Photographic Guidance for Selecting 

Flow Resistance Coefficients in 

High-Gradient Channels

Steven E. Yochum, Francesco Comiti, Ellen Wohl, 

Gabrielle C. L. David, Luca Mao

United States Department of Agriculture

Forest Rocky Mountain General Technical Report

Service Research Station RMRS-GTR-323 July 2014
Yochum, Steven E.; Comiti, Francesco; Wohl, Ellen; David, Gabrielle C. L.; 

Mao, Luca. 2014. Photographic Guidance for Selecting Flow Resistance 

Coefficients in High-Gradient Channels. Gen. Tech. Rep. RMRS-GTR-323. 

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

Mountain Research Station. 91 p.

Abstract

Photographic guidance is presented to assist with the estimation of 

Manning’s n and Darcy-Weisbach f in high-gradient plane-bed, step-pool, 

and cascade channels. Reaches both with and without instream wood are 

included. These coefficients are necessary for the estimation of reachaverage velocity, energy loss, and discharge. Using data collected in 19 

stream channels located in the State of Colorado and the Eastern Italian 

Alps, on slopes ranging from 2.4 to 21 percent, guidance is provided for 

low through bankfull flows. Guidance for low flow resistance estimation 

is additionally provided using data collected in 29 channels in the State 

of Washington, New Zealand, Chile, and Argentina. Bankfull n values 

range from 0.048 to 0.30 and low flow n values range from 0.057 to 0.96. 

Discussions of flow resistance mechanisms and quantitative prediction 

tools are also presented.

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Contents

Nomenclature ........................................................................................ii

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

Overview of Field Sites...........................................................................2

Methodology..........................................................................................3

Fraser Experimental Forest, Southern Rocky Mountains, USA ..................................4

Rio Cordon, Alps, Italy................................................................................................ 5

Cascade Mountains, USA ........................................................................................... 5

Araucania Region and Tierra del Fuego, Chile and Argentina ....................................6

New Zealand .............................................................................................................. 6

Flow Resistance Processes and Quantitative Prediction ..........................7

Flow Resistance.......................................................................................................... 7

Examples of Flow Resistance Processes..................................................................... 9

Acknowledgments................................................................................14

References ...........................................................................................15

Photographic Guidance – Multiple Discharges......................................20

Photographic Guidance – Single Discharges..........................................60

APPENDIX A: Measured Values, Multiple Discharge Reaches ................89

APPENDIX B: Measured Values, Single Discharge Reaches ....................91
Nomenclature

A = reach average flow area (m2)

d = residual

d = average residual

Dc = roughness height (m)

d84 = bed material size for which 84% of the material is finer (mm)

f = reach average Darcy-Weisbach roughness coefficient (dimensionless)

Fr = Froude number = V g / h (dimensionless)

g = acceleration due to gravity = 9.81 m/s2

h = reach average, average flow depth (m)

hm = median flow depth, computed from thalweg longitudinal profile (m)

hL = head loss from flow resistance (m) = Sf

*L

L = thalweg reach length (m)

n = reach average Manning’s roughness coefficient (s/m1/3)

n = number of measurements

P = system pressure (N/m2)

Pw = reach average wetted perimeter (m)

Q = flow discharge (m3/s)

R = reach average hydraulic radius = A/Pw (m)

S = reach slope, typically assumed to be equivalent to the water surface 

slope (m/m)

Sf

 = friction slope (m/m)

V = reach average velocity (m/s), measured via tracer travel time and flow 

path length

v = average section velocity (m/s)

W = reach average flow width (m)

z = elevation (m)

α = velocity head correction factor (dimensionless)

γ = specific weight of the fluid (N/m3)

σz

 = standard deviation of the residuals of a bed profile regression (m)

ii
USDA Forest Service RMRS-GTR-323. 2014. 1

Introduction

Practitioners are oftentimes required to predict flow resistance coefficients, such 

as Manning’s n, in high-gradient channels (slopes > ~2 %). Measurements of 

actual velocity and flow resistance indicate that reach-average resistance coefficients are substantially higher than commonly encountered in low-gradient 

channels, with Manning’s n typically falling between 0.1 to 0.3 for bankfull 

flows in step-pool and cascade channels (Yochum and others 2012) and flow resistance increasing with decreasing discharge (Lee and Ferguson 2002, Comiti 

and others 2007, Reid and Hickin 2008, David and others 2010, Yochum and 

others 2012). Photographic guides for visual comparison with actual channels 

(Barnes 1967, Aldridge and Garrett 1973, Arcement and Schneider 1989, Hicks 

and Mason 1998, Coon 1998, Phillips and Ingersoll 1998) provide assistance 

for estimating these values in lower-gradient channels, but little assistance has 

been available for high-gradient streams. The selection of appropriate resistance 

coefficients is essential for improved confidence in hydraulic modeling, stream 

assessments, stream restoration design, geomorphic analysis, and ecological 

studies.

Cascade and step-pool channels are relatively stable channel forms with high 

ratios of sediment transport capacity to sediment supply (Montgomery and 

Buffington 1997). Cascade channels are characterized by flow that is continuously tumbling, with jets and wakes over and around large clasts and wood 

features, whereas step-pool channels are characterized by a regular series of 

channel-spanning steps formed from clasts alone or in combination with inchannel wood. Plane-bed reaches have minimal bedforms, although small-scale 

bed variability can still be evident. Transitional reaches reflect the wide range 

in transitional forms between plane-bed, step-pool, and cascade morphologies. 

Instream wood is often a substantial contributor to flow resistance in these 

stream types. Step-forming wood contributes substantially to both form and 

spill resistance, due to wood increasing step heights (David and others 2011).

The Manning equation (Manning 1891) is the most common approach used 

by practitioners for estimating velocity and energy loss in streams. However, 

it is considered by some workers to be a poor method for velocity prediction 

(Ferguson 2010), due to variability by stage and non-dimensionless form. 

However, Manning’s n is the preferred method for prediction by many practitioners and is the method most typically coded into computational models. 

The Darcy-Weisbach equation (Darcy 1854, Weisbach 1865) is a dimensionless alternative argued to be more appropriate, though it is less commonly used 

in application and also varies by discharge (Comiti and others 2007, Reid and 

Hickin 2008, David and others 2010, Yochum and others 2012). The Manning 

and Darcy-Weisbach equations are:

f

gRS

n

R S

V f f 8 2 / 3 1/ 2

= = (1)
2 USDA Forest Service RMRS-GTR-323. 2014.

where V is the reach-average velocity (m/s); n is the Manning’s roughness coefficient; f is the Darcy-Weisbach friction factor; Sf

 is the friction slope (m/m); g is 

the acceleration due to gravity; R, the hydraulic radius, is computed as A/Pw; A

is the cross-sectional area (m2); and Pw is the wetted perimeter (m). In practice, 

R is often assumed to be the mean depth, though this assumption is only valid 

for wide channels (width/depth ratio > ~20). In this report, the flow resistance 

coefficients n and f represent reach-average conditions, with all the terms being reach-averaged values; as such, these coefficients are composite values that 

include all resistance components within the reach.

The appropriate selection of Manning’s n and Darcy-Weisbach f typically 

depends upon a sufficient level of user experience with the stream type in 

question. Using guides intended for lower gradient channels, practitioners commonly underpredict flow resistance when estimating resistance coefficients 

for high-gradient channels. Resistance coefficient underestimation can lead to 

substantially overestimated flow velocities, underestimated travel times, the 

miscategorization of reach-average flow regime (subcritical versus supercritical 

flow), and instability in computational models. Steps and cascades in highgradient channels, as well as instream wood, all contribute to increased flow 

resistance. Submergence of these resistance elements causes enhanced variability by stage, further complicating estimation procedures. Hence, there is a need 

for guidance tools for selecting resistance coefficients in high-gradient channels, and to provide examples for streams with steps, cascades, instream wood, 

and discharge variability.

“The principal objective of this report is to provide a photographic guide to 

assist users in the selection of reach-average values of the flow resistance coefficients n and f in high-gradient plane-bed, transitional, step-pool and cascade 

channels, both with and without instream wood. Values are provided for multiple 

discharges to illustrate the variation in flow resistance by stage, with additional 

guidance given for resistance measured during only one discharge. Initially, the 

methodologies used in the data collection are presented, followed by a section 

discussing flow resistance mechanisms and quantitative prediction methods in 

steep streams. These are followed by separate sections presenting photographs 

and coefficient estimates derived for multiple and single flow levels.”

Overview of Field Sites

Photographic guidance is provided based on data collected in North and South 

America as well as Europe and New Zealand. These channels are relatively 

small headwater streams; channel characteristics are provided (Tables 1 and 2). 

Cascade and step-pool streams are the primary channel types, although planebed and transitional reaches are also illustrated. The streams included in this 

report are A, B, C, and F type channels in the Rosgen classification system 

(Rosgen 1996), with beds dominated by gravel, cobbles, or boulders. Data included in this photographic guide include the results of fieldwork performed 

in the Southern Rocky Mountains of Colorado, USA (David and others 2010, 

Yochum and others 2012), the Cascade Mountains of Washington State, USA 
USDA Forest Service RMRS-GTR-323. 2014. 3

Slope (m/m) Width (m) Velocity (m/s) n f

Cascades 0.051 - 0.18 0.94 - 3.2 0.079 - 0.25 0.25 - 0.96 11 - 152

Chili and Argentina 0.022 - 0.15 2.8 - 5.3 0.12 - 0.77 0.059 - 0.87 0.50 - 118

New Zealand 0.003 - 0.19 1.6 - 29 0.25 - 0.81 0.021 - 0.36 0.049 - 23

Table 2: Range of average channel characteristics, single discharge reaches.

Flow Magnitude Slope (m/m) Width (m) Velocity (m/s) n f

low 0.019 - 0.17 0.67 - 3.3 0.12 - 0.40 0.10 - 0.52 1.4 - 47

moderate 0.015 - 0.18 0.92 - 4.0 0.18 - 0.61 0.078 - 0.40 0.76 - 26

~bankfull 0.024 - 0.20 1.1 - 4.0 0.51 - 1.3 0.048 - 0.30 0.28 - 11

low 0.079 - 0.21 1.9 - 4.1 0.25 - 0.39 0.18 - 0.41 5.0 - 28

moderate 0.079 - 0.18 3.5 - 4.2 0.48 - 0.64 0.16 - 0.25 3.9 - 8.3

high 0.079 - 0.21 3.8 - 5.8 0.5 - 1.6 0.12 - 0.35 0.50 - 1.6

Fraser 

Experimental 

Forest

Rio Cordon

Table 1: Range of average channel characteristics, multiple discharge reaches.

(MacFarlane and Wohl 2003, Curran and Wohl 2003), the Eastern Italian Alps 

(Comiti and others 2007), the Chilean Araucania (Comiti and others 2008), 

the Argentine Tierra del Fuego (Comiti and others 2008), and New Zealand’s 

Southern Alps (Wohl and Wilcox 2005). This aggregated dataset includes alluvial channels without instream wood (MacFarlane and Wohl 2003, Comiti 

and others 2007, Wohl and Wilcox 2005), alluvial channels with instream 

wood (Curran and Wohl 2003, Comiti and others 2008, David and others 2010, 

Yochum and others 2011), mixed alluvial and bedrock channels (MacFarlane 

and Wohl 2003, Curran and Wohl 2003, David and others 2010, Yochum and 

others 2012), and bedrock channels (MacFarlane and Wohl 2003). Hence, these 

data were collected in channels with alluvial, mixed alluvial and bedrock, and 

bedrock beds, both with and without instream wood present and enhancing step 

heights.

Methodology

Measured values of both Manning’s n and the Darcy-Weisbach f are presented (in the Photographic Guidance – Multiple Discharges section and in 

Appendices A & B), as well as the reach-average slope (S), width (W), length 

(L), reach-average velocity (V), and hydraulic radius (R). Where available, the 

discharge (Q) and reach-average Froude number (Fr) are also quantified. These 

values were computed using flow travel time measurements, longitudinal profiles, and cross-section data. The presented n and f coefficients were computed 

from these reach-average values. In channels where resistance at multiple flows 

was measured, photographs of the low-, medium-, and ~bankfull-flow conditions are provided. In channels where resistance was measured at only one flow, 

photographs are presented for only the measured condition. Longitudinal profiles are also included, to illustrate the bedforms that are the primary source of 

flow resistance in these stream types (David and others 2011).
4 USDA Forest Service RMRS-GTR-323. 2014.

The velocity data were collected using tracers (salt or dye), though different methodologies were used to compute the travel time (peak v. harmonic). 

Additionally, some of the cross-sectional data used to calculate the flow resistance coefficients were less prolific than other included datasets. Both the 

tracer travel time and the cross section spacing inconsistency may be sources 

of error, but considering the qualitative nature of using photographs to help 

choose resistance coefficients, the impact of these varying methods are likely 

inconsequential to users of this guide.

An overview of each of the data collection and processing methodologies is 

provided. For additional information, please refer to the supporting references.

Fraser Experimental Forest, Southern Rocky Mountains, USA

Data from the Fraser Experimental Forest of Colorado were collected in East 

Saint Louis (ESL) Creek and Fool Creek (FC), in the upper Colorado River 

basin on reaches of cascade, step-pool, transitional, and plane-bed form (David 

and others 2010, Yochum and others 2012). Slopes ranged from 1.5 to 20 percent, with channel reach lengths ranging from 6.2 to 31 m (21 to 101 ft) and 

widths from 1.1 to 4.0 m (3.5 to 13 ft). Reaches were initiated and terminated 

at similar bedform points and had consistent morphology, depth variability, and 

wood loading throughout. Instream wood was present, both within steps and 

dispersed throughout the reaches. Streamflow was dominated by snowmelt, 

with catchment areas from 0.69 to 8.7 km2 (0.27 to 3.4 mi2). Data collection 

consisted of longitudinal profiles at ~bankfull, medium, and low flows; bed, 

bank, and floodplain surveying; reach-average velocity measurements; bed-material characterization using pebble counts; and discharge measurements using 

V-notch weirs.

Reaches were surveyed during low flow using a terrestrial LiDAR scanner for 

above-water surface features and a gridded total station survey for below-water 

features. Cross sections were spaced at a uniform interval of 0.75 to 1.50 m (2.5 

to 4.9 ft). At each resistance measurement, longitudinal profiles of the bed and 

water surface were measured at the thalweg and left and right edges of water. 

Bed particle size was measured using a 300-point pebble count. The slope of the 

water surface was assumed to be equivalent to Sf

 and was computed using the 

upstream and downstream water surface elevations divided by the longitudinal 

profile length.

Reach-average velocities were measured using a Rhodamine WT dye tracing 

methodology, using at least four replicate injections. Velocity was computed 

as the length of the thalweg longitudinal profile divided by the dye travel time. 

These data were collected with a 1 second time step. Travel time was computed 

using a spatial harmonic mean travel time (Walden 2004, Zimmermann 2010). 

To address data noise due to sunlight and aeration, a single-pass 3-point median 

smoothing methodology was applied (Tukey 1974, Gallagher and Wise 1981, 

Ataman and others 1981).
USDA Forest Service RMRS-GTR-323. 2014. 5

Rio Cordon, Alps, Italy

The Rio Cordon data were collected in the Dolomites of the Eastern Italian 

Alps (Comiti and others 2007), in a watershed with a catchment area of 5 km2 

(1.9 mi2). The channels feature cascade and step-pool reaches, with little to no 

instream wood present as a consequence of the low forest cover (7% of the basin). Reach lengths varied from 16 to 41 m (53 to 133 ft), widths ranged from 3.8 

to 5.8 m (12 to 19 ft), and slopes varied from 7.9 to 21 percent. Resistance was 

measured at a range of flows, from low to approximately 80% of the bankfull 

discharge. Discharge ranged from 0.17 to 1.86 m3/s (6.0 to 66 ft3/s). In contrast 

to the Fraser and Curran and Wohl (2003) reaches, the Rio Cordon reaches have 

less-developed bedforms. Using a total station, thalweg longitudinal profiles 

were surveyed and three cross-sections per reach were measured to characterize 

the channel geometry. These few sections were placed at intermediate locations between pools and steps, avoiding boulders and other irregularities. This 

method is a departure to the uniform-interval sections used in the other studies. Bed material size was measured using pebble counts of at least 100 clasts. 

Average velocity was measured using a salt tracer to measure travel time, which 

was computed using a difference in tracer peak concentrations. At least three 

injection replicates were performed per resistance measurement. The conductivity probes logged with an acquisition step of 5 seconds, which can potentially 

be a velocity error source in shorter reaches. Flow discharge was measured just 

downstream of the surveyed reaches, using weirs (Mao and others 2010).

Cascade Mountains, USA

The Cascade Mountains data were collected in streams both with (Curran and 

Wohl 2003) and without (MacFarlane and Wohl 2003) instream wood present, 

with this wood influencing the bed morphology and flow resistance. These data 

were collected in Washington State, at only low flow. The Curran and Wohl 

(2003) data consist of flow resistance measurements in reaches with slopes between 9.3 and 18 percent, lengths from 48 to 74 m (156 to 244 ft), flow widths 

from 1.7 to 3.2 m (5.6 to 10 ft), and beds composed of alluvium or a combination of alluvium and bedrock. Ten to 11 cross-sections were measured for each 

reach, with a spacing of roughly 5 meters. The MacFarlane and Wohl (2003) 

data consist of measurements in streams with slopes between 5.1 and 14 percent, lengths from 48 to 58 m (156 to 192 ft), flow widths from 0.94 to 2.6 m 

(3.1 to 8.7 ft), and beds composed of alluvium, a combination of alluvium and 

bedrock, or bedrock. Ten cross-sections were measured at equal intervals, with 

sections in both steps and pools. Thalweg longitudinal profiles were surveyed 

to measure the slope, quantify the bed profile, and provide the flow path lengths 

for the velocity computations. Discharge was not measured. Bed material size 

was measured using pebble counts. Average velocity was measured using a 

salt tracer to measure travel time, computed using a difference in tracer peak 

concentrations.
6 USDA Forest Service RMRS-GTR-323. 2014.

Araucania Region and Tierra del Fuego, Chile and Argentina

Data were collected in the Tres Arroyos watershed, in the Malalcahuello 

National Reserve in the Chilean Araucania region and in the Buena Esperanza 

basin, in the Argentine Tierra del Fuego near the city of Ushuaia (Comiti and 

others 2008). Catchment areas are 9.1 and 12.9 km2 (3.5 to 5.0 mi2) for Tres 

Arroyos and Buena Esperanza, respectively. The Comiti and others (2008) 

data was collected in channels with slopes between 2.2 and 15 percent, lengths 

from 17 to 99 m (57 to 324 ft), and flow widths from 2.8 to 5.3 m (9.1 to 17 

ft). Longitudinal profiles were measured along the thalweg using a laser distance meter with inclinometer, and three cross sections per channel reach were 

surveyed using the same device. Field measurements of discharge and reachaveraged flow velocity were carried out using the salt tracer method. Portable 

conductivity meters were placed at the upstream and downstream ends of 

each study reach. A salt mixture (0.1 to 0.6 kg of salt dissolved in a plastic 

bucket) was injected into the main stream at a distance of at least 10 channel 

widths upstream from the upper probe, with the conductivity measured every 

1 second. At least three replicate injections were performed. Reach-average 

velocity was derived from the thalweg length of the reach (from the longitudinal profile) and the time lag between the conductivity peaks. Water stage at the 

selected cross sections was measured just after each discharge and velocity 

measurement. Bed surface grain size distribution of each reach was characterized by a grid-by-number survey of at least 100 particles.

New Zealand

The New Zealand data (Wohl and Wilcox 2005) were collected on the South 

Island in the Southern Alps, in the Porter, Kowai, and Camp Creek watersheds on cascade, step-pool, plane-bed and transitional reaches with little to 

no instream wood present. Average annual precipitation varies substantially 

within the studied watersheds, from about 750 – 8000 mm (29 – 310 inches), 

from frontal systems and, very occasionally, dissipating tropical cyclones. 

Catchment areas range from 0.7 to 26 km2 (0.27 to 9.9 mi2). Reach lengths 

varied from 30 to 88 meters (98 to 287 ft), widths ranged from 1.6 to 29 m (5.2 

to 96 ft), and slopes varied from 0.3 to 19 percent. The reaches were defined 

by lengths with consistent bed gradient and bedform type. Relevant data collection consisted of: (1) surveyed channel bed, water surface and high water 

mark (bankfull) gradients; (2) measured grain size, through pebble counts; 

and (3) average flow velocity using a salt tracer and conductivity probes. 

Hydraulic radius (R) values for these low flow measurements were estimated 

using approximate average flow depths, introducing uncertainty to these resistance estimates. Reach-average velocity was computed using the longitudinal 

profile length and the time lag between the conductivity peaks.
USDA Forest Service RMRS-GTR-323. 2014. 7

Flow Resistance Processes and Quantitative Prediction

Flow velocity and resistance coefficient estimates are required for numerous 

purposes, including hydraulic modeling, geomorphic and stream restoration analyses, and ecological assessments. Additionally, because small, steep channels in 

mountainous regions typically have very limited direct, systematic discharge records (Wohl 2010), indirect methods are often used to estimate discharge at the 

reach scale. Inaccurate estimation of a flow resistance coefficient will most likely 

produce inaccurate reach-average velocity and indirect discharge estimates, with 

numerous repercussions for hydrologic analyses and restoration designs. Hence, 

the selection of an appropriate Manning’s n is very important.

Most methods for predicting velocity and discharge have been developed for 

lower-gradient streams, and the assumptions that are typically made regarding 

flow state when indirectly estimating discharge have led to much discussion of 

the appropriateness of these methods for mountain channels. For example, most 

indirect methods of discharge estimation – such as slope-area computations based 

on the Manning equation (Dalrymple and Benson 1967), step-backwater methods (O’Connor and Webb 1988), or a simplified slope-area method that does not 

require an estimate of Manning’s roughness coefficient (Sauer and others 1985) 

– assume steady, uniform or gradually varied flow. However, floods along steep 

streams are typically unsteady, are rapidly varying, and may be debris-charged 

(Glancy and Williams 1994). Indirect discharge estimates can therefore be in 

error because of scour and fill, rapid changes in flow, substantial sediment transport, and flow transitions between subcritical and supercritical flow (Jarrett 1987, 

Sieben 1997).

Despite an increasing research emphasis on the hydraulics of steep streams, flow 

processes in mountain streams remain less understood than those of lower-gradient alluvial channels. Here we review the concept of resistance to flow and sources 

of this resistance in steep streams, with specific examples drawn from research 

from the Fraser Experimental Forest and Rio Cordon (Figures ESL-6 – RC-5). 

With some understanding of these processes we will also summarize the available 

quantitative methods for flow resistance prediction in these stream types.

Flow Resistance

Under conditions of steady, quasi-uniform, two-dimensional, and fully developed 

turbulent flow over a deformable channel bed, flow resistance is caused by (1) 

viscous and pressure drag on grains of the bed surface (grain roughness), (2) 

pressure drag on bed and bank undulations (form roughness), and (3) pressure 

and viscous drag on sediment in transport above the bed surface (Griffiths 1987). 

Additionally, spill resistance associated with hydraulic jumps and wave drag on 

elements protruding above the water surface (Figure RC-5) can be the dominant 

flow resistance mechanism in high-gradient channels (Curran and Wohl 2003, 

Comiti and others 2009, David and others 2011). Because vertical flow velocities 

vary from a maximum near the free-surface of the flow to zero at the wall and 

bed, shear forces are created and produce viscous energy dissipation, known as 
8 USDA Forest Service RMRS-GTR-323. 2014.

skin friction (Tritton 1988). Form or pressure drag occurs because localized flow 

separation can create a high pressure upstream from an object and low pressure in 

the object’s wake. The resulting pressure-gradient force opposes flow and creates 

viscous energy losses downstream from the object (Tritton 1988, Roberson and 

Crowe 1993).

The mechanisms producing flow resistance shift throughout a channel network, 

with transient bedforms such as dunes dominating flow resistance in low-gradient sand-bed channels, bed substrate and pool-riffle sequences creating most of 

the resistance in mid-gradient gravel-bed rivers, and boulder form drag (grain 

resistance) and spill resistance dominating high-gradient, stepped morphology 

(step-pool and cascade), boulder-bed channels (Bathurst 1993). All of these forms 

of energy dissipation are subsumed into a single resistance coefficient such as 

Manning’s n. This flow resistance tends to increase as slope increases (Figure 1), 

with variation in flow resistance sources and dominant mechanisms, as well as 

flow stage, primarily contributing to the scatter.

Grain resistance represents the channel-bed roughness that induces energy losses 

resulting from skin friction and form drag from individual bed particles (Wiberg 

and Smith 1991). As depth increases, the individual particles influence a lower 

proportion of the flow and the effect of grain resistance is diminished (Wolman 

1955). However, the coarse, poorly sorted clasts and relatively shallow flow of 

steep streams tend to make grain resistance more important in these channels 

than in most lower-gradient rivers, even during high flows. Consequently, grain 

roughness can dominate at high submergence, with form and spill roughness more 

important at low submergence (Parker and Peterson 1980, Hey 1988, Ferguson 

2007, Zimmermann 2010).

The effect of flow depth relative to the height of individual sources of roughness 

is commonly expressed by R/Dc, where Dc is the roughness height. The roughness height is most commonly equated to the grain diameter for which x% is finer. 

Figure 1: Manning’s n, at low flow, versus channel gradient for the dataset presented in this report. Note the grouping 

of channel types as gradient increases.
USDA Forest Service RMRS-GTR-323. 2014. 9

R/Dc is thus commonly expressed as R/D84, known as relative grain submergence. 

Flow can be subdivided into large-scale roughness (0 < R/D84 < 1), intermediatescale roughness (1 < R/D84 < 4), and small-scale roughness (R/D84 > 4) (Bathurst 

1985; Ugarte and Madrid 1994), with shifts in velocity distributions and hydraulic resistance mechanisms as relative submergence decreases (Nikora and others 

2001). The relative grain submergence ratio (alternatively expressed as h/D84 ) is 

widely used – stemming from the Darcy-Weisbach flow resistance approach – to 

predict channel roughness in sand- and gravel-bed rivers (see Ferguson 2007), 

but it has been applied to step-pool streams as well (Lee and Ferguson 2002, 

Zimmermann 2010). However, the relative grain submergence does not account 

for bedforms (e.g., dunes, pool riffle, and step-pool sequences) or presence of 

step-forming instream wood. These bedforms can substantially increase channel 

roughness, from form and spill resistance. To account for this, the use of the standard deviation of bed profile (σz

) has been advocated to better represent roughness 

height in channels with substantial bedforms (Aberle and Smart 2003, Coleman 

and others 2011, Yochum and others 2012).

A source of roughness in some mountain streams comes from instream wood. 

Individual pieces of wood can create grain or form resistance, depending on their 

size relative to adjacent bed topographic elements and flow depth (Wilcox and 

Wohl 2006). Logjams and wood steps substantially increase total flow resistance by altering bedform dimensions and, as a result, form and spill resistance 

(MacFarlane and Wohl 2003, Comiti and others 2008, David and others 2010, 

Wilcox and others 2011, Yochum and others 2012). Additional sources of flow 

resistance include bank roughness (Houjou and others 1990, Buffington 2012) 

and vegetation (Buffington and Montgomery 1999).

Examples of Flow Resistance Processes

To give more insight on flow resistance mechanisms, a practical illustration of 

flow resistance is provided for some of the photographic examples in this guide. 

First, brief discussions are presented on how resistance varies between reaches on 

the Rio Cordon and on East Saint Louis and Fool Creeks (Fraser Experimental 

Forest). Using the photographic guidance, more detailed descriptions are then presented on how flow resistance varies as flow changes for a subset of these reaches.

Rio Cordon

Measurements of flow resistance in the Rio Cordon were taken for a wide range of 

discharges, ranging from 0.17 to 1.86 m3/s (6.0 to 66 ft3/s; up to 80% of the bankfull 

discharge). The calculated Manning’s n ranged from 0.12 to 0.41. Measurements 

taken at similar discharges show that flow resistance is higher in steeper and 

coarser reaches, where the bed becomes increasingly irregular and hydraulically 

rougher. For example, at lower flow (around 0.2 m3/s, 7 ft3/s), Manning’s n is 0.41 

in reach 5 (cascade, RC-5), 0.30 in reach 1b (step-pool, Figure RC-1b), and 0.18 

in reach 1a (transitional from to plane-bed to step-pool, RC-1a). The presence of 

very coarse sediments, whether they are organized in steps or irregularly distributed creating a cascade morphology, is thus critical in increasing flow resistance.
10 USDA Forest Service RMRS-GTR-323. 2014.

In step-pool and cascade reaches, the spill component typically dominates over 

the other sources of resistance, mainly due to the tumbling flow generated by 

the presence of boulders or steps. Due to relatively poor development of pools 

and the virtual absence of large wood (Comiti and others 2007), flow resis-

tance in step-pool and cascade reaches may be lower than in other high-gradient 

channels with similar slope. Comparison with the Fraser Experimental Forest 

data (Figures ESL-6 – FC-4 and Appendix A) illustrates this. As expected, 

flow resistance decreases with increasing discharge in all the study reaches at 

comparable rates, but the relative grain submergence ratio (R/D84) was a poor 

predictor of flow resistance. No general thresholds (from nappe to skimming 

flows), similar to those observed in the laboratory by Comiti and others (2009), 

have been detected in the relationship between discharge and flow resistance. 

This is probably because the range of measured discharges did not exceed bank-

full conditions and, with the main steps not fully submerged and spill resistance 

still dominant, a nappe flow regime was present in all the reaches. However, in 

a few reaches, reach-averaged Froude numbers approached unity.

In step-pool reach RC-1b, flow resistance was measured at a wider range of 

discharges, and flow velocities (and Froude numbers) varied considerably. The 

longitudinal profile shows three major steps (height about 1 m) with associated 

shallow pools downstream, and two smaller steps with almost no pool in the up-

stream part of the reach. The two large pools are asymmetrical and do not span 

the whole channel width. One of these steps is built around a mid-channel large 

boulder, whereas the upper large step is part of a mid-channel bar formed by 

coarse clasts lying unorganized on the bed. Upstream and downstream of these 

two steps, the bed is more symmetrical with lower channel-spanning steps. At 

the lower discharge (summer low flows, Q = 0.17 m

3/s = 6.0 ft3/s), flow drops 

nearly vertically from the steps and dissipates all of the entire potential energy 

in the pools below. Even though these pools are quite shallow, they are still sufficient to contain the short and stable hydraulic jumps before the downstream 

steps. Jumps under these conditions are very effective in dissipating flow energy 

as the Froude number of the jet entering the pool is high (due to the large drop 

height relative to flow depth). The reach at such conditions features high flow re-

sistance (n = 0.30). Flow resistance decreases to n = 0.24 as discharge increases 

to 0.9 m

3/s (31 ft3/s) (Figure RC-1b-C), producing velocities that are sufficient 

to cause gravel transport (Lenzi and others, 2006a, 2006b). At this stage, flow 

aeration is considerably higher and small lateral bars are flooded, but the midchannel bar is still mostly exposed. All the steps are still unsubmerged, but their 

jumps are rougher, longer (comparable to pool length), and have reduced en-

ergy dissipation efficiency. However, up to this stage, reach-averaged Froude 

numbers are quite low (0.2 – 0.35). At the highest discharge measured, 1.6 m

3/s 

(57 ft

3/s) at 70-75% of bankfull flow, the mid-channel bar is nearly submerged 

and only a few boulders protrude from the water surface (Figure RC-1b-B). 

At this stage, the median grain size of the bed surface is mobilized (Lenzi and 

others 2006a, 2006b) and the flow appears turbid, very rough, and aerated. The 

water surface profile is still strongly affected by the three major steps and the 

mid-channel bar, which are not hydraulically submerged, whereas the smaller 

steps upstream are fully submerged. Hydraulic jumps are now unstable and, in 

part, oscillating, indicating little efficiency in dissipating flow energy even at the 
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taller steps. These conditions (overall, still characterizing a nappe flow regime 

for the reach) result in high reach-average velocities (V = 1.6 m/s = 5.3 ft/s), and 

the Froude number reveals that the flow is approaching critical conditions at a 

reach-averaged scale (Fr = 0.92). Flow resistance drops to n = 0.12, less than 

half the resistance of low flow.

Fraser Experimental Forest 

Measurements of flow resistance for 14 reaches in Fraser Experimental Forest 

of Colorado have been included in this guide: one plane-bed, 10 step-pool and 

transitional, and 3 cascade channels. Data were collected in East Saint Louis 

Creek, and lower and upper Fool Creek, providing data at three channel sizes. 

Manning’s n ranged from 0.05 to 0.30 for near-bankfull, 0.08 to 0.40 for midflow, and 0.10 to 0.52 for low-flow conditions. All reaches contained instream 

wood, with 95% of the wood located in the steps. The reaches typically had a 

combination of boulder steps and steps with wood held in place by one large 

keystone boulder. Two reaches without any step-forming wood (Figures FC-1 

and ESL-3) had lower measured flow resistance, with bankfull n values of 0.095 

and 0.20, and low flow n values of 0.16 and 0.25, respectively. These flow resistance values are similar to those measured in a couple of the Rio Cordon reaches 

(Figures FC-1a and RC-2a), where there was no instream wood. Conversely, 

wood-enhanced steps tend to be wider and taller than boulder-only steps, with 

typically higher measured flow resistance.

Dominant flow resistance mechanisms vary by reach and discharge, with nonstep wood resistance contributing a large proportion of the total resistance at 

high flows and a progressively smaller proportion as discharge decreased and 

logs were no longer submerged, with the spill resistance increasing progressively as discharge decreased (David and others 2011). Step-forming wood 

contributes substantially to both form and spill resistance, due to wood increasing the step heights.

We use examples from two sites to demonstrate the influence of non-step-forming wood (Figure ESL-2) and step-forming wood (Figure FC-4). Reach ESL-2 

had a bankfull n of 0.20 and low-flow n of 0.39, and reach FC-4 had a bankfull 

n of 0.22 and low-flow n of 0.52.

During high flows (Figure ESL-2-D) more than 70% of the resistance in ESL2 was due to non-step wood while this component decreased to less than 10% 

during low flow, with spill resistance becoming dominant. Conversely, in FC-4 

wood was predominantly located in steps and spill resistance was dominant for 

the full range in flows (David and others 2011). In all 13 of the cascade and step 

pool reaches, grain resistance contributed a small proportion of the total flow 

resistance, providing a contribution of less than 10% (David and others 2011).

Different components of resistance can combine to increase flow resistance at 

low flows while reducing resistance at high flows, as illustrated by the ESL-2 

measurements. This reach contains three steps: two boulder-wood and one boulder. The wood in this reach is all found in the steps, except for one log that is 

only submerged during higher flows. The channel is narrow in the upstream and 
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downstream portions, but wider in the middle. Some bedrock is exposed in the 

upstream section of the channel, prior to the final step. The farthest downstream 

step (Figure ESL-2-A, -D and -E) is taller and wider than the other two steps. 

The channel behind this step is subsequently wider and has smaller grain size 

material (D84 = 18 mm, compared to D84 = 70 mm for the entire reach) that has 

settled out. In general, step-pool reaches have greater variability in grain size 

throughout the different sections of the reach (step, tread, pool) than cascading 

reaches (David and others 2011). In ESL-2, the differences in grain sizes were 

most drastic among the various sections of the reach. Both the large step and 

the variability in grain size reveal an interaction of processes – the large step 

in the downstream portion of the reach created a large backwater area, which 

allowed the deposition of finer material and greater difference in flow acceleration between low and high flows. Grain resistance and ponding are significant at 

low flows when the grains are more exposed and water is backed up behind the 

large wood step. These two components cause a large increase in resistance and 

reduction of velocity during low flow (David and others 2010b).

As flow increases, the grains quickly become submerged, causing a substantial 

reduction in flow resistance and an increase in velocity. The upper two steps are 

almost completely submerged at bankfull flow, leaving the downstream step 

as the only significant drop and source of spill resistance. The combination of 

the reduction of grain resistance on the step tread leading up to the most downstream boulder-wood step, and the reduction of spill resistance over the upper 

two steps, results in the substantial reduction in flow resistance and increase in 

velocity, although at bankfull flow the reach-averaged Froude number is still 

substantially less than unity (0.35) and reach-average velocity is only 0.53 m/s 

(1.7 ft/s). The interaction of processes is important, because the large wood step 

in the downstream portion of this channel created a wider channel with smaller 

grain size, which then results in a reduced resistance and higher velocity at 

bankfull flows. Almost all the grains are submerged at bankfull flows, greatly 

reducing the importance of grain sorting throughout the different sections of the 

reach. At bankfull flows the drop over the step and the wood in the step become 

the most important sources of resistance.

Quantitative Prediction

This technical report was developed to provide photographic guidance for flow 

resistance in high-gradient channels, which is valuable for calibrating the predictive skills of practitioners who need to estimate resistance in these highly 

variable stream types. However, quantitative prediction methodologies are also 

needed, to reduce subjective judgment and assist with defining resistance variability as discharge changes. Simple relationships of slope versus resistance 

coefficients (Figure 1, Montgomery and Buffington 1997) are initial steps for 

quantifying flow resistance, but more comprehensive methods are desired. 

Despite the channel complexity and resulting flow resistance variability, the 

finding that bedforms provide the greatest contribution to flow resistance in 

these stream types (Comiti and others 2009, David and others 2011) can be leveraged for developing quantitative prediction methodologies.
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Using the Fraser dataset, a number of existing methodologies for estimating 

resistance were assessed (Yochum 2010, Yochum and others 2012) for their 

predictive capabilities in steep streams. The results indicated that many of the 

available methods for predicting Manning’s n (Jarrett 1984, Rickenmann 1994, 

Rice and others 1998) were not effective for prediction throughout the range of 

reach conditions and discharges. However, a review of the available methods 

for predicting f that are potentially applicable for these stream types (Thompson 

and Campbell 1979, Bathurst 1985, Kaufmann 1987, Lee and Ferguson 2002, 

Aberle and Smart 2003, Comiti and others 2007, Kaufmann and others 2008) 

found that the Aberle and Smart (2003) approach was relatively accurate. This 

method was developed using flume data on self-formed steps and uses the relative submergence term h /σz

,where h is the average flow depth (m) and σz is 

the standard deviation of the residuals of a bed profile regression (m). However, 

a systematic bias with this relationship was evident, with the methodology consistently underpredicting flow resistance in the Fraser streams.

Drawing on this finding, Yochum and others (2012) combined the Fraser dataset 

with the dataset developed by MacFarlane and Wohl (2003) to develop prediction equations based on field data using relative bedform submergence hm/σz

, 

where hm is the median thalweg flow depth as measured from a longitudinal 

profile or a substantial number of equally-spaced cross sections. This dataset 

consisted of primarily alluvial and mixed alluvial-bedrock channels. These regressions, along with their 95% prediction intervals, are provided (Figure 2). 

The standard deviation of a bed profile regression (σz

) is computed through 

Figure 2: Regressions and 95% prediction intervals for Manning’s n and Darcy-Weisbach f.
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measurement of a thalweg longitudinal profile – this computation is illustrated 

(Figure 3). For additional information on the method, refer to Yochum and others (2012).

These regression equations provide a method for quantitatively predicting flow 

resistance in high-gradient channels, for analysis and design purposes. However, 

limited data were available in their development and their applicability beyond 

the tested datasets is unknown. This may be especially problematic in larger 

channels; caution is warranted when extrapolating these results to channels of 

different scale, where other flow resistance mechanisms may be dominant.

Acknowledgments

Appreciation is expressed to the USDA Forest Service, the USDA Natural 

Resources Conservation Service, the National Science Foundation 

(EAR0608918), and the National Council for Air and Stream Improvement for 

project funding. The datasets and review provided by William MacFarlane and 

Janet Curran are greatly appreciated; their contributions to the single discharge 

measurements section provided substantial value to the report. Appreciation is 

expressed to the U.S. Forest Service Rocky Mountain Research Station for hosting the Fraser data collection, Tim Davies for facilitating field work in New 

Zealand, and Mario Lenzi for facilitating field work in Italy. Support from 

Dan Cenderelli and John Potyondy of the U.S. Forest Service Stream Systems 

Technology Center (STREAM TEAM) has been fundamental for the development of this report. Guidance on the execution of the research that forms 

the backbone of this work, from Brian Bledsoe, Sara Rathburn, and Chester 

Watson, has been highly valued. Appreciation is also expressed for the insightful 

comments by John Buffington and Sandra Ryan; their comments substantially 

increased the quality of the manuscript. Finally, field assistance by Mark Hussey, 

Dan Dolan, Andrew Wilcox, Lina Polvi, Bridget Diefenbach, Jason Alexander, 

Carla Brock, Paul Swartzinski, and Fred Wurster was fundamental for the development of these datasets.

Figure 3: Computation methodology for σz.
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Photographic Guidance

Multiple Discharges

Nineteen figures are provided illustrating stream reach characteristics in 

plane-bed, step-pool (Figure 4), and cascade channels. Manning’s n and DarcyWeisbach f are provided for low, mid and high flows (Figures ESL-6 – RC-5). 

Photographs of the reaches from multiple perspectives and flow magnitudes are 

provided. Profile plots are also included, to depict the bed and water surface 

during bankfull flow. The photograph locations and orientations are shown as 

letters on the profile plots. The figures are ordered from lowest to highest average measured flow resistance.

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Figure 4: Step-pool morphology in East Saint Louis Creek (ESL-1).
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Photographic Guidance

Single Discharges

Twenty-eight figures are provided illustrating stream reach characteristics, with 

Manning’s n and Darcy-Weisbach f given for a single discharge (Figures Porter 

11 – Bear Creek). This discharge was typically low flow. Single photographs 

are provided, illustrating conditions when flow resistance was measured. Profile 

plots are also included, to depict the bed morphology. The figures are ordered 

from the lowest to the highest resistance.

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Figure 5: A transitional plane-bed / step-pool morphology channel without instream wood enhancement of bedforms (Porter 

River 8).
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USDA Forest Service RMRS-GTR-323. 2014. 85
86 USDA Forest Service RMRS-GTR-323. 2014.
USDA Forest Service RMRS-GTR-323. 2014. 87
88 USDA Forest Service RMRS-GTR-323. 2014.
USDA Forest Service RMRS-GTR-323. 2014. 89

APPENDIX A: Measured Values, Multiple Discharge Reaches

Reach Flow Type Reference

Date of 

Measurement

Slope 

(m/m) n f

V 

(m/s)

Q 

(m3/s) W (m) L (m)

ESL-6 ~bankfull flow Yochum et al. 2012 6/9/2008 0.024 0.048 0.28 1.32 0.52 3.0 6.4

ESL-6 mid flow Yochum et al. 2012 7/14/2008 0.015 0.078 0.76 0.61 0.32 2.9 6.4

ESL-6 low flow Yochum et al. 2012 8/8/2007 0.019 0.099 1.4 0.39 0.10 2.7 6.2

FC-1 ~bankfull flow Yochum et al. 2012 6/11/2008 0.063 0.095 1.3 0.79 0.23 2.0 23.1

FC-1 mid flow Yochum et al. 2012 7/5/2007 0.061 0.134 3.0 0.40 0.05 1.6 23.7

FC-1 low flow Yochum et al. 2012 8/12/2007 0.057 0.196 7.6 0.19 0.01 1.3 25.1

RC-1a high flow Comiti et al. 2007 11/1/2004 0.079 0.130 1.8 1.14 1.86 4.8 24.6

RC-1a mid flow Comiti et al. 2007 10/29/2004 0.079 0.183 4.7 0.48 0.47 4.2 24.6

RC-1a low flow Comiti et al. 2007 10/27/2004 0.079 0.178 5.0 0.39 0.21 4.1 24.6

RC-2a high flow Comiti et al. 2007 11/1/2004 0.096 0.14 2.2 1.10 1.37 5.7 29.3

RC-2a mid flow Comiti et al. 2007 10/29/2004 0.096 0.16 3.9 0.54 0.45 4.2 29.3

RC-2a low flow Comiti et al. 2007 7/29/2004 0.096 0.19 6.3 0.31 0.17 1.9 29.3

FC-2 ~bankfull flow Yochum et al. 2012 6/11/2008 0.071 0.13 2.2 0.66 0.24 1.6 14.4

FC-2 mid flow Yochum et al. 2012 7/23/2008 0.072 0.22 8.0 0.28 0.04 1.4 14.2

FC-2 low flow Yochum et al. 2012 8/12/2007 0.068 0.24 11.4 0.17 0.013 1.1 14.9

ESL-7 ~bankfull flow Yochum et al. 2012 6/8/2008 0.085 0.17 3.5 0.69 0.52 3.0 22.1

ESL-7 mid flow Yochum et al. 2012 7/15/2008 0.081 0.19 4.8 0.55 0.30 2.9 24.0

ESL-7 low flow Yochum et al. 2012 8/4/2007 0.082 0.20 6.0 0.40 0.10 2.5 24.3

ESL-3 ~bankfull flow Yochum et al. 2012 6/7/2008 0.129 0.16 3.7 0.71 0.46 3.6 10.2

ESL-3 mid flow Yochum et al. 2012 7/15/2008 0.119 0.20 5.7 0.54 0.30 3.5 10.7

ESL-3 low flow Yochum et al. 2012 8/9/2007 0.119 0.25 9.2 0.38 0.089 2.4 11.3

ESL-8 ~bankfull flow Yochum et al. 2012 6/9/2008 0.094 0.18 4.2 0.64 0.46 3.2 30.7

ESL-8 mid flow Yochum et al. 2012 7/16/2008 0.087 0.20 5.3 0.53 0.29 3.0 32.6

ESL-8 low flow Yochum et al. 2012 8/5/2007 0.081 0.24 8.2 0.35 0.10 2.6 35.5

RC-1b high flow Comiti et al. 2007 11/1/2004 0.184 0.12 1.6 1.61 1.62 5.4 15.9

RC-1b mid flow Comiti et al. 2007 5/24/2004 0.184 0.24 7.4 0.64 0.89 3.8 15.9

RC-1b low flow Comiti et al. 2007 7/29/2004 0.184 0.30 16.2 0.28 0.17 2.9 15.9

ESL-1 ~bankfull flow Yochum et al. 2012 6/10/2008 0.095 0.19 4.5 0.65 0.56 2.9 29.4

ESL-1 mid flow Yochum et al. 2012 7/22/2008 0.105 0.27 9.4 0.42 0.24 2.6 27.3

RC-3 high flow Comiti et al. 2007 5/30/2004 0.136 0.20 4.5 0.86 0.93 3.8 38.2

RC-3 mid flow Comiti et al. 2007 10/29/2004 0.136 0.25 8.3 0.50 0.47 3.5 38.2

RC-3 low flow Comiti et al. 2007 10/27/2004 0.136 0.24 8.9 0.37 0.21 3.3 38.2

ESL-9 ~bankfull flow Yochum et al. 2012 6/8/2008 0.115 0.21 5.5 0.64 0.57 2.8 16.3

ESL-9 mid flow Yochum et al. 2012 7/11/2007 0.103 0.26 8.8 0.43 0.20 2.6 16.2

ESL-9 low flow Yochum et al. 2012 8/6/2007 0.090 0.28 10.9 0.33 0.11 2.3 18.6

ESL-2 ~bankfull flow Yochum et al. 2012 6/6/2008 0.094 0.20 4.8 0.61 0.53 3.2 13.7

ESL-2 mid flow Yochum et al. 2012 7/9/2007 0.093 0.23 7.0 0.45 0.22 2.9 13.9

ESL-2 low flow Yochum et al. 2012 8/9/2007 0.099 0.39 22.1 0.24 0.09 2.6 13.6

ESL-4 ~bankfull flow Yochum et al. 2012 6/7/2008 0.120 0.23 6.3 0.63 0.61 2.9 15.6

ESL-4 mid flow Yochum et al. 2012 7/14/2008 0.118 0.28 9.7 0.50 0.32 2.9 15.9

ESL-4 low flow Yochum et al. 2012 8/6/2007 0.119 0.32 14.4 0.33 0.12 2.3 16.5

FC-6 ~bankfull flow Yochum et al. 2012 6/25/2008 0.200 0.17 4.8 0.62 0.14 1.1 19.1

FC-6 mid flow Yochum et al. 2012 7/17/2008 0.178 0.32 18.9 0.23 0.02 0.9 20.6

FC-6 low flow Yochum et al. 2012 8/10/2007 0.165 0.44 41.9 0.12 0.01 0.7 22.1

ESL-5 ~bankfull flow Yochum et al. 2012 6/9/2008 0.160 0.30 11.1 0.52 0.50 4.0 12.5

ESL-5 mid flow Yochum et al. 2012 7/14/2008 0.143 0.29 10.8 0.48 0.33 4.0 13.9

ESL-5 low flow Yochum et al. 2012 8/8/2007 0.134 0.38 21.8 0.27 0.10 3.3 15.1

FC-3 ~bankfull flow Yochum et al. 2012 6/12/2008 0.092 0.20 5.4 0.51 0.22 2.1 13.5

FC-3 mid flow Yochum et al. 2012 7/22/2008 0.099 0.40 26.3 0.18 0.04 1.7 12.2

FC-3 low flow Yochum et al. 2012 8/11/2007 0.087 0.41 31.4 0.12 0.01 1.4 14.9

FC-4 ~bankfull flow Yochum et al. 2012 6/12/2008 0.135 0.22 6.3 0.59 0.22 1.6 18.9

FC-4 mid flow Yochum et al. 2012 7/21/2008 0.127 0.31 15.8 0.27 0.05 1.4 19.8

FC-4 low flow Yochum et al. 2012 8/11/2007 0.132 0.52 46.9 0.14 0.01 1.2 19.2

RC-5 high flow Comiti et al. 2007 5/28/2004 0.208 0.35 15.6 0.50 1.02 5.8 19.6

RC-5 low flow Comiti et al. 2007 7/30/2004 0.208 0.41 27.7 0.25 0.17 2.3 19.6
90 USDA Forest Service RMRS-GTR-323. 2014.

D 50 D 84 

Reach Flow Type R (m) h m (m) A (m2) Fr (mm) (mm) sz (m) M&B (1997) Rosgen

ESL-6 ~bankfull flow 0.26 0.38 0.89 0.77 22 77 0.037

ESL-6 mid flow 0.24 0.35 0.79 0.37 22 77 0.029 B3

ESL-6 low flow 0.15 0.21 0.44 0.31 22 77 0.036

FC-1 ~bankfull flow 0.16 0.29 0.38 0.58 32 84 0.063

FC-1 mid flow 0.10 0.18 0.18 0.38 32 84 0.076 A4

FC-1 low flow 0.06 0.12 0.09 0.23 32 84 0.076

RC-1a high flow 0.38 ---- 1.63 0.63 140 460 0.25

RC-1a mid flow 0.18 ---- 0.97 0.32 140 460 0.25 B3a

RC-1a low flow 0.12 ---- 0.54 0.35 140 460 0.25

RC-2a high flow 0.36 ---- 1.24 0.76 200 510 0.25

RC-2a mid flow 0.15 ---- 0.84 0.38 200 510 0.25 A3

RC-2a low flow 0.08 ---- 0.54 0.18 200 510 0.25

FC-2 ~bankfull flow 0.18 0.36 0.39 0.44 32 81 0.090

FC-2 mid flow 0.11 0.23 0.20 0.24 32 81 0.080 A4

FC-2 low flow 0.060 0.14 0.08 0.20 32 81 0.053

ESL-7 ~bankfull flow 0.25 0.53 0.97 0.39 84 174 0.129

ESL-7 mid flow 0.23 0.48 0.86 0.32 84 174 0.120 B3a

ESL-7 low flow 0.15 0.32 0.42 0.31 84 174 0.147

ESL-3 ~bankfull flow 0.18 0.51 0.87 0.46 58 151 0.155

ESL-3 mid flow 0.17 0.49 0.80 0.36 58 151 0.159 B3a

ESL-3 low flow 0.14 0.36 0.42 0.29 58 151 0.141

ESL-8 ~bankfull flow 0.23 0.48 0.91 0.38 70 172 0.139

ESL-8 mid flow 0.21 0.44 0.78 0.33 70 172 0.122 A3

ESL-8 low flow 0.16 0.33 0.48 0.26 70 172 0.129

RC-1b high flow 0.28 ---- ---- 0.92 150 330 0.36

RC-1b mid flow 0.21 ---- ---- 0.34 150 330 0.36 A3a+

RC-1b low flow 0.09 ---- ---- 0.20 150 330 0.36

ESL-1 ~bankfull flow 0.25 0.57 0.99 0.35 52 156 0.182

ESL-1 mid flow 0.20 0.46 0.70 0.26 52 156 0.207

RC-3 high flow 0.31 ---- 1.09 0.51 220 480 0.29

RC-3 mid flow 0.20 ---- 0.94 0.31 220 480 0.29 A3a+

RC-3 low flow 0.12 ---- 0.57 0.29 220 480 0.29

ESL-9 ~bankfull flow 0.25 0.52 0.92 0.36 62 153 0.163

ESL-9 mid flow 0.20 0.42 0.65 0.27 62 153 0.162 B3a

ESL-9 low flow 0.17 0.35 0.47 0.23 62 153 0.178

ESL-2 ~bankfull flow 0.25 0.53 1.00 0.35 7 70 0.192

ESL-2 mid flow 0.20 0.41 0.68 0.30 7 70 0.156 B3a

ESL-2 low flow 0.16 0.33 0.47 0.18 7 70 0.176

ESL-4 ~bankfull flow 0.26 0.53 0.99 0.34 68 173 0.182

ESL-4 mid flow 0.26 0.52 0.97 0.27 68 173 0.172 B3a

ESL-4 low flow 0.17 0.34 0.50 0.23 68 173 0.163

FC-6 ~bankfull flow 0.12 0.26 0.17 0.50 49 95 0.134

FC-6 mid flow 0.07 0.18 0.09 0.24 49 95 0.126 A4a+

FC-6 low flow 0.05 0.12 0.04 0.15 49 95 0.137

ESL-5 ~bankfull flow 0.24 0.51 1.20 0.30 53 143 0.259

ESL-5 mid flow 0.22 0.48 1.09 0.29 53 143 0.236 B3a

ESL-5 low flow 0.15 0.34 0.59 0.20 53 143 0.221

FC-3 ~bankfull flow 0.19 0.41 0.55 0.32 12 50 0.124

FC-3 mid flow 0.11 0.24 0.24 0.15 12 50 0.108 B4a

FC-3 low flow 0.07 0.15 0.12 0.13 12 50 0.112

FC-4 ~bankfull flow 0.21 0.44 0.48 0.34 47 95 0.171

FC-4 mid flow 0.12 0.26 0.21 0.22 47 95 0.151 B3a

FC-4 low flow 0.09 0.21 0.14 0.13 47 95 0.121

RC-5 high flow 0.24 ---- ---- 0.27 290 630 0.57

RC-5 low flow 0.11 ---- ---- 0.15 290 630 0.57

step-pool

step-pool

step-pool

step-pool

cascade

step-pool

A2a+

cascade

Stream Type

plane bed

plane-bed / step-pool

plane-bed / step-pool

step-pool

step-pool

B3a

cascade

cascade

step-pool

step-pool

step-pool

step-pool

cascade
USDA Forest Service RMRS-GTR-323. 2014. 91

APPENDIX B: Measured Values, Single Discharge Reaches

Reach Reference Date

Slope 

(m/m) n f

V 

(m/s)

Q 

(m^3/s)

W , meas. 

(m)

Porter-11 Wohl and Wilcox 2004 1/2003 0.013 0.057 0.44 0.68 ---- 5.1

Buena Esperanza-A Comiti et al. 2008 2/2006 0.022 0.059 0.50 0.77 0.40 4.4

Porter-4 Wohl and Wilcox 2004 1/2003 0.026 0.074 0.74 0.74 ---- 1.6

Buena Esperanza-C Comiti et al. 2008 2/2006 0.023 0.081 0.94 0.56 0.53 4.2

Porter-6 Wohl and Wilcox 2004 1/2003 0.047 0.091 1.1 0.81 ---- 3.3

Porter-9 Wohl and Wilcox 2004 1/2003 0.036 0.11 1.7 0.57 ---- 5.4

Porter-8 Wohl and Wilcox 2004 1/2003 0.045 0.13 2.3 0.55 ---- 5.1

Tres Arroyos-15 Comiti et al. 2008 4/2005 0.028 0.13 2.7 0.31 0.091 3.6

Kow ai-8 Wohl and Wilcox 2004 1/2003 0.064 0.14 3.1 0.40 ---- 2.6

Camp-5 Wohl and Wilcox 2004 3/2003 0.100 0.19 6.4 0.35 ---- 6.7

Camp-2 Wohl and Wilcox 2004 3/2003 0.16 0.22 8.6 0.38 ---- 6.0

Kellogg (Low er) MacFarlane and Wohl 2003 7/1999 0.061 0.25 11 0.18 ---- 2.1

Buena Esperanza-E Comiti et al. 2008 3/2006 0.076 0.25 8.3 0.37 0.18 4.6

Tres Arroyos-14 Comiti et al. 2008 4/2005 0.072 0.26 11 0.26 0.070 3.0

Buena Esperanza-G Comiti et al. 2008 3/2006 0.10 0.27 10 0.39 0.19 2.8

SF Mashel (Low er) MacFarlane and Wohl 2003 7/1999 0.051 0.31 16 0.17 ---- 2.6

Camp-7 Wohl and Wilcox 2004 2/2003 0.19 0.34 20 0.27 ---- 2.7

Lost Curran and Wohl 2003 7/1997 0.12 0.35 19 0.25 ---- 2.3

Kow ai-1 Wohl and Wilcox 2004 2/2003 0.18 0.36 23 0.25 ---- 3.2

Spine Curran and Wohl 2003 7/1997 0.12 0.38 27 0.16 ---- 1.5

SF Mashel (Upper) MacFarlane and Wohl 2003 7/1999 0.12 0.45 40 0.12 ---- 1.3

Busyw ild MacFarlane and Wohl 2003 7/1999 0.083 0.48 41 0.12 ---- 1.4

Caterpillar MacFarlane and Wohl 2003 7/1999 0.14 0.52 59 0.09 ---- 0.94

McLaine Curran and Wohl 2003 6/1997 0.18 0.27 45 0.21 ---- 2.0

Tacoma (Upper) MacFarlane and Wohl 2003 7/1999 0.099 0.65 71 0.11 ---- 2.3

Hard Curran and Wohl 2003 7/1997 0.093 0.74 76 0.13 ---- 3.2

Tres Arroyos-10 Comiti et al. 2008 3/2005 0.153 0.87 118 0.11 0.067 5.3

Bear Curran and Wohl 2003 6/1997 0.11 0.96 152 0.079 ---- 1.70

W , bankfull D 50 D 84 σz

Reach (m) L (m) R (m) Fr (mm) (mm) (m) M&B (1997) Rosgen

Porter-11 7.2 56.7 0.2 ---- 150 164 0.039 plane-bed C3

Buena Esperanza-A ---- 21.5 0.17 0.72 ---- 223 0.084 plane-bed / step-pool B3

Porter-4 2.7 34.8 0.2 ---- 142 235 0.115 plane-bed / step-pool E3b

Buena Esperanza-C ---- 17.2 0.16 0.37 ---- 113 0.232 plane-bed / riffle-glide B3

Porter-6 5.6 55.9 0.2 ---- 155 410 0.209 plane-bed / step-pool B3a

Porter-9 8.1 53.6 0.2 ---- 60 207 0.149 plane-bed / step-pool B4

Porter-8 9.3 49.9 0.2 ---- 109 245 0.195 plane-bed / step-pool B3a

Tres Arroyos-15 ---- 36.9 0.12 0.35 ---- 151 0.179 pool-riffle / step-pool B3

Kow ai-8 5.0 33 0.8 ---- 60 180 0.128 plane-bed / step-pool B4a

Camp-5 13.0 47 1.4 ---- 450 1350 0.396 cascade A2

Camp-2 23.0 46 0.6 ---- 300 1000 0.478 step-pool A2a+

Kellogg (Low er) ---- 48 0.076 ---- 111 294 0.142 step-pool B3a

Buena Esperanza-E ---- 23.4 0.19 0.36 ---- 260 0.122 cascade B3a

Tres Arroyos-14 ---- 50.4 0.13 0.28 ---- 341 0.263 step-pool / cascade A3

Buena Esperanza-G ---- 16.9 0.19 0.30 ---- 336 0.192 step-pool A3a+

SF Mashel (Low er) ---- 58 0.11 ---- 137 256 0.143 step-pool B3a

Camp-7 3.5 30 0.5 ---- 430 910 0.281 step-pool A2a+

Lost 3.3 54 0.13 ---- 65 187 0.256 step-pool A3a+

Kow ai-1 10.3 46 0.1 ---- 240 535 0.358 step-pool A3a+

Spine 2.2 53 0.077 ---- 33 123 0.390 step-pool / cascade B3a

SF Mashel (Upper) ---- 52 0.060 ---- 108 223 0.172 step-pool B3a

Busyw ild ---- 56 0.085 ---- 158 284 0.161 step-pool A3

Caterpillar ---- 55 0.047 ---- 111 239 0.334 step-pool B3a

McLaine 3.3 48 0.14 ---- 80 193 0.521 step-pool A3a+

Tacoma (Upper) ---- 51 0.11 ---- 158 416 0.349 step-pool B3a

Hard 4.8 61 0.18 ---- 87 203 0.348 step-pool / cascade A3

Tres Arroyos-10 ---- 32 0.13 0.11 ---- 143 0.599 step-pool A3a+

Bear 3.1 74 0.11 ---- 101 216 0.233 step-pool A3a+

σ Stream Type z

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