The Blurred Line between Form and Process: A Comparison of Stream Channel Classification Frameworks
The Blurred Line between Form and Process: A Comparison of Stream Channel Classification Frameworks
Alan Kasprak 0 2 3 4
Nate Hough-Snee 0 2 3 4
Tim Beechie 0 1 2 4
Nicolaas Bouwes 0 2 4
Gary Brierley 0 2 4
Reid Camp 0 2 3 4
Kirstie Fryirs 0 2 4
Hiroo Imaki 0 2 4 5
Martha Jensen 0 2 3 4
Gary O'Brien 0 2 3 4
David Rosgen 0 2 4
Joseph Wheaton 0 2 3 4
0 Current address: U.S. Geological Survey, Grand Canyon Monitoring and Research Center , Flagstaff, AZ, 86001 , United States of America
1 Fish Ecology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration , Seattle, WA 98112 , United States of America, 4 Eco Logical Research , Providence, UT , United States of America, 5 School of Environment, University of Auckland , Auckland , New Zealand , 6 Department of Environmental Sciences, Macquarie University , Sydney , Australia
2 Funding: Support for this manuscript was provided by grants from the Bonneville Power Administration to Eco Logical Research (BPA Project Number: 2003- 017), Inc. and subsequent grants from ELR to Utah State University (USU Award ID: 100652). NH-S was supported in part by STAR Fellowship Assistance Agreement no. 91768201 - 0 awarded by the U.S. Environmental Protection Agency (EPA). This research has not been formally reviewed by the EPA , USA
3 Department of Watershed Sciences, Utah State University , Logan, UT 84322-5210 , United States of America, 2 Ecology Center, Utah State University , Logan, UT, 84322-5210 , United States of America
4 Editor: Julia A. Jones, Oregon State University , UNITED STATES
5 Pacific Spatial Solutions, Reston, VA, United States of America , 8 Wildland Hydrology, Fort Collins, CO, 80524 , United States of America
Stream classification provides a means to understand the diversity and distribution of channels and floodplains that occur across a landscape while identifying links between geomorphic form and process. Accordingly, stream classification is frequently employed as a watershed planning, management, and restoration tool. At the same time, there has been intense debate and criticism of particular frameworks, on the grounds that these frameworks classify stream reaches based largely on their physical form, rather than direct measurements of their component hydrogeomorphic processes. Despite this debate surrounding stream classifications, and their ongoing use in watershed management, direct comparisons of channel classification frameworks are rare. Here we implement four stream classification frameworks and explore the degree to which each make inferences about
hydrogeomorphic process from channel form within the Middle Fork John Day Basin, a
watershed of high conservation interest within the Columbia River Basin, U.S.A. We
compare the results of the River Styles Framework, Natural Channel Classification, Rosgen
Classification System, and a channel form-based statistical classification at 33 field-moni
tored sites. We found that the four frameworks consistently classified reach types into
similar groups based on each reach or segment’s dominant hydrogeomorphic elements. Where
classified channel types diverged, differences could be attributed to the (a) spatial scale of
input data used, (b) the requisite metrics and their order in completing a framework’s
decision tree and/or, (c) whether the framework attempts to classify current or historic channel
NOAA or BPA and the views expressed herein are
solely those of the authors. The EPA, NOAA, and
BPA do not endorse any products or commercial
services mentioned in this publication. The funders
had no role in study design, data collection or
analysis, decision to publish, or preparation of the
Competing Interests: Nicolaas Bowes is the Owner
of Eco Logical Research; Hiroo Imaki is the Owner of
Pacific Spatial Solutions; David L. Rosgen is the
Owner of Wildland Hydrology. This does not alter the
authors' adherence to PLOS ONE policies on sharing
data and materials.
form. Divergence in framework agreement was also observed at reaches where channel
planform was decoupled from valley setting. Overall, the relative agreement between
frameworks indicates that criticism of individual classifications for their use of form in grouping
stream channels may be overstated. These form-based criticisms may also ignore the
geomorphic tenet that channel form reflects formative hydrogeomorphic processes across a
The physical form of a stream channel is the result of the coupled climatic, biotic, and
hydrogeomorphic processes acting upon it [
]. Accordingly, the classification of rivers into reach
types based on their physical characteristics lends insight into both the formative processes
that shape rivers, and the diversity of rivers that occur across an area of interest [
]. There are
numerous frameworks for classifying streams, many of which have diverse spatial and
temporal output scales (see [
]). Classification applications range from river maintenance to flood
control to channel and riparian protection from land use [
], and more broadly help to
disentangle natural and anthropogenic influences on channels, determine current channel
condition, and forecast response to future disturbance [
]. Over the past two decades, there has been
intense debate and criticism of the utility of particular frameworks [
], largely in
the context of river management and restoration. These criticisms range from the limitations
of a given framework at some spatial and temporal scales, to criticisms of the decisions that can
arise when a framework is misapplied, to the fact that most frameworks lack measurements of
process rates (e.g. sediment flux, bank migration) and process must instead be inferred from
channel form. An unfortunate effect of these criticisms is that river classification frameworks,
regardless of their utility, have been overlooked not for what they provide, but for perceptions
of classifications’ past (mis)applications.
The discussion of individual stream classification frameworks has been subsumed in a
broader conversation [
] that differentiates frameworks in terms of whether they are
‘form-based’ or ‘process-based.’ Form- based frameworks, those that classify stream reaches
based on their physical attributes, are often criticized as being overly simplistic. Yet to criticize
frameworks on the notion that they are ‘only form-based’ is to ignore a basic tenet of
geomorphology: that form implies process [
]. That is, measurements of river form are direct reflections
of the processes acting to shape that form [
]. Indeed, nearly all classification
frameworks use metrics that describe the capacity of a channel to perform geomorphic work and
adjust laterally within a valley bottom. For example, many classifications include measures of
channel gradient, valley setting or entrenchment, and sediment characteristics [
The fundamental basis of geomorphology is concerned both with landforms and the processes
that shape them. While the focus on quantifying process has helped geomorphology mature
beyond its observational and empirical roots [
], the notion that the study of form, or
inference about process from form, is inherently flawed is short-sighted. This over-simplification
implies that form and process are at best, distinct, and at worst, mutually exclusive. In reality,
the line between form and process is blurred, as river form and hydrogeomorphic processes are
In a long history of disagreement between proponents and detractors of particular
classification frameworks, and over the relative utility of form- versus process-based classification in
general, it is of note that direct comparisons of frameworks are exceedingly rare [
2 / 31
This may be due to the inherent difficulty in comparing methodologies that produce results at
different spatial scales and which seek to describe past or present river condition. These
methodologies also often require disparate types and amounts of input data and varying degrees of
geomorphic expertise to complete. Nevertheless, the geomorphic community would benefit
from a more clear understanding of the degree to which various river classifications, which
differ in whether or how they include or infer process from form, reach similar or disparate
conclusions with regard to their output [
This paper applies four classification frameworks across a watershed of high conservation
interest in the Pacific Northwest, USA (Table 1). Each of these frameworks contains, to varying
degrees, metrics that reflect the form of channels and floodplains, and/or the processes
operating upon those channels and floodplains. Our goal is to perform the first direct comparisons
of classification frameworks at the watershed scale, and in so doing, to elucidate the reasons
for similarities and differences between classification outputs. Where frameworks differ in the
geomorphic attributes (e.g. channel planform, bed material, valley confinement) of their
output, we attempt to ascertain the methodological differences that lead to divergence in
classification. Herein we focus on the River Styles Framework (RSF; [
]), the Natural Channel
Classification system (NCC; developed by Beechie and Imaki [
]), and the Rosgen
Classification System (RCS; [
]). We contrast these with an example of a flexible statistical
classification approach that clusters field-measured, reach-scale data into channel-form-based groups
(Table 1). The RSF and RCS are commonly applied in Australian and North American stream
and watershed assessments, respectively. The NCC framework, as presented here, uses
elements of the Montgomery and Buffington classification [
] to create a network of
pre-disturbance channel types across the U.S. Pacific Northwest [
]. Statistical classification is a flexible
family of approaches to grouping stream channels, and is increasingly common in
geomorphology (e.g. [
]), hydrology (e.g. [
]) and ecology (e.g. [
]) for identifying patterns among
observations. These frameworks have been selected because of (a) their popularity in the
geomorphology and aquatic habitat communities, (b) the wide spatiotemporal range of their
outputs, and (c) the varying degrees to which they directly or indirectly account for processes
operating on river systems (Table 1).
The Middle Fork of the John Day River (MFJD; Oregon, USA) is 117 km long and drains 2051
km2 within the broader Columbia River Basin (Fig 1). The MFJD watershed was chosen for
this research both for its physiographic diversity and due to the wealth of stream data available
there, largely as a result of ongoing watershed monitoring aimed at understanding physical
factors limiting salmonid population resilience (Section 2.2). These data enabled completion of
the four classification frameworks herein (Table 1; Sections 2.3–2.6).
Landscape, hydrologic, and ecological setting
The Middle Fork John Day Basin is largely composed of metamorphic and igneous rocks
underlain by basalt and older extrusive rock, which have been uplifted and reworked to create
a watershed marked by steep-sloped canyons, deeply dissected highlands, dissected tablelands,
and rounded uplands containing broad meadows. The watershed is generally semi-arid,
receiving 560 mm of annual precipitation throughout the basin on average [
]. However, the MFJD
basin is also marked by a distinct elevation-dependent precipitation gradient: the upper 10% of
elevations receive an average of 880 mm of precipitation, while the lowest 10% receive 370
mm. Average annual streamflow measured at the Ritter, Oregon gauging station (USGS
#14044000, Ad = 1334 km2; 83 years of record) is 7.4 m3s-1.
3 / 31
Individual reaches within
a stream network
Individual reaches within
a stream network
(fieldmonitored reaches). Can
be applied to networks if
inputs are available for
stream segments or
Salmonid conservation and watershed monitoring
Reductions in native fish populations throughout the Columbia River Basin, including the
MFJD, have led to large-scale aquatic habitat monitoring across the region. In particular,
steelhead trout (Oncorhynchus mykiss), listed as threatened under the U.S. Endangered Species Act,
have seen drastic reductions in the size of their runs [
] as a direct effect of anthropogenic
4 / 31
Fig 1. Map of the Middle Fork John Day Basin, Oregon, USA. The 33 Columbia Habitat Monitoring Program (CHaMP) reaches monitored between 2012–
2013 are shown in circles. The National Landcover Dataset is presented as the base map to illustrate biophysical gradients across the watershed. Four
photos illustrate the diversity of landscapes encountered across the basin.
habitat degradation, in part due to hydropower development, land use change, and direct
channel modification such as loss of large woody debris [
]. As a result, watersheds throughout the
Columbia River Basin have received intensive monitoring efforts to document the status and
trend of fluvial habitats that support salmonid populations [
The MFJD is monitored as part of the Columbia Habitat Monitoring Program (CHaMP;
http://www.champmonitoring.org). CHaMP data, which are used to complete the four
classification frameworks, are collected at wadeable, perennial streams throughout the Columbia
River Basin [
]. Reaches were selected for sampling using a generalized random tessellation
stratified sampling design to prevent spatial sampling bias [
]. Here we use CHaMP data
from the MFJD watershed collected during 2012 and 2013 (n = 33 sites) describing channel
5 / 31
bankfull width and depth, gradient, substrate, and sinuosity. CHaMP sampling reaches are
twenty times as long as the bankfull channel width at each site (120–360 m in length).
The River Styles Framework
The River Styles framework (Table 1) seeks to provide a “coherent set of procedural guidelines
with which to document the geomorphic structure and function of rivers, and appraise patterns
of river types and their biophysical linkages in a catchment context” [
]. In practice, the RSF
offers the potential for a process-based, watershed-scale classification system for rivers. The
RSF consists of four distinct stages that progress from (1) classifying landscapes and current
river form and function, to (2) assessing geomorphic river condition in context of reach
evolution, to (3) understanding and forecasting trajectories of river change, and (4) prioritizing
catchment management. A full description of the methods entailed in the RSF can be found in
]. Here we describe the application of stage one of the RSF, which has been completed for the
MFJD as part of an ongoing effort to contextualize site-specific CHaMP monitoring data in a
watershed setting [
]. Stage one provides a baseline assessment of current reach types
(referred to as ‘river styles’) in a system with emphasis on longitudinal variability of river form
(i.e. longitudinal profile analyses) along the mainstem channel and tributary network.
The RSF begins with the classification of landscape units (Fig A in S1 File), each of which
contain a unique distribution of river styles. Within a given landscape unit, stream reaches are
classified based on their valley confinement, presence or absence of floodplains, channel
planform, distribution of in-channel and floodplain geomorphic units, and dominant channel
substrate (Table 2). In contrast to the other classification systems presented here and those used
among practitioners (e.g. [
]), there is no intrinsic limit on the number of river styles that
may occur in a watershed of interest. In practice, once the diversity of river styles for a
particular watershed is known, a river styles tree (Figs B–D in S1 File) can be constructed that allows
for the classification of any stream segment from those found in the watershed. The top-level
discriminator in the RSF is valley confinement (Figs B–D in S1 File), which Brierley and Fryirs
 define as “the proportion of the channel length that abuts a confining margin on either
We used O’Brien and Wheaton’s [
] delineation of river styles for the MFJD where the
boundaries between landscape units were defined using remote sensing data including
elevation (10 m and 1 m digital elevation models; ), slope, underlying geology [
], and Level IV EcoRegion boundaries [
]. Following the delineation of
landscape units, individual river styles were initially digitized on the National Hydrography Dataset
(NHD; as polylines in ArcGIS; ESRI, Redlands, CA) using aerial photos ([
]; 1 m resolution)
and elevation datasets as a guide. O’Brien and Wheaton [
] conducted field visits in the
summer of 2012 and 2013 to confirm the accuracy of these delineations, refine the distinguishing
characteristics of each river style and its location in the river style tree (Figs B–D in S1 File) and
pinpoint boundaries between river styles when they could not be delineated using remote
A hierarchical framework, components of the RSF can be considered both form- and
process-based (Table 2). Individual River Styles are classified in part by their behavior, that is,
interpreting how instream and floodplain geomorphic features (landforms) are created and
reworked under various flow regimes. This interpretation is field-checked via geomorphic
mapping during visits to sites [
]. The initial differentiation of reaches is conducted at the
valley setting scale, based on valley confinement. This is analogous to Montgomery’s [
domains, which reflect the channel’s access to sediment sources and the mechanisms through
which sediment reaches the channel (Table 2). Stream power is estimated continuously along
6 / 31
Note that inclusion of metrics in each classification framework reflects only the stages that were completed in this research, and that ‘processes’ only
include geomorphic dynamics, and exclude ecological processes.
the channel and can be used to infer reach boundaries [
]. Within each valley setting, river
styles are classified based on metrics of channel form that are directly tied to geomorphic
processes like stream discharge and power that govern sediment transport, along with channel
planform (including the presence or absence of a channel), the array of instream and floodplain
geomorphic units along the reach, and bed material texture (Table 2).
Natural channel classification
Natural channel classification [
] seeks to predict the background, or pre-disturbance,
planform of alluvial channels found in an area of interest (Table 1). To this end, Beechie and Imaki
] constructed a probabilistic map of pre-disturbance channel planforms across the
Columbia River Basin, USA (drainage area 674,500 km2). Channel classes identified in NCC include
confined channels and four channel patterns for unconfined reaches: straight, meandering,
island-braided, and braided. These four unconfined channel patterns are common planforms
for alluvial, floodplain rivers [
], which have distinctly different morphologies,
dynamics, and ecological attributes [
]. In NCC, confinement is defined as the ratio of bankfull
width to valley width, and unconfined channels are those where the valley floor width is more
than four times the bankfull width. Predictor variables in the model were based on known
physical controls on channel pattern, including channel gradient, discharge, valley
confinement, sediment supply, and sediment size . Channel slope, discharge, and confinement
were estimated from digital elevation models. Relative reach slope, percent of the watershed in
7 / 31
unvegetated alpine terrain, and percent of the watershed in fine-grained erosive sediments
were hypothesized to be surrogates for sediment supply and size, respectively. Relative slope is
the slope of a reach minus the slope of its upstream neighbor. Positive relative slope values
indicate that a reach is steeper than its upstream neighbor (likely sediment supply limited or
undersupplied), and for a given slope and discharge is likely be narrower, deeper, and more
], whereas negative values indicate that a reach is more likely to have low
transport capacity relative to bed load supply (i.e., transport limited or oversupplied), and will likely
be wider, shallower, and finer grained or less armored.
For all channel segments with bankfull width > 8 m, attributes were assigned to each 200-m
long reach in the study area (> 2,000,000 reaches) based on available geospatial data, and
adjacent reaches with similar characteristics were then aggregated into sets of geomorphically
meaningful reaches. A sample of more than 30 relatively natural reaches of each channel
pattern was selected as the training data set (i.e., the natural channel pattern was not obscured by
contemporary land use or dams). Hence, the model should predict channel patterns expected
in the absence of human impacts, rather than current channel form. A support vector machine
(SVM) classifier was used to relate all 63 possible combinations of reach attributes to channel
pattern using a total training data set of 147 reaches. The multiple models were cross-validated
for classification accuracy, and the most accurate SVM model was then used to predict channel
pattern for all reaches in the study area. Bootstrapping of the final model created 1000 separate
predictions of channel pattern for each reach, and the consistency of predictions can be used as
an indicator of model uncertainty for each reach. For example, if 85% of the predictions for a
reach were ‘braided,’ we considered that reach to have a high likelihood of having a braided
channel pattern. This statistical approach produces maps of (1) the most likely channel pattern
for each reach in the Columbia River Basin, and (2) uncertainty in the channel pattern
prediction. For channels with bankfull width < 8 m, reaches were classified based on gradient [
pool-riffle (slope < 0.02), plane-bed (slope between 0.02 and 0.03), step-pool (slope between
0.03 and 0.08) or cascade (slope > 0.08).
Like the RSF, NCC contains elements based in process and form. NCC uses basin-scale
measurements of land cover and surficial geology to estimate sediment supply, along with
estimated valley confinement, the combination of which reflects sediment delivery to channels
]. In addition, remotely sensed measurements of channel form (i.e. channel width and
gradient) reflect the ability of a reach to transport supplied sediment [
]. Together, these
can be used to estimate the form of a given reach under baseline conditions.
Rosgen classification system
The Rosgen Classification System (RCS; [
]) provides a standardized workflow for river
classification based on a field survey of the geomorphic characteristics of a particular stream
reach (Table 1). RCS consists of four hierarchical stages of classification moving from coarse to
fine spatial scales . In Level I, the system uses spatial data describing valley confinement,
channel planform, local soil types, hydrologic regime, and watershed physiography to establish
a broad geomorphic characterization of river reaches. In Level II, the geomorphic
characteristics of a site (e.g. entrenchment ratio, width/depth ratio, sinuosity, median grain size, and
gradient) are assessed and a particular stream type is assigned to the reach using the decision tree
first presented by Rosgen . Like the RSF and NCC, in Level II the RCS emphasizes valley
setting and confinement early in the process. RCS uses a field-measured entrenchment ratio
(channel wetted width at two times bankfull depth divided by the bankfull width), which is
analogous to the bankfull to valley width ratio that NCC uses as a proxy for confinement. In
Level III, the stream’s condition is assessed based on channel planform, bed and bank stability,
8 / 31
occurrence and type of riparian vegetation, and any alterations in flow regime. Finally, stream
types delineated in Levels II and III are field-checked by direct measurements of sediment
transport and size, flow, bed/bank stability, and rates of bank erosion to ensure a valid stream
type classification has been made (Level IV).
We classified the 33 CHaMP reaches in the Middle Fork John Day Basin (Fig 1) using Levels
I and II of the RCS. We used DEMs (10 m and 0.1 m grid resolution), aerial imagery (1 m
resolution), and ground-based assessments to infer the Level I valley types surrounding each
CHaMP reach. Delineation of bankfull elevation was completed by trained technicians in the
field and surveyed as part of the CHaMP topographic survey. Calculations of width-to-depth
ratio, channel sinuosity, entrenchment ratio, and channel gradient were derived from CHaMP
topographic survey DEMs (0.1 m grid resolution) using the River Bathymetry Toolkit (RBT;
]). A bankfull water surface was derived by detrending a DEM and best-fitting a water stage
through the measured bankfull points and examining inflections in the hydraulic geometry
using the CHaMP Topo Toolbar (https://sites.google.com/a/northarrowresearch.com/
champtools/). Measurements that typically are derived from cross sections using RCS were
derived from averages of 100+ cross sections spaced at 1-meter intervals at every CHaMP site
and processed using the RBT. These metrics allowed us to categorize each CHaMP reach into
broad level RCS stream types (A-G). By combining broad RCS stream types with median grain
size data (D50) collected during CHaMP surveys, we classified each site into a final channel
type according to the RCS classification. Although we did not explicitly validate our reach type
delineations in the field (e.g. Level IV as described above), the wealth of on-the-ground
photographs and high-resolution topographic data (0.1 m-resolution DEMs) collected as part of
CHaMP surveys were used to ensure the validity of classified reaches.
Level II of the RCS is a form-based approach, relying on measurements of channel geometry
and bed material size to classify stream reaches (Table 2; ). It has received criticism in the
geomorphic literature for its methods, more so than the other classification frameworks used
], on the assertion that distinctions between stream types may not represent a
distinct suite of processes, but rather simply reflect different points along a process continuum
]. We would argue, however, that this latter criticism may be an inherent drawback to nearly
all hierarchical classification frameworks [
]. At the same time, the RCS, like the other
classification frameworks used here, relies on measurements of channel form as surrogates for
geomorphic process, and perhaps more so than the other three approaches, requires direct
fieldbased measurements to do so.
Multivariate statistical classification provides a flexible framework to identify patterns between
reaches based on channel form and/or landscape setting (Table 1). Multivariate statistical
approaches, including hierarchical clustering, use distance measures to group stream reaches
based on their similarity (or dissimilarity) across multiple physical attributes [
classification is a family of techniques, rather than a single technique, allowing flexibility in the
input data used, the distance measure used to compare similarity across observations, and in
the case of clustering, the algorithm used to identify meaningful groups of observations [
Here we provide an example of how these techniques can be employed in the same capacity as
the other stream classifications used herein.
We classified the 33 CHaMP sites in the Middle Fork John Day Basin by clustering reaches
on multiple instream geomorphic attributes: bankfull width, wetted width, site sinuosity,
stream gradient, bankfull width to depth ratio, and D16, D50, and D84 particle size. CHaMP
metrics that reflect sediment size and channel form were selected in order to maintain
9 / 31
consistency with data used in the classifications presented in Sections 2.3, 2.4, and 2.5. We
selected a partitioning around medoids clustering algorithm in R (‘cluster’ package; [
divisive clustering technique, to identify groups of distinct reach types based on the Euclidean
distance between reaches’ instream geomorphic attributes. We validated differences in stream
attributes between reach clusters using PERMANOVA . We plotted the cluster solution
within a principal components analysis (PCA) of the same stream channel attributes, visually
comparing CHaMP reaches classified under each method (RSF, NCC, RCS, clustering). Full
statistical methods and results are presented in the supporting information (Text A in S1 File).
The statistical classification applied here is purely form-based, incorporating geomorphic
process only by grouping channels on their physical attributes’ similarity (Table 2).
Fieldderived measurements of channel gradient, bankfull channel dimensions, and bed material size
were used to describe channel form, which, in aggregate, reflect the ability of a given stream
reach to transport supplied sediment, similar to how RCS estimates process using form-based
attributes (Section 2.5). An important distinction between the statistical classification and the
other three classifications used here is how they incorporate valley setting. While RSF, NCC,
and RCS estimate sediment supply and delivery processes by classifying valley setting (albeit at
a later stage in RCS), the statistical clustering employed here does not use valley confinement
or surrogates (stream order, valley slope) as a discriminator in its classification.
Statistical clustering approaches are relatively rare in geomorphic channel classification
compared to the other three frameworks described here. Despite the need for further
exploration of this technique, the purpose of this research was not to explore the effect of various
statistical classification algorithms (e.g. agglomerative versus divisive clustering, different distance
measures, etc.), but to select a parsimonious framework that aggregates channels into
userdefined sets of groups based on channel metrics. Future comparisons of stream channels
should build on this example by comparing multiple statistical classification methodologies in
Assessing classification framework agreement
To compare agreement between classification frameworks at the 33 CHaMP sites discussed in
Section 2.2, we compared classification outputs by using both (a) expert judgment and (b) a
multivariate comparison of reaches based on their resulting outputs in each of the four
To compare the frameworks’ outputs using an expert judgment approach, we began by
using the eight reach types identified by Natural Channel Classification, as these classes
provided intuitive and straightforward descriptors of channel planform [
]. For each NCC reach
type, we identified the most closely related reach types from the RSF, the RCS (using top-level
channel types A-G), and statistical clustering. Where available (RSF, RCS), decision trees were
used to select those reach types that best approximated each NCC type based on common
geomorphic metrics (gradient, geomorphic units present, planform). In the case of statistical
clustering, the geomorphic attributes inherent to each of the four clusters (Fig 2) were used to
approximate the corresponding NCC reach type.
Those RSF, RCS, and statistical clustering reach types that were most closely related to each
NCC type were classified as being in “good” agreement (e.g. all geomorphic attributes of the
reach type could conceivably be present in the associated NCC channel class), while those
which were only marginally related to each NCC class (that is, some aspects of the reach types
fit with an NCC class while others did not) were classified as having “moderate” agreement
(Table 3). RSF, RCS, and clustering reach types with no characteristics in common with NCC
classes were classified as having “poor” agreement. While this method is inherently qualitative,
10 / 31
Fig 2. Statistical clustering of reaches using principal components analysis (PCA) based on gradient, D16, D50, D84, bankfull width, bankfull width:
depth ratio, and integrated wetted width (i.e. channel width at time of sampling), classified into four discrete groups using partitioning around
medoids. Vectors of stream channel variables are plotted based on the strength of their correlation to the PCA (e.g. longer vectors are more strongly
correlated to the channel form variable PCA). The first and second principal components explained 85.6% and 10.9% of the variability in the reach attribute
data within the PCA. Point colors represent which cluster each reach was classified into, and representative photographs provide examples of characteristic
reach morphology for each cluster.
we attempted to take an inclusive approach when determining agreement among reach types
between frameworks, as considerable geomorphic variability can exist across each reach type
within a given framework [
In addition to our expert judgment-based, qualitative comparison of framework outputs, we
also quantitatively assessed reaches’ classification output agreement using a multivariate
11 / 31
RSF reach type
Low Sinuosity Planform Controlled Anabranching (G); Intact Valley Fill (M);
Alluvial Fan (M)
Meandering Gravel Bed (G); Meandering Planform-Controlled
Discontinuous Floodplain (G); Low-Moderate Sinuosity Gravel Bed (M);
Low-Moderate Sinuosity Planform-Controlled Disc. Floodplain (M);
BedrockControlled Elongate Discontinuous Floodplain (M); Low-Moderate Sinuosity
Gravel Bed (M)
Boulder Bed (G); Meandering Planform-Controlled Disc. Floodplain (G);
Confined Valley—Floodplain Pockets (G); Low-Moderate Sinuosity Partly
Confined Disc. Floodplain (G); Low-Moderate Sinuosity Gravel Bed (G);
Alluvial Fan (M); Bedrock-Controlled Elongate Discontinuous Floodplain (M)
Entrenched Bedrock Canyon (G); Confined Valley—Floodplain Pockets (G);
Step Cascade (G); Steep Perennial Headwater (M); Steep Ephemeral
Step Cascade (G); Boulder Bed (G); Floodplain Pockets (M); Steep
Perennial Headwater (M); Steep Ephemeral Hillslope (M)
Meandering Gravel Bed (G); Meandering Planform Controlled
Discontinuous Floodplain (G); Confined Valley—Floodplain Pockets (G);
Bedrock-Controlled Elongate Discontinuous Floodplain (G); Low-Moderate
Sinuosity Planform Controlled Disc. Floodplain (M); Meandering
Confined Floodplain (M)
Boulder Bed (G); Step Cascade (G); Steep Perennial Headwater (G); Steep
Ephemeral Hillslope (G); Confined Valley—Floodplain Pockets (M)
B (G); F (G); G
(G); A (M)
Entrenched Bedrock Canyon (G); Confined Valley—Floodplain Pockets (G); A (G); B (G); C
Bedrock Controlled Elongate Discontinuous Floodplain (G); Low-Moderate (G); F (G); G (G)
Sinuosity Planform Controlled Disc. Floodplain (G); Meandering Planform
Controlled Floodplain (M); Boulder Bed (M); Steep Perennial Headwater
(M); Steep Ephemeral Hillslope (M)
3: Steep, Narrow (G); 1: Narrow,
3: Steep, Narrow (G); 1: Narrow,
Sinuous (M); 4: Wide, Sinuous (M)
RCS reach type
C (G), E (G), G
(M), F (M)
2: Wide, Sinuous (M)
4: Wide, Sinuous (G); 1: Narrow,
Sinuous (M); 2: Wide, Low-Gradient
A (G); B (G); G
2: Wide, Low-Gradient (G); 3: Steep,
A (G); F (G); G
(G); B (M)
B (G); F (G); G
(G); A (M)
C (G); F (G); G
(G); E (G); B (M)
1: Narrow, Sinuous (G); 3: Steep,
Narrow (G); 2: Wide, Low Gradient
3: Steep, Narrow (G) 1: Narrow,
1: Narrow, Sinuous (G); 2: Wide, Low
Gradient (G); 4: Wide, Sinuous (M)
approach. We created a table of each reach’s classification outputs, calculated a Gower’s
dissimilarity matrix between each reach, and then visualized reaches in a cluster analysis and
ordination. Gower’s distance scales nominal variables between 0 and 1, allowing us to calculate the
similarity of discrete reaches’ agreement using the categorical outputs from each framework.
We clustered reaches based on their output dissimilarity using an average linkage clustering
algorithm. We then conducted a two-dimensional principal coordinates analysis (PCoA) on
the same Gower’s dissimilarity matrix of classification outputs, and used this to visualize
similarity between classifications at each reach. It is important to note that while the qualitative,
expert judgment-based approach above uses NCC as the top-level discriminator in assessing
framework agreement, the statistical assessment described here is a multivariate comparison of
outputs between all four frameworks.
The River Styles framework
In total, 14 distinct river styles were classified across the MFJD Watershed. To begin, landscape
units were classified across the watershed (Fig A in S1 File). The river styles trees showing the
characteristics of each river style are shown in Figs B–D in S1 File, and the distribution of river
styles within the MFJD Watershed is shown in Fig 3A, with distinctions made based on valley
confinement (confined, partly confined, laterally unconfined; [
]). Overall, confined valley
channels were the most common river styles across the MFJD Watershed (86% of total stream
12 / 31
Fig 3. Results of the four classifications. (A) River Styles, (B) Natural Channel Classes, (C) Rosgen Classification System, and (D) statistical classification
with clustering (partitioning around medoids) mapped across the Middle Fork John Day Basin. River Styles and Natural Channel Classes are mapped across
the entire stream network, while Rosgen Classification System and statistical classification results are presented only for CHaMP reaches. Full River Style
and Natural Channel Class results for CHaMP reaches are presented in Table 4.
length; Table 4), whereas channels in partly confined valleys (8%) and laterally unconfined
valleys (6%) were far less common (Fig 3A). Small, low-order, confined channels (boulder bed
and steep ephemeral hillslope river styles) comprised the majority of total stream length within
the watershed (68%, Table 4). Regarding the most common classifications of CHaMP sites,
33% of sites were classified as partly confined valley with low-moderate sinuosity
planform13 / 31
River Styles and Columbia Basin Natural Channel Classification are summarized across the entire network and at CHaMP sites, while the Rosgen
Classification System and clustering classifications are summarized only for reaches with CHaMP channel data.
controlled discontinuous floodplain reach types, 15% were classified as confined valley with
occasional floodplain pockets, and 12% each were classified as partly confined valley with
meandering planform-controlled discontinuous floodplain and bedrock-controlled elongate
discontinuous floodplain reach types (Fig 3A; Table 4).
Natural channel classification
Natural Channel Classification derived nine channel patterns across the Columbia River Basin
], eight of which were predicted within the MFJD Watershed (Figs 3B and 4B). By total
stream length, the majority of reaches (83%) were small channels with bankfull width < 8 m
(Table 4). Across the MFJD, 35% of the total reach length was classified as step-pool channels,
and 25% classified as plane-bed channels [
]. For channels > 8 m bankfull width, 8% of the
total reach length was classified as having a straight planform, 3% of channels classified as
island-braided, and 2% classified as meandering (Fig 3B; Table 4). The remaining reaches >8
m were classified as confined channels because valley width was less than four times bankfull
channel width [
]. With regard to the most common classifications of CHaMP sites, 25% of
sites each were classified as straight or plane bed reaches, with an additional 15% of sites
classified as pool riffle (Fig 3B; Table 4).
Rosgen classification system
We classified 11 RCS stream types within 33 CHaMP reaches in the MFJD Watershed (Fig 3C;
Table 4). The most common stream types, each containing 24% of the CHaMP reaches, were
B4 (stable plane bed with occasional pools) and C4b (low gradient, meandering, riffle/pool
sequences; Fig 4C). In total, 50% of the reaches were B stream types, all of which were within
valley type II (colluvial, moderately steep and confined), with a single exception. C stream
types (sinuous, wide and low-gradient) were the next most common (27%) and E (highly
sinuous, coarse-fine bed), F (entrenched, wide, moderately sinuous, low gradient), and G
(entrenched, low-gradient, low width:depth ratio) types were the least common (3% each).
Only one CHaMP site had a substantial length of side channels (24%), however the other
metrics did not fit a D stream type. Therefore, we did not delineate any multi-threaded channels
(RCS stream type D).
15 / 31
Fig 4. Classification results across network and sites. Percent of total network channel length and percent of CHaMP sites classified into reach types
using each classification framework (A-D).
Because statistical clustering does not test for an a priori set of outcomes as other statistical
approaches might, we compared multiple clustering results (two to ten clusters of channels)
from clusters generated using a partitioning around medoids algorithm. We selected a four
cluster final solution based on cluster fidelity; that is, the statistical and geomorphic differences
in the multiple attributes used to distinguish between groups, minimizing overlap between
cluster groups (Fig 2; Tables A–C in S1 File). We did this objectively rather than trying to create
an a priori number of reach types to match the other frameworks’ number of outputs. After
plotting the final cluster solution within a principal component analysis (PCA), the clustered
stream channel attributes showed that each group differed based on multiple channel form
attributes. The PCA indicated that the four identified clusters were meaningful representations
of the sampled reaches and not just statistical artifacts. Each cluster was named based on the
dominant attributes that differentiated clusters from one another. The four final groups
consisted of (1) narrow, sinuous, high-gradient reaches (n = 7), (2) wide, low-gradient, coarse
substrate reaches with high width to depth ratios (n = 5), (3) high-gradient, narrow reaches with
moderate-sized substrates (n = 16), and (4) moderate gradient, wide and sinuous,
coarse-substrate reaches (n = 5; Fig 2; Table 4). The number of CHaMP sites assigned to each cluster are
16 / 31
shown in Fig 4D. Channel clusters were significantly different from one another
(PERMANOVA; p < 0.05), and particle D16, D50, and D84 were the attributes that were most strongly
correlated to the principal component analysis (Tables A–C in S1 File). Clusters in the final
four-cluster solution were distinct (silhouette widths 0.24–0.60; mean width 0.41; Fig 2). The
cluster group assigned to each CHaMP site is shown in Fig 3D and Fig G in S1 File.
Comparison of framework agreement
The analysis of agreement between reach types of each framework (Section 2.8; Table 5)
generally indicates that far more often than not, frameworks produced reach type classifications that
were congruent with one another. Clustering of the classification outputs at each reach showed
that seven reaches had perfect agreement with at least one other reach; that is, each of the four
classification outputs were identical across the four classifications (Table 5; Fig 5; distance of 0
in Figs 6 and 7). Seven reaches’ outputs agreed for three of the four outputs (distance of 0.2 in
Fig 6). A majority of the reaches agreed on two of the four classification outputs (Table 5; Figs
6 and 7). Very few reaches classified as a combination of vastly different classification outputs
than the other streams (Fig 6). These trends were apparent in the PCoA ordination of reaches
based on their classification outputs (Fig 7). Similarly, when using expert judgment to compare
the level of agreement between NCC and each of the other three frameworks at 33 CHaMP
sites (for a total of 99 comparisons; Table 5), we found “good” agreement at 60 sites (61%),
“moderate” agreement at 19 sites (19%), and “poor” agreement at 20 sites (Table 5). Thus,
reasonable agreement (good or moderate) was found at 80% of sites.
There were consistent relationships between site morphology and the level of classification
agreement (good, moderate, or poor). In the qualitative analysis of classification agreement
(Table 5), individual reaches classified into groups of similar morphologies within one
framework sometimes failed to align with a comparable group under another classification
framework. This pattern was most apparent in confined reach types that did not aggregate into
consistent groups across statistical clusters, Rosgen Classification System types, and natural
channel classes. For example, River Styles’ confined valley with occasional floodplain pockets
were classified as all four statistical clusters, five different RCS reaches, and three NCC classes
(Table 5; Fig 7). In contrast, partly confined channel types were more likely to be grouped into
only one or two channel types from other classifications. For example, River Styles’ partly
confined low-moderate sinuosity, planform-controlled discontinuous floodplain grouped into
RCS types of C4b and B4, and NCC classes of plane bed or straight planform, and steep/narrow
and narrow/sinuous statistical clustering classes.
Additionally, the partly confined low-sinuosity planform-controlled anabranching river
style occurred exclusively as B4 RCS classes, straight, narrow statistical cluster, and straight
NCC. The partly confined bedrock-controlled elongate discontinuous floodplain river style
classified as slightly to moderately entrenched, moderate sinuosity RCS types (C, B channels),
and wide, low-gradient clusters, but was less consistently grouped by NCC (straight, confined,
and island braided). While strict fidelity between groups within each classification did not
occur, partly confined River Styles grouped well with the other classifications based on their
Comparison of classification outputs: example sites
Here we highlight four example sites to illustrate the varying degrees of framework agreement
found during our classification output comparison. An example of poor agreement between
the four frameworks was found at a confined valley reach on the Middle Fork John Day River
17 / 31
(CHaMP site: CBW05583-004682), we found a B4c RCS type, wide, low-gradient statistical
cluster, island-braided NCC, and entrenched bedrock canyon river style (Figs 5 and 6). Readers
are referred to the supporting information (Fig F in S1 File) for the characteristics of each RCS
channel type. The statistical classification matched the definition of a wide, low-gradient, B4c
RCS channel type. While it is plausible that a B4c RCS channel type and an entrenched bedrock
canyon river style could be applied to the same reach, the island-braided NCC classification is
deserving of further exploration as it may hint at a departure from historic channel condition,
which NCC attempts to predict. Subsequent field visits to this site by O’Brien [Personal
Communication] note the presence of numerous legacy sediment deposits (e.g. [
]) above the
active channel, within a wide valley bottom that allows for channel adjustment. These
observations may imply that the system was overwhelmed by sediment during the early Holocene.
Accordingly, the pre-disturbance classification of an island-braided channel using NCC may
be appropriate in this case, and could hint at the background morphology of the channel.
In contrast, we found good agreement between all classification frameworks at two of the
four example reaches (Fig 5) and seven total reaches (Figs 6 and 7). The first is a laterally
unconfined reach on the Middle Fork John Day River (Fig 5; CHaMP site: CBW05583-003826)
classified as a G4c RCS type, narrow sinuous statistical cluster, pool-riffle NCC, and
meandering gravel bed river style. The second site is a partly confined reach on Slide Creek (Fig 5;
CHaMP site: CWB05583-144394), classified as a meandering planform-controlled
discontinuous floodplain river style. This site was further classified as an E4 RCS reach, pool riffle RCC
type, and narrow, sinuous statistical cluster. At these locations, the combination of geomorphic
characteristics produced a reach classification that was highly similar in terms of valley setting,
planform, and assemblage of geomorphic units between all four frameworks. In the case of the
former site, the reach occurs within a broader ~10 km segment of the Middle Fork John Day
that exhibits a sinuous planform in an unconfined valley. The latter site also occurs in a ~5 km
20 / 31
Fig 5. Illustrative example reaches describing agreement between classification outputs. Reaches at which the four classifications had poor
agreement, moderate agreement, and good agreement in the observed channel planform.
segment of Slide Creek that exhibits a consistent meandering planform. These more
longitudinally-continuous reaches are undoubtedly helpful for agreement in classification among
continuous frameworks (e.g. RSF and NCC) that may use disparate spatial scales of data (e.g.
NHD+ and field-based validation versus NHD and basin-scale 10 m DEMs, respectively) and
derive classifications remotely prior to field-based verification.
21 / 31
Fig 6. Dendrogram of clustered reaches based on their classification outputs from each of the four
frameworks. Reaches with a distance of zero that occur on adjacent nodes of the same length are identical.
For example, reaches CBW05583-381682 and CBW05583-383986 are identical in how they were classified
by all four frameworks. Reaches were clustered using an average linkage clustering algorithm and Gower’s
22 / 31
Fig 7. Principal coordinates analysis ordination showing reaches’ relative similarity based on the outputs of the four classification frameworks.
Each reach is plotted within each classification output for ease of interpretation. Reaches were grouped within the ordination space using Gower’s distance.
Reaches that are more similar to one another are closer together in the ordination space. R2 values correspond to the fit of a given classification framework’s
outputs to the ordination of all classification outputs.
An example of a site with moderate agreement was found in a partly-confined valley setting
on Slide Creek (Figs 5 and 6; CHaMP Site CBW05583-013322). This reach showed different,
but plausible combinations of channel types. The reach was classified as a partly-confined
valley with meandering planform-controlled discontinuous floodplain river style—whose
inchannel geomorphic unit assemblage is essentially repeating pool-riffle sequences—and
poolriffle in NCC, but was classified as a B4 RCS and steep, narrow statistical cluster. Reaches like
this that exhibit mixed agreement between classification frameworks highlight that subtle
differences in channel form, such as channel gradient and sinuosity, can lead to significant
differences in the classification of an individual reach. These differences arise as a result of the
hierarchical and statistical clustering classifications used here, as the order of appearance of
23 / 31
geomorphic metrics in a decision tree can vary between frameworks and subsequently affect
Why do classification frameworks differ?
Differences in classification frameworks’ outputs ultimately arise because each framework
emphasizes physical variables differently throughout the classification process. Although the
data requirements between classification frameworks are similar, including channel planform
metrics, substrate, and the ability of a channel to migrate and access sediment sources
(Table 2), the order in which these attributes appear within a particular framework’s decision
tree may vary markedly (see Figs B–F in S1 File). For example, at the broad planform scale, the
first step in the differentiation of reach types within the RCS is to distinguish between
singleand multi-thread channels. In contrast, this characterization of channel planform is completed
several steps later in the River Styles framework, which instead places the greatest importance
on the degree of valley confinement. Both RCS and River Styles, however, make their final
differentiation between stream types based on the bed material texture within a reach.
When considering statistical approaches such as NCC and statistical clustering, all physical
attributes are used in the grouping algorithm, and hierarchical decision trees are foregone.
Because most statistical classification techniques computationally determine which of the
input variables are most important in differentiating stream types, ranking them accordingly, a
priori importance is not placed upon a given variable. While variables can be weighted in
clustering and machine-learning algorithms to emphasize the importance of specific processes,
many classifications, like NCC’s support vector machine, instead use training data to fit
algorithms before computing classes for a data set. This approach is limited not by what variable is
perceived to be most important, but rather, by what training data are available from which to
build a model.
Similar constraints exist on the clustering method used here, which can only group reaches
for which data are available. In building representative statistical classifications, having
spatially-balanced, randomized sampling is ideal [
]. Another key methodological consideration
in using statistical classification approaches is that the number of classes is often determined by
the strength of the fit between data and algorithm, and must be validated by expert judgment
of the classified statistical groups and their geomorphic likelihood. Robust clustering was
observed here with a relatively small number of classes (four), whereas the other three
classification schemes had between eight and fourteen classes. Accordingly, parameter and cluster
algorithm selection, data transformation or standardization can all influence how well data fits
a given clustering algorithm, with consequences for whether geomorphically meaningful
groups correspond to statistically grouped data.
More generally, the difference in the relative importance of each physical variable within a
particular classification framework points to the form-process interactions that each
classification method attempts to document or explain. Particularly in the hierarchical approaches (e.g.
RSF, RCS), the order of appearance of variables in the classification has a large impact on the
classification of an individual channel reach. Distinct differences are also evident when the
original intent of the classification framework is considered. Some frameworks produce
analyses of current reach type (e.g. RSF, RCS, statistical clustering), while others predict
pre-disturbance or natural channel morphology (e.g. NCC). Differences in the temporal output of each
framework may not be intuitive, but provide a critical context for interpreting and using the
outputs derived [
Classification frameworks appeared to disagree most frequently in settings where channel
form was incongruous relative to valley width. That is, narrow valleys typically contain narrow,
24 / 31
straight streams. Conversely, in wide valleys, we expect to find relatively wide, freely
meandering streams that occupy large portions of the valley bottom [
]. In locations where this is not
the case, we note that channel classification frameworks exhibited strong disagreement in their
output (Table 5; Fig 7). For example, the frameworks disagreed in relatively wide valleys where
channels occupied a small area of the valley floor (sites CBW05583-004682,
CBW05583289522, and CBW05583-415218) or exhibited unusually low sinuosity despite flowing through
a wide valley. In contrast, some channels exhibited moderate sinuosity despite flowing in very
narrow valleys (e.g. site CBW05583-051954; Table 5). A number of factors can lead to these
scenarios, including anthropogenic straightening of channels to reduce bank erosion or lateral
], livestock grazing , or in the case of sinuous channels in narrow valleys,
the presence of jams or dams associated with large woody debris accumulation and beaver
activity, respectively [
]. The frameworks used here incorporate valley setting to draw
inference on channel sinuosity, slope, and ultimately, the form of the channel. Particularly in
NCC and RSF, valley setting is a top-level discriminator of channel classification (Figs B–E in
S1 File). Components of RCS similarly leverage valley setting to infer the stream types found
there [26,27]. Thus, we caution that despite the overall agreement between frameworks that we
observed, classification outputs may differ markedly in locations where geomorphic, biotic, or
anthropogenic factors cause channels to diverge from expected forms given a particular valley
setting. We note that further research is needed to assess classification agreement in areas
where valley setting may not be a reliable predictor of channel form (e.g., ephemeral channels,
channels at the meandering/braided transition, and in heavily disturbed watersheds).
Form and process in channel classification
Our comparison of four distinct classification frameworks demonstrates that there is
significant overlap and agreement between outputs of the classifications used here. The most
common result in all four frameworks was some variant of moderately-high gradient channel, in a
partly-confined valley setting, with coarse gravel substrate, reflecting the high relief nature of
the Middle Fork John Day Basin resulting from resistant igneous and metamorphic lithologies
(Fig 3). Similarly, the least common channel types in all four frameworks were those variants
corresponding to wide, freely meandering, low-gradient streams. These laterally unconfined
streams are the ones most emphasized in classic channel planform classification and the fluvial
geomorphology literature [
], although they are rare in many montane regions [
The four classification frameworks showed widespread agreement between their outputs
despite being variably based in either form or process (Table 2). While all four frameworks
contained metrics that either directly described the processes at work in channel reaches or
employed measurements of channel form as surrogates for geomorphic processes, the relative
role of form- and process-based components varied between frameworks. For example, while
the RSF depends on observation of processes (e.g. channel behavior at overbank flow,
interaction with vegetation), NCC and RCS rely on measurements of channel form that are directly
related to sediment supply and transport competence at individual channel reaches. Taken to
the extreme, the statistical clustering approach used here exclusively relies on field-based
measures of channel form in an attempt to differentiate individual reaches. Despite the range of
form- and process-based metrics in each framework, the four approaches exhibited overall
agreement, suggesting that a simple differentiation in terms of form or process does not
characterize the utility of a particular approach.
When considering how the geomorphic community groups classification frameworks (see
]), the line between those based in form and those based in process is not necessarily clear.
Many common stream classification frameworks defy such simple binning, instead combining
25 / 31
aspects of form and process to group river reaches. In general, the use of channel form metrics
as surrogates for stream or valley-scale processes is widespread [
]. This is perhaps a reflection
of the complexity involved in a purely process-based classification framework, which would
require high-resolution measurement of rates of sediment transport, supply, and channel
adjustment at many sites throughout a stream network [
]. Such approaches are only possible
under exceptional mandates that require a great deal of human and financial capital (e.g. [
In most basins, classification frameworks based on channel form metrics are practical
surrogates for inferring process.
Similarly, rapid geomorphic assessment (RGA) methods that nominally characterize
process domains along streams often rely on form-based measurements or observations (e.g.
degree of bank erosion or channel incision) to infer processes related to channel stability [
We acknowledge that the degree to which cutoffs and thresholds between channel types,
particularly in hierarchical classification schemes (e.g. RSF, NCC, RCS), reflect true transitions in
process domains likely requires further investigation. At the same time, form-based
assessments have been borne of a necessity to characterize river reaches over large spatial scales
within a reasonable timeframe and at moderate costs, leading to their widespread application
in the geomorphic, land management, and hydrologic communities.
While classifications represent “snapshots” of reaches, rivers are dynamic and adjust in
response to water and sediment supply [
]. If these boundary conditions are not
considered, assumptions of stability may be made when channel form may actually indicate a
transient, or responding state, given altered sediment or water availability. For this reason, some
classification frameworks separate current character and behavior from past evolution,
condition and trajectory (e.g. the RSF), and others separate condition (e.g. RCS). In other systems,
the degree of channel departure from background conditions is considered and may
completely invalidate certain frameworks. For example, in watersheds heavily influenced by
mill dams or beaver ponds and their associated legacy sediment deposits [
], the NCC
classification approach may not provide an informative river classification as this method predicts
pre-disturbance channel planform.
There are instances where applying multiple classification frameworks may yield insight
into channel processes or watershed disturbance history that individual frameworks may not
reveal. For example, divergence between frameworks that classify current versus historic
channel form (e.g., RSF and NCC, respectively) may point to reaches that have undergone
significant disturbance and planform alteration, and thus differ from expected background
characteristics. Alternatively, the comparison of watershed-scale frameworks with reach-based
classifications (e.g., RSF versus RCS) may aid in pinpointing reaches that differ from the
characteristic downstream progression of channel patterns within a basin, thus providing insight
into local geologic/geomorphic controls or areas of intense disturbance that drive channel
Finally, it is worth exploring whether our use of a single watershed may have influenced the
results of this study, particularly with regard to the performance of, and subsequent agreement
between, the classification frameworks employed here. The MFJD basin was specifically chosen
for this research for its geomorphic diversity: a wide range of bedrock and surficial geologies
drive large gradients in elevation that correspond to variations in precipitation and climate,
which in turn lead to a diversity of vegetation assemblages across the watershed [
these biophysical gradients create a range of channel types that are reflected in the classification
frameworks’ outputs used in our comparison: ten of fourteen possible river styles, seven of
nine possible NCC classes, and six of seven possible RCS channel types were found at the 33
CHaMP sites used for direct comparison in this study. Overall, the agreement between
classification outputs across a wide range of channel types gives us confidence that these frameworks
26 / 31
will likely demonstrate similarly robust agreement when applied to basins other than the
MFJD. At the same time, we observed that disagreement in the frameworks’ output was
common when valley confinement was not a reliable predictor of the resulting channel form (e.g.
narrow, straight channels in wide valleys; Table 5). As a result, basins that have undergone
pervasive and widespread disturbance, leading to divergence between channel form and valley
confinement (whether natural or anthropogenic; [
]) may be particularly susceptible to
classification disagreement, and we encourage similar classification comparisons in these
Stream classification schemes are widely used to make inference about how channel and
floodplain landforms respond to hydrologic and geomorphic processes. In the absence of exhaustive
sediment transport, hydrologic, and/or hydraulic data, and the budget and personnel to collect
and analyze them, stream classification provides a critical tool for watershed managers to
understand the range of potential reach types in a watershed and their likely driving processes.
Here we have applied four distinct classifications, finding that each methodology is informative
of the range of potential channel types within the Middle Fork John Day River Watershed. We
assessed overall agreement between frameworks using both a qualitative, expert
judgmentbased approach, as well as a quantitative statistical intercomparison of outputs; both methods
pointed to widespread agreement between framework output. Classification outputs were often
highly comparable based on their component data inputs, and the four frameworks used here
frequently output groups of reaches with similar hydrogeomorphic settings, distributions of
inchannel geomorphic units, and bed sediment characteristics. Classification frameworks’
outputs diverged in locations where valley setting was not a reliable predictor of channel form.
The classifications used here range from thorough network based approaches (RSF, NCC)
to more rapid reach-based approaches (statistical clustering, RCS) that categorize current
(RCS, statistical clustering, RSF), historic (NCC; later stages of RSF not discussed here), and
potential future trajectories of stream channels (later stages of RSF not discussed here). To
varying degrees, they classify stream reaches based on aspects of process and form, yet the
overall agreement between the frameworks indicates that, because process and form are
intrinsically linked in rivers, form- and process-based approaches to assessing stream channel
diversity are not mutually exclusive. In many cases, inference about fluvial dynamics can be
readily drawn from measurements of physical channel characteristics.
S1 File. Supporting text, figures, and tables for manuscript.
We thank Brett Roper, Michael Pollock, and Brian Laub for critical reviews that greatly
improved the manuscript, the many collaborators involved in the collection and stewarding of
CHaMP data, and Pete McHugh, Wally MacFarlane, Michael Render, and Alex Walker for
thoughtful comments on this work. This manuscript is a research communication of the
Columbia Habitat Monitoring Program, http://CHaMPMonitoring.org.
27 / 31
Conceived and designed the experiments: AK NH-S RC. Performed the experiments: AK
NHS RC. Analyzed the data: AK NH-S RC HI GB TB JW NB GO KF. Contributed
reagents/materials/analysis tools: TB HI DR GB KF NH-S GO JW. Wrote the paper: AK NH-S TB NB GB
RC KF HI MJ GO DR JW.
28 / 31
24. Southerland WB. Stream geomorphology and classification in glacial-fluvial valleys of the North
Cascade mountain range in Washington State [dissertation]. Pullman (WA): Washington State University;
29 / 31
51. U.S. Geological Survey. National Elevation Dataset. 2014. Available from: http://nationalmap.gov/
30 / 31
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