U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2006–5061

Modeling the Effects of Fire Frequency and 

Severity on Forests in the Northwestern 

United States
Modeling the Effects of Fire Frequency and 

Severity on Forests in the Northwestern 

United States

By Richard T. Busing and Allen M. Solomon

Scientific Investigations Report 2006–5061

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

P. Patrick Leahy, Acting Director

U.S. Geological Survey, Reston, Virginia: 2006

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Suggested citation:

Busing, Richard, Solomon, Allen, 2006, Modeling the Effects of Fire Frequency and Severity on Forests in the 

Northwestern United States: U.S. Geological Survey Scientific Investigations Report 2006-5061, 16 p.
iii

Contents

Conversion Factors, Datums, Definitions, and Abbreviations and Acronyms....................................iv

Abstract...........................................................................................................................................................1

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

Purpose and scope ..............................................................................................................................1

The study region....................................................................................................................................2

Methods...........................................................................................................................................................2

The empirical data................................................................................................................................2

The model...............................................................................................................................................2

Model comparisons with field data ...................................................................................................6

Results and Discussion.................................................................................................................................7

Forest basal area ..................................................................................................................................7

Forest composition ...............................................................................................................................7

Summary..........................................................................................................................................................9

Acknowledgements.......................................................................................................................................9

References Cited............................................................................................................................................9

Appendix A Implementation of fire disturbance simulations with FORCLIM 2.9..............................11

Figures

Figure 1. Map of western Oregon showing the ecoregions used in the study (Thorson et al. 

2002). The pool of field sites located within each ecoregion is displayed.........................3

Figure 2. Graphs of monthly climatic means in a) a coastal site (Newport, Oregon), and b) an 

inland site (Bend, Oregon)...........................................................................................................4

Figure 3. Graphs of mortality responses of trees by stem diameter and fire tolerance class 

under a) low-severity fire, and b) high-severity fire. Equations are provided in the 

Appendix. .......................................................................................................................................5

Figure 4. Graphs of mean species composition and basal area by ecoregion: a) actual data, 

b) simulated data, and c) simulated data with fire-induced mortality. Ecoregion codes 

are described in Figure 1.............................................................................................................8

Tables

Table 1. Mean fire return interval applied in the simulations by severity and ecoregion..............7

Table 2. The FORCLIM species parameter input file for the Pacific Northwest. .........................12
iv

Conversion Factors, Datums, Definitions, and 

Abbreviations and Acronyms

Conversion Factors

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

mile, nautical (nmi) 1.852 kilometer (km)

yard (yd) 0.9144 meter (m)

Area

acre 4,047 square meter (m2

)

acre 0.4047 hectare (ha)

acre 0.4047 square hectometer (hm2

) 

acre 0.004047 square kilometer (km2

)

square foot (ft2

) 929.0 square centimeter (cm2)

square foot (ft2

) 0.09290 square meter (m2)

square inch (in2

) 6.452 square centimeter (cm2)

section (640 acres or 1 square mile) 259.0 square hectometer (hm2

) 

square mile (mi2

) 259.0 hectare (ha)

square mile (mi2

) 2.590 square kilometer (km2) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F-32)/1.8

DBH is the abbreviation for stem diameter at breast height (1.37 m above ground)

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Altitude, as used in this report, refers to distance above the vertical datum.
Abstract

This study used a model of forest dynamics (FORCLIM) 

and actual forest survey data to demonstrate the effects of various fire regimes on different forest types in the Pacific Northwest. We examined forests in eight ecoregions ranging from 

wet coastal forests dominated by Pseudotsuga menziesii and 

other tall conifers to dry interior forests dominated by Pinus 

ponderosa. Fire effects simulated as elevated mortality of 

trees based on their species and size did alter forest structure 

and species composition. Low frequency fires characteristic 

of wetter forests (return interval >200 yr) had minor effects on 

composition. When fires were severe, they tended to reduce 

total basal area with little regard to species differences. High 

frequency fires characteristic of drier forests (return interval 

<30 yr) had major effects on species composition and on total 

basal area. Typically, they caused substantial reductions in 

total basal area and shifts in dominance toward highly fire tolerant species. With the addition of fire, simulated basal areas 

averaged across ecoregions were reduced to levels approximating observed basal areas. 

Introduction

The ecological impacts of severe forest fires are significant. Loss of live forest biomass and sharp declines in the 

abundance of most tree species are well-known consequences 

of severe fire events (Whelan, 1995). The effects of less 

severe fire events on forests are more subtle, but the cumulative effects of multiple low-severity fires demand attention. Across the Pacific Northwest, forests are subject to a 

wide diversity of fire regimes (Agee, 1993). It follows that 

the region is well-suited for investigation of contrasting fire 

regimes on forest dynamics. 

How fire severity and frequency impact forest composition and structure is best studied or simulated over the long 

periods of time required to generate forest tree life cycles. 

This research requires mechanistic models that consider both 

tree species and stem size susceptibility to fire. Models that 

can simulate changes in forest dynamics over centuries are 

also needed to investigate the effects of global climate change 

and shifting fire regimes on forests. The FORCLIM forest gap 

model (Bugmann, 1996) is suitable for such investigations. It 

operates on time scales from individual years to centuries and 

it simulates changes in both species composition and the size 

of tree stems. It also projects the effects of climate on trees 

(Bugmann and Solomon, 2000), so it is a versatile tool in the 

study of forest responses to environmental change. With the 

added capability of simulating fire frequency and severity it 

becomes particularly useful for simulating forest dynamics 

in the western U.S. Our objectives are to: 1) develop a fire 

disturbance routine for FORCLIM, and 2) apply the model in 

evaluating the consequences of fire frequency and severity on 

a variety of forest types representative of forests in the western 

U.S.

This study is novel because other forest gap models 

that include wildfire routines do not generate realistic results 

across the broad gradients or variable terrain of the Pacific 

Slope regions of the western U.S. (e.g. ZELIG, Miller and 

Urban, 1999; see Busing and Solomon, 2004). None of the 

gap models applied to these regions (e.g. CLIMACS, Dale and 

Hemstrom, 1984; SILVA, Kercher and Axelrod, 1984; FIRESUM, Keane et al., 1990; ZELIG, Urban et al. 1993) have a 

demonstrated ability to simulate vegetation forcing by and 

response to seasonal moisture variations, which control both 

species composition and fire regimes in the Pacific Northwest.

Purpose and scope 

In earlier modeling investigations we demonstrated 

that FORCLIM was able to simulate forest composition and 

structure across broad climatic gradients in western Oregon 

(Busing and Solomon, 2004, 2005). This was particularly 

true in forests subject to infrequent fire. However, in some 

ecoregions with high fire frequencies, simulated composition 

and structure did not match that of actual forests. 

In this study we examine whether the addition of fire 

disturbance to the simulations improves agreement between 

simulated and actual forest characteristics. We simulate a 

wide range of fire regimes from high severity, low frequency 

events to low severity, high frequency events to see how fire 

regime affects forest structure and composition. By way of 

simulation we also contrast these regimes to those devoid of 

fire disturbance. Comparison of various simulated forests to 

actual forests is made. In this way we test the suitability of 

the FORCLIM model in forests of the northwestern United 

States. We also gain understanding of the long-term effects of 

fire on forest dynamics.

Modeling the Effects of Fire Frequency and Severity on 

Forests in the Northwestern United States

By Richard T. Busing and Allen M. Solomon

Introduction   
The study region

To evaluate the modeled fire routine and its simulated 

fire effects we chose a series of eight ecoregions along a broad 

climatic gradient in western Oregon from the Pacific Coast to 

the interior high desert (Fig. 1). The study region spanned a 

wide variety of climates, forest types and fire regimes representing many forested regions of the western United States. 

Coastal forests of the region are some of the wettest forests 

in the western United States with mean annual precipitation 

exceeding 200 cm (Fig. 2a; Sternes, 1960; Franklin and Dyrness, 1988). Fires are very infrequent in wet, coastal forests 

but when such forests are dry enough to burn, fires are almost 

invariably stand-replacing events (Agee, 1993). Forests and 

rangelands of interior Oregon east of the Cascades undergo 

little annual rainfall and experience particularly dry summers 

(Fig. 2b). They have high frequencies of ground fires and 

few stand-replacing crown fires, resembling fire regimes in 

of much of the western U.S. (e.g., Swetnam and Betancourt, 

1998; Brown and Wu, 2005).

Methods

The empirical data

In order to assess the existing vegetation of western 

Oregon a set of data on 2323 forest stands in the region was 

assembled from USDA and USDI databases (Busing, 2004). 

All stands were inventoried between 1993 and 2002 using 

USDA Forest Service conventions adopted for an ecological 

survey of forested federal lands (Max et al., 1996). In that 

system, known as the Current Vegetation Survey, plots were 

established on a square grid at 5.5 km intervals. A five-subplot design covering a 1-ha area was used for tree stratum 

observations at each plot site. Measurements on live trees 

included diameter at breast height and, for selected individuals 

at most sites, tree height and age. We selected sites in which 

all five subplots were inventoried and tree age data were collected. 

Our analyses in processing the data for this modeling 

investigation were restricted to all live trees >10 cm DBH. We 

calculated basal area for dominant species and for all species 

combined. Stand age, or time since the last stand-replacing 

disturbance, was estimated as the maximum age of a set of 

overstory trees selected for coring at each plot site. Climate 

data were assigned to each plot site using the geographic 

coordinates of the plot and interpolated climate data sets. The 

climate data consist of monthly mean precipitation and temperature from 1961 to 1990 on a 4-km grid across the conterminous United States (Lugo et al., 1999). 

The model

The FORCLIM model of forest dynamics is designed to 

simulate forest responses to disturbance events and to climatic 

change in space and time (Bugmann, 1996). Assessments of 

simulated forest composition along climatic gradients have 

demonstrated the wide applicability of advanced versions 

of FORCLIM in temperate forest regions (Bugmann and 

Solomon, 2000). The model generates tree species composition along climatic gradients that tends to follow patterns of 

potential natural vegetation (sensu Kuchler, 1964). It also 

simulates stand development and succession as a tree-bytree replacement process. Projected changes are driven by 

tree-level responses to precipitation, temperature and disturbance regimes. Thus, the model can simulate forest response 

to changing regimes of climate and disturbance (Busing and 

Solomon, 2004, 2005).

The FORCLIM model is individual-based and can operate across a range of spatial and temporal scales (Bugmann 

and Solomon, 1995). It is a gap model (sensu Shugart, 1984) 

in which a set of independently simulated 0.08-ha plots, each 

representing a gap-sized patch, is taken to represent a forest stand or landscape (Bugmann, 1996). Compositional 

and structural dynamics are driven by tree species responses 

within the dynamic biotic and abiotic environments modeled 

and assessed annually for each plot. A pool of 18 tree species 

(20 taxa when two subspecies are distinguished) common 

to northwestern Oregon is used in this study (Bugmann and 

Solomon, 2000; Busing and Solomon, 2004; Appendix). Key 

species parameters affecting dynamics include a maximum 

diameter at breast height (DBH), a maximum age, a unique set 

of resource-growth-response functions, and a set of conditions 

for establishment of new trees. General carrying capacity (or 

biomass capacity) functions are not used in FORCLIM but 

rather are emergent properties of the model.

The model applied in this study (FORCLIM version 2.9, 

Bugmann and Solomon, 2000) resulted from a long series of 

enhancements to the JABOWA model (Botkin et al., 1972). 

The fundamental tree competition, growth and mortality considerations in JABOWA were employed in FORCLIM version 

1.0 with some modifications (Bugmann, 1994, 1996; Bugmann and Solomon, 1995). They were also used in the southern Appalachian model FORET (Shugart and West, 1977) 

and the southern central European model FORECE (Kienast, 

1987) from which FORCLIM 1.0 evolved. Unique features of 

FORCLIM 2.9, a version developed for the strongly seasonal 

climate of the Pacific Northwest, center on the effects of climate on tree performance (Bugmann and Solomon, 2000). 

Like earlier gap models (e.g. Botkin et al., 1972; Shugart, 

1984), monthly mean temperature and precipitation serve as 

the basis for climate calculations affecting annual growth of 

trees. Modifications in FORCLIM 2.9 include a new temperature-growth relationship, a new soil moisture submodel, 

a deciduous species growing season, and a chilling requirement for establishment. The widely used parabolic curve of 

potential tree growth versus total annual growing degree days 

   Modeling the Effects of Fire Frequency and Severity on Forests in the Northwestern United States
Figure 1. Map of western Oregon showing the ecoregions used in the study (Thorson et al. 2002). The pool of field sites located within 

each ecoregion is displayed.

Methods   
0

5

10

15

20

25

30

J F M A M J J A S O N D

Month

Mean value

(b) Bend, Oregon

Precipitation (cm)

0

5

10

15

20

25

30

J F M A M J J A S O N D

Month

Mean value

Temperature (deg. C)

(a) Newport, Oregon

Figure 2. Graphs of monthly climatic means in a) a coastal site (Newport, Oregon), and b) an inland site (Bend, Oregon).

   Modeling the Effects of Fire Frequency and Severity on Forests in the Northwestern United States
0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100 120 140 160 180 200

Bole diameter (DBH cm)

Probability of death

Tolerance 1

Tolerance 2

Tolerance 3

Tolerance 4

(a)

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100 120 140 160 180 200

Bole diameter (DBH cm)

Probability of death

Tolerance 1

Tolerance 2

Tolerance 3

Tolerance 4

(b)

Figure 3. Graphs of mortality responses of trees by stem diameter and fire tolerance class under a) low-severity fire, and b) highseverity fire. Equations are provided in the Appendix.

Methods   
(Botkin et al., 1972; Shugart, 1984; Botkin, 1992) is replaced 

with an asymptotic curve assuming reduced potential growth 

only at the colder edge(s) of a species’ range. In addition, 

the potential growth of deciduous species is determined from 

climate data only for months when deciduous trees are in leaf 

(April to October). The winter chilling requirement is added 

to the regeneration “filters” (sensu Shugart, 1984) for each 

species. If the simulated minimum winter temperature for a 

year is too warm to permit winter dormancy, then regeneration 

of the species is temporarily reduced. This chilling requirement is defined by a species-specific temperature value. A 

drought index is applied to constrain tree growth based on the 

species’ moisture stress tolerance including their ability to 

withstand many consecutive weeks with soil moisture below 

the wilting point. The drought index is associated with a 

new water balance submodel of soil moisture using a bucket 

scheme and evapotranspiration estimates (Bugmann and 

Cramer, 1998) rather than the more commonly used and more 

empirical submodel derived from Thornthwaite and Mather 

(1957). These and other modifications leading to version 2.9 

of FORCLIM, which is suited to Pacific Northwest forests 

and climates, are discussed in further detail by Bugmann and 

Solomon (2000).

We modified the FORCLIM 2.9 model to simulate effects 

of fire frequency and severity. Species were assigned to one of 

four fire tolerance classes based on natural history information 

provided by Minore (1979) and the USDA Fire Effects Database. The least fire tolerant class contained Abies amabilis, 

Abies lasiocarpa, Chamaecyparis nootkatensis, Picea engelmannii, Picea sitchensis, Tsuga heterophylla, and Tsuga mertensiana. These species (class 1) were assumed to undergo 

complete stand-wide mortality during a fire event regardless 

of fire severity. For the three remaining classes of tolerance, 

fire effects were simulated so that large trees had greater 

survivorship than small trees (Fig. 3; Dale and Hemstrom, 

1984). Species in the mildly tolerant category (class 2) (Abies 

grandis, Abies procera, Acer macrophyllum, Alnus rubra, 

Arbutus menziesii, Pinus contorta, Pinus monticola and Thuja 

plicata) were assumed to suffer heavy mortality, particularly 

in severe fires. Pseudotsuga menziesii was the sole species in 

the moderately fire tolerant category (class 3). Typically, it 

suffered low to intermediate levels of mortality. Survivorship 

depended strongly on stem size and fire severity. Trees in the 

highly tolerant category (class 4), containing Pinus ponderosa

and Quercus garryana, were subject to relatively low mortality 

from fire events.

Fires regimes were simulated as a combination of lowseverity and high-severity events (see Appendix). Simulated 

events of low severity had little impact on mortality of larger 

trees of fire-resistant species. However, trees of fire-intolerant 

species, and saplings and small trees of species with low to 

moderate fire tolerance were impacted by events of low severity as well as those of high severity. The likelihood of tree 

mortality among fire-resistant species (class 2-4) increased 

with fire severity. 

Model comparisons with field data

The ability of FORCLIM 2.9 to generate accurate vegetation patterns across environmental gradients was tested using 

the forest survey data set. The test involved older forest sites 

(>150 yr) surveyed within selected ecoregions representing 

much of the variation in precipitation, temperature, and natural 

disturbance in forests of western Oregon. Simulations were 

run for a set of 20 randomly selected sites in each ecoregion 

having more than 20 sites. The Willamette Valley and Ponderosa Pine/Bitterbrush Woodland ecoregions each had only 

20 suitable sites and so all sites from each of these ecoregions 

were used in the analyses. Critical input parameters that were 

varied among simulations included site monthly mean climate, 

simulation length (yr) and, in certain cases, annual fire disturbance frequency and severity. Simulation length was set to the 

approximate age of each stand, which was assessed from tree 

cores (see above). Mean ages of stands by ecoregion varied 

from 100 yr near the coast to 300 yr in the western Cascades 

(Busing, 2004); the duration of most simulations was 200 to 

600 yr. Species parameters were not varied among simulations. 

Interpolated climate data were used for each site (Lugo et 

al., 1999). Nutrient supplies were set at adequate levels so as 

not to limit tree growth. Except for fire, large-scale natural disturbances were not employed. Small-scale disturbance in the 

form of canopy gap formation from endogenous tree mortality 

is a fundamental characteristic of the gap model. In this case, 

endogenous mortality refers to the death of trees simulated 

as a probabilistic function of maximum lifespan (background 

mortality) or in association with reduced annual tree growth 

(stress mortality). The primary abiotic environmental modifiers of tree growth were suboptimal temperatures, light availability, and drought stress.

The tests focused on eight forested ecoregions in western 

Oregon. Forested ecoregions (Omernik classification; Thorson et al., 2002) centered north of 43o

 N latitude and having 20 

or more field data sites with old trees were used in the analysis 

(Fig. 1). In the Willamette Valley (ecoregion 3), where survey 

data on old stands were scarce, data from stands with trees 

approaching or exceeding 100 yr were collected to supplement 

the survey data set. For each ecoregion, measured basal area 

of dominant species, and of the entire stand served to quantify 

composition and stand dimensions. We used the selected sets 

of field plot sites (n=20, see above) within each ecoregion. 

Prior to the simulations, we determined that the selected field 

sites represented the larger pool of eligible field sites available 

in the ecoregions. We did so by examining means and distributions for measured variables describing stand composition 

and structure for each ecoregion. All analyses included only 

live trees >10 cm DBH.

Initial simulations were constrained by climate regime 

and age of the oldest tree (which dictated simulation length) 

at each randomly selected site. In additional simulations, 

regimes of fire disturbance were superimposed on the stands. 

Various regimes of fire, differing in disturbance frequency and 

   Modeling the Effects of Fire Frequency and Severity on Forests in the Northwestern United States
severity, characteristic of each ecoregion (USDA Fire Regime 

Database, J. Kertis, Siuslaw National Forest, personal communication) were simulated (Table 1). Fire return intervals 

ranged from 15 to 400 yr (frequency 0.067 to 0.0025 yr -1). 

Fire regimes ranged from severe events with long return intervals (mean 400 yr) in ecoregions 1d and 4c to those dominated 

by low severity events with short return intervals (15 yr) in 

ecoregions 3, 9d and 9e. 

Additional assumptions and procedures for the 

FORCLIM exercises followed Busing and Solomon (2004, 

2005). Further details related to the fire disturbance simulations are presented herein (see Appendix). 

Table 1. Mean fire return interval applied in the simulations by 

severity and ecoregion.

Ecoregion Return 

interval of 

light fires 

(yr)

Return 

interval 

of severe 

fires (yr)

Coastal Volcanics (1d) none 400

Mid-coastal Sedimentary (1g) 400 400

Willamette Valley (3) 25 300

West Cascade Lowlands (4a) 200 200

West Cascade Montane (4b) 300 300

Cascade Crest (4c) none 400

East Cascade Ponderosa pine (9d) 15 400

East Cascade Pumice Plateau (9e) 15 400

Results and Discussion

Forest basal area

The simulations lacking fire disturbance gave total basal 

area values that greatly exceeded actual values. Simulated 

mean basal area was higher than the actual mean basal area in 

each of the eight ecoregions (Fig. 4a & b). When appropriate 

fire regimes were simulated for each ecoregion, the agreement between simulated and actual mean basal area improved 

markedly (Fig. 4c). The low values of total basal area in the 

Willamette Valley (ecoregion 3) were captured only by simulations with fire. Similarly, only the simulations including fire 

approximated the low total stand basal area of forest dominated by Pinus ponderosa in the Pumice Plateau (ecoregion 

9e). 

Forest composition

The data on actual vegetation showed a shift in tree 

species composition from Pseudotsuga dominated forests in 

coastal and montane regions of western Oregon (ecoregions 1-

4) to Pinus dominated forests in central Oregon (ecoregion 9) 

(Fig. 4). An increase in subalpine conifers was evident at high 

elevations of the Cascade Crest (ecoregion 4c). Simulations 

without fire produced forests dominated by Pseudotsuga in 

all eight ecoregions studied. The simulation clearly overestimated biomass in drier forests subject to high frequencies of 

low-severity fires (ecoregions 3, 9d & 9e). Simulations with 

high fire frequencies reduced the levels of Pseudotsuga and 

increased the relative dominance of very fire-tolerant species 

such as Pinus ponderosa and Quercus garryana in these ecoregions. 

With the addition of fire to the simulations the only major 

discrepancy between actual and simulated composition is 

abundance of Pinus ponderosa in the simulated forests of the 

Willamette Valley (ecoregion 3). Although Pinus ponderosa

occurs in the valley it is not currently a dominant species. It 

may have been more important prior to settlement and timber 

harvesting by Europeans (Johannessen et al., 1971). The past 

century of fire suppression in the valley (Robbins, 1997) may 

have inhibited Pinus ponderosa regeneration after initial losses 

to logging.

Other discrepancies include the presence of several minor 

species in the simulations that are rare or absent in actual forests. For example, Abies procera, Picea sitchensis, Pinus ponderosa, and Quercus garryana all occurred as minor species in 

certain simulated forests where they were not expected. Their 

presence reflects the fact that the factors limiting establishment and growth of tree species in the simulations are simplifications of reality. The most critical assumption in this regard 

was that species’ propagules were available in all simulated 

stands. In reality they were probably available only in the 

vicinity of their rare parent trees, and hence, not consistently 

available at many sites. 

Assumptions of climatic tolerances also may have played 

a role in the simulated presence of rare species. While all 

simulated dominant species within the ecoregions we examined were anticipated given the climatic conditions simulated, some discrepancies were noted for minor species. For 

example, Picea sitchensis appeared as a very minor species in 

some simulated montane forests. In reality, it is rare except 

along the coast. In this case, the simplified climatic regimes 

of the simulations do not fully account for coastal fog and they 

cannot restrict fog-belt species accordingly.

In conclusion, fire effects simulated as elevated mortality 

of trees based on their species and size do alter forest structure 

and composition. Low frequency fires (return interval >200 

yr) have minor effects on composition. When they are severe, 

they tend to reduce total basal area with little regard to species 

differences. In the current study, simulated basal areas averaged across ecoregions were reduced to levels approximating 

actual basal areas. High frequency fires (return interval <30 

yr) have major effects on species composition and on total 

basal area. They can cause substantial reductions in total basal 

area and shifts in dominance toward highly fire tolerant species.

Results and Discussion 
0

10

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30

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50

60

70

80

1d 1g 3 4a 4b 4c 9d 9e

Ecoregion

Basal area (sq m/ha)

(a)

0

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1d 1g 3 4a 4b 4c 9d 9e

Ecoregion

Basal Area (sq. m/ha)

(b)

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1d 1g 3 4a 4b 4c 9d 9e

Ecoregion

Basal Area (sq. m/ha)

(c)

Abies amabilis Abies grandis Abies procera

Chamaecyparis nootkatensis Picea sitchensis Pinus ponderosa

Pseudotsuga menziesii Thuja plicata Tsuga heterophylla Tsuga mertensiana

Quercus garryana

Other

Figure 4. Graphs of mean species composition and basal area by ecoregion: a) actual data, b) simulated data, and c) simulated data with 

fire-induced mortality. Ecoregion codes are described in Figure 1.

 Modeling the Effects of Fire Frequency and Severity on Forests in the Northwestern United States
Summary

The FORCLIM model of forest dynamics was applied to 

investigate the effects of various fire regimes on structure and 

composition of Pacific Northwest forests. Eight ecoregions in 

western Oregon with contrasting climate and fire regimes were 

studied. They ranged from wet coastal forests with infrequent 

fires to dry interior forests with frequent fires of low severity. 

The mesic coastal and montane forests were dominated by 

tall conifers, particularly Pseudotsuga menziesii. Dry forests 

of the interior were dominated by Pinus ponderosa. Fire 

disturbance simulated over centuries as elevated mortality of 

trees based on their species and size affected forest structure 

and composition. Fires of low frequency (return interval >200 

yr) had minor effects on species composition, but severe fires 

reduced total basal area. The addition of severe fires often 

resulted in simulated average ecoregion basal areas closer to 

actual basal areas. High frequency fires (return interval <30 

yr) had major effects on species composition and on total 

basal area. Even low-severity fires reduced total basal area 

and caused shifts in tree species composition when they were 

frequent.

Acknowledgements

Harald Bugmann and Rusty Dodson gave helpful advice 

on use of the FORCLIM model. Richard Hardt, Bob McNitt, 

and David Hibbs helped us find remnant forest vegetation 

in the Willamette Valley ecoregion. Don Phillips and Sarah 

Shafer provided constructive comments on earlier drafts of the 

manuscript. The field data used in comparisons were collected 

under ecological survey programs of the U.S. Department of 

Agriculture and the U.S. Department of the Interior.

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Bugmann, H. 1994. On the ecology of mountainous forests 

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Bugmann, H.K.M. 1996. A simplified forest model to study 

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Bugmann, H., and Cramer, W. 1998. Improving the behaviour 

of forest gap models along drought gradients. For. Ecol. 

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10 Modeling the Effects of Fire Frequency and Severity on Forests in the Northwestern United States
Appendix A

Implementation of fire disturbance 

simulations with FORCLIM 2.9

The 2004 version of FORCLIM with fire disturbance of 

mixed severity is described here. It is important to note that 

this simulator is based on the C program of model version 

2.9. The C program was created by Rusty Dodson in 1996. 

It was converted from the MODULA2 program version 2.9, 

written by Harald Bugmann in the mid 1990s and documented 

by Bugmann and Solomon (2000). An earlier version of 

FORCLIM was documented by Bugmann (1994, 1996).

In the current study, fire disturbance was simulated as 

either a severe event or a low-severity event. Species were 

assigned to one of four fire tolerance classes. Fire tolerances 

were determined from Minore (1979), Dale and Hemstrom 

(1984), and the USDA Fire Effects Database.

The variable kFiT is the fire tolerance class, with class 

1 being the least tolerant and most likely to be killed by fire. 

Values of all input variables, including fire tolerance, are 

provided below. Note that some values of drought tolerance 

(kDrT) and degree-day minimum (kDDMin) differ from those 

used by Bugmann circa 1996. These alterations were documented by Busing and Solomon (2004).

The simulator randomly determines in what year(s) a fire 

event occurs. The constants setting disturbance frequency 

are assigned in program module SET_CONSTANTS.C by 

the user. Two levels of severity are used in this exercise and 

each is simulated independently, with separate frequencies (or 

annual probabilities) and random number seeds for fire events. 

Low severity fire frequency is held in the constant kPGrFire; 

severe fire frequency is held in the constant kPFire. In a fire 

year an additional death probability is determined for each tree 

based on the fire tolerance class of the species, fire severity, 

and tree diameter at breast height (dbhcm). In this investigation fire severity (fsev) is assumed to be 0.1 for low-severity 

fires and 1.0 for severe fires. Non-random interdependence 

of fire events among simulated plots or years is not considered here. More advanced fire regimes are possible through 

modification of the program code, however. The following 

equations are used to assign death probabilities (FireDP variable) to each individual in a fire year.

Fire tolerance equals 1: 

FireDP=1.0

Fire tolerance equals 2: 

FireDP=exp(((-(1-fsev)*0.00202)+0.00053)*dbhcm)

Fire tolerance equals 3: 

FireDP=exp(((-(1-fsev)*0.02745)+0.00255)*dbhcm)

Fire tolerance equals 4: 

FireDP=(exp(-0.00053*dbhcm))-0.5+((1-fsev)*0.5)

The equations are for values of fsev >0 to 1. The resulting curves for two contrasting levels of fire severity are shown 

in Fig. 3. All tolerance classes other than class 1 exhibit a 

decrease in mortality probability with tree size. Equations 

are based on exponents from Dale and Hemstrom (1984) for 

severe fires. We have modified the equations of Dale and 

Hemstrom to accommodate 1) low-severity fires and 2) species with low susceptibility to fire mortality (class 4).

Appendix A 11
cies parameter input file for the Pacific Northwest. 

ology and growth form), kDm (max. diameter), kHm (max. height), kAm (max. age), kG (growth scaling constant), kDDMin (min. degree day sum), kWiTN (min. temperature 

ure required), kDrT (drought tolerance), kFlT (flood tolerance), kNTol (nitrogen demand), kBrow (browse tolerance), kLy (light demand), kLa (shade tolerance), kLQ (litter 

hreshold), and kFiT (fire tolerance). This table contains values modified or added after 2002 (Busing and Solomon, 2004). 

es sType kDm kHm kAm kG kDDMin kWiTN kWiTX kDrT kFlT kNTol kBr- ow kLy kLa kLQ kImmYr kFiT

E5 200 75 600 340 390 -10 0 0.2 3 3 1 0.08 1 3 N 1E5 225 76 300 357 600 -12 3 0.45 3 3 1 0.14 3 3 N 2E5 80 40 300 359 300 N -7 0.35 3 3 1 0.14 3 3 N 1E2 275 85 600 363 550 -7 3 0.25 3 3 1 0.22 7 3 N 2

llum D1 120 28 300 280 705 -3 7 0.45 3 3 2 0.14 3 2 N 2

D1 130 38 150 551 600 0 8 0.3 3 3 2 0.47 7 2 N 2

E2 150 34 500 154 965 0 8 0.45 3 3 1 0.22 3 3 N 2

sis E5 370 53 3500 171 390 -14 -1 0.25 3 3 1 0.08 3 3 N 1

nii E5 244 55 600 211 400 N -3 0.4 3 3 1 0.14 3 3 N 1

E5 500 90 800 374 1252 0 5 0.2 3 3 1 0.08 3 3 N 1

) E3 50 10 500 447 1252 3 7 0.35 3 3 1 0.47 9 3 N 2

) E3 213 46 600 226 524 -15 -9 0.35 3 3 1 0.47 9 3 N 2

E3 200 75 600 359 589 -12 3 0.3 3 3 1 0.14 5 3 N 2

a E5 275 80 600 324 965 -12 8 0.55 3 3 1 0.47 7 3 N 4

(g) E3 250 54 700 403 633 -15 -5 0.4 3 3 1 0.22 7 3 N 3

(m) E3 425 117 1400 315 633 -10 5 0.4 3 3 1 0.22 7 3 N 3

D2 250 37 500 161 677 -4 6 0.5 3 3 2 0.47 5 2 N 4

E5 350 76 1500 282 748 -8 3 0.25 3 3 1 0.08 3 3 N 2

lla E5 275 80 700 351 719 -8 4 0.25 3 3 1 0.08 1 3 N 1

na E3 150 46 800 203 300 -15 -3 0.35 3 3 1 0.08 1 3 N 1