United States

Department of

Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS–GTR–55

June 2000

Modeling Large Woody Debris

Recruitment for Small Streams of

the Central Rocky Mountains

Don C. Bragg, Jeffrey L. Kershner, and David W. Roberts
Bragg, Don C.; Kershner, Jeffrey L.; Roberts, David W. 2000. Modeling Large Woody Debris

Recruitment for Small Streams of the Central Rocky Mountains. Gen. Tech. Rep. RMRS-GTR55. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research

Station. 36 p.

Abstract

As our understanding of the importance of large woody debris (LWD) evolves, planning for its

production in riparian forest management is becoming more widely recognized. This report details

the development of a model (CWD, version 1.4) that predicts LWD inputs, including descriptions

of the field sampling used to parameterize parts of the model, the theoretical and practical

underpinnings of the model’s structure, and a case study of CWD’s application to a stream in

Wyoming’s Bridger-Teton National Forest.

Keywords: riparian, FVS, CWD, spruce-fir, old-growth, dead wood, disturbance, management

The Authors

Dr. Don C. Bragg is a research forester at the Southern Research Station, USDA Forest Service,

P.O. Box 3516 UAM, Monticello, AR 71656; phone (870) 367-3464 ext. 18, fax (870) 367-1164,

e-mail dbragg@fs.fed.us

Dr. Jeffrey L. Kershner is a fish ecologist at the Fish Ecology Unit, USDA Forest Service,

Department of Fisheries and Wildlife, Utah State University, Logan, UT 84322-5210; phone

(435) 797-2500, fax (435) 797-1871, e-mail kershner@cc.usu.edu

Dr. David W. Roberts is an associate professor with the Department of Forest Resources and

Ecology Center, Utah State University, Logan, UT 84322-5215; phone (435) 797-2416, fax

(435) 797-4040, e-mail dvrbts@cnr.usu.edu

Acknowledgments

We thank the staff of the Bridger-Teton National Forest (BTNF) for exceptional cooperation with

this project, as well as logistics and feedback in development of the final product. Special thanks to Mark

Novak, Kurt Nelson, and Larry Warren of the BTNF for their patience and assistance. Richard Teck

of the Timber Management, Mensuration and Systems Development Section of the USDA Forest

Service provided valuable technical support with Forest Vegetation Simulator. We are also

thankful for the skills of others at Utah State University, notably Hope Bragg and Michele Weidner.

Dave Turner, Mike Young, and Madelyn Dillon of the USDA Forest Service Rocky Mountain

Research Station provided numerous review comments that improved the quality of this paper.

You may order additional copies of this publication by sending your

mailing information in label form through one of the following

media. Please send the publication title and number.

Telephone (970) 498-1392

E-mail rschneider@fs.fed.us

FAX (970) 498-1396

Mailing Address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526-2098

Cover photo: Beginning its journey as riparian large woody debris, this recently toppled blue

spruce in the upper Greys River drainage of the Bridger-Teton National Forest in Wyoming will

contribute much to the structure and function of this stream.
Contents

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

Study Area Description ..................................................................................... 1

Regional Physiography and Climate ............................................................. 1

Stream Systems............................................................................................ 2

Vegetation ..................................................................................................... 2

Methods ............................................................................................................ 3

Field Sampling .............................................................................................. 3

Application of the Forest Vegetation Simulator (FVS) ................................... 5

CWD Model Development............................................................................. 6

Field Sampling Results ...................................................................................... 9

Riparian Forest Conditions ........................................................................... 9

Riparian Forest LWD Conditions................................................................... 9

Stream LWD Conditions................................................................................ 15

Correlation of Stream Size with LWD Loading .............................................. 16

A Simulated Case Study: Dry Lake Creek, Wyoming........................................ 18

Validation of Model ............................................................................................ 19

Discussion ......................................................................................................... 20

Watershed-Scale Applications of CWD ............................................................. 21

Conclusions ....................................................................................................... 21

Literature Cited .................................................................................................. 21

Appendix A: User’s Guide to the CWD Post-Processor (version 1.4) ............... 25

Preface.......................................................................................................... 25

Where to Get CWD and FVS ........................................................................ 25

Preliminary Steps to Run CWD ..................................................................... 25

Introductory Screens ..................................................................................... 26

Model Initiation Queries ................................................................................ 27

Output Files Created by CWD ...................................................................... 33

Troubleshooting ............................................................................................ 34

Modeling Large Woody Debris

Recruitment for Small Streams of

the Central Rocky Mountains

Don C. Bragg, Jeffrey L. Kershner, and David W. Roberts

RMRS-GTR-55. June 2000 1

Introduction

After decades of neglect and abuse, riparian

zones are now recognized as critical components

of aquatic and terrestrial ecosystems (Johnson et al.

1995, Sparks 1995). Streams link seemingly disparate communities with flows of energy, nutrients,

biomass, and water (Swanson et al. 1982, Gregory

et al. 1991, Chen et al. 1995); sustain movement

corridors (Knight 1994, Sparks 1995); provide localized increases in diversity, both biotic and structural (Swanson et al. 1982, Gregory et al. 1991,

Knight 1994, Sparks 1995); and support numerous

human activities (Budd et al. 1987, Meehan 1991,

Knight 1994). Despite the importance of these dynamic systems, little has been done to ensure their

retention of structural, biotic, and functional integrity when the surrounding forests are intensively

managed. Most riparian zone management strategies rely on fixed buffers to protect critical processes. However, buffers often fail to include important sources of biomass or nutrients or they

may include too expansive an area, thereby including parts of the landscape better suited for more

intensive management (Bren 1995). Recent reviews

of the impacts of riparian buffer zones (Steinblums

et al. 1984, Budd et al. 1987, Ralph et al. 1994) have

quantified the ecological differences between managed and unmanaged watersheds, indicating the

need for more attention to riparian management

area (RMA) design and implementation.

To address both ecological and socioeconomic

concerns, the RMA must preserve ecosystem function rather than focusing on administrative expedience. Factors that determine the effectiveness of

riparian forests include the regulation of thermal

and solar regimes, sedimentation rate, habitat quality, and large woody debris (LWD) production

(Swanson et al. 1982, Steinblums et al. 1984, Budd

et al. 1987, Gregory et al. 1991). Monitoring and

maintaining LWD (we define LWD as any structurally sound piece of wood ≥1 m in length and ≥ 10

cm diameter) is important because of its influence

on channel development (Triska and Cromack 1980,

Bisson et al. 1987, Ralph et al. 1994, Ruediger and

Ward 1996), sediment trapping and storage

(Beschta 1979, Likens and Bilby 1982, Megahan

1982, Bilby 1984, Bilby and Ward 1989, Potts and

Anderson 1990), oxygenation and turbulent mixing of water (Bisson et al. 1987, Sedell et al. 1988),

organic carbon and nutrient cycling (Bilby and

Likens 1980, Swanson et al. 1982, Harmon et al.

1986, Gregory et al. 1991), and species habitat

(Maser and Trappe 1984, Harmon et al. 1986, House

and Boehne 1987). Riparian LWD characteristics

vary, depending on the condition and distribution of the pieces, stream width and volume,

disturbance regime, and the degree of human

intervention (Swanson et al. 1982, House and

Boehne 1987, Bilby and Ward 1989, Ralph et al.

1994, Berg 1995, Bragg 1997).

Anticipating changes to riparian LWD pattern

and process provides managers with the ability to

assess the impacts of forest management practices

on this resource, predict long-term riparian debris

dynamics, and assist in the recovery of channels

altered by severe changes. Although models to

predict LWD delivery for some regions are available (Swanson et al. 1976, Rainville et al. 1985,

Budd et al. 1987, Lienkaemper and Swanson 1987,

Bilby and Ward 1989, McDade et al. 1990, Van

Sickle and Gregory 1990, Ralph et al. 1994), the

next step is to develop usable management tools

and strategies. A holistic approach would integrate the components that determine riparian system behavior and readily provide information on

which resource managers can base decisions. This

involves incorporation of existing technology, including predictive management models. In this

paper, we present a riparian LWD recruitment model

(CWD, version 1.4), to predict LWD delivery to small

riparian systems of the central Rocky Mountains as

a tool to assist in riparian zone management.

Study Area Description

Regional Physiography and Climate

Data to assist in model development were collected on the Bridger-Teton National Forest (BTNF)

during the summer of 1995. Located in the northwestern corner of Wyoming, the BTNF extends from

the southern boundary of Yellowstone and Grand

Teton National Parks down the spines of the Wind

River, Gros Ventre, Absaroka, Hoback, Wyoming,

and Salt River Mountains. Elevations range from

2000 to > 4000 m, with most of the BTNF exceeding

2100 m. Annual precipitation (primarily as snow)
2 RMRS-GTR-55. June 2000

varies from 35 to 150 cm, and the growing season

tends to be short (< 100 d).

Geologically, the BTNF encompasses a diverse

mixture of landforms and processes. Many of the

sedimentary mountains arose from low-angle faulting, producing a geological province known as the

Overthrust Belt (Lageson and Spearing 1988, Knight

1994). The Wind River Mountains are of granitic

rather than sedimentary origin, and some areas

(especially the Absaroka Mountains) resulted from

volcanic activity. During the Quaternary Period,

ice shaped the surface of the BTNF as most ranges

experienced extensive glaciation (Knight 1994).

Erosion, landslides, vulcanism, tectonics, biota,

and human activity have further shaped the face of

the region in the intervening millennia (Love and

Love 1980, Lageson and Spearing 1988, Knight 1994).

Stream Systems

The BTNF drains into 3 primary watersheds: the

Snake, Green, and Bear. The highly dissected landscape and heavy winter snows sustain many

streams, ranging from small snow-melt ephemerals to large permanent flows like the Snake, Buffalo

Fork, Hoback, Green, and Gros Ventre Rivers. We

selected individual streams for consistent, oldgrowth spruce-fir cover, low levels of human disturbance, accessibility, and relatively small channels. Most of our sample streams were moderate in

gradient (2% to 10%), with sinuosities on sampled

reaches ≤ 1.5 (table 1). Low sinuosity streams were

chosen to minimize channel braiding, meandered

cutoffs, and other difficult-to-model stream patterns.

Vegetation

Watersheds with a high proportion of forest were

selected, but some areas along major drainages included grassland or rock (especially along major

drainages). Vegetation cover is frequently patchy in

the central Rocky Mountains, with sagebrush/

grassland occurring on exposed southern and western aspects. Forests typically occupy northern and

eastern slopes, higher elevations, and sheltered

bottomlands and bowls, often interspersed with steep

rocky slopes, cliff faces, and summits. Timberlands

on the BTNF are primarily coniferous, dominated by

lodgepole pine (Pinus contorta Dougl. ex Loud.), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), Engelmann spruce (Picea engelmannii Parry ex Engelm.),

and subalpine fir (Abies lasiocarpa (Hook.) Nutt.).

Minor components of blue spruce (Picea pungens

Engelm.), limber pine (Pinus flexilis James), whitebark

Table 1. Characteristics of stream reaches in northwestern Wyoming.

Upstream Mean Mean Reach

basin elevation bankfull gradient Reach

Stream Order size (ha) (m) width (m) (%) sinuosity

Adams Creek 1 1098 2067 3.0 6.9 1.13

Moose Gulch Creek 1 417 2152 3.4 2.0 1.12

Murphy Creek 1 858 2061 6.1 2.9 1.15

Blind Bull Creek 2 2318 2122 5.7 4.9 1.17

Buck Creek 2 1326 1897 4.8 8.5 1.09

Dry Lake Creek 2 1033 2565 5.5 3.5 1.05a

South Fork Gypsum Creek 2 2309 2395 12.0 10.0 1.02a

Sheep Creek 2 1958 2450 4.6 6.7 1.06

Willow Creek 2 1774 2407 4.6 4.8 1.10

Blackrock Creek 3 9078 2498 17.6 5.0 1.07a

Ditch Creek 3 4450 2240 11.2 2.2 1.20

Hoback River 3 9965 2271 14.4 1.5 1.02a

Mosquito Creek 3 2512 2119 9.2 2.0 1.23

a Estimated from 1:24,000 topographic maps.
RMRS-GTR-55. June 2000 3

pine (Pinus albicaulis Engelm.), and Rocky Mountain

juniper (Juniperus scopulorum Sarg.) are also present.

Clonal trembling aspen (Populus tremuloides Michx.)

is found in sheltered sites, while some larger streams

are lined with groves of cottonwoods (primarily

Populus angustifolium James and Populus trichocarpa

Torr. & Gray). Spruce-fir-dominated unmanaged oldgrowth riparian forests, including transitional stands

of lodgepole pine and Douglas-fir, were of primary

interest in the development of CWD, but the model

can simulate other types.

Methods

Field Sampling

To provide an adequate estimate of in-stream

LWD loading (Nakamura and Swanson 1994), we

established 13 plots (figure 1) along 300 m reaches

flowing through relatively continuous, spruce-firFigure 1. Large woody debris study plot locations established on the Bridger-Teton National Forest

(BTNF) during the summer of 1995. A— Blackrock Creek; B— Dry Lake Creek, used for the simulations in

this paper; C— Ditch Creek; D— Moose Gulch Creek; E— Mosquito Creek; F— Buck Creek; G— Adams

Creek; H— South Fork Gypsum Creek; I— Willow Creek; J— Hoback River; K— Murphy Creek; L— Blind

Bull Creek; and M— Sheep Creek.
4 RMRS-GTR-55. June 2000

dominated old-growth. Measurements of stream

and riparian forest characteristics of LWD conditions yielded a comparison with other regions and

described important riparian LWD patterns (e.g.,

angle of stem fall, snag fragmentation). In field

sampling, we focused on stand characterization,

volume of riparian forest LWD, stream characterization, and stream LWD volume. Most LWD

inventory variables are in terms of volume (m3

)

because this was easily calculated from field

data.

Stand Characterization

Six 0.1 ha circular subplots centered 20 m from

the bankfull edge were established along two-250

m transects parallel to each bank. We recorded the

following for all living stems ≥ 10 cm diameter at

breast height (DBH): species, height (m), diameter

(cm), and angle of lean (in degrees) relative to the

stream recorded. From these measurements, individual tree volume (m3

), stand volume (m3/ha),

stand density (m2

/ha), and species composition

(%) were estimated. Habitat type (Steele et al. 1983)

was also determined for each subplot.

Riparian Forest LWD

In addition to living biomass measurements,

data were taken on riparian forest LWD. Only

pieces or portions of pieces within the plot radius

were measured. When possible, LWD species was

identified. We measured the end diameters and

length for all pieces of debris and estimated its

angle relative to the channel (figure 2). Mean piece

volume (m3

), LWD volume (m3/ha), and number

of pieces (per ha) were derived from this data.

Plot

center

OUT

IN

IN

OUT

Wetted

width

Bankfull

width

0

90

180

90

Snag fall direction,

this bank

0

90

180

90

Snag fall direction,

this bank

Figure 2. Diagrammatic view of field sampling procedures. Plot center for each 0.1 ha subplot was located

20 m from the stream. Each piece of riparian forest large woody debris (LWD) that fell entirely within plot

radius was tallied. Long pieces that extended outside of the plot radius were measured to the edge of the

subplot, but not beyond, ensuring accurate per ha estimates of LWD volume. Each “in” piece also had its

angle relative to the stream (0° ≤ θ ≤180°) estimated.
RMRS-GTR-55. June 2000 5

Stream Characterization

Streams were characterized by order (Strahler

1957), basin size (ha), reach midpoint elevation

(m), mean bankfull width (m), reach gradient (%),

and sinuosity. Reach midpoint elevation, gradient,

and some sinuosities (table 1) were taken from

1:24,000 topographic quad sheets, while mean

bankfull width and the remaining sinuosities were

measured in the field.

Riparian LWD

Since we were interested in the debris that affected stream processes, and because channels can

shift rapidly or pieces could be forcefully moved by

the stream, we defined riparian LWD differently

from forest LWD. Any LWD that extended 1 m or

more over bankfull width was tallied and the entire

length (to a 10 cm diameter) was included (figure 3).

Although this delineation has been used by some

(e.g., Richmond and Fausch 1995), others define riparian LWD differently (Lienkaemper and Swanson

1987, Carlson et al. 1990, Robison and Beschta

1990a, Ralph et al. 1994). The lack of standardization makes it difficult to cross-reference stream

LWD loads between studies. However, we felt our

approach to riparian LWD best addressed the uncertain nature of dynamic stream systems because

it allowed us to tally of the most influential pieces.

Application of the Forest

Vegetation Simulator (FVS)

The Forest Vegetation Simulator (FVS, also

known as Prognosis (Wykoff et al. 1982)) is a growth

and yield model that is used throughout the United

States (Teck et al. 1996). FVS presented several

definite advantages for our efforts. First, FVS is

capable of simulating forest dynamics over extensive (up to 400 y) periods, allowing for long-term

predictions of management effects. Second, FVS is

flexible in the scheduling and application of timber

harvests and regeneration, allowing the user to

design harvest treatments for specific scenarios.

Finally, FVS generates sufficiently detailed output

(i.e., individual tree-based) to allow CWD to function. CWD was initially designed to operate with

the Teton and Utah variants (version 6.1) of FVS.

Reference Wykoff et al. (1982), Crookston (1990),

Ferguson and Crookston (1991), Ferguson and

Carlson (1993), and Teck et al. (1996) for information about FVS operation.

FVS simulated all modeled forest dynamics (e.g.,

regeneration, growth, mortality). FVS determines

when to establish new trees, the growth of existing

ones, and tree mortality based on user-defined

defaults and internalized vegetation relationships.

FVS was instructed by the key word file to generate

a dead tree list, which provided attributes (current

DBH, height, volume, crown condition, etc.) read

by CWD. CWD acts as a post-processor on the

output of FVS (Appendix A contains more detail

on the interaction between FVS and CWD).

Generating Stand Dynamics with FVS

Modeling the behavior of woody debris in

streams involved linking forest processes with

stream dynamics. Trees become established, grow,

die, and sooner or later fall over. Some pieces will

eventually enter the channel and affect the pattern

and process of that stream. While mortality rates,

stem failure, and the interaction between them are

random events, they can be estimated. For this

Bankfull width

Wetted width

A

B

C

Figure 3. A different approach was taken to

determine riparian large woody debris (LWD)

because of uncertainties related to bankfull width

and piece stability through time. Any piece (A)

that did not enter the bankfull width by more than

1 m or did not extend more than 1 m over bankfull

width with a minimum diameter of 10 cm was not

considered riparian LWD. However, once the

piece met this criteria (B and C), then the entire

length of the piece was tallied. Any stump or

rootwad (C) with > 0.25 m3

 estimated volume

within the bankfull zone was also counted as

riparian LWD.
6 RMRS-GTR-55. June 2000

project, we assumed that self-thinning yielded the

most frequent mode of LWD production. FVS calculates mortality from quadratic mean diameter and

stand basal area (density-dependent factors), which

eventually becomes:

Rp= 1 - (1 - Rt)p (1)

where Rp is the periodic survival rate and Rt is the

estimated annual mortality rate (Wykoff et al. 1982).

Additional mortality can be fixed within FVS by

the user. Given a set of forest conditions, therefore,

we can estimate the number of individual trees

dying over a predetermined period. FVS apportions a number of stems into a mortality queue

after every iteration. However, this only reveals

the number of dead, standing snags, not the rate at

which they fall over and become riparian LWD. To

do this, one must determine the frequency of stem

failure, which is a primary function of CWD.

CWD Model Development

Snag Spatial Location

After selection, each snag is randomly assigned

a positional coordinate relative to the stream (not a

Cartesian coordinate) to mark its distance from the

stream. Stem location is important, as snags within

30 m of the channel have the greatest odds of

stream entrance (Murphy and Koski 1989, McDade

et al. 1990, Robison and Beschta 1990a, Van Sickle

and Gregory 1990). With CWD, the user provides

the breadth of the simulated riparian forest that

controls snag proximity to the channel (see Appendix A). Unlike other regions (e.g., McDade et al.

1990), there seemed to be only minor upslope LWD

contribution from debris flows or other log transport in the BTNF, so we assumed a riparian forest

recruitment depth roughly equal to maximum

potential tree height or about 35 to 40 m.

CWD defaults to random snag locations. Random

spatial distributions have been noted for some forests

(Ek 1969, Ishizuka 1984, Moeur 1993), but it is possible that riparian spruce-fir stands follow a different

pattern. Therefore, CWD was designed to permit

user flexibility in determining snag distribution (see

Appendix A). Assigning the location relative to the

channel also allowed us to avoid detailing stream

position through time, so shifts in channel location do

not effect the results. In CWD, users can include bank

steepness to heslp determine if debris enters the

bankfull channel by defining the elevation of the

edge of the forest plot above the stream channel.

Snag Residency

Once a tree has been categorized as a snag, CWD

determines how long it remains standing. In CWD,

snag residency is a random process dependent

upon species, diameter, and time since death, probably the most critical factors (e.g., Keen 1929, 1955;

Mielke 1950, Hinds et al. 1965, Cline et al. 1980, Bull

1983, Cimon 1983, Raphael and Morrison 1987,

Harrington 1996). However, there is little information about which factor is most important and

about the timing of snag failure. In CWD, the user

may adjust the weighting values for each component based on their particular needs (Appendix A).

We subjectively biased these factors:

Time >> Diameter >> Species

Thus, we weighted time more heavily than diameter, which in turn was weighted more than

species. Ample evidence suggests rapid snag fall

within the first few decades after death, regardless

of species or diameter (Keen 1929, 1955; Bull 1983,

Schmid et al. 1985, Harrington 1996, Mitchell and

Preisler 1998). A number of studies (Lyon 1977,

Cline et al. 1980, Bull 1983, Cimon 1983, Raphael

and Morrison 1987) found large-diameter trees fall

at a slower rate than small-diameter stems. Bull

(1983) and Raphael and Morrison (1987) have also

compared snag failure rates between species and

while some differences did exist, they were minor

and perhaps more directly related to either diameter or cause of mortality (see also Hinds et al.

1965).

To calculate snag failure, each snag is processed

through several steps. First, the snag is categorized

by species to determine what inherent resistance it

may have to failure. Since little information exists

on the dynamics of stem breakage by species, we

developed a formula using modulus of rupture

(the maximum bending load to failure) data gathered from Panshin and deZeeuw (1980) for each

species or a similar species. This allows for differences in the mechanical strength between species

but does not account for unpredictable degradations (e.g., species-specific susceptibilities to heart

rots, root rots, insect attack) that may weaken the

stem.

After calculating the species modifier, we derived a diameter-based modifier. Although Curtis

(1943) noted an exponential relationship between

diameter and stem breakage caused by catastrophic

winds, we cannot assume this because of other

factors that also influence snag failure (e.g., wood

decay, root rots, disturbance type). Again, sparse
RMRS-GTR-55. June 2000 7

data were available for diameter-based snag failure; most studies consider decay rates only after

the snag has fallen (e.g., Aho and Cahill 1984,

Harmon et al. 1986, Harmon et al. 1987, Spies et al.

1988). We therefore simplified the relationship

between stem failure and diameter to a negative

linear trend that assigns the lowest vulnerability to

large stems and the highest to small stems. This

assumption probably best accounts for the leading

factor in snag failure– wood decay. Mielke (1950)

and Hinds et al. (1965) suggested that decay in the

roots and lower portions of the bole was responsible for the majority of Engelmann spruce and

lodgepole pine snag failures. Since the rate of

wood decay is related to diameter, the larger the

diameter of the tree, the longer it may persist as a

snag. To determine the time of snag fall, where

even partial loss of structural integrity may cause

stem failure, we felt that a linear model of diameter-interaction was appropriate. The relationship

between diameter and stem failure is also probably

true for live trees broken by wind, snow, and other

forces (Mergen 1954, Petty and Worrell 1981,

Valinger and Fridman 1997).

To incorporate the effects of aging on snag residency, a time-based modifier was developed following a negative relationship between time and

snag residency, a pattern supported by numerous

studies (Keen 1929, 1955; Mielke 1950, Hinds et al.

1965, Schmid and Hinds 1974, Lyon 1977, Sollins

1982, Bull 1983, Schmid et al. 1985, Harrington

1996, Huggard 1997). Evidence indicates that snags

rarely persist beyond 50 y (Schmid and Hinds

1974, Lyon 1977, Sollins 1982, Huggard 1997,

Harrod et al. 1998), so a simple linear trend provided us with a conservative estimate of the relationship between time since death and snag residency. In CWD, snags that stand for more than one

cycle are reduced in size to reflect their tendency to

slowly fragment from the top down.

Finally, we included a harvest-based snag failure

modifier to adjust for the impact of harvest on snag

residency because as cutting intensity increases,

snag density declines. Safety regulations require that

most snags be felled at harvest time (e.g., Huggard

1997). This practice typically occurs when clearcutting,

although ecosystem management protocols often

call for the retention of some snags. Selective cuts

and thins may preserve greater snag densities, but

even then the impacts of felling and skidding logs

may eliminate many snags. Harvest intensity can

also be adapted to simulate the effects of a natural

catastrophic disturbance on LWD recruitment (see

Appendix A for other snag customization options).

Determining Angle of Snag Fall

The species, diameter, time, and harvest modifiers are summed for each snag and compared

against a failure benchmark. If snag failure does

not occur, the snag remains standing for another 10

y cycle and it is then reevaluated. If the snag falls,

it is transferred into the LWD pool and an angle of

fall is determined. Snags closer to the stream and

falling towards it have a greater chance of influencing recruitment than a distant stem falling away

from the channel (Van Sickle and Gregory 1990,

McDade et al. 1990). Predicting this behavior, unfortunately, is more difficult than it seems. The

most common approach assumes a random angle

of tree fall (Maser and Trappe 1984, Rainville et al.

1985, McDade et al. 1990, Robison and Beschta

1990a, Van Sickle and Gregory 1990). Van Sickle

and Gregory (1990) modeled the probability (Ps

) of

a tree entering the stream channel as:

−

= ∫

s

a

s

a

P f (a)da

180

(2)

where as = sin-1(z/h), z is the perpendicular

downslope distance from standing tree to nearest

channel boundary, h is the effective tree height,

and f(a) is the pattern of tree fall. Their integration

of random fall direction yielded:

s

(z / h) P cos− = 1

180 (3)

However, because of asymmetry in biomass distribution, differences in root support, and unpredictable disturbance patterns, some have questioned

the randomness of treefall, especially when trees

are in steep drainages and or immediately adjacent

to the channel (Lienkaemper and Swanson 1987,

Van Sickle and Gregory 1990). For example, when

measuring the direction of windthrow in sprucefir forests of Colorado, Veblen (1986) found that

95% of downed trees fell in an easterly direction,

indicating the influence of strong westerly winds

(similar to the results of Alexander and Buell (1955)

and Schmid et al. (1985)). Along stream bottoms,

snag dynamics are also complicated, so we were

uncomfortable using either random or unimodal

fall patterns. Unlike some modeling efforts, we

had an available database on piece angle relative to

stream location. Using this as a guide, we determined that along the reaches we observed, snags

did not fall randomly but instead fit a trimodal

distribution (figure 4). According to our samples,
8 RMRS-GTR-55. June 2000

stem immediately after it falls and consider no

further breakage. The number of pieces formed

and their lengths were estimated from distributions similar to those measured in the field for

downed debris (see Appendix A, figure A3).

Redimensioning LWD

After fragmenting each snag, it is necessary to

redimension all of the new pieces to allow for

calculation of piece volume. To do this, we applied

a stem taper equation1

 modified from Czaplewski

et al. (1989):

D ˆ = + d (c c DBH ) 11 2 (4)

where c1 and c2 are species-specific coefficients

and:

hh h h ˆ

d DBH b b b a I b a I

HH H H

       = −+ −+ − + −             

2 2 2

1 1 2 31 1 42 2 2 1 1 (4a)

For Equation 4a, b1, b2, b3, and b4 are speciesspecific regression coefficients, h is the height at

the predicted diameter, H is the total tree height,

ai are species-specific join points (i = 1 (upper), i

= 2 (lower)), and Ii

 = 1 if h/H < ai, otherwise Ii =

0. These equations provided the end diameters

for the newly created pieces of LWD. Subtracting

the location of breaks from the old end positions

provided the new piece lengths. To calculate

individual piece volume, we applied a geometric

volume equation (Lienkaemper and Swanson

1987, Richmond and Fausch 1995):

(D D L) V olume

π + =

2 2

1 2

8 (5)

where D1 is the top and D2 is the bottom end

diameters and L is piece length.

Selection of LWD Pieces to Enter Stream

Once CWD redimensions each piece, it selects

those that enter the bankfull channel. Because the

distance from the stream (DISTs

), elevation of the

snag relative to bankfull channel (ELEV), angle of

stem fall (θH), and length (L) of the individual

pieces of LWD are known, it is possible to calculate

10 30 50 70 90 110 130 150 170

Angle class

0

200

400

600

800

1000

1200

Snags (field sample)

0

4

8

12

16

20

Live trees (field sample)

Snags

Live trees

Modeled

Figure 4. Distribution of angle-of-debris fall that

was selected. The large woody debris (both

downed logs and standing snags) and live tree

categories reflect actual number of individuals

(n = 4330 and 1310, respectively) measured from

13 streams in northwestern Wyoming. Model

values represent those applied by CWD.

most snags fell in 1 of 3 directions (± 10°): towards

the stream (0°); parallel to the stream (90°); or away

from the stream (180°). Using a χ2 test on a circular

distribution (Zar 1984), our sample differed significantly from a random distribution for both live

tree lean (χ2

 = 458.9, P < 0.001) and LWD fall (χ2 =

1338.6, P < 0.001) angle distributions, so we biased

the subroutine that determined the angle of fall to

reflect this pattern. While the angles of fall were

strongly biased around 0, 90, and 180°, it is possible

to fall between these values. 0° and 180° were

assigned 17.8% of the grand total, 90° was given

17.6%, and the other six 20°-wide categories were

each assigned 7.8%. On nonmeandering stream

systems (those with channel sinuosity < 1.5 (Ritter

1986)), a snag falling within an angle of 0° ≤θ≤ 90°

was considered potentially capable of delivering

LWD to the stream. Riparian LWD recruitment

was then developed as a function of angle of snag

fall, distance from the stream, and snag fragmentation patterns.

LWD Fragmentation

Dead trees fragment in a highly unpredictable

manner. For example, if a snag has stood for a long

time, some sections of the top are likely to have

broken off. Other snags fracture only when they

strike the earth (or other objects) with further breakage occurring when the snag is struck (e.g., another

piece of debris falling on it). Because we did not

know the spatial location of any piece of LWD, we

could not account for its fragmentation history.

Therefore, we decided to completely fragment the

1

 CWD versions before version 1.4 used Kozak et al.

(1969) to predict stem taper. While more complicated to

use, Czaplewski et al.’s (1989) taper equation is more

accurate since it was derived from trees in Wyoming

and Colorado for most species on the BTNF.
RMRS-GTR-55. June 2000 9

which pieces meet the recruitment criteria (L ≥

DISTs) with the following equation:

s

H v

DIST L

cos cos

   ≥       θ θ

1

(6)

where the elevational piece adjustment (θv

) equals

v

s

ELEV

arctan

DIST

  θ =     (6a)

If DISTs overlaps the distance from the piece by at

least 1 m, the piece is delivered to the stream

(figure 3). Since it was possible for a snag to fall

across the stream and have pieces break off outside

of the required bankfull zone, CWD was instructed

to disregard those pieces.

Within-Stream LWD Attrition

To be most beneficial, a piece of riparian LWD

should be retained within the active channel for

extended periods of time. Retention is a function of

stream width, flow characteristics, LWD size (especially length), and decay rates (Triska and

Cromack 1980, Likens and Bilby 1982, Lienkaemper

and Swanson 1987, Bilby and Ward 1989, McDade

et al. 1990). Small pieces of LWD are often highly

transient, which limits their value (Sedell et al.

1988, McDade et al. 1990). Large pieces are retained, on average, longer because of lower mobility and slower decay. LWD retention is related to

length; the longer a piece is, the more likely it is to

get wedged along the bank or aggregated with

other debris. While seasonal floods, debris torrents, or channel shifts can eliminate more LWD

than decay, decomposition is an important variable in determining piece residency. For example,

decomposed logs are more susceptible to fragmentation, impacts from other falling trees, or bank

shifts than structurally sound ones.

Since CWD does not explicitly model in-stream

debris movement, our approach assumed that riparian LWD follows a long-term steady state

(Froehlich 1973, Likens and Bilby 1982, Murphy

and Koski 1989, Bragg and Kershner 1997) and that

to do so, losses should roughly equal inputs in

unmodified old-growth stands. For this paper, the

volume of debris lost is a function of the initial

quantity in the studied reach. The assumptions

used to generate riparian LWD turnover rates are

similar to those reported elsewhere (Murphy and

Koski 1989, Bilby and Ward 1991, Maser and Sedell

1994). The following relationship describes this

dynamic:

LWD load previous LWD new LWD LWD lost = +− (7)

The volumetric turnover rate of LWD was then

fixed as a constant ratio, which was applied during

each delivery cycle. If losses exceeded recruitment,

then the net riparian LWD volume decreased. While

this generated a fluctuating LWD load, it implied

nothing about specific piece attrition within size

classes. However, further refinements to address

piece demographics are possible since CWD output includes delivery by size class.

Field Sampling Results

Riparian Forest Conditions

All 13 stands sampled on the BTNF (table 2)

were classified as subalpine fir-series habitat types

(Steele et al. 1983). Engelmann spruce and subalpine fir dominated most riparian forests, although

some plots (Adams Creek, Buck Creek, Blind Bull

Creek, and the Hoback River) were predominantly

blue spruce and/or Douglas-fir. Stand densities

ranged from a minimum basal area of 14.2 to a

maximum of 40.6 m2/ha (mean = 25.3, standard

deviation (SD) = 8.2). Stocking varied from a minimum of 182 trees/ha to a maximum of 655 trees/

ha (mean = 373.5, SD = 141.7). While no age data

were gathered, judging from tree size, most of

these riparian stands likely exceeded 200 y. Since

they were also selected for minimal human intervention, we felt they were representative of oldgrowth riparian spruce-fir forests in the central

Rocky Mountains.

Riparian Forest LWD Conditions

Examination of riparian forest LWD found that

most of this material was not visually identifiable

to species because of decay and erosion (table 3).

Since we were more concerned with LWD as a unit

of volume, no further effort was made to identify
10 RMRS-GTR-55. June 2000

Table 2. Stand characteristics of sampled riparian forestsa in northwestern Wyoming (standard deviations

are in parentheses).

Basal Plot Mean Mean

Habitat Species area Trees volume DBH height

Stream typeb composition (m2/ha) (per ha) (m3/ha) (cm) (m)

Adams Creek ABLA/PHMA 36% Picea pungens 14.2 243 129.9 23.4 15.6

35% Pseudotsuga menziesii (1.12) (121) (13.1) (14.1) (7.9)

17% Abies lasiocarpa

11% Pinus contorta

1% Pinus flexilis

1% Picea engelmannii

Moose Gulch ABLA/ACRU 56% Picea engelmannii 23.0 320 224.8 26.5 16.6

 Creek 42% Abies lasiocarpa (0.61) (79) (10.0) (14.7) (9.6)

2% Pinus contorta

Murphy Creek ABLA/ACGL 60% Abies lasiocarpa 17.8 240 157.6 25.3 13.3

28% Picea engelmannii (2.65) (194) (26.0) (17.6) (8.2)

11% Pseudotsuga menziesii

Blind Bull Creek ABLA/ACRU 47% Picea pungens 32.5 435 300.7 26.8 15.9

31% Picea engelmannii (4.14) (186) (47.5) (15.3) (8.7)

16% Abies lasiocarpa

6% Pinus contorta

1% Pseudotsuga menziesii

Buck Creek ABLA/PHMA 52% Pseudotsuga menziesii 15.4 182 136.3 29.7 19.1

28% Picea pungens (1.57) (86) (17.2) (14.0) (8.9)

16% Abies lasiocarpa

2% Pinus flexilis

1% Picea engelmannii

1% Populus spp.

Dry Lake Creek ABLA/VASC- 56% Picea engelmannii 33.2 655 277.3 22.6 15.0

VASC 28% Abies lasiocarpa (1.05) (197) (15.7) (11.6) (6.3)

14% Pinus contorta

2% Picea pungens

1% Pinus flexilis

South Fork ABLA/ARCO- 44% Picea engelmannii 30.8 423 315.2 26.8 19.4

 Gypsum Creek SHCA 30% Picea pungens (2.67) (178) (29.7) (14.5) (9.1)

17% Abies lasiocarpa

9% Pseudotsuga menziesii
RMRS-GTR-55. June 2000 11

Table 2. (Cont’d.)

Basal Plot Mean Mean

Habitat Species area Trees volume DBH height

Stream typeb composition (m2/ha) (per ha) (m3/ha) (cm) (m)

Sheep Creek ABLA/ACRU 72% Picea engelmannii 40.6 392 489.4 30.9 20.8

27% Abies lasiocarpa (1.07) (118) (24.0) (19.1) (11.4)

1% Pinus contorta

Willow Creek ABLA/VASC- 36% Abies lasiocarpa 26.8 530 252.7 22.6 17.1

VASC 35% Picea engelmannii (1.17) (94) (13.5) (11.5) (7.0)

25% Pinus contorta

4% Picea pungens

Blackrock Creek ABLA/JUCO 55% Picea engelmannii 17.1 537 116.5 18.6 12.8

38% Abies lasiocarpa (1.71) (438) (10.8) (7.9) (4.7)

4% Pinus contorta

2% Picea pungens

2% Populus spp.

Ditch Creek ABLA/ACRU 83% Picea engelmannii 19.4 213 185.1 30.2 18.9

11% Abies lasiocarpa (2.95) (174) (30.2) (15.8) (8.0)

6% Alnus tenuifolia

1% Pinus contorta

Hoback River ABLA/ACRU 47% Picea pungens 30.7 372 334.6 28.0 19.4

28% Abies lasiocarpa (3.57) (188) (45.7) (16.3) (9.2)

14% Picea engelmannii

11% Pinus contorta

1% Pinus flexilis

Mosquito Creek ABLA/ACRU 66% Picea engelmannii 27.5 313 290.9 28.9 18.2

27% Abies lasiocarpa (2.02) (65) (26.6) (16.9) (9.8)

6% Pinus contorta

1% Alnus tenuifolia

a Only trees > 10 cm DBH were sampled.

b Predominant plot habitat type, taken from Steele et al. (1983). ABLA = Abies lasiocarpa; ACRU = Actaea

rubra; ACGL = Acer glabrum; PHMA = Physocarpus malvaceus; VASC = Vaccinium scoparium; ARCO = Arnica

cordifolia; SHCA = Shepherdia canadensis; JUCO = Juniperus communis.
12 RMRS-GTR-55. June 2000

Table 3. Large woody debris (LWD) inventory of sampled riparian forests in northwestern Wyoming

(standard deviations are in parentheses).

Mean Mean Mean

large-end piece piece LWD Pieces

LWD species diameter length volume volume LWD

Stream composition (cm) (m) (m3) (m3/ha) (per ha)

Adams Creek 36% Abies lasiocarpa 23.1 5.7 0.23 93.6 410

26% Unknown spp. (13.3) (5.8) (0.36) (11.7) (213)

22% Pseudotsuga menziesii

11% Picea pungens

3% Pinus contorta

2% Populus spp.

Moose Gulch 62% Unknown spp. 24.5 6.3 0.31 190.1 614

 Creek 26% Abies lasiocarpa (10.5) (6.0) (0.48) (19.0) (196)

10% Picea engelmannii

1% Pinus contorta

1% Populus tremuloides

1% Alnus tenuifolia

Murphy Creek 57% Unknown spp. 27.7 5.0 0.42 113.2 268

28% Abies lasiocarpa (17.6) (4.9) (0.90) (19.7) (207)

9% Picea engelmannii

6% Pseudotsuga menziesii

Blind Bull Creek 59% Unknown spp. 19.3 5.1 0.16 113.3 715

27% Abies lasiocarpa (9.6) (4.9) (0.29) (10.1) (196)

6% Picea engelmannii

6% Pinus contorta

3% Picea pungens

Buck Creek 62% Unknown spp. 22.3 4.2 0.16 57.2 367

13% Abies lasiocarpa (10.2) (3.8) (0.22) (8.3) (288)

12% Pseudotsuga menziesii

5% Populus spp.

4% Picea pungens

2% Juniperus scopulorum

1% Picea engelmannii

Dry Lake Creek 69% Unknown spp. 22.9 5.9 0.28 149.2 537

21% Abies lasiocarpa (12.3) (5.8) (0.68) (14.2) (250)

6% Pinus contorta

3% Picea engelmannii

South Fork 66% Unknown spp. 21.9 6.1 0.27 217.4 807

 Gypsum Creek 13% Picea engelmannii (11.2) (6.3) (0.65) (22.6) (325)

10% Abies lasiocarpa

5% Picea pungens

3% Populus tremuloides

2% Pseudotsuga menziesii
RMRS-GTR-55. June 2000 13

Table 3. (Cont’d.)

Mean Mean Mean

large-end piece piece LWD Pieces

LWD species diameter length volume volume LWD

Stream composition (cm) (m) (m3) (m3/ha) (per ha)

Sheep Creek 56% Unknown spp. 23.7 5.9 0.36 206.9 582

22% Picea engelmannii (14.2) (6.4) (0.86) (26.7) (332)

21% Abies lasiocarpa

1% Pinus contorta

Willow Creek 55% Unknown spp. 19.9 6.1 0.19 171.3 917

27% Pinus contorta (7.5) (6.3) (0.28) (12.3) (380)

14% Abies lasiocarpa

4% Picea engelmannii

Blackrock Creek 71% Unknown spp. 20.6 4.2 0.17 81.2 490

20% Abies lasiocarpa (9.5) (4.4) (0.38) (11.0) (359)

5% Picea engelmannii

2% Pinus contorta

1% Picea pungens

1% Populus tremuloides

Ditch Creek 46% Unknown spp. 22.1 5.1 0.23 84.9 372

23% Picea engelmannii (12.0) (5.5) (0.44) (19.2) (423)

20% Abies lasiocarpa

6% Alnus tenuifolia

4% Populus spp.

Hoback River 44% Unknown spp. 20.7 10.1 0.33 135.4 412

26% Pinus contorta (9.2) (7.1) (0.51) (10.9) (176)

19% Abies lasiocarpa

10% Picea pungens

1% Picea engelmannii

Mosquito Creek 48% Unknown spp. 27.7 5.0 0.42 113.2 268

30% Abies lasiocarpa (10.5) (5.5) (0.52) (12.4) (167)

19% Picea engelmannii

4% Pinus contorta
14 RMRS-GTR-55. June 2000

the species. Most pieces averaged 4.2 m (SD = 1.5)

in length and 22.8 cm (SD = 2.6) in large-end

diameter, although this varied by site (table 3).

LWD volume ranged from a minimum of 57.2 to a

maximum of 217.4 m3/ha (mean = 132.8, SD =

50.9). This is similar to LWD loads reported for

spruce-fir forests in other regions, but differs from

other cover types (table 4). Ultimately, riparian

forest LWD volume is limited by the productivity

of the spruce-fir forests in this area, which in turn

are controlled by stand age, precipitation, growing

season length, and disturbance regime.

Table 4. Forest large woody debris (LWD) loads by area and forest type. Unless provided by the original

authors, we used a conversion factor of 0.2 Mg/m3

 (Harmon et al. 1986) to convert LWD volume into LWD

biomass (note that researchers define LWD differently).

Predominant Volume LWD biomass Minimum piece

forest type Locationa (m3/ha) (Mg/ha)b dimensions Sourcec

Engelmann spruce- Bridger-Teton N.F., 132.8 26.6 1 m length this

subalpine fir Wyoming (57.2 - 217.4)d (11.4 - 43.5)d 10 cm diameter study

Engelmann spruce- Gallatin N.F., 79.8 16.0 no length 1

subalpine fir Montana 7.5 cm diameter

Engelmann spruce- Nez Perce N.F., 117.5 23.5 no length 1

subalpine fir Idaho 7.5 cm diameter

Engelmann spruce- Dear Lodge N.F., 135.7 27.1 no length 1

subalpine fir Montana 7.5 cm diameter

Engelmann spruce- Flathead N.F., 190.3 38.1 no length 1

subalpine fir Montana 7.5 cm diameter

Engelmann spruce- Clearwater N.F., 97.3 19.5 no length 1

subalpine fir Idaho 7.5 cm diameter

Engelmann spruce- Colville N.F., 255.4 51.1 no length 1

subalpine fir Washington 7.5 cm diameter

Engelmann spruce- northern 131.5 26.3 no length 1

subalpine fir Idaho 7.5 cm diameter

Mixed conifers Sequoia N.P., 311.1 89.6* 1.5 m length 2

California > 15 cm diameter

Spruce-fir not stated 416 97* no length- only logs 3

7.5 cm diameter

Douglas-fir Coastal Range, 379 95 10 cm diameter 4

Oregon

Douglas-fir Cascades Mtns., 544 121 10 cm diameter 4

Washington

Douglas-fir Cascade Mtns., 594 136 10 cm diameter 4

Oregon

Hemlock- northern Michigan- 121.3 24.3 no length 5

hardwood northern Wisconsin (55.1 - 207.2)d (11.0 - 41.4)d 20 cm diameter

a

 N.F. = National Forest, N.P. = National Park.

b

 * = values provided by the original study, using their own estimates of LWD density.

c

 Source codes: 1 = Brown and See (1980), 2 = Harmon et al. (1987), 3 = Harmon et al. (1986), 4 = Spies et al.

(1988), 5 = Tyrell and Crow (1994).

d

 Numbers in parentheses represent the minimum and maximum values reported by that study.
RMRS-GTR-55. June 2000 15

Stream LWD Conditions

Very few pieces of riparian LWD were identified to species (typically 20% to 30%), which is

expected because moving water rapidly removes

bark and other distinguishing characteristics. Mean

large-end diameters did not vary appreciably from

those found in adjacent riparian forests. Unlike

some studies (Bisson et al. 1987, Bilby and Ward

1989, Nakamura and Swanson 1994) that noted a

positive shift in mean piece length with increasing

stream bankfull width, no such trend was apparent in our data (table 5). Riparian LWD volume

varied from a low of 4.8 up to 54.5 m3/100 m reach

(mean = 19.4, SD = 14.3). Table 6 compares this

study with other work on riparian LWD.

Table 5. Large woody debris (LWD) inventory of sampled streams in northwestern Wyoming (standard

deviations are in parentheses).

Mean Mean Mean

large-end piece piece LWD Pieces

LWD species diameter length volume volumea per Volumea

Stream composition (cm) (m) (m3) (m3/100 m) 100 ma (m3/m2)

Adams Creek 66% Unknown spp. 19.2 4.1 0.18 8.7 48 0.029

18% Abies lasiocarpa (9.5) (4.2) (0.39)

5% Pseudotsuga menziesii

6% Populus spp.

2% Picea pungens

2% Picea engelmannii

1% Salix spp.

Moose Gulch 88% Unknown spp. 24.1 4.3 0.27 21.0 76 0.062

 Creek 6% Abies lasiocarpa (12.2) (5.1) (0.60)

5% Picea engelmannii

1% Alnus tenuifolia

Murphy Creek 78% Unknown spp. 26.3 4.3 0.64 27.5 43 0.045

17% Picea engelmannii (19.1) (6.5) (1.93)

6% Abies lasiocarpa

Blind Bull Creek 67% Unknown spp. 24.2 4.8 0.41 15.4 37 0.027

14% Abies lasiocarpa (13.3) (4.6) (0.88)

13% Picea pungens

3% Picea engelmannii

3% Pinus contorta

Buck Creek 76% Unknown spp. 23.0 4.6 0.25 16.8 66 0.035

12% Abies lasiocarpa (10.7) (4.6) (0.56)

6% Picea pungens

4% Populus spp.

2% Pseudotsuga menziesii

Dry Lake Creek 85% Unknown spp. 20.6 3.9 0.18 8.6 48 0.016

10% Abies lasiocarpa (10.8) (3.7) (0.41)

2% Picea engelmannii

2% Pinus contorta

South Fork 81% Unknown spp. 25.7 5.8 0.46 54.5 118 0.045

 Gypsum Creek 14% Picea engelmannii (13.9) (6.2) (1.04)

3% Abies lasiocarpa

1% Pseudotsuga menziesii

Sheep Creek 76% Unknown spp. 21.5 3.6 0.21 13.5 66 0.029

14% Abies lasiocarpa (11.1) (3.7) (0.41)

10% Picea engelmannii
16 RMRS-GTR-55. June 2000

Table 5. (Cont’d.)

Mean Mean Mean

large-end piece piece LWD Pieces

LWD species diameter length volume volumea per Volumea

Stream composition (cm) (m) (m3) (m3/100 m) 100 ma (m3/m2)

Willow Creek 71% Unknown spp. 21.0 5.9 0.27 23.4 88 0.051

17% Abies lasiocarpa (10.3) (6.0) (0.64)

6% Picea engelmannii

6% Pinus contorta

Blackrock Creek 74% Unknown spp. 24.7 4.4 0.24 6.5 27 0.004

16% Picea engelmannii (11.2) (3.4) (0.30)

10% Abies lasiocarpa

Ditch Creek 72% Unknown spp. 18.2 2.8 0.20 4.8 24 0.004

20% Alnus tenuifolia (12.8) (3.6) (0.39)

8% Picea engelmannii

Hoback River 83% Unknown spp. 21.0 5.4 0.31 11.9 38 0.008

9% Picea pungens (11.7) (5.4) (0.71)

4% Abies lasiocarpa

2% Pinus contorta

1% Picea engelmannii

Mosquito Creek 83% Unknown spp. 21.8 4.1 0.29 39.8 140 0.043

11% Picea engelmannii (13.3) (5.1) (0.68)

5% Abies lasiocarpa

1% Pinus contorta

a

 Within-stream estimate of standard deviation is unavailable because these parameters were measured as a

single unit without replicates.

Correlation of Stream Size with

LWD Loading

Generally, as stream size increases, its hydraulic

potential during high flows also increases, resulting in a greater capacity of that flow to attenuate

LWD (Bilby and Ward 1989). It is unlikely that

under undisturbed conditions reach LWD load

ever reaches zero with increasing width because

there are always locations along the edge of the

channel capable of debris retention. Figure 5 details the relationship between LWD load and mean

bankfull width. An exponential function fit the

LWD debris load suitably (R2

 = 0.326):

. BFW VM . e− = 0 0843 2 0 0561 (8)

where BFW is the mean bankfull width of that

particular reach. This corresponds with the work

of others who found riparian LWD loads inversely

Figure 5. Trend between mean bankfull width and

large woody debris volume per m2 of stream

surface area. The negative exponential curve

reflects the increased transport power and more

open forests of larger streams sampled in the

Bridger-Teton National Forest.

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

y = 0.07123e-0.14509x

R = 0.50755, n = 13 2

Mean bankfull width (m)

CWD volume (m3

m-2 of stream surface)
RMRS-GTR-55. June 2000 17

Table 6. Stream large woody debris (LWD) loads for largely undisturbed forests by region and cover type.

Values are not directly comparable because of differences in definition of riparian LWD. Biomass conversion assumes 0.2 Mg/m3

 (Harmon et al. 1986).

LWD Piece Minimum

Forest type Locationa Volumea biomassb densityc dimensions Sourced

Engelmann spruce- B.T.N.F., 19.4 (A) 3.9 (C) 63.0 (F) 1 m length this

subalpine fir Wyoming (4.8 - 54.5)e (1.0 - 10.9) (24 - 140) 10 cm diameter study

Engelmann spruce- north central 13.3 (A) 2.7 (C) 43.4 (F) 1 m length 1

subalpine fir Colorado (6.6 - 27.1) (1.3 - 5.4) (18 - 64) 10 cm diameter

Spruce-lodgepole Idaho — 7 (D) — > 10 cm diameter 2

pine

Mixed conifers northeast 140 (B) 28 (C) 390 (G) 1 m length 3

Oregon 10 cm diameter

Mixed conifers northeast 145 (B) 29 (C) 450 (G) 1 m length 3

Oregon 10 cm diameter

Mixed conifers southeast 195 (B) 39 (C) 250 (G) 1 m length 3

Washington 10 cm diameter

Mixed conifers Stanislaus N.F., 136 (B) 27.2 (C) 17.8 (F) 1 m length 4

California 10 cm diameter

Sitka spruce- southeast 58 (A) 11.6 (C) 33 (F) 1.5 m length 5

western hemlock Alaska (36 - 100) (7.2 - 20) (25 - 42) 20 cm diameter

Douglas-fir Willamette N.F., 478 (B) 191.2 (C) — 1.5 m length 6

Oregon (230 - 750) (92 - 300) 10 cm diameter

Mixed conifers southwest British 43.2 (A) 234.5 (E) 42.0 (F) 2 m length 7

Columbia (16.6 - 85.0) (74 - 448) (26 - 60) 10 cm diameter

eastern hemlock- Blue Ridge Mtns., 21.9 (A) 8.6 (C) 51.4 (F) 1.5 m length 8

hardwoods southeastern U.S. 10 cm diameter

a

 Volume codes: A = m3/100 m stream reach, B = m3/ha.

b

 LWD biomass codes: C = Mg/100 m stream reach, D = kg/m2, E = Mg/ha.

c

 Piece density codes: F = pieces/100 m stream reach, G = pieces/ha.

d Source codes: 1 = Richmond and Fausch (1995), 2 = Triska and Cromack (1980), 3 = Carlson et al. (1990),

4 = Ruediger and Ward (1996), 5 = Robison and Beschta (1990a), 6 = Lienkaemper and Swanson (1987),

7 = Fausch and Northcote (1992), 8 = Hedman et al. (1996)

e Numbers in parentheses represent the minimum and maximum values reported by that study.
18 RMRS-GTR-55. June 2000

related to stream dimensions (Bilby and Ward

1989, Robison and Beschta 1990b, Bilby and Ward

1991, Gippel 1995, Richmond and Fausch 1995).

Bilby and Ward (1989) found a similar decreasing

pattern between piece frequency and stream size

in the Pacific Northwest, while Keller and Swanson

(1979) noted a decline in LWD biomass (in kg/m)

with increasing channel width.

A Simulated Case Study:

Dry Lake Creek, Wyoming

Perhaps the best way to exhibit the potential of

CWD is through simulation2

 of an undisturbed

old-growth riparian stand. Since FVS can generate

forest management practices, we simulated a

clearcut (in year 50) of the initial old-growth stand

to evaluate the impact of human disturbance on

LWD dynamics (the clearcut scenario). FVS then

generated output files for each scenario that CWD

processed into 300 y debris dynamics. The results

in figures 6 to 8 represent 10 replicate runs.

Undisturbed riparian forests along Dry Lake

Creek are predicted to deliver about 2.6 m3

 (SD =

1.33) of LWD/100 m reach every 10 y (figure 6a).

Since we know the current in-stream LWD load

(8.6 m3

/100 m reach, table 5), this suggests that Dry

Lake Creek experiences a riparian LWD turnover

rate of 20% to 60% per decade, assuming the inventory of 1995 reflects a long-term steady- state riparian LWD load. Other authors (Murphy and Koski

1989, Bilby and Ward 1991, Maser and Sedell 1994)

have noted similar riparian LWD turnover rates.

Clearcutting has a different impact on riparian

LWD dynamics. During the first 50 y of the simulation (figure 6b), LWD recruitment for the

preharvest stand did not differ significantly from

the undisturbed stand (the peak in year 50 resulted

from harvest-accelerated snag failure). After

clearcutting occurs, delivery declined to zero by

year 60 and stayed negligible over the next 60 y.

Post-clearcut LWD recruitment did not reach noharvest levels until 200+ y after the disturbance.

The cumulative effect of changes in recruitment

can be seen in figure 7. The old-growth scenario

steadily added LWD, reaching 79.4 m3

/100 m over

the simulation period (figure 7a). The clearcut reach,

however, delivered only 40.7 m3

/100 m, approximately half the volume it would have accumulated

under an undisturbed scenario. Figure 7b shows

how the cumulative recruitment following harvesting differed for 150+ y after the clearcut; its

trajectory flattened to virtually zero for almost a

century. The difference in recruitment resulted

from the naturally slow recovery of the postharvest

riparian stand and the absence of post-event snags

that could have provided, however reduced, LWD

delivery during stand regrowth. Grette (1985) noted

similar trends in LWD loads measured in

postharvest watersheds in western Washington.

2

 CWD version 1.3 was used for this case study, which

is only cosmetically different from version 1.4.

(a)

Time (years)

LWD recruitment (m3/100 m reach) -0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

 - - - ± Standard error

Mean recruitmen

(b)

Time (years)

LWD recruitment (m3/100 m reach) -0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

 - - - ± Standard error

Mean recruitmen

Figure 6. Large woody debris recruitment predicted by CWD over the 300 y simulation period.

Undisturbed riparian forests along Dry Lake

Creek (a) delivered almost 2.6 m3

/100 m reach/10

y cycle, while after clearcutting occurs (b), the

simulated stand averaged less than 1.4 m3

/100 m

reach. The sharp, relatively short-term oscillations apparent for both scenarios likely resulted

from FVS- and CWD-programming effects rather

than ecological processes.

Standard error

Mean recruitment

Standard error

Mean recruitment
RMRS-GTR-55. June 2000 19

(a)

Time (years)

Instream LWD load (m3/100 m reach)

0

2

4

6

8

10

12

14

16

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

 - - - ± Standard erro

Mean LWD load

(b)

Time (years)

Instream LWD load (m3/100 m reach)

0

2

4

6

8

10

12

14

16

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

 - - - ± Standard erro

Mean LWD load

(a)

Time (years)

Cumulative LWD delivery (m3/100 m reach)

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

 - - - ± Standard error

Mean cumulative delive

(b)

Time (years)

Cumulative LWD delivery (m3/100 m reach)

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

 - - - ± Standard error

Mean cumulative delive

The net impact of these scenarios on in-stream

LWD loads is shown in figure 8. Using our assumption of in-stream LWD attrition (see Bragg and

Kershner 1997), the undisturbed conditions

achieved a relatively constant LWD stocking of 8.6

m3

/100 m reach. The volumes delivered under this

scenario were consistent from cycle to cycle (SD =

1.89). Clearcutting loads averaged only 4.6 m3/100

m reach and lingered at < 15% of undisturbed

levels for several consecutive decades. Under certain conditions, both LWD delivery and in-stream

loads may fall to zero with potentially significant

impacts on stream habitat for decades following

the clearcutting of riparian forests.

Figure 7. Cumulative large woody debris delivery

for Dry Lake Creek, WY, under undisturbed (a)

and clearcut (b) simulations. Both scenarios

developed similar recruitment patterns until

clearcutting occurred in year 50, and they remained different throughout the rest of the

simulation.

Figure 8. Calculated in-stream large woody debris

(LWD) load for Dry Lake Creek, WY, under undisturbed (a) and clearcut (b) simulations.

Clearcutting occurred in year 50. Less than 70 y

later riparian LWD loads were forecast to decline

to < 15% of the long-term undisturbed riparian

LWD average.

Standard error

Mean LWD load

Standard error

Mean LWD load

Standard error

Mean cumulative delivery

(a)

Time (years)

Cumulative LWD delivery (m3/100 m reach)

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

 - - - ± Standard error

Mean cumulative delive

(b)

Time (years)

Cumulative LWD delivery (m3/100 m reach)

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

 - - - ± Standard error

Mean cumulative delive

Standard error

Mean cumulative delivery

Standard error

Mean cumulative delivery

Validation of Model

Without detailed, long-term surveys of LWD

recruitment, it is difficult to evaluate the performance of CWD. Typically, we hope for an approximation of known (although instantaneous) conditions of our studied reaches. To this end, we have

several measures with which to compare model

behavior against field conditions. Figure 9 lists 2

tangible attributes of riparian LWD measured in

Dry Lake Creek: piece diameter and length. The
20 RMRS-GTR-55. June 2000

top graph (a) compares predicted piece large-end

diameter against that inventoried in 1995. Some

significant differences existed for diameter; however, the predicted distribution is averaged over a

300 y period while the field data represents that

sampled in the stream at a particular point in time.

The apparent shift in piece diameter in figure 9a

could have resulted from the maturation and concurrent increase in tree size of the riparian forest,

which in turn generated larger pieces of debris.

More importantly, predicted and observed piece

length data show much better agreement. With the

exception of the smallest (1 to 2 m) and largest (18

to 24 m) length classes, piece length patterns are

generally similar. The model likely over predicts

the shortest pieces because of the tendency of small

debris to be flushed out quickly. Mean predicted

piece length (3.2 m, SD = 0.6) is similar to that

measured in Dry Lake Creek (3.9 m, SD = 3.7, see table

5). Accurate representation of piece length patterns is

especially critical because of the relationship between debris length and in-stream retention.

Discussion

As with any model that is partially derived from

empirical data, CWD’s predictions are sensitive to

initial conditions and subject to considerable variance from one reach to another. However, the

information generated does not exceed the natural

limits involved and indicates which factors and

processes are most important in determining patterns of LWD recruitment.

Riparian forest conditions heavily influence riparian LWD loading. The more mature and intact

the adjacent riparian forest is, the greater the likelihood of sustained LWD delivery. Streams that

have forested cover to bankfull width also contribute significant new LWD. The likelihood of delivery declines steeply with increasing distance to the

stream (McDade et al. 1990, Robison and Beschta

1990a, Van Sickle and Gregory 1990), making the

timber adjacent to the stream the most vital reserve

of LWD. Successional status of the riparian forest

has a multifaceted impact on in-stream LWD volumes. Although vigorous early successional stands

experience greater mortality rates from self-thinning, they produce only small dead trees and hence

smaller pieces of LWD, which can be quickly moved

downstream. Old-growth stands, on the other hand,

generate fewer snags for recruitment, but those

created are often large and difficult to flush out.

Old-growth may also be more susceptible to some

catastrophic disturbances (Schmid and Hinds 1974,

Romme 1982, Holsten et al. 1991, Everham and

Brokaw 1996), thereby increasing the chance that

extensive volumes of material can be added periodically. Wider floodplains and sparser vegetation

along larger and, in our study, lower elevation

streams contributed significantly to lower riparian

LWD loads. Stream size is also responsible for

much of the difference in LWD loading; larger

streams, especially during high-water events have

greater volumes of water flowing through them,

(a)

Diameter class (inches)

Proportion of pieces 0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

4-6 6-8 8-10 10-15 15-20 20-30 30-40 40-50 50-60 60+

Model prediction

Field data

(b)

Length class (feet)

Proportion of pieces 0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

3-6 6-10 10-15 15-20 20-30 30-40 40-60 60-80 80-100 100+

Model prediction

Field data

Figure 9. Initial validation of large woody debris

(LWD) modeling results using recruited piece

diameter (a) and piece length (b). The discrepancies between field data and model predictions for

piece diameter probably resulted from attrition of

LWD once in the channel and the maturation and

concurrent increase in tree size of the riparian

forests during the simulation period. More

importantly, a better fit of the piece length data

indicates realistic projections of riparian LWD

recruitment patterns.

Field data

Model prediction

Field data

Model prediction
RMRS-GTR-55. June 2000 21

providing the force to remove even the largest

pieces of LWD.

While this study deals with volumetric measures of LWD, not all similar volumes have identical qualities. Because of their permanence, large

pieces of LWD are more valuable than smaller,

more ephemeral ones (Sedell et al. 1988, McDade et

al. 1990). Large debris can easily span the small

streams we sampled, bisecting the flow and trapping smaller pieces as they are washed into the

superstructure. This newly imbedded matrix then

traps smaller and smaller debris until very fine

particles are trapped, leading to the accumulation

of considerable amounts of sediment behind these

debris dams (Marston 1982, Megahan 1982). On

the downstream side of debris dams, water cascading over the dam quickly forms plunge pools and

other scour-related structures that can provide

critical habitat for many species. These debris dams

can last for many years before decay and flowing

water gradually erode away their structural integrity, or they can fail rapidly if high water or human

actions weaken them. Turnover rates for small

pieces are likely to be rapid in all but the smallest

flows, while large pieces can persist for extended

periods even in large rivers. Therefore, a stream

with many small pieces is less structurally productive because of debris instability, while a stream

with a few large pieces will have a value disproportionate to the abundance of LWD.

Watershed-Scale

Applications of CWD

Application of CWD is adaptable to provide

watershed-scale analysis of riparian LWD dynamics. Because small tributaries in many regions flow

through continuous forest, they are vital contributors to the structural and functional characteristics

of distant ecosystems (Minckley and Rinne 1985,

Maser and Sedell 1994). Since these areas are heavily

forested, they experience significant natural and

human disturbance, which in turn affects the recruitment patterns of LWD (Bragg 1997). It should

be possible to simulate an entire watershed (one

100 m reach at a time) and, using some assumptions on in-stream LWD attrition, predict how

much debris each tributary contributes to the larger

downstream flows. Different watershed-level

timber harvests or natural disturbance regimes can

then be applied to the tributary, allowing for assessment of large-scale impacts on systems that

may not have otherwise been considered.

Conclusions

Pairing a forest growth and yield simulator with

a riparian LWD recruitment model shows considerable promise for projecting the influence of a

range of riparian forest management activities. As

expected, riparian LWD recruitment depends

heavily on the factors affecting the adjacent forest.

Old-growth stands, with their abundance of large

trees, delivered more and larger woody debris

than a comparable reach stocked with younger,

smaller individuals arising after a disturbance.

CWD appears to do a good job generating recruitment patterns, debris loads, and other long-term

dynamics. While reliable field validation will take

years, preliminary evidence suggests CWD follows expected patterns. Improvement of some of

the assumptions in this paper (e.g., stream LWD

attrition over time) and more field data should

improve our ability to predict stream response to

changes in riparian zone management.

Predicting LWD recruitment using FVS and

CWD can help develop strategies to remediate

streams lacking LWD or can determine the potential effects of management activities on riparian

zones. CWD provides forest managers and aquatic

biologists with a tool to help understand the implications of forest structure and dynamics on riparian processes. The ability to customize both FVS

and CWD for local conditions and management

objectives provides managers with the flexibility

to anticipate changes initiated by different treatments before implementing them in the field.

Literature Cited

Aho, P.E. and J.M. Cahill. 1984. Deterioration rates blowdown

timber and potential problems associated with product

recovery. USDA Forest Service General Technical Report

PNW-167.

Alexander, R.R. and J.H. Buell. 1955. Determining the direction of destructive winds in a Rocky Mountain timber

stand. Journal of Forestry 53:19-23.
22 RMRS-GTR-55. June 2000

Berg, D.R. 1995. Riparian silvicultural system design and

assessment in the Pacific Northwest Cascade Mountains,

USA. Ecological Applications 5:87-96.

Beschta, R.L. 1979. Debris removal and its effects on sedimentation in an Oregon Coast Range stream. Northwest Science 53:71-77.

Bilby, R.E. 1984. Removal of woody debris may affect stream

channel stability. Journal of Forestry 82:609-613.

Bilby, R.E. and G.E. Likens. 1980. Importance of organic debris

dams in the structure and function of stream ecosystems.

Ecology 61:1107-1113.

Bilby, R.E. and J.W. Ward. 1989. Changes in characteristics

and function of woody debris with increasing size of

streams in western Washington. Transactions of the American Fisheries Society 118:368-378.

Bilby, R.E. and J.W. Ward. 1991. Characteristics and function

of large woody debris in streams draining old-growth,

clear-cut, and second-growth forests in southwestern Washington. Canadian Journal of Fisheries and Aquatic Sciences

48:2499-2508.

Bisson, P.A., R.E. Bilby, M.D. Bryant, C.A. Dolloff, G.B. Grette,

R.A. House, M.L. Murphy, K.V. Koski, and J.R. Sedell.

1987. Large woody debris in forested streams in the Pacific

Northwest: past, present, and future. In Streamside Management: Forestry and Fishery Interactions, E.O. Salo and

T.W. Cundy, eds. University of Washington Press, Seattle,

WA. pp. 143-190.

Bragg, D.C. 1997. Simulating catastrophic disturbance effects

on coarse woody debris production and delivery in a

northwestern Wyoming riparian system. In Proceedings:

Forest Vegetation Simulator Conference, R. Teck, M. Moeur,

and J. Adams, eds. USDA Forest Service General Technical

Report INT-373. pp. 148-156.

Bragg, D.C. and J.L. Kershner. 1997. Evaluating the long-term

consequences of forest management and stream cleaning

on coarse woody debris in small riparian systems of the

central Rocky Mountains. FHR Currents, No. 21.

Bren, L.J. 1995. Aspects of the geometry of riparian buffer

strips and its significance to forestry operations. Forest

Ecology and Management 75:1-10.

Brown, J.K. and T.E. See. 1981. Downed dead woody fuel and

biomass in the northern Rocky Mountains. USDA Forest

Service General Technical Report INT-117.

Budd, W.W., P.L. Cohen, P.R. Saunders, and F.R. Steiner. 1987.

Stream corridor management in the Pacific Northwest:

determination of stream-corridor widths. Journal of Environmental Management. 11:587-597.

Bull, E.L. 1983. Longevity of snags and their use by woodpeckers. In Snag Habitat Management: Proceedings of the Symposium. USDA Forest Service General Technical Report

RM-99. pp. 64-67.

Carlson, J.Y., C.W. Andrus, and H.A. Froehlich. 1990. Woody

debris, channel features, and macroinvertebrates of streams

with logged and undisturbed riparian timber in northeastern Oregon, U.S.A. Canadian Journal of Fisheries and

Aquatic Sciences 47:1103-1111.

Chen, J., R.J. Naiman, and J.F. Franklin. 1995. Microclimatic

patterns and forest structure across riparian ecosystems.

Final Project Report to USDA Forest Service Pacific Northwest Research Station.

Cimon, N. 1983. A simple model to predict snag levels in

managed forests. In Snag Habitat Management: Proceedings of the Symposium. USDA Forest Service General

Technical Report RM-99. pp. 200-204.

Cline, S.P., A.B. Berg, and H.M. Wright. 1980. Snag characteristics and dynamics in Douglas-fir forests, western Oregon.

Journal of Wildlife Management 44:773-786.

Crookston, N.L. 1990. User’s guide to the Event Monitor: part

of Prognosis Model version 6. USDA Forest Service General Technical Report INT-275.

Curtis, J.D. 1943. Some observations on wind damage. Journal

of Forestry 41:877-882.

Czaplewski, R.L., A.S. Brown, and R.C. Walker. 1989. Profile

models for estimating log end diameters in the Rocky

Mountain Region. USDA Forest Service Research Paper

RM-284.

Ek, A.R. 1969. Stem map data for three forest stands in northern Ontario. Canadian Forest Service, Department of Fisheries and Forestry, Forestry Research Laboratory, Ontario

Region. Information Report O-X-113.

Everham, C.M., III and N.V.L. Brokaw. 1996. Forest damage

and recovery from catastrophic wind. Botanical Review

62:113-185.

Fausch, K.D. and T.G. Northcote. 1992. Large woody debris

and salmonid habitat in a small coastal British Columbia

stream. Canadian Journal of Fisheries and Aquatic Sciences 49:682-693.

Ferguson, D.E. and C.E. Carlson. 1993. Predicting regeneration establishment with the Prognosis Model. USDA Forest

Service Research Paper INT-467.

Ferguson, D.E. and N.L. Crookston. 1991. User’s guide to

version 2 of the Regeneration Establishment Model: part of

the Prognosis Model. USDA Forest Service General Technical Report INT-279.

Froehlich, H.A. 1973. Natural and man-caused slash in headwater streams. Loggers Handbook 33:15-17, 66-70, 82-86.

Gippel, C.J. 1995. Environmental hydraulics of large woody

debris in streams and rivers. Journal of Environmental

Engineering 121:388-395.

Gregory, S.V., F.J. Swanson, W.A. McKee, and K.W. Cummins.

1991. An ecosystem perspective of riparian zones.

BioScience 41:540-551.

Grette, G.B. 1985. The role of organic debris in juvenile salmonid rearing habitat in small streams. M.S. Thesis, University of Washington, Seattle, WA.

Harmon, M.E., K. Cromack, Jr., and B.G. Smith. 1987. Coarse

woody debris in mixed-conifer forests, Sequoia National

Park, California. Canadian Journal of Forest Research

17:1265-1272.

Harmon, M.E., J.F. Franklin, F.J. Swanson, P. Sollins, S.V.

Gregory, J.D. Lattin, N.H. Anderson, S.P. Cline, N.G.

Aumen, J.R. Sedell, G.W. Lienkaemper, K. Cromack, Jr.,

and K.W. Cummins. 1986. Ecology of coarse woody debris

in temperate ecosystems. Advances in Ecological Research

15:133-302.

Harrington, M.G. 1996. Fall rates of prescribed fire-killed

ponderosa pine. USDA Forest Service Research Paper INT489.

Harrod, R.J., W.L. Gaines, W.E. Hartl, and A. Camp. 1998.

Estimating historical snag density in dry forests east of the

Cascade Range. USDA Forest Service Research Paper PNW428.

Hedman, C.W., D.H. Van Lear, and W.T. Swank. 1996. Instream large woody debris loading and riparian forest

seral stage associations in the southern Appalachian Mountains. Canadian Journal of Forest Research 26:1218-1227.

Hinds, T.E., F.G. Hawksworth, and R.W. Davidson. 1965.

Beetle-killed Engelmann spruce: its deterioration in Colorado. Journal of Forestry 63:536-542.

Holsten, E.H., R.W. Thier, and J.M. Schmid. 1991. The spruce

beetle. USDA Forest Service Forest Insect and Disease

Leaflet 127.

House, R.A. and P.L. Boehne. 1987. The effect of stream

cleaning on salmonid habitat and populations in a coastal
RMRS-GTR-55. June 2000 23

Oregon drainage. Western Journal of Applied Forestry

2:84-87.

Huggard, D. 1997. Fall-down rates of subalpine fir snags at

Sicamous Creek: implications for worker safety and habitat supply. British Columbia Ministry of Forests Research

Program Extension Note 16.

Ishizuka, M. 1984. Spatial pattern of trees and their crowns in

natural mixed forests. Japanese Journal of Ecology 34:421-

430.

Johnson, B.L., W.B. Richardson, and T.J. Naimo. 1995. Past,

present, and future concepts in large river ecology.

BioScience 45:134-141.

Keen, F.P. 1929. How soon do yellow pine snags fall? Journal

of Forestry 27:735-737.

Keen, F.P. 1955. The rate of natural falling of beetle-killed

ponderosa pine snags. Journal of Forestry 53:720-723.

Keller, E.J. and F.J. Swanson. 1979. Effects of large organic

material on channel form and fluvial process. Earth Surface Processes 4:361-380.

Knight, D.H. 1994. Mountains and Plains: the Ecology of

Wyoming. Yale University Press, New Haven, CT.

Kozak, A., D.D. Munro, and J.H.G. Smith. 1969. Taper functions and their application in forest inventory. Forestry

Chronicle 45:278-283.

Lageson, D.R. and D.R. Spearing. 1988. Roadside Geology of

Wyoming. Mountain Press Publishing Company, Missoula,

MT.

Lienkaemper, G.W. and F.J. Swanson. 1987. Dynamics of large

woody debris in streams in old-growth Douglas-fir forests.

Canadian Journal of Forest Resource 17:150-156.

Likens, G.E. and R.E. Bilby. 1982. Development, maintenance,

and role of organic-debris dams in New England streams.

In Sediment Budgets and Routing in Forested Drainage

Basins, F.J. Swanson, R.J. Janda, T. Dunne, and D.N.

Swanston, eds. USDA Forest Service General Technical

Report PNW-141. pp. 122-128.

Love, J.D. and J.M. Love. 1980. Road log, Jackson to Dinwoody

and return. In Stratigraphy of Wyoming, Wyoming Geological Association 31st Annual Field Conference. pp. 283-

317.

Lyon, L.J. 1977. Attrition of lodgepole pine snags on the

Sleeping Child Burn, Montana. USDA Forest Service Research Note INT-219.

Marston, R.A. 1982. The geomorphic significance of log steps

in forest streams. Annals of the Association of American

Geographers 72:99-108.

Maser, C. and J.R. Sedell. 1994. From the Forest to the Sea: the

Ecology of Wood in Streams, Rivers, Estuaries, and Oceans.

St. Lucie Press, Delray Beach, FL.

Maser, C. and J.M. Trappe [tech. eds.]. 1984. The seen and

unseen world of the fallen tree. USDA Forest Service

General Technical Report PNW-164.

McDade, M.H., F.J. Swanson, W.A. McKee, J.F. Franklin, and

J. Van Sickle. 1990. Source distances for coarse woody

debris entering small streams in western Oregon and Washington. Canadian Journal of Forest Research 20:326-330.

Meehan, W.R. [ed.]. 1991. Influences of Forest and Rangeland

Management on Salmonid Fishes and Their Habitats.

American Fisheries Society Special Publication 19, Bethesda,

MD.

Megahan, W.F. 1982. Channel sediment storage behind obstructions in forested drainage basins draining the granitic

bedrock of the Idaho Batholith. In Sediment Budgets and

Routing in Forested Drainage Basins, F.J. Swanson, R.J.

Janda, T. Dunne, and D.N. Swanston, eds. USDA Forest

Service General Technical Report PNW-141. pp. 114-121.

Mergen, F. 1954. Mechanical aspects of wind-breakage and

windfirmness. Journal of Forestry 52:119-125.

Mielke, J.L. 1950. Rate of deterioration of beetle-killed Engelmann spruce. Journal of Forestry 48:882-888.

Minckley, W.L. and J.N. Rinne. 1985. Large woody debris in

hot-desert streams: an historical review. Desert Plants

7:142-153.

Mitchell, R.G. and H.K. Preisler. 1998. Fall rate of lodgepole

pine killed by the mountain pine beetle in central Oregon.

Western Journal of Applied Forestry 13:23-26.

Moeur, M. 1993. Characterizing spatial patterns of trees using

stem-mapped data. Forest Science 39:756-775.

Murphy, M.L. and K.V. Koski. 1989. Input and depletion of

woody debris in Alaska streams and implications for streamside management. North American Journal of Fisheries

Management 9:427-436.

Nakamura, F. and F.J. Swanson. 1994. Distribution of coarse

woody debris in a mountain stream, western Cascade

Range, Oregon. Canadian Journal of Forest Research

24:2395-2403.

Panshin, A.J. and C. deZeeuw. 1980. Textbook of Wood Technology. 4th ed. McGraw-Hill Publishing Company, New York, NY.

Petty, J.A. and R. Worrell. 1981. Stability of coniferous tree

stems in relation to damage by snow. Forestry 54:115-128.

Potts, D.F. and B.K.M. Anderson. 1990. Organic debris and the

management of small stream channels. Western Journal of

Applied Forestry 5:25-28.

Rainville, R.P., S.C. Rainville, and E.L. Lider. 1985. Riparian

silvicultural strategies for fish habitat emphasis. In Foresters’ Future: Leaders or Followers, Society of American

Foresters National Conference Proceedings. SAF Publication 85-13. pp. 186-196.

Ralph, S.C., G.C. Poole, L.L. Conquest, and R.J. Naiman. 1994.

Stream channel morphology and woody debris in logged

and unlogged basins of western Washington. Canadian

Journal of Fisheries and Aquatic Sciences 51:37-51.

Raphael, M.G. and M.L. Morrison. 1987. Decay and dynamics

of snags in the Sierra Nevada, California. Forest Science

33:774-783.

Richmond, A.D. and K.D. Fausch. 1995. Characteristics and

function of large woody debris in subalpine Rocky Mountain streams in northern Colorado. Canadian Journal of

Fisheries and Aquatic Sciences 52:1789-1802.

Ritter, D.F. 1986. Process Geomorphology. 2nd ed. William C.

Brown Publishers, Dubuque, IA.

Robison, E.G. and R.L. Beschta. 1990a. Identifying trees in

riparian areas that can provide coarse woody debris to

streams. Forest Science 36:790-801.

Robison, E.G. and R.L. Beschta. 1990b. Characteristics of coarse

woody debris for several coastal streams of southeast

Alaska, U.S.A. Canadian Journal of Fisheries and Aquatic

Sciences 47:1684-1693.

Romme, W.H. 1982. Fire and landscape diversity in subalpine

forests of Yellowstone National Park. Ecological Monographs 52:199-221.

Ruediger, R. and J. Ward. 1996. Abundance and function of

large woody debris in central Sierra Nevada streams. FHR

Currents, No. 20.

Schmid, J.M., S.A. Mata, and W.F. McCambridge. 1985. Natural falling of beetle-killed ponderosa pine. USDA Forest

Service Research Note RM-454.

Schmid, J.M., and T.E. Hinds. 1974. Development of spruce-fir

stands following spruce beetle outbreaks. USDA Forest

Service Research Paper RM-131.

Sedell, J.R., P.A. Bisson, F.J. Swanson, and S.V. Gregory. 1988.

What we know about large trees that fall into streams and

rivers. Chapter 3 in: From the Forest to the Sea: A Story of

Fallen Trees. C. Maser, R.F. Tarrant, J.M. Trappe, and J.F.

Franklin, tech. eds. USDA Forest Service General Technical

Report PNW-229. pp. 47-81.
24 RMRS-GTR-55. June 2000

Sollins, P. 1982. Input and decay of coarse woody debris in

coniferous stands in western Oregon and Washington.

Canadian Journal of Forest Research 12:18-28.

Sparks, R.E. 1995. Need for ecosystem management of large

rivers and their floodplains. BioScience 45:168-182.

Spies, T.A., J.F. Franklin, and T.B. Thomas. 1988. Coarse

woody debris in Douglas-fir forests of western Oregon and

Washington. Ecology 69:1689-1702.

Steele, R., S.V. Cooper, D.M. Ondov, D.W. Roberts, and R.D.

Pfister. 1983. Forest habitat types of eastern Idaho-western

Wyoming. USDA Forest Service General Technical Report

INT-144.

Steinblums, I.J., H.A. Froehlich, and J.K. Lyons. 1984. Designing stable buffer strips for stream protection. Journal of

Forestry 82:49-52.

Strahler, A.N. 1957. Quantitative analysis of watershed geomorphology. American Geophysical Union Transactions

38:913-920.

Swanson, F.J., G.W. Lienkaemper, and J.R. Sedell. 1976. History, physical effects, and management implications of

large organic debris in western Oregon streams. USDA

Forest Service General Technical Report PNW-56.

Swanson, F.J., S.V. Gregory, J.R. Sedell, and A.G. Campbell.

1982. Land-water interactions: the riparian zone. In Analysis of Coniferous Forest Ecosystems in the Western United

States, R.L. Edmonds, ed. US International Biological Program Synthesis Serial 14. pp. 267-291.

Teck, R. 1995. Keyword reference guide for the Forest Vegetation Simulator. USDA Forest Service WO-TM Service Center, Fort Collins, CO.

Teck, R., M. Moeur, and B. Eav. 1996. Forecasting ecosystems

with the Forest Vegetation Simulator. Journal of Forestry

94:7-10.

Teck, R., M. Moeur, and J. Adams. 1997. Proceedings, Forest

Vegetation Simulator Conference. USDA Forest Service

General Technical Report INT-373.

Triska, F.J. and K. Cromack, Jr. 1980. The role of wood debris

in forests and streams. In Forests: Fresh Perspectives from

Ecosystem Analysis, R.H. Waring, ed. Oregon State University Press, Corvallis, OR. pp. 171-190.

Tyrell, L.E. and T.R. Crow. 1994. Dynamics of dead wood in

old-growth hemlock hardwood forests of northern Wisconsin and northern Michigan. Canadian Journal of Forest

Research 24:1672-1683.

Valinger, E. and J. Fridman. 1997. Modelling probability of

snow and wind damage in Scots pine stands using tree

characteristics. Forest Ecology and Management 97:215-

222.

Van Sickle, J. and S.V. Gregory. 1990. Modeling inputs of large

woody debris to streams from falling trees. Canadian

Journal of Forest Research 20:1593-1601.

Veblen, T.T. 1986. Treefalls and the coexistence of conifers in

subalpine forests of the central Rockies. Ecology 67:644-

649.

Wykoff, W.R., N.L. Crookston, and A.R. Stage. 1982. User’s

guide to the Stand Prognosis Model. USDA Forest Service

General Technical Report INT-133.

Zar, J.H. 1984. Biostatistical Analysis. 2nd ed. Prentice Hall,

Englewood Cliffs, N.J.
RMRS-GTR-55. June 2000 25

Preface

CWD has been a work-in-progress for several years

now, and as with all technology, staying on the cutting

edge is virtually impossible. The Forest Vegetation Simulator (FVS) has evolved since this project began, and since

the CWD post-processor is at the mercy of FVS development, it is easy to fall behind the curve. We urge caution

in application of CWD. We have written this user’s guide

as a blueprint for those interested in using CWD to

investigate the ecological impacts of various treatments on

riparian large woody debris (LWD) recruitment.

—D.C.B., J.L.K., D.W.R.

Where to Get CWD and FVS

The executable file for CWD, associated model

default file, and a digital version of this documentation are available from the primary author (D.C.

Bragg) or the USFS Fish Ecology Unit

(http://www.fs.fed.us/biology/fishecology). CWD is

available as a self-extracting ZipTM file that creates

its own subdirectory (default = C:\CWD1_4 ) on

your hard drive. FVS files and documentation can

be downloaded for free from:

http://www.fs.fed.us/fmsc/fvs_software.htm

Preliminary Steps to Run CWD

CWD is a riparian LWD recruitment model that

predicts the amount of debris delivered to the

bankfull channel as determined by forest dynamics generated with FVS (version 6.1). We assume

some level of proficiency with FVS in the design of

this user’s guide. Contact the USDA Forest Service

Timber Management group if specific questions arise

in the operation of FVS. An example of a FVS keyword file (the one used to generate the tree list file for

this report) is in Bragg (1997). Teck et al. (1997) also

provides good examples of other work done with

FVS, and valuable information can be found in Wykoff

et al. (1982) and other Prognosis references.

CWD has largely been written in standard

(ANSI) Fortran 90 code for MS-DOSTM (or in a MSDOS shell sponsored by WindowsTM 3.x, 95, or 98).

Several files are necessary for successful operation

of CWD:

• CWD1_4.EXE (executable file that

processes the data)

• DEFAULT1.CWD (data file containing CWD

default parameters)

• FVS tree list file (individual tree list file

generated by FVS)

DEFAULT1.CWD must be in the same subdirectory

as CWD1_4.EXE, while the FVS tree list file can be

in any locatable subdirectory. The FVS tree list file

must meet the following formats to be incorporated

by CWD. The tree list file needs to be either space

or comma delimited, with no text headers. The text

header option can be turned off when creating the

FVS tree list file (see Teck (1995) for the fields to

alter on the TREELIST keyword) or deleted manually. FVS tree list files produce a series of tree

records detailing the information tracked by each

representative tree used to create the simulation

Appendix A: User’s Guide to the CWD Post-Processor (version 1.4)

Table A1. Forest Vegetation Simulator (FVS) tree

list output file formatting. FVS version 6.2 tree list

output code is different and will not work with

CWD.

Use

Data type Description (yes/no)

character tree number no

integer FVS tree index no

integer FVS species number yes

integer point number no

real mortality per acre yes

real current DBH yes

real diameter increment no

real current height yes

real height increment no

integer crown ratio code no

integer crown ratio (percent) no

real basal area percentile no

integer tree class no

real total cubic foot volume no

real merchantable cubic no

 foot volume

real merchantable board no

 foot volume

integer truncated height no

integer cycle number yes

integer regeneration code no

integer miscellaneous codes no
26 RMRS-GTR-55. June 2000

(table A1). It is possible to use other models to

create a tree list file acceptable to CWD, assuming

identical formatting and the same type of information. If this is done, keep in mind that FVS tracks

trees as representative individuals on an acre of

land, so that mortality represents a fraction of that

record per acre. Also note that CWD assumes a 10

y interval for its cyclic time step. CWD requires

that English units will be used for all parameters

because it was developed to smoothly integrate

with FVS, which uses English units of measure.

However, most scientific literature uses the metric

system ( including this paper). Table A2 provides

a measurement unit conversions.

Introductory Screens

Once all files are available, begin execution by

typing CWD at the DOS prompt of the appropriate

subdirectory. As CWD loads, the first screen identifies the program and the version (figure A1). The

version number is critical information as updates

of the CWD post-processor are developed as new

versions of FVS become available. We also expect

to develop CWD versions for other regions. Version 1.4 is designed to work with the Utah and

Teton variants of FVS (version 6.1 tree list file

format) only. Use of version 1.4 with tree list files

from other variants is not advised because of differences in species codes for early (version 6.1 and

older) variants. Old versions of FVS (Prognosis)

may have unsuitable formatting to be read by

CWD (see previous section for required tree list

input file format). The tree list output file of FVS

version 6.2 has some new columns, and lacks other

important data, and cannot be interpreted by CWD

version 1.4. To continue past the introductory

screen, press <ENTER>.

Table A2. Relevant English-to-metric conversion factors.

To convert from: To: Multiply by:

inches centimeters 2.54

feet meters 0.305

square feet square meters 0.0929

acres hectares 0.4046

square feet/acre square meters/hectare 0.2296

cubic feet cubic meters 0.02832

pounds kilograms (1kg = 0.001 Mg) 0.4536

pounds/cubic foot kilograms/cubic meter 16.02

pounds/square inch pascals 6895

Figure A1. Introductory screen of CWD, version 1.4.

************************************************************************

* CWD, version 1.4 *

************************************************************************

WRITTEN FOR THE TETON AND UTAH VARIANTS OF THE FOREST VEGETATION SIMULATOR

Developed as a cooperative effort between the USFS National Fish Habitat

Relationships Unit and the College of NaturalResources, Utah State University

April 27,2000

+++++++++++++++++++++++ PRESS <ENTER> TO CONTINUE ++++++++++++++++++++++
RMRS-GTR-55. June 2000 27

Model Initiation Queries

Defining Stream and Forest Parameters

To begin data processing, CWD requires information about the stream, so users should have this

data available before beginning the analysis. The

first question refers to how long CWD simulates

debris recruitment:

Enter the number of cycles for simulation (3 - 40)... >>

Users can simulate LWD delivery during the entire

length of time FVS processed the inventory data,

from 3 cycles (30 y) to 40 cycles (400 y), or a subset

of a longer simulation run. Throughout the data

query section of CWD there are limits that must be

met. If these limits are violated, the user is asked to

provide a suitable value. For example, CWD must

run for at least 3 cycles but not more than 40 cycles;

therefore, entering -10 or 5000 will result in an

error message. The next question requests the afforestation of the stream reach:

Number of banks (1 or 2) CWD is recruited from ... >>

Remember that FVS tracks individual trees as representative of those found on an acre of forest, so

there is a 2-dimensional component to CWD operation. In the Intermountain West, it is common to

find one bank of a stream forested while the other

is open. This query allows the user to adjust the

simulation of a particular reach to the current

conditions. More recruitment is expected from a

stand with forest on both banks, and inventory

procedures should include data from both sides of

the channel. Entering 1 distributes the stand along

one side of the bank, while a 2 places trees along

both banks. The next query asks the user to:

Enter the average stream bankfull width (in feet)... >>

Bankfull width is the channel area affected by a 1 to

2 y return-interval flood (wetted width is where

the water currently is, regardless of flood stage).

These frequent high flows define the area that

woody debris can be regularly incorporated into

the stream, as substantial riparian LWD transport

occurs during flood events (Bisson et al. 1987).

Although a considerable amount of variability in

bankfull channel width along the study reach likely

exists, for predictive purposes the mean value is

most appropriate. We developed CWD for streams

flowing through well-defined, constrained channels. Therefore, highly braided systems should not

be modeled with the current version. By default, a

piece of wood is considered recruited to the

riparian zone if it extends at least one meter3

 into

the bankfull width zone. Once CWD is given the

stream width, it prompts:

Enter the fixed forest plot width (in feet)... >>

Fixed forest plot width relates to how FVS projects

stands. Since FVS operates on an areal basis, all tree

parameters in the tree list file represent a sample of

those located on a hypothetical acre of the stand.

The fixed forest plot width demarcates the extent

of sampling for the field inventory data. FVS can

predict stand dynamics from different plot sizes,

but moderate-sized (perhaps 0.1 ha) circular plots

provide a good compromise between sampling

efficiency and forest pattern. Sampling further from

the channel than maximum site-potential tree

height from the outer perimeter of bankfull width

only surveys trees with a slight probability of

entering the channel. In addition, even a few hundred feet from the channel riparian forest vegetation can differ dramatically in density and species

composition, so inventory data are best gathered

adjacent to the stream. CWD takes the fixed forest

plot width data and uses it to standardize LWD

recruitment. The results are given in volume per

100 ft (30.5 m) stream reach.

CWD subdivides the fixed forest plots into 9

zones for the purpose of determining snag spatial

location. Snags can be distributed within the riparian forest zone at different densities and patterns

by manipulating the number of snags within each

zone. CWD defaults to a random snag location

protocol, so that each of the zones have the same

potential number of snags (table A3). If a different

distribution is desired, simply alter the number of

potential snags for each zone, keeping in mind that

9 zones are required and that the sum of all potential snags cannot exceed 50,000. CWD distributes

the angle of snag fall into zones representing a

progression of 20o

 angle classes starting at 0o (falling directly towards the channel) and ending with

180o

 (falling directly away from the channel). The

default distribution has been fit to our field data on

angle-of-fall patterns but other distributions are

possible. Changing the potential number of snagfall angles changes the distribution patterns, with

the same constraints as snag locations (9 zones, not

exceeding 50,000 records).

3 This distance can be altered by changing the minimum piece length (see later section on default

customization).
28 RMRS-GTR-55. June 2000

After the initial questions have been satisfactorily answered, CWD prompts for a confirmation of

this data:

Please confirm the following riparian zone parameters:

= = = = = = = = = = = = = = = = = = = = = =

| Number of cycles to run —> _____ |

| Number of forest banks —> _____ |

| Bankfull width —> _____ |

| Fixed forest width —> _____ |

= = = = = = = = = = = = = = = = = = = = = =

Are these values correct (type ‘N’ to reset) (Y/N)? >>

Typing a Y accepts the data while entering N

rejects all of the entered information and returns

the user to the beginning of the parameterization

screen.

Harvest/Disturbance Accelerated Snag Failure

Up to this point, the user has defined the physical parameters of the stream and riparian forest,

but this does not affect snag behavior. The harvest

and disturbance accelerated snag failure subroutine allows the user to accelerate snag failure (i.e.,

failure rates in excess of model defaults) that may

occur with disturbance. Intended to address the

impacts of timber harvesting, this subroutine can

be adapted to simulate the effects of a natural

catastrophic disturbance. Intense fires, for example,

frequently consume much of the structural support of snags, causing them to fall at a rate exceeding unburned conditions.

A separate subroutine has been created to allow

modification of other snag defaults (see Defaults

customization subroutine), but accelerated snag failure due to disturbance was kept separate because

of the instantaneous nature of these events. Accelerated failure is a cycle-specific event that affects

the snags of all species in the riparian forest. With

this system, the user can define multiple disturbance events of different intensities. To access the

disturbance-mediated snag failure acceleration subroutine, type Y at the following prompt:

Accelerate snag failure due to

harvest or disturbance (Y/N)? >>

This option should be used when estimating riparian LWD recruitment from a FVS run that has

experienced harvesting, as snags are lost from

harvesting and compliance with safety regulations.

New ecosystem management practices that retain

some snags after harvest can still be simulated,

as accelerated snag failure rates can be set for a

number of different intensities. The next screen

begins:

*************************************************************************

* HARVEST/DISTURBANCE ACCELERATED

 SNAG FAILURE SUBROUTINE *

*************************************************************************

You will be prompted to enter the cycle in which the event

occurs and the intensity of the event. Note that the cycle

number cannot exceed the number used by FVS, and that

the intensity of the disturbance is bounded between 0 and

100.

Cycle of disturbance event

(press only <ENTER> when done) >>

The disturbance is called an event, the cycle number cannot exceed the number of cycles possible (as

indicated by the simulation period), and disturbances events cannot have intensities < 0 or > 100

(the higher the number, the more strongly the

event accelerates snag failure). Note the cycle of

the disturbance event should be noted by the user

before beginning CWD to ensure coordination between the event and the accelerated snag failure. If

multiple events are used, enter them in any order,

and correct errors simply by reentering the cycle

and event intensity. Once CWD has the cycle of the

event, it asks for:

Intensity of disturbance event (0 - 100) >>

CWD then confirms the information entered by

displaying:

Disturbance event of intensity ___ recorded in cycle ___

Proceeding to the next event...

The blanks represent the new data entered. After

this information is gathered, the user can either

add additional disturbance events by typing the

cycle of the event or exit back into the main program by pressing <ENTER> at the prompt. Disturbance accelerated snag failure is possible at every,

only one, or no cycles.

File Management System

The next section of CWD concerns the file management processes needed for successful operation. The first prompt:

Enter file name containing the FVS treelist data... >>

queries the user to enter the file name where the

FVS projected tree list data are contained. This file

can be located in a subdirectory different from the
RMRS-GTR-55. June 2000 29

one in which CWD is located as long as the full path

is provided. The user is then asked for names of the

primary output files CWD generates:

Enter file name for CWD.EXE

demographic results... >>

Enter file name for CWD.EXE summary results... >>

CWD creates each file as if new, so make sure that

files get unique names or CWD will overwrite them.

The demographics file contains information on the

probability of piece recruitment based on piece

size, while the summary file provides a list of

reach-related LWD attributes by cycle. Both of

these files are discussed in detail later in this appendix. After CWD is instructed where to place the

output information, it creates each file and displays the following message:

Capturing CWD.EXE default settings...

At this point, CWD readings the DEFAULT1.CWD

file from the same subdirectory in which CWD is

located. DEFAULT1.CWD is an ASCII file that

contains many of the default settings for CWD

(table A3). The program displays an error message

if it cannot find DEFAULT1.CWD and operation

ceases. Do not rename DEFAULT1.CWD, and make

sure it is in the correct subdirectory. These defaults

can be edited, but keep in mind that some functions

within CWD are strictly bounded so adjustments

may not have the desired effect.

Default Customization Subroutine

The user is allowed to customize within bounds

many of the defaults used by CWD to fit local

conditions. After the file management section of

CWD has finished, the following question appears:

Would you like to customize

CWD.EXE defaults (Y/N)? >>

A negative response immediately begins CWD

processing with model defaults, while a positive

response initiates the following screen:

*************************************************************************

* CWD.EXE DEFAULTS CUSTOMIZATION

 SUBROUTINE *

*************************************************************************

You will be prompted to adjust the simple functions used

to calculate the default environmental modifiers. Please

consult the manual if you have any detailed questions on

how the modifiers work. Press <ENTER> at any prompt

to retain the default value and skip to the next section.

Consult table A3 for existing default values. When

annotating CWD model defaults, pressing <ENTER> allows you to skip to the next subsection. If

an error is made while entering the data and <ENTER> is pressed, continue pressing <ENTER> to

move to the end of the default modification section

and repeat the customization routines. The first

defaults that are customized are the snag failure

modifiers. As mentioned, several factors are used

to predict snag longevity: species, diameter, and

time since death. Harvest, which also accelerates

snag failure, has already addressed. Each factor is

formulated with the following response function:

c Y a bX = + (A1)

Table A3. Data contained in DEFAULT1.CWD.

5555 5555 5555 5555 5555 5555 5555 5555 5555 (snag distribution)

8900 3900 3900 3900 8800 3900 3900 3900 8900 (AOF distribution)

0.000000 (ASPPMOD)

1.000000 (BSPPMOD)

1.000000 (CSPPMOD)

17.000000 (ADBHMOD)

-0.333333 (BDBHMOD)

1.000000 (CDBHMOD)

6.000000 (ATIMEMOD)

3.000000 (BTIMEMOD)

1.200000 (CTIMEMOD)

10.000000 (ASNGBRK)

15.000000 (BSNGBRK)

2.000000 (CSNGBRK)

33.333333 (FAIL_NUM)
30 RMRS-GTR-55. June 2000

where magnitude of the predicted response (Y) is

a function of the factor and 3 coefficients (a, b, and

c). The minimum value of every function is zero

and the maximum = 100. This simple equation can

generate a number of different patterns (figure

A2), so examine modifications to model behavior

before adjusting the modifiers. Species modifiers

are based on modulus of rupture data gathered

from Panshin and deZeeuw (1980). Species are

aggregated into 1 of 7 classes (table A4), but the

species modifier has only a slight impact on snag

behavior. The other modifiers vary continuously

with size, time since snag creation, and harvest

intensity. All modifiers are summed together, and

this grand total cannot be less than zero nor exceed

100. This sum is converted to a ratio with a value

between 0 and 1, multiplied by a random number

(also between 0 and 1). The sum is then compared

to a threshold fail value (FAIL_NUM in table A3).

If the threshold fail number is greater than the

computed modifier total, the snag is transferred to

the failure pool for stream recruitment. Otherwise,

it is assumed that it “survived” into the next cycle,

although pieces may be fragmented off the standing snag.

Snag fragmentation occurs to all snags to varying

degrees depending on how long they have been

standing. More pieces will break off and potentially become LWD themselves the longer a snag

remains standing. As with the snag residency modifiers, snag fragmentation follows equation [A1]

where X becomes snag residency. Pieces that fragment off of standing snags are randomly sized, but

Table A4. Species-based modulus of rupture (MOR) classes used by CWD.

Modifier class

MOR classa (SPP_MOD) Species

11,000 + 1

8,000 - 11,000 2

7,000 - 8,000 3 Douglas-fir

6,000 - 7,000 4 Limber pine, Populus, lodgepole pine,

Engelmann spruce, blue spruce,

subalpine fir

5,000 - 6,000 5

4,000 - 5,000 6

< 4,000 7

a

 MOR in pounds per square inch. Values are adapted from Panshin and

deZeeuw (1980).

b = 0, c = 0.2

X

Y

-10

10

30

50

70

90

110

0 10 20 30 40 50 60 70 80 90 100

a = 0

a = 10

a = 35

b = 2, c = 0.2

X

Y

-10

10

30

50

70

90

110

0 10 20 30 40 50 60 70 80 90 100

b = 10, c = 0.2

X

Y

-10

10

30

50

70

90

110

0 10 20 30 40 50 60 70 80 90 100

Figure A2. Possible outcomes of equation A1 (Y =

a + bXc) using different values for a, b, and c.
RMRS-GTR-55. June 2000 31

they increase in size as snag residency increases so

that young snags may lose only a small portion of

their length but old snags could lose most of it. This

concludes the capacity the user has to modify snag

dynamics. The next customization section deals with

LWD pattern and process:

Default minimum diameter for inclusion as CWD = 4 in.

New minimum diameter threshold...? >>

Since there is no standard definition of LWD (either piece diameter or length), CWD was designed

to allow the user to determine what size pieces

qualify. Minimum piece diameter is the minimum

small-end diameter and must be > 0 but less than

360 inches (915 cm) (CWD defaults to 4 inches (10

cm)). This diameter also defines the smallest recruited pieces reported in the output tables generated by CWD. Although the user is granted considerable latitude in determining minimum diameter

and length, the largest tree in the world has a

diameter of ~ 320 inches (~ 812 cm). Also note that

there is not a maximum diameter or length for

inclusion as LWD. CWD assumes that once the

minimum thresholds are surpassed, the piece qualifies. After the minimum diameter threshold has

been set, the next option appears:

Default minimum length for inclusion as CWD = 3 ft.

New minimum length threshold...? >>

Piece length is the most critical dimension of LWD

as it is most directly related to retention within the

channel. There is little difference in retention time

from a piece that is 4 inches (10 cm) from one that

is 8 inches (20 cm) in diameter, but there is a major

difference for a piece that is 6 ft (1.8 m) versus one

that is 12 ft (3.7 m) long. CWD assumes pieces are

at least 3 ft (1 m) long. The user is allowed to

customize minimum piece length so that it ranges

from 1 to 400 ft (0.3 to 122 m). Again, user discretion is advised in setting minimum piece length.

Because CWD lacks the exact spatial and temporal location of each fallen snag, predicting how

they fragment is complicated. Rather than estimating fragmentation over time while the piece lays on

the ground, the snag is assumed broken as possible

the instant it falls. To estimate how many pieces are

formed, a field survey of fallen snags was taken

(figure A3) in which the number of pieces were

tallied. CWD assumes the snag breaks randomly

along the bole, and that the likelihood of a certain

number of breaks (e.g., none, 1, 2, 3, ...) fits a

probability distribution. CWD’s default probability distribution was designed to fit the piece length

distribution noted in Dry Lake Creek, but the user

has the flexibility to reset this distribution to fit

their region:

Maximum POSSIBLE number of breaks

for fallen snags = 20

CURRENT maximum number of breaks

for fallen snags = 12

New maximum number of breaks...? >>

While the maximum possible number of breaks

(20) in CWD and the default maximum number of

breaks (12) may seem high, the distribution

achieved by the default distribution used by CWD

makes it very unlikely that 12 breaks per snag will

occur. Although this fits central Rocky Mountain

spruce-fir forests well, it may not be appropriate

for other cover types. If a new maximum number of

breaks is selected (0 to 20), then the user is prompted

to enter the break sensitivity thresholds:

Default thresholds for number of breaks =

...CWD then lists the default thresholds for up to

12 breaks...

Threshold 1 ...is ALWAYS zero...

Threshold 2 = 0.06

New threshold...? >>

This process continues until all new thresholds are

defined. Users can still use 12 breaks as the default

by simply changing the individual thresholds for a

different pattern. Keep in mind that the propensity

of a snag to fragment is a product of how long the

snag remains standing and its height (more breaks

occur in old, tall snags). A random component is

also factored in so that snag break thresholds are

0 12 34 5

Number of breaks

0

5

10

15

20

25

Number of pieces

10 - 29.9

30 - 49.9

50 - 69.9

70 +

Snag largeend diameter

(in cm)

Figure A3. Field samples by snag size, collected

to determine the likelihood of snag fragmentation

when it is on the ground.
32 RMRS-GTR-55. June 2000

most effective when they are fractionally small and

they have less impact as they increase. Because

determining an effective break threshold distribution is difficult, we caution against changing the

defaults.

A new component of version 1.4 is a slope factor

used to determine piece recruitment. Earlier versions

of CWD assumed that the riparian forest covered a

flat surface at the same elevation as the stream channel. Since many forested streams flow through steep,

confined drainages, we included the capacity to

address slope effects on LWD recruitment:

Default elevation at the furthest edge of the forest plot = 0

New elevation...? >>

Elevation is the height of the furthest edge of the

forest plot (see earlier definition) above the channel (not sea level). While relatively crude, it adds a

vertical dimension to piece recruitment. Steep

slopes limit recruitment by lengthening the amount

of bank a piece has to cover before it enters the

channel (figure A4). Plot edge elevation must be

between 0 and 400 ft (0 to 122 m).

The user can also select the number and dimensions of the size classes used in the CWD

demographics output file. The first option is to

alter the number of large-end diameter classes:

Default number of CWD large-end diameter classes = 10

New number of CWD diameter classes...? >>

Up to 20 diameter classes are allowed (minimum

number = 1). After the number of classes has been

selected, the range of each class can be defined.

CWD first lists the 10 default diameter classes:

Default diameter thresholds for CWD diameter classes:

DIAMETER CLASS 1 = 6 TO 7.99 INCHES

DIAMETER CLASS 2 = 8 TO 9.99 INCHES

DIAMETER CLASS 10 = 60 TO 360.00 INCHES

New default diameter thresholds

(minimum diameter has already been set)...?

Diameter class: 1 >>

If the user has redefined minimum piece diameter,

it becomes the new minimum diameter for the

demographics output file. Maximum piece diameter is constrained at 360 inches (915 cm) (see

earlier maximum diameters discussion). Each progressive diameter class must be larger than the

previous one, but it is unnecessary for all diameter

classes to be proportionally wide. This allows variable-sized diameter classes. A similar strategy is

used for length classes:

Default number of riparian CWD length classes = 10

New number of riparian

CWD length classes...? >>

There can be anywhere from 1 to 20 length classes.

As with the diameter classes, CWD provides a list

of the 10 default length classes:

Default length thresholds for riparian CWD length classes:

LENGTH CLASS 1 = 6 TO 9.99 FEET

LENGTH CLASS 2 = 10 TO 14.99 FEET

LENGTH CLASS 10 = 100 TO 400.00 FEET

New default length thresholds

(minimum length has already been set)...?

Length class: 1 >>

The user can reset the number of internal selfreplicates that CWD will automatically process:

Current number of model self-replicates = 1

New number of model self-replicates...? >>

A self-replicate is one loop through the entire CWD

process for a simulation run. Because of the stochastic nature of some CWD subroutines, there can

be considerable variance in debris production from

one run to the next. Thus, to provide a better

estimate of mean recruitment, CWD automatically

runs 5 self-replicates. However, if the user is interested in portraying some of the variance in recruitment, the number of self-replicates can be changed

(to a range of 1 to 5 per run).

The last stage of the CWD default customization

subroutine is a series of review screens to confirm

the modified. After the number of self-replicates

Figure A4. CWD slope effect on piece recruitment.

CWD assumes slope reduce recruitments by

forcing a piece of large woody debris to cover

more distance to reach the stream (dashed lines).
RMRS-GTR-55. June 2000 33

has been determined, the following prompt appears:

===> Press <ENTER> to see the new default settings...

After the enter key is pressed, modified default

settings appear (this example uses CWD defaults):

SPPMOD: A = 0.000000 B = 1.000000 C = 1.000000

DBHMOD: A = 17.000000 B = -0.333333 C = 1.000000

TIMEMOD: A = 6.000000 B = 3.000000 C = 1.200000

SNGBRK: A = 10.000000 B = 15.000000 C = 2.000000

MINDIA = 4.00

MINLEN = 3.00

MAXBREAK = 12

BREAK THRESHOLDS = 0.00 0.06 0.12 0.24 0.48 0.96 1.92

BREAK THRESHOLDS = 3.84 7.68 15.36 30.72 100.00

BREAK THRESHOLDS =

MAXIMUM EDGE OF FOREST PLOT ELEVATION = 0.0

SELF-REPLICATES = 5

===> Press <ENTER> to continue viewing new default

settings...

...next screen...

NUMBER OF DIAMETER CLASSES = 10

DIAMETER CLASS 1 = 4 TO 6 INCHES

DIAMETER CLASS 2 = 6 TO 8 INCHES

DIAMETER CLASS 3 = 8 TO 10 INCHES

DIAMETER CLASS 4 = 10 TO 15 INCHES

DIAMETER CLASS 5 = 15 TO 20 INCHES

DIAMETER CLASS 6 = 20 TO 30 INCHES

DIAMETER CLASS 7 = 30 TO 40 INCHES

DIAMETER CLASS 8 = 40 TO 50 INCHES

DIAMETER CLASS 9 = 50 TO 60 INCHES

DIAMETER CLASS 10 = 60 TO 360 INCHES

===> Press <ENTER> to continue viewing new default

settings...

...next screen...

NUMBER OF LENGTH CLASSES = 10

LENGTH CLASS 1 = 3 TO 6 FEET

LENGTH CLASS 2 = 6 TO 10 FEET

LENGTH CLASS 3 = 10 TO 15 FEET

LENGTH CLASS 4 = 15 TO 20 FEET

LENGTH CLASS 5 = 20 TO 30 FEET

LENGTH CLASS 6 = 30 TO 40 FEET

LENGTH CLASS 7 = 40 TO 60 FEET

LENGTH CLASS 8 = 60 TO 80 FEET

LENGTH CLASS 9 = 80 TO 100 FEET

LENGTH CLASS 10 = 100 TO 400 FEET

Are these values correct (type ‘N’ to reset) (Y/N)? >>

To change one of the values listed, answer N to

return to the initial customization stage. This requires

going through the default customization subroutine

again. If new default values are acceptable, enter Y at

the prompt to begin model processing:

*************************************************************************

* USE <CTRL> + <BREAK> TO INTERRUPT

MODEL AT ANY TIME *

*************************************************************************

Reading input treelist file data...

 Locating snags...

 Determining snag residency...

 Determining angle of snag fall...

 Fragmenting fallen snags...

 Writing to P_CWD.OUT file...

...then 4 more layers of these screen registers...

Recapturing P_CWD.OUT

 Opening CWDIN.OUT

 Determining which pieces enter the stream...

 Creating CWD.EXE output files...

CWD.EXE PROGRAM FINISHED!!

The first group of comments indicates stages that

CWD passes through as it manipulates snags. The

second group indicates the treatments being performed and other file management processes.

Output Files Created by CWD

CWD generates a series of ASCII output files

during its processing, each of which can be used for

model adjustment or recruitment analysis. These

files are:

• DEAD.OUT

• BRK_DIST.OUT

• P_CWD.OUT

• CWDIN.OUT

• demographics file (user-provided file name)

• summary file (user-provided file name)

DEAD.OUT is a list of the snags extracted from the

FVS tree list file that provide the basis for all CWD

analysis. BRK_DIST.OUT is a file that lists the num-
34 RMRS-GTR-55. June 2000

ber of snag break distributions generated by CWD.

The snags are only fragmented when they fall, so

accurate piece size distributions are vital for further

analysis. BRK_DIST.OUT can be used to calibrate

break point distribution patterns to fit the desired

pattern. This file contains 3 columns: snag number

(SNAG#), snag residency (RES), and number of breaks

(#BRK). These data can be aggregated into snag

residency or number of snag break distributions.

The next file generated (P_CWD.OUT) contains

information on pieces with the potential to enter the

bankfull width channel zone, including: piece number (PIECE#), species (SPP), large-end piece diameter

(D_BOT), small-end piece diameter (D_TOP), piece

length (LEN), distance from bankfull channel edge

(XLOC), angle of snag fall (AOF), cycle of snag fall

(CYCLE), and self-replicate number (SERP). The only

way debris enters this file is if it meets the minimum

length and small-end diameter criteria.

After passing through a final filter which determines the debris actually recruited to the bankfull

width zone, CWD writes a file with the properties of

all pieces (CWDIN.OUT), including piece number

(PIECE NUMBER), FVS species code (SPP CODE),

bottom-end piece diameter (BOT. DIA.), top-end

piece diameter (TOP DIA.), LWD piece length (PIECE

LENGTH), location of piece relative to the bankfull

channel (PLANE X LOC.), direction of LWD piece

fall (PIECE AOF), cycle the piece was generated

(CYC), and self-replicate number (SRP).

These intermediate files always have the same

content and file name. To keep a specific run of these files,

it is necessary to rename the files after that run is complete. The demographic and summary files, conversely,

are named by the user when the run is initiated, and

unique file names should be created at that time.

Demographic and Summary Files

The files most critical to analysis of LWD recruitment are the demographic and summary files.

The demographic file summarizes the riparian piece

recruitment patterns expected for all self-replicates, standardized per 100 ft (30.5 m) reach of

stream channel. This file (figure A5) begins by

listing the FVS tree list and demographic file names

for this particular run, followed by the user-defined (or default) diameter and length class definitions (listed because only codes of size class are

provided in the following tables). The number of

pieces delivered per 100 ft (30.5 m) reach is then

provided, which can be fractional due to how

CWD operates. First, remember that CWD uses a

stand expansion factor to boost mortality sensitivity.

This expansion factor translates into mortality per

unit area because FVS tracks individual tree records

as representative over an acre, so each record usually represents multiple trees. Therefore, as the

contributing stand is defined and recalibrated to fit

the standard stream reach used in CWD, fractions

of pieces are generated. Second, both the demographics and summary output files are averages of

multiple self-replicates. The stochastic routines in

CWD generate different recruitment during each

self-replicate so when the average is calculated fractions can occur.

Most of the demographics file is a table of recruitment probabilities by diameter and length

class generated for every cycle. Probability of recruitment (to 3 decimal places) is calculated by

dividing the number of pieces CWD recognizes as

delivered to each size class by the total number of

pieces recruited in that cycle. Probabilities are difficult to interpret, but they indicate the recruitment

patterns experienced in each cycle. For example,

figure A5 displays the demographics of the first

cycle in a demographics file in which most pieces

recruited to the channel are small. Occasionally,

larger pieces appear, but these are relatively infrequent (probabilities ~ 0.009).

The summary file contains the predictions generated by CWD (figure A6). The data are a summary of the recruitment patterns by each 10 y cycle

for a standard 100 ft (30.5 m) reach of the subject

stream. Mean diameter averages large-end piece

diameter, while mean length represents the average

piece length. Both of these values can be used to help

evaluate CWD’s performance to that experienced

in the field. The final columns provide estimates of

LWD volume recruited to the channel by cycle. If the

user prefers recruitment as biomass rather than volume, we recommend consulting Harmon et al. (1986)

for appropriate conversion factors.

Troubleshooting

We realize this is not a comprehensive list of

potential problems, and we welcome comments on

errors, interpretation difficulties, or source code

bugs. Contact D.C. Bragg to report problems.

Use of the Metric System

CWD is designed to operate with English units

because FVS uses the English system. Keep this

in mind when field sampling, running FVS and

CWD, interpreting CWD output, and especially

when customizing CWD operation. Using
RMRS-GTR-55. June 2000 35

centimeters or meters rather than inches or feet

could cause CWD to function improperly or to

generate illogical results (table A2 converts English to metric units).

Illegal File Names or Missing Data Files

CWD requires that DEFAULT1.CWD exists in

the same subdirectory as the executable CWD.EXE

and that its formatting matches CWD’s

expectations (see table A3). FVS tree list file can be

in any path that DOS can recognize, but it also

needs to follow the formatting expected by CWD.

Deviation from required patterns can lead to either

error messages or incorrect analysis. Any file name

and extension acceptable for DOS can be used to

label the demographics and summary files, but it is

up to the user to ensure that unique file names are

given to avoid over-writing old data files.

CWD VERSION USED TO MAKE THIS FILE: 1.4

DATE AND TIME THIS FILE WAS CREATED: 10/15/98 13:51:36

FVS TREELIST FILE NAME: NOTMT1.LST

PIECE DEMOGRAPHICS FILE NAME: RUN1_1.DEM

=======================================================

DIAMETER CLASS DEFINITIONS:

D_CLASS 1 R A N G E S F R O M 4.0 INCHES TO 6.0 INCHES

D_CLASS 2 R A N G E S F R O M 6.0 INCHES TO 8.0 INCHES

D_CLASS 3 R A N G E S F R O M 8.0 INCHES TO 10.0 INCHES

D_CLASS 4 RANGES FROM 10.0 INCHES TO 15.0 INCHES

D_CLASS 5 RANGES FROM 15.0 INCHES TO 20.0 INCHES

D_CLASS 6 RANGES FROM 20.0 INCHES TO 30.0 INCHES

D_CLASS 7 RANGES FROM 30.0 INCHES TO 40.0 INCHES

D_CLASS 8 RANGES FROM 40.0 INCHES TO 50.0 INCHES

D_CLASS 9 RANGES FROM 50.0 INCHES TO 60.0 INCHES

D_CLASS 10 RANGES FROM 60.0 INCHES TO 360.0 INCHES

LENGTH CLASS DEFINITIONS:

L_CLASS 1 RANGES FROM 3.0 FEET TO 6.0 FEET

L_CLASS 2 RANGES FROM 6.0 FEET TO 10.0 FEET

L_CLASS 3 RANGES FROM 10.0 FEET TO 15.0 FEET

L_CLASS 4 RANGES FROM 15.0 FEET TO 20.0 FEET

L_CLASS 5 RANGES FROM 20.0 FEET TO 30.0 FEET

L_CLASS 6 RANGES FROM 30.0 FEET TO 40.0 FEET

L_CLASS 7 RANGES FROM 40.0 FEET TO 60.0 FEET

L_CLASS 8 RANGES FROM 60.0 FEET TO 80.0 FEET

L_CLASS 9 RANGES FROM 80.0 FEET TO 100.0 FEET

L_CLASS 10 RANGES FROM 100.0 FEET TO 400.0 FEET

=======================================================

PIECES DELIVERED PER 100 ft (30.5 m) STREAM REACH (THIS CYCLE): 1.446

PROBABILITY OF RECRUITMENT WITHIN A GIVEN CLASS:

LENGTH CLASS

CYCLE DCLS 1 2 3 4 5 6 7 8 9 10

1 1 .116 .089 .027 .000 .009 .000 .000 .000 .000 .000

1 2 .045 .054 .054 .027 .000 .000 .000 .000 .000 .000

1 3 .018 .045 .063 .080 .036 .000 .000 .000 .000 .000

1 4 .089 .045 .071 .027 .018 .018 .000 .000 .000 .000

1 5 .000 .018 .009 .000 .000 .009 .009 .000 .000 .000

1 6 .000 .000 .000 .000 .000 .009 .000 .000 .000 .000

1 7 .000 .000 .009 .000 .000 .009 .000 .000 .000 .000

1 8 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000

1 9 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000

1 10 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000

Figure A5. Example CWD demographics output file.
36 RMRS-GTR-55. June 2000

Input Fle Too Large or Too Many Pieces of

CWD Generated

An error may arise if the input tree list file is

too large. CWD can only read a file with ≤50,000

tree records, which should be plenty for one-acre

plots covered with LWD-sized trees for 40 cycles.

However, it is possible to exceed this number if

dense regeneration is included in the FVS tree

list file. If this is the case, eliminate saplings until

there are ≤ 50,000 records. CWD can only manage 250,000 pieces of debris, if this array size is

exceeded adjust FVS so it creates fewer pieces.

Exceeding Reasonable Value Limits

It is possible to exceed parameter value limits.

While most of the code of CWD has been written to

avoid unreasonable customization, careless

alteration of defaults can lead to nonsensical relationships between parameters and therefore generate unrealistic values. While we believe the

customization capacity of CWD adds strength and

flexibility to its performance, this ability can be a

double-edged sword. Check the relationships between parameters and how they react to

customization before attempting significant adjustments.

CWD VERSION USED TO MAKE THIS FILE: 1.4

DATE AND TIME THIS FILE WAS CREATED: 10/15/98 13:51:36

FVS TREELIST FILE NAME: NOTMT1.LST

RIPARIAN CWD SUMMARY FILE NAME: RUN1_1.SUM

RIPARIAN CWD SUMMARY PER 100 ft (30.5 m) STREAM REACH BY CYCLE

NEW MEAN MEAN MEAN MEAN C W D C W D

PIECES DIA. DIA. LENGTH LENGTH VOLUME VOLUME

CYCLE (#) (in) (cm) (ft) (m) (ft3) (m3)

======================================================================

1 1.45 9.40 23.89 11.66 3.56 10.65 .30

2 8.62 9.09 23.10 9.81 2.99 47.61 1.35

3 8.44 9.43 23.95 10.34 3.15 50.20 1.42

4 9.44 9.82 24.95 9.61 2.93 54.79 1.55

5 6.66 10.60 26.94 9.84 3.00 42.06 1.19

6 5.83 10.09 25.62 10.35 3.16 38.80 1.10

7 5.79 10.66 27.09 9.12 2.78 34.45 .98

8 4.40 10.54 26.78 10.34 3.15 31.17 .88

9 4.74 10.49 26.63 10.57 3.22 33.36 .94

10 7.03 11.58 29.42 9.50 2.90 52.46 1.49

11 4.76 11.00 27.93 9.06 2.76 31.68 .90

12 3.15 10.58 26.88 10.04 3.06 20.95 .59

13 4.75 10.61 26.94 9.22 2.81 33.59 .95

14 2.88 11.08 28.14 9.89 3.02 21.95 .62

15 3.52 10.58 26.88 9.74 2.97 26.40 .75

16 5.78 11.08 28.15 8.79 2.68 40.88 1.16

17 3.19 9.66 24.53 8.21 2.50 17.02 .48

18 3.19 9.64 24.48 9.28 2.83 19.26 .55

19 4.92 10.79 27.42 8.22 2.51 28.81 .82

20 3.32 10.15 25.78 9.09 2.77 23.42 .66

21 2.83 11.18 28.41 9.92 3.03 26.48 .75

22 3.96 10.77 27.35 7.75 2.36 21.95 .62

23 3.85 10.88 27.63 8.93 2.72 29.02 .82

24 3.47 11.19 28.43 10.88 3.32 32.19 .91

25 3.42 11.26 28.60 10.61 3.24 29.43 .83

26 3.03 9.74 24.73 9.36 2.85 22.60 .64

27 2.60 10.24 26.02 9.20 2.81 18.70 .53

28 3.47 10.66 27.08 8.74 2.66 20.91 .59

29 3.17 11.64 29.57 10.39 3.17 30.73 .87

30 2.84 11.31 28.72 9.26 2.82 22.14 .63

Figure A6. Example CWD summary output file.

The Rocky Mountain Research Station develops scientific information

and technology to improve management, protection, and use of forests

and rangelands. Research is designed to meet the needs of National

Forest managers, federal and state agencies, public and private

organizations, academic institutions, industry, and individuals.

Studies accelerate solutions to problems involving ecosystems,

range, forests, water, recreation, fire, resource inventory, land

reclamation, community sustainability, forest engineering technology,

multiple use economics, wildlife and fish habitat, and forest insects and

diseases. Studies are conducted cooperatively, and applications can

be found worldwide.

Research Locations

Flagstaff, Arizona

Fort Collins, Colorado*

Boise, Idaho

Moscow, Idaho

Bozeman, Montana

Missoula, Montana

Lincoln, Nebraska

* Station Headquarters, 2150 Centre Avenue, Building A, Fort Collins, CO

80526

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its

programs and activities on the basis of race, color, national origin, sex, religion,

age, disability, political beliefs, sexual orientation, or marital or family status.

(Not all prohibited bases apply to all programs.) Persons with disabilities who

require alternative means for communication of program information (Braille,

large print, audiotape, etc.) should contact USDA’s TARGET Center at

(202) 720-2600 (voice and TDD).

To file a complaint of discrimination, write USDA, Director, Office of Civil

Rights, Room 326-W, Whitten Building, 1400 Independence Avenue, SW,

Washington, D.C. 20250-9410 or call (202) 720-5964 (voice and TDD).

USDA is an equal employment opportunity provider and employer.

Reno, Nevada

Albuquerque, New Mexico

Rapid City, South Dakota

Logan, Utah

Ogden, Utah

Provo, Utah

Laramie, Wyoming

Printed on recycled paper