United States 

Department 

of Agriculture

Forest Service

Rocky Mountain 

Research Station

General Technical Report 

RMRS-GTR-255

March 2011

Simulation Year 100 Simulation Year 250 Simulation Year 500

The FireBGCv2 Landscape Fire 

Succession Model:

A Research Simulation Platform for Exploring 

Fire and Vegetation Dynamics

Robert E. Keane, 

Rachel A. Loehman, 

Lisa M. Holsinger
Keane, Robert E.; Loehman, Rachel A.; Holsinger, Lisa M. 2011. The FireBGCv2 

landscape fire and succession model: a research simulation platform for 

exploring fire and vegetation dynamics. Gen. Tech. Rep. RMRS-GTR-255. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 137 p.

Abstract

Fire management faces important emergent issues in the coming years such as climate 

change, fire exclusion impacts, and wildland-urban development, so new, innovative 

means are needed to address these challenges. Field studies, while preferable and 

reliable, will be problematic because of the large time and space scales involved. 

Therefore, landscape simulation modeling will have more of a role in wildland fire 

management as field studies become untenable. This report details the design and 

algorithms of a complex, spatially explicit landscape fire and vegetation model 

called FireBGCv2. FireBGCv2 is a C++ computer program that incorporates several 

types of stand dynamics models into a landscape simulation platform. FireBGCv2 is 

intended as a research tool. Descriptions of FireBGCv2 code, sample input files, and 

sample output are included in this report, but this report is not intended as a user’s 

manual because the inherent complexity and wide scope of FireBGCv2 makes it 

unwieldy and difficult to use without extensive training. The primary purpose of this 

report is to document FireBGCv2 in adequate detail to interpret simulation results.

Keywords: spatially explicit, simulation, mechanistic, multi-scale, gap model, 

climate change, process modeling

Authors

Robert E. Keane is a Research Ecologist with the U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station at the Missoula Fire Sciences 

Laboratory in Missoula, Montana. Since 1985, Keane has developed various 

ecological computer models for research and management applications. His most 

recent research includes: (1) developing ecological computer models for the exploring 

landscape, fire, and climate dynamics; (2) describing, classifying, and mapping of 

fuel characteristics; (3) investigating the ecology and restoration of whitebark pine; 

and (4) conducting fundamental wildland fuel science. He received his B.S. degree 

in Forest Engineering from the University of Maine, his M.S. degree in Forest Ecology 

from the University of Montana, and his Ph.D. in Forest Ecology from the University 

of Idaho.

Rachel A. Loehman is a Research Ecologist at the Missoula Fire Sciences Laboratory 

in Missoula, Montana. Loehman’s research includes ecosystem modeling, climatefire interactions, climate change effects on ecosystem patterns and processes, 

landscape carbon dynamics, and fire and forest history. She holds a B.S. degree in 

Archaeology from the University of New Mexico, a Master’s degree in Biogeography 

from the University of New Mexico, and a Ph.D. in Forestry/Ecosystems Ecology 

from the University of Montana.

Lisa M. Holsinger is a GIS Specialist for the Rocky Mountain Research Station. Since 

2002, she has developed and analyzed spatial data for simulation models that are 

applied to large landscapes. She has developed GIS data and input for the WX-FIRE 

and LANDSUM simulation models and HRVSTAT program, conducted simulations, 

and produced associated spatial data. She has also conducted sensitivity analyses 

for both the WXFIRE and LANDSUM models. Holsinger received her B.S. degree 

in Biological Sciences from U.C. Davis and her M.S. degree in Fisheries at the 

University of Washington.
Acknowledgments

We acknowledge those who helped with primary development of this 

model: Russ Parsons, Rocky Mountain Research Station Missoula Fire 

Sciences Laboratory; Joseph White, Baylor University; Peter Thornton, 

Oak Ridge National Laboratory; Wendel Hann (retired) U.S. Forest Service 

Washington Office; Dan Fagre, U.S. Geological Survey (USGS) Glacier 

Field Station; Dave Peterson, U.S. Forest Service, Pacific Northwest 

Research Station; Rebecca Kennedy and Don McKenzie, U.S. Forest 

Service, Pacific Northwest Research Station; and Bob Riggs, Eastern 

Oregon University. We also thank the attendees of a FireBGCv2 workshop 

that was held in November 2009 for their insightful comments and review.

This work was funded by the USGS National Biological Service and 

Glacier National Park’s Global Change Research Program under Interagency 

Agreements 1430-1-9007 and 1430-3-9005 for the USGS CLIMET project 

and the USGS Western Climate Initiative; the Joint Fire Sciences Program 

grants 09-3-01-17 and 09-1-02-8; and by the FIRECLIM project that was 

funded by the Dynamics of Coupled Natural and Human Systems program 

of the U.S. National Science Foundation.

Research Summary

Fire management will face many complex and novel issues in this coming century, including dealing with decades of fuel accumulation from past 

fire exclusion policies, protecting the expanding wildland-urban interface, 

and managing fire in landscapes that are being invaded by exotic plants 

and diseases—all in the face of possible warmer and drier climates. Land 

managers need innovative tools and information to describe the impacts of 

past and future human activities on ecosystem dynamics so that they can 

plan for and respond to the burgeoning issues related to land management 

and conservation. Field studies, while preferable and reliable, will be problematic to implement because of the large time and space scales involved. 

Therefore, landscape simulation modeling will play a more substantial role 

in wildland fire management as field studies become untenable and the 

pressing issues become more complex.

This report details the design and algorithms of a complex, spatially explicit landscape fire and vegetation model called FireBGCv2. FireBGCv2 is 

a C++ computer program that incorporates several types of stand dynamics 

models into a spatially explicit landscape simulation platform. FireBGCv2 

is intended as a research tool, but it can be employed in various land management applications providing sufficient expertise exists to parameterize 

and initialize the model. This report is divided into four parts. In the first 

section, the FireBGCv2 program is presented in detail with description of 

most algorithms and protocols. The next section describes how the model 

is initialized and parameterized and how the results can be interpreted. The 

third section demonstrates FireBGCv2 by showing output, including maps, 

graphs, and charts, for two contrasting simulation scenarios—fire regimes 

under current and future climate for a landscape in Glacier National Park, 

Montana, USA. The last section details the strengths and limitations of 

FireBGCv2. The FireBGCv2 code, sample input files, and sample output 

from this demonstration are available upon request. This report is not intended as a user’s manual because the inherent complexity in FireBGCv2 

makes it unwieldy and difficult to use without extensive training and experience in geographical analysis, simulation modeling, and data management. 

i
Contents

Acknowledgments i

Research Summary i

Introduction 1

Model Design 3

Background 3

History 3

Terminology 5

Report Conventions 6

Model Description 7

Design and Structure 10

Organizational Scales 10

Major Design Assumptions 12

Module Design 12

Landscape Simulation 13

Site Simulation 13

Fuel Representation 13

Stand Simulation 15

Carbon and Nitrogen Pools 16

Ground Simulation 16

Undergrowth Simulation 17

Tree Simulation 20

Program Flow 20

Programming Specifics 22

Model Algorithms 23

General Algorithms 23

Landscape Processes 23

Cone Crop 23

Seed Dispersal 24

Wildland Fire 25

Insects and Diseases 28

Management Actions 29

Hydrology 30

Site Processes 32

Climate 32

Weather 33

Phenology 35

Wildland Fire 36

Stand Processes 37

Vegetation Development 37

Undergrowth Dynamics 44

Ground Dynamics 45

Canopy Dynamics 48

Wildland Fire 50

ii
Management Actions 54

Wildlife 54

Species Processes 55

Regeneration 55

Tree Processes 58

Regeneration 58

Growth 59

Mortality 62

Snags 63

Phenology 63

Wildland Fire 64

Insects and Diseases 66

Program Implementation 67

Parameterization 69

Initialization 70

Simulation Initialization 70

Model Initialization 73

Calibration 73

Simulation 74

Output 75

Demonstration 80

Graphical Output 83

Tabular Output 85

Discussion 90

Model Limitations 90

Simulation Issues 91

Potential Improvements 92

Input File Structure 94

File Design 94

Driver.in File 94

Sim.in File 95

Climate.in File 98

Map.in File 99

Species.in File 101

Wildlife.in File 103

Manage.in File 104

Plant.in File 106

Pial.in File 107

Fuel.in File 108

Site.in File 109

Stand.in File 111

Tree.in File 112

References 114

Index of Variables 127

iii

USDA Forest Service RMRS-GTR-255. 2011. 1

Introduction

Fire management will face many complex and novel issues in this coming 

century. Past fire exclusion policies combined with a successful fire suppression 

program in the western United States and Canada over the last several decades has 

resulted in high accumulations of surface and canopy fuels that have increased the 

potential for severe fires in many ecosystems (Ferry and others 1995, Kolb and 

others 1998, Keane and others 2002b). Residential expansion into the wildlands 

increasingly places human populations within fire-prone areas, further escalating 

the wildfire-caused risk to life and property (Radeloff and others 2005, Berry and 

others 2006). Rapid climate change over the next several decades may compound 

the potential for uncharacteristically severe wildfires and increase fire hazard because current projections indicate that future fire seasons may be longer, warmer, 

and drier (Flannigan and van Wagner 1991, Lenihan and others 1998, Bachelet 

and others 2000). Emerging climates may also accelerate fuel accumulation and 

alter vegetation such that future landscapes will experience larger and more 

intense fires (Keane and others 1996a, Bachelet and others 2001). To complicate matters, invasions of exotic plants and diseases are creating novel fuelbeds 

that also alter fire regimes and burn severities (Whisenant 1990, Billings 1992). 

Land management agencies have initiated extensive fuel treatment and ecosystem restoration activities to reduce the possibility of severe wildfires that could 

damage ecosystems, destroy property, and take human life (Laverty and Williams 

2000, GAO 2007). But land managers need tools and information to describe the 

impacts of future climates and management strategies on ecosystem dynamics 

(Keane and Finney 2003).

The study of today’s landscapes and the complex ecosystems that comprise 

them is increasingly difficult because of the extensive interactions among vegetation, disturbance, climate, and many other biophysical factors that act across 

multiple scales of time and space (Starfield and Chapin 1996). Field studies that 

explore landscape and ecosystem dynamics are becoming problematic because of 

the large space and time scales that are involved in such interactions. Exploring 

changes in fire regimes that are caused by mountain pine beetles, for example, 

might require sampling across large regions for many years or centuries in ecosystems with long fire return intervals (Jenkins and others 2008). Simulation 

modeling is rapidly becoming indispensible in wildland fire research and management because it synthesizes field study results into a platform that can expand 

time and space scales to investigate complex landscape and ecosystem feedbacks 

and relationships (Lauenroth and others 1993, Gardner and others 1999). Recent 

advances in computer software and hardware technology coupled with development of Geographic Information Systems (GIS), spatial statistics, and new field 

methodologies have facilitated the creation of complex, mechanistic, spatially 

explicit landscape simulation models (Keane and Finney 2003, Keane and others 

2010). These models can be employed to study the consequences of the interactions of changing climates, fire management, urban development, and disturbance 

dynamics on landscape ecosystem dynamics.

Simulation modeling will be one of the most important tools for fire research 

and management because it offers an effective and objective context within which 

to explore and evaluate management actions and ecological change. Models can 

be used to simulate effects of potential alternative treatments in order to determine 

the most effective fuel reduction or ecosystem restoration strategy. Novel treatments can be simulated to determine resultant short- and long-term effects on a 

diverse array of ecosystem elements. Fire hazard and risk can be simulated to 

prioritize areas for treatment and to design the most effective treatment prescriptions (Keane and others 2010). Simulation will also be essential to approximate 
2 USDA Forest Service RMRS-GTR-255. 2011.

historical or future landscape conditions that can then be used as reference for 

ecologically based landscape prioritization and planning (Wimberly and others 2000, Keane and others 2009). Predictive landscape models can also update 

broad-scale digital maps and inform future sampling strategies for assessing 

change. The inherent design of simulation models can be used to identify areas of 

possible research and to prioritize possible research directions. Most importantly, 

mechanistic landscape simulation models can be used to explore fire, climate, and 

vegetation interactions; quantify spatial and temporal dynamics of fire regimes; 

and describe potential fire dynamics under future climates and land management 

strategies to provide critical information that can help fire managers mitigate potential adverse effects.

This report details the design and algorithms of a complex, spatially explicit 

landscape fire and vegetation model called FireBGCv2. FireBGCv2 is a C++ 

computer program that incorporates several types of stand dynamics models into 

a landscape simulation platform. FireBGCv2 is intended as a research tool, but it 

can be employed in various land management applications, providing sufficient 

expertise exists to parameterize and initialize the model. This report is divided 

into several parts. The FireBGCv2 program is presented first, complete with a 

detailed description of most algorithms and protocols. Extensive references are 

provided for algorithms that are not presented. Execution of the model is then 

described, including initialization, calibration, and parameterization. The third 

section demonstrates various types of FireBGCv2 output including maps, graphs, 

and charts that describe and contrast two simulation scenarios–fire regimes under current and future climate. The discussion section details the strengths and 

limitations of FireBGCv2. Execution commands are described in the next section, followed by a listing of all FireBGCv2 input files. The FireBGCv2 code, 

sample input files, and sample output from the demonstration are also included. 

This report is not intended as a user’s manual because the inherent complexity of 

the FireBGCv2 simulation platform makes it unwieldy and difficult to use without 

extensive training and experience in geographical spatial analysis, ecological and 

simulation modeling, and data management. The primary purpose of this report is 

to document FireBGCv2 in enough detail for proper interpretation of simulation 

results.
USDA Forest Service RMRS-GTR-255. 2011. 3

Model Design

Background

History

FireBGCv2 is the second version of the original FIRE-BGC model (Keane 

and others 1995, 1996b) that was created by merging the gap model FIRESUM

(Keane and others 1989, Keane and others 1990c) with the “big-leaf” mechanistic, biogeochemical BIOME-BGC model (Running and Coughlan 1988, Running 

and Hunt 1993, Thornton 1998) (Figure 1). FireBGCv2 merges the latest ecophysiological simulations of BIOME-BGC, as updated by Thornton (1998), into 

the FIRE-BGC design. FIRE-BGC simulated individual tree growth, regeneration, and mortality across a landscape using the mechanistic ecophysiological 

approach of BIOME-BGC, and then simulated the ignition, spread, and effects of 

wildland fire using the FARSITE fire growth model (Finney 1996) coupled with 

FIRESUM algorithms (Keane and others 1989). FIRESUM was created from the 

SILVA gap model (Kercher and Axelrod 1984) which was developed from the 

original gap model JABOWA (Botkin and others 1972).

H2OTRANS

DAYTRANS

FORESTBGCBIOMEBGC

JABOWA SILVA FIRESUM

FireBGCv2

FIRE-BGC

Figure 1. The lineage of the FireBGCv2 model. It is a significant revision of the original FIRE-BGC model (Keane and others 

1996) in that it includes many algorithms from BIOME-BGC (Thornton and others 1994). FIRE-BGC was a melding of 

the FOREST-BGC big-leaf model (Running and Coughlan 1988), which was developed from the Running (1984) models 

DAYTRANS and H2OTRANS), and the FIRESUM model that was developed by Keane and others (1989) from SILVA 

(Kercher and Axelrod 1984), which was a variant of the original JABOWA gap model (Botkin and others 1972).
4 USDA Forest Service RMRS-GTR-255. 2011.

Climate change and fire management scenarios were simulated in FIRE-BGC 

for the McDonald and St. Mary drainages of Glacier National Park to explore pos-

sible future landscape composition and structure (Keane and others 1997, Keane 

and others 1998, Keane and others 1999). However, many computational and 

design problems with FIRE-BGC necessitated a complete revision of the model. 

For example, FIRE-BGC was implemented in a specialized simulation platform 

called Loki that precluded distribution of FIRE-BGC code because the Loki soft-

ware was short-lived, unmaintained, and computer platform-specific (Bevins and 

Andrews 1994, Bevins and others 1995). The FARSITE model that was used to 

spread fires for FIRE-BGC simulations had high computational demands, so it 

could not be executed simultaneously with FIRE-BGC to achieve a fully inte-

grated simulation, and FIRE-BGC lacked modules needed to comprehensively 

explore fire management futures and issues, such as prescribed burning, fuel treat-

ment, silvicultural cutting, and wildlife habitat modules. Therefore, a complete 

redesign was initiated in 1999 to create the new FireBGCv2 model.

FireBGCv2 differs from FIRE-BGC in many ways:

•	 One program. All modules are contained in one program. No external 

simulation platforms, such as Loki for FIRE-BGC, are used, meaning the 

program can be ported to any computer.

•	 Modeling platform. FireBGCv2 contains many routines that perform the 

same function in different ways to enable greater modeling flexibility so that 

simulation comparisons can be made across algorithms and parameter sets. For 

example, there are three vegetation dynamics modules to explore the influence 

of model design in succession simulation; FireBGCv2 contains a simple gap 

model (FIRESUM), a mechanistic gap model, and a comprehensive BIOMEBGC module.

•	 Management actions. FireBGCv2 includes a suite of routines that implement 

prescribed fire and silvicultural cuttings on the landscape based on stand 

conditions.

•	 Fire simulation modifications. The fire spread algorithm has been changed 

to a cell percolation model to speed up execution times. This has resulted in 

major changes on how fire behavior and effects are calculated. The First Order 

Fire Effects Model (FOFEM) (Reinhardt and others 1997) has also been added 

to improve the quantitative estimation of fire effects on ecosystem elements.

•	 Wildlife habitat analysis. A wildlife suitability model has been added to assess 

the relative value of a stand’s cover type and structural stage to any number of 

wildlife species.

•	 Mountain pine beetle and blister rust. Along with the detailed simulation of 

wildland fire, FireBGCv2 also simulates the initiation, spread, and effects of 

mountain pine beetle and white pine blister rust epidemics.

•	 Tree regeneration improvements. The regeneration factors of serotiny and 

sprouting were added at the species level. The regeneration module was also 

updated to include the influence of weather on reproduction dynamics.

•	 Stream temperature simulation. There is an empirical module in FireBGCv2 

that simulates stream temperature on riparian sites to investigate effects of fire 

management and climate change on stream temperature and to investigate its 

implications for fish habitat.

New modules are also being added, such as grazing, weather variability, and 

fire hazard and risk evaluation, but they are not discussed in this report because 

they are not fully implemented.

FireBGCv2 was originally developed for the CLIMET project to explore the 

effects of climate change on fire regimes, vegetation migration, and landscape 

structure (McKenzie 1998). But, because the renovation of FireBGCv2 took 

nearly a decade, its scope was expanded to explore many other ecological inter-

actions, so additional modules were integrated into FireBGCv2 to facilitate the 
USDA Forest Service RMRS-GTR-255. 2011. 5

investigation of the effects of climate change on other ecosystem properties, such 

as stream temperature, carbon, and mountain pine beetles. The model presented 

here is a snapshot of a fluid landscape simulation tool.

Terminology

The design and structure of FireBGCv2 is complex and, as a result, this report 

requires a somewhat unique terminology to clearly describe the intricate details 

of the simulation modules. While this report uses commonly accepted landscape 

ecology, simulation modeling, and ecosystem science terms, it also includes 

modified or new terminology to more adequately explain the complexity in the 

FireBGCv2 design. Since general modeling terminology is especially perplexing, 

it is important to define those terms.

The first term, model parameterization, is the quantification of the major parameters required as input to the model. Parameters are static variables, such 

as smoke emission factors or duff bulk density, which are estimated by the user 

or model author, and their values remain static throughout the simulation. Input 

parameters for some models can be emergent properties (dynamically simulated 

output) or output variables from other models. Fire return interval, for example, 

is an input parameter in LANDSUM (Keane and others 2006), but it is an explicitly simulated output variable in FIRESCAPE (Cary 1997). Model initialization

is the quantification of the state variables in a model to begin model execution. 

State variables are those that are explicitly simulated by a model, such as tree 

carbon, stand nitrogen, or fuelbed loading. LANDIS, for example, represents tree 

dynamics using diameter-species cohorts of tree densities as the state variables 

(Mladenoff and others 1996). Most state variables are quantified from plot data, 

GIS maps, and previous simulation results. Model execution refers to actually 

running the model to create outputs to analyze. A model component is the abstract 

representation of a simulated process or characteristic used for descriptive purposes: whereas a module is the quantification and representation of that process into 

a computer algorithm. Model compartments are the state variables that represent 

characteristics or properties of an ecosystem, such as leaf carbon or soil nitrogen 

(Swartzman 1979). Processes are the vectors that affect the dynamic exchange 

of energy and mass across the landscape, such as photosynthesis and respiration 

(Forman and Godron 1986). Mechanisms refer to the underlying biophysical factors, such as temperature and radiation, that influence the flow of energy across 

model components.

Many types of variables are incorporated into a simulation model’s design 

(Swartzman 1979). Intermediate variables are temporarily computed in the model 

to be used as input to other equations, algorithms, and decision thresholds and 

also as output for the user to interpret the results. Flux variables represent vectors that move energy, water, and carbon across model state variables. The output 

contains predictions or estimations of response variables, which, in this paper, are 

directly needed to fulfill the study or project objective. Explanatory variables are 

output from the model that explains model behavior.

There are many types of simulation models and many types of modules that 

are included within simulation models. They are usually categorized into four 

approaches: empirical, deterministic, stochastic, and mechanistic (Table 1). 

Empirical models are primarily built on statistical relationships that are derived 

from actual data. Deterministic models use generalized functions to represent the 

relationships that drive simulation dynamics. Stochastic models use probability 

distributions to represent primary ecosystem processes. Mechanistic models attempt to use fundamental biological and physical relationships to simulate the 

underlying processes or causal mechanisms that dictate system behavior. While all 

of these model approaches have various advantages and disadvantages (Table 1), 

the best simulation models and modules are often combinations of all four types.
6 USDA Forest Service RMRS-GTR-255. 2011.

Landscape structural and compositional terms can also be confusing. The 

spatial and size distribution of patches describes landscape structure, while landscape composition is often described by the relative abundance of ecosystem 

features across the spatial domain (percent area by cover types, for example). A 

stand is often referred to as an area of homogeneous vegetation, but in this report, 

stand also represents an inherent scale within the model. Polygons often refer to 

stands that are digitally mapped. The term patches is synonymous with stands or 

polygons in this paper. Ecosystem features, such as the dominant plant species 

(cover type) or vertical stand structure (structural stage), and disturbance properties, such as fuel models and fire regime, can be related to each polygon. Several 

terms are used to describe spatial data. A data layer is a digital map that describes 

a particular attribute. Data layers can be raster or vector. Raster layers are comprised of a grid of square pixels that represent the mapped area, and each pixel has 

a resolution (size) and is assigned an attribute. Vector layers are represented by 

lines that represent polygon boundaries. FireBGCv2 only uses ASCII raster data 

for inputs and outputs both in maps and input files. In this report, the terms data 

layer, raster layer, and digital map are used synonymously.

There are several ecological terms that need further description. In FireBGCv2, 

the term undergrowth describes all the non-tree species that can inhabit a stand. 

The understory is composed of trees in the lower canopy stratum, and overstory

is comprised of trees in the highest canopy stratum. Fuel, in this report, is the 

biomass of dead and live organic material in the unique classes or categories that 

are required by the fire behavior models. The fuelbed is synthesized from various 

state variables at the different organizational scales.

Report Conventions

This report uses many unique conventions and symbologies to describe the 

internal workings of the FireBGCv2 model. First, most variable names are in upper case (RNUM, for example) and they are defined in the text and in an index 

at the end of this report. However, a special class of variables has a lower-cased 

letter that starts the variable name, such as rTREE. Variables that have the lowercased “r” as the first letter are called “reduction” variables or scalars, which are 

commonly used in ecological gap modeling (Botkin 1993). In many gap models, 

simulated processes are often scaled from 0.0 to 1.0 to reflect their effect on state 

variables. In FireBGCv2, reduction variables are multiplied by a state or flux variable to scale effects and simulate interactions. For example, rTREE is a reduction 

variable that represents the influence of the number of seed trees on seed dispersal; it is scaled from 0.0 to 1.0 (0.0 for no seed-producing trees and 1.0 for greater 

than five seed-producing trees per hectare).

Variables with lower-cased letters in the middle of their names are often 

major state variables that represent important carbon, nitrogen, or water pools. 

Table 1. Advantages and disadvantages of the four approaches used in simulation modeling.

Attribute Empirical Deterministic Stochastic Mechanistic

Complexity Low Low Moderate High

Parameter requirements Low Moderate Moderate High

Accuracy High Variable Low Variable

Exploratory uses Low Moderate Moderate High

Management application High High Low Moderate

Portability to other situations Low Moderate Moderate High

Expand-ability Low Moderate Moderate High

Required Expertise Low Moderate Moderate High

Computer requirements Low Moderate Moderate High

Preparation time Low Low Moderate High
USDA Forest Service RMRS-GTR-255. 2011. 7

For example, LeafC is the leaf carbon pool of a stand or tree, and AvailN is the 

available nitrogen pool. Variables with all capital letters are usually intermediate 

variables or input parameters, while variables with lower-cased letters within the 

name are state variables.

The units throughout the report follow SI but some have special coding, especially the mass units. It is difficult to keep track of the origin of the mass of certain 

state and flux variables. State variables that have mass, for example, have the unit 

of kg, but it is unknown what that mass represents–it could be kg of carbon or kg 

of biomass. Therefore, we have placed a capital letter after the kg mass unit that 

indicates the origin of that biomass:

• kgW signifies kg of water for all water fluxes and state variables,

• kgC is kg of carbon,

• kgB is kg of biomass, and

• kgN is kg of nitrogen.

All files in this report have a unique suffix where “.in” is used for input files and 

“.out” is used for output files. All input file names are specified by the user, so they 

can have any name. We refer to them as “Filename.suffix” to simplify discussion, 

where the Filename is an informative name for a file and the suffix represents its 

use. For example, simulation parameters are input to the simulation in an ASCII 

file, which we have named “Sim.in,” where Sim refers to the “Simulation parameters” and .in refers to the fact that this is an “input file.” Throughout this report, 

we have used the file name to reference where the input variable is specified by 

the user. For example, a species’ specific leaf area (SLA) is specified by the user in 

the Species.in file. The content and structure of all files are detailed in the “Input 

File Structure” section.

All models or important modules are listed in italics and capital letters, such 

as FOREST-BGC, except for FireBGCv2. The variable names used in this report 

are not the same variable names used in the FireBGCv2 program because C++ 

program variable names are often long, connected with a pointer that references 

the scale, and redundant across program modules. Therefore, it would be difficult 

to debug the FireBGCv2 program using the variable names in this report; instead 

use the extensive documentation that is embedded in the code.

Model Description

FireBGCv2 is best described as a mechanistic, individual-tree, gap model that 

is implemented in a spatial domain (Keane and others 2004a). The model was 

developed by integrating empirically derived deterministic functions with stochastically driven algorithms to approximate landscape and ecosystem behavior 

across time and space. Empirical and deterministic functions are used to represent ecological processes, such as autotrophic respiration, that are heavily studied 

and to show where the variability of the process is somewhat understood and 

predictable. Stochastic functions are used to represent ecological processes that 

are highly variable, somewhat unstudied, and difficult to quantify, such as fire 

ignition, tree mortality, and snag fall. FireBGCv2 simulates ecological processes 

across and within scales so that cross scale interactions can be appropriately represented and accounted for in landscape behavior.

From an applications viewpoint, FireBGCv2 is more of a simulation platform 

than a single, integrated, linear model. Some ecosystem processes are simulated 

in multiple ways in the model. Fire spread, for example, can be simulated using a 

cell-automata model or a simplistic spread model. Tree dynamics can be simulated using the BGC algorithms, a mechanistic gap model, or a simplistic gap model. 

Fire behavior can be simulated using the Rothermel spread model (Rothermel 

1972) or the Albini FIREMOD model (Albini 1976). Therefore, FireBGCv2 often has multiple approaches and redundant modules that enable users to simulate 
8 USDA Forest Service RMRS-GTR-255. 2011.

important ecosystem processes in several ways. This platform structure accommodates explorations in the efficiency and accuracy of modeling approaches and 

predictions. Moreover, FireBGCv2 includes a number of other previously developed programs, namely BIOME-BGC, FIREMOD (Albini 1976), and FOFEM

(Reinhardt and others 1997).

FireBGCv2 simulates landscape dynamics by integrating fundamental ecological processes across multiple spatial and temporal scales (Figure 2). Wildland fire 

ignition and spread, along with cone crops and seed dispersal, are simulated at the 

landscape level at the end of the simulation year. Weather and species phenology 

are simulated at the site level at a daily time step. Most action in a FireBGCv2 

simulation occurs at the stand level where the flow of carbon, nitrogen, and water 

are moved across various components within the model (Figure 3). Litterfall and 

decomposition dictate forest floor dynamics; rainfall, leaf area, and temperature 

govern water dynamics; and photosynthesis and respiration dictate carbon dynamics (Figure 4). Tree growth, establishment, and mortality are simulated at the 

tree level. Disturbance effects, such as fuel consumption, tree mortality, and soil 

heating, are computed at each scale. All simulated processes have cross scale 

implications (Figure 2). For example, carbon is fixed by tree leaves (needles) via 

photosynthesis using solar radiation and precipitation weather inputs, and it is 

then distributed to leaves, stems, and roots, and accumulated litter, duff, and soil 

to eventually act as fuel for a fire (Figure 4). These forest floor compartments lose 

carbon through decomposition. Nitrogen is cycled through the system from the 

available nitrogen pool.

FireBGCv2 is a cumulative effects model best used to study long-term changes 

to landscape ecological regimes rather than as a prognostic model to predict the 

near future. Since FireBGCv2 has many stochastic elements, it shouldn’t be used 

to move landscapes forward or backward in time to determine what will be where 

and when. Rather, FireBGCv2 is best used to simulate the long-term interactions 

of disturbance, climate, and vegetation across several model runs to determine 

trends in landscape behavior and response. Because of this, FireBGCv2 should be 

run at least five times for time periods that are at least two to five times longer than 

the longest fire return interval, and results should be summarized to determine relative trends in ecological patterns and processes. And, as with most mechanistic 

models, the simulation results are best used when compared relatively with other 

simulation results in a scenario approach to evaluate trends; the absolute values of 

FireBGCv2 predictions can often be inaccurate, but they are precise, especially if 

averaged over many simulation replicates.

Figure 2. A general description of the 

ecosystem processes integrated into 

the FireBGCv2 model at multiple 

scales of time and space at both the 

landscape and stand scales.
USDA Forest Service RMRS-GTR-255. 2011. 9

Figure 4. General illustration of the important processes and ecosystem components represented in a FireBGCv2 

simulation.

Figure 3. A generalized 

diagram that illustrates 

the complexity of 

ecological processes 

that are simulated on the 

landscape. The biomass 

that is dropped to the 

ground from the simulated 

trees and undergrowth is 

stratified by seven ground 

components to facilitate 

the simulation of fire 

behavior and effects. 
10 USDA Forest Service RMRS-GTR-255. 2011.

Design and Structure

As mentioned, FireBGCv2 was designed as a research platform to explore cli-

mate, fire, and vegetation dynamics across small landscapes. It was also designed 

to conduct various simulation experiments in modeling science to understand 

consequences of simulation design, such as investigating appropriate and opti-

mum simulation detail when simulating landscape dynamics. This program was 

not developed to be used by managers to simulate possible management alterna-

tives, even though it could be used for some management applications, providing 

sufficient expertise exists to parameterize and initialize the model. Because of 

this, FireBGCv2 has some major drawbacks for general use:

•	 No graphical user interface. There is not a slick, user-friendly interface 

to facilitate execution, parameterization, and initialization. All inputs are in 

ASCII files that are edited with a standard text editor and are read directly by 

the program.

•	 No standardized output. FireBGCv2 was built to be flexible in the output 

generated by the model, especially in time and space. As a result, there is no 

general set of outputs that are created by the model. The user must design the 

output files to fit the simulation objective.

•	 No version control. FireBGCv2 was designed so that code could be modified 

by the user to accommodate the simulation of a new ecosystem element, 

process, or characteristic. As a result, the FireBGCv2 versions are highly fluid 

and dynamic, and there is no organization and maintenance of code updates 

and version control. However, we will manage FireBGCv2 versions by posting 

the most recent version at the www.firelab.org website.

•	 No maintenance. There is no organization or person who manages FireBGCv2 

code across multiple users. The latest versions will be posted to www.firelab.

org, but there will be no formal maintenance plan associated with posting.

The model description and code in this report represent one version in a long 

line of updates made to FireBGCv2 to facilitate new modeling projects. We an-

ticipate more modifications to the code in the future with each novel application 

of FireBGCv2.

Organizational Scales

There are five inherent scales built into FireBGCv2 design that correspond to 

spatial domains or organizational layers represented within the model (Figure 5). 

The first is the landscape scale, which is the spatial context for all processes and 

characteristics that occur in the simulation. It is usually defined as a large expanse 

of land (greater than 10,000 ha) that is delineated by the natural boundaries that 

control the major properties of that ecosystem, such as climate, vegetation, hy-

drology, and disturbance. The landscape is divided into biophysical units of land 

called sites that have similar topography, soils, weather, and potential vegeta-

tion. The model keeps the spatial boundaries of sites static within the landscape 

throughout FireBGCv2 simulations. Nested within each site is another scale 

called stands. That is, each site is comprised of a number of stands that can be differentiated by vegetation composition and structure. Each stand is assumed to be 

comprised of pixels with homogeneous vegetation and disturbance conditions. By 

definition, stand boundaries cannot extend past site boundaries. Stand boundaries 

are not stationary in FireBGCv2. Instead, simulated succession, fire, insects, and 

pathogens serve to alter stand boundaries within a site. Stand boundaries almost 

always change after major disturbances. Because of computational limitations, 

FireBGCv2 does not explicitly model all entities across the entire spatial extent of 

a stand. Instead, the model simulates ecosystem processes in a small representa-

tive portion of the stand called the simulation plot. The size and characteristics 
USDA Forest Service RMRS-GTR-255. 2011. 11

of the simulation plot are input to FireBGCv2 and can be adjusted to improve 

computation time and to better simulate ecological processes. Conditions within 

the simulation plot are assumed to be representative of the entire stand.

The fourth organizational scale is the species level. Any number of species 

can inhabit a stand and many modeled processes, such as cone production and 

phenology, are performed at the species level. The fifth and finest level of organization is the tree level. Each tree within a simulation plot is explicitly modeled in 

the FireBGCv2 architecture. Many structural and ecophysiological attributes of 

each tree, such as leaf carbon, diameter, and height, are simulated in FireBGCv2. 

Discussion of FireBGCv2 simulation methods is stratified by these organizational 

levels in this report.

FireBGCv2 links many cross-scale interactions in the simulation of ecosystem 

processes. The treatment of weather in the model is a good example of linkages 

that progress downward in organizational scale. The weather year is selected for 

the entire landscape and then used to access weather for a site. Each site is assigned a separate daily weather file. Photosynthesis and respiration are computed 

from the daily weather data at the stand level for that site. Important weather 

events such as frost and drought are computed at the stand level for the simulation 

of species dynamics, such as regeneration. Carbon that is fixed through photosynthesis at the stand level is allocated to the trees based on the distribution of 

radiation in forest canopy, which is computed from the site weather file and the 

stand’s canopy structure. FireBGCv2 also accounts for interactions that occur upward in organizational scale. At the end of the simulation year, FIREBGCv2 sums 

all carbon and nitrogen tree compartments for a new estimate of stand carbon and 

nitrogen components. Simulated fires burn a stand’s forest floor compartments 

(fuels) but use site-level weather and topography for computation of fire spread 

and intensity at the stand level.

Figure 5. The five 

organizational 

scales built 

into the design 

of FireBGCv2: 

landscape, site, 

stand, species, 

and tree.

Tree

Stands

Sites

Simulation landscape

Simulation

plot
12 USDA Forest Service RMRS-GTR-255. 2011.

Major Design Assumptions

In the previous version of FireBGCv2 (FIRE-BGC), the melding of the BGC 

models with a gap model created some undesirable behavior and odd side-effects 

in simulation results. BIOME-BGC and FOREST-BGC are equilibrium models 

that simulate ecological processes and their interactions with climate to describe 

changes in ecosystem properties that are explicitly represented in the model by 

carbon, water, and nitrogen pools. The problem with using BGC models to rep-

resent tree and undergrowth dynamics is that most ecosystem elements, such as 

trees, fuels, and grasses, are rarely in equilibrium but rather are always experi-

encing perturbations from a diverse number of agents, such as fire, disease, and 

insects. These perturbations constantly and abruptly change carbon, nitrogen, and 

water pools over short time periods. The BGC equilibrium model cannot respond 

to these instantaneous perturbations to produce realistic carbon, nitrogen, and wa-

ter fluxes because the model state variables must first come into equilibrium with 

the climate. As a result, there was often a lag in FIRE-BGC carbon, water, and 

nitrogen flows to tree growth after major changes to stand-level carbon pools 

brought about by disturbance that caused unrealistic tree diameter and height 

growth patterns (Keane and others 1996b). To remedy this, we removed the in-

timate link between tree and stand carbon that was present in FIRE-BGC, and 

instead, stand-level carbon and nitrogen pools are recalculated each year based on 

empirically derived carbon estimates at the tree, ground, and undergrowth levels. 

These updated carbon pools are then passed to the BGC routines to mechanisti-

cally compute the ecological processes that govern the fluxes of carbon, water, 

and nitrogen. So, FireBGCv2 is missing the seamless, comprehensive, and com-

plete linkages between stand and tree biogeochemical processes and now uses the 

list of trees, undergrowth, and ground components as the critical state variables 

instead of carbon, water, and nitrogen state variables. We feel this loss in ecologi-

cal process interaction is more than balanced by the gain in consistency, accuracy, 

and model robustness.

Another design change from FIRE-BGC to FireBGCv2 is the simulation of the 

nitrogen pools. The BGC simulation of vegetation dynamics still simulates chang-

es in nitrogen pools, but now, all changes in nitrogen pools are calculated from 

changes in the carbon pools, and nitrogen pools are updated at the beginning of a 

simulation year. So, for example, instead of having a dynamic, independent nitro-

gen pool for foliar nitrogen and corresponding nitrogen fluxes, the foliar nitrogen 

is now calculated from the carbon:nitrogen ratios entered in input files. Only the 

available nitrogen pool (AvailN) is dynamically modeled across simulation years. 

The nitrogen pools are only used in a BGC simulation, and again, the carbon and 

nitrogen pools are updated each year.

All parameters and initialization data are input into the FireBGCv2 program 

using ASCII files, and all simulated outputs are printed to ASCII files. These files 

are specified by name in a metafile called the Driver.in file. All input files have 

the .in suffix and will be presented by name in the following sections. Their for-

mat and structure is presented in the “Input File Structure” section. All output 

files have a fixed column format with each column containing a value of a userselected variable output at a user-specified time interval. Output files have an .out 

suffix. The following sections will refer to various parameters and the files in 

which they are specified.

Module Design

This section presents the major state variables and parameters that are simulat-

ed or referenced at each organizational scale in the FireBGCv2 model (Figure 5). 

This is not an exhaustive list of state variables: these are the most important state 

variables needed to understand model dynamics and algorithms.
USDA Forest Service RMRS-GTR-255. 2011. 13

Landscape Simulation

The landscape-level state variables are: (1) area burned, (2) number of fires, 

and (3) area in mountain pine beetle host species. These variables are updated 

yearly and are used in finer scale process simulations. From the burned area, the 

model computes fire regime information including fire return interval, fire rotation (cycle), and number of fires per year.

Site Simulation

Site state variables are used to simulate ecological processes at finer scales. 

The dynamic variables that are simulated at the site level include:

• DSS (days). Days since last snowfall.

• DSR (days). Days since last rainfall.

• RUST (flag). Flag indicating the initiation of a rust epidemic.

• BEETLE (flag). Flag indicating the initiation of a beetle epidemic.

• NFREEZDAY (days). Number of days it is freezing for mountain pine beetles 

(>-30 oC).

• NFROZDAY (days). Number of days it is colder than freezing for beetles 

(>-40 oC).

• RUSTrh (%). Average relative humidity in September used for simulating rust 

dynamics.

• RUSTtemp (oC). Average temperature in September used for simulating rust 

dynamics.

The values of these variables are updated each year or day, depending on the 

algorithm and variable type.

There are a number of site variables that are assigned as parameters in the Site.

in file or computed at the start of the simulation and used in various processes 

throughout the simulation, especially in the phenology simulation. These variables are introduced in the following sections where they are first used. Some 

important site parameters used throughout the program are:

• LAImax (m2 m-2). Maximum projected leaf area index for the site.

• BAmax (m2 ha-1). Maximum basal area for the site.

• SI (m). Site index for all tree species for that site.

• SAPmax (saplings m-2). Maximum number of saplings (trees greater than 1.37 

m tall) that can regenerate in any one year across all species.

• LAG (year). Number of years after a fire before regeneration can occur.

• FRI (year). Fire return interval or number of years between fires for all land 

area within a site.

• FSIZE (ha). Average fire size.

These are the most important site-level parameters, and they dictate many processes at several scales. Another component that is specified at the site scale is 

fuel characteristics, which is how fuels are represented in the model.

Fuel Representation

All fuel properties in FireBGCv2, except for loading and moisture, are static 

input parameters that are specified by the user in the Fuel.in file. Fuels attributes 

are assigned by live and dead components, which are further stratified by shrub, 

herbaceous, and four size classes of downed dead woody fuel (twigs, branches, 

large branches, and logs that correspond to 1, 10, 100, and 1000 hr fuels, respectively) (Figures 4 and 6). Each component has a list of attributes that are used in 

the calculation of fire intensity and fire effects. Currently, FireBGCv2 recognizes 
14 USDA Forest Service RMRS-GTR-255. 2011.

a maximum of eight dead and two live fuel components, but these parameters 

can be easily changed for future applications (Figure 7). Some fuel model parameters are not stratified by fuel component. Values for duff and litter bulk densities 

(BULK) and surface area to volume ratios (MPS) are taken from Brown (1974, 

1981) and are used to calculate duff and litter depth (see “Regeneration” in the 

“Species Processes” section).

The following parameters are specified in the Fuel.in file for each of the live 

and dead fuel components:

• RHOP (kg m-3). Fuel particle density.

• LHV (BTUs). Heat content of fuel.

• MPS (m2 m-3). Surface area to volume ratio.

• MOIST (proportion). Fuel moisture at the time of fire, expressed as a proportion 

of dry weight.

• CONSUME (proportion). Proportion of fuel that will be consumed in the fire.

Also specified in the Fuel.in file are the moisture of extinction of live and 

dead fuels (MEXT, proportion), bulk density (kgB m-3) of the duff and litter 

ground layers (BULKd and BULKl) and the live and dead fuelbed (FBULKl and 

FBULKd), and depth of the fuelbed (FDEPTH, m). The spread component and 

wind reduction factors are also specified for a fuel model but are not used in the 

model. These fuel models are specified at the site level.

Figure 6. A generalized figure of the important components of a stand that are simulated in FireBGCv2. The undergrowth 

contains all non-tree species and is represented by a set of guilds or plant functional types. The forest floor includes 

all wood, litter, and duff, and wood is separated into four size classes based on diameter of the woody particle (twigs, 

branches, large branches, and logs). The overstory is composed of all trees greater than 10 cm DBH and the remaining 

small trees are the understory.
USDA Forest Service RMRS-GTR-255. 2011. 15

Stand Simulation

The stand scale is the primary scale of analysis in the FireBGCv2 model, and it 

is at this scale where most important ecological processes are simulated (Figures 

2, 3, and 7). Each stand has two types of state variables: (1) variables that describe 

general stand dynamics, and (2) variables that describe the dynamics of unique 

stand compartments.

The important dynamic stand state variables that are critical to the simulation 

of most other modeled ecological processes are as follows:

• LA (m2), LAI, PLAI, PLAIsun, PLAIshade (m2 m-2). The leaf area (LA), 

all-sided leaf area index (LAI), projected leaf area index (PLAI), PLAI for 

only the foliage in the sun, and PLAI for the shade leaves, respectively.

• ALBEDO (proportion). The proportion of the direct radiation that is reflected 

back into the atmosphere as indirect radiation.

• FRI (year), YSF (year), NFIRES (number). Fire return interval, years since 

last fire, and the number of fires recorded for this stand during the simulation.

• FBFM (index). Fire behavior fuel model number (Anderson 1982).

• BA (m2 ha-1). Basal area of stand.

• HT (m). Stand height or average height of all trees in the overstory.

• HBC (m). Height to base of canopy or average height to crown base for all 

trees in the overstory (>10 cm DBH).

• CAREA (m2). Canopy area of all trees in the stand.

• CC (%). Percent canopy cover of all trees in the stand.

• PLAj (m2), ALj (index). Projected leaf area and proportion of available light 

for canopy layer j.

• SEEDTREEi (trees), SEEDPROBi (probability), SPPBAi (m2 ha-1), 

SPPBIOMASSi

 (kgB m-2). The number of seed producing trees, average 

relative probability of a seed hitting the stand, basal area, and amount of 

aboveground biomass by species i.

• HABSUITk (index). Habitat suitability index for wildlife species k.

There are three major stand compartments that are simulated separately from 

other stand-level processes. Their design is discussed next.

Figure 7. The forest floor 

components that comprise 

the fuelbed and the 

processes that affect the 

flux of carbon and water to 

these components.
16 USDA Forest Service RMRS-GTR-255. 2011.

Carbon and Nitrogen Pools

The carbon pool state variables at the stand level are as follows:

• LeafC (kgC). Carbon in leaf biomass.

• LeafCmax (kgC). Carbon in leaf biomass at peak of growing season.

• FrootC (kgC). Fine root carbon.

• LivestemC (kgC). Amount of carbon in the live stem, including twigs, 

branches, and boles.

• DeadstemC (kgC). Amount of carbon in the dead stem, including bark, twigs, 

branches, and boles.

• LivecrootC (kgC). Amount of carbon in the live coarse roots.

• DeadcrootC (kgC). Amount of carbon in the dead coarse roots.

• PSN (kgC). Amount of carbon fixed as photosynthate during the year for that 

tree.

• RESP (kgC). Amount of carbon lost to respiration during the year for that tree.

• StemC (kgC). Previous year’s stem carbon (live and dead).

Ground Simulation

The forest floor is an important component in FireBGCv2 design because it 

represents the dynamic properties of the fuelbed that are used to simulate fire 

(Figure 7). There are eight ground components that are represented as state variables at the stand level. They were designed to accurately represent forest floor 

processes and to be used directly in the fire behavior models (Table 2):

• LeaffallC (kgC m-2). The total leaf carbon that has fallen on the ground during 

the simulation year.

• LitterC (kgC m-2). The total leaf carbon in the litter layer, comprised of the 

remaining LeaffallC after many highly mobile substances have been lost.

• DuffC (kgC m-2). The decomposed organic material from woody and nonwoody sources that represents a long-term carbon pool on the soil surface.

• SoilC (kgC m-2). The total sloughed fine root carbon and the carbon that 

transitions from the duff to the soil.

• W1C (kgC m-2). The carbon in the fallen twigs of trees and shrubs. Twigs 

are downed woody fuel particles that are 0.0 to 1.0 cm diameter. In fuels 

terminology, this material is called 1 hour downed dead woody fuel (1 hr), 

hence its variable name.

• W10C (kgC m-2). The carbon in the fallen branches of trees and shrubs. 

Branches are downed woody fuel particles that are 1.0 to 3.0 cm diameter. 

In fuels terminology, this material is called 10 hour downed dead woody fuel 

(10 hr).

• W100C (kgC m-2). The carbon in the fallen large branches of trees and shrubs. 

Large branches are downed woody fuel particles that are 3.0 to 7.0 cm diameter. 

In fuels terminology, this material is called 100 hour downed dead woody fuel 

(100 hr).

• W1000C (kgC m-2). The carbon in the fallen logs of trees and shrubs, such as 

snag fall. Logs are downed woody fuel particles that are greater than 7.0 cm 

diameter. In fuels terminology, this material is called 1000 hour downed dead 

woody fuel (1000 hr).

These ground components are represented by carbon pools instead of biomass 

pools so that FireBGCv2 can easily adjust the pools based on BGC ecosystem 

process simulations (Figures 3 and 7).
USDA Forest Service RMRS-GTR-255. 2011. 17

Undergrowth Simulation

The most difficult structure to understand in FireBGCv2 design is how the 

undergrowth (non-tree species) is represented in a simulation (Figure 8). The simulation of the undergrowth involves assigning a “plant model” to each site. Each 

plant model contains a set of “guilds” that represent suites of functionally similar 

undergrowth species (shrub and herbaceous plant functional types, for example).

Table 2. Description of the forest floor compartments. Forest floor fuels are litter (LitterC + LeaffallC), duff, twigs, branches, 

large branches, and logs (NA means not applicable)

Attributes

Stand ground

compartment Name Particle Size (cm) Description

LeaffallC Fresh litterfall <0.2 Freshly fallen litter less than 1 year old

LitterC Litterfall <0.2 Litterfall that has lost highly mobile materials (carbon)

DuffC Duff NA Organic carbon material with indiscernible origins

W1C Twigs <0.6 Down, dead woody carbon material, 1 hr fuels

W10C Branches 0.6 to 2.5 Down, dead woody carbon material, 10 hr fuels

W100C Large Branches 2.5 to 8.0 Down, dead woody carbon material, 100 hr fuels

W1000C Logs >8.0 Down, dead woody carbon material, 1000 hr fuels

SoilC Soil NA Organic carbon found in the mineral soil

Figure 8. The representation of the undergrowth in FireBGCv2. All biomass in the undergrowth is separated into guilds and 

parameters used to model each guild are specified in the plant models that are assigned to each site.
18 USDA Forest Service RMRS-GTR-255. 2011.

FireBGCv2 allows the stratification of undergrowth vegetation into an infinite 

number of guilds (Figure 8). A guild is a classification grouping of species with 

similar ecophysiological attributes (plant functional types, for example). The user 

can input up to 100 guilds in a simulation. This allows individual undergrowth 

species to be simulated. However, the most parsimonious simulations stratify 

undergrowth species into ecologically similar groups, such as plant functional 

types, and simulate changes in biomass within these guilds. A large number of 

guilds will make the simulation more complex and computationally intensive. We 

recommend somewhere between 4 and 20 guilds to represent the understory in a 

simulation landscape.

Parameters for these guilds are stratified by plant models (Figure 9), which 

are like fuel models in that they describe a group of static parameters that are 

used across guilds but vary nonetheless in their values across sites. Plant models 

allow the stratification of important guild parameters by biophysical setting or 

site. For example, a simulation may include two guilds—herbs and shrubs. The 

plant model for an alpine tundra site might have the maximum height as 0.02 m 

for herbs and 0.1 m for shrubs, while the lower subalpine site plant model might 

have maximum height as 1.0 m for herbs and 1.8 m for shrubs (Figure 9). Again, 

plant models are assigned by site and not by stand, and the same plant model can 

be assigned to two different sites. The design of plant models is difficult because 

of the diverse parameters, so it requires knowledge of forest undergrowth and 

seral communities. It is suggested to use the same number of guilds for each plant 

model and that these guilds be roughly equivalent across all plant models. This 

allows for a more comprehensive interpretation of results across the landscape.

The plant model consists of many parameters. An identification (ID) number, 

name, and the number of guilds are used to identify plant models during the simu-

lation. The plant model ID number is specified in the Site.in file and is used for all 

stands within that site. Each guild is assigned values for the following parameters, 

which will be different across the number of plant models specified in the Plant.

in file: • SppID. Species ID number. This index number references a species in the 

Species.in file; and when FireBGCv2 needs a species-level parameter for an 

undergrowth guild, such as SLA, it takes it for that species.

• FuelID. Fuel ID number. This index number references a fuel model in the 

Fuel.in file, so when FireBGCv2 needs a fuel parameter, such as surface area 

to volume ratio, it takes it from that fuel model.

• UHTmax. Guild height (m). The maximum height of the plants in this guild. 

This is used to distribute leaf area in the canopy and for fuelbed characteristics.

• ALPHA. Biomass growth rate coefficient (yr-1). The coefficient that defines 

the growth rate of species in a guild. See “Undergrowth Dynamics” in the 

“Stand Processes” section for more information.

• BIOMASSmax (kgB m-2). Maximum aggregate biomass attainable for all 

species in a guild. This is another coefficient that defines the biomass and fuel 

loading of species in a guild. See “Undergrowth Dynamics” in the “Stand 

Processes” section for more information.

• LEAFfrac (proportion). Fraction of guild biomass that is leaf. This is the 

proportion of the total biomass that is leaf biomass.

• FDEAD (proportion). Fraction of guild biomass that is dead at the time of a 

fire. This is used to separate live and dead fuels for this guild for fire simulation.

The user must also name each guild for organizing the Plant.in file and for provid-

ing detail in model output.

There are a number of state variables that are used to simulate undergrowth 

dynamics for each guild. While the parameters above are static, the state variables 

below are updated at daily and yearly time steps.
USDA Forest Service RMRS-GTR-255. 2011. 19

• BIOMASS (kgB m-2). The instantaneous amount of biomass on the simulation 

plot for all the plants of the species within the guild.

• BIOMASSgs (kgB m-2). The amount of biomass on the simulation plot for all 

the plants of the species within the guild at the height of the growing season 

for that year.

• UHT (m). The instantaneous height of the undergrowth guild.

• ULA (m2). The instantaneous projected leaf area of the undergrowth guild.

• UPSN (kgC m-2). The amount of carbon from photosynthesis for that guild 

during that year.

• URESP (kgC m-2). The amount of carbon lost from respiration for that guild 

during that year

• UNPP (kgC m-2). The amount of carbon gain as growth for that guild during 

that year.

Biomass was used for the undergrowth guilds instead of kgC to allow for consistency with the fuel and fire algorithms and for interpretation of intermediate 

results.

Figure 9. An example of how plant models work in FireBGCv2. A plant model is a special parameterization of plant guilds 

based on environmental conditions. In a landscape simulation (A), an upper subalpine plant model (B) and a mesic 

montane plant model (C) are created that have different values for the maximum biomass and maximum height parameters 

for four guilds in a FireBGCv2 simulation (D).
20 USDA Forest Service RMRS-GTR-255. 2011.

Tree Simulation

Trees are represented in the FireBGCv2 program by two types of state variables: structural and pool variables. The structural variables describe the physical 

aspects of the tree. They include:

• SppID (index). Species index number cross-referenced to the species in the 

Species.in file

• DBH (cm). Diameter at breast height.

• HT (m). Tree height.

• HBC (m). Height to the bottom of the crown.

• AGE (year). Age of tree.

• LA (m2). All sided leaf area of the tree.

• RUST (flag). Flag indicating if this tree is rust resistant (RUST = 1).

• YSI (counter, year). Years since mountain pine beetle outbreak.

• STRESS (counter, year). Years of stress.

The carbon pool variables are used to keep track of carbon by pools as needed 

for a BGC simulation (Figures 3 and 4). They are as follows:

• LeafC (kgC). Carbon in leaf biomass.

• LeafCmax (kgC). Carbon in leaf biomass at peak of growing season.

• FrootC (kgC). Fine root carbon.

• LivestemC (kgC). Amount of carbon in the live stem, including twigs, 

branches, and boles.

• DeadstemC (kgC). Amount of carbon in the dead stem, including bark, twigs, 

branches, and boles.

• LivecrootC (kgC). Amount of carbon in the live coarse roots.

• DeadcrootC (kgC). Amount of carbon in the dead coarse roots.

• PSN (kgC). Amount of carbon fixed as photosynthate during the year for that 

tree.

• RESP (kgC). Amount of carbon lost to respiration during the year for that tree.

• StemC (kgC). Previous year’s stem carbon (live and dead).

Once a tree dies, it becomes a snag and each snag has these state variables:

• SppID (index). Species index number cross-referenced to the species in the 

Species.in file.

• DBH (cm). Diameter at breast height.

• HT (m). Tree height.

• AGE (year). Age of tree.

• WoodC (kgC). Amount of carbon in the dead wood.

Only AGE and WoodC are dynamic state variables for snags.

Program Flow

The flow of the FireBGCv2 model is somewhat complicated because of the 

cross-scale design of the program, so it is difficult to encapsulate all of the complexity into one flow chart. But Figure 10 provides sufficient detail to understand 

the order of FireBGCv2 simulations. First, the program reads all initial values and 

parameters from a series of input data files that are specified by the user in the 

Driver.in file. Then, all state and flux variables in the model are initialized with 

that input information. A comprehensive logic and error check is then performed 

on all input initial values and parameters to ensure all entered data are compatible 
USDA Forest Service RMRS-GTR-255. 2011. 21

Figure 10. Simplified flow chart of the FireBGCv2 model.
22 USDA Forest Service RMRS-GTR-255. 2011.

and rectified. This is especially important because this check also ensures critical 

issues won’t emerge after hundreds of years of simulation that could take days 

in real time. Simulation of all ecological processes now starts from the coarsest 

scale to the finest scale. For example, spatial processes of cone abundance and 

seed dispersal are first simulated at the landscape level, plant phenology and fire 

ignition are simulated at the site level, then water dynamics are simulated at the 

stand level, and tree growth is simulated at the tree level.

The daily simulation of stand-level processes is then accomplished using the 

simple gap, mechanistic gap, or FOREST-BGC routines. First, weather is input by 

site from data in a weather file (Figures 3, 4, and 7). Daily photosynthesis, respiration, and water budgets are then calculated to obtain yearly carbon gains. At the 

end of the simulation year, stand carbon and nitrogen are allocated to each tree in 

the stand and then to the leaf, stem, and root components of each tree. The carbon 

allocated to the tree’s stem is converted to a diameter growth. Establishment of 

new trees is then assessed by species in the regeneration routine. The possibility 

of tree death is evaluated in the mortality algorithm. Then, fire occurrence and 

subsequent fire behavior and effects are dynamically modeled on the landscape. 

Forest floor decomposition is simulated daily, the forest canopy characteristics are 

recomputed at year’s end, and the process is repeated for the next year.

Disturbances and their effects are simulated at the end of the year, but many 

disturbance variables are calculated and summarized at a daily time step. Output 

is also printed to the appropriate files (.out files) at daily and annual time steps for 

stand, tree, and fire variables.

Programming Specifics

FireBGCv2 was programmed in the C programming language, but it does not 

use the C++ class and object structure for a number of reasons. First, we started 

programming FireBGCv2 in the late 1990s and C++ was only beginning to become popular. At that time, UNIX-based workstations were the only platforms 

available and none of our workstations had comprehensive C++ compilers and 

debuggers in a programming environment. The C programming style was primarily selected because it was the easiest to port to other platforms and it offered the 

greatest balance between speed, compatibility, and interpretability.

The programming style of FireBGCv2 emphasized flexibility, interpretability, 

and debug-ability rather than high optimization and execution speed. Many functions were written so that other programmers could understand the programming 

flow and modify code with ease. Instead of combining equations, many algorithms solve in a stepwise fashion so that intermediate results can be evaluated. As 

a result, FireBGCv2 can be somewhat slow and can consume a great deal of memory. However, it should be relatively easy for a user to read the program’s code to 

determine the logic and flow of any module, even if the user is not a programmer.
USDA Forest Service RMRS-GTR-255. 2011. 23

Model Algorithms

This section describes the approaches, methods, protocols, and algorithms that 

FireBGCv2 uses to simulate fire, vegetation, and landscape dynamics. This section is designed as an efficient reference on a particular simulation method rather 

than a step-by-step description of how the model simulates ecosystem dynamics. 

The section is organized by spatial scale from broad- (landscape) through progressively finer-scale (site, stand, species, and tree) processes, with the algorithms that 

simulate ecological processes further organized by the vegetation, disturbance, or 

biophysical factor simulated. Many processes are simulated across multiple scales; 

for example, fire ignition and spread are simulated across the entire landscape, fire 

behavior is simulated for each burned stand, and mortality is simulated for each tree.

General Algorithms

A few general algorithms are used by nearly all model routines in the FireBGCv2 

platform. Many ecological processes are simulated in FireBGCv2 using stochastic approaches, and all stochastic approaches depend on a consistent random 

number generator. The random number generators available in FireBGCv2 were 

taken from Press and others (2002) and Ecuyer (1988), but extensive tests of 

these routines revealed some inconsistencies and unwanted behavior. We tested 

the system-level random number generator in the Microsoft C++ compiler and 

found its behavior acceptable for long FireBGCv2 runs when the random generator might be called billions of times. In this report, the variable that is used to store 

the random number (number between 0.0 to 1.0) is always RNUM.

Landscape Processes

Cone Crop

Two aspects of the tree regeneration process are spatially simulated at the landscape level during a FireBGCv2 simulation. First, the occurrence of an abundant 

cone crop is stochastically determined for each tree species in the simulation for 

the entire landscape. Then, the spatial dispersal of the seeds from that cone crop is 

simulated by species across the landscape. Again, these spatial processes are fully 

linked with stand-level processes through the storage of intermediate information 

and dynamic databases.

Each year a tree species can have a good or poor cone crop. Tree regeneration only 

occurs in good cone crop years. Species’ cone crop abundances are stochastically 

modeled at the landscape level rather than the stand level because the processes that 

govern cone production, such as climate and topography, work at the coarser spatial 

scale (Boe 1954, Eis and Craigdallie 1983, Piovesan and Adams 2001). Cone crops 

are simulated by comparing a generated random number (RNUM) to the probability of a good crop for a species (PCONE) and if RNUM is less than PCONE, a 

good cone crop is initiated. This Monte Carlo stochastic method is based on the 

approach used by Kercher and Axelrod (1984) in their SILVA model and Keane 

and others (1990b, 1990c) in FIRESUM. The chance of having a good cone crop in 

subsequent years is blocked for a number of years (NOCROP, specified by the species in FireBGCv2) based on the assumption that trees must store sufficient energy 

reserves before generating another good cone crop. Future versions of FireBGCv2 

will mechanistically simulate cone crops based on carbon budgets, climate events, 

and tree health once sufficient research has been conducted.
24 USDA Forest Service RMRS-GTR-255. 2011.

Seed Dispersal

The relative probability of a seed falling onto a stand is computed in FireBGCv2 

by calculating the amount of seed available for dispersal from stand species composition and density and then dispersing the relativized seed totals across all pixels 

in a landscape using seed dispersal curves following methodologies in Kellomäki 

and others (1987), Ribbens and others (1994), Greene and Johnson (1996), and 

South (1999). First, the probability of seed dispersal to every pixel in the landscape is computed using this relativized form of the equations of McCaughey and 

others (1986):

P

e

e

]

[ ]

dist [a

a bDIST = + (1)

where Pdist is the probability of a seed landing on a pixel, DIST is the distance 

between the seed source pixel and target pixel (m), and a and b are equation coefficients by species from McCaughey and others (1986) (Table 3).

The above equation is for tree species whose seeds are dispersed by the wind. 

The probability of seed dispersal of the bird-disseminated whitebark pine seed is 

calculated from the equation of Tomback and others (1990):

. P 0 1563

10[0.8062 0.000454 ]

dist

DIST = - (2)

Calculating the spatial distribution of Pdist for each tree species across the landscape involves a “moving window” approach where a source pixel is selected 

(starting in the northwest corner of the simulation landscape) and the model obtains stand-level information for this pixel. The distance (DIST) from this source 

pixel to all other target pixels on the simulation landscape is calculated using the 

Euclidean distance formula (X2 + Y2 = DIST2) until the distance to target pixel 

yields a Pdist less than 0.001. Then, another source pixel is selected, and the procedure is repeated for all landscape pixels as targets. This process is reiterated for 

all pixels as sources with the calculated Pdist summed for each pixel. The sum of 

all computed Pdist is divided by the number of iterations and across all pixels in a 

stand to calculate an average Pdist for each stand.

The size of the cone crop is represented by the number of cone-producing trees 

per hectare (SEEDTREEi) by species i for the stand. SEEDTREE is calculated 

from the previous simulation year’s stand information by summing all trees in a 

stand that are greater than 10 cm in DBH and greater than the minimum species 

Table 3. Seed dispersal parameters for the species in the FireBGCv2 

model for the northern Rocky Mountain simulations.

Species Parameter a Parameter b

Pines (ponderosa) 13.1251 0.0255

True firs, yews 13.4099 0.0183

Douglas-fir 14.1251 0.0222

Lodgepole pine 12.6760 0.0376

Western larch 14.3257 0.0148

Subalpine fir 13.4099 0.0183

Spruces 12.7470 0.0251

Limber pine 12.7470 0.0251

Western white pine 14.3257 0.0148

Western red cedar 13.1251 0.0257

Western hemlock 11.9823 0.0297

Aspen, cottonwood 12.9129 0.0079
USDA Forest Service RMRS-GTR-255. 2011. 25

reproductive age (AGECONEi) by species i. This sum is then scaled from 0.0 (no 

trees producing seeds) to 1.0 (five or more cone-producing trees per hectare) and 

stored in a reduction variable (rTREE). Then, Pdist is multiplied by rTREE for 

every calculation and iteration above to adjust for limited seed source production.

The simulation of seed dispersal is computationally intensive because of the 

numerous and iterative moving-window computations. As a result, it is recommended that this computation only be performed every decade or two (user 

defined in the Sim.in file as SEEDOPT). The model will automatically update 

seed dispersal probabilities if more than 10 percent of the landscape has changed 

as a result of disturbance, such as fire or insects. To save time, we also recommend 

that the initial seed dispersal probabilities be saved in a file so that the seed dispersal module need not be invoked to initialize seed dispersal parameters. The seed 

dispersal output file must be specified in the Driver.in file as seeddisp.dat, and 

the option to use initial dispersal probabilities is specified in the Sim.in input file.

Effects of topography, wind, seed size, temperature, and predation on seed 

dispersal are not included in the FireBGCv2 platform (Venable and Brown 1988, 

Greene and Johnson 1995, Clark and others 1999, Groeneveld and others 2002). 

Although these are important interactions that must be modeled across spatial 

scales to fully account for species migration across a landscape, especially in 

the context of climate change, their implementation may overwhelm computing 

requirements and cause major problems with simulation times and memory requirements. Future versions of FireBGCv2 will implement these dynamics within 

dispersal routines once simulation technology has advanced.

Wildland Fire

The process of wildland fire actually occurs at multiple scales. Fire ignition and 

spread are simulated in FireBGCv2 at the landscape scale. Ignition is determined 

by the input fire frequency probability at the site level and by the amount of ignitable fuel at the stand level, but the actual simulation of fire starts occurs across 

the entire landscape. Spread is a true landscape process because it is simulated 

across sites and stands based on landscape-level attributes (wind and slope) as 

well as stand-level attributes (fuel availability). Fire behavior is simulated at the 

stand level along with the fire effects of consumption and smoke while fire-caused 

tree mortality is simulated at the tree level. This may be confusing for many but it 

is important to know that several major disturbance processes in FireBGCv2 are 

simulated at multiple scales to rectify and organize landscape dynamics.

Fire Ignition

The frequency of ignition and points of origin of simulated fires are stochastically predicted at a yearly time step across the simulation landscape. The entire 

ignition simulation algorithm scales stand-level fire occurrence based on the sitelevel fire return intervals (year) that are input by the user in the Site.in file to 

compute an instantaneous probability of fire occurrence for that site (Keane and 

others 1989). This probability is then scaled to the size of the stand, level of fire 

management, and climate using the procedure detailed below. This procedure is 

done each year for every stand by site on the simulation landscape.

The first factor evaluated in this algorithm is the burnability of a stand, or 

the potential for a stand to have an ignition. This is determined from the amount 

and type of fuel on the ground. The biomass from all flashy ground fuels (litter, 

twigs, small branches, and current year’s leaffall; see “Stand Processes” section) 

is summed for the stand and compared to a threshold value called minimum fuel 

loading or MIN_FUEL_LOADING (in FireBGCv2, the minimum fuel loading is 

0.05 kgB m-2 based on simulations in the Behave program). If the flashy biomass 

is greater than MIN_FUEL_LOADING, the stand is then considered burnable 

and a fire ignition is allowed to occur within the stand. If the amount is less than 

the threshold, then the biomass in the undergrowth guilds is summed for the stand 
26 USDA Forest Service RMRS-GTR-255. 2011.

and compared against the MIN_FUEL_LOADING in a similar manner to the 

flashy ground fuels. A fire is not allowed to burn in the stand if both the flashy and 

undergrowth biomass in a stand are below the threshold.

Fire ignition on burnable stands is simulated from a probability of fire occur-

rence (Pfire) that is computed by first using a base fire probability that is input 

to the model in the Site.in file and scaling it to account for several environmen-

tal factors, such as the area of the stand, climate for that year, and level of fire 

suppression effort. There is an option in FireBGCv2 where the base Pfire can be 

computed from a form of the Weibull equation hazard function: a-1 a P FRI FRI

YSB REBURN

fire

i

=

-

:

D

:

D (3)

where YSB is the years since a stand last burned, REBURN is an input parameter 

in Site.in file for the number of years before this stand can burn again, FRI is the 

site-level input value for fire return interval (year), and a is the shape function 

(value of 2.0 in FireBGCv2). This algorithm is based on the equations and algo-

rithms presented in many studies (van Wagner 1978, Johnson and van Wagner 

1985, Baker 1989, Fox 1989, Clark 1990). However, we found that the most 

stable method of computing Pfire is to simply take the inverse of FRI (Keane and 

others 1996). We feel this estimation for Pfire behaves best across a diverse num-

ber of ecosystems and topographical conditions (Keane and others 2006).

This base probability Pfire is then adjusted for scale to account for fire size us-

ing the following relationship:

rFIRE FSIZE

AREA

ij

= (4)

where rFIRE is a reduction variable that can range above 1.0 in some cases, 

AREAj is the AREA (m

2) of stand 

j, and FSIZE is the average fire size (m

2) 

for the site i where stand j resides, which is input to the model in the Site.in file 

(Marsden 1983, Reed 1994).

We explored many approaches for integrating climate into fire ignition process-

es from relating fire danger and fuel moisture variables to fire activity (Hargrove 

and others 2000) to simulating fire regimes from climate parameters (Gardner 

and others 1997, Cary 1998, Keane and others 2004b). In the end, we found the 

most scalable, simple, consistent, and realistic approach was to simulate climate 

effects on fire ignition using deviations from the site’s fire weather based on a 

fire index, namely the Keetch Byram Drought Index (KBDI) (Keetch and Byram 

1968, Burgan 1993). At the beginning of a FireBGCv2 simulation, the KBDI 

is computed for every day in the site’s input weather stream and the maximum 

KBDI (KBDIbase) is computed for the fire season (Julian dates 179 to 269) across 

all years. The variable rCLIMATE is then computed as the scale of current KDBI 

to base KBDI (rCLIMATE = KBDI / KBDIbase). Refer to “Weather” in the “Site 

Processes” section to understand how to compute KBDI.

The effect of wildfire management is included as an input parameter in the 

FireBGCv2 Sim.in file that is constant throughout the simulation run. The fire 

suppression reduction variable rMGT represents the level of effectiveness of the 

fire suppression effort. This variable is computed from the equation 1 - FIRESUPP 

where FIRESUPP ranges from 0.0 (no suppression) to 1.0 (total suppression) and 

is specified as a simulation input parameter in the Sim.in file.

The final probability of fire Pfire is then computed from the multiplication of all 

factors with the base probability:

P P fire = fire ( ) rCLIMATE ( ) rFIRE ( ) rMGT (5)

The stand’s final fire probability (Pfire) is then compared to a freshly generated 

random number (RNUM) and a fire start is simulated for the stand in question 
USDA Forest Service RMRS-GTR-255. 2011. 27

if RNUM is less than Pfire. FireBGCv2 randomly selects one of the pixels in the 

stand as the ignition point for the fire. The locations of this pixel and any other 

pixel that has a simulated fire start are stored until the end of the simulation year, 

when they are passed to the fire spread routine to simulate the perimeter of the 

fire.

Fire Spread

The spatial process of the spread of a fire across the landscape is somewhat 

problematic for landscape fire regime models because of the intense computational demands required over the long simulation times and large simulation areas. 

Some large, fire-prone landscapes can have many fires in a single year, and since 

spread algorithms can be quite complex (Gardner and others 1999), the simulation 

of spread from multiple fires can often overwhelm available computing resources. 

We tried many fire spread algorithms from the highly mechanistic, detailed vector 

spread algorithms in the FARSITE model (Keane and others 1997, Finney 1998) 

to the more simplistic cell automata spread models (Karafyllidis and Thanailakis 

1997, Barros and Ball 1998). Detailed spread routines often lengthened simulation times by an order of magnitude, while simplistic spread models produced 

fire perimeters that were often unrealistic. We settled on the cell percolation approach that was implemented in the LANDSUM model (Keane and others 2002a) 

because it was computationally efficient yet still seemed to produce realistic fire 

perimeters. This approach uses vectors of wind (input to the model in the Site.in 

file) and slope (computed in FireBGCv2 from the input digital elevation model 

[DEM]) to drive fire spread. There is a slot in FireBGCv2 to add a more mechanistic, highly efficient fire spread algorithm such as FARSITE, and there also is a 

cell automata model implemented in FireBGCv2. However, we feel that the cell 

percolation spread model provides the best balance between realism and computational demand.

In the cell percolation model, fire is spread across the landscape at the pixel 

level using directional vectors of wind and slope. Wind direction (degrees azimuth) is an initial input to the model by site, but then it is randomly modified 

within 45 degrees of the input direction for each simulated fire. Wind speed 

(m sec-1) is also an input parameter that is randomly adjusted within 0.5 times of a 

user-specified input value for each fire. Slope (percent) is computed by pixel from 

the input DEM. The fire spreads to pixels in eight possible directions (N, NE, E, 

SE, S, SW, W, NW), as calculated from the following relationship modified from 

Rothermel (1991):

SPIX = (WINDf)(SLOPEf) (6)

where SPIX is the number of pixels to spread in a direction, and WINDf

 and 

SLOPEf are wind and slope factors that are computed from the following 

equations:

WINDf = (1+0.125ω)(cos(abs(θs - θw)))ω0.6 (7)

(1 3.5 ) SLOPEe

4

f = + 10T (8)

where ω is wind speed (m sec-1), abs is absolute value, θs is the spread direction 

(azimuths), θw is the wind direction (azimuths), and ∆ is slope (percent, rise over 

run) (Rothermel 1991). The slope factor above applies to only positive slope values (upslope spread). Down-slope spread is computed as:

SLOPEf = e3∆2 (9)
28 USDA Forest Service RMRS-GTR-255. 2011.

These equations are solved for each pixel ignited by the fire, originating from 

a randomly selected fire start pixel, as mentioned above. Only pixels that have 

an FRI shorter than the simulation time period are allowed to burn, except for 

patches where Pfire is zero, such as in a recently burned patch. Rounding of the 

computed SPIX to the nearest pixel size (30 m for most simulation studies) is sto-

chastically determined from a uniform random number generator. Initially, fires 

were allowed to burn until they hit the landscape boundary or an unburnable patch 

(no fuel), but this resulted in too much area burning on the simulated landscapes. 

We then limited fire spread by stochastically calculating a maximum fire size 

(FIRESIZE, m-2) for each fire from the following fire size distribution equation:

FIRESIZE = αln(RNUM)

ß (10)

where α is the magnitude parameter that is approximated from average fire size 

(m-2) and is specified in the Site.in input file, RNUM is a random number from 

a uniform probability distribution, and ß is a shape parameter estimated as 3.0 in 

FireBGCv2 (Keane and others 2002a). Average fire size can be estimated from 

fire records (Schmidt and others 2002) or from previous studies (Cui and Perera 

2006). Fire growth is simulated until the size of the fire exceeds FIRESIZE, and 

the final size is stored to calculate landscape fire rotation over the simulation time 

period so users can adjust their critical fire inputs, average fire size (α) and FRI to 

simulate realistic fire regimes under historical climates. Fire spread is simulated 

at the end of the model year using weather from that year.

Insects and Diseases

Mountain Pine Beetle

There are two variables in the Sim.in file that must be set by the user to simulate 

an insect (mountain pine beetle) or disease (blister rust) outbreak in FireBGCv2. 

The BEETLE and RUST variables are initialized as simulation parameters in the 

Sim.in file. They are important because they specify if and when an insect or a 

disease epidemic occurs. The user assigns a value to these variables that indicates 

what year the simulation of an outbreak would start, and the model does not simu-

late insect/disease dynamics until that year. For rust, if the sign of the variable is 

negative (-200, for example), the program will start the rust epidemic on the abso-

lute value of the negative number (year 200, for example), but it will also assume 

that all dead five-needle pine trees (whitebark, limber, and western white pine, for 

example) that were entered into the Tree.in file will be live trees instead of snags 

so that historical stand dynamics can be modeled.

The initiation of a mountain pine beetle epidemic is simulated at the land-

scape level at an annual time step using algorithms in Cole and others (1985). 

First, the number of days when the average daily temperature was below 

-20 

oC (NFREEZDAYS) and below -40 oC (NFROZDAYS) are summed across the 

entire year of simulation for every site on the landscape. Then, if NFREEZDAYS 

is less than 11.0 or NFROZDAYS is less than 1.0 for a site, the area (m

2) of the 

site is summed (SITEAREA) and a counter (YSFREEZE) is incremented one 

year. The model also evaluates the proportion of the landscape that is occupied by 

available hosts of the mountain pine beetle (MPBHOST_AREA, percent). This is 

computed as the sum of the area of stands that have greater than 30 percent of the 

stand’s basal area (m

2 ha-1) in pine trees that are larger than 20 cm DBH divided 

by the area of the entire site on the simulation landscape (without the buffer). The 

resulting number is then multiplied by 100 to get the percent of landscape in host 

trees.

The program sets a flag (MPB_FLAG) for a site if the number of years without 

a lethal freeze (<-20 

oC for 11 days, <-40 oC for 1 day) is greater than 40 years 

and the proportion of the site in host species (MBPHOST_AREA) is greater than 
USDA Forest Service RMRS-GTR-255. 2011. 29

40 percent. Once the flag is set, the model will simulate an endemic beetle outbreak for that site using a set of modified mountain pine beetle mortality equations 

(discussed in the “Tree Processes” section). The model simulates a mountain pine 

beetle epidemic if these two conditions are met: (1) the area (over all sites) that 

has host species is greater than 40 percent summed over all sites or is greater than 

40 percent for the entire landscape, and (2) the summed area for those sites that 

have favorable weather (YSFREEZE > 40) is greater than half the landscape. In 

an epidemic, all pines above 20 cm DBH are assigned a 0.99 probability of beetlecaused tree mortality (see the “Tree Processes” section).

White Pine Blister Rust

Rust epidemics are simulated in FireBGCv2 using a simplistic method taken 

from McDonald and others (1981) and Howell and others (2006) that is based on 

whether appropriate conditions for rust infection occur during the autumn (defined 

as Julian days 230 to 260). The model averages relative humidity and temperature 

over this range of Julian days for each site, and if the average relative humidity 

is above 70 percent and mean temperature is above 10 oC, a rust flag is set at 

the site level (RUST_FLAG), indicating a rust infection year. When simulating 

tree mortality, the model checks to see if the rust flag is set to YES, and if so, the 

probability of mortality is set to 0.99 for rust-susceptible five-needle pines. The 

program determines rust resistance (susceptibility) at the beginning of a simulation by randomly assigning a percent of the trees to be rust resistant—they will 

not experience rust mortality throughout their lifetime. That percent is currently 

assigned at 1.0 percent in the code, but future versions will allow rust resistance 

to be assigned by the user by species.

Management Actions

Three types of management actions or treatments are implemented in 

FireBGCv2—clearcut harvest, partial-cut harvest (thinning, selection, and seed 

tree, for example), and prescribed burning. Both the clearcut and partial-cut harvest actions allow prescribed burning as another post-cutting treatment. The details 

of the management actions are specified in the Manage.in input file. All management actions take place at landscape- and stand-level scales. The landscape-level 

information is used to determine if a treatment should be implemented, and standlevel information is used to decide which stands should be treated and how the 

treatments should be implemented based on the stand’s condition. The following 

describes how the model decides if a treatment should be implemented.

Within the Manage.in input file, a set of parameters (ALIMIT, BLIMIT, 

CLIMIT, DLIMIT) called treatment limits dictate whether and when a treatment 

should be implemented on the landscape and for a particular stand. For all three 

treatment types, the ALIMIT parameter is the total landscape area (ha) allowed 

to be treated each year. For example, if the user enters 100 for the ALIMIT for 

partial cutting, the program cycles through all sites and stands and treats those 

stands that meet a user-defined selection criteria until the total stand area treated 

reaches the 100 ha threshold. The BLIMIT parameter specifies the largest stand 

size (ha) that can be treated. If a stand area is greater than BLIMIT, no treatment 

will be implemented, even if the stand otherwise meets the selection criteria. This 

may limit treatment options within the simulation if a simulation landscape is 

initially built with large stands to optimize simulation resources. The CLIMIT 

parameter defines the minimum basal area (m2 ha-1) that a stand must have to be 

treated with cutting treatments, and the minimum number of years since last fire 

with the prescribed burning treatment. The DLIMIT parameter specifies the lower 

DBH (cm) threshold for partial cutting treatments, but is not used for clearcut 

treatments. For prescribed burning, the DLIMIT parameter is a factor that, when 

multiplied by the stand’s site FRI (year), specifies the minimum number of years 

before the stand can be treated again by prescribed burning. The program cycles 
30 USDA Forest Service RMRS-GTR-255. 2011.

through sites and stands in the order that they are listed in the Site.in and Stand.in 

input files, respectively, and then applies the above criteria to each stand to decide 

which to treat and when there is enough treatment on the landscape for each year. 

There will be many years when no treatments are implemented. These are the only 

activities that are done at the landscape level. The treatments are implemented at 

the stand level (see the “Stand Processes” section).

Hydrology

Streamflow hydrology has only recently been implemented in FireBGCv2 as 

an optional module that is invoked only when the simulation of the hydrologic 

cycle and water resources is desired. This is accomplished by identifying a site 

biome (BIOME) as a “riparian” site using the number 5 in the Site.in file. If a 

riparian site is specified in the Site.in file, then the hydrological routing model is 

initiated, and a specialized set of algorithms is used to estimate streamflow across 

all the streams on the landscape. This hydrologic routing module was included so 

that we could empirically compute stream temperatures, which require estimates 

of streamflow, air temperature, elevation, solar radiation at the stream surface, 

stream channel slope, and area in basin that contributes water to streamflow. 

Stream temperature simulation also allows subsequent estimations of fish sur-

vival and productivity. The hydrology algorithms are computationally intensive 

and require extensive memory, particularly for large landscapes with extensive 

stream networks, so it is recommended that the hydrology module only be used if 

streamflow and stream temperatures are desired.

We had to implement a somewhat simplistic hydrologic flow model into 

FireBGCv2 because of computational, memory, and data requirement concerns. 

We used a deterministic hydrologic process modeling approach rather than a sto-

chastic data-based approach to facilitate empirical estimates of stream temperature 

that use streamflow as input. For our process-based approach, we implemented a 

hydrologic routing model that contains representations of surface runoff, subsur-

face flow, and channel flow (Srinivasan and others 1998; Zeleke and Si 2005) 

with FireBGCv2’s mechanistic water balance model that simulates evaporation, 

transpiration, soil water content, and outflow (Keane and others 1996a) (see the 

“Stand Processes” section).

One major requirement for a hydrological routing simulation is that the simu-

lation landscape (excluding the buffer) must completely encompass a watershed 

because the model assumes that all water that falls within this simulation land-

scape is routed to the stream and ends up exiting from the landscape at the lowest 

point; the simulated flow at this exit point can be compared to stream discharge 

hydrographs to calibrate and validate FireBGCv2. A complex digital map is re-

quired as input for all hydrological simulations, and this layer (called distance to 

stream) has pixels that are assigned a positive value for upland areas and contain 

the values for distance (m) to the nearest stream along flow paths from that pixel 

and the elevation difference between that pixel and the nearest stream (m) with 

the format YYYYXXXX where YYYY is the elevation difference along a flow 

path (HEAD, m) and XXXX is the distance to stream (DTS, m). All pixel values 

that are less than zero (the streams) have three stream attributes combined into 

one value. This stream pixel value has a format of –XYYY.ZZZ, where the nega-

tive sign tells the program that this is a stream pixel (upland, non-stream pixels 

are positive), the X indicates the stream order (SORDER; value from 1 to 7), YYY 

represents the stream width (SWIDTH) in tenths of meters (123 is 12.3 m wide), 

and ZZZ indicates the percent of total simulation area from which this stream 

pixel is gathering water (percent contributing area or PCA; 123 indicates that 12.3 

percent of the watershed is contributing water to this stream pixel). All of these 

values can be computed using GIS software.

The hydrologic routing model tracks the distribution of water through the eco-

system to the components of soil, subsurface flow, or surface flow. Precipitation 
USDA Forest Service RMRS-GTR-255. 2011. 31

from rainfall and snowmelt to a stand is first evaluated for loss to evapotranspiration and then infiltration to the ground. When the ground soil becomes saturated, 

the excess water (OUTFLOW) from the stand is shunted to the stream either as 

overland surface flow (OSFLOW, kgW day-1) or as subsurface flow (SSFLOW, 

kgW day-1). Specifically, any amount of water that exceeds soil saturation is allocated first to subsurface flow. Excess water can percolate through the soil layer 

during the day if the water falls as rain. This amount of subsurface water is calculated using the site’s soil hydraulic conductivity (m day-1), as calculated from the 

following Shevnin and others (2006) equation:

HCsoil = (0.00072)PCLAY2 (11)

where HCsoil is the hydraulic conductivity of the soil layer and PCLAY is the 

percent clay in the soil profile (see the “Site Processes” section). HCsoil is then 

converted to kgW day-1 and compared against the amount of total excess water 

(OUTFLOW). If HCsoil is less than OUTFLOW, all water goes into subsurface 

flow (SSFLOW, kgW day-1), but if HCsoil is greater than OUTFLOW, the difference of HCsoil - OUTFLOW is allocated to SSFLOW, and all remaining water 

goes into surface flow (OSFLOW, kgW day-1). If any SSFLOW and OSFLOW 

values are greater than zero, their daily discharge values are stored in memory by 

stand for computation of hydrologic flow processes at the end of the year.

At year’s end, FireBGCv2 invokes the routing model that incrementally evaluates each pixel within the simulation landscape (watershed) and then goes through 

stored daily SSFLOW and OSFLOW values and shunts that daily water to the 

stream and then out of the watershed. We assume that once this water enters the 

stream, it will leave the watershed the same day. Water is routed to the stream 

by calculating how long it will take to enter the stream from the current day 

(JDAY, Julian date), estimating the day in the future that the water will arrive at 

the stream, and then adding that discharge amount into a daily stream flow array 

(STREAMFLOW, kgW day-1) for the estimated day. STREAMFLOW on JDAY 

is the amount of water that leaves the watershed on the Julian date.

The time it takes for surface water to enter the stream (DAY, days) is calculated 

using the following relationship taken from USDA-NRCS (1986):

[( )( )]

bHEAD

a R cDTS DAY 0.4

0.8

= (12)

where R is roughness, DTS is distance to stream (m) as taken from the input data 

layer, HEAD is the elevational fraction between the pixel where the surface water 

originates and where it enters the stream (m m-1), and a, b, and c are coefficients 

taken from USDA-NRCS (1986). Roughness is approximated at 0.07 for stands 

with substantial duff and litter layers (> 0.5 kgC m-2), 0.17 for low duff and litter 

(between 0.1 and 0.5 kgC m-2), and 0.37 for no duff.

The time it takes subsurface water (SSFLOW) to reach the stream (DAY, days) 

is calculated using the following relationship for subsurface flow:

( )( ) DAY HC

SDEPTH

HC HEAD

DTS

soil sub

= + e e o o (13)

where SDEPTH is soil depth (m), HCsoil is soil hydraulic conductivity, as previously calculated, DTS is distance to stream (m) from this pixel to the nearest 

stream along the best flow path as input using the digital input layer described 

above, HCsub is the hydraulic conductivity of the subsurface soils layers (m day-1) 

(estimated as a default value of 100 m day-1 but adjustable in the Sim.in file), and 

HEAD is the stream head (m m-1). The first term in the above equation estimates 

the time it takes for water to go through the soil layer into the subsurface soils layers, while the second term is how long it takes for water to get to the stream once it 

hits the subsurface layers. HEAD is taken from the stream map for a grid position.
32 USDA Forest Service RMRS-GTR-255. 2011.

Discharges from surface and subsurface flow amount are added to the 

streamflow array (STREAMFLOW) in the position of JDAY + DAY. The dai-

ly additions from all pixels are then summed into the appropriate days in the 

STREAMFLOW array. Streamflow out of the simulation watershed can then be 

estimated for any given day as the JDAY element in the STREAMFLOW array. 

The STREAMFLOW array has storage for 10 years into the future or 3650 days.

Streamflow for any stream pixel (pixels with a negative value in the distance 

to stream input data layer) for any day of the year is computed by multiplying the 

STREAMFLOW value for the day in question (JDAY) by the percent contribut-

ing area (PCA) and dividing by 100. This pixel streamflow value, in addition to 

air temperature, stream elevation, solar radiation reaching the stream, elevation, 

stream channel slope, and the area within the drainage basin that contributes water 

to streamflow are used to calculate daily stream temperature, trout habitat (us-

ing stream width SWIDTH and stream order SORDER), and trout productivity 

(g day-1) in modules that are scheduled to be added in the future.

Site Processes

Climate

The FireBGCv2 model employs a simplistic method for simulating climate 

change across the landscape, and it is based on a number of approaches that mod-

ify historical daily weather to obtain future weather sequences (Malanson and 

Westman 1991, Botkin and Schenk 1996, Cary and Banks 1999, Schwalm and 

Ek 2001). The advantage to this approach is that it is scaled to the input weather 

record, and all input and simulated weather variables are compatible for any given 

day. The disadvantage is that the potential increases in climate variability, which 

are important observed and predicted components of climate change, are not di-

rectly included in the FireBGCv2 algorithms (Ibanez and others 2007, Sherriff 

and Veblen 2008). In FireBGCv2, new climates are simulated by modifying the 

input site-level weather file on a daily basis to obtain a new weather stream. For 

example, in a possible climate change scenario, the daily maximum temperature 

(Tmax) may be increased by 3 

oC for a selected season and ramped up over a userspecified number of years.

Within the FireBGCv2 Climate.in input file, the user can specify up to six 

seasons that are defined by the range of Julian days in the season. Under climate 

change scenarios, maximum and minimum daily temperature, obtained from the 

input daily weather stream (see the “Weather” section for a complete discussion 

of the input weather variables), are offset by a user-defined number of degrees. 

The precipitation variable (PPT) is modified through use of a multiplier; for ex-

ample, a PPT value of 1.25 corresponds to a 25 percent increase in precipitation 

for a particular season, and a PPT value of 0.75 corresponds to a 25 percent de-

crease in precipitation. Vapor pressure deficit (VPD) and solar radiation (SRAD) 

can also be modified through the use of a multiplier, but because these are rarely 

summarized in climate change scenarios from Global Circulation Model output, 

most climate change simulations will use the same radiation and vapor pressure 

deficit as the historical record.

The user must first specify a starting and ending simulation year of climate 

change over which the FireBGCv2 model incrementally modifies temperature, 

humidity, precipitation, and radiation. Prior to the starting climate change year, 

the weather record is used in its unmodified state. But, following the ending year, 

the weather record is fully modified by the parameters defined in the Climate.

in file. For example, if the starting and ending years of climate change scenario 

are 1 and 100, respectively, and the Tmax offset is 3.0 

oC for the summer season, 

the model will increase daily Tmax by a factor of 0.03 (1/100 years*3.0) for the 

first year, and will continue that incremental increase for the next 99 years of the 

simulation.
USDA Forest Service RMRS-GTR-255. 2011. 33

There is also a set of parameters that define starting and ending values for three 

important climate and land use change variables. These parameters are increased 

across the entire year, not simulated by season. The carbon dioxide concentration 

(ppm), nitrogen deposition rate (kgN m-2 year-1), and nitrogen fixation rate (kgN 

m-2 year-1) are specified for the beginning and end of the climate change scenario. Each is proportionally modified during the start and end year of the climate 

change scenario, as discussed for the daily weather variables.

Weather

Core Weather Variables

A daily weather data file is specified for each site that is included in a FireBGCv2 

simulation. This file contains daily weather data for all years taken from a weather 

station at or near each site. The best and most common method used to create 

this weather file is to use the Mountain Climate Model, MT-CLIM, (Running and 

others 1987, Hungerford and others 1989) to extrapolate weather from a base 

weather station to a site in mountainous terrain. MT-CLIM extrapolates daily temperature (minimum and maximum, oC) and precipitation (cm) values recorded 

at valley base stations to mountainous locations based on elevation, slope, and 

aspect. Values computed by MT-CLIM and used by FireBGCv2 are minimum 

air temperature (Tmin, oC), maximum air temperature (Tmax, oC), precipitation 

(PPT, cm), vapor pressure deficit (VPD, Pa), net daily canopy shortwave radiation 

(SRAD, W m-2 day-1), and daylength (DAY, seconds).

At the beginning of each simulation, FireBGCv2 reads and stores the six daily 

input weather variables (Tmax, Tmin, Tday, VPD, PPT, and SRAD) for all sites. 

Then, it randomly selects a weather year to use for all sites, and that daily weather 

data for that year is used to simulate ecosystem processes in all stands within that 

site. The user can decide if the sequence of weather years is random or identical to that in the weather file. The FireBGCv2 program will cycle through this 

weather record until the end of the simulation if the weather year sequence is the 

same as the input data, or it will randomly pick a year for each year of the entire 

simulation.

Many other core weather-related variables are computed from the five MTCLIM weather variables (Tmax, Tmin, VPD, PPT, and SRAD). Average daily air 

temperature (Tave, oC) is computed from an arithmetic average of Tmin and Tmax

(oC). Soil temperature (Tsoil, oC) is computed as an 11-day running average of 

Tave and is initialized at the beginning of a simulation at 0 oC. However, when 

there is snow on the ground, the soil temperature changes with the difference between average annual Tair and the previous day’s soil temperature multiplied by 

0.83 (Running and Coughlan 1988). Average daytime air temperature (Tday, oC) 

is computed from the following empirical equation from Running and Coughlan 

(1988) in MT-CLIM:

Tday = Tave + [0.212(Tmax - Tave)] (14)

Average nighttime air temperature (Tnight, oC) is computed as an average of daytime and minimum temperatures ( (Tday + Tmin) / 2.0 ) and is used to compute 

nighttime respiration.

A daily average relative humidity (Hr, %) is computed from the following relationship detailed in Campbell (1987):

. ( )

. ( )

H

e

e

100

6 1078

6 1078

.

.

.

.

r

T

T

T

T

237 3

17 269

237 3

17 269

ave

ave

dew

dew

=

+

+

R

T

S

S

S

S

V

X

W

W

W

W

(15)

where Tdew is taken from the VPD value that was computed in MT-CLIM

(Campbell 1987).
34 USDA Forest Service RMRS-GTR-255. 2011.

Net radiation to the site (Rn, kJ m-2 day-1) is estimated as the un-reflected 

proportion of daily shortwave radiation (SRAD, W m-2 day-1), as computed by 

multiplying Rn and stand albedo (ALBEDO) (Gay 1979). The photon flux density 

(umol m-2 sec-1) is computed from SRAD by multiplying it by two factors: the 

radiation to photosynthetically active radiation (PAR) ratio (0.45 in the model) 

and a conversion factor to convert radiation to umol m-2 sec-1 (4.55 in the model).

Daylength (Dl, sec) for a flat surface is computed from the equation:

Dl = 3060(∝ sin (0.01721 (YD - 79) + 12) (16)

where YD is yearday (Julian date), and α is the amplitude of the diurnal day length 

sine function estimated from:

e

3600

( . 7 42 0. ) 45LAT \= + (17)

where LAT is the latitude of the site in decimal degrees, as specified in the Site.in 

file. Daylength is available from MT-CLIM output, but it is now computed within 

FireBGCv2 to save memory.

Weather-Related Variables

There are many other ecological weather attributes estimated from the raw 

weather data that are used in algorithms throughout the model. FireBGCv2 estimates the latest spring frost as the last day before the middle of the growing 

season (GSmid, Julian date) with a minimum air temperature (Tmin) below -3 oC. 

The middle of the growing season is currently Julian day 200 in FireBGCv2. The 

earliest fall frost is computed in a similar manner where the earliest autumnal 

frost after the middle of the growing season is recorded. These parameters are 

used in the Tree Regeneration algorithms. A number of FireBGCv2 routines use 

days since last rain (DSR) and days since last snow (DSS), which are computed 

as the number of days since at least 0.05 cm precipitation (Rainfall>Effective 

Precipitation or EPPT).

There are some precipitation-related variables computed in FireBGCv2 that 

are fed to BGC and mechanistic gap simulations. First, and most importantly, 

FireBGCv2 converts all precipitation variables from cm to kgW m-2 to be compatible with BGC routines (conversion factor is 0.1). Next, snow (SnowW, kgW m-2) 

is simulated if the average temperature (Tave) is less than 1 oC. EPPT (kgW m-2) 

is rainfall that makes it to the forest floor and is calculated from the product of the 

interception coefficient (kgW m-2 LAI-1, specified in the Species.in input file) and 

the projected leaf area index (PLAI, m2 m-2) of the stand. This value is the rainfall 

that is intercepted by the canopy (RainInterceptedW, kgW m-2). If PPT is greater 

than RainInterceptedW, the difference is the amount of water that hits the forest 

floor (RainThrufallW, kgW m-2), called EPPT in the model (kgW m-2).

There are a couple of important fire-related weather variables that are computed each day. First, the KBDI is computed using the algorithms taken from 

Keetch and Byram (1968) and Burgan (1993). In FireBGCv2, KBDI is computed 

based on precipitation and temperature. If there is significant rainfall (above the 

interception coefficient) on a day, and that rainfall is above 0.5 cm or there was 

rain on the previous day, the KDBI is updated using this relationship:

KBDIt = KBDIt-1 - (0.0005 + 100NETPPT) (18)

where KBDIt-1 is yesterday’s KBDI, and NETPPT is the net precipitation (inches) 

as computed from the previous and current day’s rainfall (inches). In addition, a 

heat sum (DQ) is estimated if the maximum temperature for the day is greater 

than 10 oC:
USDA Forest Service RMRS-GTR-255. 2011. 35

. ( ) ( ( . ))

( . KBDI

e

e DQ 0 001 800 1 10 880 9676

1 .

. ) .

t AAPPT

T

0 0441

0 0486 max 8 299

= - - + -

-

= G (19)

where Tmax is the maximum temperature for that day converted to oF, and AAPPT 

is the average annual precipitation (converted to inches) as computed at the start 

of the simulation based on the weather file for that site. The complete KBDI algorithm is presented in Keetch and Byram (1968). The KBDI is initialized at 100 

at the beginning of a FireBGCv2 simulation. FireBGCv2 also contains the algorithms to compute all fire danger indices (for example, energy release component, 

burning index, and spread component), but this has been disabled during simulation to decrease simulation times.

Phenology

The phenology of each species in the Species.in file is calculated at the site 

level for each year of simulation. Phenology simulations are used to increment 

litterfall and leaf growth for all trees and undergrowth on the simulation plot at a 

daily time step. The BGC simulations can use one of two phenology simulations–

phenology can be simulated from hardwired Julian dates specified by species in 

the Species.in input file, or phenology can be modeled using the algorithms of 

White and others (1997). A detailed description of these phenology routines is 

presented in White and others (1997), so the routines won’t be discussed here 

except in the modification to simulate phenology for the mechanistic gap model. 

The simple gap model implemented in FireBGCv2 does not simulate daily phenology because it models litterfall and leaf growth at an annual time step.

The phenology algorithms for the FireBGCv2 mechanistic gap model taken 

from White and others (1997) have some major adjustments by lifeform and species. There are four stages of phenology simulated for all lifeforms (evergreen 

conifer and deciduous broadleaf, for example) in FireBGCv2: (1) dormant, (2) 

leaf out, (3) growing season, and (4) leaf fall. The model starts all species in the 

dormant phase and simulates the transition to the next phases using Julian date 

and intermediate weather variables by major lifeform. This simulation is at a daily 

time step, so that on any day, the model knows the current phenological stage and 

grows or sheds foliage accordingly.

Conifers and Shrubs

All evergreen and deciduous conifers and shrubs are initialized as dormant at 

the beginning of a simulation. Litterfall for evergreen conifers is simulated evenly 

across all days as a proportion of the annual leaf fall (leafC divided by the leaf 

retention time, as specified by species in the Species.in file) divided by 365. Leaf 

growth initiation is simulated using the White and others (1997) equations where 

each day the program calculates a soil temperature summation (SOIL_TEMP_

SUM) by summing positive soil temperatures for all days up to the seventh day 

after the day in question. It then compares the SOIL_TEMP_SUM to a tree green 

up onset date (TGOD) sum, as calculated at the beginning of a FireBGCv2 simulation for each site from the following relationship:

TGOD = e[4.795 + (0.129Taaave)] (20)

where Taaave is average annual daily average temperature (oC), which is calculated and stored by site at the very beginning of a FireBGCv2 simulation. If 

SOIL_TEMP_SUM greater than TGOD and JD is less than 182 (a date used to 

ensure green up occurs before July 1), then the phenological state of the evergreen 

transitions from the dormant stage to the leaf out stage.

Leaf out is simulated for the number of days specified for each species in the 

Species.in file, where it is called the species leaf period (assumed to be 14 in past 

Fire-BGC simulations). During this time, new leaf material is added to each 
36 USDA Forest Service RMRS-GTR-255. 2011.

conifer tree based on a maximum (described in the “Tree Processes” section). 

At the end of the leaf out stage (number of days in leaf out period>species leaf 

period), the model transitions the phenological stage to the growing season stage.

Growing season continues until day length is shorter than 39,300 seconds and 

the soil temperature (Tsoil) is less than an average summer soil temperature (Tsst), 

as computed at the beginning of the simulation by taking an average across all 

years in the weather record of soil temperature between the Julian dates of 244 

and 305. There is a logic check in the program for some rare weather years so that 

if the above mentioned conditions don’t occur, growing season stops when soil 

temperature is less than 2 

oC and Julian date is greater than 182. The species then 

transitions to the leaf fall phase, which is simulated exactly like the leaf out phase 

where the phase lasts for the input leaf period for the species. After the end of the 

leaf period, the species transitions to dormancy and stays that way until the next 

year when leaf out occurs.

Herbaceous Species

Grass and forb species’ phenology are also simulated using the White and others 

(1997) algorithms. Herbaceous species are in the dormant phase until two critical 

criteria are met: first, the soil temperature summation (SOIL_TEMP_SUM) must 

be greater than a critical threshold (HERBTEMP_GREENUP) that is computed from:

HERBTEMP_GREENUP = 900 + (0.5 * (1380 - 418))

ee

. ( . )]

32.9( 9.0)]1

Taaave

Taaave

32 9 9 - 0 1

- -

8

+

8

BB

(21)

where Taaave is average annual daily average temperature (

oC). Second, 

a precipitation sum (PPT_SUM) must be greater than a critical threshold 

(HERB_PPT_GREENUP). The precipitation sum is computed as the amount of 

precipitation accumulated from the first of the year (YD = 1), and the HERBPPT_

GREENUP threshold is computed as 15 percent of the average annual precipitation 

(m) across all weather years. If these criteria are met, the herb species transitions 

to the leaf out phase. Both the leaf out and leaf fall stages are simulated the same 

as evergreen conifers using the static number of days in the leaf period (input in 

Species.in file). At the end of the leaf period, the species transitions to the growing 

season phase.

Growing season for herbs lasts until the following criteria are met. First, the 

number of days in this stage has to be greater than 30. Then, the amount of pre-

cipitation in the previous 31 days has to be greater than 0.0114 m. Then, the 

amount of precipitation in the subsequent seven days has to be more than 0.0097 

m. And last, the maximum temperature has to be greater than 92 percent of a site 

maximum that is computed as the maximum Tmax over the entire weather record 

(computed at the beginning of the simulation). As a logic check for cold-canceling 

weather events, the herb species will also transition to the leaf fall stage when 

the three-day mean Tmin is less than the average annual Tmin for the site over all 

weather years and the Julian date is greater than 182. Leaf fall lasts for the input 

leaf period and then transitions to dormancy for the rest of the year and the begin-

ning of the following year.

Wildland Fire

Fuels

The only fuel properties that are not static at the site level are fuel loadings and 

moistures. Loadings (kgB m-2) for the eight dead fuel components (see Table 2) 

are computed from the forest floor carbon pools by dividing the carbon mass by 

the fraction of carbon to biomass (FCB, kgC kgB-1) and the simulation plot area 

(PAREA, m

2) (see the “Stand Processes” section). The moisture contents of the 

fuel components are simulated at the stand level, but when a fire occurs, the wild-

fire fuel moisture conditions specified in the Fuel.in file (see “Fuel Moisture” in 
USDA Forest Service RMRS-GTR-255. 2011. 37

the “Stand Processes” section) are used to simulate fire behavior and fire effects. 

Wind speed is another dynamic variable that is treated as static in FireBGCv2. 

Future versions of FireBGCv2 will incorporate mechanistic simulations of fuel 

moistures for fire behavior inputs.

All fuel properties, except moisture and loading, are specified in the Fuel.in 

input file as fuel models. The user can create several fuel models for the simulation area depending on vegetation and fuel characteristics. We used the fuel model 

parameters from Anderson (1982) to build a set of fuel models that we thought 

best represented the fuel conditions of the Glacier National Park simulation landscape (we used only 5 to 6 of Anderson’s original 13). Values from both Anderson 

(1982) and Scott and Burgan (2005) can be used to build fuel models in the Fuel.

in file.

Stand Processes

Vegetation Development

As mentioned, FireBGCv2 has three routines for simulating the process of 

succession or vegetation development, and the desired routine is specified in the 

Sim.in input file. The first option is a simple gap model taken directly from the 

FIRESUM model (Keane and others 1989, Keane and others 1990c). In this submodel, a maximum growth rate is reduced using reduction factors that represent 

light, stand density (nutrients), available water, and growing season temperature. 

This gap model is explained in detail in the Keane and others (1989) model documentation and, therefore, is not detailed here. The advantages of using this simple 

gap model are that it is quick, efficient, and highly generalized; however, there 

is a substantial loss of environmental feedbacks when using this simple gap approach. The primary disadvantage is that a simple gap model does not have the 

rich diversity of simulation output that is available with the other two submodels. 

This model does not simulate the flow of water, nitrogen, and carbon across stand 

carbon pools. As a result, several important ecological processes and their interactions are not represented in the simulation, such as the effect of moisture and 

temperature on fuel decomposition, the flow of carbon from plant respiration to 

the atmosphere, and the direct influence of climate on tree growth.

The most complex and detailed simulation of ecosystem dynamics occurs when 

the user selects the BGC simulation option for plant growth. The BGC algorithms 

are thoroughly documented in a number of papers (Running and Hunt 1993, 

Thornton and White 1996, White and others 2000, Thornton and others 2002) 

and on the website http://www.ntsg.umt.edu/models/bgc. The BGC submodel 

uses the routines from the big-leaf, ecosystem model BIOME-BGC (Running and 

Hunt 1993, White and others 2000, Thornton and others 2002). The first version 

of FireBGCv2 (FIRE-BGC) incorporated an early version of BIOME-BGC that 

was developed by Running and Coughlan (1988) and Running and others (1989). 

In the 1990s, a complete model revision occurred that was started by Running 

and Hunt (1993) and completed by Thornton (1998), White and others (2000), 

and Thornton and others (2002). The new BIOME-BGC model has since been 

implemented in its entirety into the FireBGCv2 code (Figure 1). When a BGC

simulation is selected, a complex and extensive daily simulation of a number 

of ecosystem processes is initiated, including carbon, nitrogen, and water flux 

across a number of ecosystem components (leaf, stem, and root compartments). 

This simulation is quite demanding and takes a great deal of simulation resources 

such as memory and computation time. In return, a rich collection of interacting 

variables are available as output to explore ecosystem functions and response. 

One major problem with the BGC simulation is that it is difficult to scale the carbon allocated to the stand’s leaf, stem, and root compartments and then allocate 

them to the tree’s leaf, stem, and root compartments. The FIRE-BGC version used 
38 USDA Forest Service RMRS-GTR-255. 2011.

leaf area and shade tolerance to perform this allocation, but many small trees in 

the understory were given insufficient carbon, especially shade-intolerant species 

(Keane and others 1996b). The new FireBGCv2 improves on this by allocating 

carbon by an annual carbon sum.

The last option is the mechanistic gap model simulation of ecosystem dynamics. This option was designed to be a compromise between the overly simplistic 

gap model and the computationally extensive BGC simulation (Figure 10). This 

option provides the full simulation of carbon and water in an ecosystem but still 

uses growth reduction factors to drive tree and undergrowth growth, and these 

scalars are now linked to the simulated ecosystem processes. This option does 

not include a cycling of nitrogen in the design, but it does simulate all other ecosystem biophysical processes at the same level of detail as the mechanistic BGC 

submodel because most of the algorithms are based on BGC design. The mechanistic gap model is the only option that is discussed in detail in this report.

Mechanistic Gap Biophysical Simulation

The mechanistic ecosystem process gap submodel for simulating plant dynamics uses many of the BGC environmental algorithms along with various additional 

biophysical relationships to simulate tree and undergrowth growth using a highly 

mechanistic gap approach (Figure 3). This gap model does not have the problems 

associated with the BGC simulation (inability to consistently allocate sufficient 

carbon to tree growth), yet still simulates the flow of carbon and water throughout 

the stand.

A mechanistic approach to simulating forest growth involves the complex simulation of all the ecological processes that drive growth for that ecosystem. This 

mechanistic simulation effort starts with the calculation of soil water potential (Ψ, 

also termed PSI, Pa) that describes the pressure (tension) needed to extract water 

from the soil matrix. In FireBGCv2, soil water potential (Ψ, Pa) is computed with 

the following relationship developed by Cosby and others (1984):

b ( ) VMC

VMC sat sat

} } = (22)

where Ψsat (Pa) is the soil water potential at soil saturation, VMCsat is the soil 

volumetric water content (percent) at saturation, VMC is the soil water content 

for the day in question (percent), and b is an empirical parameter. All of these 

variables are parameters that are estimated from the following equations using 

percent sand (SAND, %), silt (SILT, %), and clay (CLAY, %) for the free rooting 

zone for a site:

b = -(3.1 + 0.157CLAY - 0.003SAND) (23)

. . . VMC SAND CLAY

100

50 5 0 142 0 037 sat = - - (24)

Ψsat = -0.000098e[(1.54-0.0095SAND + 0.0063SILT) log(10)] (25)

( /1 b) [ . VMC VMC ] 0 015

fc sat }sat

= - (26)

where VMCfc is the soil volumetric water content at field capacity, as defined by 

Crosby and others (1986). VMC is the soil volumetric water content of a stand 

for a particular day (percent), and it is estimated from the amount of available soil 

water (SoilW, kgW m-2) in the soil profile using the following relationship:
USDA Forest Service RMRS-GTR-255. 2011. 39

( ) VMC SOILDEPTH

SoilW

1000 = (27)

where SOILDEPTH (m) is the depth of the free rooting zone, as specified for 

each site.

The available soil water (SoilW, kgW m-2) is computed from a water balance 

algorithm that attempts to balance the processes that utilize water (transpiration, 

evaporation, and runoff) with processes that fill the soil profile with water (precipitation and outflow). This algorithm first adds effective precipitation (EPPT, 

kgW m-2) to the soil water (SoilW, kgW m-2). If the new computed VMC exceeds 

the amount that the soil profile can hold (VMCfc), then the excess is shunted 

to runoff (OutflowW, kgW m-2). The model then computes estimates of actual 

evapotranspiration (AET, kgW m-2) and evaporation (EVAP, kgW m-2) using the 

Penmon equation presented in the middle of this section. The daily AET is subtracted from the soil water (SoilW) and the soil moisture properties (VMC, Ψ ) 

are recalculated. FireBGCv2 assumes there is no water flow from stand to stand.

Estimates of conductances—g (the inverse of resistance or 1 / rv)—are needed 

for computing evapotranspiration (ET, kgW m-2). This is accomplished by reducing a maximum conductance (gmax, kgW sec-1), as specified by species in 

the Species.in file and summarized to the stand level by the leaf area weighed 

average, by several scalars that account for environmental interactions using the 

following equation:

g g r

1

l max c

= e o(rTEMP)(rVPD)(rSWP)(rPPFD) (28)

where gl is the leaf-level conductance (sec), and rc is the correction factor for 

temperature and pressure as follows:

r

1

.

( . ) c .

AP

T 101300

293 15

273 15 1 75 day

= +

8 B: D (29)

where Tday is daytime temperature (oC), and AP is atmospheric pressure (MPa). 

The rest of the terms are reduction factors that are computed from the following 

relationships using previously described variables. All reduction scalars (rTEMP, 

rVPD, rSWP, rPPFD) are bounded to the range 0.0 to 1.0 (Thornton 1998, White 

and others 1998, White and others 2000). The scalar that accounts for stand temperature (rTEMP) is calculated from:

rTEMP = 1 + 0.125Tmin (30)

where Tmin (oC) is the daily minimum temperature. The reduction factor that 

accounts for the vapor pressure deficit (humidity) influences on stomatal conductance is:

( )

( ) rVPD VPD VPD

VPD VPD

close open

close = -

- (31)

where VPDclose and VPDopen are the vapor pressure deficits (Pa) for closing and 

opening stomata, as specified by species in the Species.in file and summarized for 

the canopy based on a leaf area weighted average (see the “Canopy Dynamics” 

section). The daily VPD is taken from the MT-CLIM input file as part of the 

weather stream. The scalar to account for soil water potential effects on conductance (rSWP) is similar to that for VPD:

( )

( ) rSWP

close open

close

} }

} } = -

- (32)

where Ψclose and Ψopen are the soil water potential (MPa) for stomatal closing and 

opening, and Ψ is the daily soil water potential (MPa). The scalar that represents 
40 USDA Forest Service RMRS-GTR-255. 2011.

light effects on stomatal opening (rPPFD) is calculated using the following 

equation:

rPPFD PPFD PPFD

PPFD

plai 50

plai

=

+ (33)

PPFD50 is the PAR (photosynthetically active radiation) photon flux density 

(umol sec-1) needed to open 50 percent of the stomata, and it is hardwired into the 

program as 75.0 umol m-2 sec-1 (Thornton and others 2002). PPFDplai is the PAR 

photon flux density per unit of projected leaf area index and it is computed from 

the following relationship:

PPFDk( )( ) ALBEDO PAR 1E

3 plai = - (34)

where k is the extinction coefficient specified by species in the Species.in file, 

ALBEDO is the stand albedo that is dynamically modeled using relationships 

presented in this section, and PAR is radiation flux only in the photosyntheti-

cally active range (PAR, 400 to 700 nm). E is a factor to convert PAR radiation 

flux (kW m-2) to PAR photon flux density (umol m-2 sec-1) (assigned as 4.55 in 

FireBGCv2).

These conductances are computed for both sunlit and shaded leaves using a 

variation of the rPPFD scalar where the proportion of photon flux density to the 

shaded leaves (rSHADE) is computed from the equation:

rSHADE = 1 - e-kPLAI (35)

where k is the extinction coefficient and PLAI is the projected leaf area index (m

2

m-2). So, the proportion of flux density to sunlit leaves is 1 - rSHADE. The pro-

portion of sunlit leaves in the canopy is calculated from the ratio of sun to shade 

leaves, as specified in the Species.in file according to species.

Each day, the program computes three leaf area measures at the stand level 

that are used throughout the program and described in the “Canopy Dynamics” 

section: LAI (all-sided leaf area index), and the PLAI (projected leaf area in-

dex) of the sunlit and shaded leaves. The program calculates the PLAI of sunlit 

leaves (PLAISUN) by multiplying PLAI with (1-e-PLAI), and then PLAISHADE 

(shaded projected leaf area index) is PLAI - PLAISUN. These three major canopy 

state variables change throughout the year as phenological events occur to both 

the undergrowth and trees.

Stand albedo (ALBEDO) is simulated at the stand level to account for the role 

of vegetation in light dynamics. Each day, the program evaluates stand albedo by 

first assigning a default albedo to the stand, as taken from an input in the Site.in 

file. However, this default pertains only to evergreen coniferous canopies, and the 

program changes albedo to 0.18 if the lifeform of the stand is deciduous, 0.2 if 

the stand is shrub-dominated, and 0.25 if the stand is grass-dominated. Next, the 

program checks to see if the stand has a snowpack (SnowW > 0.05 kgW m-2); if 

there is a snowpack, a second snow albedo (SALBEDO) is computed from the 

following equation:

SALBEDO = 0.88 - (SDECAY)(STDA) (36)

where SDECAY is the snow albedo decay coefficient assigned as 0.004 in 

FireBGCv2 (White and others 1998), and STDA is the snowpack temperature deficit (oC) that is calculated as the summation of daytime temperature (Tday) when 

there is snow on the ground (minimum value of -5 

oC). Then, if the projected leaf 

area index (PLAI) is below 1.0 m

2 m-2, albedo is assigned the SALBEDO value; 

but if PLAI is greater than 1.0, then ALBEDO is calculated as the average of the 

default albedo and SALBEDO.
USDA Forest Service RMRS-GTR-255. 2011. 41

Three estimates of conductance (g) are computed within the mechanistic gap 

module of FireBGCv2. The leaf-level conductance (gl) is computed as the g value in the previous equations. The leaf-level conductance to sensible heat (glsh) 

is computed as the product of the boundary-level conductance (assigned in the 

Species.in file) and the correction factor (1 / rc ), as computed in the previous 

equations. The canopy-level conductance to sensible heat (gcsh) is computed by 

multiplying glsh with PLAI.

There are a number of biophysical variables computed in FireBGCv2 at the 

stand level that depend on some form of the Penmon-Monteith equation (Monteith 

1973) calculated at a daily time step:

[ ( )]

[ ]

ET

SRAD ( )

r b

a c r

r

c p VPD

hr

p p v

hr

p a

D

D = ++

m

(37)

where ET is evapotranspiration (kgW m-2), cp is specific heat of air (1010.0 J kgW 

K-1), pa is air density estimated from pa = 1.292 - ( 0.00428Tday), λ is the latent 

heat of vaporization estimated by λ = 2.5023x10-6 - ( 2430.54Tday), b is a unitless 

ratio of molecular weights (0.6219) for water in the atmosphere, and Δ is the slope 

of the vapor pressure deficit curve estimated from the following relationship:

( )

( . .

T T

610 78 610 78

1 2

e e

. .

T

T

T

T

239

17 38

239

17 38

1

1

2

2

D = -

[ ] + + - [ ]

(38)

where T1 = Tday + 0.2 and T2 = Tday - 0.2. The term ap (Pa) is atmospheric pressure estimated from:

[ ]

p LR( / RR MA)

GS

a = - 101325[ ( 1 0. / 0065ELEV 288. ) 15 ] (39)

where ELEV is the stand elevation (m), GS is standard gravitational acceleration 

(9.80665 m sec-1), LR is standard temperature lapse rate (0.0065 –K m-1), RR is 

the gas law constant (8.3143 m3 Pa mol-1 K-1), and MA is the molecular weight of 

air (kg mol-1) (Iribane and Godson 1981, Thornton and Running 1999).

The resistance terms in this equation are:

( ) r r

r r

rhr

r h

r h = + (40)

where rr is estimated from the Stefan-Boltzmann constant ν (5.67x10-8, W [m2

oK4]-1) and Tk (Tday + 273, oK) using the following relationship:

( ) r T

p c

4 r

k

a p = y 3 (41)

In calculating potential evapotranspiration (PET, kgW m-2), the terms rh (resistance to sensible heat, m-1) and rv (resistance to latent heat, m-1) are set equal to 

boundary layer resistance (rb sec m-1) as estimated from the following equation:

rb = 107rc (42)

In calculating actual evapotranspiration (AET, kgW m-2), the terms rh and rv

must be estimated from the dynamic simulation of water flux from precipitation to 

the soil profile and then to the plants in the stand. The resistances (r) are converted 

to conductances (g) in the final output for ease of computation by simply taking 

the inverse (g = 1 / r).

To calculate bare soil evaporation (EVAP, kgW m-2), the PET is calculated, and 

then reduced based on precipitation and days since precipitation. If the effective 

precipitation (EPPT, kgW m-2) is greater than PET, then EVAP is set at 60 percent 
42 USDA Forest Service RMRS-GTR-255. 2011.

of PET. If EPPT is less than PET, then the days since last rainfall (DSR) is used to 

calculate an alternative ratio:

. ER , ( )( ) DSR then EVAP ER PET 0 3 = = 2 (43)

where ER is the evaporation ratio (dimensionless) if there is no significant rainfall.

Radiation computations are quite important in FireBGCv2, and they are calculated from the net daily canopy shortwave radiation (SRAD, W m-2 day-1) that 

is input to FireBGCv2 and is often derived using the MT-CLIM program. A daily 

estimate of photosynthetically active radiation (PAR) is calculated by multiplying 

SRAD by 0.45. Next, the shortwave reflected radiation (SWREF) is computed by 

multiplying SRAD by stand albedo (ALBEDO). Stand shortwave radiation (SW) 

is calculated as the difference between SRAD and SWREF, and the absorbed 

shortwave radiation (SWABS) is calculated from SW using the following:

swabs = sw(1 - e-kPLAI) (44)

where k is the extinction coefficient estimated each year using the weighted average 

of leaf area across all species’ extinction coefficients (see the “Canopy Dynamics” 

section), and PLAI is the projected leaf area index (m2 m-2). Transmitted shortwave radiation (SWTRANS) is the difference between SW and SWABS (SW 

- SWABS).

Snowfall dynamics are computed from algorithms taken directly from 

BIOME-BGC (Thornton 1998) and FIRE-BGC (Keane and others 1996b), where 

precipitation that falls when daytime temperature (Tday) is below freezing (0 oC) 

is considered snow. Snow melts as a function of temperature, radiation, and vapor 

pressure deficit. Snowpack (SnowW) is reduced by five factors (temperature, condensation, rain, radiation, and sublimation) when Tave is above freezing using the 

following empirical functions found in Thornton (1998). Temperature-induced 

snowmelt (TMELT, kgW m-2) is computed by multiplying the daily average 

temperature (Tave) by a snowmelt coefficient (0.65 kgW m-2 oC-1 day-1). The condensation melt (CMELT) is computed from the following equation:

cmelt = 0.0000078(230) ß [(0.42Tday) - (1.51Tmin)] (45)

where ß is the conversion from cm to kgW and Tmin is minimum daily temperature 

(

oC). Snowmelt from rainfall (RMELT, kgW) is computed from the following:

RMELT = 0.00012 ß EPPT(Tday) (46)

And the melt from the radiation load (RADMELT, kgW) is computed from SRAD 

/ LHFUS where LHFUS is the latent heat of fusion (335 kj kgW-1). Sublimation 

only occurs when Tave is below freezing and it is estimated the same way as 

RADMELT. These losses are summed and the total loss is taken from the snowpack (SnowW).

Autotrophic respiration is also simulated at the daily time step using identical 

simulation methods as in BGC. Respiration (kgC) is computed using the general 

formula:

MR = A1(Q10 A2) (47)

where MR is maintenance respiration (kgC), Q10 is set at 2.0, and coefficients A1 

and A2 are calculated based on the tree or stand carbon compartment and time of 

day. For leaf daytime respiration, the following equations are used for A1 and A2:
USDA Forest Service RMRS-GTR-255. 2011. 43

A ( ) MRP LeafCN

LeafC 1 = (48)

( ) A

T

2 10

day 20 = - (49)

where MRP is a maintenance respiration coefficient that accounts for carbon and 

nitrogen (0.218 kgC kgN-1 day-1), LeafC is the mass of carbon in leaves, Tday is 

the daytime temperature (oC), and LeafCN is the leaf carbon to nitrogen ratio, as 

specified in the Species.in file. Nighttime leaf respiration is calculated by substituting Tnight for Tday to calculate A2, while fine and coarse root respiration are 

calculated using Tsoil, the fine and coarse root carbon:nitrogen ratio (FrootCN and 

CrootCN), and the amount of fine and coarse root carbon (FrootC and CrootC). 

Live stem carbon respiration is computed using Tave, StemCN, and LivestemC. 

Daytime leaf respiration is divided into sun and shade leaves and then converted 

to a rate per second using daylength for input into the photosynthesis computation.

Gross canopy photosynthesis (PSN, kg CO2 day-1) is computed from the 

Farquhar and others (1980) algorithm, as implemented in the BGC module. This 

routine is quite complicated and is discussed in detail in von Caemmerer (2000). 

This routine uses the following input variables that are computed daily for both 

sunlit and shaded leaves:

• Tday (oC). Daytime temperature.

• Photosynthetic pathway (C3 or C4). Set as C3 for most plants in FireBGCv2, 

but this will be dynamic in future versions.

• ACO2 (ppm). Concentration of carbon dioxide in the atmosphere, as specified 

in the Climate.in file and modified in the weather routines.

• LNC (kgN m-2). Leaf nitrogen concentration per unit projected leaf area is 

calculated as 1 / [(PSLA)(LeafCN)] where PSLA is the projected specific leaf 

area.

• FLNR (proportion). Fraction of leafN in Rubisco (input in Species.in file).

• PPFD (umol m-2 sec-1). Photon flux density of sun/shade leaves, as previously 

computed.

• DLMR. Daytime respiration of sun/shade leaves per projected leaf area.

•	 gl (sec-1). Leaf conductance, as previously computed.

The Farquhar algorithm outputs a photosynthetic flux density per unit leaf area 

that is then converted to kgC day-1 based on daylength (sec) and leaf area index.

BGC Simulation

As mentioned earlier, all BGC algorithms are discussed in other documents. 

However, there are some important BGC-based simulations, performed at the 

stand level and used to allocate carbon from the stand level to the tree level, that 

merit additional description. FireBGCv2 uses BGC algorithms to estimate daily 

respiration (MR, kgC) and photosynthesis (PSN, kgC). The PSN and MR variables are then summed for each tree across all days in the year and the radiation 

input is adjusted by multiplying SRAD by the proportion of available light at the 

top of the canopy (AL), as described in the “Canopy Dynamics” section. Next, 

all stand-level BGC input parameters are replaced by the species-level parameters 

defined in the FireBGCv2 Species.in input file. Radiation transfer, as summarized 

in the “Mechanistic Gap Biophysical Simulation” and “Weather” sections, is then 

recomputed for each tree using tree-level parameters and available light at the top 

of each tree or undergrowth guild.
44 USDA Forest Service RMRS-GTR-255. 2011.

Undergrowth Dynamics

Most big-leaf models, such as FOREST-BGC, and some gap models, such as 

JABOWA (Botkin 1993), ignore undergrowth growth dynamics and interactions 

with other ecosystem processes. However, undergrowth dynamics are important 

in many ecosystems, especially rangeland, alpine, and early successional communities (Kelliher and others 1986). Undergrowth plants contain important carbon 

stocks, contribute to ecosystem productivity, and comprise the majority of fine 

fuels for most fuel beds. Their influence on tree regeneration and light distribution is included in FireBGCv2, along with their interactions with water, light, fire 

ignition, and fire spread. As mentioned in the “Undergrowth Dynamics” section in 

“Model Design,” the undergrowth is modeled in FireBGCv2 using suites of species called plant guilds, which are individually parameterized using plant models 

that are then assigned to sites. Guilds are analogous to plant functional types that 

are collections of species grouped by their functional roles in stand dynamics. 

The design and number of plant guilds in a simulation depends on the modeling 

objective and available data.

Growth in biomass of the undergrowth guild compartments (Figure 8) is 

computed using a deterministic method that estimates the maximum growth in 

biomass (kgB) of individual undergrowth compartment (UBIOMASS) and then 

reduces this maximum by reduction factors (Keane and others 1990c, Kercher and 

Axelrod 1984). The computation of the growth (UGROWTH, kgB m-2) uses the 

following equation:

UGROWTH = (rSHADE)(n)(UBIOMASSt-1)( ) UBIOMASS

UBIOMASS 1 1

max

t - - (50)

where UBIOMASSt-1 is the previous year’s biomass of the undergrowth compartment (kgB m-2), n is a growth constant for the undergrowth compartment 

(year-1), and UBIOMASSmax is the maximum attainable biomass for the compartment (kgB m-2). In previous studies, values for ni

 were estimated at 1.441 year-1

for shrubs (Keane and others 1996b) and 10.842 year-1 for herbaceous fuel from 

field data. UBIOMASSmax values can be estimated from field studies as the maximum loading of a suite of species across all field plots used in parameterization 

(specified in Plant.in file). The light reduction scalar rSHADE (dimensionless) is 

calculated from a set of relationships based on shade tolerance of the undergrowth 

component, as specified by species in the Plant.in input file and taken from the 

Species.in file. This scalar uses the shade tolerance index of a species to key to 

one of these equations:

rSHADE = e-1.2plai for very shade intolerant species (index = 1) (51)

rSHADE = e-0.4plai for shade intolerant species (index = 2) (52)

rSHADE = e-0.25plai for moderately shade tolerant species (index = 3) (53)

rSHADE = e-0.15plai for shade tolerant species (index = 4) (54)

rSHADE = e-0.05plai for very shade tolerant species (index = 5) (55)

where PLAI is stand projected leaf area index (m2 m-2) for all trees and undergrowth 

above the undergrowth component (Figure 6). The new growth in UGROWTH 

(kgB m-2) is added onto last year’s biomass (UGROWTH + UBIOMASS) to get 

the current year’s biomass estimates. This growth can be negative or positive and 

only occurs during the growing season.

The change in an undergrowth guild’s height (UHT, m) is simulated using 

the same relationships as above, except the height parameters are substituted for 

the biomass parameters. The maximum height (UHTmax) is taken from the plant 
USDA Forest Service RMRS-GTR-255. 2011. 45

model assigned to that site. The growth rate (n) and the rSHADE are the same as 

above. This height can slide up or down depending on the amount of light at the 

top of the undergrowth canopy.

Phenology, and its influence on component biomass throughout the growing 

season and remaining part of the year, is a final key component of the simulation 

of understory dynamics. Undergrowth phenology is simulated at the site level using the value for the current year UBIOMASS (UGROWTH + UBIOMASSt-1) 

as the biomass at the height of the growing season, adjusted by the fraction of 

undergrowth biomass dead at time of fire (FRAC). The FRAC value increases 

biomass during the leaf out phenological stage and reduces biomass during the 

leaf fall stage.

Ground Dynamics

The method used to calculate decomposition dynamics in FireBGCv2 is based 

on the BGC approach where forest floor respiration is influenced by climate, particle size, and vegetation inputs (Figure 3). The original FOREST-BGC and the 

new BGC decomposition routines did not provide the detail needed to simulate 

wildland fire process interactions because compartments representing the forest 

floor were not stratified by fuel size class. Therefore, FireBGCv2 was refined to 

stratify the forest floor into eight compartments to predict fire behavior and effects 

(Figure 4, Table 2).

As previously mentioned, organic matter on the forest floor is represented 

in FireBGCv2 as carbon pools that are contained within eight compartments 

(Table 2). These compartments are quantified by the loading (amount of carbon, 

kgC m-2) by fuel classes on the forest floor. The soil (SoilC, kgC m-2) and duff 

(DuffC, kgC m-2) carbon compartments are typically composed of mostly lignin-based, partially decomposed material from leaf and stem turnover. The leaf 

material falling to the ground (LeaffallC, kgC m-2) and litter (LitterC, kgC m-2) 

compartments contain freshly deposited carbon that is quickly decomposed and 

year-old leaf material that is not lignin, respectively. The four woody compartments (W1C, W10C, W100C, and W1000C; kgC m-2) are defined by the average 

diameter or size of woody particle: twigs of 0 to 1 cm diameter, branches of 1 to 3 

cm diameter, large branchwood of 3 to 7 cm diameter, and logs of 7+ cm diameter, 

respectively. These particle size classes correspond to standard woody fuel size 

classes used to predict fire behavior (1, 10, 100, and 1000 hr timelag woody fuel 

components) (Albini 1976, Fosberg 1970).

Organic Matter Accumulation

Accumulation of organic material on the forest floor is computed at both a 

daily and annual time step from stem, leaf, and root turnover of the simulated 

trees and undergrowth, depending on the succession driver module (simple gap, 

mechanistic gap, and BGC) (Figure 7). Each day, the model computes the amount 

of foliar litterfall based on the daily phenology described in the “Site Processes” 

section. This carbon is deposited into the LeaffallC ground compartment (kgC 

m-2) on a daily level, and at the end of the year, this amount is equal to the standlevel LeafC divided by leaf retention time. Currently, the leaffall from evergreen 

conifer trees is evenly distributed over the entire year with the total amount being 

the total LeafC for the tree divided by the leaf retention time (LSPAN) parameterized by species in the Species.in file. Deciduous tree species lose all of their foliar 

carbon during the leaf fall phenological stage and gain it all back during the leaf 

out stage.

Undergrowth plant contributions to the LeaffallC are computed from biomass 

turnover also estimated at a daily time step from LSPAN (year) for the species 

assigned to the plant model (Plant.in file) and parameterized in the Species.in file. 

Undergrowth LeaffallC is computed from the following:
46 USDA Forest Service RMRS-GTR-255. 2011.

LeaffallC = LeaffallC + [

( )( )( )] LSPAN

UBIOMASS LFRAC FLBC (56)

where UBIOMASS is the biomass of an undergrowth component (kgB m-2), 

LFRAC is the fraction of undergrowth biomass that is leaf (proportion), and 

FLBC is a constant that is the proportion of leaf biomass that is carbon (0.48 in 

FireBGCv2). Some twig, branch, and large branch wood are added to the woody 

carbon compartment at year’s end:

W1C = W1Ct-1[(UBIOMASS)(1-LFRAC)(DWTF)] (57)

where DWTF is the deadwood turnover fraction specified according to species in 

the Species.in file and referenced in the plant model file (Plant.in).

Decomposition

The decomposition routine detailed here only applies to the mechanistic gap 

model. If the BGC vegetation model is selected, the decomposition is done exactly 

the same as that described for BIOME-BGC by Thornton (1998). Decomposition 

for the simple gap model uses the following routines but at an annual, rather than 

a daily, time step, meaning that all weather parameters below are computed as 

averages across the year.

Decomposition from forest floor compartment carbon pools (Figure 3) is calculated as a function of the soil water and temperature. Each day, two scalars are 

calculated from the weather data and algorithms previously presented:

[

[ ]

ln

ln

rSOILW

max

min

min

=

}

}

}

}

(58)

308.56[ 71.02

1

( 273.15) 227.13

1 ] Tsoil - rSOILT = e + - b l (59)

where rSOILW and rSOILT are the soil water and temperature scalars; Ψmin and 

Ψmax are the minimum and maximum soil water potentials for opening and closing stomata, as specified per species in the Species.in file and summarized for the 

canopy (see the “Canopy Dynamics” section); Ψ is the soil water potential (MPa) 

for that day; and Tsoil is the soil temperature for that day. Both scalars are bounded 

between 0.0 and 1.0.

There are several fuel fragmentation factors built into FireBGCv2 that are 

based on the algorithms in BGC (Thornton and others 2002). A static base fragmentation factor (kFRAG, assumed to be 0.001) represents the physical breakage 

of woody fuels due to weathering and soil macrobiotic activity. There are also 

static scalars to account for the size of the fuel particles: sW1 (assigned a value of 

0.7 in FireBGCv2), sW10 (assigned 0.5), sW100 (assigned 0.2), and sW1000 (assigned 0.01). Computation of all decomposition fluxes involves the multiplication 

of the soil scalars (rSOILW and rSOILT), the base fragmentation (kFRAG), and 

size scalars ( sW1 to sW1000) with the amount of carbon in the ground component pools. For example, decomposition for twigs (W1C, kgC m-2) is calculated 

as:

W1Ct

 = W1Ct-1 -W1Ct-1 (rSOILW)(rSOILT)(kFRAG)(sW1)( ) TIME

1 60)

where t is time (day or year) and TIME is the number of time steps in the year 

(365 for a daily time step).

Additions to the DuffC (kgC m-2) ground component are computed by multiplying carbon loadings of the W1C, W10C, W100C, and W1000C by the four 

factors each day. The formula for losses to DuffC from the W1C pool is:
USDA Forest Service RMRS-GTR-255. 2011. 47

DuffCt = DuffCt-1 + (W1C)(kFRAG)(sW1)(rSOILW)(rSOILT) (61)

This formula is repeated for each woody fuel ground component, substituting the 

appropriate size scalars and fuel component carbon loadings.

There are also losses from the woody fuel components to the soil carbon pool 

(SoilC, kgC m-2). These are calculated using the four scalars and an additional 

scalar that is stratified by the proportion of the carbon that is cellulose versus 

lignin. The formula for twigs (W1C) is:

SoilCt = SoilCt-1 + [(KL2)(PCELL)(W1C) +

(LK4)(PLIG)(W1C)](kFRAG)(sW1)(rSOILW)(rSOILT) (62)

where KL2 and KL4 are decomposition constants for the cellulose and lignin proportions of the wood (assigned 0.07 and 0.014, respectively, from Thornton and 

White [1996]), and PCELL and PLIG are the proportion of the wood that is cellulose and lignin, respectively, as specified in the Species.in file and summarized 

to the stand level using methods in the “Canopy Dynamics” section.

Fresh litterfall (LeaffallC, kgC m-2) is transferred to the litter component 

(LitterC, kgC m-2) based on the two soil scalars:

LitterCt = LitterCt-1 + [(rSOILT)(rSOILW)(LeaffallC)] (63)

where t-1 indicates the LitterC loading the previous day. Any material from the 

litter component is transferred to the duff (DuffC) based on the BGC decomposition scalar KL2:

DuffCt = DuffCt-1 + [(KL2)(rSOILT)(rSOILW)(LitterC)] (64)

Material from the duff is lost to the soil carbon (SoilC, kgC m-2) based on 

another decomposition factor (KS3 = 0.0014):

SoilCt = SoilCt-1 + [(KS3)(rSOILT)(rSOILW)(DuffC)] (65)

These fluxes are summed each day to compute carbon losses from 

decomposition.

Nitrogen Cycling

There is no detailed nitrogen cycling for the simple and mechanistic gap models. In the BGC simulation, nitrogen is released from the forest floor compartments 

as a function of the fraction of carbon decomposed and a decomposition release 

factor (Running and Gower 1991). The nitrogen released by the decomposition 

process goes directly into the available nitrogen pool (AvailN, kgN m-2) except 

for a portion that is lost from the simulation plot (Fahey 1983). The mobile nitrogen retention time represents the rate of leaching, volatilization, or some other 

loss of nitrogen from the system (Aber and others 1978; Aber and others 1991; 

Berg and Ekbohm 1993). Volatilization from fire is the only other means by which 

nitrogen can be lost from the stand (see the “Wildland Fire” section). The only external sources of nitrogen input are atmospheric deposition and biological fixation 

by plants or microbes, which are specified in the Climate.in file. Both parameters 

are assumed to be constants across simulation time.

Fuel Moisture

Fuel moisture for the downed woody fine fuel ground compartment (W1C) 

is simulated in FireBGCv2 using the same routines that are implemented in 

the FARSITE program (Finney 1996). In those routines, the wind (specified as 

a constant in the Site.in file), temperature, precipitation, relative humidity, and 
48 USDA Forest Service RMRS-GTR-255. 2011.

at-the-ground radiation (attenuated through the canopy and adjusted for solar azi-

muth, zenith, and declination) are used to drive an equilibrium wetting and drying 

curve that is then used to compute an equilibrium fuel moisture content. Moisture 

content of the 1 hr woody fuels are also assigned to the litter fuels (LitterC).

Fuel moistures of the coarse woody fuel components (W10C, W100C, and 

W1000C) are computed using the algorithms from the National Fire Danger 

Rating System (NFDRS) (Deeming and others 1977). In NFDRS methods, 

temperature, humidity, and precipitation are used to compute equilibrium fuel 

moisture content (EMC). The EMC is then used to derive a moisture value for 

the W10C fuels based on empirical relationships. The W100C moisture content is 

based on the EMC and the amount and duration of precipitation. The log moisture 

content (W1000C) is estimated from a seven-day running average and the EMC. 

We assigned the log moisture content to the duff fuels (DuffC) for lack of a better 

method.

Live fuel moisture contents (MCfoliar) of the trees and undergrowth are simu-

lated in FireBGCv2 from the soil water potential and two species-level parameters 

that are summarized for the canopy (Keane and others 2010). This algorithm scales 

foliar moisture content between a minimum and maximum using this relationship:

MC FMmax ( ) FMmax FMmin ( ) foliar close open

close = - - } }

} } --

:

D (66)

where FMmax and FMmin are the minimum and maximum foliar moisture con-

tents (percent) for the landscape (hardwired in the model as 200 percent and 90 

percent, respectively), Ψclose and Ψopen are the pre-dawn leaf water potentials for 

stomata closing and opening (MPa) (specified in the Species.in file and summa-

rized for the canopy using a leaf area weighted average), and Ψ is the current soil 

water potential (MPa).

Canopy Dynamics

A major limitation of the big-leaf FOREST-BGC model is that species and 

canopy dynamics are not explicitly recognized in the model design (Running and 

Coughlan 1988, Friend and others 1997). Stand-level bioenvironmental processes 

are greatly influenced by canopy dynamics brought about by the changes in spe-

cies composition and structure resulting from succession (Busing 1991, Dalla-Tea 

and Jokela 1991, Gilmore and others 1995, Choi and others 2001). FireBGCv2 

improves the simulation of canopy dynamics over FIRE-BGC, FIRESUM, and 

FOREST-BGC by explicitly recognizing the vertical leaf area and available light 

distributions within the stand’s canopy above the ground on the simulation plot. 

This allows a more accurate characterization of forest canopy influences on the 

coarser and finer scale processes simulated by FireBGCv2 such as radiation, wa-

ter, and carbon fluxes.

The forest canopy in FireBGCv2 is represented by vertical layers above the 

ground on the simulation plot (Figure 11). FireBGCv2 “slices” the area above 

the simulation plot into vertically arranged canopy layers. The thickness of each 

canopy layer (HSIZE, m) and the number of layers (MXHGT) are defined in the 

Sim.in file. Three attributes are computed for each canopy layer—leaf area (LA, m2), projected leaf area (PLA, m2), and available light (AL, index 0 to 1). All canopy-related parameters used in the BGC routines are computed from a weighted 

average of available light and leaf area by species. As an example, the stand-level 

BGC input parameter of maximum canopy conductance (gmax) is computed by 

estimating LA for all species on the plot and then calculating a weighted average 

of gmax using the summed LA.

Stand leaf area (LA, m

2) and projected leaf area (PLA, m2) are summed with

-

in and across all vertical canopy layers using the distribution of crown biomass 

between the height (HT, m) and base of live crown (HBC, m) for each tree on 

the simulation plot. Canopy layer leaf area is then computed by multiplying the 
USDA Forest Service RMRS-GTR-255. 2011. 49

proportion of the tree crown within a canopy layer (that is, canopy layer width 

[HSIZE, m] divided by the crown length [CL, m]) by the total leaf area of the 

tree. This assumes that leaf area is evenly distributed throughout the crown and 

that each tree crown is cylindrical (Figure 11) (Keane and others 1989, Friend and 

others 1993). Crown length (CL, m) is obtained by subtracting HBC from the tree 

height (HT, m). This procedure is repeated for every canopy layer and every tree 

in the stand. Projected leaf area by canopy layer is computed by dividing leaf area 

by a weighted PLAI that is calculated by converting from an all-sided to projected 

leaf area using the factor APPF that is specified in the Species.in file.

The contribution of shrub and herb foliage to canopy layer leaf area is also accounted for in FireBGCv2. The leaf areas of all undergrowth components (ULA, 

m2 m-2) are calculated from the following:

ULA = (UBIOMASS)(FLBC)(ULF)(USLA) (67)

where UBIOMASS is the biomass of the undergrowth component at the height 

of growing season, FLBC is the fraction of that biomass that is carbon (0.48 in 

FireBGCv2), ULF is the proportion of that biomass that is leaf material (input parameter specified in the Plant.in file), and USLA is the undergrowth specific leaf 

area (m2 kgC-1, also specified in Plant.in and referenced in the Species.in file). 

Shrub and herb leaf area distribution by canopy layer is computed by multiplying 

the proportion of the shrub or herb height (UHT, m) in a canopy layer (HSIZE / 

UHT) by the respective leaf areas. If the canopy layer width (HSIZE) is greater 

than an undergrowth compartment height (UHT), then all leaf area is allocated 

to the bottom canopy layer. Shrub and herb crown shape is again assumed to be 

cylindrical, and it is also assumed that the crown encompasses the entire height of 

the undergrowth compartment (Keane and others 1989). The vertical distribution 

of projected leaf area (UPLA) is also computed by multiplying the ULA by the 

APPF for that undergrowth reference species, as specified in the Plant.in file and 

taken from the Species.in file. The projected leaf area for trees and undergrowth 

are summed and this final leaf area (PLA) is stored for reference during the simulation year.

Canopy available light is calculated by first computing the cumulative projected leaf area (CPLA, m2) for each canopy layer summing from the highest 

layer to the bottom (ground) layer. The proportion of available light in each layer 

is then estimated from this cumulative leaf area profile using the Beers-Lambert 

equation:

Figure 11. Representation of the forest canopy in FireBGCv2. The model (A) assumes even spatial distribution of trees across 

the simulation plot, then (B) slices the canopy into vertical layers of a user-specified thickness, and then (C) sums canopy 

leaf material into the layers using a cylindrical crown calculation.
50 USDA Forest Service RMRS-GTR-255. 2011.

[ ] PAREA

CPLA

-a AL = e (68)

where AL is the proportion of available light in a canopy layer, CPLA is the cumu-

lative projected leaf area (m

2) for a canopy layer, PAREA (m2) is the area of the 

simulation plot, and α is the extinction coefficient of the canopy, which is comput-

ed as a leaf area weighted average for all species’ extinction coefficients (specified 

in the Species.in file) for the canopy. Values of α in the FireBGCv2 model assume 

an average over all solar zenith angles and vegetation foliar tilt angles. The AL 

fractions for each canopy layer are stored for later reference.

Several other canopy-related values are also computed at the beginning of the 

simulation year. The canopy cover (CC, m

2) is computed by estimating the crown 

width of each tree using the empirical equations in Moeur (1981), then calculating 

a projected canopy cover by using the crown width as the radius of a circle. The 

total area of all tree crown circles are summed to approximate CC. This is divided 

by the simulation plot area (PAREA) and multiplied by 100 to get percent canopy 

cover (PCC, percent). This is estimated at the plot and species level. Canopy clo-

sure is approximated when PCC is greater than 100.

The canopy layer attributes of CPLA and AL are used throughout the model to 

simulate a variety of ecosystem processes. The relative amount of available light 

(AL) is used in the species regeneration, tree growth, and tree carbon allocation. 

Canopy closure is used as an independent variable in many of the empirical equa-

tions that approximate tree attributes, such as crown weight and tree height.

Wildland Fire

Fire behavior is computed at the stand level using a wide variety of meth-

ods. The fuelbed simulated at the stand level must be summarized from the 

stand ground and undergrowth components and then passed to the fire behavior 

model(s). Fire behavior is then estimated at the stand level. The direct effects of 

wildfire on ecosystem components and processes are computed in FireBGCv2 as 

a general cycling of carbon to and from dead and live carbon pools. Carbon and 

nitrogen losses are computed from the amount of forest floor biomass oxidized 

by the fire (fuel consumption). Most fuels are not completely consumed by a fire, 

and the level of consumption is simulated using the FOFEM model (Reinhardt 

and others 1997). Forest floor carbon that is consumed by a fire from any fuel 

compartment is assumed to be lost to the simulation plot. Tree mortality from 

fire is computed from the amount of crown scorched and size of the tree. Dead 

tree carbon and nitrogen not consumed by the fire are added to the forest floor 

compartments.

Fuel Summary

Prior to the calculation of fire behavior, the stand ground and undergrowth 

components must be summarized into fuel components for input into the fire be-

havior models. Live and dead shrub and herb component loadings are summed 

from the simulation plot’s guilds (see “Undergrowth Dynamics” in the “Stand 

Processes” section) using the following relationships:

( ) SHRUB PAREAUBIOMASS FDEAD

dead

R s s

= (69)

( ) HERB PAREAUBIOMASS FDEAD

dead

R h h

= (70)

where SHRUBdead and HERBdead (kgB m-2) are the dead shrub and herb fuel 

component loadings; UBIOMASS is a summed biomass of all undergrowth com-

ponents that are shrubs (s) or herbs (h) as specified by the lifeform for that species 

in the Plant.in file; PAREA (m

2) is the area of the simulation plot; and FDEAD is 

the fraction of the appropriate shrub or herb compartment that is dead at the time 
USDA Forest Service RMRS-GTR-255. 2011. 51

of the fire, as specified in the Plant.in file. Values of FDEAD were taken from 

Brown and Bevins (1986). The remaining shrub and herb biomasses are considered live (fraction not dead, or 1 - FDEAD) and are assigned to the live shrub and 

herb fuel components.

The stand ground components are already stratified by the fuel components 

used in the fire behavior models, except for LitterC and LeaffallC, which are 

added to the 1 hr woody fuel loadings. All loadings are converted to biomass by 

dividing by the fraction of biomass that is carbon for wood (FRAC_STEM_C_

BIOMASS = 0.48). All other fuel characteristics, besides loading, are taken from 

the fuel model for that stand and site, as specified in the Fuel.in file.

Fire Behavior

In FIRE-BGC, wildland fire was dynamically modeled on the simulation landscape using the FARSITE spatial fire model (Finney 1996). This model predicts 

the fire intensity and rate of spread of a fire as it moves across a landscape using the Rothermel (1972) spread model implemented into a spatial domain using 

Huygen’s principles. The spatial data layers of topography, vegetation, weather, 

and fuels are used to predict fire behavior, and this behavior is fed back to the 

vegetation to compute consumption, mortality, and smoke. However, the highly 

complex FARSITE model was too computationally intensive for most computers, 

so Keane and others (1996b) executed it outside of the FIRE-BGC program under 

the Loki platform. This proved problematic for the FireBGCv2 revision for two 

reasons: (1) the simulation of fire using FARSITE required extensive computer resources for a 1000-year simulation (there might have been 20,000 to 50,000 fires 

simulated during this time period), and (2) there were no FARSITE libraries to 

include in FireBGCv2. A new version of FARSITE with a simplified computation 

of fire spread is being developed and it will soon be included in the FireBGCv2 

program. Until that time, FireBGCv2 allows fire behavior to be computed at the 

stand level from either the Rothermel (1972) spread model as implemented in the 

FireLib library (Bevins 1996) or the Albini (1976) FIREMOD model.

The Rothermel (1972) model requires that the surface fuels information be 

keyed to a surface fire behavior fuel model to compute a realistic fire behavior. 

To do this, we used the key in the FVS-FFE model to key the fire behavior fuel 

model from the loadings of the stand ground components (Beukema and others 1997). This represents a significant loss in resolution of fuel loadings across 

stands, and, therefore, tends to homogenize fire behavior predictions across the 

simulated landscape, especially in long fire return interval ecosystems. We found 

that more realistic fire behaviors were simulated when the simulated fuel loadings were input to the Albini (1976) model, especially when differences between 

fuelbeds were small. We simulated running crown fire using the Rothermel (1991) 

algorithms, but we also simulated passive crown fire if the flame length (see below) was greater than height to the bottom of the live crown for a tree (HBC, m).

Fuel loading is summarized for the stand using the procedures in the “Fuel 

Summary” section. Fuel moistures are taken from the dynamic fuel moisture 

simulations described in the “Fuel Moisture” section. These data are then fed to 

the selected fire behavior model. The model (either Rothermel’s or Albini’s) then 

computes several fire behavior characteristics that are saved and used to compute 

fire effects:

• Flame length (FL, m).

• Spread rate (SR, m sec-1).

• Fireline intensity (FLI, kW m-1 fireline).

• Scorch height (SH, m).

• Crown fire index (CFI).

There are a number of caveats that are associated with this method of fire 

behavior prediction. First, only headfires are simulated; there is no simulation of 
52 USDA Forest Service RMRS-GTR-255. 2011.

backing or flanking fires. Second, both fire models tend to perform inconsistently 

when actual (simulated) fuel loadings are used. Next, fuel moisture estimates 

are somewhat coarse because of the resolution of the NFDRS algorithms. Future 

modifications will include the addition of new fuel moisture models if optimized 

for a simulation platform.

Fuel Consumption

The reduction of the forest floor compartments by fire is simulated by the 

process of consumption (Figure 12), which can be computed using one of two 

methods. The first method uses static consumption estimates by fuel component, 

as specified in the Fuel.in for the fuel model assigned to that site as follows:

ConsumeCi = CFi(GCi) (71)

where ConsumeC is the amount of carbon consumed for stand ground component 

i, CF is the consumption fraction of ground component i, and GC is the amount of 

carbon in ground component i (ground components are DuffC, LitterC, W1C, and 

so on). Under model assumptions, no soil carbon (SoilC, kg C) is consumed. It is 

important to note that while this method is quick, it is not integrated with climate, 

vegetation, and fire behavior.

The second and preferred method of computing fuel consumption is with the 

embedded FOFEM model (Reinhardt and others 1997). The FOFEM consumption 

routines were integrated into the FireBGCv2 code using dynamic link libraries, 

and the state variables in FireBGCv2 were linked to the input fields in FOFEM

to simulate fuel consumption using the BURNUP model that is integrated in the 

FOFEM code (Albini and others 1995, Albini and Reinhardt 1995). Consumption 

of all FireBGCv2 litter and woody fuel components is computed using BURNUP

algorithms, while duff consumption is computed with the FOFEM equations.

Figure 12. Simplified diagram that shows fire effects of fuel consumption on stand forest floor components.
USDA Forest Service RMRS-GTR-255. 2011. 53

Carbon and Nitrogen Cycling

All consumed fuel is removed from the ground components to update the 

stand-level carbon pools (Figure 12). The cycling of nitrogen after fire is simulated in the same manner as carbon cycling, except that some nitrogen from the 

consumed nitrogen pools goes to the available nitrogen pool (Grier 1975, Pehl 

and others 1986, Schoch and Binkley 1986). First, the amount of nitrogen in each 

ground component is calculated from the carbon:nitrogen ratios entered in the 

Species.in file and summarized for the canopy (see the “Canopy Dynamics” section). Fuel consumption fractions computed from FOFEM or input as CF in the 

Fuel.in file are multiplied by the computed nitrogen amounts for each ground 

component to obtain the amount of nitrogen lost from those compartments. These 

estimates are then multiplied by a volatilization fraction (kgN kgN-1) to obtain the 

actual loss of nitrogen from the site. Nitrogen not volatilized becomes available 

for plant growth and is added to the available nitrogen pool (AVAILn, kgN). The 

volatilization fraction is specified per site in the Site.in file and its values can be 

taken from Klemmedson and others (1962), Covington and Sackett (1984), Ryan 

and Covington (1986), Little and Ohmann (1988), Kutiel and Shaviv (1992), and 

Groeschl and others (1993). Nitrogen losses and contributions to the available 

nitrogen pool from the undergrowth biomass pools are also computed using the 

same method.

Smoke

Smoke is simulated in FireBGCv2 by multiplying the consumption of the 

fuels by specific emission factors (Figure 12). Unlike the previous version (FIREBGC), which had smoke emission factors as parameters in the fuel models (Fuel.

in input file), FireBGCv2 emission factors are hardwired in the code. The equation for predicting smoke is:

SMOKEi = (CONSUMElEFli) + (CONSUMEdEFdi) (72)

where SMOKE is the amount of smoke emissions for i element (kg), 

CONSUMEl and CONSUMEd are live and dead fuel consumption estimates 

(kg), and EF represents the live (l) and dead (d) emission factors for i element 

(kg consumed kg element-1) (Table 4). This smoke is multiplied by the stand 

area to get a total amount of smoke for the stand. There are five smoke elements 

in FireBGCv2: PM2.5 (particular matter below 2.5 microns), PM10 (PM below 

10 microns), methane, carbon monoxide, and carbon dioxide (Table 4). The 

program stores and sums all smoke emissions and it outputs results in kg for the 

entire landscape.

Table 4. Emission factors for the five smoke elements. PM2.5 is all 

particulate matter less than 2.5 microns in diameter, PM10 is 

all particulate matter less than 10 microns in diameter, CH4 is 

methane, CO is carbon monoxide, and CO2 is carbon dioxide. 

Emission factors (kg smoke kg-1 consumed)

Smoke elements Dead fuel Live fuel

PM2.5 22.64 22.64

PM10 26.71 26.71

CH4 13.76 13.76

CO 301.72 301.72

CO2 1228.1 1228.1
54 USDA Forest Service RMRS-GTR-255. 2011.

Management Actions

As mentioned in the “Landscape Processes” section, three management actions 

are implemented in FireBGCv2–clearcutting, partial cutting (thinning, selection, 

and seed tree, for example), and prescribed burning. Both the clearcut and partialcut actions allow prescribed burning as another post-cutting treatment. The details 

of the management actions are specified in the Manage.in file. The landscape-lev-

el information is used to determine whether a treatment should be implemented 

for a stand, while the stand-level information is used to decide how to treat the 

stands that are selected, using a set of treatment parameters (PARM1, PARM2, 

PARM3, and PARM4). The following paragraphs describe treatment implementa-

tion at the stand level and the parameters needed to perform this implementation 

in the model.

A clearcut is implemented in FireBGCv2 by killing and removing all of the 

trees in a stand (represented by a simulation plot) that are above a minimum DBH 

(PARM2) and removing trees that are above a minimum residual DBH (PARM1). 

All material from trees above the residual DBH is taken offsite, leaving only slash 

material that is a fraction of the total crown weight as specified by PARM3 (frac-

tion of crown biomass left on the stand). This material is added to the appropriate 

ground biomass compartments. If a prescribed burn is implemented after the 

clearcut the target intensity of that burn (kW m-1 fireline) is specified in PARM4, 

and the program simulates tree mortality, fuel consumption, and soil heating using 

that input intensity. Treatment can be prioritized by tree species in the last line of 

each treatment defined in the Manage.in file, where 0 means the species is cut and 

1 signifies that this species is retained.

A partial cut is implemented in the model by killing and removing all trees in 

the stand that are between a minimum (PARM1) and maximum (PARM2) DBH, 

where PARM3 is the fraction of the tree’s crown biomass that is added to the 

ground biomass components. A prescribed burn can be specified by entering a 

number greater than zero in PARM4–this represents the fireline intensity of the 

desired prescribed burn. Treatment prioritization by tree species occurs as de-

scribed above.

Prescribed burning without cutting is the third treatment type defined in the 

model. The minimum (PARM1) and maximum (PARM2) fireline intensity is 

specified in the Manage.in file, and the program randomly selects a fireline in-

tensity between these two parameters for each stand eligible for treatment. Fire 

effects, such as fuel consumption and tree mortality, are computed from this simu-

lated intensity and the fuel moistures entered for the fuel model.

FireBGCv2 computes a number of treatment statistics as output, including 

total area treated by each treatment type and the cost of implementing each treat-

ment for each simulation year.

Wildlife

Wildlife dynamics are simulated in FireBGCv2 using a habitat suitability ap-

proach in which all combinations of stand-level cover type and structural stage 

define a matrix of suitability values for specified wildlife species. Cover type is 

determined from the plurality of basal area for each tree species on the simula-

tion plot and structural stage is determined by the distribution of DBH of the 

trees on the simulation plot. The habitat suitability indices can be of any scale, 

such as 0 to 3, or 1 to 10, or 0 to 100, depending on the objective of the wildlife 

analysis. Lower values indicate low suitability and higher values represent areas 

of high suitability for a particular wildlife species. Any number of wildlife species 

can be included in the simulation and specified in the Wildlife.in file, along with 

associated index values for cover type and structural stage. The FireBGCv2 pro-

gram then computes a suitability value for each stand based on its cover type and 

structural stage, stores this value for every stand on the simulation landscape, and 
USDA Forest Service RMRS-GTR-255. 2011. 55

outputs map and tabular data summaries at user-defined intervals. These output 

data can be summarized or displayed across the entire simulation landscape (see 

the “Demonstration” section).

Wildlife suitability can also be computed at the tree scale by summarizing 

suitability values for each tree and undergrowth component on the simulation plot 

by species and structural stage categories. An average suitability index across all 

trees and undergrowth guilds is then computed and stored for the stand. For example, for a Douglas-fir tree of 15 cm DBH, the model keys to a Douglas-fir cover 

type and a pole structural stage, and the suitability index for this combination 

is stored and averaged across all trees and undergrowth components. All undergrowth components are treated as seedlings. Users can specify stand- or tree-level 

wildlife habitat suitability calculations in the Sim.in file.

Species Processes

Regeneration

Establishment of new trees on the simulation plot is partially accomplished at 

the species level. Climate and stand conditions are evaluated each year at both 

landscape and stand levels to determine establishment success of a tree species. 

Species regeneration is governed by many processes, including seed availability, serotiny, sprouting ability, seedbed condition, antecedent weather, and cone 

crop abundance. All of these factors are simulated as reduction factors that reduce a maximum regeneration potential (SAPmax, saplings m-2) by species. In 

FireBGCv2, there are three types of regeneration mechanisms for tree species: 

(1) wind dispersed seed, (2) serotinous seed, and (3) sprouts. Species can have 

multiple regeneration mechanisms in the model.

Sprouting Species

All species are assigned a sprouting ability in the Species.in file using an 

ordinal index that ranges from zero (no sprouting) to three (prolific sprouter). 

In FireBGCv2, sprouting can only occur if a tree dies; there is no spontaneous 

sprouting from live trees. Trees can die from being harvested (if management 

activities are specified by user), from being burned (in a management activity or 

wildfire), or from natural causes (as described in the “Mortality” section in “Tree 

Processes”). FireBGCv2 simulates a maximum of one sprout from a dead tree 

(Prentice and Leemans 1990), but there are modifications to the model to increase 

that number, pending the availability of the new research. One sprout per tree is 

a good assumption for many Rocky Mountain tree species, with the exception of 

aspen, because many sprouts never make it to 1.37 m tall (upper height threshold 

for seedlings). In addition, there are a few existing sprouting studies for western 

U.S. ecosystems that will provide for more robust parameterization, and model 

efficiency is increased by reducing the number of new saplings (Pennanen and 

Kuuluvainen 2002).

The success of a sprout is governed by two factors—shading and stand density—that are simulated as regeneration reduction factors (Kercher and Axelrod 

1984). The shading reduction factor (rSHADE) is computed from the stand projected leaf area index (PLAI) using the following set of equations:

rSHADE = e-0.8PLAI for shade intolerant species (73)

rSHADE = e-0.25(PLAI+1) for moderately shade intolerant species (74)

rSHADE = e-0.25(PLAI-0.2) for shade tolerant species (75)
56 USDA Forest Service RMRS-GTR-255. 2011.

where shade intolerant species have shade tolerance indices of 1 or 2 as specified 

in the Species.in file, moderately shade tolerant trees have an index value of 3, 

and shade tolerant species have index values of 4 or 5. The stand density reduction 

factor (rDENS) uses both a leaf area and a basal area scale to determine sprouting 

success. The current stand’s PLAI is scaled to the maximum LAI that is entered 

in the Site.in file and this fraction is subtracted from 1.0. The stand’s current basal 

area is also divided by the maximum basal area (specified in the Site.in file), and 

this fraction is subtracted from one. The factor rDENS is computed as the mini-

mum between these two scalars.

A probability of sprouting (Psprout) is calculated by multiplying a maximum 

sprouting potential (SPROUTmax) by the two regeneration reduction factors (Liu 

and Ashton 1995). The SPROUTmax is computed by scaling the ordinal sprouting 

index to the following values: 0.0, 0.5, 0.7, and 1.0. For example, if the sprouting 

index is assigned a value of 2.0 for a species in the Species.in file, SPROUTmax

is equal to 0.7, and if the sprouting index is 0.0, SPROUTmax is equal to 0.0. The 

final calculation of Psprout is the product of SPROUTmax, rDENS, and rSHADE. 

This probability is compared to a random number, and if the random number is 

less than Psprout, a sprout is simulated at that dead tree.

Serotinous Species

Serotiny is simulated in FireBGCv2 using an input parameter called pSEROT 

that is specified in the Species.in file. The variable pSEROT describes the propor-

tion of cones on a tree that are serotinous (cones that only open after a fire). A 

reduction factor (rSEROT) is calculated from the pSEROT input value by sub-

tracting pSEROT from one. In the tree regeneration routine, the effect of cone 

serotiny depends on whether the current simulation year is a fire year or non-fire 

year. A fire year is when the number of years since last fire (YSF, year) for a stand 

is greater than the input lag year (LAG, year), as specified in the Site.in file. If 

the year is a non-fire year, rSEROT is used as a reduction factor (1 - pSEROT ) 

to reduce a maximum number of saplings for a species (see the next section), and 

the reduction factor rDSUR (duff survival reduction factor) is set at 0.01 to reflect 

the marginal survival of serotinous species in duff. During fire years, rSEROT is 

set at 1 - pSEROT and then multiplied by 10.0 to reflect the great amount of seed 

that is contained in the canopy.

Species Regeneration Processes

New trees of any species can only be established on the simulation plot if 

several environmental criteria are met. First, the species must be experiencing 

an abundant cone crop, as simulated at the landscape level and described in the 

“Landscape Processes” section. Next, the last acceptable spring frost date for the 

species (FROSTspring, Julian date) is compared with the latest frost that occurred 

during a simulation year (Hanninen 1995). The earliest acceptable fall frost for 

the species (FROSTfall) is also compared with the earliest autumnal frost for that 

simulation year. If there is a late spring frost or an early fall frost, then no tree 

regeneration is simulated that year for that species. The latest and earliest frost is 

evaluated relative to the midpoint of the species’ growing season (GSmid, year-

day), as computed from the average of the leaf out and leaf fall Julian dates. These 

input values are specified in the Species.in file if the user wishes to not use the 

dynamic phenology routines of White and others (1997). The latest spring frost is 

the last frost that occurs before GSmid, and the earliest autumnal frost is the first 

to occur after GSmid.

Because stand-replacement fires often render a site inhospitable for sapling 

establishment (Agee and Smith 1984, Arno and others 1985), tree saplings can’t 

become established until an adequate time has elapsed since a major disturbance. 

The subsequent mitigation of site conditions for tree ecesis is a complex process 

and difficult to model. This process is indirectly modeled in FireBGCv2 through 

the use of a waiting period, called the regeneration lag time (LAG, years), that is 
USDA Forest Service RMRS-GTR-255. 2011. 57

input to FireBGCv2 from the Site.in file. Trees will not be established in the simulation plot until LAG number of years have elapsed after a stand-replacement fire 

(defined as fires causing 98 percent fire-caused tree mortality) (Agee and Smith 

1984). Regeneration lag times often exceed 30 years in some upper subalpine 

ecosystems (Little and others 1994).

As mentioned, the actual number of trees established on the simulation plot 

for any species is calculated as a reduction of maximum sapling establishment 

using scalars that indirectly represent environmental effects on tree species ecesis (Keane and others 1989, Urban 1990). The annual maximum sapling density 

(SAPmax, saplings m-2) is specified according to site in the Site.in file and describes new trees across all species. Values for this parameter can be taken from 

regeneration studies; the values used in FIRE-BGC simulations were taken from 

Alexander (1985), Arno and others (1985), Knapp and Smith (1981), Pfister and 

Shearer (1978), and Shearer (1975, 1985).

The SAPmax parameter is often tuned to improve model efficiency. It is 

common for many saplings to be established on the plot during a FireBGCv2 simulation and this requires a great deal of memory and simulation time. However, in 

the model as in real life, most of these saplings will eventually die due to unfavorable environments. The SAPmax parameter can, therefore, be altered or reduced 

to ensure that a realistic number of saplings become trees while also reducing 

simulation time and memory use. The FireBGCv2 model uses several scalars to 

reduce the input SAPmax parameter value.

The first regeneration scalar evaluates the effect of duff and litter depth on sapling survival for a species. This scalar (rDSUR) is calculated from a modification 

of Boyce (1985) empirical sapling survival equations of the form:

( ) rDSUR DEPTH

a

a b = - (76)

where α and ß are regression coefficients stratified by species and taken from 

Boyce (1985). These coefficients are specified in the Species.in file. DEPTH is 

the depth of the duff and litter layer (cm) and is computed from the following 

formula:

( )( ) DEPTH [ PAREA FDC BULK

LitterC

BULK

DuffC

100

1

l d

= + (77)

where PAREA is the area of simulation plot (m2), FDC is the fraction of the duff 

and litter biomass that is carbon (held constant in FireBGCv2 as 0.48), LitterC 

and DuffC are the amounts of carbon in the litter and duff forest floor compartments (kgC), and BULKl

 and BULKd are the bulk density (kgB m-3) of the litter 

and duff forest floor layers, respectively, as specified in the Fuel.in file for each 

fuel model assigned at the site level. Bulk densities are taken from Brown (1981).

The effect of shade on species establishment is represented in the second regeneration scalar (rSHADE) taken from FIRESUM. This scalar uses the shade 

tolerance index of a species to key to one of these equations:

rSHADE = e-1.2PLAI for very shade intolerant species (index = 1) (78)

rSHADE = e-0.4PLAI for shade intolerant species (index = 2) (79)

rSHADE = e-0.25PLAI for moderately shade tolerant species (index = 3) (80)

rSHADE = e-0.15PLAI for shade tolerant species (index = 4) (81)

rSHADE = e-0.05PLAI for very shade tolerant species (index = 5) (82)
58 USDA Forest Service RMRS-GTR-255. 2011.

where PLAI is stand projected leaf area index (m

2 m-2) for all trees and under

-

growth. Graphical representations of these equations show that shade tolerant 

species have the best chance of establishing under a dense canopy (Bossel and 

others 1991, Bossel 1994).

The third scalar (rDENS) adjusts sapling establishment based on an index of 

crowding in the stand. The scalar is computed from the minimum of two indices 

that indirectly assesses crowding using leaf area and stand density. The algorithm 

is as follows:

,

I rDENS MIN LA

PLAI

BA

BA

max max

=

=G (83)

where PLAI is the stand’s projected leaf area index (m

2 m-2), LAImax is the maxi

-

mum leaf area index for the site (m

2 m-2), BA is the instantaneous stand basal 

area (m

2 ha-1), and BAmax is the maximum stand basal area (m2 ha-1). Values for 

BAmax and LAImax are specified in the Site.in file.

The next scalars are computed at other scales in the model. The seed dispersal 

scalar (rDIST) represents the chance of tree species’ seed falling within the stand 

(P

d). This parameter is detailed in the “Seed Dispersal” section within “Landscape 

Processes.” In short, rDIST is set to the minimum of 1.0 or Pdist. The influence of 

serotiny on seed dispersal and availability is simulated using the scalar rSEROT, 

which is approximately the level of serotiny on the landscape for that species 

during non-fire years; and during fire years, this value is set to 10.0 (see the 

“Serotinous Species” section).

The final estimate of saplings established in the simulation plot is the product 

of all regeneration scalars and maximum sapling density:

SAP = (SAPmax)(rDSUR)(rSHADE)(rDENS)(rDIST)(rSEROT) (84)

where SAP is the number of saplings established on the simulation plot for that 

tree species (saplings PAREA-1). The model ensures that the number of trees es-

tablished across all species does not exceed SAPmax by keeping a running sum 

of the number of trees established across all species and then scaling the number 

of saplings for regeneration across all species to SAPmax. The number of new 

saplings to add to the simulation plot are stored in an array and passed to the tree 

regeneration routine for establishment (see the “Regeneration” section in “Tree 

Processes”).

Whitebark pine regeneration is computed differently from other tree species 

because the seeds of this pine are disseminated by the Clark’s nutcracker rather 

than being dispersed by the wind like all other species. The whitebark pine re-

generation module in FireBGCv2 simulates the effects of seed crop, nutcrackers, 

and light on whitebark tree sapling establishment. A complete discussion of the 

whitebark pine regeneration algorithm is presented in Keane and others (1990a, 

1990b). Parameters that quantify these algorithms are contained in the Pial.in file.

Tree Processes

Regeneration

The FireBGCv2 tree regeneration routine uses simulated results from many 

scales to predict the number of new trees to add to the simulation plot. Cone 

crop occurrence and seed dispersal probabilities are estimated across the land-

scape from data summarized at the stand level. Sapling establishment and survival 

are evaluated at the species level (see the “Regeneration” section in “Species 

Processes”), and the number of new trees to add to the simulation plot by species 

is estimated at the tree level. The number of saplings that are established on a 

simulation plot is SAP, as detailed in the “Species Processes” section.
USDA Forest Service RMRS-GTR-255. 2011. 59

Each new tree established on the simulation plot, whether it originated from a 

seed or sprout, is randomly assigned a diameter (DBH, cm) between 1 and 2 cm 

using a uniform probability distribution. The initial tree age (years), height (m), 

and height to bottom of live crown (m) are taken from parameters specified in the 

Species.in file. The initial ecophysiological characteristics (XT) of established 

saplings are then calculated from structural characteristics using empirical equations as detailed in the “Program Implementation” section.

Growth

Tree growth in both diameter and height is simulated differently for each of 

the three succession model drivers. The simple gap model simulates growth using 

growth reduction factors or scalars. The mechanistic gap model uses estimates 

of carbon growth based on estimates of photosynthesis computed at a daily time 

step. The BGC model proportions carbon to trees then stems to compute growth 

in diameter and height.

Simple and Mechanistic Gap Models

Tree growth in the simple and mechanistic gap model was taken directly from 

the FIRESUM model (Keane and others 1989). In that model, diameter and height 

growth are simulated as a reduction from a maxima that is taken from the literature. In this routine, there are four reduction factors to account for available water, 

light, crowding, and temperature.

The light reduction factor (rSHADE) is calculated from a light response function that is keyed from the shade tolerance index specified for each species in the 

Species.in file:

rSHADE = a(b - e-cAL)(AL - d) (85)

where AL is the light scaled from 0.0 (no light) to 1.0 (full sunlight) that is available the top of the crown of a tree; and a, b, c, and d are coefficients fitted to a 

growth reduction equation from previous gap modeling studies. AL is computed 

for vertical slices of the canopy each year of the simulation at the stand level 

(see the “Canopy Dynamics” section in “Stand Processes”). Hardwired values in 

FireBGCv2 for the a, b, c, and d coefficients are listed in Table 5.

The rWATER reduction factor to account for the availability of water is simulated from the actual and potential evapotranspiration (AET and PET, kgW m-2). 

The function uses the ratio of AET and PET to compute rWATER:

( )

( ) rWATER WSO

WRS 1 11 = - -- y

= G (86)

where WRS is the ratio between AET and PET (AET/PET) bounded between 0.0 and 

1.0, WSO is the minimum ratio for that tree species as input in the Species.in file, 

and v is the exponent set at 2.0 for current modeling efforts. AET is not simulated in 

Table 5. Coefficients for the available light reduction function used to compute 

diameter and height growth.

Regression coefficients

Shade tolerance class a b c d

1 1.60 1.10 1.20 0.25

2 1.22 1.00 2.00 0.13

3 1.11 0.90 3.00 0.07

4 1.04 0.80 6.00 0.04

5 1.01 0.75 10.00 0.01
60 USDA Forest Service RMRS-GTR-255. 2011.

the simple gap model so total annual precipitation is used instead of AET and PET is 

calculated from annual average temperatures (Keane and others 1989).

Temperature effects are simulated using the reduction factor rTEMP that is 

calculated from degree-days (DD) using the following formula:

( )( )

( )( ) rTEMP DD DD DD DD

DD DD DD DD

min max

min max

opt opt

= - -

- - y

y

= G (87)

and

( )

( )

DD DD

DD DD

min

max

opt

opt y = -

- (88)

where DD is the current year’s degree-days; and DDmin, DDopt, and DDmax are 

all input parameters entered in the Species.in file that define the temperature reduction factor curve and that represent the minimum, optimum, and maximum 

degree-days for that tree species. These parameters are usually estimated from 

weather stations that are on the edge of the geographic (latitudinal) or elevation 

range of the species. Degree-days (oC day) is calculated using average daily temperature and a base of 3 oC (DD = ∑min[(Tave - 3), 0.0]).

The crowding factor (rCROWD) is calculated from the minimum of two indices that indirectly assess the effect of crowding on tree growth using leaf area 

and stand density (similar to that used for regeneration simulation detailed in the 

“Species Processes” section). The algorithm is as follows:

rCROWD MIN , LAI

PLAI

BA

BA

max max = : D (89)

where PLAI is the instantaneous projected leaf area index of the stand (m2 m-2), 

LAImax is the maximum leaf area index for the site (m2 m-2), BA is the instantaneous stand basal area (m2 ha-1), BAmax is the maximum stand basal area (m2

ha-1), and MIN is a function that picks the minimum value between the two values. Values for BAmax and LAImax are specified in the Site.in file.

Tree diameter growth (DINC, cm) is computed as a reduction from a maximum diameter increment (DINCmax) using the four growth reduction factors, as 

shown below:

DINC = (DINCmax)(rSHADE)(rWATER)(rTEMP)(rCROWD) (90)

where DINC is DBH growth increment (cm). The maximum increment is computed from the maximum and current DBH using the following function:

( )( )[( ]

DINC aDBH bDBH

G DBH

274 3 4

1

2

(

( )( )

max

DBH HT

DBH HT

max max = + -

- (91)

where G is computed from equation 3 in Botkin and others (1972), and the a

and b coefficients are computed from equation 5 in Botkin and others (1972). 

Parameters G, a, and b use AGEmax for their derivation. The parameters DBHmax

and HTmax are input values entered according to species in the Species.in file. 

DINC is then compared to a minimum increment DINCmin (parameter entered 

according to species in the Species.in file), and the highest value is used, but if the 

DINC is less than DINCmin, the tree stress counter adds one year (in essence, this 

signals that the tree is under stress).

The increase in height growth is simulated from the Botkin and others (1972) 

equation:

HT = 137 + aDBH - bDBH2 (92)
USDA Forest Service RMRS-GTR-255. 2011. 61

where a and b coefficients are computed from equation 5 in Botkin and others 

(1972). Growth in leaf area and branchwood is calculated from the crown biomass 

equations (Brown 1978) using the new DBH and HT values. Last, the model increments the tree’s age (AGE) by one year.

BGC Model

Tree diameter (DBH, cm) and height (HT, m) growth increments are computed 

at year’s end from the estimates of carbon growth proportioned to the tree stem 

carbon compartment (StemC, kgC) (Gordon and Larson 1968). First, tree age is 

incremented by one year. Then, the annual photosynthesis (PSN) and respiration 

(MR) stored for each tree are used to calculate a net primary productivity for that 

tree (NPPt, kgC). This NPPt is summed across all trees in the stand to calculate 

“bottom-up” stand-level NPPs value. Then, the annual BGC stand-level estimates 

of production (NPPbgc) are adjusted for plot area and then allocated to each tree 

based on the ratio of NPPt / NPPs, as in the following equation:

NPP NPP NPP

NPP

tBGC BGC s

t = (93)

This tree-level carbon (NPPtBGC, kgC) is then allocated to the tree’s components (LeafC, FrootC, CrootC, DeadstemC, and LivestemC) using the allocation 

parameters for that species as specified in the Species.in file and the allocation algorithm from Schwalm and Ek (2004), which allocates carbon based on all-sided 

leaf area, shade tolerance, and species life form. The understory is also included 

in this computation of carbon allocation.

Tree growth in diameter and height is then computed from the difference in 

stem carbon (StemC = LivestemC + DeadstemC) from the previous year to current year. First, a minimum and maximum DBH and HT are computed to bound 

growth. The minimum diameter (DBHmin) is set from the parameter entered according to species in the Species.in file, and the maximum DBH (DBHmax) is set 

at three times the average diameter growth (computed as DBH / AGE). A maximum tree height (HTmax) is calculated using equations in Milner (1992) and a 

scaling routine, which uses the current year’s height, tree age, site index (as input 

to FireBGCv2 in the Site.in file), species site index (as specified in the Species.in 

file), and shade tolerance to compute a new height. Basically, tree height (HT) is 

calculated from the Milner (1992) equations using AGE and site index (selected 

as the minimum between the site and species input values) and then reduced by 

the shade reduction factor rSHADE, as calculated from the same formulae described in the previous gap model tree growth section, which uses the available 

light at the top of the crown (AL) and shade tolerance class (specified in Species.

in). Two heights are computed: maximum height (HTmax) that is calculated by assigning AL = 1.0, and a lower height (HT) that is calculated using the AL for the 

canopy layer at the tree’s top.

Next, the diameter increment (DINC) is calculated from the change in stem 

carbon (StemC) and the change in tree height from the following:

DBH StemC HT

StemC HT DBH 2

new old new

new old old = (94)

where DBHnew is the new diameter (cm), DBHold is the diameter last year (cm), 

StemCold and StemCnew are last and this year’s stem carbon, and HTnew and 

HTnew are last year’s and this year’s tree heights (m). This approach assumes a 

truncated frustum stem geometry and uniform diameter growth over the entire 

stem. The StemC is calculated from the tree’s live and dead stemwood carbon; the 

previous year’s StemC (StemCold) is stored in memory.

Next, the minimum possible diameter (DBHmin, cm) for this year is compared 

to the new DBH (DBHnew), and if DBHnew is less than DBHmin, a DBHnew is 

recomputed based on shade tolerance. If shade tolerance class is shade intolerant 
62 USDA Forest Service RMRS-GTR-255. 2011.

(class one or two), DBHnew is set to DBHmin and HTnew is recomputed based on 

the same proportional relationship shown above. If it is any other shade tolerance 

class, then HTnew is set to HTold + 0.01 m and DBHnew is computed using the 

proportional formula above. In addition, trees with a DBHnew that is less than 

the DBHmin are assumed to be experiencing growth-related stress and the stress 

counter Ystress is incremented.

The last computation in the dimensional growth analysis is the modification of 

tree crown height (HBC, m). The model calculates the available light conditions 

(0.0 to 1.0) at the bottom of the canopy from the vertical distribution of available 

light as detailed in the “Canopy Dynamics” section. If the shade tolerance class 

of the tree species is intolerant (class one or two) and the available light is less 

than 0.1, then the HBC is increased by the simulation height increment (HINC) 

as input by the user in the Sim.in file. For shade tolerant species (class three, four, 

or five), the available light value has to be less than 0.02 for crown pruning. All 

crown material is added to the ground carbon layers by crown fuel category.

Mortality

There are four causes of tree mortality that are stochastically simulated in 

FireBGCv2—random, stress, fire, and pathogens (Keane and others 1989). 

Random mortality is the chance of death from random events, such as endem-

ic insect attack, windthrow, or other local perturbations that a tree experiences 

throughout its lifetime (Monserud 1976, Franklin and others 1987, Hamilton 

1990). The probability of random mortality (Prandom) is calculated by the equation: P AGEmax random f = (95)

where AGEmax is the maximum age that can be attained by the tree species and ε

is an empirically fitted coefficient (years). This coefficient is estimated based on 

the assumption that 2.0 percent of the trees (98th percentile) survive to the maxi-

mum attainable age for a species. Analysis of stand data from Montana, Idaho, 

and eastern Oregon suggests that 2.0 percent is reasonable for most western co-

nifers. The value of ε was assumed to be 4.0 for most species to approximate the 

2.0 percent estimate. However, stand data collected by Keane and others (1994) 

found ε closer to a value of 3.0 for whitebark pine.

Stress mortality is tree death resulting from severe stress over long periods. 

Stress mortality can be caused by water scarcity, insufficient light, or tree crowd-

ing (Pedersen 1998). FIRE-BGC used the amount of carbon allocated to the stem 

to evaluate tree stress. In FireBGCv2, a stress counter (Ystress, years) is increment-

ed by one year if the tree cannot grow sufficiently to exceed a minimal diameter 

growth. For the simple gap model, Ystress is incremented when the current year’s 

DBH increment (DINC) is less than an input minimum (DINCmin) as specified in 

the Species.in file. For the mechanistic gap and BGC succession models, Ystress

is incremented if the NPP for the tree is less than zero. Ystress is then used in the 

following two-parameter Weibull probability function (Reed and Clark 1979): Pstress = a[1 - e-bYstress] (96)

where Pstress is the probability of mortality resulting from stress, and a and b are 

coefficients by shade tolerance class (Table 6). Stressed trees become healthy after experiencing three consecutive years of diameter growth above the minimum 

allowable growth (DINC>DINCmin or NPP>0.0).

The last two causes of mortality—fire and pathogens (only blister rust and 

mountain pine beetles are currently available in FireBGCv2)—are presented in 

the following sections. Pathogens include both insects and disease in this model-

ing effort. The probability of mortality from these factors is represented by Pfire

for fire-caused mortality, Prust for blister rust, and Pbeetle for mountain pine beetlecaused mortality.
USDA Forest Service RMRS-GTR-255. 2011. 63

Tree death is evaluated separately for each mortality agent. First, a random 

number (RNUM) is compared to Prandom and, if RNUM is less than Prandom, the 

tree dies. If the tree lives, another random number is compared to Pstress, and if the 

random number is less, the tree dies. This is repeated for Pfire, Pbeetle, and Ppath. 

The tree must pass all comparisons to continue to live.

After a tree dies, regardless of the cause of mortality, it contributes its carbon 

to the forest floor compartments. Foliage (LeafC) and branchwood (W1C, W10C, 

and W100C) carbon, computed from the Brown (1978) empirical equations, is 

allocated to the LitterC and DuffC forest floor compartments based on the species 

needle lignin fraction (see the “Decomposition” section in “Stand Processes”). 

Woody branchwood carbon is added to the W1C, W10C, and W100C forest floor 

compartments by size class using Brown’s (1978) equations. The tree is then considered a snag, and it is assigned a limited number of the structural carbon and 

dimensional attributes of the tree at the time of death.

Snags

Once a tree dies in FireBGCv2, it becomes a snag. The only characteristics that 

are simulated for a snag are its species, DBH, HT, AGE, and WoodC (carbon remaining in the stem after all woody twigs and small branchwood are added to the 

fuel compartments, kgC). The snag receives the same DBH and HT as the mother 

tree, but its AGE is set at zero, and its WoodC is estimated from the StemC values 

previously discussed. A snag’s AGE is incremented each year, and a probability 

of snag fall is calculated using the following relationship:

fall

[ ] AGE 3

AGEsnag - P e = -1 (97)

where AGEsnag is the maximum age of a snag (year), which is input to the program 

according to species in the Species.in file. Once a snag falls, all of its WoodC gets 

added to the W1000C ground pool.

A snag can also be input to FireBGCv2 in the Tree.in file by specifying the 

health rating (RATE) as the number 4. This will tell the program to make this 

entry a snag. However, if the rust flag in the Sim.in file is negative, all five-needle 

pine trees that have a health status of 4 will be input to the program as a healthy 

pine tree to simulate tree and stand structures before rust mortality.

Phenology

All phenological triggers for each tree are simulated at the site level for each 

species. Two major phenological events—leaf fall and leaf growth—require triggers. Event triggers for trees and undergrowth components are differentiated by 

evergreen or deciduous plant types. Evergreen trees and shrubs keep their needles 

all year long and drop 1/365th of their needles each day throughout the year. They 

gain new foliage only during the leaf out phenological stage, and this gain is dependent on the amount of leaf carbon computed for this tree during the previous 

Table 6. Stress mortality function parameters by shade tolerance classes 

that are used to calculate tree death from stress-related causes.

Regression coefficients

Shade tolerance class a b

1 150.0 2.0

2 180.0 2.0

3 200.0 2.0

4 240.0 2.0

5 280.0 2.0
64 USDA Forest Service RMRS-GTR-255. 2011.

year. In short, each year the model computes a tree’s leaf carbon (leafC) from allometric equations using DBH and height. This LeafC is then stored as LeafCmax for 

that year’s simulation. Foliage is lost from LeafC during the leaf fall phenological 

stage by subtracting a proportion of the LeafC (LeafC / leaf retention time, as 

specified in the Species.in file) for each day of the leaf out period (also specified 

in the Species.in file). During the leaf out stage, the same amount of foliage lost 

during leaf fall is added to LeafC. For deciduous shrubs and trees, all foliage is 

lost during leaf fall and gained during leaf out stages. The fine roots for these trees 

follow the same phenology pattern as the leaves. For the herbaceous undergrowth 

components all foliage dies during the leaf fall period and all foliage grows to its 

full potential during the leaf out period.

Wildland Fire

Tree Mortality

Fire-caused tree mortality is modeled as a function of bark thickness and 

scorch height, which is dependent on fire intensity and wind (Ryan and others 

1987, Ryan and Reinhardt 1988). When a fire spreads through an area, it can 

kill trees by consuming or scorching foliage and killing stem and root cambium 

(Peterson 1983). The scorching of crown foliage involves killing the needles from 

heat exposure without consuming them. Sometimes the fire is so intense that tree 

crowns are entirely consumed by the fire, resulting in immediate tree death. Live 

crown consumption by fire is assumed to occur if the fire’s flame length (FL) is 

greater than the height to the bottom of the crown (HBC) (Figure 13). If the flame 

length is lower than the crown, indirect mortality from crown scorch and cambial 

kill is evaluated.

The degree of crown scorch and cambial kill depends on fire intensity and duration. Ryan and Reinhardt (1988) developed an empirical mortality equation that 

Figure 13. Representation of a tree dimensions in simulating tree mortality in FireBGCv2.
USDA Forest Service RMRS-GTR-255. 2011. 65

implicitly accounts for both causes of fire death. The equation was implemented 

in FireBGCv2 as:

P [ ] -BARK DBH

1

fire thick = 2 [- + 1.941 6.32(1 -e) -0.000535CK ] 1 + e

(98)

where Pfire is the probability of mortality from fire after one year; BARKthick is 

the factor that converts DBH to bark thickness (cm bark cm DBH-1), as specified in the Species.in file; DBH is tree diameter (cm); and CK is the percent of 

scorched or consumed crown volume for the tree (percent). Values for BARKthick

can be taken from Lynch (1959), Lange (1971), Myers and Alexander (1972), 

Faurot (1977), Ryan and Reinhardt (1988), and Reinhardt and Ryan (1989). Tree 

death is evaluated using a random number approach, as described in previous 

sections. Scorched crown volume percent (CK, percent) is estimated using the 

following formula:

[ ( ) CK CS CL CS 100 2 = 2

-

CL (99)

where CS is the length of crown that is scorched (m) and CL is crown length (m) 

(Figure 13). This relationship assumes that the crown shape approximates a paraboloid (Peterson 1985). The dimension CS is solved by the equation CS = SH - (HT 

- CL), where HT is tree height (m), and SH is scorch height (m). Scorch height is 

calculated from an empirical expression developed by van Wagner (1973):

( )[ ( ) ( ) ]

( ) SH T T b FI c WINDa FI

3 0.5

1.667

kill ave

= - + (100)

where FI is fire intensity (kW m-1), WIND is wind speed (km hr-1) at mid‐flame 

height, Tave is ambient air temperature (oC), and Tkill is the lethal temperature for 

tree foliage (assumed as 60 oC in FireBGCv2). The constants a, b, and c were 

derived empirically and are 0.74183 m oC-1, 0.025574 (kW m-1)4/3, and 0.021433 

km-1 hr (kW m-1)7/9, respectively. FI is computed from the Rothermel (1972) 

spread model, as implemented in the FireLib routines (Bevins 1996) or the Albini 

(1976) FIREMOD routine (see the “Wildland Fire” section in “Stand Processes”). 

Ambient air temperature for the fire is specified as a constant in the Site.in file. 

Although the fire mortality equations are robust and allow for a wide range of 

diameters and species, data for small-diameter (<10 cm) tree mortality are lacking. Since the majority of simulated trees are less than 10 cm DBH, additional 

refinement of this equation to account for small diameter tree mortality is needed.

A tree’s foliage is considered scorched if the flame length (FL) is less than the 

height to base of live crown (HBC); and conversely, a trees foliage is consumed 

if FL is greater than HBC (Figure 13). A probability of 0.99 is assigned to Pfire

if a tree’s foliage is consumed. If a tree dies from fire scorching, its foliage and 

branchwood are added to the appropriate forest floor compartments, and its stemwood becomes a snag. If the crown material is consumed by the fire, a fraction of 

the crown fuel components gets consumed and the remaining fraction is added to 

the forest floor. The consumption fractions are specified in the Fuel.in file for live 

and dead canopy and ground fuels. There is a consumption fraction for the foliage, twigs, branchwood, and large branchwood. The unconsumed portions of the 

crown compartments are added to the appropriate forest floor carbon and nitrogen 

compartments. All consumed crown carbon and nitrogen is assumed to be lost 

from the simulation plot.

Some trees will continue to live even with some crown scorch. In these cases, 

the scorched foliage is added to the forest floor compartments. The proportion of 

crown scorched (that is, CK / 100) is multiplied by the tree’s leaf carbon (LeafC, 

kg C) and nitrogen (LeafN, kg N) to obtain the amount of killed foliage to add to 

the LitterC and DuffC compartments. No branchwood is added to the forest floor. 

The height to the bottom of crown (HBC) is then adjusted upward proportional to 

the percent crown kill (CK).
66 USDA Forest Service RMRS-GTR-255. 2011.

Insects and Diseases

Insect- and disease-caused mortality are the fourth and fifth types of tree 

mortality simulated in FireBGCv2. FireBGCv2 only simulates tree mortality 

caused by white pine blister rust (Cronartium ribicola) and mountain pine beetle 

(Dendroctonus ponderosae) in high-elevation ecosystems. Details of these mortality algorithms can be found in Keane and others (1989, 1990b). The initial 

year of blister rust (YRUST) or pine beetle (YBEETLE) epidemic is entered in 

the Sim.in file. The probability of tree mortality is assessed for each pathogen 

during the epidemic and tree death is simulated using the methods detailed in 

the “Mortality” section of “Tree Processes.” This submodel of FireBGCv2 was 

designed so that any new pathogen algorithm can be added with minimal program 

modification. Most insect and disease mortality algorithms were developed from 

empirical data using regression analysis. However, future versions of FireBGCv2 

will be linked with mechanistic simulation of pathogen dynamics to fully understand the interaction of these disturbance processes on ecosystem function.

White Pine Blister Rust Mortality

The spread of blister rust is modeled at the stand level for each western haplopaxon (five-needle) pine (whitebark pine or western white pine, for example) 

using a probability of mortality (Prust). Once a rust epidemic has been initiated 

(see the “Landscape Processes” section), all five-needle pines are evaluated for 

rust mortality, except those that are rust resistant. The probability of death from 

blister rust (Prust) is computed from the following equation:

Prust = e [-0.15DBH] (101)

where DBH is tree diameter (cm). A rust flag for each tree is incremented each 

year of infection and, if this flag reaches 20 years, the tree is unable to produce a 

cone crop.

Because blister rust kills only rust susceptible five-needle pines, resistance to 

the disease is simulated in the model by randomly assigning a portion (set at 1.0 

percent in the model) of the five-needle pine trees as rust resistant. There is no 

cone crop reduction for rust resistant five-needle pines.

Mountain Pine Beetle Mortality

The probability of mortality from mountain pine beetles (Pbeetle) is calculated 

each year of a beetle attack using the empirical equations for whitebark pine:

Pbeetle = 0.007664 (DBH) - 0.00222 (102)

and for lodgepole and ponderosa pine:

Pbeetle = 0.00555(DBH) (103)

where DBH is tree diameter (cm). These regression equations were developed 

from unpublished data provided by the USDA Forest Service, Northern Region, 

and Gene Amman, Intermountain Research Station. A pine beetle attack ends 

when the number of surviving pines divided by the number of pines prior to attack is less than a minimum infestation level (5 percent).
USDA Forest Service RMRS-GTR-255. 2011. 67

Program Implementation

To understand the complexity and detail involved in generating acceptable 

simulation results from FireBGCv2, it is important to follow the general steps 

involved for the creation of a simulated dataset (Figure 14):

1. State your objective. This is the most important step in the entire process. 

Succinctly state the objective of your FireBGCv2 simulation to select the most 

appropriate initial conditions, most accurate parameters, and best response 

variables to output.

2. Select response and explanatory variables. The decision of which variables 

to include in the output simulation results will make parameterization much 

easier and more focused. A long list of response and explanatory variables 

may overwhelm some statistical software packages that are used for results 

analysis. Response variables are used to answer or complete the simulation 

objective; they are the variables that best describe differences in between simulation scenarios. Explanatory variables are variables that provide context and 

explanation on why the response variables differ across scenarios.

3. Design simulation. Decide the important simulation specifics such as the landscape extent, pixel size, buffer width, temporal length of the simulation, and 

output reporting interval. These are decided in the context of the modeling 

objective, available computing resources, and available modeling expertise. 

Also, the design of the set of simulation scenarios needed to successfully complete the simulation objective are done at this step.

4. Gather data. All models need data for parameterization, initialization, and 

validation, and it is critical that appropriate and accurate data are available to 

quantify parameters, compute starting conditions, and test model output for 

the entire simulation area. If not, a literature search can help quantify those 

missing parameters or they can be estimated using local experts or statistical 

modeling.

5. Parameterize model. Analyze collected data to quantify parameters for direct 

input into the model. Be sure to document all sources of information used to 

quantify each parameter. A scheme will be needed to populate all of the thousands of parameters required by FireBGCv2. For example, Keane and others 

(1996a) had problems populating species parameters for FIRE-BGC, so they 

used a plant functional types approach where undergrowth and tree species 

were grouped into plant functional types based on genus, fire adaptations, and 

morphology. If parameters were unavailable for a species, they used the values 

for another species in that plant functional type. If those were unavailable, they 

used a default parameter set.

6. Initialize model. Model execution must start from someplace, so many modelers use the current conditions as a starting point for model runs. For example, 

a vegetation type layer might be overlaid with a potential vegetation map to 

create the stand map with sites (potential vegetation) that can then be used to 

initialize a landscape simulation.

7. Execute model. Run the model, then run it again, and again. Be sure that all parameters and initial conditions are entered into the model correctly by checking 

the echo and error files (see the “Model Execution” section). Clean up all error 

statements and warnings.

8. Evaluate results. Always examine model outputs in detail to determine if the 

model is computing believable and realistic answers. We suggest that a calibration scenario be designed to mimic landscape dynamics over the last 50 years 

by selecting the weather and landscape initial conditions that best capture this 
68 USDA Forest Service RMRS-GTR-255. 2011.

recent history. This landscape could be run for 50 years and resultant landscape 

dynamics can be compared with observed conditions (field data) at the landscape, stand, and tree levels.

9. Calibrate model. If unrealistic predictions are generated, then adjust parameters and initial conditions to get more realistic answers. Pay special attention 

to parameters with high uncertainty, such as those quantified from expert opinion, as they may not be rectified with the entire parameterization. Again, check 

simulated results against field data to ensure realism. Repeat steps (8) and (9) 

until satisfied with results.

10. Simulate results. The implementation of the entire simulation design should 

be done using the model. All simulation scenarios should be executed at least 

five times and results should be averaged to achieve modeling objectives.

11. Perform analysis. Use the simulated results in the appropriate analysis to successfully accomplish the simulation objective. For example, the change in response variables between modeling scenarios can be analyzed using analysis 

of variance techniques.

This entire process could take years for the inexperienced person using complex simulation designs or hours for the experienced modeler using simple, 

parsimonious scenarios. To simplify the discussion of these steps, we’ve broken 

the categories into five major tasks: parameterization, initialization, calibration, 

simulation, and output analysis.

Figure 14. A flow chart to use 

when calibrating and testing 

FireBGCv2 output to ensure the 

most realistic simulations.
USDA Forest Service RMRS-GTR-255. 2011. 69

Parameterization

FireBGCv2 requires the quantification of many algorithm parameters, as is the 

case with most multi-species mechanistic models (Burk and others 1990, Bossel 

1991). However, it is difficult, and sometimes impossible, to gather all of the 

information needed to parameterize every relationship included in the model. 

Critical ecophysiological research needed for model parameterization is lacking 

for most forest species. Therefore, a scheme should be devised to assign parameter values that are taken from well-studied tree species to the less-studied tree 

species. A suggested scheme involves grouping tree species into similar ecological groups or plant functional types (Diaz and Cabido 1997). Then, if data are not 

available to quantify a FireBGCv2 species parameter, the parameter for another 

species in the plant functional type can be used (Kellomäki and Väisänen 1991). 

Tree species can be grouped according to their role in the successional process using the Minore (1979) classifications. Tree species with similar successional roles 

seemed to display similar ecological, morphological and physiological qualities 

(Grime 1966, Drury and Nisbet 1973, Grime 1974, Bazzaz 1979, Grime 1979, 

Canham and Marks 1985, Wallace 1991). For instance, early seral species tend to 

have high photosynthetic rates, low tolerance for shade, rapid height and diameter growth, frequent cone crops, long lifespans, and short crown lengths (Grime 

1979, Bazzaz 1990). Late seral species appear to show opposite qualities (Finegan 

1984, Drake 1991). 

We suggest the following scheme for assigning model parameters:

• Perform a literature search on model parameter values and attempt to 

parameterize as many species as possible. Create a spreadsheet with parameters 

and reference citations.

• Create a default parameter file for fuels, plants, and, most importantly, species 

using the results of the literature search and input files from this report.

• Create plant functional types around the modeling objective. If the simulation 

scenarios emphasize differences in fire dynamics, then the plant functional 

types should be designed around fire adaptations or survival characteristics.

• Assign parameters from parameterized plants to other un-parameterized 

species with each plant functional type.

• If no parameters exist for a species or functional type, use the value from the 

default species parameter list.

Most selected parameters and the initial values discussed next are stored in a 

suite of ASCII data files that are immediately scanned at the start of simulation for 

validity and errors. These files are stratified based upon the overall organizational 

architecture of the model (landscape, site, stand, species, and tree) with examples 

depicted in the “Input File Structure” section. The names of these input files are 

specified in the Driver.in file that is specified as an argument in the program execution command line.

Parameterization is the most important task in conducting FireBGCv2 simulations, but there are very few ecosystems, landscapes, and species in the world that 

have sufficient ecological data across the entire simulation area, and some sort of 

data synthesis is required to extrapolate existing data across the entire simulation 

landscape. Comprehensive literature searches and expert consultations are critical 

in data-poor regions. Model parameters can be approximated from many sources 

listed here in order of preference:

1. Measurement. The actual measurement of a parameter on the landscape that is 

to be simulated will result in the most credible simulation results.

2. Literature. A review of the literature to evaluate the parameter values measured 

on other landscapes by other studies may provide a suitable alternative, but 

special attention must be made to ensure that the context of the measurement 
70 USDA Forest Service RMRS-GTR-255. 2011.

best fits the simulation landscape (for example, match vegetation types, topo-

graphic settings, and sampling methods). There are also a number of literature 

syntheses of important model parameters (Hengst and Dawson 1994, Korol 

2001, Hessl and others 2004).

3. Meta-modeling. Models can be used to estimate parameters for other models. 

Stage and others (1995), for example, used a growth and yield model (FVS) to 

estimate growth parameters for a gap model.

4. Iterative modeling. This is when a parameter is approximated and then input 

into the model, and the model is run to generate results that are then com-

pared to a reference to determine if the reference agrees with the simulated 

results. If not, the parameter is incrementally changed until the results match 

the reference.

5. Dephi approach. A parameter is approximated by a group of experts in a sys-

tematic fashion based on past their experience.

6. Default. Often, a modeler will prepare a sample model application for dem-

onstration purposes, and parameter values from these input files can be used.

7. Best guess. Sometimes, the only option is to guess at the value based on past 

experience and consultation with modelers and then use iterative modeling to 

calibrate the guess.

Technically, the author of each model should conduct an extensive sensitivity 

analysis of their model’s parameters to identify each parameter’s importance in 

order to avoid making detailed measurements or literature searches on minor pa-

rameters or using best guesses for the most important parameters.

Even the best historical data to estimate model parameters does not ensure 

simulations will have high accuracies or realism. There is always a tension be-

tween model design and model parameterization. Keane and others (2006), for 

example, found that fire regimes were simulated incorrectly when fire return 

interval parameters were quantified from tree fire scars located in topographic 

settings that experienced frequent fires, such as flat areas. The detail in the simula-

tion model will never capture the detail of environmental factors that created the 

field evidence from which parameters are quantified. As a result of this complex-

ity, it is difficult to realistically simulate disturbance or vegetation development 

without building overly complex models that are difficult to parameterize and 

inefficient to run for large landscapes over long simulation periods. As mentioned, 

it is important that results of any landscape simulation be compared with expert 

experience and any available data to determine if output is reasonable. If not, then 

the parameters should be adjusted to more closely approximate reality.

Initialization

Simulation Initialization

Quantification of site and stand initial values is also time-consuming and often 

costly to accomplish from landscape inventories. A scheme was devised in Keane 

and others (1996a) to assign information from a sampled stand to similar, un-

sampled stands. Site and stand parameter values were quantified using a potential 

vegetation approach where sites were classified to a “fire group” from topographi-

cal information provided by the GIS DEM (Fischer and Bradley 1987). Stand and 

site parameters that were not directly measured were quantified in the input file 

based on data taken from other studies or values in the literature for the fire group, 

such as fire return interval and fire size.

Real-world landscapes are climatologically, edaphically, and ecologically 

complex, to such a degree that they cannot be accurately represented in ecosys-

tem process models because of time and computational constraints. The challenge 
USDA Forest Service RMRS-GTR-255. 2011. 71

in initializing a study region for FireBGCv2 implementation is to define a set of 

initial characteristics that provide enough landscape detail to capture the driving 

ecological processes and disturbance patterns that influence the beginning of a 

simulation while not overwhelming system resources with detail that will ultimately be lost as the simulation proceeds. The first step in this process, and one 

that is critical in establishing a fieldwork sampling design, is to define a set of 

sites across the study region. Site boundaries should be established on the basis of 

similarity in soils and climatology—for example, using soils data from the State 

Soil Geographic (STATSGO) digital soils database or a biophysical landscape 

classification from the LANDFIRE Environmental Site Potential (ESP) layer. The 

ESP layer represents the vegetation type that could be supported within a given 

area based on its biophysical environment, and if aggregated to a coarse enough 

scale to eliminate sites smaller than about 5 percent of the simulation landscape, 

can approximate site boundaries. A suggested number of sites for a 50,000-ha 

simulation landscape is 8 to 12 (Figure 15). Site types in Figure 15 refer to a combination of dominant vegetation species, elevation, and climatology; for example, 

the ABLA/Low/Wet site is a relatively low-elevation, moist site that is dominated 

by subalpine fir forests.

Stand boundaries are defined on the basis of unique, existing, dominant vegetation types within a site. These types can be mapped using spatial data such 

as the LANDFIRE Existing Vegetation Type layer, stand inventory data, or other appropriately scaled land cover data layers. Stand structural characteristics 

such as overstory height can be incorporated into the stand classification using 

the LANDFIRE Existing Vegetation Height layer or other structural data layers. 

Integration of these data allows users to differentiate among stands characterized 

as having the same existing vegetation type but at different successional stages, 

perhaps as the result of a disturbance event. A simulation landscape that is 50,000 

ha or more in area may contain 300 to 500 stands, depending on the complexity of 

the existing vegetation mosaic (Figure 16).

 Figure 15. Site types mapped for 

the Lake McDonald area. “ABLA” 

represents subalpine fir, and “TSHE” 

represents western hemlock.
72 USDA Forest Service RMRS-GTR-255. 2011.

Field sample plots should ideally be established within the study area so that 

each unique site-stand combination is sampled at least once. Figure 16 shows the 

location of field sampling plots within the McDonald watershed, Glacier National 

Park simulation area. Data from each plot are extrapolated across the simulation landscape to parameterize unsampled stands. Data collected within each 

simulation plot fall within three categories: (1) general plot description, including 

latitude and longitude; elevation; aspect; slope; plot area; plot-scale percent tree, 

shrub, and forb cover; dominant over- and understory vegetation species; stand 

height; and canopy cover; (2) vegetation data, including tree height; diameter; 

status; crown base height; and shrub and forb percent cover; and (3) fuels data, 

including 1 hr to 1000 hr fuel loadings and litter and duff bulk densities. Plot 

descriptive and vegetation cover field forms can be easily modified from those 

provided in the FIREMON Fire Effects Monitoring and Inventory System (Lutes 

and others 2006). The FIREMON reference also includes detailed information on 

data collection protocols and methods. We suggest using the FIREMON data collection protocols and forms because the system can be used to post-process field 

data to the format required for FireBGCv2 initialization.

A number of methods exist for estimating fuel loadings, including planar intersect techniques such as Brown’s transects (Brown 1974), photo series (Sikkink 

and Keane 2008), and visual loading estimates such as the Photoload Sampling 

Technique (Keane and Dickinson 2007a, 2007b). The Photoload method uses 

calibrated, downward-looking photographs of known fuel beds as reference images for individual surface fuel beds. These reference loadings can be height- and 

volume-adjusted to match field observations. The Photoload method is well-suited for initialization of the fuel components of FireBGCv2 and includes sampling 

protocols and forms that can easily be modified to suit most sampling designs.

The field data types previously described, combined with site and stand spatial 

data layers, are used to build the Site.in, Stand.in, and vegetation and fuels input 

files described in the “Input File Structure” section. Together, these data comprise 

the initial conditions defined for a simulation landscape.

Figure 16. Stands mapped for the lake 

McDonald area. The red dots are field 

plots used to populate stand initial 

conditions. Black lines are the site 

boundaries, and each color represents 

a different stand vegetation type or 

structural stage.
USDA Forest Service RMRS-GTR-255. 2011. 73

Model Initialization

Once the user has specified all the initial conditions using field data, the 

model then performs a number of initializations to populate the state variables. 

FireBGCv2 uses generalized tree input data taken from the Tree.in file to compute 

tree attributes needed in the simulation. Tree data are input to the model by stand 

from tables of tree density, diameter, height, height to base of crown, and age by 

species. The model creates a list of all trees in a stand from the tree density information and assigns the appropriate attributes (DBH, HT, HBC, and AGE) to the 

trees. Then, FireBGCv2 computes those attributes that are explicitly utilized by 

the model for each tree.

The tree’s structural characteristics are computed first. The tree’s ecophysiological compartment values are initialized first from allometric equations that use 

the input structural characteristics as independent variables. Leaf carbon (LeafC, 

kg C) is approximated from the Brown (1978) equations that estimate foliar biomass from DBH and species. This biomass estimate is then multiplied by the 

fraction of leaf biomass that is carbon. Leaf area is estimated from the product of 

the specific leaf area and LeafC (kg C). Stem carbon (LivestemC and DeadstemC, 

kg C) is estimated from the stem and bark volume equations of Faurot (1977) 

and crown volume equations of Brown (1978). Stem and bark volume, computed 

from DBH and HT, are multiplied by their respective densities and added together 

to obtain stem biomass. This biomass is then multiplied by the fraction that is 

carbon to estimate stem carbon. Coarse and fine root carbon (CrootC and FrootC, 

kgC) are estimated from the empirical biomass equations. The biomass estimates 

are multiplied by the fraction of that biomass that is carbon to obtain carbon in 

the coarse and fine roots. Tree nitrogen compartments are initially computed as 

proportions of the tree’s corresponding carbon pools.

Most stand compartments are initialized from the sum of the corresponding 

tree compartments. Stand leaf area (LA, m2) is the sum of leaf area for all trees 

and undergrowth. Leaf, stem, fine root, and coarse root carbon and nitrogen are 

the sum of the corresponding tree and undergrowth components. Some initial 

stand attributes are quantified from the stand input file. Snowpack (SnowW, kgW 

m-2) and soil water (SoilW, kgW m-2) also receive initial values from the Stand.in 

file. Starting values for all compartments that make up the ground or forest floor 

(see the “Ground Dynamics” section in “Stand Processes”) and undergrowth (see 

the “Undergrowth Dynamics” in “Stand Processes”) are obtained from the Stand.

in file.

New estimates of some tree compartments are also computed at simulation 

year’s end. A new tree leaf area is estimated from leaf carbon. Effective tree area 

and the resource allocation factor is recomputed from the new stand and tree leaf 

area estimates. Photosynthesis is initialized for the year at 10 percent of last year’s 

net carbon gain to simulate the effect of carbon storage on the growth process 

(Running and Hunt 1993).

Calibration

Model calibration involves running the model under known and measureable 

conditions and generating results that are then compared against real data (that is 

hopefully collected from the landscape that is being simulated) to evaluate realism and accuracy. Rarely is there a dataset that can be used to properly calibrate 

a model because most inventories do not measure the response and explanatory 

variables output from the model. Here are some steps to take when calibrating the 

model:

1. Obtain all available data for the simulation landscape that can be used to evaluate model behavior. Possible data sources include:

• Tree ring width data. These can be compared against model growth 

predictions.
74 USDA Forest Service RMRS-GTR-255. 2011.

• Stand development data. Any data that quantifies changes in stand structure 

for any stand(s) on the target landscape can be used to determine if the 

model is predicting stand development correctly.

• Fire history data. Maps of burn boundaries can be used to compute 

landscape fire rotation and to compute fire pattern metrics to compare 

against simulation results.

• Disturbance records. Any spatial or tabular records of disturbance impacts, 

such as mountain pine beetle damage, can be used to adjust or validate 

model output.

• Digital map chronosequences. Spatial layers that show the landscape at two 

or more time periods are especially useful for comparing modeled landscape 

changes to observed changes.

2. Craft a simulation scenario that best matches the collected data. This involves 

matching the initial conditions, site parameters (including weather), and tree 

lists to the conditions that are represented by the data.

3. Execute the model for a time period that matches the comparison data and output simulation variables that match those measured variables.

4. Use qualitative or statistical techniques to compare the results.

5. If results do not agree, adjust the appropriate parameters until results are compatible. For example, if not enough area burned on the landscape, then increase 

fire return intervals or fire size parameters in the Site.in file until results match. 

However, this is somewhat problematic, and it may take trial and error to figure 

out which parameters to adjust and how much to adjust them.

6. Keep repeating steps (3) through (5) until satisfied with comparison.

Simulation

This step involves executing the model for all the scenarios in the simulation 

experiment design. There are a few cautions in this process. FireBGCv2 produces 

copious results, especially if maps are requested as output, that tend to quickly fill hard disks, so be sure the computers have sufficient disk space. Second, 

FireBGCv2 requires abundant memory for long simulations, so it is best to have 

at least 6 to 10 gigabytes of memory available during the execution. Third, it 

is important that the simulation experiment be carefully designed to minimize 

confusion of results between scenarios. A directory structure should be created 

where files that do not change across scenarios, such as Fuel.in, Species.in, and 

Tree.in, are put in one directory, and files that will change across scenarios, specifically Sim.in, Map.in, and Climate.in, are placed in directories that represent 

the appropriate scenario. Output should also be stored in directory structure that 

emphasizes differences across scenarios. This directory structure can have many 

variations, and one useful variation is contained within this report (see the “Input 

File Structure” section).

It is actually quite easy to execute FireBGCv2 by simply entering the following command in a MS-DOS Command Prompt window:

C:>firebgcv2 driver.in

where driver.in is the name of the Driver.in input file that contains the filenames 

that drive the program. The C:> prompt indicates the directory in which to start 

the FireBGCv2 program. It is important that the program is started in a directory 

where the Driver.in files reside. So, if the files are in the directory c:\firebgc\yellowstone, then use the cd command and navigate to that directory by entering:

C:>cd c:\firebgc\yellowstone
USDA Forest Service RMRS-GTR-255. 2011. 75

Then the command prompt and FireBGCv2 execution command will be:

C:\ firebgc\yellowstone> firebgcv2 driver.in

The program will immediately begin printing messages on the status of the execution. You can shunt these messages to a file by using the following command:

	 C:\ firebgc\yellowstone> firebgcv2 driver.in > messagefile.txt

where messagefile.txt is a file that will store all of the messages generated from 

the program.

Output

FireBGCv2 provides many avenues for printing intermediate, non-spatial results during program execution. Model calculations can be printed to a variety 

of ASCII files depending on the temporal and organizational scale. These ASCII 

files can then be imported into statistical and graphic software packages for analysis and display. The model was programmed to allow the printout of additional 

variables with little or no modification of the program. All output file names are 

specified in the Driver.in file. The state and intermediate variables whose values 

are to be printed at the appropriate time step are specified in the Sim.in file. Any 

ecophysiological component at the stand and tree levels and any intermediate 

variable at the stand and tree levels can be printed to the appropriate files. This 

is done by specifying the index (i) of the compartment or intermediate variable 

in the output variable list (Table 7). A seasonal window is available to limit the 

printing of daily values to only a portion of the year. Only output data that are 

calculated for days between a specified starting and ending yearday, as entered in 

the Driver.in file, will be printed in the appropriate file. No data will be printed 

to any of the output files described next if the starting yearday for the seasonal 

window is entered as zero.

The first file created by FireBGCv2 is the Echo file that contains all input data 

and initialization data, as computed by the model (Echo.out). This file is used to 

verify that input file structure and data values are correct. The remaining output 

files are stratified by the time step at which they are updated (year or day) and the 

organizational scale described by the data (tree or stand).

Stand-level predictions for specified stand state variables, compartments, and 

intermediate variables, as specified in the Sim.in file, are printed each simulation 

day for every stand in the simulation into the Stand Day file (Stand.day). No treelevel predictions are printed at a daily time step. Each year, FireBGCv2 writes 

a number of computed values to the FireBGCv2 output files. Yearly tree output 

data are written to two files by FireBGCv2. The Tree List file (Tree.list) contains 

a list of trees by simulation year, site, and stand. Each tree is described by tree ID 

number, species acronym, DBH (cm), HT (m), and AGE (years). This variable list 

is also static and cannot be changed without FireBGCv2 program modification. 

The yearend values of the compartment and intermediate variables for a selected 

tree are written to the second tree output file, the Tree.year file. The target tree is 

specified by number in the Driver.in file. These yearly values are written for that 

same tree number across every stand in the simulation. No predictions are written 

for that tree once it dies.
76 USDA Forest Service RMRS-GTR-255. 2011.

Table 7. FireBGCv2 stand-level output variables that can be specified in the Sim.in file.

ID num Variable name Description Units of measure

Tree silivicultural characteristics

0 tree->age Tree age years

1 tree->carea Tree crown area m2

2 tree->la Tree leaf area m2

3 (DREAL)tree->stress Number years of growth stress years

4 (DREAL)tree->rust Number of years infected by rust years

 

Tree xt carbon state variables

5 xt->leafC Tree leaf carbon kgC m-2

6 xt->livestemC Tree sapwood carbon kgC m-2

7 xt->deadstemC Tree heartwood carb kgC m-2

8 xt->frootC Tree fine root carb kgC m-2

9 xt->livecrootC Tree live coarse root kgC m-2

10 xt->deadcrootC Tree dead coarse root kgC m-2

11 xt->npp Tree net primary prod kgC m-2

12 xt->psn Tree photosynthesis kgC m-2

13 xt->respiration Tree respiration kgC m-2

 

Stand ecosystem descriptive variables

100 stand->fri Fire return interval years

101 stand->albedo Stand albedo dimensionless

102 stand->la Stand leaf area m2

103 stand->lai Stand allside LAI dimensionless

104 stand->plai Stand project LAI dimensionless

105 stand->plaisun Stand project LAI for sun leaves dimensionless

106 stand->plaishade Stand project LAI for shade leaves dimensionless

107 stand->ba Basal area m2 ha-1

108 stand->ht Ave stand height m

109 stand->hbc Ave stand height to crown m

110 stand->cbd Canopy bulk density crown kg m-3

111 stand->crown_area Stand total crown area m2

112 stand->canopy_cover Crown cover percent

113 stand->area Stand total area ha

114 stand->nt Stand number of trees # trees

115 stand->nt_dead Number of dead trees this year # trees

116 ecostand->degree_days Growing degree days degrees C

269 stand->nt_sap Number of new saplings this year # trees

270 stand->nsnag Stand number of snags # snags

271 stand->ysf Years since last fire years

272 stand->pstage Phenological stage categorical

273 stand->lag Years of lag after a fire years

275 stand->habsuit1 Habitat suitability index index

276 stand->habsuit2 Habitat suitability index index

277 stand->habsuit3 Habitat suitability index index

278 stand->habsuit4 Habitat suitability index index

279 stand->habsuit5 Habitat suitability index index

 

Stand ground variables

250 ground->w1C 1 hour twigwood (0-1cm) kgC m-2

251 ground->w10C 10 hour twigwood (1-3cm) kgC m-2

252 ground->w100C 100 hour twigwood (3-8cm) kgC m-2

253 ground->w1000C 1000 hour twigwood (8+cm) kgC m-2

254 ground->leaffallC Total leaffall carbon kgC m-2

255 ground->litterC Total litter carbon kgC m-2

256 ground->duffC Total duff carbon kgC m-2

257 xp->leafC Leaf C kgC m-2

258 xp->frootC Fine root C kgC m-2

259 xp->livestemC Live stem C kgC m-2

260 xp->deadstemC Dead stem C kgC m-2
USDA Forest Service RMRS-GTR-255. 2011. 77

Table 7. Continued.

ID num Variable name Description Units of measure

Stand ground variables (continued)

261 xp->livecrootC Live coarse root C kgC m-2

262 xp->deadcrootC Dead coarse root C kgC m-2

485 log_load Dead coarse woody debris kg m-2

486 fuel_load Dead fine woody debris kg m-2

487 duff_depth Duff and litter depth cm

488 stand->fbfmID Fire behavior fuel model ID number kgC m-2

489 stemC Tree stem carbon kgC m-2

490 shrubC Shrub biomass kgC m-2

491 herbC Herb biomass kgC m-2

 

Map variables

263 sstage Structural stage: seed, sap, pole categorical

264 spp_ba Species with greatest basal area categorical

265 spp_la Species with greatest leaf area categorical

266 spp_dbh Species with greatest diameters categorical

267 spp_cover Species with greatest cover categorical

268 spp_biomass Species with greatest biomass categorical

 

Leaf dynamics

650 ecostand->lai Leaf area index dimensionless

651 ecostand->plai Projected leaf area index dimensionless

652 ecostand->plaisun Sunlit projected leaf area index dimensionless

653 ecostand->plaishade Shaded projected leaf area index dimensionless

654 ecostand->sun_proj_sla Sunlit projected SLA m2 kgC-1

655 ecostand->shade_proj_sla Shaded projected SLA m2 kgC-1

656 ecostand->psn_sun Sunlit assimilation per unit pLAI umol m-2 s-1

657 ecostand->psn_shade Shaded assimilation per unit pLAI umol m-2 s-1

 

Soil variables

658 ecostand->swf Soil water fraction dimensionless

659 ecostand->psi Water potential of soil and leaves -Mpa

660 ecostand->vwc Volumetric water content scalar

661 ecostand->gsws Accumulated growing season water stress -Mpa

662 ecostand->psi_max Maximum annual leaf water potential -Mpa

663 ecostand->psi_ave Average annual leaf water potential -Mpa

664 ecostand->swf_yr Summation of soil water content proportion

665 ecostand->soil_temp_yr Soil temperature summation degrees C

 

Water fluxes

666 ecostand->rainW_thrufall Rainfall reaching the forest floor kgH2O m-2 day-1

667 ecostand->rainW_drip Rainfall drip from canopy not evaporated kgH2O m-2 day-1

668 ecostand->rainW_thrufall Rainfall to soil water kgH2O m-2 day-1

669 ecostand->snowW_fall Rainfall to snow water kgH2O m-2 day-1

670 ecostand->rainW_intercepted Rainfall captured by leaves—intercepted kgH2O m-2 day-1

671 ecostand->snowW_fall Snowfall kgH2O m-2 day-1

672 ecostand->snowW_melt Snowmelt kgH2O m-2 day-1

673 ecostand->snowW_sub Snow sublimation kgH2O m-2 day-1

674 ecostand->snowW_melt Snow water to soil water kgH2O m-2 day-1

675 ecostand->soilW_evap Evaporation of water from soil kgH2O m-2 day-1

676 ecostand->soilW_outflow Soil water lost to runoff and ground water kgH2O m-2 day-1

677 ecostand->soilW_trans Soil water transpired by canopy kgH2O m-2 day-1

678 ecostand->canopyW_drip Evaporation of water from canopy kgH2O m-2 day-1

679 ecostand->canopyW_trans Canopy transpiration kgH2O m-2 day-1

680 ecostand->canopyW_evap Canopy evaporation kgH2O m-2 day-1

681 ecostand->transW Water transpired by canopy kgH2O m-2 day-1
78 USDA Forest Service RMRS-GTR-255. 2011.

Table 7. Continued.

ID num Variable name Description Units of measure

Evapotranspiration variables (conductances and multipliers)

682 ecostand->et Evapotranspiration kg m-2

683 ecostand->pet Potential evapotranspiration kg m-2

684 ecostand->ppfd_per_plaisun PPFDensity per PLAI sunleaves dimensionless

685 ecostand->ppfd_per_plaishade PPFDensity per PLAI sunleaves dimensionless

686 ecostand->m_tmin Freezing night temperature multiplier dimensionless

687 ecostand->m_psi Water potential multiplier dimensionless

688 ecostand->m_co2 Atmospheric [CO2] multiplier dimensionless

689 ecostand->m_ppfd_sun PAR flux density multiplier dimensionless

690 ecostand->m_ppfd_shade PAR flux density multiplier dimensionless

691 ecostand->m_vpd Vapor pressure deficit multiplier dimensionless

692 ecostand->m_final_sun Product of all other multipliers for sunlit dimensionless

693 ecostand->m_final_shade Product of all other multipliers for shaded dimensionless

694 ecostand->gl_bl Leaf boundary layer conductance m s-1

695 ecostand->gl_c Leaf cuticular conductance m s-1

696 ecostand->gl_s_sun Leaf-scale stomatal conductance m s-1

697 ecostand->gl_s_shade Leaf-scale stomatal conductance m s-1

698 ecostand->gl_e_wv Leaf conductance to evaporated water m s-1

699 ecostand->gl_sh Leaf conductance to sensible heat m s-1

700 ecostand->gl_t_wv_sun Leaf cond to water vapor sunlit leaves m s-1

701 ecostand->gl_t_wv_shade Leaf cond to water vapor shaded leaves m s-1

702 ecostand->gc_e_wv Canopy conductance to evaporated water m s-1

703 ecostand->gc_sh Canopy conductance to sensible heat m s-1

704 ecostand->gcorr Conductance correction factor scalar

 

Respiration variables

705 ecostand->mr_leaf Daily leaf respiration—night and day kgC day-1

706 ecostand->mr_leafC_day Daytime leaf respiration kgC day-1

707 ecostand->mr_leafC_night Nighttime leaf respiration kgC day-1

708 ecostand->mr_lstemC Sapwood respiration assumes only sapwood live kgC day-1

709 ecostand->mr_lcrootC Coarse root respiration kgC day-1

710 ecostand->mr_frootC Fine root respiration kgC day-1

umolC m-2 projected

711 ecostand->dlmr_area_sun Sunlit leaf MR leaf area s-1

umolC m-2 projected

712 ecostand->dlmr_area_shade Shaded leaf MR leaf area s-1

713 ecostand->n_area_sun Mass Nitrogen per unit proj leaf area sunlight kgN m-2

714 ecostand->n_area_shade Mass Nitrogen per unit proj leaf area shade kgN m-2

715 ecostand->leafC_max Maximum Leaf carbon in stand kgC

716 ecostand->can_leafN2can Canopy nitrogen as percent of max canopy N proportion

717 ecostand->leafN_conc Canopy leaf N concentration kgN kgC-1

718 ecostand->leafN_trans Canopy leaf N retranslocation fraction scalar 0 to 1

719 ecostand->leaf_long Leaf retention time years

720 ecostand->leaf_falltolitC Leaffall contribution to litter carbon kgC

 

Diagnostic variables

721 ecostand->ytd_maxplai Year-to-date maximum projected LAI m2 m-2

722 ecostand->npp Net Primary Productivity = GPP - Rmaint - Regrowth kgC m-2 day-1

723 ecostand->nep Net Ecosystem Production = NPP - Rheterotroph kgC m-2 day-1

724 ecostand->nee Net Ecosystem Exchange = NEP - fire losses kgC m-2 day-1

725 ecostand->gpp Gross Primary Productivity = PSN source kgC m-2 day-1

726 ecostand->mr Maintenance respiration kgC m-2 day-1

727 ecostand->gr Growth respiration kgC m-2 day-1

728 ecostand->hr Heterotrophic respiration kgC m-2 day-1

729 ecostand->fireC Fire carbon losses kgC m-2 day-1

730 ecostand->vegC Total vegetation C kgC m-2

731 ecostand->litC Total litter C kgC m-2

732 ecostand->soilC Total soil C kgC m-2

733 ecostand->totalC Total of vegC, litrC, and soilC kgC m-2
USDA Forest Service RMRS-GTR-255. 2011. 79

Table 7. Continued.

ID num Variable name Description Units of measure

Diagnostic variables (continued)

734 ecostand->sum_npp Summed over entire simulation kgC m-2

735 ecostand->sum_nep Summed over entire simulation kgC m-2 

736 ecostand->sum_nee Summed over entire simulation kgC m-2

737 ecostand->sum_gpp Summed over entire simulation kgC m-2

738 ecostand->sum_mr Summed over entire simulation kgC m-2

739 ecostand->sum_gr Summed over entire simulation kgC m-2

740 ecostand->sum_hr Summed over entire simulation kgC m-2

741 ecostand->sum_fire Summed over entire simulation kgC m-2

 

Decomposition and accumulation variables

742 ecostand->leafC_to_leaffallC Leaf carbon to leaffall carbon kgC m-2

743 ecostand->leaffallC_to_litterC Leaf carbon to litter carbon (gap) kgC m-2

744 ecostand->dstemC_to_w1C Stem carbon to 1 hr woody carbon (gap) kgC m-2

745 ecostand->dstemC_to_w10C Stem carbon to 10 hr woody carbon (gap) kgC m-2

746 ecostand->dstemC_to_w100C Stem carbon to 100 hr woody carbon (gap) kgC m-2

747 ecostand->dstemC_to_cwd Stem carbon to 1000 hr woody carbon (gap) kgC m-2

748 ecostand->litterC_to_duffC Litter carbon to duff carbon (gap) kgC m-2

749 ecostand->duffC_to_soilC Duff carbon to soilC or sink (gap) kgC m-2

750 ecostand->w1C_to_duffC Woody 1 hr carbon to duff carbon kgC m-2

751 ecostand->w10C_to_duffC Woody 10 hr carbon to duff carbon kgC m-2

752 ecostand->w100C_to_duffC Woody 100 hr carbon to duff carbon kgC m-2

753 ecostand->w1000C_to_duffC Woody 1000 hr carbon to duff carbon kgC m-2

754 ecostand->w1C_to_soilC Woody 1 hr carbon to soil carbon kgC m-2

755 ecostand->w10C_to_soilC Woody 10 hr carbon to soil carbon kgC m-2

756 ecostand->w100C_to_soilC Woody 100 hr carbon to soil carbon kgC m-2

757 ecostand->w1000C_to_soilC Woody 1000 hr carbon to soil carbon kgC m-2

 

Grab-bag of variables added at a later date

758 ecostand->npp Species-level NPP estimate kgC m-2

760 ecostand->annoutflow Water lost from the site kgH2O m-2 year-1

 

Carbon summary variables

722 ecostand->npp Net Primary Productivity = GPP - Rmaint - Regrowth kgC m-2

723 ecostand->nep Net Ecosystem Production = NPP - Rheterotroph kgC m-2

724 ecostand->nee Net Ecosystem Exchange = NEP - fire losses kgC m-2

725 ecostand->gpp Gross Primary Productivity = PSN source kgC m-2

726 ecostand->mr Maintenance respiration kgC m-2

727 ecostand->gr Growth respiration kgC m-2

728 ecostand->hr Heterotrophic respiration kgC m-2

770 ecostand->abovegroundC Standing crop of aboveground C kgC m-2

771 ecostand->groundC All dead biomass on the ground kgC m-2

772 ecostand->deadC Aboveground dead carbon kgC m-2

773 ecostand->liveC Aboveground live carbon kgC m-2

774 ecostand->fireC Fire carbon losses kgC m-2

775 ecostand->litC Total litter C kgC m-2

776 ecostand->snagC Total snag C kgC m-2

777 ecostand->soilC Total soil C kgC m-2

778 ecostand->totalC Total of all carbon on site kgC m-2
80 USDA Forest Service RMRS-GTR-255. 2011.

Demonstration

This section presents example graphical (output maps) and tabular (output files) 

output from the FireBGCv2 model. The simulation landscape presented is the 

Lake McDonald watershed in Glacier National Park, Montana, USA (Figure 17). 

Simulation specifics are an historical weather scenario with no fire suppression 

(all fires allowed to burn) and no management actions (no clearcutting, thinning, 

or prescribed burning) implemented on the landscape. All model input files and 

parameters are quantified for the Lake McDonald landscape (Keane and others 

1996a). The following simulation specifications (as defined in the Sim.in file) 

apply: succession model driver = mechanistic gap model; simulation time = 500 

years; fire spread model = LANDSUM; fire ignition probability = site-level fire 

return intervals; and phenology = dynamic phenology model.

The Lake McDonald simulation landscape is an approximately 50,000-ha 

watershed on the western slope of the Continental Divide. Elevation within the 

watershed ranges from 950 m at lakeside locations to 2900 m in alpine areas. 

Approximately 75 percent of the watershed is forested, predominantly in coniferous types such as western hemlock (Tsuga heterophylla), western red cedar 

(Thuja plicata), western larch (Larix occidentalis), Douglas-fir (Pseudotsuga 

menziesii), and lodgepole pine (Pinus contorta). Two distinct fire regimes are 

present in the watershed: large, stand-replacement fires on moist sites with fire 

return intervals of 120 to 350 years; and mixed-severity fires in drier areas with 

fire return intervals of 25 to 75 years. The species and their parameterizations are 

shown in Table 8. Lake McDonald site types (Figure 15) and stands (Figure 16) 

were mapped using data layers from the LANDFIRE project.

As with any research tool, the types of analyses performed and output created 

should be question-driven and represent the most parsimonious and straightforward method of presenting research results. Unlike many other models, 

FireBGCv2 offers users a broad array of potential output variables and types, and 

the following sample output is not meant to be inclusive of all output variables 

and data types.

Figure 17. Lake 

McDonald watershed 

simulation landscape, 

Glacier National Park, 

Montana, USA.\
USDA Forest Service RMRS-GTR-255. 2011. 81 Table 8. FireBGCv2 species input parameters for the McDonald drainage in Glacier National Park. Parameters UNITS Ponderosa Grand Douglas Lodgepole Western Subalpine Engelmann Whitebark Alpine Western Western Western Quaking Paper pine	 fir	 -fir	 pine	 larch	 fir	 spruce	 pine	 larch	 white pine	 red cedar	 hemlock	 aspen	 birch Species name in four-letter alpha code	 PIPO	 ABGR	 PSME	 PICO	 LAOC	 ABLA	 PIEN	 PIAL	 LALY	 PIMO	 THPL	 TSHE	 POTR	 BEPA Species ID number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Vegetation lifeform	 (0-evergreen, 1-deciduous, 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2-shrub, 3-grass) Shade Tolerance Class (1-vsi, 2-si, 3-mst, 4-st, 1 4 4 2 1 4 4 2 1 2 4 5 1 1 5-vst) Sprouting ability (0-none, 1-low, 2-moderate, 0 0 0 0 0 0 0 0 0 0 0 0 1 3 3-prolific) Rain interception coefficient	 (1/LAI/day)	 0.045	 0.045	 0.045	 0.045	 0.045	 0.045	 0.045	 0.045	 0.045	 0.045	 0.045	 0.045	 0.001	 0.001 Light extinction coefficient	 dimensionless	 0.35	 0.45 0.45	 0.3	 0.312	 0.45	 0.45	 0.35	 0.312	 0.4	 0.5	 0.5	 0.5	 0.5 All-sided-to-projected conversion factor	 dimensionless	 3.54	 2.04	 2.85	 3.54	 3.54	 2.04	 2.94	 3.54	 3.54	 3.54	 2.8	 2.8	 2	 2 SapwoodC to deadwoodC ratio	 dimensionless	 0.9	 0.5	 0.9	 0.9	 0.9	 0.9	 0.9	 0.9	 0.9	 0.9	 0.9	 0.9	 0.9	 0.9 Specific leaf area of a projected area basis	 m2 kg C-1 20	 25	 22.8	 21.9	 18	 20	 20	 21	 16	 25	 20	 20	 25	 25 Ratio of shade to sunlit SLA	 dimensionless	 1	 2	 2.0 	 1	 1	 3	 2	 1	 1	 1	 3	 3	 1	 1 Fraction of leaf N in Rubisco	 dimensionless	 0.04	 0.04	 0.04	 0.04	 0.04	 0.04	 0.04	 0.04	 0.04	 0.04	 0.04	 0.04	 0.04	 0.04 Maximum canopy conductance (gmax)	 m sec-1 0.0065	 0.0065	 0.0067	 0.006	 0.006	 0.0065	 0.0064	 0.0067	 0.0067	 0.006	 0.006	 0.006	 0.003	 0.003 Leaf cuticular conductance (gc)	 m sec-1 0.00001	 0.00001	 0.00001	 0.00001	 0.00001	 0.00001	 0.00001	 0.00001	 0.00001	 0.00001	 0.00001	 0.00001	 0.00001	 0.00001 Leaf boundary layer conductance (gb)	 m sec-1 0.08	 0.08	 0.08	 0.08	 0.08	 0.08	 0.08	 0.08	 0.08	 0.08	 0.08	 0.08	 0.08	 0.08 Leaf water potential at field capacity	 -Mpa	 -0.5	 -0.5	 -0.5	 -0.5	 -0.5	 -0.5	 -0.5	 -0.5	 -0.5	 -0.5	 -0.5	 -0.5	 -0.5	 -0.5 Leaf water potential at stomatal closure	 -Mpa	 -1.65	 -1.4	 -1.6	 -1.46	 -1.5	 -1.4	 -1.3	 -1.65	 -1	 -1.5	 -1.3	 -1.3	 -2.3	 -2.3 Min vapor pressure deficit to affect conduct	 Pa	 500	 500	 700	 500	 500	 500	 500	 500	 500	 500	 500	 500	 1000	 1000 Max vapor pressure deficit to affect conduct	 Pa	 2000	 2000	 3500	 2000	 2000	 2000	 2000	 2000	 2000	 2000	 2000	 2000	 4200	 4200 Light compensation point	 W/m2 560 300 400 550 550 350 370 540 600 450 300 250 550 550 Carbon:Nitrogen (C:N) ratio for live leaves dimensionless 42 42 42 42 42 42 42 42 42 42 42 42 42 42 Critical litter C:N ratio dimensionless 93 93 93 93 93 93 93 93 93 93 93 93 93 93 Critical fine root C:N ratio	 dimensionless	 42	 42	 42	 42	 42	 42	 42	 42	 42	 42	 42	 42	 42	 42 Live wood C:N ratio	 dimensionless	 50	 50	 50	 50	 50	 50	 50	 50	 50	 50	 50	 50	 50	 50 Dead wood C:N ratio dimensionless 729 729 729 729 729 729 729 729 729 729 729 729 729 729 Critical duff C:N ratio dimensionless 500 500 500 500 500 500 500 500 500 500 500 500 500 500 Leaf and fine root turnover fraction	 kgC kgC-1year-1 0.33	 0.2	 0.25	 0.25	 1	 0.2	 0.2	 0.17	 1	 0.33	 0.25	 0.25	 1	 1 Live wood turnover fraction	 kg C kg C-1 0.7	 0.7	 0.7	 0.7	 0.7	 0.7	 0.7	 0.75	 0.75	 0.7	 0.7	 0.7	 0.75	 0.75 Stem, branch, and coarse root turnover fraction kg C kg C-1 0.04	 0.09	 0.01	 0.05	 0.05	 0.1	 0.1	 0.05	 0.05	 0.1	 0.1	 0.1	 0.05	 0.05 New fine root C to new leaf C allocation	 dimensionless	 1	 1	 1	 1	 1	 1	 1	 1	 1	 1	 1	 1	 1	 1 New stem C to new leaf C allocation	 dimensionless	 2.2	 2.2	 2.2	 2.2	 2.2	 2.2	 2.2	 2.2	 2.2	 2.2	 2.2	 2.2	 2.2	 2.2 New live wood C to new total wood C allocation	 dimensionless	 0.1	 0.1	 0.1	 0.1	 0.1	 0.1	 0.1	 0.1	 0.1	 0.1	 0.1	 0.1	 0.1	 0.1 New coarse root C to new stem wood C allocation	 dimensionless	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3 Current growth to storage C allocation	 dimensionless	 0.5	 0.5	 0.5	 0.5	 0.5	 0.5	 0.5	 0.5	 0.5	 0.5	 0.5	 0.5	 0.5	 0.5
82 USDA Forest Service RMRS-GTR-255. 2011. Table 8. Continued. Parameters UNITS Ponderosa Grand Douglas Lodgepole Western Subalpine Engelmann Whitebark Alpine Western Western Western Quaking Paper pine	 fir	 -fir	 pine	 larch	 fir	 spruce	 pine	 larch	 white pine	 red cedar	 hemlock	 aspen	 birch Species name in four-letter alpha code	 PIPO	 ABGR	 PSME	 PICO	 LAOC	 ABLA	 PIEN	 PIAL	 LALY	 PIMO	 THPL	 TSHE	 POTR	 BEPA Proportion C to allocate to indiv tree leafC	 (DIM)	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3 Leaf labile carbon fraction	 kg lab C kgC-1 0.32	 0.32	 0.32	 0.32	 0.32	 0.32	 0.32	 0.32	 0.32	 0.32	 0.32	 0.32	 0.32	 0.32 Leaf cellulose carbon fraction	 kg cell C kgC-1 0.44	 0.44	 0.44	 0.44	 0.44	 0.44	 0.44	 0.44	 0.44	 0.44	 0.44	 0.44	 0.44	 0.44 Leaf lignin fraction	 kg ligninC kgC-1 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24 Fine root labile carbon fraction kg lab C kgC-1 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3	 0.3 Fine root cellulose carbon fraction kg cell C kgC-1 0.45	 0.45	 0.45	 0.45	 0.45	 0.45	 0.45	 0.45	 0.45	 0.45	 0.45	 0.45	 0.45	 0.45 Fine root lignin fraction kg ligninC kgC-1 0.25	 0.25	 0.25	 0.25	 0.25	 0.25	 0.25	 0.25	 0.25	 0.25	 0.25	 0.25	 0.25	 0.25 Wood cellulose carbon fraction	 kg cell C kgC-1 0.76	 0.76	 0.76	 0.76	 0.76	 0.76	 0.76	 0.76	 0.76	 0.76	 0.76	 0.76	 0.76	 0.76 Wood lignin fraction	 kg ligninC kgC-1 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24	 0.24 Maximum attainable age	 years	 650	 275	 350	 220	 450	 180	 320	 1000	 800	 350	 650	 650	 120	 120 Maximum attainable height	 m	 65.63	 53.7	 57.15	 41.15	 68.58	 41.76	 54.56	 36.57	 30.48	 56	 79	 79	 35	 35 Maximum attainable diameter	 cm	 250.5	 139.4	 208.84	 110	 250	 126.7	 234.4	 182	 168	 254	 300	 300	 130	 130 Minimum number degree days	 degree days (degC)	 1350	 1200	 988	 988	 991.9	 427.7	 527.4	 426.7	 426.7	 1200	 1350	 1350	 260	 1000 Optimum number degree days degree days (degC) 1750 1650 1650 1650 1650 1400 1400 1000 1000 1650 1750 1750 1371 1650 Maximum number degree days	 degree days (degC)	 4767.4	 3979	 3979	 3500	 3979	 3426.7	 3426.7	 2871	 2871	 3593	 4000	 4000	 3000	 3979 Minimum threshold on water stress curve	 ratio	 0.15	 0.37	 0.22	 0.28	 0.28	 0.55	 0.59	 0.23	 0.65	 0.28	 0.4	 0.5	 0.1	 0.4 Site index at 50 years	 m	 14	 10	 13	 10	 15	 8	 11	 9	 8	 16	 12	 14	 6	 6 Probability of good cone crop	 probability	 0.395	 0.333	 0.446	 0.318	 0.438	 0.333	 0.167	 0.8	 0.368	 0.318	 0.25	 0.33	 0.368	 0.368 Proportion of seeds in serotinous cones	 proportion	 0	 0	 0	 0.5	 0	 0	 0	 0	 0	 0	 0	 0	 0	 0 Reproductive age years 20 15 20 15 15 25 25 60 50 15 20 20 15 15 Minimum diameter growth increment	 cm	 0.012	 0.005	 0.007	 0.015	 0.016	 0.008	 0.008	 0.006	 0.007	 0.016	 0.005	 0.005	 0.016	 0.016 Survival by duff depth—alpha coefficient	 probability	 10.59	 40.01	 38.69	 14.12	 20.17	 40.01	 40.01	 40.01	 40.01	 40.01	 40.01	 40.01	 20.17	 20.17 Survival by duff depth—beta coefficient	 probability cm-1 2.74	 5.115	 4.24	 2.28	 5.59	 6.115	 6.115	 6.115	 6.115	 6.115	 6.115	 6.115	 5.59	 5.59 Bark thickness coefficient	 cm bark cm DBH-1 0.063	 0.046	 0.063	 0.028	 0.063	 0.015	 0.022	 0.03	 0.05	 0.035	 0.035	 0.05	 0.014	 0.021 Initial live crown fraction for mature trees m m-1 0.4	 0.8	 0.8	 0.4	 0.4	 0.8	 0.8	 0.5	 0.45	 0.4	 0.8	 0.8	 0.4	 0.4 Establishment age years 15 10 15 10 7 25 25 30 27 15 30 30 7 7 Establishment live crown ratio m m-1 0.5	 0.8	 0.6	 0.5	 0.4	 0.8	 0.6	 0.4	 0.4	 0.4	 0.8	 0.8	 0.4	 0.4 Establishment height	 m	 1.4	 1.5	 1.4	 1.4	 1.8	 1.5	 1.5	 1.5	 1.4	 1.8	 1.5	 1.5	 1.8	 1.8 Leaf start date or leaf out day	 yearday	 0	 0	 0	 0	 115	 0	 0	 0	 0	 0	 0	 0	 140	 140 Leaf drop date	 yearday	 366	 366	 366	 366	 280	 366	 366	 366	 366	 366	 366	 366	 245	 245 Time for leaf drop or growth days 10 10 13 10 15 18 11 9 8 16 12 14 6 6 Latest tolerable spring frost	 yearday	 120	 160	 160	 200	 170	 210	 211	 220	 220	 180	 150	 150	 170	 170 Earliest tolerable autumn frost yearday 230 230 210 200 210 220 220 230 220 220 260 260 210 210 Average snag life years 20 20 30 20 40 20 30 90 90 20 20 20 20 20
USDA Forest Service RMRS-GTR-255. 2011. 83

Graphical Output

Graphical model output consists of thematic maps that are created at userdefined intervals (as specified in the Sim.in file) for specific annual, stand-level 

variables specified in the Map.in file. The model defines 20 variables for thematic 

output, including dominant species by basal area, cover type, leaf area, DBH, and 

biomass; structural stage, stand age, and stand LAI; cumulative number of fires; 

and gross primary, net primary, and net ecosystem productivity. Maps are output 

in ASCII format and can be converted into GIS raster layers using geoprocessing software such as ESRI ArcMap. Geoprocessing software can also be used for 

post-processing and analysis of raster layers. For example, GIS software can be 

used to strip away a buffer area from around the simulation landscape, to aggregate thematic data across multiple simulation runs, or for change detection across 

simulation time steps.

Figures 18 and 19 show straightforward visualization of thematic output for two variables—dominant species by percent cover within each stand 

(sppIDcov in Map.in; Figure 18) and cumulative number of fires (fireatls in 

Map.in; Figure 19)—at 50-year simulation time steps for a 500-year simulation 

period. These map layers display output data for a single FireBGCv2 run; there 

Figure 18. Dominant species by percent cover at 50-year intervals, 500-year simulation, Lake McDonald watershed, Glacier 

National Park, USA.
84 USDA Forest Service RMRS-GTR-255. 2011.

has been no statistical or numerical aggregation of the data across multiple replicate runs. Simple graphical outputs can be very useful for model calibration, in 

which ecosystem processes and patterns are examined for internal consistency. 

For example, in Figures 18 and 19, stands that experienced frequent fires, as indicated by a high cumulative number of fires across the simulation period, show 

significant shifts in vegetation species composition as compared with stands in 

which few fires burned over 500 years. Specifically, fire-dependent, seral species 

such as western larch (Larix occidentalis) and western white pine (Pinus monticola) increase in prominence across stands following wildfire, while fire-sensitive 

species such as Englemann spruce (Picea engelmanni) and subalpine fir (Abies 

lasiocarpa) disappear in frequently burned stands. Similarly, wildfires can result 

in short- or long-lived transitions to grasses or shrubs, especially under potential 

climate warming scenarios.

Figure 20 illustrates post-processed map layers that display information not 

directly available in the primary graphical output layers. The uppermost panels 

show stand composition in forest or shrub- and grassland for simulation years 100 

and 500, developed through a reclassification scheme of vegetation species (including shrubs and grasses) present on the simulation landscape at each time step. 

Data in these panels represent median values for each pixel across five replicate 

Figure 19. Cumulative number of fires at 50-year intervals, 500-year simulation, Lake McDonald watershed, Glacier National 

Park, USA.
USDA Forest Service RMRS-GTR-255. 2011. 85

model runs. The middle panels show mean values for stand carbon for each pixel 

across five replicate model runs. In the lower panel, a differencing algorithm has 

been used to produce a single map layer that shows the categorical change in stand 

carbon (increase, decrease, or no change) from simulation years 100 to 500, using 

the data layers in the middle panels as inputs.

Tabular Output

Tabular model output consists of horizontally arrayed ASCII flat files that 

contain values for tree- and stand-level variables, wildfires, and landscape-level 

carbon summations. There are currently more than 200 stand-level variables that 

can be summarized at annual time scales, including dominant species by basal 

area, cover type, leaf area, and biomass (Table 7). Stand-level variables are selected for output in the Sim.in file, and output file pathnames and prefixes are 

specified in the Driver.in file. As with graphical output, the choice of output variables should be question-driven to limit model processing time and file size.

The envelope-style graphs in Figure 21 show minimum (lower envelope 

bound), maximum (upper envelope bound), and mean (dashed line) values across 

five replicate simulations for proportion of the simulation landscape area occupied 

by mature versus sapling trees (Top) and western larch, subalpine fir, and western 

Figure 20. Stand composition in 

forest or shrub- and grassland 

(Upper) and mean values for 

stand carbon (Middle) for 

simulation years 100 and 500; 

and change in stand carbon 

from simulation years 100 to 

500 (Lower), Lake McDonald 

watershed, Glacier National 

Park, USA.
86 USDA Forest Service RMRS-GTR-255. 2011.

Figure 21. Minimum (lower envelope bound), maximum (upper envelope bound), and mean (dashed line) 

proportion of the simulation landscape area occupied by mature versus sapling trees (Top); and western 

larch, subalpine fir, and western hemlock (Bottom). 
USDA Forest Service RMRS-GTR-255. 2011. 87

hemlock (Bottom). These graphs illustrate change in model variables across the 

simulation period and also quantify the variability among replicate simulations. 

As with the graphical output previously described, shifts in prominence of vegetation species are likely associated with changing patterns of wildfire disturbance, 

in which repeated fire events on the landscape decrease the proportion of firesensitive subalpine fir and favor establishment of fire-adapted western larch. 

Similarly, an increase in the proportional area occupied by immature (sapling) 

trees may reflect stand initiation following disturbance events. Figure 22 shows 

another display method for structural stage output data, in which stages (seedling, 

sapling, pole, mature, large, and very large) are shown as percent of the simulation 

landscape at simulation years 100, 250, and 500. As with the envelope graphs, 

changes in structural stages reflect successional processes influenced by disturbance events, climate variability, and species interactions.

Landscape fire and carbon information can be assessed through tabular output 

in the Stand.fire and Land.year output files. For example, the Stand.fire output 

file provides information on number of fires, fire size per site and stand, and total area burned during each simulation year (Figure 23). These data can be used 

to compare wildfire dynamics across simulation replicates to assess model variability, recognize underlying driving processes in the model (for example, fire 

return intervals at site and simulation landscape scales), and understand relationships among disturbance events and ecosystem processes such as succession, net 

primary productivity, or landscape carbon dynamics. The Land.year output file 

contains landscape-scale carbon data for each simulation year, including emissions from wildfires and autotrophic and heterotrophic respiration, total landscape 

(live and dead vegetation) carbon, standing-crop (live) carbon, and gross primary 

Figure 22. Structural stages (seedling, sapling, pole, mature, large, and very large) by percent of the simulation 

landscape at simulation years 100, 250, and 500. 
88 USDA Forest Service RMRS-GTR-255. 2011.

Figure 23. Total area burned during each simulation year for three model replicates, 500-year simulation, 

Lake McDonald watershed, Glacier National Park, USA.
USDA Forest Service RMRS-GTR-255. 2011. 89

productivity. These outputs are used to evaluate carbon dynamics across the simulation landscape—for example, to compare carbon emissions from wildfires (net 

loss of carbon from the landscape) to carbon stored on the landscape in the form 

of live and dead vegetation. High annual carbon emissions from fire, resulting 

from a large number of landscape acres burned and/or high-intensity fires, temporally co-occur with a decrease in landscape total carbon stores due to consumption 

of live and dead vegetation (Figure 24). The difference between total stored (sequestered) carbon and total carbon emissions from wildfires and autotrophic and 

heterotrophic respiration can be used to describe a dynamic landscape carbon 

budget across the simulation period.

The sample graphical and tabular outputs described and presented here describe a single model scenario that represents historical climate conditions with 

no suppression or fuels treatments implemented on the landscape. Previous and 

ongoing FireBGCv2 research projects have been implemented with factorial 

simulation designs that test the effects of management scenarios and/or potential 

future climate conditions on ecosystem patterns and processes; in this context, 

visualization and analysis of output should highlight key differences that result 

from variation among factors (Keane and others 1997, 1998, 1999).

Figure 24. Carbon emissions from fire (kg m-2) and landscape total carbon (kg m-2) across the 500-year simulation 

period, McDonald watershed, Glacier National Park, USA.
90 USDA Forest Service RMRS-GTR-255. 2011.

Discussion

Model Limitations

The downside of using a highly mechanistic model to simulate landscape dynamics is that the resultant behavior can often be unstable, non-linear, and inexact. 

The response surfaces of any model, especially a mechanistic one, are bounded 

and dictated by their algorithms and the parameterization of those algorithms. 

Often, model parameters will be in conflict with each other. For example, the 

specified fire return interval could be at odds with the species parameters, which 

may result in odd behaviors and questionable predictions. It is critical that intermediate simulation results be compared with reality or field data under specially 

designed simulation parameterizations so that the model can be calibrated and adjusted to achieve sensible simulation results. However, the most difficult task of a 

modeler is to determine if a strange simulated time series is a viable, ecologically 

possible result or if it is simply wrong because of incompatible parameterizations 

and initializations. We suggest that users simulate historical landscape conditions 

using historical weather, fire frequency and fire size parameters, and species parameters and then create a suite of diagnostic output variables to evaluate whether 

the model is realistically simulating historical landscape dynamics (Figure 14). 

If not, then site and species parameters can be adjusted to nudge results closer 

to observed characteristics by comparing simulation results to historical study 

results. If actual conditions still cannot be approximated, then the model may need 

adjustment and the computer code may need to be modified.

One important limitation of FireBGCv2 is the absence of the close linkage between fire spread and fire behavior with fuels and weather. It was difficult to stop 

simulated fires from spreading into stands that tend to be moist for many parts of 

the year, such as spruce-fir forests. As a result, the model tends to overestimate 

the number of fires in mesic communities because fire is constantly spreading into 

those areas from surrounding, more flammable communities.

The relationship between the average fire size and fire size distribution 

parameters can cause some confusion and problems in parameterization and interpretation of model results. The average fire size (in Site.in) is the average size 

of the fires by site on the simulation landscape, and it is used to scale the point 

estimate of fire probability to the polygon level. The fire size distribution parameter is a constant in the fire size distribution equation that determines the ultimate 

size of a simulated fire. This parameter can be approximated from the average 

fire size, but in FireBGCv2 simulations, it often turns out to be roughly one-third 

of the average fire size to match observed fire regimes. We recommend that the 

average fire size be determined from available data, and then that the fire size distribution parameter be estimated using calibration techniques. The model should 

be executed, and fire statistics from the Fire Output file should be inspected to 

determine if the fire size distribution parameter should be altered to obtain more 

realistic fire regime simulations.

The two site-level fire parameters (average fire size and fire return interval) 

strongly influence fire and vegetation dynamics. A consistent methodology for 

setting these parameters to achieve appropriate simulated fire frequencies is important. As discussed, the historical data available for estimating these parameters 

are limited. Due to scale and model efficiency, fires smaller than 0.5 ha are not 

modeled. This removes a portion of the left end of the fire size distribution curve, 

which is where the largest numbers of fires occur, and increases the mean fire size. 

Even if there are excellent historical data to estimate the fire parameters (return 
USDA Forest Service RMRS-GTR-255. 2011. 91

interval and average size), there will still be differences between simulated fire 

regimes and actual historical fire regimes. It is difficult to simulate fire regimes 

from two sets of independent fire parameters (fire occurrence probabilities and 

fire size distributions) and expect perfect agreement with historical conditions. 

Fire, like many natural processes, is highly complex, and any attempt to model it 

is a simplification of the actual dynamics. Fire operates at many different spatial 

and temporal scales, and its occurrence is influenced by many factors, such as 

vegetation, weather, wind, and topography, that also operate at different spatial 

and temporal scales. As a result, it is difficult to realistically simulate fire without 

building overly complex models that are difficult to parameterize and inefficient 

to run for large landscapes over long simulation periods. Integrating fine-scale 

processes of weather and fuel into the model using FARSITE routines would 

make the model computationally intensive and would dramatically reduce the 

efficiency of the model runs. Therefore, results of each FireBGCv2 simulation 

should be compared with expert experience and any available data to determine 

if output is reasonable because of the overly simplistic model algorithms. If not, 

then the parameters should be adjusted to more closely approximate reality.

Simulation Issues

Much of FireBGCv2 modeling is about balancing realistic simulations of fire 

and vegetation dynamics with the often opposing goal of computational and logistical efficiency. This struggle becomes more important as simulation landscapes 

increase in size, resolution, and complexity. We found that simulation times tended to increase exponentially with increasing landscape size and time spans, but 

conversely, the use of larger simulation landscapes were logistically simpler and 

produced better simulation results. There is an optimal landscape size at which 

the model becomes efficient and overall processing time is optimized. We found 

that 50,000- to 250,000-ha landscapes at 30-m pixel resolution work well, but this 

may change with topographic and succession pathway complexity and as computer technology improves. A spatial data layer pixel resolution of 30 m works 

well for many FireBGCv2 applications, but as landscapes get large (>100,000 

ha), it is suggested that the pixel resolutions also increase proportionately. Using 

LANDSUM, we found little loss in simulation detail when we went from 30-m to 

100-m resolution (Karau and Keane 2007).

The arbitrary delineation of a simulation landscape is somewhat problematic in 

that the landscape edges create artificial boundaries across which fire cannot burn. 

In real landscapes, water, rock, or topography may create boundaries that influence fire spread, but on most simulation landscapes, the simulation edge does not 

follow natural fuel break boundaries and may cut right through areas of constant 

vegetation or topography, through which fires would naturally spread. Another 

problem is that areas near the edges of a landscape have a limited number of surrounding pixels from which a fire can spread into the landscape. This problem is 

further exacerbated by wind and its prevailing direction because fire is spread by 

wind and slope vectors at variable directions and speeds for the entire simulation. 

All things being equal, this means that pixels near the direction where the wind 

is originating (south and west edges, for example) have the lowest probability of 

burning, while those nearest to the direction where the wind is blowing (the north 

and east, for example) have the highest probability of burning. The best way to 

mitigate the edge effects in a FireBGCv2 simulation is to surround the simulation 

landscape with a buffer. A buffer is created by making the simulation landscape 

larger and then stratifying the results into two regions—the buffer and the context 

area—that together comprise the simulation landscape. If the buffer area is large 

enough, the relative position of pixels within the landscape will not influence 

burning and fire frequency within the context area, and the fire frequency in the 

leeward buffer region of the context area should not be substantially different 
92 USDA Forest Service RMRS-GTR-255. 2011.

from the fire frequency for any other part of the context area. Each landscape is 

unique, so buffers will be different for each setting.

It is important that there is a consistent methodology for testing the parameters 

established for the simulation landscape. FireBGCv2 has an extensive errorchecking routine that scans the input data for inconsistencies between the various 

input files that could cause problems during simulation. However, there may be 

problems with the input data that the model does not recognize as inconsistencies 

but that still may cause unexpected results. And while the FireBGCv2 model has 

undergone an extensive de-bugging process, it is possible that some new, unique 

circumstance will arise that will cause unexpected results. We recommend a thor-

ough quality control process for all parameters, initialization data, and simulation 

results. Ideally this could be done as a series of queries in a database environment 

that would check for consistencies between parameters and initial values. If there 

are any suspicious results, further analysis should be done to determine the reason 

for such results and whether they make sense ecologically.

It appears that a significant number of simulation years are required before the 

trends in response variables stabilize and the annual simulations achieve some 

level of equilibrium. This stabilization time may take one to three centuries on 

simple landscapes and more than 500 years on complex landscapes. We found that 

using the current landscape to initialize the historical landscape could be ineffi-

cient on two levels. First, the current landscape often contains too many polygons, 

and the simulation times increase as numerous polygons are added by the con-

tinual division of polygons by fire. Second, the current landscape is so departed 

from historical conditions that it takes an excessively long time for this landscape 

to reach equilibrium. However, in FireBGCv2, the use of current conditions for 

initialization is often the only possible option.

Potential Improvements

At the center of future simulation research is a need for comprehensive data 

to run and validate future models. The balance of data needs versus model ad-

vancement reflects an imperative for collaboration between field ecologists, who 

provide data and equations, and modelers, who must then integrate that knowl-

edge to provide descriptions of phenomena at different spatial and temporal 

scales. It is critical that extensive field programs be intimately integrated with 

simulation efforts to ensure sufficient parameter and validation data are measured 

for model applications. Temporally deep, spatially explicit databases created from 

extensive field measurements are needed to quantify input parameters, describe 

initial conditions, and provide a reference for model testing and validation, es-

pecially as landscape fire models are ported across large geographic areas and to 

new ecosystems (Cary and others 2006). For example, Hessl and others (2004) 

compiled a number of ecophysiological parameters for use in mechanistic eco-

system models, which has increased parameter standardization and decreased the 

time modelers spend on parameterization. New sampling methods and techniques 

for collecting data are needed to ensure the right variables are being compared at 

the right scales. Field data that are useful in simulation modeling should be stored 

in standardized databases, such as FIREMON (Lutes and others 2006), and stored 

on websites so that they are easily accessible for complex modeling tasks. Last, 

new instruments are needed to quantify important simulation variables such as 

canopy bulk density, to initialize and parameterize fire behavior models (Keane 

and others 2005).

Model validation and verification are difficult tasks with landscape models 

that simulate vegetation and fire dynamics over millennia time spans (Keane and 

Finney 2003, Keane and others 2004b). There is a lack of spatially explicit, his-

torical time series data that are in the right context to compare with model results. 

Validation data must have many characteristics to be useful for model validation. 
USDA Forest Service RMRS-GTR-255. 2011. 93

First, the data must be described in the same format as model output; the mapped 

data must have the same categories as those simulated by FireBGCv2. Next, the 

historical data must be from many time periods. The comparison of one historical 

map with the simulation results provides only a qualitative or descriptive means 

of testing and verifying the model. Multiple maps would provide the basis for an 

objective statistical comparison. And, since FireBGCv2 does not replicate historical fire sequences because it is a stochastic model, it would be difficult to 

compare past fire patterns with corresponding simulated patterns. Last, the data 

must be incorporated into maps that describe historical conditions across the entire landscape. Because of these limitations, we could find no historical datasets 

to comprehensively validate FireBGCv2. This leaves validation of the algorithms 

and disturbance parameters as the only way to verify that the model is producing realistic results. Keane and others (2002a) compared simulated fire area 

and pattern statistics from a 1000-year LANDSUM run (FireBGCv2 uses some 

LANDSUM fire simulation routines) to the historical fire atlas created by Rollins 

and others (2001) and found excellent agreement between the distributions of fire 

size and patch shape.
94 USDA Forest Service RMRS-GTR-255. 2011.

Input File Structure

File Design

All input and output files in FireBGCv2 are in ASCII format, also referred to 

as text or flat file format (Figure 10). Each ASCII file is configured as a vertical 

or horizontal file, where vertical files contain only one input value per line with a 

descriptor of that input value following the number, and horizontal files contain 

more than one value per line and resemble tables. Most FireBGCv2 input files are 

vertical (provides greater understanding and error management) and all output 

files are horizontal (increases efficiency). The first line of each FireBGCv2 input 

file is called a title line and is used to describe the file and its origins. There are 

256 characters that are available for this descriptive title, which may also be used 

to identify the file author, project affiliation, model version, creation/edit date, and 

other useful information.

There are two types of files in FireBGCv2: parameter files (scenario) that describe various aspects of the simulation and input files (description) that represent 

site, stand, and tree conditions. Scenario files are totally subjective, but description files must describe true ecological conditions and are usually developed from 

primary field data. FireBGCv2 files are also cycling or discrete. Cycling files 

contain the same format that is cycled downward or repeated for each similar 

instance. For example, species parameters are cycled vertically for each species 

in the simulation. Discrete files do not cycle and always contain a discrete number 

of fields.

Model parameter and input files are described next, including suggestions for 

file management and user-specified customizations for simulation development. 

In addition, parameters and input files are described in the order in which they 

should be modified by new users.

Driver.in File

This driver file is a vertical discrete scenario file that contains the names and 

pathnames of all input files required for FireBGCv2 execution. The order of these 

files is absolutely critical. The structure of the file is as follows:

Rec Variable Description

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

1 None This line is reserved for the title line

2 Sim Input simulation file containing general simulation parameters

3 Climate Input climate file containing climate parameters

4 Map Input file containing names of input and output ASCII maps

5 Species Input file containing parameters for individual tree species

6 Wildlife Input file containing habitat suitability parameters

7 Manage Input file containing specifics of management activities

8 Plant Input file containing parameters for plant models

9 Pial Input file containing special parameters for whitebark pine

10 Fuel Input file with fuel model parameters for effects and behavior

11 Site Input file containing parameters for sites on landscape

12 Stand Input file containing initial input values for stand components

13 Tree Input file with initializing values for all trees in stands
USDA Forest Service RMRS-GTR-255. 2011. 95

14 Echo Output file displaying a summary of all input variables

15 Error Output file showing all errors in input files and values

 16 Restart Output file for BGC restart variable initial values

17 SeedDispersal Output file for seed dispersal parameters

18 StandDay Output file containing daily values for specified stand-level 

 variables for each day

19 StandYear Output file containing values for specified stand-level variables 

 for each year

20 TreeYear Output file containing values for specified tree-level variables 

 for each year

21 TreeList Output file containing a tree list for each stand for each year

22 StandFire Output file showing all fire statistics for individual fires

23 Disturb Output map prefix for storing fire and management maps

24 Manage Output file for storing all management actions

25 LandYear Output file reporting carbon and fire variables at landscape scale

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

All pathnames in this file should be valid filenames in that they start in column 

one and proceed left to right without blank spaces or commas. It is recommended 

that a space be skipped after the file name and then that a descriptive statement 

be included that describes the file content. There is space for only 256 characters 

on this record. For all output variables, the word NONE entered in place of a 

filename and pathname dictates that no output will be written for that file type.

Sim.in File

The simulation file is a vertical discrete scenario file that contains general 

simulation parameters related to the execution of the entire program. Some parameters in this file serve as error checks for parameters in other files, while other 

parameters are critical for FireBGCv2 simulation. The structure of the file is as 

follows:

Rec Variable Description

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

1 None This line is reserved for the title line

2 verbose Verbose flag (0-no messages, 1-partial, 2-full, 3-full for 

 debugging)

3 span Simulation time span in years

4 bgcinit BGC process (0-initialize from bare ground and store, 

 1-initialize from stand data and store, 2-use stored values, 

 4-stand data, 5-no spinup)

5 nsites Number of sites on the modeled landscape

6 nspecies Number of species in the species parameter file

7 nwild Number of wildlife species in the wildlife file

8 nmanage Number of management activities in the management file

9 nplant Number of undergrowth plant models in the plant input file

10 nfmod Number of fuel models in the fuel model input file

11 nwyr Number of weather years in the daily weather file for all sites

12 wxseq Weather data cycle (0-in sequence, 1-random, 2-front to back)

13 smod Succession model driver (0-BGC, 1-mechanistic gap, 2-simple gap)

14 seedint Seed dispersal map update interval in years

15 outint Output thematic map interval in years

16 seedrun Seed dispersal option (0-new run, 1-old run, 2-new run store 

 values)

17 runopt Fire sim option (0-new run, 1-old run, 2-new run store ignitions)

18 firemod Fire spread model (1-FARSITE, 2-LANDSUM, 3-Cell automata)

19 fireprob Fire ignition probability calculation: (0-default, 1-weibull 

 hazard, 2-weibull survivor, 3-fri)
96 USDA Forest Service RMRS-GTR-255. 2011.

20 firesupp Level of fire suppression: number between 0 and 1 where 0 is 

 historical fire regime and 1 is total suppression

21 habsuit Method for computing wildlife habitat suitability (0-no 

 calculation, 1-stand level, 2-tree level)

22 beetle Starting year of beetle epidemic (YYYY)

23 rust Starting year of rust infection epidemic (YYYY or -YYYY)

24 phenology Phenology option (0-constant, 1-simulated from White model)

25 rnscheme Random number scheme (0-fully random, 1-use same random numbers)

26 rngen Random number generator (See tools.c for IDs)

27 parea Area of simulation plot in square meters

28 hsize Canopy height increment in meters

29 treemax Maximum tree density (t/m2) use as multiplier (use -1 for no use)

30 print_int Print interval for outputting stand and tree data (years)

31 bday Beginning day for daily stand output (Julian date)

32 eday Ending day for daily stand output (Julian date)

33 treeID Tree ID number for yearly tree output

34 varindxnum Variable index numbers for printing to daily stand output file

35 varindxnum Variable index numbers for printing to annual stand output file

36 varindxnum Variable index numbers for printing to tree yearly output file

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

A few of these parameters warrant additional discussion. The verbose flag 

describes how many messages the user wishes displayed while the program is 

executing. The simulation time span (span) in years is the number of years that 

will be simulated by the model.

The variable bgcinit is a flag that represents how the BGC succession model 

driver within FireBGCv2 is initialized. A value of 0 initializes the BGC submodel 

in the current execution, while a value of 1 uses a previous initialization. The 

previous initialization is stored in the restart file identified in the Driver.in file.

The next set of parameters identify the number of entities in the simulation: 

nsites is the number of sites represented in the Site.in file; nspecies is the number 

of species in the Species.in file; nwild is the number of wildlife species in the 

Wildlife.in habitat input file; nmanage is the number of management activities 

defined in the Manage.in file; nplant is the number of plant models in the Plant.in 

file; and nfmod is the number of fuel models in the Fuel.in file.

The next two variables describe the weather data specified by site in the Site.in 

file. All weather files in the simulation (one per site) must have the same number 

of records (days) and same number of weather years (whole years with 365 days, 

no leap years), as identified with the variable wxyr. The flag wxseq identifies how 

this weather record will be used over the long simulation time: a value of 0 results 

in random selection of each year from the full weather record, a value of 1 results 

in the entire record being cycled for the entire simulation, and 2 indicates that the 

starting year of the cycle is randomly chosen.

The smod variable indicates the succession model to use for vegetation simulation. Options include a fully integrated BGC simulation (option 0), a mechanistic 

gap model with over 70 percent of the BGC structure built into a gap model without the overhead (option 1), and a simple gap model, in this case the FIRESUM

model (option 2). For additional discussion of succession model drivers, refer to 

the “Stand Processes” section.

The next two variables identify the interval at which processes are simulated. 

The seedint variable represents the number of years between seed dispersal simulations. Because seed dispersal simulations are computationally intensive, this 

process should not be performed every year. The outint variable determines the 

output map (thematic) update intervals in years.

The seedrun flag indicates the seed dispersal model run option—as a new 

run without any storage of information for use subsequent runs (option 0), using 
USDA Forest Service RMRS-GTR-255. 2011. 97

the previous run’s initialization as a primer with no new initial dispersal run (option 1), or as a new run with storage of initial values for use in subsequent runs 

(option 2).

The runopt flag is used to describe how fires are simulated in FireBGCv2. If 0 

is entered, the program will call the fire spread models (FARSITE or LANDSUM) 

each time a fire is simulated. This is the most computationally-intensive option; 

alternatively, if 1 is entered, fires are taken from a set of files that were stored from 

previous runs. Option 2 indicates that new fires are generated for the simulation 

run, but all simulated fires are stored for use in subsequent runs.

Three variables implement fire behavior options in the simulation. The 

firemod flag indicates the fire spread model that is used to model fire across 

the landscape. A 0 selects the FARSITE model, and a 1 selects the LANDSUM

fire spread algorithms. For additional discussion of these fire spread models see 

“Wildland Fire” in the “Landscape Processes” section. No cell automata model 

has been implemented in the FireBGCv2 model yet but will be included in a later 

release as option 2. The fireprob variable is a fire ignition probability calculation 

in which 0 is the default value, 1 indicates a weibull hazard function, 2 selects a 

weibull survivor function, and 3 uses the fire return interval defined for each site 

on the simulation landscape. The firesupp variable specifies the level of fire suppression, entered as a number between zero and one where 0.0 is the historical or 

native fire regime and 1.0 is the full suppression of all fires.

The habsuit flag defines the method for habitat suitability calculations. The 

habitat suitability algorithm can key habitat suitability indices to stand-level 

characteristics (option 1) or tree-level characteristics (option 2). The tree-level 

calculation option computes habitat suitability indices for each tree in the stand.

The beetle and rust variable values are used to indicate the simulation year 

in which a beetle epidemic or rust epidemic starts, respectively. A value of 9999 

is used to specify no rust or beetle epidemic in the simulation. For the rust variable, a negative value (for example, -9999) indicates that all five-needled pines 

included in the tree list (Tree.in) will be counted as live trees, even if they are 

entered as dead trees in the input file.

The phenology flag is used to specify either a static simulation of phenology 

using constants in the Species.in file (option 0), or dynamic simulation of phenology using algorithms from White and others (1997) (option 1). The FireBGCv2 

model does not use the phenology flag unless BGC is selected as the succession 

model driver (smod).

The rnscheme and rngen flags are used to specify how stochastic algorithms 

will use random numbers in the model. The rnscheme has two modes: option 

0 specifies that the random number sequence will be different across multiple 

FireBGCv2 executions and option 1 specifies that the random number sequence 

will be exactly the same across multiple executions. This is important if a sensitivity analysis requires that model stochasticity remain constant.

The variables parea and hsize are the only decimal values in the simulation 

input stream. The variable parea is the area of the simulation plot (m2) that is 

used to represent the stand area. The variable hsize is the thickness (m) of the 

canopy layer, which is important for computing light dynamics under the canopy. 

It is recommended that parea be set to 400 m2 and hsize be set to 1.0 m when 

the majority of trees in a plot are below 35 m in height. The treemax variable is 

a multiplier for maximum tree density that is used to govern the number of trees 

in a simulation.

The next set of parameters defines how intermediate output will be printed 

from FireBGCv2. The variable print_int indicates the printing interval in years 

for tabular output—for example, if 10 is entered, output will be printed every 10 

years. The variables bday and eday specify the beginning and ending Julian dates 

to print daily calculations (for example, 1 and 365 for an entire simulation year). 

TreeID indicates the tree ID number for which to print daily calculations between 

bday and eday in Tree.Year output file. For example, a value of 1 for TreeID will 
98 USDA Forest Service RMRS-GTR-255. 2011.

print species, DBH, height, and height to base of crown for the first tree in every 

stand throughout the simulation period.

The final three variables in the Sim.in file are sets of numbers that specify the 

output variables printed to the stand.day (daily stand-level intermediate variables), 

stand.year (annual stand-level intermediate variables), and tree.year (annual treelevel intermediate variables) output files. There are a maximum of 40 variables 

that can be printed out for each set. See Table 7 for a list of available variables 

and ID numbers.

Climate.in File

The climate file contains parameters that alter daily input weather streams to 

create a climate change scenario, including offsets of atmospheric CO2, temperature, precipitation, and nitrogen deposition and fixation rates. The climate file 

consists of both universal and seasonal parameters, in which universal parameters 

define a ramping period for climate changes and a consistent CO2 and nitrogen 

environment, and seasonal parameters allow for intra-annual offsets in temperature, precipitation, vapor pressure deficit, and solar radiation. The structure of the 

file is as follows:

Rec Variable Description

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

1 None This line is reserved for the title line

2 year_start Starting year of climate change scenario

3 year_end Ending year of climate change scenario

4 seas Number of seasons in this climate change scenario

5 co2_start Starting atmospheric CO2 concentration (ppm)

6 co2_end Ending atmospheric CO2 concentration (ppm)

7 nd_start Starting nitrogen deposition rate (kg N m-2 year-1)

8 nd_end Ending nitrogen deposition rate (kg N m-2 year-1)

9 nf_start Starting nitrogen fixation rate (kg N m-2 year-1)

10 nf_end Ending nitrogen fixation rate (kg N m-2 year-1)

11 filename Filename of these parameters by year of simulation

12 None Filler line noting each weather scheme is repeated for each season

13 seas1 Title for season 1, ex: Winter season (jan-feb)

14 jdate Julian Date of last day in this season

15 tmax Final Tmax offset (degC) by end of climate change scenario

16 tmin Final Tmin offset (degC) by end of climate change scenario

17 ppt Multiplier for precipitation (dimensionless)

18 vpd Multiplier for VPD (vapor pressure deficit) (dimensionless)

19 srad Multiplier for radiation (dimensionless)

Repeat lines 13-19 to define another season (ex: season 2, Spring (mar-may)

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

The universal parameters year_start and year_end define the simulation period over which the climate offsets entered in the Climate.in file will be applied; 

for example, year_start and year_end values of 1 and 100, respectively, indicate 

that the offsets defined for each season will be ramped incrementally across the 

first 100 years of the simulation. The next set of parameters defines starting and 

ending values for three important climate and land use change variables: (1) the 

carbon dioxide concentration (ppm), (2) nitrogen deposition rate (kgN m-2 yr-1), 

and (3) nitrogen fixation rate (kgN m-2 yr-1), specified by co2_start, co2_end,

nd_start, nd_end, nf_start, and nf_end, respectively. The final universal parameter in the Climate.in file identifies a secondary file that can be used to specify 

climate change parameters by simulation year. If the filename parameter is entered as “NONE,” FireBGCv2 uses the parameters in the previous nine file lines 
USDA Forest Service RMRS-GTR-255. 2011. 99

to simulate a climate change scenario; however, if a filename is entered, a file 

must be created that lists the nine previous parameters by year. This option is used 

if a ramping strategy is not warranted or sufficient.

The seasonal portion of the climate file may consist of up to six user-defined 

seasons, where each season is defined by a last Julian date and a number of abiotic climate-related variables. Variables tmax and tmin are final maximum and 

minimum temperature (°C) offsets used during the climate change scenario. For 

example, the maximum temperature for any given day will be T + tmax at the end 

of the climate change scenario. The variables ppt, vpd, and srad are multipliers 

(values from 0.0 to 10.0) that define the maximum precipitation, vapor pressure 

deficit, and solar radiation, respectively, at the end of the climate change scenario. 

For example, a ppt value of 1.2 is equal to a 20 percent increase in precipitation 

at the end of the climate change scenario.

Map.in File

This critical file contains all the descriptors and filenames for all thematic layers (maps) used in model execution and output from FireBGCv2 simulations. This 

is a vertical discrete scenario file with the following structure:

Rec Variable Description

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

1 None This line is reserved to describe the file

2 polymeth Method of polygon creation

3 cellsize The size of a square pixel in meters

4 rows Number pixels in a row of the map

5 cols Number pixels in a column of the map

6 xllcorner UTM North coordinate of the lower left pixel

7 yllcorner UTM East coordinate of the lower left pixel

8 nodata Value for missing data (-1)

9 minmapunit Minimum map unit for output (ha)

10 demname DEM filename or pathname: Elevation grid map filename

11 initstandname Initial polygon filename

12 zone Zone filename for simulation area, context landscape, buffer

13 outmapname Dynamic thematic map filename

14 firemap Fire intensity maps for each fire year

15 xul,yul x,y coor upper right submap

16 xlr,ylr x,y coor lower left submap

17 None This line is reserved to describe the next two fields

18 None This line is reserved to describe the next two fields with headers

19 mapper[] Array storing the requested FireBGCv2 output maps

20 None This line is reserved to describe the next two fields

21 None This line is reserved to describe the next two fields with headers

22 wildlife[] Array storing the requested wildlife habitat suitability maps

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

The polymeth flag describes the method of polygon creation, in which a value 

of 0 indicates that new stands can be created along the boundaries of a fire and an 

entry of 1 means that the entire stand will burn if a fire enters that stand. The cellsize should correspond to the pixel size (m) in the maps’ ASCIIGRID file headers. 

The variables rows and cols, and the coordinates xllcorner and yllcorner, are 

taken directly from the ASCIIGRID headers and specify the number of rows and 

columns in the map and the UTM coordinates of the lower left corner of the map. 

These variables serve as an error check for map input. The nodata variable is the 

value of a pixel given if there is no data for that cell, such as any pixel outside the 

simulation area. The demname is the name of the digital elevation model (DEM) 
100 USDA Forest Service RMRS-GTR-255. 2011.

used to define the simulation area. This map is important because any value in this 

map that is not equal to nodata is considered inside the simulation area. The initialstandname file is a raster file for which each pixel is assigned a stand number. 

Stand numbers should be ordered to reflect their location within sites. The zone 

filename is an ASCIIGRID map in which cell values of 0 define a buffer around 

the core simulation landscape (see the “Map Creation” section for more information), and cell values of 1 or higher define the context landscape for which results 

will be printed. The outmapname sets the prefix and pathname for thematic maps 

defined with the mapper and wildlife arrays (described below).

If thematic output is only needed for a small portion of the entire simulation 

landscape the upper and lower left and right coordinates (xul, yul, xlr, and ylr) 

(in pixel coordinates, not map coordinates) can be entered. Enter -999 for these 

four values if no subarea is desired. The next two lines are used to describe the 

thematic and wildlife habitat maps that are to be printed at the time interval specified in the Sim.in file. The arrays mapper and wildlife are used to store toggle 

switch values (1 for each map that is to be printed, and 0 if the map is not desired) 

for variables whose order corresponds to thematic output variables or wildlife 

species, respectively. There is space for twenty values per line. The wildlife maps 

reference the wildlife ID numbers in the Wildlife.in file, and thematic maps are 

listed in the following order:

Index Description Variable

1 Polygon ID number for this stand polyID

2 Fire Atlas with number cumulative fires numfires

3 Dominant species ID (Canopy Cover) sppID

4 Dominant species ID (Basal Area) sppID

5 Dominant species ID (DBH) sppID

6 Dominant species ID (leaf area) sppID

7 Dominant species ID (biomass) sppID

8 Structural stage of stand code

9 Age of stand based on years since dist years

10 Age of oldest individual on stand years

11 Stand basal area m2 ha-1

12 Stand canopy cover percent

13 Stand leaf area index m2 m-2

14 Gross primary productivity kgC m-2 year-1

15 Net primary productivity kgC m-2 year-1

16 Net ecosystem production kgC m-2 year-1

17 Evapotranspiration kgH2O m-2 year-1

18 Maintenance respiration kgC m-2 year-1

19 Aboveground carbon kgC m-2

20 Total carbon kgC m-2

Input Map Creation

More needs to be said about the creation of the FireBGCv2 input maps. 

FireBGCv2 reads thematic layers into the model in ASCIIGRID format, with a 

file structure that contains header information, data values, and “no data” values. 

These ASCIIGRID layers are created from raster maps (gridded pixels) that are 

usually processed in a GIS software package such as ESRI ArcMap. To create the 

required set of input maps, a user first selects a simulation area, ideally near 1000 

by 1000 pixels in size. All attempts should be made to minimize the number of 

“no data” pixels within the map. Watershed boundaries make acceptable simulation landscape boundaries, but the odd shapes of watersheds almost always result 

in an abundance of “no data” pixels outside of the watershed boundary because 

ASCIIGRID input maps are always represented as a square array of pixels. Users 
USDA Forest Service RMRS-GTR-255. 2011. 101

should add a spatial buffer around the core simulation landscape to ensure that 

fires are allowed to spread into the simulation landscape without artificial edge 

effects resulting from the restricted simulation boundary.

Three map layers (ASCIIGRID files) are required for FireBGCv2 execution. 

The first input map is a DEM. The DEM data can be acquired from the USGS 

National Elevation Dataset (http://ned.usgs.gov/). Users should order an area 

much larger than the proposed simulation area (at least five times the size) and 

then clip out an area that corresponds to the project simulation landscape plus an 

additional buffered area around the core landscape.

The second input map is the initial stand (polygon) map. This map spatially 

represents all simulation stands on the landscape. The stand map is created from 

satellite imagery, digitized photo-interpreted stand delineations, or some other existing vegetation mapping product. We recommend that stands be at least 5 ha to 

avoid bogging down simulations with an excessive number of stands. Also, stands 

should not exceed 250 ha in order to avoid oversimplification. Each stand must be 

assigned a unique stand ID number.

The third required input map layer is a zone map. This map is a grid array with 

cell values of 0 that define the buffer area around the context simulation landscape 

and cell values of 1 or higher that delineate the context or core landscape. The 

zone map restricts output data printed in tables and thematic layers to just the core 

simulation landscape by trimming the buffer area.

The three required input map layers must share a common map area and pixel 

size, as defined in the ASCIIGRID header information, and must be present in the 

appropriate location, as defined in the Map.in file, in ASCIIGRID format.

Species.in File

The most important file in the FireBGCv2 simulation is the Species.in file. It 

contains all the parameters for the individual plant species modeled in FireBGCv2. 

Most of these parameters are described in detail in the Keane and others (1996) 

FIRE-BGC document. Species.in is a cycling vertical scenario file where the first 

line contains an ID or title information and the second line lists the number of 

species contained in the file. The third line is a species ID line that is not used in 

the model. The name variable is entered on the fourth line. A description of each 

variable is usually written after the numeric or alphanumeric input value—but at 

least one space must be skipped between the value and the description. Once all 

parameters for a single species are entered (see Table 8), the following line is the 

ID line for the next species, followed by a repeating set of parameters for that 

species. This structure continues for the entire tree species defined for a simulation landscape plus entries for grass and shrub functional types. The following 

describes the species parameters:

Var Description Units

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

name Species 4-character name (2-genus, 2-species) alphanumeric

sppID Species ID number from master table code

lifeform Species lifeform index 0-trees, 1-shrubs, 2-herb code

vegtype Vegetation type: 0-evergreen, 1-deciduous code

Ecophysiological parameters

ic Canopy interception coefficient m LAI-1 day-1

k Canopy light extinction coefficient dimensionless

all_to_proj All-sided to projected leaf area ratio m2 m-2

saplai SapwoodC to leaf area ratio kgCsap m-2leaf

sla Specific leaf area of a projected area basis m2 kg C-1

shade_sunSLA Ratio shade to sunlit SLA dimensionless
102 USDA Forest Service RMRS-GTR-255. 2011.

leafNrub Fraction of leaf N in Rubisco dimensionless

gl_smax Maximum canopy conductance (gmax) m sec-1

gl_c Leaf cuticular conductance (gc) m sec-1

gl_bl Leaf boundary layer conductance (gb) m sec-1

psi_open Leaf water potential at field capacity -MPa

psi_close Leaf water potential at stomatal closure -MPa

vpd_open Min Vapor pressure deficit to affect conduct Pa

vpd_close Max Vapor pressure deficit to affect conduct Pa

Carbon to nitrogen ratios

leafCN C:N ratio for live leaves ratio

litterCN Critical litter C:N ratio ratio

frootCN Critical fine root C:N ratio ratio

lstemCN Live wood C:N ratio ratio

dwoodCN Dead wood C:N ratio ratio

duffCN Critical duff C:N ratio ratio

Annual turnover fractions

leaffr_turn Leaf and fine root turnover fraction kgC kgC-1 year-1

lstem_turn Live wood turnover fraction kgC kgC-1

stem_turn Stem, branch, and coarse root turnover frac kgC kgC-1

Allocation parameters

alloc_frootC_leafC New fine root C to new leaf C proportion

alloc_stemC_leafC New stem C to new leaf C proportion

alloc_lstemC_twoodC New live wood C to new total wood C proportion

alloc_crootC_stemC New coarse root C to new stem wood C proportion

alloc_growthC_storC Current growth to storage C proportion

alloc_leafC Proportion to individual tree leaf C proportion

Decomposition parameters

leaf_lab Leaf labile carbon fraction kg lab C kgC-1

leaf_cell Leaf cellulose carbon fraction kg cell C kgC-1

leaf_fscell Leaf shielded cellulose fraction kg ligninC kgC-1

leaf_fucell Leaf unshielded cellulose fraction kg ligninC kgC-1

leaf_lig Leaf lignin fraction kg ligninC kgC-1

froot_lab Fine root labile carbon fraction kg lab C kgC-1

froot_cell Fine root cellulose carbon fraction kg cell C kgC-1

froot_fscell Fine root shielded cellulose fraction kg ligninC kgC-1

froot_fucell Fine root unshielded cellulose fraction kg ligninC kgC-1

froot_lig Fine root lignin fraction kg ligninC kgC-1

dwood_cell Wood cellulose carbon fraction kg cell C kgC-1

dwood_lig Wood lignin fraction kg ligninC kgC-1

dwood_fscell Downed wood shielded cellulose fraction kg ligninC kgC-1

dwood_fucell Downed wood unshielded cellulose fraction kg ligninC kgC-1

Gap model parameters

r_shade shade tolerance 1-5, 1-low to 5-highly tolerant code

age_max Maximum attainable age years

ht_max Maximum attainable height m

dbh_max Maximum attainable diameter cm

ddmin Minimum number degree-days for the species degree-day

ddopt Optimum number degree-days for the species degree-day

wso Minimum AET:PET ratio for the species ratio

si50 Site index at 50 years m
USDA Forest Service RMRS-GTR-255. 2011. 103

pcone Probability of good cone crop probability

nocrop Years after cone crop when there is no crop years

age_cone Reproductive age years

dinc_min Minimum diameter growth increment cm

duff_sura Survival by duff depth - alpha coefficient probability

duff_surb Survival by duff depth - beta coefficient probability cm-1

btc Bark thickness coefficient cm bark cm DBH-1

crat_init Initial live crown ratio for mature trees m m-1

age_est Tree establishment age years

crat_est Tree establishment live crown ratio m m-1

ht_est Tree establishment height m

Phenological parameters

leaf_out Leaves start date yearday

leaf_shed Leave drop date yearday

leaf_period Time for leaf drop or growth days

frost_late Latest tolerable spring frost yearday

frost_early Earliest tolerable autumn frost yearday

Snag parameters

snag_long Average snag life years

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

Species names and IDs must correspond for each FireBGCv2 run. The following are the species, species IDs, and four-letter acronyms for the Glacier National 

Park simulation design, as described in the “Demonstration” section:

Master species file

SppID Common name Four-letter acronym

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

1 Ponderosa pine PIPO

2 Grand fir ABGR

3 Douglas-fir PSME

4 Lodgepole pine PICO

5 Western larch LAOC

6 Subalpine fir ABLA

7 Engelmann spruce PIEN

8 Whitebark pine PIAL

9 Alpine larch LALY

10 Western white pine PIMO

11 Western red cedar THPL

12 Western hemlock TSHE

13 Quaking aspen POTR

14 Grass GRSS

15 Shrub SHRB

It is important to note that the four-letter acronym used to name the species is 

the same acronym used in the Tree.in file.

Wildlife.in File

The Wildlife.in file is a cycling vertical scenario file with horizontal properties. 

It contains habitat suitability index values (scaled, often from 0 to 100) for wildlife 

species by cover type and structural stage. This file is a double-cycling file where 

the first or primary cycle is wildlife species and the secondary cycle is cover type. 

Any wildlife species or group of species can be implemented in this file, and there 
104 USDA Forest Service RMRS-GTR-255. 2011.

is a limit of 20 wildlife species per FireBGCv2 run. A list of cover types is defined 

for each wildlife species. A cover type is nothing more than a dominance species 

classification with the species specified in the Species.in file as the list of possible 

cover types. Cover types that are not important habitat for a wildlife species need 

not be represented in the wildlife cover type list. Habitat suitability indices are 

then assigned to five structural stages for each cover type and wildlife species. 

A habitat suitability index can represent anything about a wildlife species. For 

example, it may represent the importance of cover type-structural stage combinations for the nesting of a particular woodpecker species or the relative abundance 

of forage for grizzly bears by each defined cover type-structural stage combination. The indices are assigned to each structural stage horizontally in the file (that 

is, there are five habitat suitability indices for each cover type line in the Wildlife.

in file). The format of the Wildlife.in file is as follows:

Var Record Description Units

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

name 1 Wildlife species name alphanumeric

label 2 Wildlife species label (2-genus, 2-species) alphanumeric

wildID 3 Wildlife species ID number code

code 4 Number of included plant species number

name 1 Four-letter acronym of plant species code

sppID 2 Plant species ID number referencing SPECIES file number

seedling 3 Wildlife habitat suitability index for seedlings index

sapling 4 Wildlife habitat suitability index for saplings index

pole 5 Wildlife habitat suitability index for poles index

mature 6 Wildlife habitat suitability index for mature index

oldgrowth 7 Wildlife habitat suitability index for oldgrowth index

The variable name is a moniker for this wildlife species (for example, 

GrizzlyBear, Ungulates). The user has up to 64 characters to specify a name, 

but be sure there is no space if the name is more than one word. The label is a 

unique wildlife species acronym used in FireBGCv2 output, conventionally labeled as the first two letters of the genus and first two letters of the species. The 

wildID variable is a unique, sequential identification number that is used in model 

computations.

The cover type cycle now starts where habitat suitability values are specified 

in one record in the Wildlife.in file. The name is a four-character acronym for the 

cover type (plant species ID number). The user can put any label, but the sppID

must correspond to a species ID number in the Species.in file. A habitat suitability value (number between 0 and 100, for example) must be entered for each of 

the five structural stages (seedling, sapling, pole, mature, and oldgrowth). The 

structural stages are defined by diameter classes as:

Seedling: Less than 1.37 m tall

Sapling: 0 to 10 cm DBH

Pole: 10 to 25 cm DBH

Mature: 25 to 50 cm DBH

Oldgrowth: Greater than 50 cm DBH

This file structure is repeated for multiple wildlife species.

Manage.in File

The Manage.in file is a cycling vertical scenario file that contains generalized 

parameters that define various management activities. This file is built as a list of 

management activities (called treatments) that are user-defined from three classes 

of anthropogenic disturbances. The first disturbance class is a clearcut that may 
USDA Forest Service RMRS-GTR-255. 2011. 105

be specified with or without a subsequent broadcast burn. The second is a partial 

cut that can also be specified with or without prescribed fire. The partial cut can 

be tailored to mimic a thinning, selection cut, or any other silvicultural treatment. The third anthropogenic disturbance class is a prescribed burn that can be 

implemented at a wide variety of intensities. The user can specify any number or 

combination of these activity types to create a unique treatment by modifying the 

parameters that describe each activity type. This file is different from any other 

FireBGCv2 input file in that the variables that represent the parameters in a treatment are different across management classes. The format of the Manage.in file 

is as follows:

Rec Variable Description Units

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

1 None This line is reserved to describe the file none

THESE VARIABLES ARE CYCLED FOR EACH MANAGEMENT TREATMENT

2 name Unique Management ID name alphanumeric

3 manage_type Management type ID number (1-cc, 2-pc, 3-pb) Code

4 alimit First limitation parameter variable

5 blimit Second limitation parameter variable

6 climit Third limitation parameter variable

7 dlimit Fourth limitation parameter variable

8 parm1 First design parameter variable

9 parm2 Second design parameter variable

10 parm3 Third design parameter variable

11 parm4 Fourth design parameter variable

12 spp1[ MXSPP ] First species design parameter spp code

13 spp2[ MXSPP ] Second species design parameter spp code

NEXT MANAGEMENT TREATMENT

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

The parameters that comprise management types (limit and parm roots) can 

be changed to create different management treatments. For example, there may 

be two clearcut treatments where clearcut (option 1) is specified as the management type, but there is a different list of retention species for each treatment. 

FireBGCv2 can handle any number of treatments, but each treatment must be defined as one of the three management types previously described. The definitions 

of each management type follow:

Clearcut harvest management type (code 1)

name Name of this first management activity– no spaces; use only a single

 word

manage_type Management treatment type ID number: (1-clearcut, 2-partial-cut, 

3-prescribed burn)

alimit Landscape annual clearcut maximum in hectares; the most area that 

can be harvested in one year

blimit Maximum size of a clearcut in hectares

climit Minimum basal area to be considered for harvest; all stands above 

this will be clearcut

dlimit Not used in this management type

parm1 Residual minimum tree DBH in cm

parm2 Smallest merchantable tree DBH in cm

parm3 Fraction of slash left on the stand

parm4 Broadcast burn intensity kW m-1; enter 0.0 if no burn is desired

spp1[ MXSPP ] Retention species; (0-no, 1-yes, order corresponds to speciesID in 

SPECIES file

spp2[ MXSPP ] Placeholder--This field is not used for clearcut management type
106 USDA Forest Service RMRS-GTR-255. 2011.

Partial-cut harvest management type (code 2)

name Name of this first management activity- no spaces; use only a single 

 word

manage_type Management treatment ID number: (1-clearcut, 2-partial-cut, 

3-prescribed burn)

alimit Landscape annual partial-cut maximum in hectares; the most area 

that can be harvested in one year

blimit Maximum size of a partial-cut area in hectares

climit Minimum basal area between min and max DBH classes specified below 

to harvest

dlimit Smallest merchantable tree DBH in cm

parm1 Minimum DBH to cut in cm

parm2 Maximum DBH to cut in cm

parm3 Fraction of slash left on the stand

parm4 Prescribed burn intensity kW/m; enter 0.0 if no burn is desired

spp1[ MXSPP ] Residual species: 0-no, 1-yes, order corresponds to species ID in 

SPECIES file

spp2[ MXSPP ] Placeholder–-Not used for this management type

Prescribed burn management type (code 3)

name Name of this first management activity– no spaces; use only a 

single word

manage_type Management treatment ID number: (1-clearcut, 2-partial-cut, 

3-prescribed burn)

alimit Landscape annual burned with planned burns in hectares; the most 

that can be harvested in one year

blimit Maximum size of the prescribed burn in hectares

climit Minimum stand age for treatment

dlimit Factor when multiplied by fire frequency is minimum age of 

treatment since last fire

parm1 Minimum fire intensity in KW m-1

parm2 Maximum fire intensity in KW m-1

parm3 Placeholder–-Not used for this management type

parm4 Placeholder–-Not used for this management type

spp1[ MXSPP ] Residual species: 0-no, 1-yes, order corresponds to species 

ID in SPECIES file

spp2[ MXSPP ] Placeholder–-Not used for this management type

Plant.in File

This file contains the parameters for the plant model and guilds assigned at 

the site level. This is a cycling vertical scenario file that is used in the simulation 

of the undergrowth or non-forest species. This is actually a double-cycling file in 

that the file cycles for all plant models, and then through all guilds included in that 

plant model. The following is the generalized format for the Plant.in file:

Rec Variable Description

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

1 None This line is reserved to describe the file

2 pnum Number plant models in simulation--Models are assigned to site

3 None This line describes the plant model

4 name Name of plant model

5 plantID Identification number of plant model

6 nguilds Number of guilds (species) for this plant model

7 None This line describes the guilds in the model

8 name Guild name
USDA Forest Service RMRS-GTR-255. 2011. 107

9 id Guild ID number

10 sppID Species ID number--Reference species file

11 ht Guild height (m)

12 bio_alpha Biomass growth rate alpha (year-1)

13 bio_max Maximum biomass(kg m-2)

14 pleaf Proportion biomass leaf

15 pdead Proportion dead at fire

16 pfuel Dead fuel index (1-duff, 2-litter, 3-1hr, 4-10hr, 5-100hr, 

 6-1000hr, 7-shrub, 8-herb)

. . .

. . .

. . . Continue fields 8 to 16 until all guilds in model entered

. . . Start fields 2 to 7 for next plant model

. . . Continue fields 8 to 16 until all guilds in model entered

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

The plant model variables are defined as follows: pnum is the number of plant 

models in this file and is the only line that is not repeated or cycled in this file. 

The first cycle for each plant models then starts. The variable name is a moniker 

for this particular plant model. The user has up to 64 characters (for example, 

EricaceousShrubs) to specify a name, but be sure there is no space if the name is 

more than one word. The plantID is a unique plant model identification number 

that is used throughout FireBGCv2. The variable nguilds specifies the number of 

guilds present in each plant model.

The second cycle within plant models is then initiated and it is repeated for 

each guild in the plant model. Again, name is a useful label for this guild and this 

label shouldn’t exceed 64 characters or contain spaces. The guildID is a unique 

number that identifies a guild across all plant models. The guildID field can be 

used to build several shrub and herb models stratified by productivity gradients. 

Species ID (sppID) is the ID number of a species represented in the Species.

in file that best describes this guild. Guild height (ht) is the maximum height 

of each guild, which is derived from field data or other sources in the literature. 

Biomass growth is described by two exponential curve parameters: bio_alpha is 

the growth rate (kgB m-2 year-1) and bio_max is the maximum biomass for this 

guild kgB m-2). The proportion of total guild biomass that is leaf biomass is represented by pleaf and the proportion of total biomass that is dead after a fire event is 

pdead. The proportion pdead is assigned a fuel index pfuel that best describes the 

fuel component of this dead material (woody, shrub, or grass, for example). Cycle 

through these parameters until all guilds are represented for each plant model, 

then cycle through each plant model.

Pial.in File

This file contains the parameters for modeling whitebark pine regeneration on 

a landscape. This routine was originally included in the first version of FIRESUM

and has been modified to be included in FireBGCv2. This is a static, descriptive 

file with the following fields:

Record Variable Description Units

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

1 cmax Maximum number of whitebark pine cones on a tree cones

2 amin Minimum cone producing age years

3 dbhmin Minimum diameter for cones to be produced cm

4 birds Number of birds on landscape that are caching seed birds

5 spc Number of whitebark pine seeds per cone seeds cone-1

6 spcache Average number of seeds a nutcracker caches seeds cache-1
108 USDA Forest Service RMRS-GTR-255. 2011.

7 pfind Proportion cached seeds recovered by the nutcracker proportion

8 cyr[0] Cone crop probability for a non-cone crop year probability

9 cyr[1] Cone crop probability for a poor cone crop year probability

10 cyr[2] Cone crop probability for a moderate cone crop year probability

11 cyr[3] Cone crop probability for a good cone crop year probability

12 fmax Maximum amount of canopy leaf area for caching m2 m-2

13 cpt Maximum number of cones per tree cones tree-1

14 seeds_eat Consumed seeds proportion proportion

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

These values will probably not change by landscape or simulation area, so it is 

best to keep the values of all variables in this file.

Fuel.in File

This file contains the parameters for the fire behavior and fire effects simulation. This is a uniquely structured, descriptive file that vertically cycles by fuel 

model (with the number of fuel models specified in the Sim.in file), with each line 

representing an important parameter in the fire behavior or fire effects models. 

However, each line has eight values for each parameter for each fuel component 

(litter; duff; 1, 10, 100, and 1000 hr fuels; shrubs; and herbs). In addition, there 

are two sets of five parameters in each fuel model representing different sets of 

values for live and dead fuels. Therefore, this file can be described as a doublecycling vertical file (fuel type, live or dead, and fuel model) that is horizontally 

arranged. The final unique characteristic of this file is that there are three additional parameters that have only two horizontal values, where each value is a general 

representation of live and dead fuels; and three more single-value parameters that 

describe general characteristics of this fuel model. This file should probably never 

be modified once created, and the structure of this file is so singular that there is an 

example at the end of this section. The format of the Fuel.in file follows:

Record Variable Description Units

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

1 None Empty line used to describe file contents none

2 None This has headings for the live and dead none

3 fuelID Fuel model identification number none

4 rhop_dead[ MXDEAD ] Particle density kg m-3

5 lhv_dead[ MXDEAD ] Heat content of fuel btu

6 mps_dead[ MXDEAD ] Surface area:volume ratio cm-1

7 moist_dead[ MXDEAD ] Fuel moisture percent

8 consume_dead[ MXDEAD ] Percent fuel consumption percent

9 rhop_live[ MXLIVE ] Particle density kg m-3

10 lhv_live[ MXLIVE ] Heat content of fuel btu

11 mps_live[ MXLIVE ] Surface area:volume ratio cm-1

12 moist_live[ MXLIVE ] Moisture content percent

13 consume_live[ MXLIVE ] Percent fuel consumption percent

14 mext[2] Moisture of extinction percent

15 fuel_bulk[2] Bulk density of live and dead fuel kg m-3

16 om_bulk[2] Bulk density of litter and duff kg m-3

17 fuel_depth Depth of fuel bed m

18 scm Spread component when ignited dim

19 wndfc Wind reduction factor dim

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

These fields are repeated for each fuel model specified in the SIMULATION 

file.
USDA Forest Service RMRS-GTR-255. 2011. 109

The variable fuelID is a unique identification number for this fuel model that 

can be used to identify the 13 Anderson (1982) fuel classes or any other fuel 

model classification. The next set of parameters represents the eight fuel components of dead fuel. The parameters are particle density (rhop_dead), heat content 

(lhv_dead), surface area to volume ratio (mps_dead), fuel moisture at time of fire 

(moist_dead), and percent fuel consumption at the time of fire (consume_dead). 

The same five variables are used to describe the live fuel components (rhop_live,

lhv_live, mps_live, moist_live, and consume_live). The variables MXDEAD

and MXLIVE are set at a value of 8 inside the FireBGCv2 program and are not 

allowed to vary.

The next set of three parameters are used to describe general characteristics 

about the live and dead fuels, which means there are only two values per line 

rather than eight. The variable mext is the moisture of extinction of live and dead 

fuels, fuel_bulk is the fuel bulk density of the live and dead fuel beds, and om_

bulk is the bulk density of only litter and duff.

The last three parameters describe some general information on the fuel model 

as a whole (that is, only one value per parameter). The fuel_depth variable is the 

depth of the fuel bed. The scm and wndfc variables are the spread component and 

wind reduction factor in the NFDRS fire model parameters used to compute fire 

danger ratings in the model. The following is a sample of a Fuel.in file:

FUELS This file contains the fuel model parameters for FireBGCv2 applications

Fuel Comp Duff Litter 1 hour 10 hour 100 hr 1000 hr Shrub Herb

1 Fuel Model Number - Low elevation FBM 8

rhop-dead 0.550 0.510 0.390 0.390 0.390 0.390 0.510 0.510

lhv-dead 18586.7 18586.7 18586.7 18586.7 18586.7 18586.7 18586.7 18586.7

mps-dead 111.0 57.410 61.16 11.760 2.880 0.980 3.156 91.8560

moistdead 0.60 8.000 0.060 0.080 0.100 0.150 0.1000 0.0500

consume-dead 0.90 0.900 0.950 0.845 0.845 0.790 0.9000 0.9900

rhop-live 0.550 0.510 0.390 0.390 0.390 0.390 0.513 0.513

lhv-live 18586.7 18586.7 18586.7 18586.7 18586.7 18586.7 18595.0 18595.0

mps-live 111.0 57.410 61.166 11.760 2.880 0.980 49.200 91.860

moistlive 0.60 8.000 0.060 0.080 0.100 0.150 0.800 0.500

consume-live 0.90 0.900 0.900 0.800 0.500 0.010 0.900 0.900

mext 0.300 0.300

bulk-d/l 0.0115 0.001

duflitbulk 76.9 44.1

fdepth 0.06

spreadcomp 1.0

windmultfactor 1.0

Site.in File

This is a vertically cycling, descriptive file that repeats for each site specified on the landscape. Each site is defined as a unique biophysical setting where 

weather, soils, and potential vegetation characteristics are specified as static variables for the FireBGCv2 simulation. This biophysical setting is usually taken 

from a digital map that delineates settings by topography, soils, weather, and 

landform. The site is a very important construct in FireBGCv2 because it is used 

to represent biophysical processes unique to the stands within each site, such as 

weather, soils, and fire regime. It is suggested that the mapping of sites use the 

biophysical settings that represent potential vegetation (habitat type groups, for 

example) to delineate each site because, that way, the parameterization of the 

Species.in and Tree.in files will be easier. Create the site map using any number 

of multivariate statistical analyses such as clustering, discriminant analysis, or 

regression trees; however, be sure that the final site map matches the final stand 

map in terms of its biophysical setting. For example, you should not have a stand 

that has lodgepole pine on a dry ponderosa pine site type. Assign to each stand a 
110 USDA Forest Service RMRS-GTR-255. 2011.

site ID once the site map is created. Each site in the Site.in file is specified by the 

following parameters:

Rec Variable Description Units

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

1 Title Title line for site file none

These fields are cycled for each site

1 NONE This is a record to identify or label the site none

2 id Site ID number ID number

3 biomeID Biome ID number 1-evergreenforest, 2-deciduous forest, code

 3-shrubland, 4-grass

4 nstands Number of initial stands at start of simulation number

5 fuelID Fuel model ID number for this site ID number

6 plantID Understory plant model ID number for this site ID number

7 prate Treatment priority rating by site rating

8 weather_file Weather filename for the site--up to 128 characters filename

9 wind_speed Ave annual wind speed m s-1

10 wind_dir Ave annual wind direction degrees

11 latitude Latitude of site decdeg

12 elev Elevation of site m

13 albedo Default site albedo dim

14 maxlai Maximum leaf area index (all sided) m2 m-2

15 maxba Maximum stand basal area m2 ha-1

16 siteindex Site index at year 50 m

17 spm Maximum number of seedlings per unit area seed m-2

18 seed_lag Number of lag years for establishment after fire years

19 fire_freq Average fire frequency years

20 fire_size Average fire size ha

21 soil_depth Depth of soil (free rooting zone) m

22 sand Percent sand in soil profile for soil depth percent

23 silt Percent silt in soil profile for soil depth percent

24 clay Percent clay in soil profile for soil depth percent

25 volN Fraction nitrogen loss that is volitalized proportion

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

The site id number is a unique identification number for this site. The biomeID

number is a flag that identifies whether the site can support evergreen forests (option 1), deciduous forest (option 2), shrublands (option 3), or grasslands (option 

4). The nstands variable indicates the number of stands that are located within 

each site. The fuelID and plantID numbers reference the fuel and plant model 

identification numbers that are defined in the Fuel.in and Plant.in files, respectively. The variable prate is used to rate the site for prescribed burn and harvest 

treatments according to which sites should be treated first and last. The weather_

file is a full pathname of the file that contains the daily weather for this site. The 

format for this file is Julian date, minimum temperature (oC), maximum temperature (oC), and precipitation (cm). The wind_speed and wind_direction variables 

store the average wind speed (m/sec) and wind direction (azimuths) for fire spread 

and some evapotranspiration calculations.

The latitude, elevation, and albedo of the site can be taken from digital spatial 

datasets such as DEMs and thematic map layers, or they can be assigned from 

paper maps. The latitude is in decimal degrees, the elevation is the average elevation of the site above mean sea level. The albedo is for the site at near climax 

conditions.

The next set of parameters defines the potential productivity of the site. The 

maxlai is the maximum leaf area index that the site can support. The maxba is 
USDA Forest Service RMRS-GTR-255. 2011. 111

the maximum basal area ever to occur on this site or the potential basal area for 

this site. The siteindex is the site index of the trees for the site; the site index is 

the height of the trees at 50 years. The variable spm is the maximum number of 

seedlings per meter on this site. The seedlag is the time it takes in years to ameliorate site conditions so that successful regeneration is possible. The seedlag at high 

elevations can sometimes exceed 20 to 50 years. The fire_freq is the average fire 

return interval (years) for this site based on fire history evidence. Last, fire_size is 

the average size of individual fires within this site based on observed data.

The last set of parameters quantifies site soil conditions. The soil_depth is the 

average depth of the free-rooting zone across the site in meters. The variables 

sand, silt, and clay identify the average percent sand, silt, and clay of the soil 

profile defined by the soil depth. These four parameters can be taken from digital soils databases such as STATSGO (http://soils.usda.gov/survey/geography/

statsgo/). Last, the volN variable specifies the fraction of nitrogen in the downed 

wood, litter, duff, and logs that is lost to the site via volatization to the atmosphere. 

This parameter can be found in various fire publications.

Stand.in File

The stand file is a horizontal, cycling, descriptive file that contains important 

stand-level ecosystem information that is critical for FireBGCv2 parameterization and initialization. This file is also a little more complex than the others in 

that the width of the file (number of fields on one line) varies by the number of 

undergrowth guilds for the plantID model specified in the Site.in file. The first line 

of the file is the title line where anything can be written to describe this file. The 

second line is a column header line for ease of entering data. The remaining lines 

have the following format:

Variable Field Size Description Units

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

siteID 1 5 Plot site ID number none

standID 2 10 Plot stand ID number none

ntree 3 5 Number of trees in stand number

elev 4 10.4 Ave elevation for stand m

slope 5 10.4 Ave slope for stand percent

aspect 6 10.4 Ave aspect for stand degrees azimuth

snowW 7 10.4 Snow water kg m-2

soilW 8 10.4 Soil water kg m-2

litter 9 10.4 Litter biomass kg m-2

duff 10 10.4 Duff biomass kg m-2

1hr 11 10.4 Down woody 1 hr biomass kg m-2

10hr 12 10.4 Down woody 10 hr biomass kg m-2

100hr 13 10.4 Down woody 100 hr biomass kg m-2

1000hr 14 10.4 Down woody 1000 hr biomass kg m-2

guild1 15 10.4 Undergrowth guild 1 biomass kg m-2

guild2 16 10.4 Undergrowth guild 2 biomass kg m-2

guild3 17 10.4 Undergrowth guild 3 biomass kg m-2

guildn 10.4 Undergrowth guild n biomass kg m-2

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

The siteID identifies the biophysical setting of this stand, and the site ID number is specified in the Site.in file. The standID is a unique identification number 

that is assigned to this stand. It is important to note that all of the stand IDs specified in the Stand.in file must appear on the initial stand map (see the “Map.in 

File” section). The variable ntree specifies the number of trees (living and dead) 

on the stand. This variable is used to read the details of these trees (DBH, height, 
112 USDA Forest Service RMRS-GTR-255. 2011.

age, etc.) from the Tree.in file. The elev, slope, and aspect variables describe the 

average elevation (m), slope (%), and aspect (az) of the stand. The beginning 

snow water (water in the snowpack) and soil water (kgW m-2) are specified in the 

variables soilW and snowW.

The remaining fields are used to define the biomass on the forest floor. The 

biomass in the duff and litter layers is specified in the variables duff and litter. Downed woody fuel (coarse and fine woody debris) is divided into four size 

classes: 1hr (0 to 0.5 cm diameter), 10hr (0.5 to 2.5 cm), 100hr (2.5 to 7.0 cm) 

and 1000hr (>7.0 cm).

The live biomass in the undergrowth guilds is specified in the last set of variables. The number of fields must correspond to the number of guilds in the plant 

model specified in the Site.in file. These will be stretched horizontally across the 

Stand.in file so the file width could be quite wide. Each stand in the simulation 

landscape is assigned a site ID number, as described in the “Site.in File” section. 

Sites are identified by individual site ID numbers and represent unique biophysical settings on the simulation landscape.

Tree.in File

The tree file is a cycling, horizontal, descriptive file that specifies all of the 

trees in a simulation plot that represents a stand. Various silvicultural metrics are 

recorded for each group of trees. These data reflect a list of all trees within the 

reference stand. These data can be taken from one plot and entered as a tree list 

for the entire plot, or they can be summarized across a stand so that the tree silvicultural measurements are roughly an average for all trees in a size class. It is 

important that the number of trees in a stand correspond to the number of trees 

entered here.

A list of trees must be assigned to each stand and site combination using field 

data. This is done by summarizing the stand and site combinations from the maps 

(lodgepole pine sapling stand type on an upper subalpine site type, for example) 

into a table. Then, the available field data should be summarized in a similar manner. Finally, choose the field plot that best represents each stand-site combination 

and assign the sampled tree characteristics to that stand-site combination in this 

format of the Tree.in file:

Variable Field Size Description Units

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

siteID 1 5 Site ID number none

standID 2 10 Stand ID number none

treeID 3 5 Tree ID number None

sppID 4 5 Tree species 4-character code code

stat 6 5 Tree health (1-healthy, 4-snag) none

tdens 5 10.1 Tree density t ha-1

dbh 7 10.1 Tree DBH cm

ht 8 10.1 Tree height m

hbc 9 10.1 Height to base of crown m

age 10 10.1 Tree age years

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

The siteID number corresponds to the siteID in the Site.in file; the standID

corresponds to the standID in the Stand.in file; the treeID is an ID number for 

this tree; and the sppID is the four-letter species code that is identified in the 

Species.in file. The variable stat is tree status; there are only two status codes: 1 is 

a live tree and 4 is a snag. The variable tdens is tree density on the plot. Variables 

dbh, ht, hbc, and age are average diameter, height, crown height, and age for 

this tree class or actual values for this tree. Trees are best entered as a list in this 
USDA Forest Service RMRS-GTR-255. 2011. 113

format, in which case the dbh, ht, hbc, and age would be actual sampled values 

for that tree. For simplicity the user can enter trees as diameter classes with the 

midpoint of the diameter class as the dbh and the average height, crown height, 

and age as ht, hbc, and age. The trees are entered in sequence by their ID numbers 

by stand and then by site. 
114 USDA Forest Service RMRS-GTR-255. 2011.

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Index of Variables

Page

35 AAPPT

The average annual precipitation (kgW m-2)

43ACO2

Concentration of carbon dioxide in the atmosphere (ppm)

39AET

Actual evapotranspiration (kgW m-2 day-1)

20AGE

Age of stand or tree in years

25AGECONEi

Minimum reproductive age for species i (year)

60AGEmax

Maximum possible age for a species (year)

63AGEsnag

Maximum possible time that a snag can stay erect (year)

43 AL

Proportion of available light (proportion)

15ALBEDO

The proportion of the direct radiation that is reflected back into the atmosphere as 

 indirect radiation

18 ALPHA

Biomass growth rate coefficient. The coefficient that defines the growth rate of species 

 in a guild (kg year-1)

39 AP

Atmospheric pressure (MPa)

15 BA

Basal area of stand (m2 ha-1)

65 BARKthick

Bark thickness factor to convert DBH to bark thickness

13BEETLE

Mountain pine beetle infection flag (0-no, 1-yes)

19BIOMASS

The instantaneous amount of biomass on the simulation plot for all the plants of the 

 species within the guild (kgB m-2)

19BIOMASSgs

The growing season biomass on the simulation plot for all the plants of the species 

 within the guild (kgB m-2)

18BIOMASSmax

Maximum biomass attainable for the species in that guild (kgB m-2)

14 BULKd

Bulk density of duff (kg m-3)

Bulk density of the duff (kg m-3)
128 USDA Forest Service RMRS-GTR-255. 2011.

14 BULKl

Bulk density of litter (kg m-3)

Bulk density of the litter (kg m-3)

15CAREA

Canopy area of all trees in the stand (m2)

15CC

Percent canopy cover of all trees in the stand (%)

52CF

Consumption factor input to model (proportion)

65CK

Scorched tree crown volume (%)

49CL

Tree crown length (m)

14CONSUME

Proportion of fuel that will be consumed in the fire (proportion)

52ConsumeC

Amount of fuel consumed across all components (kgC m-2)

41 cp

specific heat of air (1010.0 J kgW K-1)

49CPLA

Cumulative projected leaf area (m2)

65CS

Length of crown that is scorched (m)

14 DBH

Diameter at breast height (cm)

60 DD

Days that daytime temperature is above 2 °C

60 DDmax

Maximum number of degree-days for a species to account for temperature effects on 

 tree growth

60 DDmin

Minimum number of degree-days for a species to account for temperature effects on 

 tree growth

60 DDopt

Optimum number of degree-days for a species to account for temperature effects on 

 tree growth

16DeadcrootC

Dead coarse root carbon at stand or tree level (kgC)

16DeadstemC

Dead stem or heartwood carbon at stand or tree level (kgC)

57DEPTH

Depth of litter and duff layer (cm)

60DINC

Diameter increment for tree growth in a year (cm)

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USDA Forest Service RMRS-GTR-255. 2011. 129

60DINCmax

Maximum possible diameter increment for tree growth (cm)

24DIST

The distance between the seed source pixel and target pixel (m)

34 Dl

Daylength (seconds)

43 DLMR

Daytime respiration of sun/shade leaves per projected leaf area

13 DSR

Days since last rainfall (days)

13 DSS

Days since last snowfall (days)

16DuffC

Amount of carbon in the duff on the forest floor (kgC m-2)

46 DWTF

Turnover fraction specified by species in the Species.in file

53EF

Emission factor (kg consumed kg-1 of smoke element)

41ELEV

Stand elevation (m)

48EMC

Equilibrium fuel moisture content (%)

34EPPT

Effective daily precipitation (kgW m-2)

39ET

Evapotranspiration (kgW m-2)

39EVAP

Evaporation (kgW m-2)

18FDEAD

Fraction of guild biomass that is dead at the time of a fire

14FDEPTH

Fuel depth (m)

65FI

Fire intensity (kw m-1)

28FIRESIZE

Size of a simulated fire (m2)

26FIRESUPP

Index that ranges from 0 (no suppression) to 1 (total suppression)

46FLBC

Fraction of that biomass that is carbon

43 FLNR

Fraction of leafN in Rubisco

48 FMmax

Maximim foliar fuel moisture for tree crowns (%)

Page
130 USDA Forest Service RMRS-GTR-255. 2011.

48 FMmin

Minimum foliar fuel moisture for tree crowns (%)

13FRI

Average fire return interval for a point in a site (years)

Fire return interval or number of years between fire for all land area within a site (years)

16FrootC

Fine root carbon at stand or tree level (kgC)

56 FROSTfall

Earliest acceptable fall frost (yearday)

56 FROSTspring

Last acceptable spring frost (yearday)

13FSIZE

Average fire size (ha)

18FUELID

Fuel ID number referenced in the Fuel.in file

39 gl

Leaf-level stomatal conductance (kgW sec-1)

39 gmax

Maximum stomatal conductance (kgW sec-1)

41GS

Standard gravitational acceleration (9.80665 m sec-1)

34GSmid

Midpoint of the growing season (yearday)

15HABSUITk

Habitat suitability index for wildlife species k

15HBC

Height to base of crown (tree) or canopy (stand) (m)

50HERBdead

Dead herbaceous fuel component loading (kgB m-2)

36HERBPPT_GREENUP

Critical threshold for herbaceous greenup based on precipitation (kgW m-2)

36HERBTEMP_GREENUP

Critical threshold for herbaceous greenup based on soil temperature (°C)

33 Hr

Relative humidity for a day (%)

48HSIZE

Thickness of a canopy layer (m)

15 HT

Stand height or average height of all trees in the overstory (m)

Tree height or stand height (m)

40 k

Extinction coefficient

26KBDI

Keetch Byram Drought Index

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USDA Forest Service RMRS-GTR-255. 2011. 131

26KBDIbase

Mean across all years of the maximum KBDI value for one year

46kFRAG

Base fragmentation rate for decomposition

15 LA

Leaf area of the canopy or tree (m2 m-2)

13LAG

Number of years after a fire before regeneration can occur (years)

Number of years after a fire before tree regeneration (year)

13LAImax

Maximum projected leaf area index for the site (m2 m-2)

34 LAT

Latitude (degrees)

7LeafC

Leaf carbon at either stand or tree level (kgC)

16LeafCmax

Maximum leaf carbon at stand or tree level in one year (kgC)

16LeaffallC

This year’s leaf fall that hits the ground (kgC m-2)

18LEAFfrac

Fraction of guild biomass that is leaf. This is the proportion of the total biomass that 

 is leaf biomass

46LFRAC

Fraction of undergrowth biomass that is leaf (proportion)

14LHV

Heat content of fuel (BTUs)

16LitterC

Amount of carbon in litter on the forest floor (kgC m-2)

16LivecrootC

Live coarse root carbon at stand or tree level (kgC)

16LivestemC

Live stem or bole carbon at stand or tree level (kgC)

43LNC

Leaf nitrogen concentration per unit projected leaf area calculated as 1 / PSLA

48MCfoliar

Foliar moisture content (%)

14MOIST

Fuel moisture at the time of fire expressed as a proportion of dry weight (proportion)

14 MPS

Surface area to volume ratio (m2 m-3)

42 MR

Maintenance respiration (kgC)

43 MRP

Maintenance respiration coefficient

Page
132 USDA Forest Service RMRS-GTR-255. 2011.

48MXHGT

Maximum number of canopy layers (number)

34NETPPT

The net precipitation (inches) as computed from the previous and current day’s 

 rainfall (inches)

13NFREEZDAY

Number of freezing days in the year (>-30 °C) (days)

28NFREEZDAYS

Number of days below 20 °C (days)

13NFROZDAY

Number of frozen days in a year (>-40 °C) (days)

28NFROZDAYS

Number days below 40 °C (days)

23NOCROP

Number of years to block a cone crop after a good crop (years)

Years before another cone crop is possible (years)

39OutflowW

Excess water that is drained off the stand via runoff or subsurface flow (kgW m-2)

41 pa

Air density

34 PAR

Photosynthetically active radiation

37PAREA

Simulation plot area (m2)

63 Pbeetle

Probability of tree mortality from mountain pine beetle

47PCELL

Proportion of wood that is cellulose

23PCONE

Probability of a good cone crop (probability 0 to 1)

24 Pdist

Probability of a seed landing on a pixel

26 Pfire

Probability of a fire

Probability of tree mortality from wildland fire

48 PLA

Projected leaf area of the tree or canopy (m2 m-2)

15PLAI

Projected leaf area index (m2 m-2)

40PLAISHADE

Projected leaf area index of shaded leaves (m2 m-2)

40PLAISUN

Projected leaf area index for sunlit leaves (m2 m-2)

47PLIG

Proportion of wood that is lignin

Page
USDA Forest Service RMRS-GTR-255. 2011. 133

40 PPFD

Photon flux density of sun/shade leaves

40 PPFD50

PAR photon flux density (umol sec-1) needed to open 50 percent of the stomata

40 PPFDplai

PAR photon flux density per unit of projected leaf area index

32 PPT

Daily precipitation (cm in input, kgW m-2 in model)

62 Prandom

Probability of tree mortality from random events

63 Prust

Probability of tree mortality from white pine blister rust

56pSEROT

Probability of serotiny of lodgepole pine

16 PSN

Amount of carbon from photosynthesis at stand or tree level (kgC)

56 Psprout

Probability of sprouting

62 Pstress

Probability of tree mortality from stress

39 rc

Correction factor for temperature and pressure for stomatal conductance

26rCLIMATE

Reduction factor that scales ignition probabilities to climate signal (0 to 1 value, 

 dimensionless)

60rCROWD

Reduction factor to account for the effect of crowding or resource availability in 

 tree growth

56rDENS

Density reduction factor for regeneration

58rDIST

Reduction factor for regeneration accounting for seed dispersal distance

56 rDSUR

Reduction factor for seedling survival in duff

26REBURN

Number of years before a stand can burn again (year)

16RESP

Amount of carbon from respiration at stand or tree level (kgC)

26rFIRE

Reduction factor that scales fire ignition probabilities to account for size of stand 

(0 to 1, dimensionless)

14 RHOP

Fuel particle density (kg m-3)

Page
134 USDA Forest Service RMRS-GTR-255. 2011.

26rMGT

Reduction value that scales fire ignition probabilities to level of suppression 

effecicacy (0 to 1, dimensionless)

6 RNUM

Random number between 0.0 and 1.0 (dimensionless)

Random number returned from the random number generator

39 rPPFD

Scalar that represents light effects on stomatal opening

41 RR

Gas law constant (8.3143 m3 Pa mol-1 K-1)

40rSHADE

Proportion of photon flux density to the shaded leaves

46rSOILT

Soil temperature scalar for decomposition

46rSOILW

Soil water scalar for decomposition

39 rSWP

Reduction factor for soil water potential effects on conductance

39rTEMP

Reduction factor to account for the effect of temperature on tree growth

6rTREE

Reduction factor accounting for number of cone producing trees in a stand (0 to 1, index)

13 RUST

Flag indicating if this tree is rust resistant

Rust infection flag (0-no, 1-yes)

13 RUSTrh

Average humidity in September (%)

13 RUSTtemp

Average temperature in September (°C)

59rWATER

Reduction factor to account for the availability of water

40SALBEDO

Stand albedo with snowpack

13 SAPmax

Maximum number of saplings per meter to be established in the simulation 

 plot (sap m-2)

Maximum number of saplings that can regenerate in any one year across all species 

 (saplings m-2)

40SDECAY

Snow albedo decay coefficient

25SEEDOPT

Simulation option number of simulation years before seed dispersal is updated (year)

15SEEDTREEi

Number of cone producing trees for species i—trees>10 cm DBH, >min reproductive 

 age (trees)

Page
USDA Forest Service RMRS-GTR-255. 2011. 135

51 SH

Scorch height (m)

50 SHRUBdead

Dead shrub fuel component loading (kgB m-2)

53SMOKE

Smoke emissions by factor (kg)

34 SnowW

Snowpack (kgW m-2)

16SoilC

Amount of carbon in mineral soil on the forest floor (kgC m-2)

27SPIX

Number of pixels to spread the fire in one time step (pixels)

18SppID

A species ID number referenced in the Species.in file

56 SPROUTmax

Maximum sprouting potential (sprouts)

32 SRAD

Net daily canopy shortwave radiation (W m-2 day-1)

40 STDA

snowpack temperature deficit (°C)

16StemC

Total live and dead stem carbon at stand or tree level (kgC)

20STRESS

Years of stress (years)

42 SW

Stand-level shortwave radiation (W m-2 day-1)

46sW1 to sW1000

Decomposition scalars for woody particle size for 1 hr to 1000 hr woody fuels

42 SWABS

Absorbed shortwave radiation (W m-2 day-1)

42SWREF

Reflected shortwave radiation (W m-2 day-1)

35 Taaave

average annual daily average temperature (°C)

33 Tday

Daytime temperature (°C)

34 Tdew

Dewpoint temperature (°C)

35TGOD

Tree greenup onset date (Julian date)

65 Tkill

Lethal temperature for tree foliage (assumed as 60 °C)

32 Tmax

Maximum daily temperature (°C)

Page
136 USDA Forest Service RMRS-GTR-255. 2011.

33 Tmin

Minimum daily temperature (°C)

33 Tnight

Nighttime daily temperature (°C)

33 Tsoil

Daily soil temperature (°C)

44UBIOMASS

Undergrowth biomass (kgB m-2)

44UBIOMASSmax

Maximum undergrowth biomass for a guild (kgB m-2)

44UGROWTH

Undergrowth growth by guild (kgB m-2)

19 UHT

The instantaneous height of the undergrowth guild (m)

18 UHTmax

Maximum undergrowth guild height (m)

19 ULA

The instantaneous projected leaf area of the undergrowth guild (m2)

19 UNPP

The amount of carbon gain as growth for that guild during that year (kgC m-2)

19 UPSN

The amount of carbon from photosynthesis for that guild during that year (kgC m-2)

19URESP

The amount of carbon lost from respiration for that guild during that year (kgC m-2)

49 USLA

Undergrowth specific leaf area (m2 kgC-1)

38VMC

Soil water content (%)

38VMCsat

Soil volumetric water content (percent)

32VPD

Vapor pressure deficit (Pa)

39VPDclose

Vapor pressure deficit for closing stomata, as specified by species (MPa)

39VPDopen

Vapor pressure deficit for opening stomata, as specified by species (MPa)

16W1000C

Amount of carbon in logs (1000 hr woody) on the forest floor (kgC m-2)

16W100C

Amount of carbon in large branches (100 hr woody) on the forest floor (kgC m-2)

16W10C

Amount of carbon in branches (10 hr woody) on the forest floor (kgC m-2)

16W1C

Amount of carbon in twigs (1 hr woody) on the forest floor (kgC m-2)

Page
USDA Forest Service RMRS-GTR-255. 2011. 137

34YD

Yearday or Julian date

26YSB

Years since last fire (year)

20YSI

Years since mountain pine beetle epidemic (years)

41  λ

Latent heat of vaporization

38 Ψ

Soil water potential (MPa)

40 Ψclose

soil water potential for stomatal closing (MPa)

40 Ψopen

soil water potential for stomatal opening (MPa)

38 Ψsat

Soil water potential at soil saturation (MPa)

Page
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