Douglas-fir 3

Cool dry Douglas-fir 4

Dry grand fir 5

Wet grand fir 6

Warm dry subalpine fir 7

Wet subalpine fir 8

High water table subalpine fir 9

Persistent lodgepole pine 10

High elevation subalpine fir with whitebark pine 11

Table 4.110—Habitat code and corresponding Potential Vegetation Groups (PVG) in the CIFFE variant.

Habitat Habitat Habitat Habitat

code PVG code PVG code PVG code PVG

50 10 330 2 525 6 691 7

60 10 331 4 526 6 692 10

70 10 332 4 527§ 6 694 11

80 10 334 2 580 6 700 7

100 1 340 2 585 6 705 7

120 1 341 4 590 5 720 7

130 1 343 4 591 6 721 7

140 1 344 2 592 6 723 7

160 1 360 4 593 6 730 7

161 1 370 4 600 6 731 7

162 1 371 4 605 7 732 10

170§ 2 372 4 620 9 734 11

190§ 2 375 4 621 8 740 8

195 1 380 1 625 8 745 10

200 1 385 1 635 8 750 7

210 1 390 3 636 9 780 7

220 1 392 3 637 9 790 10

221 1 393 3 638 9 791 10

222 1 395 4 640 10 793 11

250 3 396 4 645 7 810 11

260§ 2 397 4 650 9 830 10

262§ 2 398 4 651 9 831 10

264§ 2 400 7 652 9 833 11

265§ 4 410 7 654 9 850 11

280 3 440 7 655 9 870 11

290 3 490 9 660 8 900 10

310§ 2 493 7 661 8 905 10

313§ 4 500 5 662 8 920 10

315§ 2 505 5 663 10 940 10

320 2 510 6 670 8 955 10

323 4 511 671 8 999 4

324 2 515 6 672 8

325 4 520 6 690 7

§ These habitat codes map to Snowberry/Ninebark, as shown in figure 4.9.2
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 183

10 20 30 40

10

20

30

40

0

3" + fuels (tons/acre)

0"-3" fuels (tons/acre)

FM10 - natural fuels

FM11 - activity fuels

FM12 - natural fuels

FM14 - activity fuels

FM13

Fuel model, 100% weight

low fuels

models

0

Figure 4.12—If large and small fuels map to the shaded area, candidate fuel models

are determined using the logic shown in figure 4.13. Otherwise, flame length is

based on the distance to the closest fuel models, identified by the dashed lines, and

on recent management (see “Model Description” chapter, section 2.4.8 for further

details).

provided by Kathy Geier-Hayes (Fire Ecologist, USFS Boise, ID pers. comm.,

2001).

When the combination of large and small fuel lies in the lower left corner

of the graph shown in figure 4.12, one or more low-fuel fuel models become

candidate models. In other regions of the graph, other fuel models may also

be candidates. The cover types described above, along with the flow diagrams

in figure 4.13, define which low fuel model(s) will become candidates.

According to the logic of the figure, only a single fuel model will be chosen for

a given stand structure. Consequently, as a stand undergoes structural

changes due to management or maturation, the selected fire model can jump

from one model selection to another, which in turn may cause abrupt changes

in predicted fire behavior. To smooth out changes resulting from changes in

fuel model, the strict logic is augmented by smooth linear transitions using

percent canopy cover.

If the STATFUEL keyword is selected, fuel model is determined by using

only the closest match fuel model identified by either figure 4.12 or 4.13. The

FLAMEADJ keyword allows the user to scale the calculated flame length or

override the calculated flame length with a value they choose.
184 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

YES PVG

n

CC

<30%

NO

YES

FMx

CC

<50%

NO

YES

FM8 FM9

FMx

8

9

if n = 1 x = 1

 = 2 and snowberry/nine bark x=5

 = 2 = 2

 = 3 = 5

 = 4 = 2

PVG

m

YES CCGF

<50

NO

YES

NO

YES

FMx

CC

<40

CC

<60

NO

8

9

FMx

8

9

YES

if m = 5 x = 2

 = 6 = 5

PVG

else

YES FM

8 * 8 based on % short needle vs. long needle

where 100% long needle = 9 and 100% short needle = 8

9

CCGF

<70

NO

YES

NO

YES

FM5

CC

<40

CC

<60

NO

8

9

FM5

8

9

YES

FM5

Figure 4.13—Logic for modeling fire at “low” fuel loads in the CI-FFE variant. The ‘n’ and ‘m’ indices are the PVG

groups defined in table 4.104.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 185

4.10 Tetons (TT) ___________________________________________________

4.10.1 Tree Species

The Tetons variant models the seven tree species shown in table 4.111.

One additional category, “other” is modeled using whitebark pine to simulate

other pines.

4.10.2 Snags

The majority of the snag model logic is based on unpublished data provided

by Bruce Marcot (USFS, Portland, OR, unpublished data 1995). Snag fall

parameters were developed at the FFE design workshop. A complete description of the Snag Submodel is provided in section 2.3 of the FFE “Model

Description” chapter.

Four variables are used to modify the Snag Submodel for the different

species in the TT-FFE variant:

• A multiplier to modify the species’ fall rate

• Amultiplier to modify the time required for snags to decay from a “hard”

to “soft” state

• The maximum number of years that snags will remain standing

• A multiplier to modify the species’ height loss rate

These variables are summarized in tables 4.112 and 4.113.

Table 4.111—Tree species simulated by the Tetons variant.

Common name Scientific name Notes

whitebark pine Pinus albicaulis

limber pine Pinus flexilis

Douglas-fir Pseudotsuga menziesii

quaking aspen Populus tremuloides

lodgepole pine Pinus contorta

Engelmann spruce Picea engelmannii

subalpine fir Abies lasiocarpa

other = other pines

Table 4.112—Default snag fall, snag height loss and oft-snag characteristics for

15 inch DBH snags in the TT-FFE variant. These characteristics are

derived directly from the parameter values shown in table 4.113.

Species 95% fallen All down 50% height Hard-to-soft

- - - - - - - - - - - - - - - - - - -Years - - - - - - - - - - - - - - - - - - -

whitebark pine 185 90 — 29

limber pine 185 90 — 29

Douglas-fir 94¶ 100 30§ 36

quaking aspen 32 15 — 29

lodgepole pine 47 50 — 29

Engelmann spruce 124¶ 100 — 29

subalpine fir 94 40 — 29

other 185 90 — 29

§

 95% of original height is lost at 79 years.
186 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.113—Default snag fall, snag height loss and softsnag multipliers for the TT-FFE. These parameters result in the values shown in table

4.112. (These three columns are the default

values used by the SNAGFALL, SNAGBRK,

and SNAGDCAY keywords, respectively.)

Snag Height

Species fall loss Hard-to-soft

whitebark pine 0.41 — 0.9

limber pine 0.41 — 0.9

Douglas-fir 0.81¶ 1.01§ 1.1

quaking aspen 2.40 — 0.9

lodgepole pine 1.60 — 0.9

Engelmann spruce 0.61¶ — 0.9

subalpine fir 0.81 — 0.9

Other 0.41 — 0.9

¶ This value apples to Douglas-fir and spruce snags <18” DBH;

see text for details. § height loss coefficient = 4.61 after 50% height

loss

Height loss is only significant for Douglas-fir and is set to zero for all other

species. After Douglas-fir snags have lost half their original height, the rate

of height loss increases markedly, as shown in table 4.112. In the case of

Douglas-fir and spruce snags greater than 18 inches DBH, the fall rate is

reduced to 32 percent of the rate predicted by Marcot’s equation. Finally, the

fall rate of aspen is also halved in the 10 years following a burn.

Snag bole volume is determined in using the base FVS model equations.

The coefficients shown in table 4.114 are used to convert volume to biomass.

Soft snags have 80 percent the density of hard snags.

Snag dynamics can be modified by the user using the SNAGBRK,

SNAGFALL, SNAGDCAY and SNAGPBN keywords described in the FFE

“Model Description” chapter.

4.10.3 Fuels

Information on live fuels was developed using FOFEM 4.0 (Reinhardt and

others 1997) and FOFEM 5.0 (Reinhardt and others 2001) and in cooperation

with Jim Brown (USFS, Missoula, MT pers. comm. 1995). A complete

description of the Fuel Submodel is provided in section 2.4 of the FFE “Model

Description” chapter.

Table 4.114—Wood density (ovendry

lb/ft3) used in the TTFFE variant.

Species Density (lb/ft3)

whitebark pine 24.8

limber pine 24.8

Douglas-fir 31.9

quaking aspen 24.1

lodgepole pine 26.4

Engelmann spruce 22.6

subalpine fir 21.1

other 24.8
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 187

Fuels are divided into to four categories: live tree bole, live tree crown, live

herb and shrub, and down woody debris (DWD). Live herb and shrub fuel

load, and initial DWD are assigned based on the cover species with greatest

basal area. If there is no basal area in the first simulation cycle (a “bare

ground” stand) then the initial fuel loads are assigned by the vegetation code

provided with the STDINFO keyword. If the vegetation code is missing or

does not identify an overstory species, the model uses a lodgepole cover type

to assign the default fuels. If there is no basal area in other cycles of the

simulation (after a simulated clearcut, for example) herb and shrub fuel

biomass is assigned by the previous cover type.

Live Tree Bole: The fuel contribution of live trees is divided into two

components: bole and crown. Bole volume is transferred to the FFE after

being computed by the FVS model, then converted to biomass using ovendry

wood density calculated from table 4-3a and equation 3-5 of The Wood

Handbook (Forest Products Laboratory 1999). The coefficient in table 4.114

for Douglas-fir is based on Douglas-fir north.

Tree Crown: As described in the section 2.2 of the FFE “Model Description” chapter, equations in Brown and Johnston (1976) provide estimates of

live and dead crown material for most species in the TT-FFE (table 4.115).

Live leaf lifespan is used to simulate the contribution of needles and leaves

to annual litter fall. Dead foliage and branch materials also contribute to

litter fall, at the rates shown in table 4.116. Each year the inverse of the

lifespan is added to the litter pool from each biomass category. Leaf lifespan

data are from Keane and others (1989).

Table 4.115—The crown biomass equations listed here determine the biomass of

foliage, branch, and stem wood. Species mappings are done for species

for which equations are not available.

Species Species mapping and equation source

whitebark pine Brown (1978)

limber pine lodgepole pine: Brown and Johnston (1976)

Douglas-fir Brown and Johnston (1976)

quaking aspen Ruark (1987) <1” DBH, Standish and others (1985) >1” DBH

lodgepole pine Brown and Johnston (1976)

Engelmann spruce Brown and Johnston (1976)

subalpine fir Brown and Johnston (1976)

other whitebark pine; Brown (1978)

Table 4.116—Life span of live and dead foliage (years) and dead branches

for species modeled in the TT-FFE variant.

Live Dead

Species Foliage Foliage <0.25” 0.25–1” >1”

whitebark pine 3 2 5 5 15

limber pine 3 2 5 5 15

Douglas-fir 5 2 5 5 15

quaking aspen 1 1 5 5 10

lodgepole pine 3 2 5 5 15

Engelmann spruce 6 2 5 5 10

subalpine fir 7 2 5 5 15

other 3 2 5 5 15
188 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Live Herbs and Shrubs: Live herb and shrub fuels are modeled simply

by the FFE. Shrubs and herbs are assigned a biomass value based on total

tree canopy cover and dominant overstory species (table 4.117). When there

are no trees, habitat type is used to infer the most likely dominant species of

the previous stand (“Model Description” chapter, section 2.4.2). When total

tree canopy cover is less than 10 percent, herb and shrub biomass is assigned

an “initiating” value (the ‘I’ rows from table 4.117). When canopy cover is

greater than 60 percent, biomass is assigned an “established” value (the ‘E’

rows). Live fuel loads are linearly interpolated when canopy cover is between

10 and 60 percent. Data are taken from FOFEM 4.0 (Reinhardt and others

1997) with modifications provided by Jim Brown (USFS, Missoula, MT pers.

comm., 1995). Data on pine were developed after examining live fuels

reported in the Stereo Photo Guides for Quantifying Natural Fuels (Ottmar

and others 2000b).

Dead Fuels: Initial default DWD pools are based on overstory species.

When there are no trees, habitat type is used to infer the most likely

dominant species of the previous stand (“Model Description” chapter, section

2.4.2). Default fuel loadings were provided by Jim Brown (USFS, Missoula,

MT pers. comm., 1995) (table 4.118). If tree canopy cover is less than 10

percent, the DWD pools are assigned an “initiating” value, and if cover is

greater than 60 percent they are assigned the “established” value. Fuels are

linearly interpolated when canopy cover is between 10 and 60 percent. Initial

fuel loads can be modified using the FUELINIT keyword.

4.10.4 Bark Thickness

Bark thickness contributes to predicted tree mortality from simulated

fires. The bark thickness multipliers in table 4.119 are used to calculate

single bark thickness as described in the “Model Description” chapter,

section 2.5.5. The bark thickness equation used in the mortality equation is

Table 4.117—Values (dry weight, tons/acre) for live fuels used in the TT-FFE. Biomass is linearly

interpolated between the “initiating” (I) and “established” (E) values when canopy

cover is between 10 and 60 percent.

Species Herbs Shrubs Notes

whitebark pine E 0.20 0.10 Use lodgepole pine

I 0.40 1.00

limber pine E 0.20 0.10 Use lodgepole pine

I 0.40 1.00

Douglas-fir E 0.20 0.20

I 0.40 2.00

quaking aspen E 0.25 0.25 Ottmar and others 2000b

I 0.18 1.32

lodgepole pine E 0.20 0.10

I 0.40 1.00

Engelmann spruce E 0.15 0.20

I 0.30 2.00

subalpine fir E 0.15 0.20

I 0.30 2.00

other E 0.20 0.10 Use whitebark pine

I 0.40 1.00
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 189

Table 4.118—Canopy cover and cover type are used to assign default dead fuel loads (tons/acre) by size class

for established (E) and initiating (I) stands.

Size class (inches)

Species < 0.25 0.25 – 1 1 – 3 3 – 6 6 – 12 > 12 Litter Duff

whitebark pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

limber pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

Douglas-fir E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 15.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

quaking aspen E 0.2 0.6 2.4 3.6 5.6 0.0 1.4 16.8

I 0.1 0.4 5.0 2.2 2.3 0.0 0.8 5.6

lodgepole pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

Engelmann spruce E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

subalpine fir E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

other E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

Table 4.119—Species specific constants

for determining single

bark thickness.

Species Multiplier (Vsp)

whitebark pine 0.030

limber pine 0.030

Douglas-fir 0.063

quaking aspen 0.044

lodgepole pine 0.028

Engelmann spruce 0.036

subalpine fir 0.041

other 0.030

unrelated to the bark thickness used in the base FVS model. Data are from

FOFEM 5.0 (Reinhardt and others 2001).

4.10.5 Decay Rate

Decay of down material is simulated by applying loss rates to biomass in

categories based on the original size-class of the branch and bole pieces

described in section 2.4.5 of the “Model Description” chapter (table 4.120) By

default, down material decays at the rate used by the UT-FFE: 55 percent

lower than the default decay rates based on Abbott and Crossley (1982). A

portion of the loss is added to the duff pool each year. Loss rates are for hard

material; soft material in all size classes, except litter and duff, decays 10

percent faster.

By default, the FFE decays all wood species at the rates shown in table

4.120. The decay rates of species groups may be modified by users, who can

provide rates to the four decay classes shown in table 4.121 using the

FUELDCAY keyword. Users can also reassign species to different classes

using the FUELPOOL keyword.
190 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.120—Default annual loss rates are applied based on

size class. A portion of the loss is added to the

duff pool each year. Loss rates are for hard

material. The rates for woody material are the

same as those used by the UT-FFE. If present,

soft material in all size classes except litter

and duff decays 10 percent faster.

Table 4.121—Default wood decay classes

used in the TT-FFE variant.

Classes are from The Wood

Handbook (1999). (1 = exceptionally high; 2 = resistant or very resistant; 3 =

moderately resistant, and

4 = slightly or nonresistant.)

Species Decay class

whitebark pine 4

limber pine 4

Douglas-fir 3

quaking aspen 4

lodgepole pine 4

Engelmann spruce 4

subalpine fir 4

other 4

4.10.6 Moisture Content

Moisture content of the live and dead fuels is used to calculate fire intensity

and fuel consumption (Model Description, section 2.5.2). Users can choose

from four predefined moisture groups (table 4.122) or they can specify

moisture conditions for each class using the MOISTURE keyword.

Size class

(inches)

Annual loss rate

Proportion of loss

becoming duff

< 0.25

0.25 — 1

0.054

1 — 3 0.041

3 — 6

6 — 12

> 12

0.0068

Litter 0.50

0.02

Duff 0.002 0.0
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 191

4.10.7 Fire Behavior Fuel Models

Fire behavior fuel models (Anderson 1982) are used to estimate flame

length and fire effects stemming from flame length. Fuel models are determined using fuel load and stand attributes (“Model Description” chapter,

section 2.4.8) specific to each FFE variant. In addition, stand management

actions such as thinning and harvesting can abruptly increase fuel loads and

can trigger ”Activity Fuels” conditions, resulting in the selection of alternative fuel models. At their discretion, FFE users have the option of:

1. Defining and using their own fuel models

2. Defining the choice of fuel models and weights

3. Allowing the FFE variant to determine a weighted set of fuel models

4. allowing the FFE variant to determine a weighted set of fuel models,

then using the dominant model

This section explains the steps taken by the TT-FFE to follow the third and

fourth of these four options.

When the combination of large and small fuel lies in the lower left corner

of the graph shown in figure 4.14, one or more low-fuel fuel models become

candidate models. In other regions of the graph, other fuel models may also

Table 4.122—Moisture values, which alter fire intensity and consumption, have been predefined for four groups. In

general they are drier than the default values used in

the NI-FFE.

Moisture group

Size class Very dry Dry Moist Wet

0 – 0.25 in. (1-hr) 4 5 8 10

0.25 – 1.0 in. (10-hr) 4 6 10 12

1.0 – 3.0 in. (100-hr) 5 8 12 15

> 3.0 in. (1000+ -hr) 10 15 16 18

Duff 15 50 125 200

Live 70 90 120 140

Figure 4.14—If large and

small fuels map to the shaded

area, candidate fuel models

are determined using the logic

shown in figure 4.15. Otherwise, flame length is based on

the distance to the closest fuel

models, identified by the

dashed lines, and on recent

management (see “Model

Description” chapter, section

2.4.8 for further details). 10 20 30 40

10

20

30

40

0

3" + fuels (tons/acre)

0"-3" fuels (tons/acre)

FM10 - natural fuels

FM11 - activity fuels

FM12

Fuel model, 100% weight

low fuels

models

0
192 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

be candidates. The logical flow shown in figure 4.15 defines which low fuel

model(s) will become candidates. According to the logic of figure 4.15, only a

single fuel model will be chosen for a given stand structure. Consequently, as

a stand undergoes structural changes due to management or maturation, the

selected fire model can jump from one model selection to another, which in

turn may cause abrupt changes in predicted fire behavior. To smooth out

changes resulting from changes in fuel model, the strict logic is augmented

by linear transitions between states that involve continuous variables (for

example, percent canopy cover, average height, snag density, and so forth).

The program logic shown in figure 4.15 also uses stand structure classes

in some decision rules. The TT-FFE uses the default structure class rules

documented in Crookston and Stage (1999) unless model users alter those

definitions using the STRCLS keyword.

If the STATFUEL keyword is selected, fuel model is determined by using

only the closest match fuel model identified by either figure 4.14 or figure

4.15. The FLAMEADJ keyword allows the user to scale the calculated flame

length or override the calculated flame length with a value they choose.

YES

YES

NO Spruce

fir cover

type

Structural

class ≤ 2

YES

NO YES

Lodgepole NO

pine cover

type

Stand

density ≤

1000 stems

per acre

Top

height > 10’

and mid-flame

wind speed

≤7 mph

NO

Begin

Lodgepole

pine site index <35

NO

YES

Structural NO

class ≤2

YES

YES

YES NO Stand

density > 1000 stems/

acre

NO YES

NO

YES

Top

height

>10’

Douglas fir

cover

type

YES NO

Aspen

basal area

>80% of

stand

BA

NO YES

Douglas fir

cover

type

YES

NO

NO

YES

Crown

cover of over

10’ conifers

>40%

* Trees shorter than 1/2 the top height

Live

conifer under- story*

> 1 ft

2

/ac

Use rules for

Douglas fir

cover type

FM 5

FM 2

FM 8

FM 2

FM 8

FM 8

FM 5

end

FM 2

FM 8

FM 5

FM 5

FM 8

FM 8

Figure 4.15—Logic for modeling fire at “low”

fuel loads in the TT-FFE variant.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 193

4.11 Blue Mountains (BM) ___________________________________________

4.11.1 Tree Species

The Blue Mountains variant models the nine tree species shown in table

4.123. One additional category, “other” and is modeled using juniper.

4.11.2 Snags

The majority of the snag model logic is based on unpublished data provided

by Bruce Marcot (USFS, Portland, OR, unpublished data 1995). Snag fall

parameters were developed at the FFE design workshop. A complete description of the Snag Submodel is provided in section 2.3 of the FFE “Model

Description” chapter.

Four variables are used to modify the Snag Submodel for the different

species in the BM-FFE variant:

• A multiplier to modify the species’ fall rate

• Amultiplier to modify the time required for snags to decay from a “hard”

to “soft” state

• The maximum number of years that snags will remain standing

• A multiplier to modify the species’ height loss rate

These variables are summarized in tables 4.124 and 4.125.

Snag dynamics are similar to the NI-FFE variant, with the exception that

height loss rates for all species increase markedly after half the original

height is lost (coefficients not shown here). In addition, the default coefficient

for snag height loss is changed from 0.0228 to 0.03406.

Snag bole volume is determined in using the base FVS model equations.

The coefficients shown in table 4.126 are used to convert volume to biomass.

Soft snags have 80 percent the density of hard snags.

Snag dynamics can be modified by the user using the SNAGBRK,

SNAGFALL, SNAGDCAY and SNAGPBN keywords described in the FFE

“Model Description” chapter.

Table 4.123—Tree species simulated by the Blue Mountains variant.

Common name Scientific name Notes

western white pine Pinus monticola

western larch Larix occidentalis

Douglas-fir Pseudotsuga menziesii

grand fir Abies grandis

mountain hemlock Tsuga mertensiana

lodgepole pine Pinus contorta

Engelmann spruce Picea engelmannii

subalpine fir Abies lasiocarpa

ponderosa pine Pinus ponderosa

other = western juniper
194 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.124—Default snag fall, snag height loss and soft-snag characteristics for

15 inch DBH snags in the BM-FFE variant. These characteristics

are derived directly from the parameter values shown in table

4.125.

Species 95% fallen All down 50% height Hard-to-soft

- - - - - - - - - - - - - - - - - - -Years - - - - - - - - - - - - - - - - - - -

western white pine 20 75 30 36

western larch 30 100 55 36

Douglas-fir 30 75 30 36

grand fir 25 60 20 29

mountain hemlock 25 70 20 29

lodgepole pine 20 50 30 29

Engelmann spruce 40 75 30 29

subalpine fir 30 60 25 29

ponderosa pine 20 80 35 32

other 40 100 75 29

Table 4.126—Wood density (ovendry

lb/ft3

) used in the BMFFE variant.

Species Density (lb/ft3)

western white pine 24.8

western larch 34.3

Douglas-fir 32.7

grand fir 24.1

mountain hemlock 29.5

lodgepole pine 26.4

Engelmann spruce 22.6

subalpine fir 21.1

ponderosa pine 26.4

other 34.9

Table 4.125—Default snag fall, snag height loss and softsnag multipliers for the BM-FFE. These parameters result in the values shown in table

4.124. (These three columns are the default

values used by the SNAGFALL, SNAGBRK,

and SNAGDCAY keywords, respectively.)

Snag Height

Species fall loss Hard-to-soft

western white pine 1.21 0.68 1.1

western larch 0.81 0.37 1.1

Douglas-fir 0.81 0.68 1.1

grand fir 0.97 1.02 0.9

mountain hemlock 0.97 1.02 0.9

lodgepole pine 1.21 0.68 0.9

Engelmann spruce 0.61 0.68 0.9

subalpine fir 0.81 0.81 0.9

ponderosa pine 1.21 0.58 1.0

other 0.49 0.27 0.9
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 195

4.11.3 Fuels

Information on live fuels was developed using FOFEM 4.0 (Reinhardt and

others 1997) and FOFEM 5.0 (Reinhardt and others 2001) and in cooperation

with Jim Brown (USFS, Missoula, MT pers. comm. 1995). A complete

description of the Fuel Submodel is provided in section 2.4 of the FFE “Model

Description” chapter.

Fuels are divided into to four categories: live tree bole, live tree crown, live

herb and shrub, and down woody debris (DWD). Live herb and shrub fuel

load, and initial DWD are assigned based on the cover species with greatest

basal area. If there is no basal area in the first simulation cycle (a “bare

ground” stand) then the initial fuel loads are assigned by the vegetation code

provided with the STDINFO keyword. If the vegetation code is missing or

does not identify an overstory species, the model uses a Douglas-fir cover type

to assign the default fuels. If there is no basal area in other cycles of the

simulation (after a simulated clearcut, for example) herb and shrub fuel

biomass is assigned by the previous cover type.

Live Tree Bole: The fuel contribution of live trees is divided into two

components: bole and crown. Bole volume is transferred to the FFE after

being computed by the FVS model, then converted to biomass using ovendry

wood density calculated from table 4-3a and equation 3-5 of The Wood

Handbook (Forest Products Laboratory 1999). The coefficient in table 4.126

for Douglas-fir is based on Douglas-fir west.

Tree Crown: As described in the section 2.2 of the FFE” Model Description” chapter, equations in Brown and Johnston (1976) provide estimates of

live and dead crown material for most species in the BM-FFE (table 4.127).

Mountain hemlock biomass is based on Gholz (1979), using western hemlock

equations from Brown and Johnston to partition the biomass and also to

provide estimates for trees less than 1 inch diameter. Western juniper

(“other”) equations are based on a single-stem form.

Live leaf lifespan is used to simulate the contribution of needles and leaves

to annual litter fall. Dead foliage and branch materials also contribute to

litter fall, at the rates shown in table 4.128. Each year the inverse of the

lifespan is added to the litter pool from each biomass category. Leaf lifespan

Table 4.127—The crown biomass equations listed here determine the

biomass of foliage, branch, and stem wood. Species mappings are done for species for which equations are not

available.

Species Species mapping and equation source

western white pine Brown and Johnston (1976)

western larch Brown and Johnston (1976)

Douglas-fir Brown and Johnston (1976)

grand fir Brown and Johnston (1976)

Mountain hemlock Gholz (1979); Brown and Johnston (1976)

lodgepole pine Brown and Johnston (1976)

Engelmann spruce Brown and Johnston (1976)

subalpine fir Brown and Johnston (1976)

ponderosa pine Brown and Johnston (1976)

other Chojnacky (1992), Grier and others (1992)
196 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

data are from Keane and others (1989). Lifespans of western white pine and

mountain hemlock are mapped using ponderosa pine, and western hemlock

and western redcedar are based on Douglas-fir.

Live Herbs and Shrub: Live herb and shrub fuels are modeled simply

by the FFE. Shrubs and herbs are assigned a biomass value based on total

tree canopy cover and dominant overstory species (table 4.129). When there

are no trees, habitat type is used to infer the most likely dominant species of

the previous stand (“Model Description” chapter, section 2.4.2). When total

Table 4.129—Values (dry weight, tons/acre) for live fuels used in the BM-FFE.

Biomass is linearly interpolated between the “initiating” (I) and

“established” (E) values when canopy cover is between 10 and 60

percent.

Species Herbs Shrubs Notes

western white pine E 0.15 0.10

I 0.30 2.00

western larch E 0.20 0.20

I 0.40 2.00

Douglas-fir E 0.20 0.20

I 0.40 2.00

grand fir E 0.15 0.10

I 0.30 2.00

mountain hemlock E 0.20 0.20 Use Douglas-fir

I 0.40 2.00

lodgepole pine E 0.20 0.10

I 0.40 1.00

Engelmann spruce E 0.15 0.20

I 0.30 2.00

subalpine fir E 0.15 0.20

I 0.30 2.00

ponderosa pine E 0.20 0.25

I 0.25 0.10

other E 0.04 0.05 Ottmar and others 2000a

I 0.13 1.63

Table 4.128—Life span of live and dead foliage (years) and dead branches

for species modeled in the BM-FFE variant.

Live Dead

Species Foliage Foliage <0.25” 0.25–1” >1”

western white pine 4 3 10 15 15

western larch 1 1 10 15 15

Douglas-fir 5 3 10 15 15

grand fir 7 3 10 15 15

mountain hemlock 5 3 10 15 15

lodgepole pine 3 3 10 15 15

Engelmann spruce 6 3 10 10 10

subalpine fir 7 3 10 15 15

ponderosa pine 4 3 10 10 10

other 4 3 10 15 20
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 197

tree canopy cover is less than 10 percent, herb and shrub biomass is assigned

an “initiating” value (the ‘I’ rows from table 4.129). When canopy cover is

greater than 60 percent, biomass is assigned an “established” value (the ‘E’

rows). Live fuel loads are linearly interpolated when canopy cover is between

10 and 60 percent. Data are taken from FOFEM 4.0 (Reinhardt and others

1997) with modifications provided by Jim Brown (USFS, Missoula, MT pers.

comm., 1995).

Dead Fuels: Initial default DWD pools are based on overstory species.

When there are no trees, habitat type is used to infer the most likely

dominant species of the previous stand (“Model Description” chapter, section

2.4.2). Default fuel loadings were provided by Jim Brown (USFS, Missoula,

MT pers. comm., 1995) (table 4.130). If tree canopy cover is less than 10

percent, the DWD pools are assigned an “initiating” value, and if cover is

greater than 60 percent they are assigned the “established” value. Fuels are

linearly interpolated when canopy cover is between 10 and 60 percent. Initial

fuel loads can be modified using the FUELINIT keyword.

4.11.4 Bark Thickness

Bark thickness contributes to predicted tree mortality from simulated

fires. The bark thickness multipliers in table 4.131 are used to calculate

single bark thickness as described in the “Model Description” chapter,

section 2.5.5. The bark thickness equation used in the mortality equation is

unrelated to the bark thickness used in the base FVS model. Data are from

FOFEM 5.0 (Reinhardt and others 2001).

Table 4.130—Canopy cover and cover type are used to assign default down woody debris (tons/acre) by size class

for established (E) and initiating (I) stands.

Size class (inches)

Species < 0.25 0.25 – 1 1 – 3 3 – 6 6 – 12 > 12 Litter Duff

western white pine E 1.0 1.0 1.6 10.0 10.0 10.0 0.8 30.0

I 0.6 0.6 0.8 6.0 6.0 6.0 0.4 12.0

western larch E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

Douglas-fir E 0.9 0.9 1.6 3.5 3.5 0.0 0.6 10.0

I 0.5 0.5 1.0 1.4 1.4 0.0 0.3 5.0

grand fir E 0.7 0.7 3.0 7.0 7.0 0.0 0.6 25.0

I 0.5 0.5 2.0 2.8 2.8 0.0 0.3 12.0

western hemlock E 2.2 2.2 5.2 15.0 20.0 15.0 1.0 35.0

I 1.6 1.6 3.6 6.0 8.0 6.0 0.5 12.0

lodgepole pine E 0.9 0.9 1.2 7.0 8.0 0.0 0.6 15.0

I 0.6 0.7 0.8 2.8 3.2 0.0 0.3 7.0

Engelmann spruce E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

subalpine fir E 1.1 1.1 2.2 10.0 10.0 0.0 0.6 30.0

I 0.7 0.7 1.6 4.0 4.0 0.0 0.3 12.0

ponderosa pine E 0.7 0.7 1.6 2.5 2.5 0.0 1.4 5.0

I 0.1 0.1 0.2 0.5 0.5 0.0 0.5 0.8

other E 0.2 0.8 2.3 1.4 3.0 0.0 9.3 0.0

I 0.0 0.1 0.0 0.0 0.0 0.0 2.0 0.0
198 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.131—Species specific constants

for determining single

bark thickness.

Species Multiplier (Vsp)

western white pine 0.035

western larch 0.063

Douglas-fir 0.063

grand fir 0.046

mountain hemlock 0.040

lodgepole pine 0.028

Engelmann spruce 0.036

subalpine fir 0.041

ponderosa pine 0.063

other 0.025

4.11.5 Decay Rate

Decay of down material is simulated by applying loss rates to biomass in

categories based on the original size-class of the branch and bole pieces as

described in section 2.4.5 of the “Model Description” chapter (table 4.132).

Default decay rates are based on Abbott and Crossley (1982). A portion of the

loss is added to the duff pool each year. Loss rates are for hard material; soft

material in all size classes, except litter and duff, decays 10 percent faster.

Decay rates on moist sites are one-third higher than the rates shown in

table 4.132; dry sites are one-third lower. The habitat code set by the

STDINFO keyword determines whether a stand is defined as a moist, mesic,

or dry site, as shown in table 4.133. These assignments were provided by

Table 4.132—Default annual loss rates are applied based

on size class. A portion of the loss is added to

the duff pool each year. Loss rates are for

hard material. If present, soft material in all

size classes except litter and duff decays 10

percent faster.

Size class

(inches)

Annual loss rate

Proportion of loss

becoming duff

< 0.25

0.25 — 1

0.12

1 — 3 0.09

3 — 6

6 — 12

> 12

0.015

Litter 0.65

0.02

Duff 0.002 0.0
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 199

David Powell (Silviculturist, Umatilla NF, USFS, Pendleton, OR pers.

comm. 2001)).

By default, the FFE decays all wood species at the rates shown in table

4.132. The decay rates of species groups may be modified by users, who can

provide rates to the four decay classes shown in table 4.134 using the

FUELDCAY keyword. Users can also reassign species to different classes

using the FUELPOOL keyword.

Table 4.133—Habitat type - moisture regime relationships for the BM-FFE variant. Moisture classes modify

default decay rates, as described in the text.

Habitat Habitat Habitat Habitat

code Regime code Regime code Regime code Regime

CAG111 Dry CES311 Mesic CPG111 Dry CWF312 Mesic

CDG111 Dry CES314 Mesic CPG112 Dry CWF421 Mesic

CDG112 Dry CES315 Mesic CPG131 Dry CWF431 Mesic

CDG121 Dry CES411 Dry CPG132 Dry CWF512 Moist

CDS611 Mesic CES414 Mesic CPG221 Dry CWF611 Moist

CDS622 Dry CES415 Dry CPG222 Dry CWF612 Moist

CDS623 Dry CLF211 Mesic CPM111 Dry CWG111 Dry

CDS624 Dry CLG211 Dry CPS131 Dry CWG112 Dry

CDS634 Dry CLM112 Mesic CPS221 Dry CWG113 Dry

CDS711 Dry CLM113 Moist CPS222 Dry CWG211 Mesic

CDS722 Mesic CLM114 Moist CPS226 Dry CWS211 Mesic

CDS821 Dry CLM312 Moist CPS232 Dry CWS212 Mesic

CEF221 Mesic CLM313 Mesic CPS233 Dry CWS321 Dry

CEF311 Moist CLM314 Moist CPS234 Dry CWS322 Dry

CEF331 Mesic CLM911 Moist CPS511 Mesic CWS412 Mesic

CEM111 Moist CLS411 Mesic CPS522 Dry CWS541 Moist

CEM221 Moist CLS415 Dry CPS523 Dry CWS811 Dry

CEM222 Moist CLS416 Dry CPS524 Dry CWS812 Mesic

CEM311 Moist CLS511 Mesic CPS525 Dry CWS912 Moist

CEM312 Moist CLS515 Mesic CWC811 Moist HQM121 Moist

CES131 Mesic CMS131 Dry CWC812 Moist HQM411 Moist

CES221 Mesic CMS231 Mesic CWF311 Mesic HQS221 Mesic

Table 4.134—Default wood decay

classes used in the BMFFE variant. Classes are

from The Wood Handbook (1999). (1 = exceptionally high; 2 =

resistant or very resistant; 3 = moderately resistant, and 4 = slightly

or nonresistant.)

Species Decay class

western white pine 4

western larch 3

Douglas-fir 3

grand fir 4

mountain hemlock 4

lodgepole pine 4

Engelmann spruce 4

subalpine fir 4

ponderosa pine 4

other 2
200 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Table 4.135—Moisture values, which alter fire intensity and consumption, have been predefined for four groups.

Moisture group

Size class Very dry Dry Moist Wet

0 – 0.25 in. (1-hr) 4 8 12 16

0.25 – 1.0 in. (10-hr) 4 8 12 16

1.0 – 3.0 in. (100-hr) 5 10 14 18

> 3.0 in. (1000+ -hr) 10 15 25 50

Duff 15 50 125 200

Live 70 110 150 150

4.11.6 Moisture Content

Moisture content of the live and dead fuels is used to calculate fire intensity

and fuel consumption (“Model Description” chapter, section 2.5.2). Users can

choose from four predefined moisture groups (table 4.135) or they can specify

moisture conditions for each class using the MOISTURE keyword.

4.11.7 Fire Behavior Fuel Models

Fire behavior fuel models (Anderson 1982) are used to estimate flame

length and fire effects stemming from flame length. Fuel models are determined using fuel load and stand attributes (“Model Description” chapter,

section 2.4.8) specific to each FFE variant. In addition, stand management

actions such as thinning and harvesting can abruptly increase fuel loads and

can trigger “Activity Fuels” conditions, resulting in the selection of alternative fuel models. At their discretion, FFE users have the option of:

1. Defining and using their own fuel models

2. Defining the choice of fuel models and weights

3. Allowing the FFE variant to determine a weighted set of fuel

models

4. Allowing the FFE variant to determine a weighted set of fuel

models, then using the dominant model

This section explains the steps taken by the BM-FFE to follow the third

and fourth of these four options.

The fuel model logic for the BM variant is based on stand classification

tables provided by Les Holsapple (USFS, Pendleton, OR pers. comm., 2001).

When the combination of large and small fuel lies in the lower left corner

of the graph shown in figure 4.16, one or more low-fuel fuel models become

candidate models. In other regions of the graph, other fuel models may also

be candidates. The stand classification system shown in table 4.136 and the

flow diagrams in figure 4.17 define which low fuel model(s) will become

candidates.

Figure 4.17 uses size class, canopy cover of the dominant canopy layer, and

canopy cover in the canopy layers, to assign stands to a single fuel model. To

implement the logic of table 4.136 and figure 4.17, two additional processing

steps are made. The first step uses a simplified and hardwired version of the

FVS stand structure logic (Crookston and Stage 1999) to provide estimates

of canopy cover in up to two vertical layers of the stand. The second step
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 201

Table 4.136—Size classes used in the BM-FFE fuel model

selection logic.

Code Notes

1 Seedlings; trees less than 1 inch DBH

2 Seedlings and saplings mixed

3 Saplings; trees 1 – 4.9” DBH

4 Saplings and poles mixed

5 Poles; trees 5 – 8.9” mixed

6 Poles and small trees mixed

6.5 Small trees 9 – 14.9” DBH

7 Small trees 9 – 20.9” DBH

7.5 Small trees 15 – 20.9” DBH

8 Small and medium trees mixed

9 Medium trees 21 – 31.9” DBH

10 Medium and large trees mixed

11 Large trees 32 – 47.9” DBH

10 20 30 40

10

20

30

40

0

3" + fuels (tons/acre)

0"-3" fuels (tons/acre)

FM10 - natural fuels

FM11 - activity fuels

FM12 - natural fuels

FM14 - activity fuels

FM13

Fuel model, 100% weight

low fuels

models

0

Figure 4.16—If large and small fuels map to the shaded

area, candidate fuel models are determined using the

logic shown in figure 4.17. Otherwise, flame length is

based on the distance to the closest fuel models, identified by the dashed lines, and on recent management (see

“Model Description” chapter, section 2.4.8 for further

details).
202 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003

Figure 4.17—Logic for modeling fire at “low” fuel loads in the BM-FFE variant.

begins by classifying the stand into one of the 13 size class codes shown in

table 4.136.

As stand structure changes with time or management, the classification of

the dominant size class may also change. This can lead to abrupt changes in

the fuel model selection. To smooth out these discontinuities, the sample

treelist is further processed by repeatedly classifying the stand based on

adding a uniform random deviate with a range equal to +/–20 percent of the

diameter of each tree. This is repeated 50 times, potentially generating more

than one size classification. When the classification weights are taken into

the fuel model selection, the fuel model selection varies more smoothly as

class boundaries are approached.

Introducing gradual transitions at all the logical breakpoints of the fuel

model selection diagram also supports smoother transitions between fuel

models. These transitions begin 5 percent below the nominal breakpoint for

dominant overstory, percent total canopy cover (CC), percent lower canopy

(CCA). and percent upper canopy (CCB).

In the accompanying diagram showing the BM fuel model logic, PP and DF

refer to the percentage of stand basal area in ponderosa pine and Douglasfir, respectively. The size categories referred to in table 4.136 are abbreviated

as ‘SZ’.

If the STATFUEL keyword is selected, fuel model is determined by using

only the closest match fuel model identified by either figure 4.16 or 4.17. The

FLAMEADJ keyword allows the user to scale the calculated flame length or

override the calculated flame length with a value they choose.

PP > 50%

or

DF > 50%

SZ

< 3

SZ

< 7

FM 5

FM 8 FM 10

FM 10

FM 9

FM 2

FM 5 FM 5

FM 1

FM 8

FM 2

FM 8

NO NO

NO NO

NO

NO

NO

NO

NO

NO

NO

YES

YES

YES YES

YES

YES

YES

YES

YES

YES

YES

CC

< 30

CC

< 30

CCA

< 20

CCB

< 15

CCA

< 30

CCB

< 50

CC

< 10

PP

< 50
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 203

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USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 207

Keyword Index

Keyword Note Page numbers

Chapter 2 Model Chapter 3 User’s Guide Chapter 4

Description Variants

BURNREPT 55 86, 87

CRBASEHT Event Monitor variable 90

CRBULKDN Event Monitor variable 90

CROWNIDX Event Monitor variable 90

DEFULMOD 35 71, 72, 73, 74

DROUGHT 66, 71

DUFFPROD 32 79

END 62

FIRE Event Monitor variable 89

FIREYEAR Event Monitor variable 89

FLAMEADJ 48, 49 66, 67, 68, 69, 91 102, 111, 123,

137, 147, 158,

168, 183

FMIN 62, 73, 91

FUELDCAY 32 78, 79 100, 109,

122, 135, 145,

154, 165, 180,

189, 199
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 208

FUELINIT 26 64, 78 99, 107, 119,

134, 143, 153,

163, 177, 188, 197

FUELLOAD Event Monitor function 89

FUELMODL 38 71, 72, 73, 74

FUELMOVE 40 81, 83, 84, 85, 86

FUELMULT 32 78, 79, 80

FUELOUT 40 80, 86, 87

FUELPOOL 27 79, 80 100, 109, 122,

135, 145, 154,

 165, 180, 189, 199

FUELREPT 56 86, 87

FUELTRET 40 81, 83, 84

MINSOIL Event Monitor variable 89

MOISTURE 44 66, 67, 69, 78 100, 109, 122,

135, 145, 154,

166, 180, 190, 200

MORTCLAS 86, 87

MORTREPT 56 86, 87

PILEBURN 46, 47 68, 78, 81, 83, 91

POTFIRE 53, 57 62, 69, 74, 86, 87

POTFLEN Event Monitor function 90

POTFMOIS 53 70

POTFTEMP 53 70

POTFWIND 48, 53 70

PRUNE Base Model keyword 81, 82

SALVAGE 21 81, 82, 83

SIMFIRE 44, 46, 48 66, 67, 68, 69, 73, 78 95, 105, 128, 139,

161, 175, 186, 193

SNAGBRK 17 74, 75

SNAGCLAS 23, 24 86, 87

SNAGDCAY 18 75, 76 95, 105, 128, 139,

161, 175, 186, 193

Keyword Note Page numbers

Chapter 2 Model Chapter 3 User’s Guide Chapter 4

Description Variants
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-116. 2003 209

Keyword Note Page numbers

Chapter 2 Model Chapter 3 User’s Guide Chapter 4

Description Variants

SNAGFALL 20 76, 77 95, 105, 128, 139,

161, 175, 186, 193

SNAGINIT 15 64, 65, 76 149

SNAGOUT 21 86, 88

SNAGPBN 20 77, 78 95, 105, 128, 139,

161, 175, 186, 193

SNAGPSFT 15 64, 74, 75, 76

SNAGS Event Monitor function 90

SNAGSUM 23 86, 87

STATFUEL 38 71, 74 102, 111, 123, 137,

147, 168, 183

SVIMAGES 88

TORCHIDX Event Monitor variable 90

YARDLOSS Base Model keyword 21, 31 78, 81, 82, 83


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