United States Department of Agriculture / Forest Service

Rocky Mountain Research Station

General Technical Report RMRS-GTR-321

June 2014

A Landscape Scale Valley Confinement Algorithm: 

Delineating Unconfined Valley Bottoms for Geomorphic, 

Aquatic, and Riparian Applications

David E. Nagel, John M. Buffington, Sharon L. Parkes, Seth Wenger, and Jaime R. Goode
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Nagel, David E.; Buffington, John M.; Parkes, Sharon L.; Wenger, Seth; Goode, Jaime R. 

2014. A landscape scale valley confinement algorithm: Delineating unconfined valley 

bottoms for geomorphic, aquatic, and riparian applications. Gen. Tech. Rep. RMRSGTR-321. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 42 p.

Abstract

Valley confinement is an important landscape characteristic linked to aquatic habitat, riparian 

diversity, and geomorphic processes. This report describes a GIS program called the Valley 

Confinement Algorithm (VCA), which identifies unconfined valleys in montane landscapes. 

The algorithm uses nationally available digital elevation models (DEMs) at 10-30 m resolution to generate results at subbasin scales (8 digit hydrologic unit). User-defined parameters 

allow results to be tailored to specific applications and landscapes. Field data were sampled 

to verify geomorphic characteristics of valley types identified by the program, and a detailed 

accuracy assessment was conducted to quantify the reliability of the algorithm output.

Keywords: valley confinement, valley bottom, stream channel morphology, fish habitat, 

riparian habitat

Authors

David E. Nagel is a Spatial Analyst with the Rocky Mountain 

Research Station, Boise Aquatic Sciences Laboratory in Boise, 

Idaho. He holds a B.S. degree from Michigan State University 

and an M.S. degree from the University of Wisconsin-Madison. 

He has worked for private, state, and Federal agencies for 

more than 25 years in the fields of GIS and remote sensing. 

He is currently involved with developing spatial analysis tools 

for watershed and aquatic applications.

John M. Buffington is a Research Geomorphologist with the 

Rocky Mountain Research Station, Boise Aquatic Sciences 

Laboratory in Boise, Idaho. He holds a B.A. degree in geology 

from the University of California, Berkeley, with M.S. and Ph.D. 

degrees in geomorphology from the University of Washington. 

He was a National Research Council Fellow from 1998 to 2000 

and a professor in the Center for Ecohydraulics Research at the 

University of Idaho from 2000 to 2004. His research focuses on 

fluvial geomorphology of mountain basins, biophysical interactions, and the effects of natural and anthropogenic disturbances 

on salmonid habitat.

Sharon L. Parkes is GIS Specialist with the Rocky Mountain 

Research Station, Boise Aquatic Sciences Laboratory in Boise, 

Idaho. She holds a B.S. degree from Lincoln University and 

graduated Summa Cum Laude as an 1890 USDA Land-grant 

Scholar. She conducts GIS analyses and web program development.

Seth Wenger is an Assistant Professor at the University of 

Georgia, Odum School of Ecology. He holds an undergraduate degree from Lebanon Valley College, with M.S. and Ph.D. 

degrees from the University of Georgia. He studies potential 

effects of climate change on native and invasive trout in the 

Western United States.

Jaime R. Goode is a Lecturer at the University of Aberdeen, 

Scotland. She holds a B.S. degree from Connecticut College 

with M.S. and Ph.D. degrees from Colorado State University. 

Her interests include interactions among ecologic and geomorphic processes in mountain environments, specifically rivers.
Acknowledgments

Bruce Rieman and Jason Dunham provided the initial impetus and guidance 

for developing a GIS based valley confinement algorithm for use in fisheries 

applications. David Theobald and Kurt Fesenmyer provided valuable reviews 

of this report and their time and insight are greatly appreciated. Thank you to 

Leslie Jones and Joel Murray who provided useful feedback by testing the VCA 

for their respective study sites.

Disclaimer of Non-endorsement—Reference herein to any specific commercial products, process, 

or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government. The 

views and opinions of authors expressed herein do not necessarily state or reflect those of the 

United States Government, and shall not be used for advertising or product endorsement purposes.

ArcGIS, ArcMap, and ArcCatalog are registered trademarks of ESRI, Inc., 380 New York Street, Redlands, 

CA 92373

The Python Programming Language and Python are registered trademarks of the Python Software Foundation, http://www.python.org/psf/.

Executive Summary

Valley confinement describes the degree to which 

bounding topographic features, such as hillslopes, alluvial 

fans, glacial moraines, and river terraces, limit the lateral 

extent of the valley floor and the floodplain along a river. 

Valleys can be broadly classified as confined or unconfined, with corresponding differences in their appearance, 

vegetation, ground water exchange rates, topographic 

gradient, and stream characteristics. Unconfined valleys are generally less extensive than confined valleys 

in montane environments, but host a diverse array of 

terrestrial and aquatic organisms and provide disproportionately important ecosystem functions. Consequently, 

identifying the location and abundance of each valley 

type is increasingly recognized as an important aspect 

of ecosystem management. In this report, we describe 

a GIS program called the Valley Confinement Algorithm 

(VCA) that maps the extent and shape of unconfined 

valley bottoms using readily available spatial data as 

input.

The VCA is designed to operate using ESRI ArcGIS 

software with 1:100,000 scale stream lines from the National Hydrography Dataset (NHDPlusV1) and 10-30 m 

digital elevation models (DEMs). The algorithm focuses 

on fluvial applications and therefore only considers channeled valleys. The smallest unconfined valley that can be 

resolved by the VCA depends on the resolution of the DEM; 

the VCA is unable to resolve unconfined valleys that are 

narrower than about two to three times the DEM cell size 

(i.e., valleys that are 60-90 m in width for a 30 m DEM or 

20-30 m for a 10 m DEM). In addition, as bankfull width 

approaches two times the DEM cell size, the VCA may 

misinterpret the channel as a narrow unconfined valley. 

Consequently, care should be exercised in interpreting 

results in such locations. 

We conducted field work in central Idaho to document 

channel characteristics in confined and unconfined valleys 

mapped by the algorithm. Results showed that channel 

confinement measured in the field (ratio of valley width 

to bankfull width) agreed with valley confinement predicted by the algorithm 79% of the time and that channel 

characteristics were similar to those documented in other 

studies of confinement. In particular, confined channels 

typically exhibited steep-gradient step-pool and planebed morphologies composed of coarse-grained bed 

material, with a median channel confinement of about 

2 bankfull widths. In contrast, unconfined channels were 

primarily low-gradient pool-riffle and plane-bed streams 

composed of finer substrate, with a median channel 

confinement of about 10 bankfull widths. 

We further assessed the accuracy of the algorithm by 

generating a stratified random sample of points equally 

partitioned between confined and unconfined valleys 

as identified by the VCA. Predicted valley types were 

compared with those observed from digital photos and 

quadrangle maps. Results showed that the algorithm 

could differentiate between the two valley types with 

89-91% accuracy.

Contents

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

Part I—Review.......................................................................................................... 6

Morphogenesis of Unconfined Valleys ......................................................... 6

Application and Significance of Valley Confinement..................................... 7

Previous Algorithms and Comparison to the VCA Approach........................ 9

Part II—VCA Software ............................................................................................11

Software Requirements and Overview........................................................11

Scale Considerations...................................................................................11

Input and Ancillary Data Overview.............................................................. 14

Input and Ancillary GIS Data Download Instructions.................................. 15

Input and Ancillary GIS Data Pre-processing Instructions.......................... 16

Substituting Higher Resolution Input Data.................................................. 17

Program Inputs, Parameters, and Output................................................... 18

Algorithm Sequence ................................................................................... 19

Output Shapefiles....................................................................................... 22

Part III—Field Assessment ................................................................................... 23

Approach .................................................................................................... 23

Findings and Discussion............................................................................. 25

Part IV—Statistical Validation............................................................................... 30

Methods...................................................................................................... 30

Results ....................................................................................................... 31

Part V—Online Database ...................................................................................... 33

Part VI—Summary ................................................................................................. 35

References ............................................................................................................. 35

Appendix A. Field Collected Data ........................................................................ 40

Appendix B: Examples of Channel Types........................................................... 41

Appendix C: Examples of Substrate Types ........................................................ 42

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 1

A Landscape Scale Valley Confinement Algorithm: 

Delineating Unconfined Valley Bottoms for 

Geomorphic, Aquatic, and Riparian Applications

David E. Nagel, John M. Buffington, Sharon L. Parkes, 

Seth Wenger, and Jaime R. Goode

Introduction__________________________________________

Looking across a mountainous landscape, an observer will immediately perceive the 

peaks, ridges, and valleys that comprise the terrain. Our attention is often drawn to the 

peaks, but it is the valleys that carry surface water, harbor fish, provide riparian habitat, 

and impart critical resources to the montane ecosystem. The morphology, or shape of 

the valley, is an important predictor of the types of services that the valley can provide 

for the ecosystem. One of the most fundamental characteristics of a valley is its degree 

of lateral confinement by topographic features, such as hillslopes, alluvial fans, glacial 

moraines, and relict river terraces.

Confined valleys in mountain basins are typically narrow and v-shaped, with little 

alluvial fill (figure 1). These valleys have relatively steep, erosive gradients, and contain 

coarse-grained, high-energy streams with little to no floodplain (e.g., Montgomery and 

Buffington’s (1997) transport reaches [cascade and step-pool channels] or Rosgen’s 

(1994, 1996) A, B, and G channel types) (figure 2).

Figure 1—Graphic of a typical confined valley illustrating shallow alluvial deposits.
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

Figure 2—Typical confined valleys in the project study area, described in Part III.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 3

In contrast, unconfined valleys are wider depositional areas, with extensive alluvial 

fill and broad floodplains that allow active channel migration and the development of 

channel sinuosity or braiding (figure 3). Unconfined valleys typically have relatively 

lower gradients and finer-grained sediment (e.g., Montgomery and Buffington’s (1997) 

response reaches [pool-riffle and dune-ripple channels] or Rosgen’s (1994, 1996) C, 

E, and F channel types) (figure 4), except where coarse-grained braided rivers occur 

below alpine glaciers.

Both confined and unconfined valleys are associated with different process domains 

and ecosystems (e.g., Brierley and Fryirs 2005; Buffington and Tonina 2009; Montgomery 1999; Paustian and others 1992; Wohl and others 2013). Knowing the location 

and abundance of each valley type can be a key component for addressing a variety of 

management issues related to aquatic species and terrestrial riparian animals.

To help address management and research endeavors in fluvial systems where valley 

confinement is of interest, a GIS-based software program called the Valley Confinement Algorithm (VCA) was developed that identifies unconfined valleys using readily 

available, nationwide spatial data. The program uses stream line data from the National 

Hydrography Dataset (NHDPlusV1) and digital elevation models (DEMs) with 10-30 m 

resolution as input. Unconfined valley bottom polygons are delineated by the algorithm 

and output in ArcGIS shapefile format. All areas within these polygons are considered 

unconfined valleys, while other areas along streams but outside of the polygons are 

considered confined valleys (figure 5). The output is generated at landscape scales 

composed of subbasins defined by 8-digit (4th code) hydrologic units (Seaber and others 1987). Valley confinement can be identified along stream reaches as small as 100 

m in length. The algorithm optionally produces a second output that computes network 

distance along the stream channel to the nearest unconfined valley bottom polygon 

(figure 6), which was developed for fisheries applications (Wenger and others 2011). 

In addition to the VCA, this report describes an online database of valley confinement 

data that have been processed for a substantial portion of the western United States.

Figure 3—Graphic of a typical unconfined valley illustrating relatively deep alluvial deposits.
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

Figure 4—Typical unconfined valleys in the project study area, described in Part III.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 5

Figure 5—Example output from the VCA, where unconfined valleys are 

delineated as polygons and all other valleys outside of the polygon and 

along stream channels are considered confined.

Figure 6—Distance from unconfined valleys as measured along the 

stream network.
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

Part I of this document reviews the formation of unconfined valleys, the application 

and significance of valley confinement, and prior automated routines for identifying 

unconfined valleys. Part II describes the VCA software and its technical implementation, including details for downloading and preprocessing the input GIS data and an 

overview of the algorithm methods. Part III presents field data documenting channel 

characteristics in confined and unconfined valleys mapped by the VCA for central Idaho, 

and qualitatively assesses the results of the algorithm. Part IV describes a statistical 

assessment of the VCA output to examine classification accuracy. Part V describes an 

online database of valley confinement data for the Intermountain Region of the western 

United States, and Part VI summarizes the report.

The VCA program described herein may be accessed from the Rocky Mountain 

Research Station valley confinement website: 

http://www.fs.fed.us/rm/boise/AWAE/projects/valley_confinement.shtml

Part I—Review________________________________________

Morphogenesis of Unconfined Valleys

In the broadest sense, an unconfined valley is a landscape feature that is low lying and 

relatively flat compared to its surroundings (Gallant and Dowling 2003). Unconfined 

valleys may be formed by a variety of geologic processes in mountainous environments. 

Some unconfined valleys may be structural features, such as fault-bound grabens, like 

those of the basin and range physiography of the western United States. Others may be 

self-formed depositional features that occur where the long-term sediment supply exceeds 

the channel transport capacity (typically lower-sloped portions of the stream network). 

In these self-formed cases, floodplain initiation and the development of an unconfined 

valley may be related to downstream gradients in stream power (Jain and others 2008), 

while absolute values of stream power affect the type of floodplain environment that 

occurs (Nanson and Crooke 1992).

In northerly and high elevation systems, Pleistocene alpine glaciers are one of the 

primary agents that have influenced valley form. Where glaciers have scoured bedrock 

to form U-shaped valleys, valley width is generally greater than in fluvially formed 

environments (Amerson and others 2008; Montgomery 2002). In addition, glaciers can 

have indirect effects on valley form by delivering large sediment supplies to valleys 

(e.g., Wohl 2000). When glacial sediment cannot be transported because of inadequate 

stream power or because it is blocked by a downstream obstruction, such as a moraine 

or bedrock constriction, a wide valley may be formed with deep alluvial deposits.

Valleys may also fill with alluvium from more recent geomorphic activity, such as 

debris flows and landslides. Again, if a valley obstruction or inadequate stream power 

reduces sediment transport, a wide valley bottom will often form. Valleys may become 

wider still if side slopes are composed of low-strength material. These side slopes may 

become over-steepened by lateral erosion, causing slope failure and facilitating additional 

sediment input (Lifton and others 2009). Valley width has also been linked to the occurrence of deep-seated landslides that are controlled by lithology and geologic structure 

and that, in turn, influence habitat availability for salmonids (May and others 2013).

Riverine animals and riparian vegetation can also modulate fluvial processes and 

affect valley form. For example, beaver dams alter channel slope, transport capacity, 

and the frequency of overbank flooding, promoting deposition and the development 

or expansion of unconfined valleys, particularly in smaller channels (e.g., Butler and 

Malanson 1995; 2005; Pollock and others 2007; Persico and Meyer 2009; Polvi and 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 7

Wohl 2012). Similarly, riparian vegetation creates roughness that increases floodplain 

sedimentation and stability (Allmendinger and others 2005; Smith 2004) that over time 

may lead to expansion of unconfined valleys.

Application and Significance of Valley Confinement

Valley confinement mapping has been used for a variety of geomorphic, aquatic, and 

riparian applications. Some applications pertain to physical modeling, such as debrisflow routing, while others are concerned with in-stream biological habitat, botanical 

predictions, or wildlife management.

Valley confinement in mountain basins is widely used for classifying process domains 

and stream reach morphology due to the strong effect of confinement and associated 

valley slope on fluvial processes and hillslope–channel coupling (e.g., Bengeyfield 

1999; Brierley and Fryirs 2005; Montgomery 1999; Montgomery and Buffington 1997; 

Paustian and others 1992; Rosgen 1994; 1996; Schumm 1977). In addition, confinement 

is recognized as an important factor in debris-flow routing because entrained material 

is likely to be deposited in unconfined valley segments due to declining gradients and 

dissipation of energy by overbank flow (Benda and Cundy 1990; Fannin and Wise 

2001; Miller and Burnett 2008). This is true for fluvial sediment routing as well, with 

the occurrence of unconfined valleys along the stream network influencing system-wide 

sediment routing and residence times (Goode and others 2012).

Unconfined valleys in montane systems are also recognized for their propensity to 

exchange ground and surface water, creating a unique environment for supporting various 

life stages of riparian and aquatic species (e.g., Baxter and Hauer 2000; Malard and others 

2002; Poole and others 2006; Stanford 2006; Stanford and Ward 1993). The mixing of 

groundwater with stream flow is known as hyporheic exchange, and the character of this 

exchange will vary depending on the nature of the stream and underlying alluvium (e.g., 

Buffington and Tonina 2009; Wondzell and Gooseff 2013). Higher-gradient streams in 

confined valleys exhibit shallow hyporheic exchange because these reaches lack deep, 

unconsolidated sediments. In contrast, unconfined valleys with deep alluvial deposits 

often support pool-riffle channels that promote lateral water movement across bars and 

between meanders, enhancing sub-reach scale exchange rates (Tonina and Buffington 

2007) and providing deep and long hyporheic flow paths through the extensive alluvial 

deposits that characterize unconfined valleys (Stanford 2006; Stanford and Ward 1993). 

The broad distribution of flow paths and hyporheic residence times in unconfined valleys influences stream temperature and enhances biochemical reactions which, in turn, 

affect the character of the surface and subsurface water and their associated habitats 

(e.g., Buffington and Tonina 2009; Malard and others 2002; Poole and others 2006; 

2008; Wondzell 2011).

In addition to affecting hyporheic exchange and water quality, numerous studies 

document the role of valley confinement in structuring fish populations. For example, 

juvenile coho salmon (Oncorhynchus kisutch) have been noted in greater densities in 

unconfined stream reaches, while 1-year old steelhead trout (O. mykiss) often avoid 

these same reaches, preferring confined stream segments (Burnett and others 2003; 

Gresswell and others 2006). Similarly, field studies in central Idaho demonstrate that 

Chinook salmon (O. tshawytscha) preferentially spawn in unconfined valleys (Buffington 

and others in prep.; Isaak and Thurow 2006; McKean and Tonina, 2013). In general, 

the current distribution of anadromous salmon populations in the Pacific Northwest, 

United States, is closely associated with the distribution of unconfined floodplain 

channels (Hall and others 2007). This result may reflect a variety of factors, including (1) increased habitat diversity in unconfined valleys, such as side channels and 

pools for juvenile fish (Hall and others 2007); (2) availability of spawning gravels 
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

in pool-riffle and plane-bed channels that commonly occur in unconfined valleys due 

to lower stream gradients (Buffington and others 2004; Coulombe-Pontbriand and 

Lapointe 2004; Wilkins and Snyder 2011); (3) favorable hyporheic flows for mitigating 

adverse summer and winter stream temperatures (Baxter and Hauer 2000; Boxall and 

others 2008); and (4) potentially greater spawning success due to extensive floodplains 

that effectively mitigate high flows and reduce the risk of bed mobility and scour during 

embryo incubation (e.g., Goode and others 2013; Magiligan 1992; McKean and Tonina, 

2013). The lower stream gradients and higher sinuosity of channels in unconfined valleys 

also reduce stream velocity, providing refugia for aquatic organisms during high flow 

periods (e.g., Wenger and others 2011). Furthermore, field studies in eastern Oregon 

demonstrate that unconfined channels have approximately 80% more pool area and 

40% deeper pools than channels in confined valleys, providing higher quality habitat 

for salmonids (McDowell 2001).

Unconfined valleys are bounded longitudinally at their upstream and downstream 

ends by confined valley segments (Baxter and others 1999). Consequent downstream 

changes in valley width and alluvial volume force large-scale hyporheic circulation and 

upwelling of groundwater at the downstream ends of unconfined valleys (Poole and 

others 2008; Stanford 2006; Tonina and Buffington 2009) that has been shown to affect 

trout distributions. For example, bull trout (Salvelinus confluentus) in western Montana 

preferentially spawn in downstream portions of unconfined valleys, where hyporheic 

upwelling may warm stream temperatures and enhance overwintering success of bull 

trout embryos (Baxter and Hauer 2000). Similarly, Lahontan cutthroat trout (O. clarki 

henshawi) in southeastern Oregon show a preference for locations directly downstream 

from unconfined valleys, where hyporheic upwelling likely modulates stream temperature, providing cooler flow in the summer and warmer flow in the winter, as well as 

greater flow depths in all seasons (Boxall and others 2008).

Brook trout (S. fontinalis) are considered an invasive species in the western United 

States, and can displace native fish such as cutthroat trout (Dunham and others 2002). To 

help reduce brook trout populations, researchers have attempted to identify their preferred 

habitat. Wenger and others (2011) found that brook trout had a positive association with 

unconfined valleys, and where these fish were present, cutthroat trout demonstrated a 

negative association with unconfined valleys. These findings indicate that brook trout 

prefer unconfined channels, possibly because warmer groundwater in winter provides 

favorable conditions for egg incubation (Curry and others 1995).

Valley confinement also has a strong control on riparian habitats. Riparian zones 

form a narrow interface between aquatic and terrestrial ecosystems and represent only 

a small fraction of the terrestrial landscape. However, these areas harbor high levels 

of species richness (Birken and Cooper 2006; Kovalchick and Chitwood 1990; Ward 

1998) and provide disproportionately important ecosystem functions (Wissmar 2004). 

Such environments are characterized by unique soils (Bohn and Buckhouse 1985), 

plant species (Dwire and others 2006), and wildlife (Morrison and others 1994). Valley 

confinement is strongly linked to riparian width in montane systems, with unconfined 

valleys supporting wider riparian zones (Polvi and others 2011). In the western United 

States, these riparian areas support some of the most important vegetative communities 

for providing wildlife habitat at both the site and landscape scales (USFWS 2009; Ward 

1998) and are thought to be the most productive type of wildlife habitat on land (Kauffman and Krueger 1984). Confined riparian areas are preferred by some species, such as 

bats (Hagen and Sabo 2011), but unconfined valleys provide more suitable habitat for 

fauna, such as beaver (Polvi and Wohl 2012), elk, waterfowl, and bird species like the 

Willow Flycatcher (Sanders and Flett 1989). Unconfined valleys are associated with 

the presence of montane riparian meadows (Polvi and Wohl 2012) that are critical for 

wildlife, but are often targeted by livestock (Kauffman and others 1997). As a result, 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 9

many of these areas see decreased diversity, function, and productivity. Because of its 

relevance to riparian habitat, valley confinement has been used as a baseline factor for 

classifying and mapping riparian zones (Hemstrom and others 2002; Kovalchik and 

Chitwood 1990; Manning and Maynard 1994; Verry and others 2004; Winters and others, submitted). As such, identifying unconfined valleys in montane regions may be an 

important step toward restoring riparian habitat.

Previous Algorithms and Comparison to the VCA Approach

Automated valley confinement and valley width algorithms have been previously 

developed for a range of aquatic and riparian applications. Strager and others (2000) 

developed a landscape scale riparian habitat model that used 1:100,000 scale stream 

lines and 30 m DEMs as input. The algorithm delineated near-stream riparian habitat 

using a path-distance function. The path-distance method computed the relative “cost” 

of moving laterally away from the stream channel while subject to a resistance measure, 

defined as ground slope. The result was a raster with cost increasing as the cumulative 

product of ground slope and distance from the stream, with values rapidly increasing 

where slope was greater. Low-cost, relatively flat-lying areas were associated with 

wetland/riparian habitat.

Williams and others (2000) mapped valley bottoms using an elevation approach, 

where valley bottom polygons were delineated when cells proximal to the channel fell 

within a user-defined elevation threshold compared to the elevation of the stream as 

depicted by the DEM. All cells with elevations less than or equal to the local threshold 

were included in a valley bottom zone. Valley width was then computed using transects 

generated across the zone.

Elevation and ground slope were used by Gallant and Dowling (2003) to generate 

an index of valley bottom flatness from DEM data. The algorithm used basic concepts 

of flatness and lowness to identify valley bottoms at various scales. Flatness was derived from a standard percent slope measure and lowness was determined by elevation 

percentile, a ranking of the elevation of a grid cell with respect to the surrounding cells 

in a circular region.

Vertical change and valley bottom width thresholds were used to map probable riparian areas by Ruefenacht and others (2005). The first threshold restricted the valley 

bottom based on vertical rise from the DEM modeled channel and the second threshold 

constrained the valley bottom by a maximum width (Finco and others 2008).

Hall and others (2007) used cross-valley transects and 10-m DEMs to estimate 

horizontal floodplain extent and to classify stream reaches as confined or unconfined. 

In their approach, bankfull width and depth were predicted for each stream reach as 

functions of contributing area and precipitation. The cross-valley transects were placed 

above the stream at a height of three times the modeled bankfull depth. Where transects 

intersected the valley side-slopes, the edge of the floodplain was demarcated. The channel confinement ratio was next computed as a function of floodplain width divided by 

bankfull width. Channels were classified as confined if the ratio was <3.8 and unconfined if the ratio was >3.8, an empirical threshold based on the observed potential for 

lateral channel migration.

Walterman and others (2008) used a three-step approach to delineating valley bottom extent, with each step subsequently refining the likelihood of identifying riparian 

habitat. First a stream buffer distance, scaled by stream order, was computed for the 

study area. This layer was refined by thresholding a change in height above the stream 

elevation, which was further refined using a ground slope threshold.

Benda and others (2007, 2009, 2011) developed a tool called NetMap that generates numerous watershed attributes and indices, including a channel confinement 
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

classification. Channel confinement is calculated as floodplain width divided by channel width. Floodplain width is calculated by using a specified height above the stream 

elevation; the specified height may be a user-defined constant or computed as a function 

of bankfull depth, similar to the approach of Hall and others (2007).

Most recently, Housman and others (2012) developed a logistic regression approach 

to modeling valley bottom areas for identifying riparian zones. Representative training 

data are used to determine the probability of each cell belonging to the valley bottom 

class by using topographic derivatives: height above the channel elevation, Euclidean 

distance from the channel multiplied by the ground slope, and topographic position 

index. Riparian areas are further refined with the use of spectral reflectance information from remotely sensed data.

Our VCA approach builds from many of the techniques used in these previous studies and employs four primary variables: (1) cost-weighted distance; (2) flood height; 

(3) ground slope; and (4) maximum valley width. The approach is described in detail 

in Part II and is summarized here. A modified version of the cost-distance approach 

(distance times ground slope) is used as an initial gross scale technique for identifying 

relatively flat valley bottoms near streams, while excluding other flat features beyond 

stream valleys. In this stage, only contiguous polygons meeting the distance times slope 

threshold that include an adjoining stream channel are preserved for further processing 

(the VCA does not consider unchanneled valleys). The results from the cost-distance 

process are refined using a valley filling procedure, which is best described conceptually 

as flooding the valley to a designated height above the channel elevation in the DEM. 

This process is similar to the one employed by Hall and others (2007) using transects, 

except that our method is implemented in a raster environment. A flood height is spread 

laterally outward from the stream channel until the valley side-slope is intersected. The 

flood height is controlled by the bankfull depth of the stream channel, predicted as an 

empirical function of drainage area and precipitation (Hall and others 2007), similar to 

the approach used by Benda and others (2011). A user-defined “flood factor” is multiplied by bankfull depth to determine the flood height. The resultant flood height and 

valley extent are thereby scaled to the stream size, similar to Rosgen’s (1994, 1996) 

flood prone width. In addition, a user-defined ground slope threshold is used to restrict 

the unconfined valley extent. This parameter may be used in combination with the flood 

height variable as an additional criterion for constricting valley area, or may be used 

alone if drainage area and precipitation data are not available for predicting bankfull 

depth and the flood height parameter. Finally, a user-defined maximum valley width 

limits floodplain extent in exceptionally wide valleys and plateau regions.

The VCA algorithm uses techniques similar to previous methods, but is unique because 

extensive user input allows the valley confinement product to be customized for a range 

of applications. User-defined parameters (flood height, ground slope, maximum valley 

width, minimum valley bottom area, and minimum valley stream length) can be used 

to tailor the mapping to one’s specific goals. In addition, processing is predominantly 

completed on raster datasets, which makes computations at the landscape scale operate 

more quickly than similar functions using vector data. The algorithm includes an option for computing stream network distance away from unconfined valley bottoms for 

use in aquatic habitat studies (e.g., Wenger and others 2011). The VCA uses standard, 

nationally available input data, such as NHDPlus, and the executable can be freely 

downloaded. The script may also be modified by knowledgeable Python developers. 

In addition, an online database of valley bottom extent, including the network distance 

output, is available for download.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 11

Part II—VCA Software _________________________________

Software Requirements and Overview

The VCA program requires ArcGIS version 10.0 or higher, with an ArcInfo level 

license and the Spatial Analyst extension. Python version 2.6.2 (ArcGIS 10.0) or higher 

is also required.

The program is run from the ArcToolbox interface as a script. Following installation 

(see http://www.fs.fed.us/rm/boise/AWAE/projects/valley_confinement.shtml) a Toolbox called Valley Confinement is added to the ArcToolbox interface and a script called 

Valley Confinement Algorithm will appear within the Toolbox.

Three input GIS files are used by the script, the first two are required and the third is 

optional: (1) a 10- or 30-m DEM clipped to a watershed boundary, (2) NHDPlus stream 

lines, and (3) NHDPlus water bodies. A watershed boundary GIS file is also useful to clip 

all three input layers to the correct spatial domain. A number of user input parameters 

that modify the algorithm results are required at the program interface.

The algorithm will output one of two possible products: (1) a polygon shapefile 

representing relatively flat, unconfined valley bottoms, or (2) a polygon shapefile 

representing relatively flat, unconfined valley bottoms with distance along the stream 

network measured to the closest valley bottom polygon. These products are referred to 

as Valley Type 1 and Valley Type 2, respectively. Although both products are generated 

from the raster DEM, the first output incorporates a line smoothing routine to create 

more realistic looking valley bottom polygons. The second output does not incorporate 

a smoothing routine because the nature of stream lines represented in a raster environment precludes this type of enhancement. As a result, the second product has a stairstepped appearance that mimics the underlying raster data model. Valley Type 1 and 

2 will provide slightly different valley bottom results because two slightly different 

processing procedures are used.

The VCA program and online documentation may be accessed from the Rocky 

Mountain Research Station (RMRS) valley confinement website: 

http://www.fs.fed.us/rm/boise/AWAE/projects/valley_confinement.shtml.

Scale Considerations

The USGS National Elevation Dataset (NED; USGS 2006), with a 30-m spatial resolution, is well suited for valley confinement mapping at a landscape scale. These data 

are generally able to identify relatively flat valley bottoms as small as 60-90 m wide, 

depending on the VCA user-supplied input parameters and the quality of the DEM. 

The output will be generally applicable at the valley segment scale (100 to 10,000 m) 

(Bisson and others 2006; Fausch and others 2002) and suitable for mapping at about 

1:50,000 to 1:100,000 scales. Assuming that the DEM processing units are clipped to 

the subbasin (8-digit) USGS Hydrologic Unit boundaries (USGS 2011), the VCA will 

run approximately 5 to 10 minutes per subbasin.

DEMs with a 10-m spatial resolution can also be used, but run times will increase 

substantially. For 10-m DEMs, the program’s default ground slope threshold should 

be decreased because smaller cells will result in less slope averaging at the interface 

between the valley bottom and side slopes. The user should experiment with different 

slope thresholds until a suitable value is obtained. Although 10-m DEM data may be 

used as input for the VCA, 30-m data are recommended due to their availability as part 

of the NHDPlus dataset.
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

The smallest unconfined valley that can be resolved by the VCA depends on the cell 

size of the DEM. In particular, the VCA has difficulty resolving unconfined valleys that 

are narrower than about two to three times the DEM cell size (60-90 m in width for a 

30 m DEM or 20-30 m in width for a 10 m DEM).

Errors may also occur due to interactions between cell size and channel width. As 

bankfull width approaches two times the DEM cell size, the VCA may misinterpret the 

channel as a narrow unconfined valley (figure 7). This condition occurs as two or more 

adjacent raster cells at the same elevation abut perpendicular to the stream centerline, 

producing the appearance of a relatively flat valley bottom to the algorithm logic. These 

cells may actually represent the water surface in wider streams. Consequently, care 

should be exercised when narrow unconfined valleys are predicted in locations where 

channels have bankfull widths larger than twice the DEM cell size (i.e., bankfull widths 

larger than 60 m for a 30 m DEM or larger than 20 m for a 10 m DEM).

In addition, some valley bottoms are not mapped accurately in the USGS NED data 

and may not be identified by the VCA. The NED data were produced using an algorithm 

called LT4X (USGS 1997). The LT4X program used scanned topographic contours from 

quadrangle maps, tagged with elevation values, for generating the raster DEM data 

(Underwood and Crystal 2002). LT4X performed well in areas with sufficient contour 

density; however, where the concentration of contours changed from dense to sparse at 

unconfined valley bottoms, elevation errors were sometimes created. This type of error 

is most obvious in relatively flat, wide valley bottoms with relatively steep adjacent 

side slopes (Nagel and others 2010). Since these valley bottoms exhibited very little 

relief, there was a paucity of contours for guiding the LT4X algorithm, resulting in valleys having a “half-pipe” shape that should have a more planar morphology (figure 8). 

Figure 7—A channel may be misinterpreted as unconfined because the channel width is larger 

than twice the DEM cell size. In this case the area of the stream’s surface was interpreted as 

an unconfined valley bottom.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 13

Figure 8—The original quadrangle map (a) from which the DEM 

data were created does not contain any elevation contours 

across the unconfined valley at the arrow location. However, 

2-m contours derived from the 10-m resolution DEM (b) show 

an elevation change of at least 6 m across the valley. This 

gradual “rounding” of the valley bottom creates higher slope 

readings in the DEM than one is likely to encounter in the field. 

In addition, the DEM stream channel location is likely to be set 

deeper into the valley, relative to the valley side slopes, than 

one would encounter in the field.

a

b
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

Consequently, some valley bottoms may not be accurately captured by the VCA output. 

Increasing the input ground slope parameter or increasing the flood factor (described in 

the program parameters section below) may help alleviate this problem.

It should also be recognized that the VCA is unable to capture geomorphic features 

that are smaller than the resolution of the DEM. As such, ground truthing is advised to 

identify sub-cell topography, such as small terraces or channel entrenchment that may 

confine the river and its active floodplain. Riparian vegetation can also confine the 

river (e.g., Manners and others 2013; Smith 2004) and will not be visible to the DEM.

The VCA has been tested most extensively in montane regions of the Intermountain 

West and its performance in other landscapes is uncertain. Consequently, we recommend 

that users validate the VCA results using local field sampling. Low-relief landscapes 

may be particularly challenging in terms of setting an appropriate lateral ground-slope 

threshold.

Input and Ancillary Data Overview

It is necessary to download and pre-process five input GIS data themes (one DEM, three 

shapefiles, and one table) to successfully run the VCA program. This section provides a 

brief overview of the required inputs. The input data can be obtained from the National 

Hydrography Dataset Plus, Version 1 (NHDPlusV1) website. Additional information 

about downloading NHDPlusV1 data is provided in the next section. Instructions for 

using NHDPlusV2 data are provided on the RMRS valley confinement website.

Average annual precipitation (cm/yr) is needed if using the flood height factor. The 

precipitation value is implemented as a single value for the watershed being processed, 

so the GIS data layer is not required as input. The user is required to enter an integer 

value as an input parameter in the VCA interface. Readily available online sources of 

precipitation data include the PRISM Climate Group (http://www.prism.oregonstate.

edu/) or the nationalatlas.gov website.

Elevation GIS raster

Source: NHDPlusV1 or USGS NED

NHDPlus download layer name: elev_cm

Format: ESRI GRID

Description: This raster layer is the underlying basis for most computations in the 

VCA, such as ground slope and flood height. The elevation units are centimeters. 

If the NHDPlus layer is used, it will need to be converted to meters during the preprocessing phase.

Stream lines GIS layer

Source: NHDPlusV1

NHDPlusV1 download file name: nhdflowlines.shp

Format: ESRI shapefile

Description: This layer is used to restrict valley bottoms to relatively flat areas directly 

adjacent to the stream network. Unchanneled valleys are not considered. The user 

will join a field to the attribute table called CUMDRAINAG, which may be used to 

control the minimum drainage area (surrogate for stream size) where valley bottom 

polygons will be delineated. The CUMDRAINAG field is also used in an equation 

to control the flood height parameter.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 15

Water bodies GIS layer

Source: NHDPlusV1

NHDPlusV1 download file name: NHDWaterbody.shp

Format: ESRI shapefile

Description: The layer is used to exclude water bodies such as lakes and reservoirs 

from analysis so that these features are not misinterpreted as valley bottoms.

Drainage area attribute table

Source: NHDPlusV1

NHDPlusV1 download file name: flowlineattributesflow.dbf

Format: dBase

Description: This dBase file must be joined to the stream lines theme using the key 

field COMID. The attribute of interest from this file is CUMDRAINAG, which is 

the cumulative drainage area for the stream segment.

Watershed boundary

Source: NHDPlusV1

NHDPlusV1 download file name: Subbasin.shp

Format: ESRI shapefile

Description: This layer is an ancillary shapefile that contains watershed boundaries, 

which may be used to clip the elevation raster and hydrography shapefiles.

Average annual precipitation

Source: PRISM (Daly and others 1994, 1997) recommended; however, other sources 

are acceptable

Download file name: Varies with source

Format: ESRI grid or ESRI shapefile

Description: This is a required input if the flood height option is activated for the 

VCA input parameters; however, only a single integer value is required as input.

Input and Ancillary GIS Data Download Instructions

Obtain NHDPlus data for the production unit of interest from the website: 

http://www.horizon-systems.com/nhdplus/

Note that these instructions were written for NHDPlusV1. These data can be obtained 

from the archive site at:

http://www.horizon-systems.com/NHDPlus/NHDPlusV1_home.php.

Data are downloaded under the “Additional information” heading at this website.

NHDPlusV1 data were used to develop the VCA; however, since that time NHDPlus 

Version 2 (NHDPlusV2) data have been released. NHDPlusV2 data may be substituted 

for NHDPlusV1. The processing instructions will be very similar, but may require 

substitution of certain file names. See the RMRS valley confinement website for more 

information.

Download Instructions:

1. Download the elevation GIS raster, which will have a file description similar to: 

Region 17, Version 01_02, Elevation Unit a

2. Download the NHDPlus stream lines, water bodies, and subbasins, which will have 

a file description similar to: Region 17, Version 01_02, National Hydrography 

Dataset

3. Download the associated attribute tables, which will have a file description similar 

to: Region 17, Version 01_04, Catchment Flowline Attributes
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

Input and Ancillary GIS Data Pre-processing Instructions

These pre-processing instructions should provide an intermediate level GIS user with 

enough information to prepare the input data files for the VCA program. An intermediate level of GIS theory and ArcGIS knowledge is necessary to complete these steps.

Map projection—Choosing an appropriate map projection for processing the data is 

an important consideration. The elevation raster resides in the following native projection when downloaded from the NHDPlus website:

Albers Conical Equal Area

Standard Parallel 1: 29.5

Standard Parallel 2: 45.5

Longitude of Central Meridian: -96.0

Latitude of Projection Origin: 23.0

False Easting: 0.0

False Northing: 0.0

Datum: NAD 83

This projection may not be appropriate for all study areas. The user may select a 

more appropriate projection such as Universal Transverse Mercator (UTM) if desired, 

which will require projecting the elevation raster to the desired coordinate system. In 

doing so, it is important to intentionally select an output cell size of 30 m to match the 

native cell size. In addition, the bilinear interpolation or cubic convolution resampling 

algorithm should be used.

The vector GIS data from the NHDPlus dataset (stream lines, water bodies, and subbasins) reside in the geographic coordinate system (latitude-longitude). Because this 

coordinate system is based on units of degrees rather than meters, these shapefiles must 

be projected to a rectangular coordinate system, such as Albers Conical Equal Area or 

UTM. It is assumed that the user is experienced in projecting raster and vector data and 

can accomplish these tasks without detailed instructions.

The workflow for projecting the various GIS layers will vary by user and is complicated by the fact that the NHDPlus elevation and hydrography data reside in different 

projections. It will be incumbent on the user to devise a workflow for reconciling the 

GIS layers into a single projection for processing with the VCA.

Watershed boundary—The subbasin shapefile will have been extracted to a folder 

named \HydrologicUnits. The subbasin data reside in the geographic coordinate system 

and should first be projected to the NHDPlus Albers coordinate system. This step will 

produce a subbasin dataset matching the projection of the elevation data. Next, a single 

watershed should be selected from the projected subbasin dataset to use for clipping the 

elevation data. The extent of a single subbasin is an appropriate size for implementing 

a VCA run. Subbasins are defined by 8-digit or 4th code hydrologic units (Seaber and 

others 1987).

Elevation GIS raster—After unzipping the download file, the DEM data will reside 

in a folder named \Elev_Unit_x. The DEM raster is named elev_cm. This DEM has 

vertical units of cm that must be converted to units of meters.

To convert the units with ArcGIS 10 use the Raster Calculator tool as follows:

Arc Toolbox > Spatial Analyst Tools > Map Algebra > Raster Calculator

Raster calculator input box:

Float(elev_cm)/100

Use the subbasin shapefile to extract the elevation data from the NHDPlus DEM.

Spatial Analyst Tools > Extraction > Extract by Mask.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 17

Fill in the required fields. Click the “Environments...” button and then the “Processing Extent” option.

Extent: Use the selected subbasin shapefile name

Snap Raster: Use the DEM name

Output: elevclip

This clipped raster may now be projected to the user’s study area projection, if desired.

Stream lines and water bodies—Use the same selected subbasin shapefile to clip 

the NHDPlus stream lines from the shapefile called nhdflowline.shp, within the \Hydrography folder:

Analysis Tools > Extract > Clip

Output: FlowLine.shp

Also clip the shapefile called NHDWaterbody.shp using the same procedure.

Output: Waterbody.shp.

Project the shapefiles to the appropriate projection as necessary.

Drainage area attribute—Find the table flowlineattributesflow.dbf in the folder \

NHDPlusXX. This file will need to be joined to the shapefile FlowLine.shp. Implement 

the join using COMID as the common ID. Export to a new shapefile.

Output: FlowLineJoin.shp.

The flow line shapefile, water body shapefile and DEM will be used as input by 

the valley bottom algorithm. All of the files should now reside in the same projection.

Precipitation—Download data from the PRISM Climate Group (Daly and others 

1994, 1997) or the nationalatlas.gov website. Project the GIS data as necessary and 

convert units from inches to centimeters if required.

Substituting Higher Resolution Input Data

Higher resolution data such as 10 m National Elevation Dataset (NED) DEMs and 

1:24,000 scale National Hydrography Dataset (NHD) may be substituted for the NHDPlus data. However, care must be taken to ensure that the substituted data adhere to 

certain consistency standards.

1. It is always a good idea to clip the DEM, stream lines, and water bodies to a watershed boundary. This step ensures that all layers have the same extent and that 

streams and valleys maintain their full extent.

2. If 1:24,000 scale NHD data or other stream lines are used, the shapefile must contain a numeric attribute field called CUMDRAINAG. This field must be populated 

with a number, such as the value 1, even if it is a mock number used to satisfy the 

program requirements. The CUMDRAINAG attribute has two important functions 

in the VCA program. First, it is used for computing the modeled bankfull depth 

that, in turn, is used by the flooding function. Second, it is used to exclude streams 

with a drainage area smaller than the user-defined threshold.

3. If water bodies such as lakes or reservoirs are present in the study area, a water 

body shapefile, defined as polygon type, should be input into the program. The 

shapefile may come from the NHDPlus data, another source, or be hand digitized. 

This layer will prevent water bodies from being identified as unconfined valley 

bottoms.
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

Program Inputs, Parameters, and Output

The program inputs, parameters, and output are defined through the Arc Toolbox user 

interface (figure 9) The interface options are described below.

Inputs

Workspace location—The computer location where the input data are stored and 

where the output shapefile will be saved. Scratch data are also written to this folder 

and cleaned up at the end of the process. Available scratch space should equal 12 

times the size of the input DEM.

Input DEM—Name of the input 10- or 30-m digital elevation model (DEM), derived from the NHDPlus data, USGS National Elevation Dataset, or other source.

Input streams—Name of the input flow line data derived from the NHDPlus 

dataset or other source.

Input waterbodies—Name of the input waterbodies shapefile derived from the 

NHDPlus dataset or other source.

Parameters

Output valley type—Two output products are available: (1) unconfined valley 

bottoms only, or (2) unconfined valleys with distance measures along the stream 

network to the nearest unconfined valley.

Maximum ground slope threshold—A ground slope threshold with units of percent 

slope. Only grid cells below the threshold will be preserved to represent unconfined valley bottom extent.

Figure 9—Screen shot of the Arc Toolbox VCA interface.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 19

Flood factor—The flood factor is a dimensionless value that is multiplied by the 

estimated bankfull depth to generate a nominal “flood height” above the channel elevation (e.g., Hall and other’s (2007) figure 2b). Bankfull depth (hbf, m) is 

empirically predicted by the VCA as a function of drainage area (A, km2) and 

average annual precipitation (cm/yr) by combining hydraulic geometry equations 

developed by Hall and others (2007) for channels in the Columbia River Basin: 

hbf=0.054A0.170P0.215. The flood height (m) is used to conceptually flood the valley floor to a variable height above the channel elevation. Only grid cells that are 

“submerged” are preserved.

Average annual precipitation—The precipitation value (cm/yr) is used, as described 

above, to predict bankfull depth and flood height. An average annual precipitation value equal to the highest estimate in the watershed is recommended. Using 

the highest estimate will produce a more liberal valley bottom extent, helping to 

compensate for the DEM rounding of valley bottoms discussed above (figure 8).

Maximum valley width—This parameter allows the user to select a width (m) for 

clipping the extent of the valley floor orthogonal to the channel. This parameter 

is useful in very low relief regions where a valley side slope is not portrayed in 

the DEM data, causing the valley bottom polygon to extend beyond the influence 

of the stream channel.

Minimum drainage area—This parameter deletes valley bottom polygons that are 

smaller than the user-defined minimum drainage area (km2).

Minimum stream length—This parameter will delete valley bottom polygons that 

do not contain the specified minimum total stream length (m).

Minimum valley area—This parameter is used to delete valley bottom polygons 

under a specified minimum area (m2).

Output

Output Shapefile—The name of the output shapefile.

Algorithm Sequence

The input GIS layers work together to create intermediate files that will vary based 

on the input data characteristics and user supplied parameters. These intermediate files 

are referred to as variables and fall into two general categories: (1) valley bottom extent 

variables, and (2) valley bottom exclusion variables. The valley bottom extent variables 

control polygon initiation and the extent of the valley bottom area. The valley bottom 

exclusion variables operate on the output from the extent procedures to eliminate selected 

valley bottom polygons based on user-defined criteria. All calculations are completed 

using standard programs within ArcGIS version 10.0.

Controlling Valley Bottom Polygon Initiation and Size—Valley Bottom Extent 

Variables

Four rasters are computed and used as variables for controlling valley bottom initiation 

and extent: (1) slope cost distance, (2) flood height, (3) ground slope, and (4) maximum 

valley width. A threshold is applied to each variable to generate a binary (0 or 1) intermediate output. Each output grid has equal weight in the algorithm. When overlain 

together, each theme must have a value of 1 to instantiate a valley bottom cell. Variable 

1 above sets the gross extent of unconfined valleys. Variables 2 and 3 refine the gross 

valley bottom extent, while variable 4 is a user-defined channel buffer that restricts the 

unconfined valley width.
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

Variable 1—Slope cost distance

Function: Generates an initial valley bottom domain that is refined by subsequent 

procedures in the algorithm.

The slope cost distance variable is derived from the ArcGIS Cost Path tool. A rasterized version of the stream channel is used as the algorithm source. This cell-by-cell 

operation computes accumulated cost, using a cost grid, as the focal cells are processed 

with increasing distance from the source cells (stream channel). The cost grid for this 

operation is the result from the ArcGIS Slope tool, with percent rise as the measurement 

parameter. The cost at each cell is computed as the cell size (10 or 30 m) multiplied by 

the cell slope (percent). As distance from the stream channel increases, the accumulated 

product of slope and distance are computed. In wide unconfined valleys with relatively 

low ground slope values, the slope cost distance measure increases gradually, whereas 

in confined valleys with steep side slopes the value increases rapidly. Empirical testing in the Intermountain West indicates that a slope cost distance threshold of 2,500 

adequately captures an initial valley bottom domain that can be refined by further processing. This variable has no physical meaning and is simply an empirical rule that is 

used to set the initial processing domain for subsequent operations in the algorithm. The 

variable is intended to capture a relatively low-sloped domain near the stream network 

and eliminate low slope features outside of valleys. The variable threshold intentionally 

overestimates valley extent to ensure that all legitimate valley bottom cells are included 

for subsequent processing.

Variable 2—Flood factor

Function: Controls the lateral extent of valley bottom polygons by conceptually 

raising the stream water surface above the channel elevation.

The flood factor is a user-defined multiplier that conceptually raises the stream water 

surface to “flood” the valley to a specific height above the channel elevation. Bankfull 

depth is predicted by the VCA using an equation developed by Hall and others (2007) as 

described in the previous section. The flood factor variable is multiplied by the predicted 

bankfull depth to determine the flood height for each stream segment. Stream segments 

are defined by the NHDPlus data model.

Selecting the most appropriate flood factor is an important component for regulating the VCA results. Previous investigators have used similar approaches for defining 

valley extent, but different flood heights have been proposed. For example, based on 

field studies Rosgen (1994, 1996) defined the flood prone extent of a valley as the width 

measured at an elevation twice the maximum bankfull depth. This value roughly corresponded with the 50-year flood stage or less (Rosgen 1994). However, investigators 

working with DEM data have found that larger flood height multipliers are needed. Using 10-m DEM data, Hall and others (2007) found that an elevation of three times the 

bankfull depth provided the best results for estimating the historical floodplain width. 

In contrast, Clarke and others (2008) used a factor of five times the bankfull depth to 

estimate the elevation for measuring valley-floor width when using 10-m DEM data. 

This seemingly high multiplier is necessary because, as discussed above, the rounding of valley bottoms by the DEM may sometimes generate stream channels that are 

inset deeper into the valley than would be encountered in the field (figure 8). The VCA 

flood factor parameter defaults to a value of 5; however, a value of 5-7 is recommended 

based on the user’s familiarity with the terrain and field observations. Comparison of 

predicted and observed valley extent for field sites in central Idaho (Part III) indicates 

that a flood factor of seven is most appropriate for 30-m DEMs. The coarser vertical 

resolution of 30-m DEMs relative to 10-m data requires a larger flood factor (7 vs. 5) 

to obtain similar results.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 21

For the VCA, the unconfined valley bottom is defined by the “flooded” area below 

the elevation where the flooded height intersects the valley side slope. This extent is 

further modified by the ground slope variable.

Variable 3—Ground slope

Function: Sets an upper ground slope threshold controlling the extent of valley 

bottom polygons.

Ground slope is generated from the input DEM using the ArcGIS Slope tool. Slope 

is computed by querying the eight neighboring cells to the focal cell and finding the 

neighbor with the maximum elevation change. Although the VCA queries all nearest 

neighbors, rather than specifically examining lateral slopes, the steepest slope generally 

becomes a lateral value as cells approach confining features. Percent slope is computed 

by dividing the elevation change (rise) by the distance between cell centers (run), and 

multiplying by 100. Only cell values less than the user-defined slope criteria will be 

included in the output unconfined valley bottom layer. A default slope threshold of 9% 

is used by the VCA. This value was derived empirically by generating transects across 

30-m DEMs in unconfined valleys that were identified from field surveys in central 

Idaho (Part III). The slope value of 9% seems high; however, valley bottoms in USGS 

DEMs are generally modeled at a higher gradient than their true value on the ground 

(Nagel and others 2010) (figure 8). Users are advised to run the ArcGIS slope program 

independently to estimate an appropriate slope threshold value for the project area, prior 

to running the VCA.

Variable 4—Maximum valley width

Function: Controls lateral width of valley bottom polygons at a specific userdefined value.

This parameter is meant to confine the valley extent within wide floodplains and 

plateau regions. The width parameter (m) includes the entire valley width on both sides 

of the stream.

Eliminating Valley Bottom Polygons—Valley Bottom Exclusion Variables

Valley bottom polygons can be eliminated from the above data set based on the following criteria.

Variable 5—Drainage Area

Function: Eliminates valley bottom polygons where all of the stream segments 

are smaller than the minimum specified value (km2).

Variable 6—Stream Length

Function: Eliminates valley bottom polygons with a cumulative internal stream 

length less than the minimum specified length (m).

This variable is included for fisheries applications where a minimum stream length 

is necessary to support biological requirements. Total stream length is computed for 

each valley bottom polygon. The sum is calculated using all stream segments within the 

polygon, including streams smaller than the minimum drainage area. Polygons with a 

total stream length smaller than the cumulative stream length threshold will be eliminated.

Variable 7—Valley Area

Function: Eliminates valley bottom polygons smaller than a specified area (m2).
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

Output Shapefiles

Two output products are possible from any given input dataset as described below. 

Only one product may be generated per algorithm run.

Output Valley Type 1—Valley Bottoms Only

Choosing Output Valley Type 1 as the output type will produce a polygon shapefile 

that only includes the unconfined valley bottoms. The polygon outline for each valley 

is smoothed to remove the stair-stepped appearance resulting from the underlying raster 

DEM. The shapefile will have an attribute field called VB_CLASS. All records will be 

populated with value = 1 (figure 10).

Output Valley Type 2—Valley Bottoms With Distance Measures

Choosing Output Valley Type 2 as the output will produce a polygon shapefile that 

includes unconfined valley bottoms as well as confined stream reaches (both represented as polygons). Each polygon feature is attributed with distance along the stream 

network to the nearest unconfined valley bottom. The polygon outline for each valley 

is not smoothed for this output type because the linear nature of the confined valleys 

does not support this procedure (figure 11).

The attribute field will contain integer records representing the following classes:

0—Valley bottom polygon

1-30—Network distance to nearest unconfined valley bottom (km)

31—Distance greater than 30 km

50—Lakes and reservoirs

Figure 10—Valley Type 1 with smoothed output polygon and example attribute table. VB_

CLASS is equal to value 1 for all records in the attribute table.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 23

Part III—Field Assessment ______________________________

Approach

Field data were collected to characterize channel conditions associated with confined 

and unconfined valleys mapped by the algorithm in two study areas of central Idaho 

(figure 12). The first study area was within the Secesh River drainage, located in the 

northwestern portion of the South Fork Salmon River basin, which is comprised of 

montane, mesic forests and meadows, with numerous high alpine lakes and a variety 

of channel morphologies. The second study area covered the South Fork Boise River 

basin, a more arid and lower elevation watershed, with mesic to dry forest and shrubland 

environments, also containing a variety of stream types typical of mountain basins.

The VCA was run for each study area and field sites were deliberately chosen to 

capture a range of valley characteristics. Sites were located near roads and trails for 

accessibility. In general, field sites were paired to produce an equal number of confined 

and unconfined samples. The pairs were often arranged near the boundary between confined and unconfined valley segments predicted by the algorithm, intentionally locating 

one point within, and a second point outside of a valley bottom polygon (figure 13). 

This configuration made data collection more efficient and also allowed us to evaluate 

the spatial delineation of the valley bottom polygons at the geomorphic transition zone 

between confined and unconfined valleys.

Figure 11—Valley Type 2 with stair-stepped polygon outline and example attribute table. The 

VB_CLASS field is attributed with distance along the stream network (km) from the nearest 

unconfined valley bottom. The white polygon is unconfined valley bottom and colored polygons 

represent distance classes along the stream network.
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

Figure 12—Study sites for field measurements and 

accuracy assessment of the VCA.

Figure 13—Example of field site locations relative to unconfined 

valley bottom polygons predicted by the VCA.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 25

Bankfull channel width and valley width were measured at each field site to determine the channel confinement ratio (valley width normalized by bankfull channel 

width) (Bisson and others 2006; Clarke and others 2008; Hall and others 2007; Moore 

and others 2002; WFPB 1993). Channel morphology was noted as one of four types: 

cascade, step-pool, plane-bed, or pool-riffle (Montgomery and Buffington 1997). Channel substrate was classified as either alluvial or bedrock and reach-average grain size 

was visually estimated using Buffington and Montgomery’s (1999) procedure for the 

relative abundance of primary size classes (sand, gravel, cobble, and boulder). Bankfull 

width was measured with a tape to the nearest meter, while valley width was measured 

to the nearest meter using a tape or range finder, depending on the field conditions. Here, 

valley width corresponds with the active (contemporary) floodplain of the channel. In 

measuring valley width, small-scale confining features (e.g., riparian vegetation, wood 

debris, and hummocky topography) were ignored, but larger confining features were 

included (e.g., river terraces and cut banks of entrenched channels; figure 14). Note that 

in the case of entrenched channels, the historic valley might be classified as unconfined, 

but the channel would be classified as confined (i.e., incised into the surrounding valley flat and incapable of flooding the historic valley). Because DEMs typically cannot 

identify channel entrenchment, field observations are needed to identify such cases. In 

these instances where channels were entrenched and confined by their cut bank, the 

confining width was measured at the cut bank rather than the historical floodplain.

The field measurements from both study areas were combined into a single data set. 

Thirty-eight field sites were sampled in the Secesh River basin and 25 in the South Fork 

Boise River basin, for a total of 63 sites.

The channel confinement ratio was computed for field locations to classify each site 

as either confined or unconfined. Channels with a confinement ratio less than 4.0 

were deemed confined (figure 15), whereas those with a ratio greater than or equal to 

4.0 were considered unconfined (figure 16). Threshold values for confinement ranging 

from 2.5 - 5 have been used previously by others (e.g., Clarke and others 2008; Moore 

and others 2002; WFPB 1993). We chose a value of 4.0 to closely align with the threshold 

of 3.8 used by Hall and others (2007). Channel type and grain size observations were 

summarized by confinement class as determined by this method, and channel confinement was compared with valley confinement predicted by the VCA.

Findings and Discussion

Measured channel confinement ratios were plotted against the bankfull channel width 

for each site and compared with the VCA predictions of valley confinement (figure 17). 

Results show that channel confinement agreed with valley confinement 79% of the time. 

Further inspection of the data revealed that disagreement between channel and valley 

confinement occurred due to VCA prediction errors. Although the prediction errors were 

small overall (79% correct classification), erroneous predictions of unconfined valleys 

were most common in locations with wide channels (bankfull width >20 m), while 

incorrect predictions of confined valleys occurred in locations with narrow channels 

(bankfull width of about 10 m or less) (figure 17).
26 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

Figure 14—Examples of wide valley bottoms with entrenched channels, showing the elevation 

of the abandoned (historic) floodplain.

Abandoned floodplain

Typical high water

Abandoned 

floodplain
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 27

Figure 16—Example of an unconfined channel (ratio of valley width to bankfull width ≥ 4).

Figure 15—Example of a confined channel (ratio of valley width to bankfull width < 4).
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

Of the 13 sample sites that were incorrectly classified, eight were errors of commission, where the valley bottom was delineated as unconfined by the VCA but deemed 

confined in the field. Five of these errors occurred because the channel width was more 

than two times the DEM cell size (cell size 10 m, channel width > 20 m). As a result 

the algorithm interpreted the stream surface as an unconfined valley bottom. Two of 

the errors occurred because the valley was too narrow to be resolved by the DEM and 

historic terraces were misidentified as unconfined valleys. An additional error occurred 

because the channel was incised, so the confining feature measured in the field was 

narrower than the historical floodplain identified by the algorithm in the DEM. 

Five of the errors were errors of omission where valleys were not delineated by the 

VCA, but were deemed unconfined in the field. The errors of omission often occurred 

because the DEM did not consistently provide a good representation of narrow flat valley 

bottoms. As noted previously (figure 8), valley bottoms are sometimes over-steepened 

by the DEM due to a lack of guiding contours on the valley floor. Field observations 

and predicted valley types are shown in Appendix A.

The field measurements further show that all of the streams in both study areas had 

alluvial beds, except for one confined channel that was predominantly bedrock. Channel morphology varied substantially with confinement, as expected from prior studies 

(e.g., Montgomery and Buffington 1997). Unconfined channels were primarily poolriffle (63%) and plane-bed streams (30%) (figure 18). In contrast, confined channels 

exhibited predominantly step-pool (58%) and plane-bed morphologies (39%). Example 

photographs of each channel type are given in Appendix B (figures B1-B4).

As expected, we also found that unconfined channels exhibited smaller grain sizes, 

with cobble and gravel sediments dominating, while boulder- and cobble-sized material 

were more prominent in confined channels (figure 19). Example photographs of each 

substrate type are given in Appendix C (figures C1-C4).

Our observed values of channel confinement, channel type, and grain size in each 

valley type are generally consistent with those of prior investigations, indicating that the 

VCA is identifying unconfined valleys with characteristics similar to those previously 

studied (Bisson and others 2006; Coulombe-Pontbriand and Lapointe 2004; Clarke 

Figure 17—Field measurements of channel confinement ratio (valley width/bankfull width) 

plotted against bankfull width. The channel confinement threshold (dashed line) is compared 

with VCA predictions of valley confinement for each site (blue circles = unconfined valleys; red 

diamonds = confined valleys).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 29

Figure 18—Composition of channel types within unconfined and confined channels.

and others 2008; Hall and others 2007; Montgomery and Buffington 1997; Moore and 

others 2002; Rosgen 1996; Wilkins and Snyder 2011; WFPB 1993). This finding does 

not validate the VCA results sensu stricto, but confirms that the algorithm is consistent 

with studies published in other geographic regions. However, our results may be biased 

because the field sites were not randomly selected and the actual population of confined 

and unconfined valleys along the stream network is greatly imbalanced, with a higher 

incidence of confined valleys over the landscape. For a more unbiased evaluation, the 

sampling of each valley type should be in proportion to its occurrence within the stream 

network in order to detect type 1 and 2 errors at equal rates (Snedecor and Cochoran 

1978; Zar 1999). In addition, because the field sample sites were paired near the transition between confined and unconfined valleys, results may be sensitive to how well the 

VCA locates the boundary between the two valley types; consequently, our agreement 

between channel confinement and valley confinement is likely lower than if the sites 

were assigned randomly and located near the center of each valley type. To account for 

these concerns, a more rigorous assessment of accuracy is provided in Part IV.

Figure 19—Proportion of grain size by channel confinement class.
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

Part IV—Statistical Validation ___________________________

Methods

An office assessment of the VCA was conducted for the two study areas (figure 12). 

Two sources were used for verifying VCA predictions of valley type: scanned USGS 

1:24,000 scale topographic maps and 1-m digital aerial photography. The VCA was 

run for each study area using the NHDPlusV1 30-m elevation data and 1:100,000 scale 

stream lines. For each study area, 120 random points were generated along the stream 

lines, stratified so that an equal number of points fell within confined and unconfined 

valley types, as identified by the algorithm. For the South Fork Salmon River drainage, 

a balanced sample of 60 sites in each valley type was generated. For the South Fork 

Boise River drainage, 61 points were located within unconfined valleys and 59 points 

within confined valleys. The following VCA program parameters were used to create 

the polygon output.

Output valley type: Type 1, valley bottoms only

Ground slope threshold: 9%

Flood factor: 3

Average annual precipitation: 115 cm/yr

Maximum valley width: 1000 m

Minimum stream length threshold per valley polygon: 360 m

Minimum polygon size threshold per valley polygon: 10,000 m2

The 9% ground slope threshold was chosen based on empirical evidence indicating 

that slopes less than 9% in the DEM likely correspond to unconfined valleys, as discussed in Part II, VCA Software, Algorithm Sequence. The flood factor of 3 was based 

on that recommend by Hall and others (2007) and the average annual precipitation value 

was derived from PRISM data (Daly and others 1997) for the study area. The 1000 m 

maximum width is an arbitrary measure, whereas the 360-m stream length threshold 

was chosen because the narrowest valleys identified by the algorithm are approximately 

60-90 m wide. This threshold helped eliminate smaller spurious circular polygons by 

ensuring that most delineated valley bottoms adhered to an elongated form that was 

about four times longer than the valley width. The minimum polygon size threshold 

was set at an arbitrary size of 10,000 m2, equal to 1 HA.

An unbiased analyst who was unfamiliar with the VCA output for this region conducted 

the analysis. At each point location, the analyst inspected the underlying topographic map 

and aerial photography. Each site was then classified as either confined or unconfined. 

To be classified as unconfined, a valley was required to meet the following criteria:

1. Valley width between confining topographic contours was greater than 60 m. This 

width is approximately the narrowest valley that can be identified by the VCA 

using 30-m DEM data.

2. Distance between upstream and downstream contours along the stream channel 

was greater than 406 m. Given the contour interval of 40 ft. (12.192 m) on the 

topographic maps, 406 m is the shortest length for obtaining a 3% stream gradient. 

This stream gradient approximates the upper limit for the occurrence of plane-bed 

channels (Buffington and others 2004), which is the steepest channel type generally found within unconfined valleys (Part III).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 31

3. Context indicates a relatively flat, unconfined valley bottom considering the following elements:

a. Stream sinuosity. A meandering stream pattern was taken as evidence of an 

unconfined valley.

b. Topographic contour pattern. Specifically, a paucity of contours near the channel along the valley length (indicative of a low-slope, relatively flat valley).

c. Riparian vegetation. Meadow or shrub vegetation was used as an indicator of 

unconfined valleys in predominantly forested areas.

Each sample location was classified as confined or unconfined based on convergence of evidence using the above criteria. Lateral valley width and distance between 

upstream-downstream contours along the channel were used as quantifiable variables. 

Stream sinuosity, topographic contour pattern, and riparian vegetation were used as 

supporting contextual evidence to help make the final classification decision. The contextual characteristics were useful when one of the quantifiable variables did not meet 

the literal measurement criteria, such as when stream line distance between contours 

was less than 406 m.

Results

To understand the results of the validation effort it is important to recognize that our 

sample data are proportionally unbalanced compared to the actual number of unconfined 

and confined valleys typically found in the field. Our sample sites were chosen from the 

VCA model output and we chose roughly 50% of our samples in unconfined valleys and 

50% in confined valleys, with approximately 60 sites in each class. However, within 

the South Fork Boise River basin the length of unconfined and confined streams is 296 

km and 2995 km, respectively, and for the South Fork Salmon River basin the measures 

are 299 km and 2301 km, respectively. This imbalance between the class populations 

would require that we modify our sample proportions to mimic the stream length in order 

to detect false positive errors and false negative errors at equal rates. However, if the 

number of sample sites were stratified proportionally to the stream length in each class 

(assuming 100 sample sites) we would have only 9 sites within unconfined valleys, but 

91 sites within confined valleys. To account for this problem, we report our results in 

two confusion matrices; one standard matrix (balanced sample) and one adjusted (unbalanced sample reflective of the expected occurrence of each valley type) (Baldi and 

others 2000). The matrices convey the results as described in table 1, where a positive 

event is associated with unconfined valley polygons and a negative instance refers to 

confined features. For example, a true positive occurs where a predicted unconfined valley was confirmed by the topographic and photographic data. A true negative indicates 

Table 1—Sample confusion matrix. The predicted values represent 

the output from the VCA and the actual values represent 

results from the office assessment based on topographic 

maps and aerial photography.

Actual

Predicted Unconfined Confined

Unconfined True positives (TP) False negatives (FN)

Confined False positives (FP) True negatives (TN)

Column count Pc Nc
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

a correctly classified confined valley. False results imply an incorrect classification. 

The accuracy can then be computed as:

Accuracy = (TP + TN) / (PC + NC)

where TP and TN are true positive and true negative counts, respectively. Pc and Nc are 

the positive and negative column counts, respectively, which sum to the total number 

of samples. The above estimate of accuracy should be used with caution since it is not 

adjusted for the expected occurrence of each valley type; as such, we have a low probability of detecting false positives because few samples fall within confined valleys, 

relative to their abundance in the data set. To account for this issue an adjusted confusion 

matrix accompanies each study area, providing normalized values based on the expected 

proportion of each valley type. In addition, a Matthews correlation coefficient (Baldi 

and others 2000; Matthews 1975) is computed from the normalized table to assess the 

significance of the accuracy measure of the original sample set. The procedure yields a 

statistic between -1 and 1, with -1 indicating all false predictions, 0 implying completely 

random predictions, and 1 indicating perfect predictions. The procedure also yields a 

p-value, which represents the commonly understood significance measure, indicating 

whether the result is statistically different from zero.

For the South Fork Boise River sites, we observed that 109 samples were correctly 

classified for an accuracy of 91% for the unadjusted data (table 2). The adjusted confusion table was computed for these data (table 3) and a Matthews correlation coefficient 

of 0.77 was obtained, with a p-value of <0.00001.

Likewise for the South Fork Salmon River basin, we observed that 107 samples 

were correctly classified for an accuracy of 89% for the unadjusted data (table 4). The 

adjusted confusion table was computed for these data (table 5) and a Matthews correlation coefficient of 0.70 was obtained, with a p-value of <0.00001.

Table 3—Adjusted confusion matrix for the South 

Fork Boise River study area.

Actual

Predicted Unconfined Confined

Unconfined 10.28 3.6

Confined 1.72 102.73

Column count 12 106.33

Table 2—Confusion matrix for the South Fork 

Boise River study area.

Actual

Predicted Unconfined Confined

Unconfined 52 2

Confined 9 57

Column count 61 59
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 33

The overall, unadjusted accuracy of 89-91% for our study sites indicates good 

performance of the algorithm relative to the 85% accuracy standard that is often cited 

for classifying raster remote sensing data (Anderson and others 1976). The Mathews 

coefficients (0.70-0.77) indicate that the true accuracy may be lower than the above 

unadjusted values, but nonetheless demonstrate a high level of correct classification 

(Mattews coefficients closer to 1 than not). The highly significant p-values for both 

study areas indicate that a sufficient number of samples were tested for computing the 

Mathews coefficient.

The above analysis indicates that the VCA is reliable, producing accuracy results 

similar to other landscape scale classification efforts (Clarke and others 2008; Hall and 

others 2007). However, accuracy may vary by geographic region and use of the VCA 

should be limited to montane areas where the algorithm has been validated. We recommend that users conduct additional field work or consult independent digital sources, 

such as quadrangle maps and remotely sensed data, to ascertain the reliability of the 

VCA in untested landscapes.

Part V—Online Database _______________________________

An online database of valley bottom confinement is available for an area covering 

most of the native range of inland cutthroat trout, except the Rio Grande and Arkansas 

basins. This database primarily encompasses the montane regions of Colorado, Idaho, 

Montana, Nevada, Utah, and Wyoming (figure 20) and is organized by USGS 4th code 

Hydrologic Units. These data were generated to support the research effort published 

in Wenger and others (2011) to study the distribution trends of cutthroat trout and other 

trout species.

Table 4—Confusion matrix for the South Fork 

Salmon River study area.

Actual

Predicted Unconfined Confined

Unconfined 51 4

Confined 9 56

Column count 60 60

Table 5—Adjusted confusion matrix for the South 

Fork Salmon River study area.

Actual

Predicted Unconfined Confined

Unconfined 13.25 6.96

Confined 2.34 97.45

Column count 15.59 104.41
34 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

The data were generated using an earlier version of the VCA, called VCA Cutthroat 

Trout (VCA-CT). There are three primary differences between the VCA-CT and the 

current VCA version described in this document:

1. The VCA-CT version used an additional 5 x 5 range filter to refine the Euclidean 

distance * slope variable.

2. The VCA-CT version did not utilize the flood height variable.

3. The VCA-CT version used a stream discharge variable for identifying the minimum 

stream size to be mapped, rather than the cumulative drainage area threshold.

The following parameters were used to generate the VCA-CT database:

Output valley type – Output option 2, flat valleys with distance measures.

Euclidean distance * slope – 850

5 x 5 range filter – 2500 (A VCA-CT option only, not found in the current version of VCA)

Ground slope – 8%

Flood height – Not used

Maximum valley width – 500 m

Drainage area – This variable was not used. A mean stream discharge value of 

3.19 feet per second was used instead.

Stream length – 1500 m

Valley area – 3700 m2

Figure 20—Extent of USGS 4th code Hydrologic Units processed for the VCA-CT database.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 35

These VCA-CT data may be downloaded from the Forest Service Valley Confinement website at:

http://www.fs.fed.us/rm/boise/AWAE/projects/valley_confinement.shtml.

Part VI—Summary_____________________________________

This report describes the Valley Confinement Algorithm (VCA), a GIS program that 

delineates unconfined valley bottoms and optionally computes the distance along the 

stream network to the nearest valley bottom for fisheries applications (Wenger and others 2011). Although prior studies have developed similar algorithms, to our knowledge 

this is the only such program that accepts extensive user input to vary the results for 

the intended application. As such, the algorithm should appeal to a variety of users for 

applications in hydrology, geomorphology, aquatic biology, and riparian studies.

The VCA was developed for use at landscape scales and should provide a good approximation of the total area and location of unconfined valley bottoms at a mapping 

scale of about 1:50,000 – 1:100,000. Field measurements demonstrated that channel 

confinement agreed with VCA predictions of valley confinement 79% of the time and 

that channel characteristics were similar to those documented in other studies of confinement. In addition, and desk audits using topographic maps and aerial photography 

showed that the algorithm correctly identified unconfined valley bottoms with an unadjusted accuracy of 89 - 91% at our sample locations.

Although the above results are encouraging, users should be aware of limitations 

imposed by the resolution (cell size) of the DEM. In particular, because stream width 

cannot be ascertained from DEM data, the algorithm will identify wide streams greater 

than twice the width of the DEM resolution (60 m for 30-m resolution DEMs and 20 

m for 10-m resolution DEMs) as unconfined valleys, even though the true field condition may be confined. Therefore, care should be exercised in interpreting results where 

narrow unconfined valleys are predicted in areas containing wide channels. In addition, 

the VCA has difficulty resolving unconfined valleys that are narrower than about two to 

three times the DEM cell size (60-90 m in width for a 30-m DEM or 20-30 m in width 

for a 10-m DEM).

The VCA is one of many emerging GIS tools focused on riverine resources. Recent 

emphasis on landscape scale analysis highlights the importance of these tools for meeting 

management goals over regional domains. With a growing body of research emphasizing the importance of valley bottom confinement for aquatic and riparian applications, 

we believe that the VCA will be a useful tool for mapping and managing resources in 

riverine landscapes of mountain basins.

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Appendix A. Field Collected Data

ID STR_WDTH VAL_WDTH RATIO STRM_TYPE GRAIN SIZE

S1-1 3 374 124.7 Pool-riffle NR

S2-1 3 80 26.7 Pool-riffle NR

S3-1 3.5 98 28.0 Pool-riffle NR

S7-1 8.5 70 8.2 Pool-riffle Gravel

S8-1 5.5 22 4.0 Plane-bed NR

S9-1 3.5 64 18.3 Step-pool NR

S11-1 5.3 120 22.6 Pool-riffle Gravel

S12-1 6 12.5 2.1 Step-pool Boulder

S13-1 15 88 5.9 Pool-riffle Cobble

S14-1 12.5 180 14.4 Pool-riffle Cobble

S21-1 4.5 72 16.0 Pool-riffle Gravel

S26-1 24 34 1.4 Plane-bed Cobble

S27-1 29 71 2.4 Plane-bed Cobble

S28-1 5.5 90 16.4 Pool-riffle Sand, gravel

S29-1 18 170 9.4 Pool-riffle Gravel, cobble

S30-1 14 275 19.6 Pool-riffle Sand

S31-1 7 38 5.4 Pool-riffle Gravel

S32-1 19 95 5.0 Step-pool NR

S34-1 12.5 80 6.4 Pool-riffle Sand, gravel

S35-1 7 270 38.6 Pool-riffle Sand

S40-0 3 6.5 2.2 Step-pool NR

S41-0 7 72 10.3 Pool-riffle NR

S42-0 7 62 8.9 Pool-riffle NR

S43-0 6 16 2.7 Step-pool Cobble

S45-0 6 12 2.0 Step-pool Boulder, gravel

S50-0 4.5 5.5 1.2 Step-pool Boulder

S51-0 22 32 1.5 Plane-bed Cobble, boulder

S52-0 20 24 1.2 Plane-bed Cobble, boulder

S57-0 5 10.5 2.1 Step-pool Cobble, boulder

S58-0 17 23 1.4 Step-pool NR

S59-0 10.2 21.4 2.1 Step-pool Boulder

S60-0 11 22 2.0 Step-pool NR

S61-0 13 19 1.5 Step-pool Boulder

S62-0 12.7 26 2.0 Plane-bed Boulder

S63-0 7 7 1.0 Cascade Boulder

S65-0 4 6 1.5 Step-pool Cobble

S71-0 19.5 26 1.3 Plane-bed NR

S72-0 5 5 1.0 Step-pool Boulder

B6-1 18 96 5.3 Plane-bed Cobble, boulder

B7-0 18 60 3.3 Plane-bed Cobble, boulder

B8-1 16 122 7.6 Plane-bed Cobble

B9-0 16 30 1.9 Step-pool Cobble, boulder

B10-0 11 74 6.7 Plane-bed Cobble, gravel

B11-1 9 181 20.1 Plane-bed Cobble, gravel

B12-1 11 91 8.3 Plane-bed Cobble

B13-0 10 41 4.1 Pool-riffle Sand, gravel, cobble

B14-0 9 20 2.2 Plane-bed Gravel, sand, cobble

B15-1 10 260 26.0 Pool-riffle Gravel, cobble

B18-1 4 60 15.0 Plane-bed Cobble, sand

B19-0 4 10 2.5 Step-pool Cobble

B20-1 13 90 6.9 Pool-riffle Cobble, sand

B21-0 6 8 1.3 Step-pool Cobble, gravel, sand, boulder

B30-0 1.5 16 10.7 Step-pool Boulder, sand

B33-1 31 182 5.9 Plane-bed Cobble, boulder

B34-0 2 5 2.5 Step-pool Sand, gravel

B35-1 30 59 2.0 Plane-bed Cobble, boulder

B36-1 38 90 2.4 Plane-bed Cobble, boulder

B37-0 26 30 1.2 Step-pool Boulder, cobble

B38-1 20 22 1.1 Plane-bed Cobble

B39-1 3 4 1.3 Step-pool Gravel, cobble

B40-1 48 504 10.5 Plane-bed Cobble, boulder

B1x-1 30 86 2.9 Plane-bed Cobble, boulder

B1x-0 26 30 1.2 Plane-bed Boulder, cobble

Key

ID – Unique ID for each site. 1 = unconfined valley delineated by VCA, 0 = confined valley.

STR_WDTH – Bankfull width measured in the field

VAL_WDTH – Valley width measured in the field

RATIO – Confinement ratio (VAL_WDTH/STR_WDTH)

STRM_TYPE – Channel morphology (cascade, step-pool, plane-bed, pool-riffle) (Montgomery and Buffington 1997)

GRAIN SIZE – Grain size observed in the field. NR = none recorded 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014 41

Appendix B: Examples of Channel Types

Figure B4—Pool-riffle channel type, most common in 

unconfined valleys.

Figure B1—Cascade channel type 

found almost exclusively in confined 

valleys.

Figure B2—Step-pool channel type most common in 

confined valleys.

Figure B3—Plane-bed channel type, often intermediate or 

transitioning between confined and unconfined valleys.
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-321. 2014

Appendix C: Examples of Substrate Types

Figure C4—Boulders found most often in confined 

valleys.

Figure C1—Sand substrate commonly found in unconfined 

valleys.

Figure C2—Gravel substrate typical of unconfined valleys.

Figure C3—Cobble substrate common in either confined or 

unconfined valleys.

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