U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2010–5208

Prepared in cooperation with the Missouri River Recovery—Integrated Science Program

U.S. Army Corps of Engineers, Yankton, South Dakota

Development of a Channel Classification to Evaluate 

Potential for Cottonwood Restoration, Lower Segments 

of the Middle Missouri River, South Dakota and Nebraska
Cover photograph: Landsat 5 imagery of Big Bend in Lake Sharpe segment, Middle Missouri River, 

acquired on 9/17/2009. U.S. Geological Survey, Earth Resources Observation and Science Data Center.
Development of a Channel Classification 

to Evaluate Potential for Cottonwood 

Restoration, Lower Segments of the Middle 

Missouri River, South Dakota and Nebraska

By Robert B. Jacobson, Caroline M. Elliott, and Brittany L. Huhmann

Prepared in cooperation with the Missouri River Recovery—Integrated Science Program

U.S. Army Corps of Engineers, Yankton, South Dakota

Scientific Investigations Report 2010–5208

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2010

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Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Jacobson, R.B., Elliott, C.M., and Huhmann, B.L., 2010, Development of a channel classification to evaluate potential 

for cottonwood restoration, lower segments of the Middle Missouri River, South Dakota and Nebraska: U.S. 

Geological Survey Scientific Investigations Report 2010–5208, 38 p.
iii

Contents

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

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

Background............................................................................................................................................2

Scope and Objectives ..........................................................................................................................2

Approach and Methods................................................................................................................................4

Definitions ..............................................................................................................................................5

Data Resources.....................................................................................................................................5

Hydrologic Data............................................................................................................................5

Aerial Orthophotography............................................................................................................9

Land-Surface Elevations ............................................................................................................9

Information Content Assessment.......................................................................................................9

Geomorphic Measurements .............................................................................................................10

Channel and Valley Boundary Determinations .....................................................................10

Longitudinal Addressing System.............................................................................................11

Channel and Valley Width ........................................................................................................11

Sinuosity Index...........................................................................................................................11

Braiding Index and Bar Count .................................................................................................11

Statistical Classification ....................................................................................................................11

Results ...........................................................................................................................................................12

Information Content Analysis ...........................................................................................................12

Elevation Data.............................................................................................................................12

Soils Data ....................................................................................................................................14

Water-Surface Elevation Data.................................................................................................16

Longitudinal Classification ................................................................................................................16

Flood-Plain Elevations...............................................................................................................16

Geomorphic Characteristics....................................................................................................17

Sharpe Segment ...............................................................................................................17

Fort Randall Segment.......................................................................................................20

Statistical Classification ...........................................................................................................20

Discussion.....................................................................................................................................................22

Data Availability and An Alternative LCPI.......................................................................................22

Flood-Plain Elevation Data .......................................................................................................22

Soil Characteristics as Indicators for Cottonwood Restoration ........................................23

Water-Surface Elevations Compared to Flood-Plain Elevations .......................................26

Longitudinal Geomorphic Classification of the Middle Missouri River .....................................28

Segment Scale Classification..................................................................................................28

Reach-Scale Classification......................................................................................................30

Implications for Cottonwood Restoration..............................................................................32

Conclusions...................................................................................................................................................35

References Cited..........................................................................................................................................36
iv

Figures

1. Map showing Missouri River Basin and segment-scale classification of the 

mainstem........................................................................................................................................3

2. Map showing the Sharpe segment of the Missouri River .....................................................4

3. Map showing the Fort Randall segment of the Missouri River.............................................5

4. Graph showing stage, discharge, and aerial photography acquisition data for 

the Missouri River in the Sharpe and Fort Randall segments...............................................8

5. Map showing locations of comparison reaches on Lower Missouri River and 

distribution of wetness classes. Wetness classes are indices based on 

hydraulically modeled flood return intervals .........................................................................10

6. Graphic showing elevation maps of comparison reach at river mile 770 using 

1-arc-second, 1/3-arc-second, and 5-meter datasets .........................................................12

7. Graphic showing elevation maps of comparison reach at river mile 672 using 

1-arc-second, 1/3-arc-second, and 5-meter datasets .........................................................13

8. Graphic showing elevation maps of comparison reach at river mile 560 using 

1-arc-second, 1/3-arc-second, and 5-meter datasets .........................................................14

9. Graphs showing cumulative frequency distributions of floodplain topography and 

water-surface elevations in comparison reaches at river mile 770, river mile 672,

and river mile 560. The construction reference plane is a datum used by the 

U.S. Army Corps of Engineers to indicate water-surface elevation at approximately 

75 percent flow exceedance ....................................................................................................15

10. Cross section showing longitudinal profiles of water surface elevations for 2-, 5-, 

and 10-year return floods, and distributions of flood-plain elevations on the Sharpe 

and Fort Randall segments........................................................................................................17

11. Graphs showing longitudinal geomorphic characteristics for the Sharpe segment 

of the Missouri River..................................................................................................................20 

12. Graphs showing longitudinal geomorphic characteristics for the Fort Randall 

segment of the Missouri River..................................................................................................21

13. Graphs showing bivariate and frequency plots of selected, non-transformed 

classification variables for the Sharpe segment of the Missouri River ............................23

14. Graphs showing principal components analysis of selected variables of for the 

Sharpe segment of the Missouri River. All variables have been standardized to 

the range ......................................................................................................................................24

15. Graphs showing bivariate and frequency plots of selected, non-transformed 

classification variables for the Fort Randall segment of the Missouri River....................25

16. Graphs showing principal components analysis of selected variables of for the Fort 

Randall segment of the Missouri River. All variables have been standardized to the 

range.............................................................................................................................................26

17. Graphs showing bivariate and frequency plots of a reduced set of selected 

classification variables for the Sharpe segment of the Missouri River ............................27

18. Graphs showing bivariate and frequency plots of a reduced set of selected 

classification variables for the Fort Randall segment of the Missouri River....................27

19. Graphic showing hierarchical cluster dendogram of the reduced dataset for the 

Sharpe segment of the Missouri River. Selected variables were standardized for 

this analysis .................................................................................................................................28

20. Graphic showing hierarchical cluster dendogram of the reduced dataset for the 

Fort Randall segment of the Missouri River. Selected variables were standardized 

for this analysis ...........................................................................................................................28
v

21. Graph showing changes in sum of square differences within and among clusters as 

number of clusters changes for the Sharpe segment of the Missouri River....................29

22. Graph showing changes in sum of square differences within and among clusters as 

number of clusters changes for the Fort Randall segment of the Missouri River...........29

23. Graphs showing cluster-parallel plots from the K-means procedure for the Sharpe 

segment of the Missouri River..................................................................................................30

24. Graphs showing cluster-parallel plots from the K-means procedure for the Fort Randall 

segment of the Missouri River..................................................................................................31

25. Graphs showing canonical scores plot for the four-cluster classification, Sharpe 

segment........................................................................................................................................32

26. Graphs showing canonical scores plot for the four-cluster classification, Fort 

Randall segment .........................................................................................................................32

27. Map showing reach-scale cluster classifcations for the Sharpe segment of the Missouri River....................................................................................................................................33

28. Map showing reach-scale cluster classifcations for the Fort Randall segment of the 

Missouri River .............................................................................................................................33

29. Schematic diagram showing summary of classification of the lower segments and 

reaches of the Middle Missouri River, with interpreted implications for cottonwood 

restoration....................................................................................................................................34

Tables

1. Geomorphic segments of the Missouri River...........................................................................6

2. Discharges for National Aerial Imagery Program (NAIP) digital aerial orthophotography used in this study...................................................................................................................8

3. Sources for stage infomation used in this study .....................................................................9

4. Percent correct classification of 0–5 year wet area based on known soil characteristics ................................................................................................................................................14

5. Geomorphic characteristics of reaches within the Sharpe and Fort Randall Missouri 

River segments............................................................................................................................18

6. Result of jackknife validation of discriminant function classifications for the Sharpe 

segment........................................................................................................................................21

7. Result of jackknife validation of discriminant function classifications for the Fort 

Randall segment .........................................................................................................................22

8. Names and interpreted physical significance of the clustered classes in the Sharpe 

and Fort Randall segments........................................................................................................31
vi

Conversion Factors, Abbreviations, and Datum

Inch/Pound to SI

Multiply By To obtain

Length

mile (mi) 1.609 kilometer (km)

Flow rate

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

SI to Inch/Pound

Multiply By To obtain

Length

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd) 

Area

square meter (m2) 10.76 square foot (ft2) 

square kilometer (km2) 0.3861 square mile (mi2)

Flow rate

cubic meter per second (m3/s) 35.31 cubic foot per second (ft3/s)

meter per second (m/s) 3.281 foot per second (ft/s) 

meter per day (m/d) 3.281 foot per day (ft/d)

To communicate effectively with stakeholders, managers, and other scientists working on the 

Lower Missouri River, this report uses a mix of U.S. customary units and International System 

of Units (SI) units of measure. Distances along the Missouri River are given in river miles 

upstream from the junction with the Mississippi River at St. Louis, Missouri, as measured by 

the U.S. Army Corps of Engineers in 1960. Discharges are provided in the customary units of 

cubic feet per second. Reach-scale hydraulic variables—velocity and depth—are in SI units 

of meters per second and meters.

Horizontal coordinate information is referenced to the World Geodetic System of 1984 

(WGS 84).
Abstract

This report documents development of a spatially explicit 

river and flood-plain classification to evaluate potential for 

cottonwood restoration along the Sharpe and Fort Randall 

segments of the Middle Missouri River. This project involved 

evaluating existing topographic, water-surface elevation, and 

soils data to determine if they were sufficient to create a classification similar to the Land Capability Potential Index (LCPI) 

developed by Jacobson and others (U.S. Geological Survey 

Scientific Investigations Report 2007–5256) and developing 

a geomorphically based classification to apply to evaluating 

restoration potential. 

Existing topographic, water-surface elevation, and soils 

data for the Middle Missouri River were not sufficient to replicate the LCPI. The 1/3-arc-second National Elevation Dataset 

delineated most of the topographic complexity and produced 

cumulative frequency distributions similar to a high-resolution 

5-meter topographic dataset developed for the Lower Missouri 

River. However, lack of bathymetry in the National Elevation 

Dataset produces a potentially critical bias in evaluation of 

frequently flooded surfaces close to the river. High-resolution 

soils data alone were insufficient to replace the information 

content of the LCPI. In test reaches in the Lower Missouri 

River, soil drainage classes from the Soil Survey Geographic 

Database database correctly classified 0.8–98.9 percent of the 

flood-plain area at or below the 5-year return interval flood 

stage depending on state of channel incision; on average 

for river miles 423–811, soil drainage class correctly classified only 30.2 percent of the flood-plain area at or below the 

5-year return interval flood stage. Lack of congruence between 

soil characteristics and present-day hydrology results from 

relatively rapid incision and aggradation of segments of the 

Missouri River resulting from impoundments and engineering. The most sparsely available data in the Middle Missouri 

River were water-surface elevations. Whereas hydraulically 

modeled water-surface elevations were available at 1.6-kilometer intervals in the Lower Missouri River, water-surface 

elevations in the Middle Missouri River had to be interpolated 

between streamflow-gaging stations spaced 3–116 kilometers. 

Lack of high-resolution water-surface elevation data precludes 

development of LCPI-like classification maps.

An hierarchical river classification framework is proposed to provide structure for a multiscale river classification. 

The segment-scale classification presented in this report is 

deductive and based on presumed effects of dams, significant 

tributaries, and geological (and engineered) channel constraints. An inductive reach-scale classification, nested within 

the segment scale, is based on multivariate statistical clustering of geomorphic data collected at 500-meter intervals along 

the river. Cluster-based classifications delineate reaches of the 

river with similar channel and flood-plain geomorphology, and 

presumably, similar geomorphic and hydrologic processes. 

The dominant variables in the clustering process were channel 

width (Fort Randall) and valley width (Sharpe), followed by 

braiding index (both segments).

Clusters with multithread and highly sinuous channels are 

likely to be associated with dynamic channel migration and 

deposition of fresh, bare sediment conducive to natural cottonwood germination. However, restoration potential within these 

reaches is likely to be mitigated by interaction of cottonwood 

life stages with the highly altered flow regime.

 Introduction

Riparian cottonwood communities have been in general 

decline in rivers of the Great Plains because of lack of reproduction and recruitment. Causes for recruitment failures typically have been linked to river-management actions that have 

decreased channel-migration processes that would naturally 

provide bare mineral soils for seedling germination or actions 

that promote hydrologic conditions that give a competitive 

advantage to invasive species. 

Lack of cottonwood recruitment on the Missouri River 

has been recognized as a threat to habitat needed by the 

threatened bald eagle (Haliaeetus leucocehpalus). A 2000 

Biological Opinion recommended that the U.S. Army Corps 

of Engineers implement reasonable and prudent alternatives to 

maintain cottonwood communities by avoiding bank stabilization and purchasing land where cottonwood could be restored 

(U.S. Fish and Wildlife Service, 2000). Implementation of this 

goal requires an understanding of hydrologic and geomorphic 

processes that promote natural cottonwood regeneration or 

Development of a Channel Classification to Evaluate 

Potential for Cottonwood Restoration, Lower Segments of 

the Middle Missouri River, South Dakota and Nebraska 

By Robert B. Jacobson, Caroline M. Elliott, and Brittany L. Huhmann
2 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

that would support active restoration efforts. The objective 

of the study described here was to provide a spatially explicit 

river and flood-plain classification that resolves differences 

in biophysical capacity for cottonwood restoration along the 

Middle Missouri River.

Background

Riparian cottonwood communities are considered to be in 

decline along the Missouri River as a result of bank stabilization and flow-regime regulation (U.S. Fish and Wildlife 

Service, 2000). Bank stabilization prevents channel migration 

and consequent formation of cottonwood regeneration sites in 

freshly deposited sediment in point bars. Flow regulation that 

decreases magnitude and frequency of flood peaks decreases 

nutrient deposition in flood plains, may decrease soil moisture, and further diminishes potential for channel migration. 

Flow regulation may also result in germination of seedlings 

in places where they will be subject to scour; flow regulation 

may also change flood-recession rates and thereby alter root 

growth patterns (Scott and others, 1997; Auble and Scott, 

1998; Mahoney and Rood, 1998; Kalischuk and others, 2001). 

As a result, regeneration has not been sustained in many 

places and cottonwood communities have become senescent 

or invaded by nonnative species (U.S. Fish and Wildlife Service, 2000; National Research Council, 2002). 

Lack of cottonwood regeneration has been considered a 

factor in continuing concern for the status of the bald eagle. 

Large, old cottonwood trees have been identified as important 

nesting habitat for bald eagles along the Missouri River, and 

lack of cottonwood regeneration threatens habitat availability. 

Although the bald eagle was delisted as an endangered species 

in 2007 the U.S. Fish and Wildlife Service recognized the 

value of cottonwood communities and their role in providing habitat for the bald eagle in a 2000 Biological Opinion 

(U.S. Fish and Wildlife Service, 2000). To avoid take (harm or 

destruction) of bald eagles from loss of cottonwood community habitat, the U.S. Fish and Wildlife Service stipulated three 

reasonable and prudent measures:

• Map and evaluate current health of cottonwood forests 

on the Missouri River;

• Develop a management plan to allow for natural regeneration, seed germination, and seedling establishment 

to maintain populations;

• Implement management actions to ensure that no more 

than 10 percent of cottonwood habitat suitable for bald 

eagles is lost.

This report addresses the physical processes and geomorphic framework that define river flood-plain ecosystems in part 

of the Middle Missouri River. The objective is to develop a 

classification system that delineates relatively discrete reaches 

with common suites of geomorphic processes and rates [process domains, in the sense of (Montgomery, 1999)]. Similar 

process domains are inferred to have similar potential for natural cottonwood regeneration or active mechanical restoration. 

Previous classification work on the Lower Missouri River 

has delineated the river based on specific project needs. The 

broadest classification has been into segments generally delineated at major tributaries or geologic or engineering boundaries (Laustrup and LeValley, 1998; U.S. Fish and Wildlife Service, 2000). Segment-scale classifications have been used to 

describe broad variation in hydrology and channel hydraulics 

(Galat and Lipkin, 2000; Pegg and others, 2003; Reuter and 

others, 2008; Reuter and others, 2009) and have not explicitly 

considered adjacent flood plains. 

Classifications that expand laterally from the channel to 

address adjacent flood plain and valley-bottom terraces generally require relative elevation data, soils data, or both. Classification schemes for parts of the Lower Missouri River flood 

plain have been based on surficial geology (Holbrook and 

others, 2006; Ghimire and others, 2007) and hydrogeomorphic classifications that combine relative elevation, measures 

of soils wetness, topography, and soil moisture retention 

(Jacobson and others, 2007). Other approaches to flood-plain 

classification have been based on existing vegetation classes 

(Rieman and others, 2001) or vegetation and mappable water 

bodies [National Wetlands Inventory Program, (U.S. Fish and 

Wildlife Service, 1994–2000)].

Classifications of the Missouri River at finer scales 

include reach scale (10s of kilometers), bend scale (bendcrossover units or channel units, 100s to 1,000s of meters), 

and macro-, meso-, and micro-scale units (ranging from 100s 

of meters to millimeter scale). At finer scales, classifications 

are more specific to particular intended uses. Reach-scale 

classifications have been developed for part of the Missouri 

River between Gavins Point Dam and Sioux City, Iowa (Elliott 

and Jacobson, 2006) and parts of the adjacent Platte River in 

Nebraska (Elliott and others, 2009b); these classifications have 

been intended to resolve geomorphically similar reaches that 

would have similar channel dynamics and suites of channel 

and sandbar habitats. Macro- and mesoscale classifications on 

the Missouri River have been used to define areas with similar 

depth, velocity, and substrate characteristics and for which 

specific gear types would be used in fishery studies (Welker 

and Drobish, 2009). Mesoscale aquatic habitat classifications 

of the Lower Missouri River also have been defined using 

species-specific criteria to subdivide spatially continuous maps 

of derivative hydraulic variables (Reuter and others, 2008; 

Jacobson and others, 2009b; Reuter and others, 2009)

Scope and Objectives

This report documents development of a channel floodplain classification for the Sharpe and Fort Randall segments 

of the Middle Missouri River. The Middle Missouri River 

generally is defined as segments between Gavins Point Dam, 

South Dakota, and the headwaters of Lake Sakakawea in 

North Dakota (fig. 1). This section of the Missouri River is 
 Introduction 3

"

"

"

"

"

"

Three ForksRiverine

Canyon FerryReservoirs

Cascade/ SmithRiver Segment

Great Falls Dams

Fort BentonRiverineMissouri River

Breaks Riverine

Fort PeckDelta

Fort PeckLake Fort Peck

Riverine

LakeSakakaweaDelta

UPPER MISSOURI RIVER

MIDDLE MISSOURI RIVER

LOWER MISSOURI RIVER

OAHE 

DA

M

BIG

BEND

DA

M

GARRISON

DA

M

FORT

PECK

DA

M

GAVINS

POINT

DA

M

FORT

RANDALL

DAM

0

600

700

500

900

800

300

400

200

100

2300

1000

1200

1100

1300

2200 1900 1800

1400

2100 2000

1700 1600 1500

84° 90° 96° 102° 108° 114°

46°

42°

38°

I O

W A

M I S S O U R I

K A N S A S

N E B R A S K A

S O U T HD A K O T A

Base data from the National Atlas, 2006Universal Transverse 

Mercator projection

Zone 15

Waterbody data from the U.S. Geological Survey National Hydrography Dataset, 2006;

River miles from U.S. Army Corps of Engineers

W Y O M I N G

C O L O R A D O

M O N T A N A N O R T H

D A K O T

Lak

A

e

Sakakawea

Garrison Riverine

Lake Oahe Delta

EX

P

L

A

N

ATIO

N

Missouri River Segment and 

 Subsegment

Type

Lake Oahe 

Sharpe Riverine

Lake Sharpe Delta

Lake Sharpe

Lake Francis Case

Fort Randall Riverine

Le

wis and Clark Delta

Lake Francis Case

Gavins Riverine

Ponca Riverine

Sioux Riverine

Platte Riverine

KansasRiverine

Grand Riverine

Osage Riverine

M i s s o u r i

R i v e r

b a s i n

Channelized

U

Delta/headwaters

nchannelized 

Minimally channelized 

Reservoir 

Segment, subsegment boundary

"

Mainste

m

Missouri River dams

100

Sioux City

Platte

River

0 50 100 150 200 MILES

0 50 100 150 200 KILOMETERS

River-mile marker—Number is

distance upstream from the junction

with

the Mississippi River, in

miles

Figure 1. Missouri River Basin and segment-scale classification of the mainstem.
4 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

characterized by five large reservoirs separated by relatively 

free-flowing segments. This report concentrates on segments 

of the river downstream from Oahe Dam, South Dakota, and 

upstream from Lewis and Clark Lake, South Dakota (figs. 1–3, 

table 1). These are interreservoir segments Oahe Dam to Big 

Bend Dam, Fort Randall Dam to the Niobrara River, and Niobrara River to the headwaters of Lewis and Clark Lake. The 

segment defined by Lake Francis Case is not considered in this 

report because it is unlikely to provide cottonwood habitat. 

The primary objective is to develop a classification 

system similar to the Land Capability Potential Index (LCPI) 

that has been applied to the Lower Missouri River downstream 

from Gavins Point Dam (Jacobson and others, 2007). Because 

the LPCI was developed in an area where high-resolution 

elevation and water-surface profile data were available, it was 

not clear whether a similar classification could be developed 

in the Middle Missouri River where data were more sparse. 

Therefore, two subobjectives were identified. The first was to 

evaluate the utility of existing low-resolution data in development of an alternative LCPI by comparing LCPI metrics in 

areas with high-resolution and low-resolution data. The second 

was to develop a classification system based on available geomorphic data that would serve as the alternative LCPI as well 

as a general classification for the Middle Missouri channel and 

flood plain.

Approach and Methods

We first assessed the information content of generally 

available, low-resolution datasets relative to the LCPI to identify which, if any, physical variables in low-resolution datasets 

are useful in classifying the potential for cottonwood regeneration. In particular, we were interested in the ability of soils 

data alone to classify sites similar to classifications from LCPI 

datasets, and whether low-resolution, national elevation data 

(NED) adds significantly to our ability to classify these sites. 

Based on the limitations in using low-resolution data 

to develop an LCPI-like classification, we then developed a 

general geomorphic reach-scale classification that would capture many of the salient geomorphic processes of the Middle 

Missouri River. Although not as detailed or directly relevant to 

cottonwood regeneration as the LCPI, the geomorphic process 

domains delineated in the reach-scale classification approach 

should provide useful information for guiding restoration 

efforts. 

The approach to geomorphic classification is to employ 

generally available geospatial data arrayed in a longitudinal 

framework to develop quantitative classification indices. 

Many river classifications exist, but utility of a classification system rests on its ability to discriminate characteristics 

that are important for specific uses (Kondolf and others, 

2003). To achieve the most utility, we applied the continuous 

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990

1020

1030

1000

1060

1010

1050

1040

1070

Pierre

100°20' 100°00' 99°40'

44°20'

44°10'

0 5 10 15 KILOMETERS

0 5 10 15 MILES

S O U T H D A K O TA

Bad River

Missouri River

Lake

Sharpe

Lake

Oahe

Oahe

Dam

Big

Bend

Dam

Base from National Elevation Dataset digital data, 2007

Universal Transverse Mercator projection

Zone 14

Pierre

Pierre

La

Framboise

Island

Below La Framboise Island

Farm

Island

Bad River

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P

#*

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1030

EXPLANATION

Mainstem Missouri River Dam

U.S. Geological Survey streamflow-gaging station

River mile—Number is distance upstream from the junction with the Mississippi River, in miles

Boundary of Missouri River Valley

Figure 2. The Sharpe segment of the Missouri River.
Approach and Methods 5

longitudinal classification method of Elliott and Jacobson 

(2006) to develop a general classification of USACE segments 

6, 8, and 9 (table 1) in a manner consistent with previous 

classifications. 

Definitions

We use “segment” to denote lengths of the river having 

similar flow regime and valley-scale geologic constraints on 

form and process (Frissell and others, 1986). Segments usually 

are defined at major tributary junctions where flow regime or 

water quality might be expected to change river processes substantially. Segments also are defined by bedrock or engineering changes that might exert strong effects on channel morphology. In the Middle Missouri River, segments are clearly 

defined by dams. Downstream from dams, the free-flowing 

parts of the river typically undergo a gradual transition into 

the headwaters of the next downstream reservoir incorporating riverine, deltaic, and lacustrine conditions. Conceptually, 

lengths of river characterized by each of these three conditions can be considered subsegments, although the transitions 

vary in position within and among years. In this report, the 

three subsegments between Oahe Dam and Big Bend Dam are 

referred to collectively as the Sharpe segment and the three 

subsegments of river between Fort Randall Dam and Gavins 

Point Dam are referred to collectively as the Fort Randall 

segment. 

Segments generally contain multiple “reaches” or lengths 

of river characterized by uniform multiples of channel units or 

bend-crossover units (Frissell and others, 1986). The concept 

of reaches is used less precisely than segments. It is meant to 

describe varying lengths of channel with at least one bendcrossover sequence. Typically, a reach is defined with multiple, similar bend-crossover sequences.

Data Resources

Several types of widely available sources of data are used 

for this analysis including U.S. Department of Agriculture 

National Aerial Imagery Program (NAIP) digital aerial othophotography and National Elevation Dataset (NED) digital 

elevation models. Hydrologic information was synthesized 

using U.S. Geological Survey (USGS) streamflow-gaging 

stage and discharge records, supplemented with U.S. Army 

Corps of Engineers (USACE) records for Oahe Dam upstream 

from the Sharpe segment and for the Fort Randall Dam at the 

upstream end of the Fort Randall segment. 

Hydrologic Data

Hydrologic data were used to assess variation within the 

aerial photography datasets and to develop stage frequency 

analyses. Big Bend and Fort Randall Dams are managed for 

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850

840

830 820

870

860

880

810

98°20' 98°00' 97°40'

43°00'

42°50'

S O U T H D A K O TA

Niobrara River

Missouri River

Lewis and Clark Lake

Lake

Francis

Case

Fort

Randall

Dam

Gavins

Point

Dam

N E B R A S K A

Springfield

Verdel

Niobrara

Pickstown

Verdel

Niobrara

Lewis and

Clark Lake

Niobrara

0 5 10 15 KILOMETERS

0 5 10 15 MILES

Base from National Elevation Dataset digital data, 2007

Universal Transverse Mercator projection

Zone 14

!

P

#*

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830

EXPLANATION

Mainstem Missouri River Dam

U.S. Geological Survey streamflow-gaging station

River mile—Number is distance upstream from the junction with the Mississippi River, in miles

Boundary of Missouri River Valley

Figure 3. The Fort Randall segment of the Missouri River.
6 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration Table 1. Geomorphic segments of the Missouri River. [River mile, distance upstream from the Junction with the Mississippi River, in miles; MOREAP, Missouri River Environmental Assessment Program; USACE, U.S. Army Corps of Engineers] River mile start River mile end Length, miles Segment Number from MOREAP document2 USACE Segment Pallid Sturgeon Population Assessment (Welker and Drobish, 2009) Segment number, this docu- ment Segment Segment names Type/class/ environment Upper Missouri River 2,320 2,278.3 41.7 -- -- -- 28 Confluence of Gallatin, Madison, and Jefferson Rivers to Canyon Ferry Lake Three Forks Riverine Unchannelized River 2,278.3 2,211.1 67.2 -- -- -- 27 Canyon Ferry Lake to Holter Dam Canyon Ferry Reservoirs Series of dams and run-of- river reservoirs 2,211.1 2,121 90.1 -- -- -- 26 Holter Dam to Black Eagle Reservoir Cascade/Smith River Unchannelized River 2,121 2,105.7 15.3 -- -- -- 25 Black Eagle Reservoir to Morony Dam Great Falls Dams Series of dams and run-of- river reservoirs 2,105.7 2,052 53.7 -- -- -- 24 Morony Dam to Marias River Fort Benton Riverine Unchannelized River 2,052 1,9051 147 1 -- -- 23 Marias River to Fort Peck Delta Missouri River Breaks Riv- erine Unchannelized River 1,9051 1,8581 47 2 1 -- 22 Fort Peck Delta to Fort Peck Lake Fort Peck Delta Delta/Headwaters 1,8581 1,771.5 86.5 2 1 -- 21 Fort Peck Lake to Fort Peck Dam Fort Peck Lake Reservoir 1,771.5 1,5681 203.5 3 2 1,2,3,4 20 Fort Peck Dam to Lake Sakakawea Delta Fort Peck Riv- erine Inter-Reservoir/Unchannel- ized River Middle Missouri River 1,5681 1,5121 56 -- 2 4 19 Lake Sakakawea Delta to Lake Saka- kawea Lake Saka- kawea Delta Delta/Headwaters 1,5121 1,389.9 122.1 5 3 -- 18 Lake Sakakawea to Garrison Dam Lake Sakakawea Reservoir 

1,389.9 1,3041 85.9 6 4 -- 17 Garrison Dam to Lake Oahe Delta Garrison Riverine Inter-Reservoir/Unchannel- ized River

1,3041 1,2281 76 7 4 -- 16 Lake Oahe Delta to Lake Oahe Lake Oahe 

Delta

Delta/Headwaters

1,2281 1,072.3 155.7 7 5 -- 15 Lake Oahe to Oahe Dam Lake Oahe Reservoir

1,072.3 1,065.21 7.1 8 6 -- 14 Oahe Dam to Bad River Sharpe Riverine Inter-Reservoir/Unchannelized River1,065.21 1,052.51 12.7 8 6 13 Bad River to Lake Sharpe Lake Sharpe 

Delta

Delta/Headwaters
Approach and Methods 

7

Table 1. Geomorphic segments of the Missouri River.—Continued[River mile, distance upstream from the Junction with the Mississippi River, in miles; MOREAP, Missouri River Environmental Assessment Program; USACE, U.S. Army Corps of Engineers]

River mile 

start

 River mile 

end

Length, 

miles 

Segment 

Number from 

MOREAP 

document2

USACE 

Segment

Pallid 

Sturgeon 

Population 

Assessment 

(Welker and 

Drobish, 

2009)

Segment 

number, 

this document Segment Segment names Type/class/ environment

Middle Missouri River—Continued

1,052.51 987 65.5 8 6 -- 12 Lake Sharpe to Big Bend Dam Lake Sharpe Reservoir

987 880 107 9 7 -- 11 Big Bend Dam to Fort Randall Dam Lake Francis 

Case

Reservoir

880 8441 36 10 8 5 10 Fort Randall Dam to Niobrara River 39-mile 

MNRR; Fort Randall Riverine

Inter-Reservoir

8,441 8261 18 11 9 6 9 Niobrara River to Lewis and Clark 

Lake

Lewis and 

Clark Delta

Delta/Headwaters

8,261 811.1 14.9 12 9 -- 8 Lewis and Clark Lake to Gavins Point 

Dam

Lewis and 

Clark Lake

Reservoir

Lower Missouri River

811.1 753 58.1 13 10 7 7 Gavins Point Dam to Ponca, Nebraska 59-mile Missouri National Recreational River; 

Gavins 

Unchannelized River

753 734 19 14 11 8 6 Ponca, Nebraska to Big Sioux River Ponca Minimally Channelized734 594.5 139.5 15 12 8 5 Big Sioux River to Platte River Sioux Channelized River594.5 367.5 227 16 13 9 4 Platte River to Kansas River Platte Channelized River367.5 250 117.5 17 14 10 3 Kansas River to Grand River Kansas Channelized River250 130.4 119.6 18 14 13 2 Grand River to Osage River Grand Channelized River130.4 0 130.4 19 15 14 1 Osage River to Mississippi River Osage Channelized River

1 River miles marking the beginning and end of delta/reservoir headwater reaches are approximate because of fluctuating water levels in the reservoirs.2 Laustrup and LeValley (1998).
8 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

load-following power production causing discharges downstream from these dams to fluctuate daily. Aerial orthophotography is indexed by the date but not the time of day the photography was taken, therefore, we use daily average discharges 

downstream from these dams for reference. Daily fluctuations 

in discharge exist within the imagery data sets illustrated by 

the differences in daily minimum, maximum, and mean stage 

in the Fort Randall segment (fig. 4, table 2). Such hydrologic 

variation may result in nonuniform mapping of geomorphic 

features that are dependent on water levels, although our 

focus on geomorphic features rather than waterlines minimized this effect. Mean daily stage data are available from 

four USGS streamflow-gaging stations in the Sharpe segment 

and three locations in the Fort Randall segment of the river 

(table 3). Midnight tailwater elevations were compiled from 

USACE gaging stations at Oahe Dam and Big Bend Dam. 

USGS gage heights were converted to elevations using the 

individual gage datums, and both USGS and USACE elevations were converted from National Geodetic Vertical Datum 

of 1929 (NGVD 29) to North American Vertical Datum of 

1988 (NAVD 88) for consistency with the National Elevation 

Dataset. Mean daily stage data were available for the past 10 

years from the Sharpe gaging stations and 13 years from the 

Fort Randall gaging stations. 

Determination of the elevations of floods with expected 

2-, 5-, and 10-year recurrence interval floods was completed 

for each gaging station along the Fort Randall and Sharpe 

segments. From the daily stage data, an annual exceedance 

series was identified, consisting of the top n independent high 

flow events within the n years of available data. For the Fort 

Randall segment n=13, and for the Sharpe segment n=10. 

The events for each gaging station were ranked in order of 

magnitude, with the highest elevation event receiving a rank 

7/11/2006 7/14/2006 7/17/2006 7/20/2006 7/23/2006 7/26/2006 7/29/2006

0

200

400

600

800

1,000

Daily average discharge

Imagery Dates

Daily average discharge

Imagery Dates

DISCHARGE, IN CUBIC METERS PER SECOND

0

200

400

600

800

1,000

1,200

Oahe Dam, Pierre, South Dakota

 U.S. Army Corps of Engineers data

Fort Randall Dam, Pickstown South Dakota,

U.S. Army Corps of Engineers Data

6.2

6.4

6.6

6.8

7.0

7.2

Mean daily stage

Maximum daily stage

Minimum daily stage

STAGE ACCORDING TO AN

ARBITRARY DATUM,

IN METERS

Missouri River below Ponca Creek near Verdel, Nebraska

U.S. Geological Survey streamflow-gaging station 6453620

Figure 4. Stage, discharge, and aerial photography acquisition data for the Missouri River in the Sharpe and 

Fort Randall segments. 

Table 2. Discharges for National Aerial Imagery Program 

(NAIP) digital aerial orthophotography used in this study.

[USACE, U.S. Army Corps of Engineers; m3/s, cubic meters per second]

NAIP aerial 

photography dates 

(month/day/

year)

Discharge at 

Oahe Dam, 

USACE data 

(m3/s)

NAIP aerial 

photography dates 

(month/day/

year)

Discharge at Fort 

Randall Dam, 

USACE data 

(m3/s)

Sharpe segment Fort Randall segment

7/11/2006 943.0 7/12/2006 676.8

7/12/2006 883.5 7/15/2006 702.3

7/15/2006 863.7 7/18/2006 716.4

7/28/2006 826.9 7/23/2006 798.5

7/30 2006 866.5 7/26/2006 824.0

7/10/2008 39.6 7/29/2006 889.1

7/11/2008 218.0 7/30/2006 894.8

7/12/2008 212.4 7/6/2007 563.5

7/7/2007 594.7

7/15/2007 625.8

7/16/2007 625.8

7/17/2007 597.5
Approach and Methods 9

of 1 and the lowest elevation event receiving a rank of n. The 

average recurrence interval for each event was then calculated 

as T = (n+1)/m, where n = number of years of record and m = 

event rank. Elevations for floods with recurrence intervals of 

2, 5, and 10 years were determined directly as a result of these 

calculations or were found by linearly interpolating between 

the two events with recurrence intervals most closely bracketing the desired interval.

Aerial Orthophotography

We used two-meter (m) resolution digital natural-color 

orthophotography quarter-quadrangles (DOQQ) from 2006, 

2007, and 2008 taken by the Farm Service Agency’s National 

Agricultural Imagery Program (FSA/NAIP). The 2008 NAIP 

imagery was used for digitizing channel boundaries in the 

Sharpe segment and 2007 NAIP imagery was used for channel 

boundary determination in the Fort Randall segment (table 

2). All in-channel features were digitized from 2006 imagery 

(table 2). The photographs were compiled in a geographic 

information system (GIS) using the ArcGIS software package 

(ESRI, Redlands, Calif.). All data derived from the orthophotography have the horizontal datum North American Datum 

of 1983 (NAD 83). The projection used for all data associated 

with this report is Universal Transverse Mercator Zone 14, 

Meters (UTM 14).

Land-Surface Elevations

We used 1/3-arc-second (approximately 6.5-m cell size) 

resolution digital elevation models from the NED to assess 

elevations within the Sharpe and Fort Randall segments (U.S. 

Geological Survey, 2009). The data are provided using North 

American Datum of 1983 (NAD 83) as a horizontal reference 

and North American Vertical Datum of 1988 (NAVD 88) as a 

vertical reference. The raster maps were mosaicked and compiled using the ArcGIS software package (ESRI, Redlands, 

Calif.). To evaluate information content of different available 

elevation data, we also used 1-arc-second (approximately 

26-m cell size) and a high-resolution, 5-m dataset (Jacobson 

and others, 2007). 

The following sections provide detail on methods used 

in the two subobjectives—assessment of information content 

for LCPI-like classification and geomorphic classification of 

the channel and flood plain. The first objective is addressed 

through exploratory statistical evaluations of available 

topographic, water-surface elevation, and soils data, and the 

second objective is addressed through multivariate statistical 

clustering of geomorphic data compiled in a longitudinal GIS 

framework.

Information Content Assessment

We used graphical and tabular statistical assessments to 

evaluate information content of datasets readily available in 

the Middle Missouri River relative to those used to construct 

the LCPI for the Lower Missouri River (Jacobson and others, 

2007). Geomorphic adjustments of channels in regulated rivers generally result in channel incision or widening directly 

downstream from dams (Williams and Wolman, 1984); channel aggradation generally occurs upstream from reservoirs in 

headwaters and can potentially occur far downstream from 

dams if sediment delivered by tributaries cannot be transported 

by regulated flows. These changes in local channel geometry can combine with changes to the natural flow regime to 

alter the magnitude, frequency, duration, and timing for flood 

events that connect the channel and flood-plain surfaces. In the 

LCPI approach, modern flood-elevation profiles were available to assess connectivity between the channel and floodplain; similar flood profiles were not available for the area of 

interest on the Middle Missouri River. 

To explore the information content of soils and NED 

elevation data relative to the high resolution LCPI datasets 

(Jacobson and others, 2007), we identified three comparison 

flood-plain reaches in the Lower Missouri River downstream 

from Gavins Point Dam (fig. 5). The upstream-most reach 

(river mile 770) is incising; the middle reach (river mile 672) 

has water-surface elevations nearly the same as those that 

existed before Gavins Point Dam was closed; and the downstream-most reach (river mile 560) is stable to slightly aggrading (U.S. Army Corps of Engineers, 2007; Jacobson and 

others, 2009a). These three flood-plain reaches were selected 

to document the range of geomorphic adjustments expected 

because of impoundments on the Missouri River. The reaches 

were each defined by 1 km length of the river channel and the 

valley bottom from bluff to bluff. 

Table 3. Sources for stage infomation used in this study.

[USACE, U.S. Army Corps of Engineers; USGS, U.S. Geological Survey]

Gaging station Source River mile

Sharpe segment

Oahe Dam USACE 1,071.8

Missouri River at Pierre, South Dakota USGS 1,066.4

Missouri River at LaFramboise Island 

at Pierre, South Dakota

USGS 1,064.8

Missouri River Below La Framboise 

Island at Pierre, South Dakota

USGS 1,062.8

Missouri River at Farm Island near 

Pierre, Dakota

USGS 1,060.0

Fort Randall segment

Fort Randall Dam USACE 880.1

Missouri River below Ponca Creek 

near Verdel, Nebraska

USGS 846.3

Missouri River at Niobrara, Nebraska USGS 842.8

Lewis and Clark Lake at Springfiled, 

South Dakota

USGS 832.0
10 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

The information contents of different elevation source 

data were assessed by comparing cumulative frequency distributions of elevations relative to water-surface elevations. The 

information content of soils data was evaluated by calculating 

how well soil characteristics correctly classify the area of the 

flood plain within the 5-year return flood stage. 

Geomorphic Measurements

Geomorphic channel attributes were extracted from the 

maps using automated procedures and scripts within ArcMap 

(ESRI, Redlands, Calif.). The geomorphic information was 

attributed to address points at 500-m spacing along the channel and analyzed statistically for naturally occurring classification units.

Channel and Valley Boundary Determinations

The river channel boundary was manually digitized on 

a computer screen at a 1:3,000 scale using the most recent 

NAIP imagery—2008 for the Sharpe segment and 2007 for the 

Fort Randall segment. All areas of the river that appeared to 

be “active channel” were digitized as part of the channel. The 

active channel was identified by steep slope breaks on banks, 

presence of open water, and geomorphic and sedimentological 

features indicative of frequent sediment transport. In contrast 

to defining the channel boundary by water’s edge, this method 

allowed for comparison of channel and bank position between 

orthophotographs with different discharges. The river valley 

boundary was digitized at a 1:10,000 on the 2007 and 2008 

NAIP orthophotography. Geologic maps and NED digital 

elevation models aided in determining the valley boundary.

Figure 5. Locations of comparison reaches on Lower Missouri River and distribution of wetness 

classes. Wetness classes are indices based on hydraulically modeled flood return intervals (U.S. 

Army Corps of Engineers, 2004). Map development is detailed in Jacobson and others (2007).

98° 96° 94°

42°

40°

Universal Transverse Mercator projection

Zone 14

0 50 100 KILOMETERS

0 50 100 MILES

< 2 year

>2–5 year

>5–10 year

>10–20 year

>20–50 year

>50–100 year

>100–200 year

>200–500 year

>500 year

SOUTH DAKOTA

IOWA

NEBRASKA

MISSOURI

KANSAS St. Joseph

Omaha

Sioux City

Gavins Point Dam

EXPLANATION

Wetness Class

River mile 770 reach

River mile 672 reach

River mile 560 reach

<, less than

>, greater than
Approach and Methods 11

Longitudinal Addressing System

The 2007 and 2008 NAIP orthophotography was also 

used to establish a longitudinal address system for the collection of geomorphic data. To establish the address system, the 

channel centerline was drawn between the digitized channel 

boundaries using a computer-automated method. Address 

points were defined along this centerline with 500-m spacing, 

and a polygon address system was created by using generally 

perpendicular transects drawn through the address points to 

divide the channel into quadrilateral polygons as described in 

Elliott and Jacobson (2006) and Elliott and others, (2009b). 

Address polygons were intersected with polygon datasets to 

assign attributes to address points.

Channel and Valley Width

The digitized channel boundary lines were converted 

to closely-spaced points (1 m) to calculate river width. A 

distance function was then used to measure the distance from 

each address point to the closest channel boundary point on 

the right and left banks. These values were added together to 

calculate total channel width. Channel width, therefore, measures the width of the active channel, including all midchannel 

bars and islands, as well as bars along the sides of the channel. 

The same process was used to calculate valley width for both 

segments. 

Sinuosity Index

Sinuosity is calculated by dividing the distance between 

points as measured along the thalweg by the straight-line 

distance between points. It was not possible in some reaches 

to map the thalweg accurately solely from aerial photographs. 

We therefore used the channel centerline as the basis for calculating a modified channel sinuosity index. This effectively 

defines a minimum scale of sinuosity resolution. Additionally, 

because sinuosity can vary greatly with the scale of measurement, we calculated sinuosity values for 1-kilometer (km), 

2-km, 4-km, 8-km, and 16-km reaches of the river in both 

segments. 

Braiding Index and Bar Count

Bars (islands and sandbars surrounded by inundated 

channels) were manually digitized from the 2006 NAIP aerial 

orthophotography in both segments at a scale of 1:5,000 or 

less. The dominant surface material was used to classify each 

bar as “vegetated” or “bare sand,” with vegetated bars having 

50 percent or greater vegetation coverage. A braiding index 

representing the total number of channels was calculated at 

each address point. Perpendicular transects running through 

each address point were intersected with the bar polygons, 

such that gaps were created in the transects when they overlapped the bars. An automated count was made of the number 

of resultant line segments for each transect to define the braiding index.

The number of vegetated and bare sand bars within 

each address polygon and bar area as a percentage of address 

polygon area were also calculated. Bar number was tabulated 

by counting the number of bar polygon centroids within an 

address polygon. Percent area was calculated by tabulating the 

total area of all bars or portions of bars within each address 

polygon and dividing this number by the address polygon area.

Statistical Classification 

Statistical classification involved an exploratory analysis 

of the entire set of address point variables and a cluster classification based on a selected, reduced dataset. Methods are 

similar to those of Elliott and others (2009b). 

Variables collected at each address point considered for 

analysis included valley and channel width, braiding index, 

the number of vegetated and bare sand bars, and sinuosity at 

2,000, 4,000, and 8,000 m. In each of the two segments, the 

variable data were examined for normality and for correlations 

using bivariate scatterplots and principal component analysis 

(PCA). 

To simplify the classification process, a reduced number 

of variables was determined from the original dataset. The 

chosen variables were valley width, channel width, braiding 

index, and 2,000-m sinuosity. One-third of the address points 

was randomly selected for clustering analysis to minimize 

dependence among spatially adjacent data points in the initial 

clustering process. Variables were standardized by the range 

of values and examined for normality, which is not critical for 

cluster analysis but is a fundamental assumption of discriminant analysis. Nonnormality is not critical for useful analysis 

(Hill and Lewicki, 2006), and the variables used in this analysis were not considered critically nonnormal.

Hierarchical cluster analysis was used to examine the 

clustering structure of the standardized, randomly selected 

datasets in the two Missouri River segments. K-means analysis was applied to the standardized, randomly selected datasets 

separately for each segment. This type of cluster analysis 

begins with a predefined number of clusters (K) and divides 

the data into K mutually exclusive groups by maximizing the 

between-group variation (Wilkinson and others, 2004). Euclidean distance was applied as the distance metric, and betweengroup sum of square differences were plotted with withingroup sum of square differences for the K-means analysis of 2 

to 10 clusters. Similar to a “scree” plot, slope breaks in these 

curves can be interpreted as numbers of clusters where the 

information content of the clustering process changed. Cluster 

parallel plots were used to assess influences of variables in 

defining clusters. Cluster parallel plots show standardized 

values (z scores) of selected variables relative to the mean for 

each cluster, with the most influential variable at the top and 

least influential at the bottom.
12 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

Discriminant analysis was used to inspect data fit within 

defined clusters and to classify the remaining, nonrandomly 

selected address points. Classification into clusters was crossvalidated using a jackknife procedure. 

Results

The following sections provide results of the information 

content assessment for three reaches of the Lower Missouri 

River, river miles 420–811, and for the statistical classification 

of the channel flood-plain complexes for the lower segments 

of the Middle Missouri River. The information content assessment demonstrates the dependence of LCPI classification on 

well-constrained flood profiles and indicates that existing, lowresolution data lack the ability to resolve LCPI classes in the 

Middle Missouri River. The statistical classification presents 

an alternative to LCPI that delineates the main geomorphic 

process domains along the Middle Missouri River and thereby 

provides some guidance to cottonwood restoration planning.

Information Content Analysis

The component data sources for the LCPI are hydraulically modeled water-surface elevation profiles for floods of 

a range of return intervals, high-resolution elevation data 

amenable to gridding at 5-m cell size, and detailed soils maps 

(Jacobson and others, 2007). Of these, only the soils maps are 

presently available for the Middle Missouri River at a resolution equal to that used in the original LCPI. The following 

sections present results of evaluations of alternative elevation 

data, soils data, and water-surface elevation data in creating 

LCPI-type classifications.

Elevation Data

 On the Lower Missouri River, elevation data gridded 

at 5 m were available with root-mean square vertical error of 

approximately 0.2 m (U.S. Army Corps of Engineers, 2004; 

Jacobson and others, 2007). In contrast, the two generally available elevation datasets for the Middle Missouri 

River are the NED 1-arc-second and 1/3-arc-second. The 

97°00' 96°50' 97°00' 96°50'

42°48'

42°45'

42°42'

42°48'

42°45'

42°42'

Universal Transverse Mercator projection

Zone 14

High : 290

Low : 270

EXPLANATION

355 

330

1-arc-second elevations

1/3-arc-second elevations

5-meter elevations

0 2 4 6 KILOMETERS

0 2 4 6 MILES

Meters above North American

Vertical Datum of 1988

Figure 6. Elevation maps of comparison reach at river mile 770 using 1-arc-second, 1/3-arc-second, and 5-meter datasets.
Results 13

1-arc-second dataset has a grid-cell size of approximately 26 

m and a vertical root-mean square error of approximately 2.44 

m; the 1/3-arc-second dataset has a grid-cell size of approximately 6.5 m and shares the vertical root-mean square error of 

approximately 2.44 m.

Maps of the Lower Missouri River comparison reaches 

illustrate the qualitative differences among the datasets (figs. 

6–8). In all three comparison reaches, the 1-arc-second elevation map shows substantially less topographic detail. Differences between the 1/3-arc-second and 5-m elevation maps are 

less discernible except near the channel where the 5-m dataset 

shows more detail because of the inclusion of bathymetric 

data. Lack of topographic information near the channel in the 

1-arc-second and 1/3-arc-second datasets may be attributable 

to high water stages during acquisition of the topographic data.

Cumulative distribution functions of elevations from 

these examples document that the 1/3-arc-second data conform better to the 5-m data than the 1-arc-second data (fig. 9). 

Differences in the distributions are most apparent in the tails, 

especially the lower elevations. Because the 5-m data were 

gridded with added bathymetric data (Jacobson and others, 

2007), they more accurately represent low elevations near the 

channel. Differences among elevation datasets are especially 

important for frequent floods in the river mile 770 comparison 

reach; for example, the 5-m dataset indicates that 11 percent 

of the valley bottom is at or below the elevation of the 2-year 

return interval flood, whereas the 1/3-arc-second dataset 

indicates 7 percent, and the 1-arc-second dataset indicates 0 

percent. 

The cumulative distribution functions illustrate the general differences in the relations between floods and flood-plain 

elevations among the three reaches. In the unchannelized, 

incised reach at river mile 770, there is a broad distribution 

of low elevations, but because of incision and flow regulation much of the distribution of elevations is not inundated by 

even 100- to 500-year return-interval floods. In contrast, the 

channelized reaches at river mile 672 and 560 have a narrow distribution of available valley-bottom elevations. In the 

stable-incising reach at river mile 560 over 90 percent of the 

valley bottom is within the range of elevations associated with 

10- to 25-year return-interval floods. The reach at river mile 

672 is intermediate with less than 10 percent of the valley bottom within the elevation of 25-year return-interval floods.

96°10 96°00' 96°10 96°00'

41°54'

41°51’

41°48'

41°54'

41°51’

41°48'

High : 290

Low : 270

1-arc-second elevations

1/3-arc-second elevations

5-meter elevations

Universal Transverse Mercator projection

Zone 14

High : 290

Low : 270

EXPLANATION

323

298

0 2 4 6 KILOMETERS

0 2 4 6 MILES

Meters above North American

Vertical Datum of 1988

Figure 7. Elevation maps of comparison reach at river mile 672 using 1-arc-second, 1/3-arc-second, and 5-meter datasets.
14 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

Soils Data

The utility of using high-resolution soils data as a surrogate for the combination of water-surface elevations and highresolution topography was explored by calculating how well 

soil attributes corresponded with modern flood classes. The 

question is: If only soils data were available, could accurate 

predictions be made for areas of good potential for cottonwood regeneration? In this case, we assumed that nonirrigated 

cottonwood regeneration required relatively wet conditions as 

indicated by LCPI classes with elevations within the 5-year 

return-interval flood (0- to 5-year wet area). Table 4 indicates 

the information content of soils data relative to known wetness 

conditions. 

Table 4. Percent correct classification of 0–5 year wet area based on known soil characteristics.

River mile

Drainage classes1 Wetland classes2 Entisols

Percent of 

class actually 

wet

Percent of 

wet correctly 

classified

Percent of 

class actually 

wet

Percent of 

wet correctly 

classified

Percent of 

class actually 

wet

Percent of 

wet correctly 

classified

560 98.9 62.6 97.8 52.6 89.6 49.6

672 .8 23.2 3.4 22.9 4.2 96.2

770 2.8 34.7 3.5 32.6 4.8 100

All 30.2 66.3 18.8 51.1 33.5 61.6

1

 Combined somewhat poorly, poorly, and very poorly drained classes.

2

 Good wetland class.

Figure 8. Elevation maps of comparison reach at river mile 560 using 1-arc-second, 1/3-arc-second, and 5-meter datasets.

95°50' 95°40' 95°50' 95°40'

40°45'

40°42'

40°39'

40°36'

40°45'

40°42'

40°39'

40°36'

1-arc-second elevations

1/3-arc-second elevations

5-meter elevations

290

0 2 4 6 KILOMETERS

0 2 4 6 MILES

Universal Transverse Mercator projection

Zone 14

High : 290

Low : 270

EXPLANATION Meters above North American Vertical Datum of 1988

270
Results 15

Soil drainage classes integrate saturated hydraulic conductivity of the soil and underlying geologic materials, and to 

some extent, contain information related to surface topography (Soil Survey Staff, 1993). At river mile 560, 98.9 percent 

of the soil area classed as poorly, somewhat poorly, or very 

poorly drained was within the 0–5 year wet area. Moreover, 

62.6 percent of the wet area was comprised of these drainage 

classes. These data indicate that in stable to aggrading reaches 

of the river, knowledge of soil drainage classes is useful for 

classifying wetness. However, in upstream reaches that are 

characterized by ongoing incision, such as river mile 672 and 

770, the percentage of poorly drained classes that is actually 

wet is quite small (0.8 and 2.8 percent, respectively), and the 

percentage of these classes within the wet area is only 23.2 

and 34.7 percent, respectively. For river miles 423 to 811, 

30.2 percent of poorly drained class area is actually wet, and 

0

336 338 340 342 344 346 348 350 352

0.2

0.4

0.6

0.8

1.0

CUMULATIVE PROPORTION OF AREA

0

306 308 310 312 314 316 318 320 322

0.2

0.4

0.6

0.8

1.0

ELEVATION, IN METERS ABOVE NORTH AMERICAN VERTICAL DATUM OF 1988

0

274 276 278 280 282 284 286 288 290

0.2

0.4

0.6

0.8

1.0

RIVER MILE 770

RIVER MILE 672

RIVER MILE 560

5-meter dataset

1-arc-second dataset

1/3-arc-second dataset

Construction reference plane

2-year return flood

5-year return flood

10-year return flood

25-year return flood

50-year return flood

100-year return flood

200-year return flood

500-year return flood

EXPLANATION

Figure 9. Cumulative frequency distributions of floodplain topography and 

water-surface elevations in comparison reaches at river mile 770,river mile 672, 

and river mile 560. The construction reference plane is a datum used by the U.S. 

Army Corps of Engineers to indicate water-surface elevation at approximately 

75 percent flow exceedance.
16 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

66.3 percent of the wet area is underlain by soils in the poorly 

drained classes. These data indicate that the utility for soil 

drainage classes to predict actual wetness in the flood plain 

depends on the state of geomorphic adjustment of the channel; 

drainage class is less predictive of wetness in reaches undergoing channel incision.

The wetland index and shallow water index are wildlife 

habitat interpretations that rate the soil as good, fair, poor, or 

very poor for wetland restoration purposes (Soil Survey Staff, 

1993). A good rating indicates the habitat is easily established; 

fair indicates that the habitat can be established in most places 

with moderate management effort; poor indicates that limitations on the habitat development are severe; and very poor

indicates that restrictions on habitat development are very 

severe and unsatisfactory results can be expected. The wetland 

and shallow-water habitat indices produce practically identical 

results so only wetland index is shown in table 4.

Wetland suitability classes show relations similar to 

drainage classes. Soils with wetland suitability in the good 

category predict wetness relatively well at river mile 560, but 

do poorly at river miles 672 and 770 where only 3.4 and 3.5 

percent, respectively, of the good classes are coincident with 

the wet area. On average, through river miles 423 to 811, 18.8 

percent of good wetland suitability is coincident with wet 

areas and 51.1 percent of the wet area has soils rated as good 

wetland suitability.

We also explored whether soils classified as entisols 

would be a useful indicator of wetness, as they are typically 

developed in recently deposited alluvium. Although 89.6 

percent of soil area classified as entisols was wet at river 

mile 560, only 49.6 percent of the wet area was comprised of 

entisols; the remainder was dominantly mollisols. The ability 

of entisols to predict wetness was poor at river mile 672 and 

770, with only 4.2 and 4.8 percent, respectively, of the entisols 

coincident with wet areas. In contrast, the wet areas in these 

reaches were dominated by entisols because channel incision 

has resulted in only very young, sandy sediments being deposited at elevations coincident with 0- to 5-year return- interval 

floods.

Soil attributes are highly variable in their ability to 

predict wet areas conducive to cottonwood restoration in these 

segments of the Lower Missouri River. At river mile 560, 

soil attributes predict wetness very well, but this is because 

aggradation in this reach has put nearly all of the flood plain 

in this reach in the 0- to 5-year wet area. In upstream reaches 

that are stable or incising, soil attributes do not perform as 

well because soils with pedogenic characteristics indicative 

of wetness have been stranded at elevations where they are 

infrequently inundated because of recent incision.

Water-Surface Elevation Data

The LCPI benefitted from hydraulically modeled watersurface elevations for floods with return intervals from 2 to 

500 years (U.S. Army Corps of Engineers, 2004). Hydraulic 

flood models generally were not available for the Middle 

Missouri River. The next best source of information for watersurface elevations is from a sparse network of streamflowgaging stations maintained by the U.S. Geological Survey and 

the U.S. Army Corps of Engineers. Water-surface elevations 

for stages attained with return intervals of 2, 5, and 10 years 

show little differences because of the highly regulated flows 

(fig. 10). Because of long distances between gages (3–116 km) 

water-surface elevations would have to be interpolated extensively to model water-surface elevations similar to the LCPI. 

In areas where water- surface elevations change rapidly, such 

as the deltas at the junctions of the Niobrara and Bad Rivers 

(fig. 10), these interpolations would not be valid. 

Longitudinal Classification

Although the LCPI process cannot be replicated in the 

Middle Missouri River, longitudinal classification of the channel and flood plain can be used to inform understanding of cottonwood restoration potential. The longitudinal classification 

is based on quantifiable geomorphic characteristics that are 

indicative of channel and flood-plain form and process.

Flood-Plain Elevations

Comparison of existing water-surface elevation data 

and the range of elevations of adjacent flood plain and valley 

bottom indicate the strong effect of reservoirs and headwater 

deltas in the Middle Missouri River (fig. 10). Figure 10 shows 

the water-surface elevations of 2-, 5-, and 10-year floods compared to percentiles of the adjacent valley bottom elevations. 

The colored bands indicate 0–2, greater than 2–5, greater than 

5–8, and greater than 8–10 percent of the adjacent valley bottom exists at the indicated range of elevations. Because these 

are subaerial, 1/3-arc-second data derived from photogrammetric measures, the lowermost extent of elevation data may 

be limited by the water-surface elevation that existed on the 

day of the data acquisition.

Water-surface elevations in the Sharpe segment are very 

flat because the effects of Big Bend Dam extend nearly to the 

base of the next upstream dam (Oahe Dam, figs. 2, 10). Most 

of the segment has 2–5 percent of the adjacent land subject to 

inundation by the 2-, 5-, and 10-year floods. High longitudinal 

variation results from juxtaposition of reaches where nearly 

the entire valley is inundated by the lake and reaches where 

flood plain and terraces flank the river (for example, 28–35 

km, 45–47 km, 77–84 km, and 105–115 km upstream from 

Big Bend Dam, fig. 10). The flood-plain and terrace reaches 

have as much as 5–10 percent of the adjacent land within the 

2-, 5-, and 10-year flood elevations.

In the reservoir part of the Fort Randall segment, the 2-, 

5-, and 10-year water-surface elevations intersect the 8–10th 

percentile of elevations, indicating that 8–10 percent of the 

adjacent valley bottom would be potentially inundated by 

the 2-, 5-, and 10-year floods (fig. 10). Upstream, the watersurface elevations intersect smaller percentiles indicating that 
Results 17

smaller areas of the adjacent flood plain would be inundated. 

In the reaches 70–95 km upstream from Gavins Point Dam, 

these water-surface elevations would affect only 0–2 percent 

of the adjacent flood plain.

Geomorphic Characteristics

Geomorphic characteristics are illustrated in a longitudinal framework for the Sharpe and Fort Randall segments 

(figs. 11, 12). Valley width, channel width, sandbar frequency, 

and sinuosity vary substantially within both segments. Both 

segments have reservoirs at their downstream ends that fluctuate in water level and influence the base level of the riverine 

reaches. Both segments also have zones of sediment deposition, or deltas, between their riverine and reservoir reaches.

Sharpe Segment

The reach between Oahe Dam and the headwaters of 

Lake Sharpe occurs in a fairly narrow valley that range from 

about 1,960 to 2,740 m (fig. 11, table 5). The valley widens to 

as much as 11,630 m in the Lake Sharpe reservoir reach (figs. 

2, 11) where there are large bends in the bedrock valley of the 

Missouri River. The reservoir itself occupies an average of 36 

percent of the valley width in the reservoir segment. Channel 

widths generally increase in a downstream direction as the 

short riverine segment transitions to delta and then to reservoir 

(fig. 11).

The Bad River enters the Missouri River near Fort Pierre, 

South Dakota, on the right descending bank at river mile 

1,065.2 (fig. 2). The Bad River generally contributes a small 

percentage of flow to the Missouri River (less than 1 percent 

of mean annual discharge), although infrequent flood events 

have contributed considerably more discharge. Five Bad 

River floods in the last 83 years have been 1.2 to 3.0 times the 

annual mean discharge of the Missouri River.). Sediment from 

the Bad River is primarily fine material and enough sediment 

has been deposited downstream from the confluence with the 

Missouri River/Lake Sharpe to form a large delta (U.S. Army 

Corps of Engineers, 2006). The effects of the delta are evident 

in increasing sandbar frequency downstream from river mile 

1,065.2 (fig. 11). Because of reservoir level fluctuations, exact 

2-year return flood elevation

5-year return flood elevation

10-year return flood elevation

0 − 2nd percentile

>2 − 5th percentile

>5 − 8th percentile

>8 − 10th percentile

Greater than 10th percentile

EXPLANATION

DISTANCE UPSTREAM OF GAVINS POINT DAM, IN KILOMETERS

110 100 90 80 70 60 50 40 30 20 10 0

365

370

375

380

385

390

DISTANCE UPSTREAM OF BIG BEND DAM, IN KILOMETERS

140 120 100 80 60 40 20 0

430

435

440

445

450

455

Sharpe Segment

Fort Randall Segment

Bad River

Niobrara River

ELEVATION, IN METERS ABOVE NORTH AMERICAN VERTICAL DATUM OF 1988

<, less than; >, greater than

Figure 10. Longitudinal profiles of water surface elevations for 2-, 5-, and 10-year return floods, and distributions of flood-plain 

elevations on the Sharpe and Fort Randall segments.
18 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration Table 5. Geomorphic characteristics of reaches within the Sharpe and Fort Randall Missouri River segments. [%, percent] Sharpe segment Oahe Dam to Bad River Bad River to Lake Sharpe (Delta) Lake Sharpe Oahe Dam to Big Bend Dam (entire segment) River mile start 1,072.3 1,065.2 1,052.5 1,072.3 River mile end 1,065.2 1,052.5 987.0 987.0 total distance (miles) 7.1 12.7 65.5 85.3 Mini- mum Maxi- mum Mean Mini- mum Maxi- mum Mean Mini- mum Maxi- mum Mean Mini- mum Maximum Mean Valley width (meters) 2,017 2,745 2,331 1,962 2,646 2,295 1,434 11,627 5,378 1,434 11,627 4,691 Channel width (meters) 426 1,746 846 545 1,854 1,369 961 3,973 1,911 426 3,973 1,749 Percent channel width of valley width 21% 64% 36% 28% 70% 60% 67% 34% 36% 30% 34% 37% Braiding index (number of individual channels) 1.0 2.0 1.2 1.0 6.0 2.9 1.0 2.0 1.0 1.0 6.0 1.3 Number of sandbars in a channel address polygon

0 0 0 0 7.0 1.3 0 0 0 0 7.0 0.2

Sandbar percent area in a channel address 

polygon

0 0 0 0 5.7 0.9 0 0 0 0 5.7 0.1

Number of vegetated bars in a channel address 

polygon

0 2.0 0.4 0 20 3.4 0 1.0 0 0 20 0.5

Vegetated bar percent area in a channel address 

polygon

0 55.9 5.1 0 59.6 24.2 0 0.6 0 0 59.6 3.8

Total number of bars in a channel address 

polygon

0 2.0 0.4 0 21.0 4.7 0 1.0 0 0 21.0 0.7

Total percent of bars in a channel address 

polygon

0 55.9 5.1 0 59.6 25.1 0 0.6 0 0 59.6 3.9

Channel sinuosity calculated over a 1,000 

meter-ruler

1.00 1.06 1.01 1.00 1.03 1.00 1.00 1.07 1.01 1.00 1.07 1.01

Channel sinuosity calculated over a 2,000 

meter-ruler

1.00 1.06 1.02 1.00 1.03 1.01 1.00 1.10 1.01 1.00 1.10 1.01

Channel sinuosity calculated over a 4,000 

meter-ruler

1.01 1.05 1.03 1.00 1.05 1.01 1.00 1.21 1.04 1.00 1.21 1.03

Channel sinuosity calculated over a 8,000 

meter-ruler

1.03 1.10 1.06 1.00 1.09 1.02 1.00 1.64 1.12 1.00 1.64 1.10

Channel sinuosity calculated over a 16,000 

meter-ruler

1.15 1.20 1.18 1.02 1.20 1.07 1.04 2.69 1.39 1.02 2.69 1.33
Results 19

Table 5. Geomorphic characteristics of reaches within the Sharpe and Fort Randall Missouri River segments.—Continued[%, percent]

Fort Randall segment Fort Randall Dam to Niobrara River Niobrara River to Lewis and Clark Lake (Delta) Lewis and Clark Lake Fort Randall Dam to Gavins Point Dam

River mile start 880 844.0 826.0 880River mile end 844.0 826.0 811.1 811.1Total distance (miles) 36.0 18.0 14.9 68.9

Minimum Maximum Mean Minimum Maximum Mean Minimum Maximum Mean Minimum Maximum Mean

Valley width (meters) 1,488 3,075 2,379 2,199 4,600 2,972 2,143 4,239 3,171 1,488 4,600 2,710Channel width (meters) 372 2,297 954 1,694 4,564 2,738 2,138 4,223 3,078 372 4,564 1,893Percent channel width of valley width 25% 75% 40% 77% 99% 92% 100% 100% 97% 25% 99% 70%Braiding index (number of individual channels) 1.0 16.0 2.5 3.0 29.0 8.5 1.0 1.0 1.0 1.0 29.0 3.7Number of sandbars in a channel address 

polygon

0 8.0 0.9 0 18.0 1.4 0 0 0 0 18.0 0.8

Sandbar percent area in a channel address 

polygon

0 25.7 2.3 0 11.5 0.7 0 0 0 0 25.7 1.4

Number of vegetated bars in a channel address 

polygon

0 66.0 4.2 0 70 21.4 0 0 0 0 70 7.7

Vegetated bar percent area in a channel address 

polygon

0 0.7 0.2 0 0.8 0.6 0 0 0 0 0.8 0.3

Total number of bars in a channel address 

polygon

0 66.0 5.1 0 70 22.8 0 0 0 0 70 8.6

Total percent of bars in a channel address 

polygon

0 69.6 25.1 0 80.5 56.4 0 0 0 0 80.5 27.6

Channel sinuosity calculated over a 1,000 

meter-ruler

0.99 1.08 1.01 0.99 1.04 1.01 0.99 1.02 1.00 0.99 1.08 1.00

Channel sinuosity calculated over a 2,000 

meter-ruler

1.00 1.10 1.10 1.00 1.04 1.01 0.99 1.02 1.00 0.99 1.10 1.01

Channel sinuosity calculated over a 4,000 

meter-ruler

1.00 1.17 1.17 1.00 1.06 1.02 0.99 1.02 1.01 0.99 1.17 1.02

Channel sinuosity calculated over a 8,000 

meter-ruler

1.00 1.12 1.12 1.01 1.14 1.05 1.00 1.04 1.01 1.00 1.14 1.04

Channel sinuosity calculated over a 16,000 

meter-ruler

1.01 1.01 1.01 1.03 1.23 1.13 1.02 1.03 1.02 1.01 1.23 1.07
20 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

delineation of the boundary between lake and riverine subsegments is not possible. Substantial decreases in sandbar occurrence downstream from river mile 1,052.5 are apparent in 

aerial photography in 2004, 2005, 2006, and 2008, indicating 

that river mile 1,052.5 is a reasonable downstream boundary 

of the Lake Sharpe delta. 

Sinuosity generally increases with scale in the Sharpe 

segment of the Missouri River because sinuosity measured 

over longer lengths picks up the large bends of the bedrock 

valley. Meander bends in Lake Sharpe have sinuosity values 

measured over scales of 8,000 and 16,000 m ranging up to 1.6 

and greater than 2.2 (fig. 11). 

Fort Randall Segment

Valley widths in the Fort Randall segment are quite 

narrow ranging from about 1,490 to 4,600 m (fig. 12, table 

5). Channel widths vary in the riverine subsegment including 

narrow and wider reaches ranging from about 380 to 2,300 

m wide upstream from the Niobrara River confluence and 

become increasingly wide (about 1,690 to 4,560 m) in the 

delta and reservoir subsegments (fig. 12, table 5). The river 

occupies an average of 92 and 97 percent of the valley width 

in the delta and reservoir subsegments of the Fort Randall segment (table 5).

In the Fort Randall segment, the sand-bedded Niobrara 

River enters the Missouri River at river mile 843. The Niobrara River contributes about 6.4 percent of the Missouri River 

discharge on a mean annual basis but as much as 55 percent of 

the sediment load to the Missouri River and Lewis and Clark 

Lake (Engineering and Hydrosystems Inc., 2002). Consequently, a large delta has formed at the headwaters of Lewis 

and Clark Lake (Elliott and Jacobson, 2006; U.S. Army Corps 

of Engineers, 2006). The rapid increase in braiding index at 

approximately river mile 850 defines the upstream boundary 

of the delta. This delta has grown at a rate of approximately 

105 meters per year from 1993–2003 and as of 2006 was 

located near river mile 826 (Elliott and Jacobson, 2006). 

Downstream from river mile 826 to Gavins Point Dam at river 

mile 811.1 is Lewis and Clark Lake where the reservoir fills 

nearly the entire bedrock-defined valley (figs. 3, 12).

Statistical Classification

In the Sharpe segment, channel width and valley width 

are interrelated (fig. 13) and positively loaded on factor 1 in 

the PCA (fig. 14). Sinuosity at all scales is interrelated and 

positively loaded on factor 1 as well. In-channel characteristics such as the number of vegetated bars, sand bars, and braid 

index are related and negatively loaded on factor 2 (fig. 14). 

Figure 11. Longitudinal geomorphic characteristics for the Sharpe segment of the Missouri River. BAR COUNT

0

5

10

15

20

BRAIDING INDEX

1

2

3

4

5

6

7

Sandbar count

Braiding index

WIDTH, IN METERS

0

2,000

4,000

6,000

8,000

10,000

12,000

Valley

Channel

DISTANCE UPSTREAM FROM THE JUNCTION WITH THE MISSISSIPPI RIVER, IN RIVER MILES

1,070 1,060 1,050 1,040 1,030 1,020 1,010 1,000 990

SINUOSITY INDEX

1.0

1.2

1.4

1.6

1.8

2.0

2.2

1,000 m

2,000 m

4,000 m

8,000 m

16,000 m

Oahe

Dam

Big

Bend

Bad River Lake Sharpe Dam
Results 21

In the Fort Randall segment, there is also a strong relation 

between valley width and channel width as seen in the scatterplots (fig. 15), and these variables are positively loaded on 

factor 2 in the PCA (fig. 16). As seen in the Sharpe segment, 

sinuosity at all scales is interrelated and positively loaded on 

factor 1 (figs. 15, 16) Characteristics related to channel complexity (the number of sand and vegetated bars and braiding 

index) are positively loaded on factor 1, although the number 

of sand bars and vegetated bars are less related than in the 

Sharpe reach (figs. 15, 16). 

Based on correlations among variables evident in the 

scatterplots and PCA (figs. 13–16), we selected a reduced set 

of variables to be used in cluster analysis (figs. 17, 18). A subset of all address points was randomly selected and standardized to act as a training classification set. Hierarchical cluster 

analysis of the standardized, randomly selected data indicated 

four to five distinct clusters in the Sharpe segment (fig. 19). In 

the Fort Randall segment hierarchical cluster analysis supported the identification of four clusters (fig. 20 ). 

K-means analysis supported the hierarchical clustering 

analysis. Breaks in slope of the “scree” plots occur in both of 

the Missouri River segment datasets at four clusters (figs. 21 

and 22) . Cluster parallel plots support the understanding that 

valley width, braiding index, and channel width are the most 

Figure 12. Longitudinal geomorphic characteristics for the Fort Randall segment of the Missouri River. WIDTH, IN METERS

0

1,000

2,000

3,000

4,000

5,000

Valley

Channel

BAR COUNT

0

20

40

60

80

BRAIDING INDEX

5

10

15

20

25

30

35

Bar count

Braiding index

DISTANCE UPSTREAM FROM THE JUNCTION WITH THE MISSISSIPPI RIVER, IN RIVER MILES

880 870 860 850 840 830 820

SINUOSITY INDEX

1.00

1.05

1.10

1.15

1.20

1.25

1,000 m

2,000 m

4,000 m

8,000 m

16,000 m

Niobrara River Lewis and Clark Lake

Gavins

Point

Dam

Fort

Randall

Dam

Table 6. Result of jackknife validation of discriminant function classifications for the 

Sharpe segment.

Classification

Classification

Percent correct

1 2 3 4

Four-cluster classification

1 51 1 0 0 100

Classified by 

jackknife

2 1 23 0 0 100

3 0 0 10 5 100

4 0 0 0 5 100

Total 51 23 10 10 100
22 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

influential variables defining clusters in the Sharpe segment 

whereas channel width, braiding index, and sinuosity are the 

most influential variables in the Fort Randall segment (figs. 

23, 24).

Discriminant plots show distinct groupings of cluster 

classes (figs. 25, 26). A five-cluster plot in the Sharpe segment 

indicated two clusters with nearly complete overlap, therefore 

a four-cluster classification was determined to be more appropriate (fig. 25). The remaining points that were not randomly 

selected were classified by discriminant functions into the 

best fit cluster (figs. 27, 28). Cross validation of classifications 

using a jackknife procedure indicated that 100 percent of 

all points were classified correctly in the Sharpe segment 

(table 6), and 77–100 percent of points were classified correctly in the Fort Randall segment (table 7). 

Discussion

This report addressed two objectives related to understanding and prioritizing restoration of cottonwood communities along the Middle Missouri River. The first objective was 

to assess the utility of available soils, topography, and watersurface elevation data in defining a Land Capability Potential 

Index (LCPI) similar to that developed for the Lower Missouri 

River (Jacobson and others, 2007). The second objective was 

to develop a geomorphic classification for the Sharpe and Fort 

Randall segments of the Middle Missouri River using existing 

GIS data. The following section presents discussion about the 

prospects for developing a LCPI-like index from relatively 

low-resolution data, development of the longitudinal classification, and application of the classification to assess potential 

for cottonwood restoration.

Data Availability and An Alternative LCPI

The original LCPI combined county-level soils data 

(SSURGO) (Jacobson and others, 2007; Soil Survey Staff, 

2009) high-resolution topographic data (U.S. Army Corps 

of Engineers, 2004), and hydraulically modeled floodwatersurface elevations (U.S. Army Corps of Engineers, 2004) to 

create the LCPI for the Lower Missouri River. Of these data 

sources, only the SSURGO data are readily available for the 

Middle Missouri River segments considered in this report. 

Hence, a specific objective of this study was to determine 

whether the soils data could be used alone or in combination 

with other sources of topography and water-surface elevations 

to create an LCPI-like classification for the Middle Missouri 

River.

Flood-Plain Elevation Data

Alternative elevation data available in the Middle Missouri River are the 1-arc-second and 1/3-arc-second digital 

elevation models from the National Elevation Dataset (Gesch 

and others, 2009; U.S. Geological Survey, 2009). The 1-arcsecond data have a cell size of approximately 26 m and the 

1/3-arc-second data have a cell size of approximately 6.5 m in 

this region. Compared to the 5-m cell size dataset developed 

for the LCPI, the 1-arc-second data poorly represent elevation cumulative frequency distributions (fig. 9). One can infer 

from the stepped distributions that the 1-arc-second data fail 

to capture details of the actual distribution of elevations on 

the flood plain. The lack of correspondence to the 5-m data 

in the low-elevation tail of the distribution also indicates that 

frequently flooded areas near the channel are underrepresented 

in the data. This is an important bias in the incised reach at 

river mile 770 where water-surface elevations of a wide range 

of floods occur in this range of elevations. The 1-arc-second 

data match better with the 5-m data in the upper parts of the 

flood plain (fig. 9).

The 1/3-arc-second topographic data generally conform 

better to the frequency distribution of the 5-m data compared 

to the 1-arc-second data. Small potential deviations from 

the 5-m distribution occur at the lowest elevations, probably 

because the 1/3-arc-second data lack the bathymetric data 

included with the 5-m data. NED datasets typically are derived 

by methods confined to subaerial data collection (for example, 

photogrammetry) that are limited to the water-surface elevations existing at the time of data collection. If the original data 

are collected at relatively high water, less flood-plain elevation will be present in the NED. Nevertheless, good general 

Table 7. Result of jackknife validation of discriminant function classifications for the Fort 

Randall segment.

Classification

Classification

Percent correct

1 2 3 4

Four-cluster classification

1 25 1 0 0 96

Classified by 

jackknife

2 0 11 0 0 100

3 1 0 10 2 77

4 0 1 0 13 93

Total 26 13 10 15 92
Discussion 23

correspondence between the 1/3-arc-second and the 5-m data 

frequency distributions indicates that 1/3-arc-second data 

should be sufficient for LCPI-like classifications for much of 

the flood plain. Exceptions would exist if the 1/3-arc-second 

data were collected during high flow events or if the flow 

stages of interest are concentrated in a small range of elevations where the data are obscured by high water, or if both 

conditions existed. Concentration of flow stages in a narrow 

range is typical of highly regulated systems downstream of 

dams and is typified by the narrow range shown at river mile 

770 (fig. 9).

Soil Characteristics as Indicators for Cottonwood 

Restoration

The Middle Missouri River is composed of reservoir 

and interreservoir river segments. Missouri River mainstem 

reservoirs were not designed to pass sediment (Ferrell, 1993; 

Engineering and Hydrosystems Inc., 2002) and, consequently 

sediment tends to accumulate in the reservoir headwaters 

while sediment deficits in the tailwaters downstream from 

dams tend to lead to channel incision (Williams and Wolman, 1984; Schmidt and Wilcock, 2008; Jacobson and others, 

2009a). The general model of channel incision downstream 

from dams and channel aggradation in headwaters can be 

modified by tributary inputs. For example, sediment input 

from the Bad River causes moderate aggradation in the reach 

downstream from Oahe Dam, which might otherwise be 

expected to be affected by incision. Similarly, bed aggradation in the headwaters of Lewis and Clark Lake is accelerated 

because of sediment influxes from the Niobrara River (U.S. 

Army Corps of Engineers, 2007).

Without recent geomorphic adjustments, it would be 

expected that characteristics and spatial arrangements of 

flood-plain soils would relate to an equilibrium among prevailing rates of flood-plain erosion, flood-plain deposition, 

Figure 13. Bivariate and frequency plots of selected, non-transformed classification variables for 

the Sharpe segment of the Missouri River. VAL_WIDTH CHAN_WIDTH BRAID_IND NUM_SBARS NUM_VBAR SIN_2000 SIN_4000

VAL_WIDTH

SIN_8000

CHAN_WIDTH BRAID_IND NUM_SBARS NUM_VBAR SIN_2000 SIN_4000 SIN_8000

EXPLANATION

VAL_WIDTH Valley width

CHAN_WIDTH Channel width

BRAID_IND Braiding index calculated as number of individual channels 

 at a channel address transect

NUM_SBARS Number of sandbars in a channel address polygon

NUM_VBARS Number of vegetated bars in a channel address polygon

SIN_2000 Channel sinuosity calculated over a 2,000 meter-ruler

SIN 4000 Channel sinuosity calculated over a 4,000 meter-ruler

SIN_8000 Channel sinuosity calculated over a 8,000 meter-ruler
24 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

and flood-plain inundation. Fragmentation of the sediment 

transport system by dams alters that equilibrium to the point 

where soil characteristics may no longer be reliable indicators 

of prevailing hydrology or geomorphic processes. In incising 

reaches, former flood plains become terraces that are no longer 

inundated by floods of 1- to 2-year return interval. Soils on the 

former flood plain will retain pedogenic and sedimentologic 

characteristics indicative of their origin in an active flood 

plain, slowly evolving to characteristics adjusted to their new 

hydrologic and geomorphic regimes (Chadwick and Chorover, 

2001; Cornu and others, 2009). Hence, soils with pedogenic 

features indicative of water saturation and reducing geochemical conditions (redoximorphic features; Soil Survey Staff, 

1993) may be found in landscape positions where soil saturation is now rare. In contrast, channel reaches characterized 

by rapid aggradation tend to have soils dominated by deposition of fine, organic-rich sediments, frequent inundation, and 

high water tables. Rapid aggradation of organic rich alluvial 

-1.0

-0.5

0

0.5

1.0

FACTOR (1)

-1.0

-0.5

0

0.5

1.0

FACTOR (2)

-0.5

0

0.5

1.0

FACTOR (3)

VAL_WIDTH

CHAN_WIDTH

BRAID_IND

NUM_SBARS NUM_VBAR

SIN_2000

SIN_4000

SIN_8000

EXPLANATION

VAL_WIDTH Valley width

CHAN_WIDTH Channel width

BRAID_IND Braiding index calculated as number of individual channels 

 at a channel address transect

NUM_SBARS Number of sandbars in a channel address polygon

NUM_VBARS Number of vegetated bars in a channel address polygon

SIN_2000 Channel sinuosity calculated over a 2,000 meter-ruler

SIN 4000 Channel sinuosity calculated over a 4,000 meter-ruler

SIN_8000 Channel sinuosity calculated over a 8,000 meter-ruler

FACTOR (2)

BRAID_IND

NUM_SBARS

CHAN_WIDTH

SIN_8000

VAL_WIDTH

FACTOR (3)

BRAID_IND NUM_VBAR

NUM_SBARS

CHAN_WIDTH

SIN_8000

SIN_4000

VAL_WIDTH

SIN_2000

VAL_WIDTH

CHAN_WIDTH

SIN_8000

SIN_4000

BRAID_IND

NUM_SBARS

SIN_2000

FACTOR (1) FACTOR( 2)

NUM_VBAR

SIN_4000

SIN_2000

NUM_VBAR

Figure 14. Principal components analysis of selected variables of for the Sharpe segment of the Missouri 

River. All variables have been standardized to the range.
Discussion 25

sediments leads to cumulative A-horizons in soils that consequently classify as mollisols (Jacobson and others, 2003); 

these soils retain their sedimentologic characteristics and may 

not develop redoximorphic pedogenic characteristics related 

to their frequent inundation until years to decades have passed 

(Cornu and others, 2009). 

Distributions of soil characteristics among comparison 

reaches along the Lower Missouri River support the idea 

that soil characteristics may be poor indicators of hydrologic 

condition in a geomorphically active river. In the incising 

reach at river mile 770, the 0–5 year wet area was dominated 

by relatively well-drained entisols, but a large area of entisols 

existed within the valley bottom outside of the 0- to 5-year wet 

area, resulting in entisols predicting only 4.2 percent of the 

0- to 5-year wet area (table 4). The 0- to 5-year wet area in the 

stable-aggrading reach at river mile 560 was slightly dominated by mollisols, but mollisols were also dominant throughout the entire valley bottom resulting in only 45.1 percent 

correct classification of the 0- to 5-year wet area. Similar rates 

of classification errors occurred with other soil characteristics 

(table 4). In general, soil characteristics were better at classifying wet areas in aggradational reaches, with up to 98.9 percent 

correct classification rate. 

Longitudinal variation in classification performance relative to the LCPI identification of wet areas indicates that soils 

alone are not robust indicators of wetness conditions along the 

reaches of the Missouri River affected by reservoirs. Independent assessment of hydrologic condition, like that afforded 

through modeled water-surface elevations, is necessary to 

assess the hydrologic condition of flood plains along geomorphically active rivers.

Figure 15. Bivariate and frequency plots of selected, non-transformed classification variables for 

the Fort Randall segment of the Missouri River.

EXPLANATION

VAL_WIDTH Valley width

CHAN_WIDTH Channel width

BRAID_IND Braiding index calculated as number of individual channels 

 at a channel address transect

NUM_SBARS Number of sandbars in a channel address polygon

NUM_VBARS Number of vegetated bars in a channel address polygon

SIN_2000 Channel sinuosity calculated over a 2,000 meter-ruler

SIN 4000 Channel sinuosity calculated over a 4,000 meter-ruler

SIN_8000 Channel sinuosity calculated over a 8,000 meter-ruler

VAL_WIDTH CHAN_WIDTH BRAID_INDEX NUM_SBARS NUM_VBAR SIN_2000 SIN_4000

VAL_WIDTH

SIN_8000

CHAN_WIDTH BRAID_INDEX NUM_SBARS NUM_VBAR SIN_2000 SIN_4000 SIN_8000
26 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

Water-Surface Elevations Compared to FloodPlain Elevations

Whereas hydraulic models provided estimates of watersurface elevations for floods at intervals of 1.6 km on the 

Lower Missouri River (U.S. Army Corps of Engineers, 2004), 

streamgage spacings on the Sharpe and Fort Randall segments 

of the Middle Missouri River range from 3 to 116 km, and 

average 30.5 km (fig. 10). The long interpolations of watersurface elevations between streamgages challenges the validity 

of applying an LCPI-like intersection of water-surface elevations and topography to delineate wetness classes. Interpolation of water-surface elevations over long distances may be 

more valid in reservoir segments where water-surface elevations can be assumed to be flat to gently sloping. 

The longitudinal inventory of elevation and interpolated 

water-surface elevations of 2-, 5-, or 10-year floods (fig. 10) 

provides some limited information on where land exists at 

elevations that can be inundated by these floods. Reaches 

where the water-surface elevations intersect higher percentiles 

-1.0

-0.5

0

0.5

1.0

FACTOR (1)

-1.0

-0.5

0

0.5

1.0

FACTOR (2)

-0.5

0

0.5

1.0

FACTOR (3)

VAL_WIDTH

CHAN_WIDTH

BRAID_INDEX

NUM_SBARS

NUM_VBAR

SIN_2000

SIN_4000

SIN_8000

EXPLANATION

VAL_WIDTH Valley width

CHAN_WIDTH Channel width

BRAID_IND Braiding index calculated as number of individual channels 

 at a channel address transect

NUM_SBARS Number of sandbars in a channel address polygon

NUM_VBARS Number of vegetated bars in a channel address polygon

SIN_2000 Channel sinuosity calculated over a 2,000 meter-ruler

SIN 4000 Channel sinuosity calculated over a 4,000 meter-ruler

SIN_8000 Channel sinuosity calculated over a 8,000 meter-ruler

FACTOR (1) FACTOR (2)

FACTOR (2)

CHAN_WIDTH VAL_WIDTH

SIN_2000

BRAID_INDEX

FACTOR (3)

CHAN_WIDTH

VAL_WIDTH NUM_SBARS

NUM_VBAR

SIN_4000

SIN_8000

SIN_4000

SIN_2000

NUM_SBARS

NUM_VBAR

NUM_VBAR

NUM_SBARS

SIN_4000

SIN_8000

SIN_2000

BRAID_INDEX

SIN_8000 CHAN_WIDTH

VAL_WIDTH

BRAID_INDEX

Figure 16. Principal components analysis of selected variables of for the Fort Randall segment of 

the Missouri River. All variables have been standardized to the range.
Discussion 27

Figure 17. Bivariate and frequency plots of a reduced set of selected 

classification variables for the Sharpe segment of the Missouri River. 

EXPLANATION

VAL_WIDTH Valley width

CHAN_WIDTH Channel width

BRAID_IND Braiding index calculated as number of individual 

 channels at a channel address transect

VAL_WIDTH

SIN_2000 Channel sinuosity calculated over a 2,000 meter-ruler

CHAN_WIDTH BRAID_IND SIN_2000

VAL_WIDTH CHAN_WIDTH BRAID_IND SIN_2000

SELECTED VARIABLES IN THE SHARPE SEGMENT

SELECTED VARIABLES IN THE SHARPE SEGMENT

Figure 18. Bivariate and frequency plots of a reduced set of selected 

classification variables for the Fort Randall segment of the Missouri River. 

EXPLANATION

VAL_WIDTH Valley width

CHAN_WIDTH Channel width

BRAID_IND Braiding index calculated as number of 

 individual channels at a channel address transect

SIN_2000 Channel sinuosity calculated over a 2,000 meter-ruler

SELECTED VARIABLES IN THE FORT RANDALL SEGMENT

VAL_WIDTH CHAN_WIDTH BRAID_IND SIN_2000

VAL_WIDTH CHAN_WIDTH BRAID_IND SIN_2000

SELECTED VARIABLES IN THE FORT RANDALL SEGMENT
28 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

indicate more land is available at elevations where wetness 

conditions may favor cottonwood restoration. Examples from 

the Sharpe segment are reaches at 28–35 km, 45–47 km, 

77–84 km, and 105–115 km upstream from Big Bend Dam 

where 2-, 5-, and 10-year flood elevations affect 5–10 percent 

of the valley bottom.

Longitudinal Geomorphic Classification of the 

Middle Missouri River

Rivers can be classified in many ways, but a generally 

accepted criterion is that a classification should be developed 

to address specific applications (Kondolf and others, 2003). 

In this project, our objective was to develop a classification 

for the Middle Missouri River and its flood plain that would 

delineate units useful for cottonwood restoration. As discussed 

in the previous sections, available data were not of sufficient 

resolution to replicate the LCPI classification developed for 

the Lower Missouri River. This section of the report discusses 

a longitudinal geomorphic classification of the Middle Missouri River and its application to cottonwood restoration.

Segment Scale Classification

River classifications fall into two broad categories: 

deductive and inductive. Deductive classification systems are 

based on assumptions that well-understood processes determine channel form, and that the channel form is indicative of 

geomorphic processes. The most frequently cited example of 

deductive reach-scale classification is the Rosgen classification that places river reaches in predetermined classes based 

on channel planform, slope, and cross-sectional morphology 

(Rosgen, 1996). 

In contrast, an inductive approach measures a suite of 

geomorphic variables and uses statistical clustering techniques 

to determine naturally occurring geomorphic sections of the 

river (Kondolf and others, 2003; Elliott and Jacobson, 2006; 

Elliott and others, 2009a). The inductive approach can be 

hierarchical depending on the number of criteria and number of clusters desired, and can, therefore, be used to define 

subreaches, reaches, or segments. For example, Elliott and 

Jacobson (2006) identified nested 2, 4, and 10-cluster classifications for the Missouri National Recreational River segment 

of the Lower Missouri River.

A combination of deductive and inductive approaches 

may be justified in a hierarchical approach. Elliott and Jacobson (2006) defined broad-scale features that clearly delineate 

physically distinct parts of the river and the flood plain at the 

segment scale. These features included boundaries imposed by 

dams, tributary junctions, and headwaters of lakes. A similar 

segment-scale classification is used in this report (figs. 1, 29, 

table 1), with segments delineated either by tributary junctions 

where flow regime and material fluxes change abruptly, or by 

hard structures such as dams. In the inter-reservoir reaches, 

we define riverine, deltaic, and lacustrine subsegments. The 

Figure 19. Hierarchical cluster dendogram of the reduced 

dataset for the Sharpe segment of the Missouri River. Selected 

variables were standardized for this analysis. 

Figure 20. Hierarchical cluster dendogram of the reduced 

dataset for the Fort Randall segment of the Missouri River. 

Selected variables were standardized for this analysis. SITES

Clusters resulting

at this distance

4

0 1 2 3 4 5 6 7

DISTANCES

SITES

Clusters resulting

at this distance

4

0 1 2 3 4 5

DISTANCES
Discussion 29

transitions between subsegments are not clearly delineated 

because they vary with seasonal variation in water levels and 

with decadal variation in channel incision and aggradation.

The proposed inductive, reach-scale classification of the 

Middle Missouri River is nested within the deductive, segment-scale classification (figs. 1, 29, table 1). Other segmentscale classifications have been proposed for the Missouri 

River, and the proliferation of classifications, numbering protocols, and naming systems has caused some confusion (table 

1). The numbering system proposed in our classification starts 

with 1 in the downstream segment (Osage River to Mississippi River) and counts up in the upstream direction. The logic 

for upstream incremental counting is that the upstream limit 

of the mainstem segments doesn’t need to be defined, allowing segment numbering to continue to successively smaller 

channels. If the numbering system starts with 1 upstream and 

counts down, then some arbitrary starting place needs to be 

defined, and if there is subsequent interest in smaller channels 

upstream, the system has to be revised or negative numbers 

need to be used. An alternative to numbering systems is to use 

names as also proposed in table 1.

The definition of river segment originally proposed by 

Frissell and others (1986) was intended to indicate longitudinal parts of a stream system between substantial tributaries 

and with relatively uniform properties of bedrock and valley 

physiography. This definition leaves some latitude in determining substantive hydrologic and physiographic variation. 

Clearly, segments should be defined where large storage 

reservoirs, like those on the mainstem Missouri River, impose 

substantive hydrologic change on downstream channels. 

Impounded sections of the river also typically have a combination of lacustrine, deltaic, and riverine conditions. Because 

NUMBER OF CLUSTERS IN SHARPE SEGMENT

Between clusters

Within clusters

2 4 6 8 10 12

SUM OF SQU

ARES

0

5

10

15

20

Figure 21. Changes in sum of square differences within and among 

clusters as number of clusters changes for the Sharpe segment of the 

Missouri River.

Figure 22. Changes in sum of square differences within and among 

clusters as number of clusters changes for the Fort Randall segment of 

the Missouri River.

NUMBER OF CLUSTERS IN FORT RANDALL SEGMENT

2 4 6 8 10 12

SUM OF SQUARES

0

5

10

15

20

Between clusters

Within clusters
30 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

these conditions have a substantial effect on the distributions 

of water, sediment, and nutrients over time and space, they 

are important delimiters of riverine habitat and appropriate to 

define as subsegments. However, the locations and characteristics of the transitions between these conditions are highly 

dynamic. The transitions vary with seasonal to multiyear 

variations in lake levels and over longer periods as channel 

incision and deltaic sedimentation change channel-floodplain 

geomorphology. Consequently, lakes, deltas, and riverine parts 

of the Missouri River can be defined as separate subsegments, 

but transitions among them are dynamic.

Reach-Scale Classification

Reaches within segments are characterized conceptually 

as uniform lengths of river with similar channel and floodplain morphology generally containing multiples of channel 

units or bend-crossover units (Frissell and others, 1986). Our 

statistical classification attempts to define naturally occurring 

clusters of uniform channel and flood-plain geomorphology 

at the reach scale (table 8). Because reaches are defined at a 

substantially finer scale compared to segments, statistically 

defined reaches may cluster within identified segments or 

subsegments or they may occur in multiple segments. In interreservoir reaches where subsegment boundaries are difficult to 

define, the spatial distribution of statistically defined reaches 

provides an independent basis for delineating riverine, deltaic, 

and lacustrine subsegments (figs. 27, 28).

The number of clusters used in the classification is a 

matter of judgment that is based on a combination of physical understanding of the river, the structure of the hierarchical 

clustering (figs. 19, 20) and the shape of the “scree” plots from 

the K-means clustering (figs. 21, 22). More than four clusters 

could be used in the Sharpe and Fort Randall segments but it is 

unclear whether subdivided clusters would result in increased 

ability to interpret and apply the classification. 

Because the Sharpe and Fort Randall segments were clustered separately, the numbering and the characteristics of the 

clusters are not the same in the two segments. In the Sharpe 

segment, valley width was the most influential variable in the 

K-means cluster procedure, followed by braiding index, channel width, and sinuosity (fig. 23). In contrast, channel width 

was most influential in the Fort Randall segment, followed by 

braiding index, sinuosity, and valley width (fig. 24). The influence of valley width in the Sharpe segment results from the 

greater variability, especially relative to channel width, within 

the segment (fig. 11). In contrast, channel width was more 

variable than valley width in the Fort Randall segment (fig. 

12). Valley width can be of considerable importance in riverine habitat assessments because it is a measure of total valley 

Figure 23. Cluster-parallel plots from the K-means procedure for the Sharpe segment 

of the Missouri River. VARIABLES IN THE SHARPE SEGMENT

EXPLANATION

VAL_WIDTH Valley width

CHAN_WIDTH Channel width

BRAID_IND Braiding index calculated as number of individual channels 

 at a channel address transect

SIN_2000 Channel sinuosity calculated over a 2,000 meter-ruler

STANDARDIZED VALUE OF THE VARIABLE RELATIVE TO THE MEAN

1 2

SIN_2000

CHAN_WIDTH

BRAID_IND

VAL_WIDTH

0 0.4 0.8 1.2 SIN_2000

CHAN_WIDTH

BRAID_IND

VAL_WIDTH

0 0.4 0.8 1.2

3

SIN_2000

CHAN_WIDTH

BRAID_IND

VAL_WIDTH

0 0.4 0.8 1.2

4

SIN_2000

CHAN_WIDTH

BRAID_IND

VAL_WIDTH

0 0.4 0.8 1.2
Discussion 31

VARIABLES IN THE FORT RANDALL SEGMENT

STANDARDIZED VALUE OF THE VARIABLE RELATIVE TO THE MEAN

EXPLANATION

VAL_WIDTH Valley width

CHAN_WIDTH Channel width

BRAID_IND Braiding index calculated as number of individual channels 

 at a channel address transect

SIN_2000 Channel sinuosity calculated over a 2,000 meter-ruler

SIN_8000 Channel sinuosity calculated over a 8,000 meter-ruler

0.0 0.4 0.8 1.2

1 2

VAL_WIDTH

SIN_2000

BRAID_IND

CHAN_WIDTH

0 0.4 0.8 1.2 VAL_WIDTH

SIN_2000

BRAID_IND

CHAN_WIDTH

VAL_WIDTH

SIN_2000

BRAID_IND

CHAN_WIDTH

0 0.4 0.8 1.2

3 4

VAL_WIDTH

SIN_2000

BRAID_IND

CHAN_WIDTH

0 0.4 0.8 1.2

Figure 24. Cluster-parallel plots from the K-means procedure for the Fort Randall 

segment of the Missouri River.

Table 8. Names and interpreted physical significance of the clustered classes in the Sharpe and Fort Randall segments.

Classification Descriptive name Physical process interpretation

Sharpe segment

1 No braiding, variable valley width, channel 

width, and sinuosity

This unit occurs in multiple settings within the Sharpe segment, including the reaches 

above and below the Bad River. It is associated with variable valley and channel 

widths, no braiding and variable sinuosity. 

2 Wide valley, no braiding, medium channel 

width

This unit occurs entirely within Lake Sharpe and is characterized by a wide valley, 

medium channel widths, and no braiding consistent with a reservoir environment.

3 Medium to highly braided, narrow valley This unit occurs downstream from the Bad River in the delta region of the Sharpe 

segment and is characterized by braiding and a narrow valley.

4 Wide valley and channel, no braiding This unit is restricted to Lake Sharpe and is characterized by wide valley and channel 

widths and no braiding consistent with a reservoir environement.

Fort Randall segment

1 Low to medium channel and valley width, 

variable braiding

This unit is made up primarily of the riverine reaches of the Missouri River below 

Fort Randall Dam and its occurrence is limited downstream from the Niobrara 

River.

2 Low braiding index, low sinousity, high 

channel and valley width

This unit is restricted to the reservoir and delta of Lewis and Clark Lake characterized 

by wide valley and channel widths, low braiding and sinuosity consistent with a 

reservoir environment.

3 Lower channel widths, medium braiding 

high sinuosity

This unit occurs in the riverine reach mostly upstream from the Niobrara River and is 

characterized by measures of channel complexity such as braiding, high sinuosity 

and lower channel widths.

4 High channel width, medium to high braiding, low sinuosityThis unit occurs in the highly braided delta primarily downstream from the Niobrara 

River. There is some occurance in the reaches with high channel complexity immediately upstream from the Niobrara River. 
32 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

bottom area available for riparian restoration. The relation of 

channel width to valley width also determines interactions of 

the channel with the valley wall, which can be associated with 

bedrock substrate availability and convergent flow hydraulics 

(Laustrup and others, 2007; Jacobson and others, 2009b).

Braiding index is the second most influential variable in both segments (figs. 23, 24). 

Braiding index is calculated from the number of 

channels and is useful for distinguishing between 

single-thread and multithread channels. Braiding 

index can be sensitive to prevailing discharge 

when aerial photography is obtained, especially 

for reaches with many low-elevation sand bars. 

Discharges during aerial photographs used in 

this analysis varied little among dates (fig. 4) and 

ranged from approximately 25 to 50 percent flow 

exceedance. Therefore, braiding indices were 

measured under fairly consistent conditions that 

represent typical discharge conditions. 

Braiding index is a useful measure of complexity of aquatic habitats and is an indicator of 

channel dynamics in the Missouri River (Elliott 

and Jacobson, 2006). The index was influential in defining cluster 3 in the Sharpe segment 

in the riverine section upstream from the Bad 

River junction, and it was influential in defining 

clusters 3 and 4 in the Fort Randall reach. Cluster 

3 is characterized by multithread channels in the 

riverine part of the segment upstream from the 

Niobrara River junction and cluster 4 is characterized by the multithread, distributary channels of 

the Lewis and Clark Lake delta.

Sinuosity is a measure of the curviness of the 

centerline of the channel and an index of aquatic 

habitat complexity. Sinuosity also is related 

directly to channel migration rates (Johannesson 

and Parker, 1989). Sinuosity was least influential in the Sharpe reach because it tended to co 

vary with valley width at all scales (figs. 11, 23). 

In the Fort Randall segment, sinuosity was the 

third most influential variable and helped define 

cluster 3 (fig. 24). Cluster 3 occurs in a complex 

part of the channel at river miles 860–870 and in 

the riverine segment upstream from the Niobrara 

junction.

Implications for Cottonwood 

Restoration

Under natural conditions, cottonwoods 

germinate after seeds are deposited by water on 

freshly deposited sediment, usually on point bars 

created by channel migration (Johnson, 1992; 

Scott and others, 1996; Scott and others, 1997; 

Johnson, 2000). If the seeds are deposited high 

enough to avoid subsequent scour by water or 

ice (Friedman and others, 1995; Auble and Scott, 1998) and 

groundwater does not decline so rapidly that root growth 

cannot keep pace during the summer and fall (Mahoney and 

Rood, 1998), seedlings may survive to the next year. High 

Figure 25. Canonical scores plot for the four-cluster classification, Sharpe 

segment.

Figure 26 Canonical scores plot for the four-cluster classification, Fort 

Randall segment.

EXPLANATION

4

3

2

1

Cluster number

 Sharpe segment

FACTOR (1) FACTOR (2) FACTOR (3)

FACTOR (1) FACTOR (2) FACTOR (3)

EXPLANATION

4

3

2

1

Cluster number

 Fort Randall 

 segment

FACTOR (1) FACTOR (2) FACTOR (3)

FACTOR (1) FACTOR (2) FACTOR (3)
Discussion 33

!(

!! !!!! !!!! !!! !! !!

!!

!

!

!

!

!

!!

!

!

!

!!!

!

!

!

!

!

!

!

!

!

!!

!! !! !! !! !!! !!!!!!!!! !!

!

!!

!!!!!!!!!!!!!!!!!!!!

!!!!

!! !!!!!!!!! !!! !! !! !! !! !! !!! !!!!!!!!!!! !!!!!!!! !! !!

!!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!!!!!!!

!

!

!

!

!

!! !!!!!!!!!!!!!! !! !! !! !!! !! !! !! !! !!! !!!!!! !!! !!!!!!! !!!!!!!!!! !!! !!! !!!! !!!! !!!!

!

!

!

!

!

!

!

!

!!

!

!

!

!

!

!

!

!

!

"

"

Pierre

100°20' 100°00' 99°40'

44°20'

44°10'

S O U T H D A K O TA

Bad River

Missouri River

Lake

Sharpe

Lake

Oahe

Oahe

Dam

Big

Bend

Dam

EXPLANATION

Four-cluster classification

! 1

! 2

! 3

! 4

0 5 10 15 KILOMETERS

0 5 10 15 MILES

Base from National Elevat

Universal Transverse Mercator projection

Zone 14

Figure 27. Reach-scale cluster classifcations for the Sharpe segment of the Missouri River.

!(

!(

!(

!(

!!! !! !!!!!! !!!!!!! !!!!!!!!!! !!!! !!!!!!!!!!!!! !!!! !!! !!!!!!!!!! !!! !! !!

!!

!!

!!!!!!

!!

!!

!!

!! !! !! !!!!!!! !!!! !! !! !!! !!

!! !!!! !! !!

!

!!

!! !!!!!!! !!! !!! !! !! !! !! !!! !!! !!! !!! !! !! !! !! !! !! !! !!! !!!!!! !!!

!

!!

!!

!!

!!

!

!

!

!

!

!

!

!

!

!

!

!

!!

!!

!! !!!

"

" """"""

S O U T H D A K O TA

Niobrara River

Missouri River

Lewis and Clark Lake

Lake

Francis

Case

Fort

Randall

Dam

Gavins

Point

Dam

N E B R A S K A

Springfield

Verdel

Niobrara

Pickstown

EXPLANATION

Four-cluster classification

! 1

! 2

! 3

! 4

43°00'

42°50'

Base from National Elevat

Universal Transverse Mercator projection

Zone 14

0 5 10 15 KILOMETERS

0 5 10 15 MILES

98°20' 98°00' 97°40'

Figure 28. Reach-scale cluster classifcations for the Fort Randall segment of the Missouri River.
34 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

water tables also may limit cottonwood growth and survival 

by contributing to root hypoxia or encouraging shallow root 

distributions (Kozlowski, 2009). 

Restoration strategies for cottonwoods have focused on 

restoring the natural hydrologic and geomorphic dynamics of 

river reaches (Scott and others, 1997; Johnson, 2000; Shafroth 

and others, 2002) or on artificial mechanisms (Friedman and 

others, 1995). In artificial regeneration experiments, Friedman 

and others (1995) found that natural seed sources on disturbed 

or irrigated ground increased cottonwood seedling density by a 

factor of more than 10 over nondisturbed, nonirrigated ground 

(0.03 seedlings/ square meter (m2). Irrigation and disturbance 

increased seedling density by another order of magnitude to 

10.3 seedlings/m2. These results indicate that artificial regeneration of cottonwoods is possible if costs of plowing and 

irrigation are acceptable. 

For natural or artificial regeneration of cottonwoods, flow 

regime and relative elevation of ground above water-surface 

elevations are critical factors for germination and survival. 

For natural regeneration, a dynamically migrating channel is 

also critical. While lacking the detail of the LCPI, the distribution of elevations along the Sharpe and Fort Randall segments 

provides an indication of the interaction of flow regime and 

ground-surface elevations (fig. 10). The base of the shaded 

bands on this figure indicates approximate river level on the 

day that the topographic elevations were recorded, presumably 

a relatively frequent flow exceedance. The elevation difference between the river level and the 2, 5, and 10-year recurrence stages indicates the range of relatively frequent floods. 

The intersection of the floodwater-surface elevations with the 

colored bands indicates what percentage of the valley bottom 

would be affected by stages of that magnitude. Sections of 

the profile where the floodwater-surface elevations intersect 

bands indicating 8 to 10 percent of the valley bottom is at or 

below that elevation present more restoration opportunity than 

sections where intersecting bands indicate that 0 to 2 percent 

of the valley bottom is at or below that elevation. It should 

be noted that the valley-bottom elevations are in percentiles 

MIDDLE

MISSOURI

RIVER

SHARPE

RIVERINE

...

SHARPE

DELTA

...

LAKE SHARPE

GAVINS

SEGMENT

MAINSTEM

SECTIONS HYDROLOGIC AND

GEOLOGIC

DIFFERENTIATION

(DEDUCTIVE)

FOUR-CLUSTER

STATISTICAL

CLASSIFICATION

(INDUCTIVE)

SEGMENT SCALE REACH SCALE

UPPER

MISSOURI

RIVER

LOWER

MISSOURI

RIVER

MAINSTEM

SYSTEM

MISSOURI

RIVER

LAKE

FRANCIS

CASE

FORT

RANDALL

RIVERINE

...

LEWIS & CLARK

DELTA

...

LEWIS & CLARK

LAKE

LAKE

OAHE 1

4

3

2

1

4

3

2

Wide valley, wide single-thread

channel, Lake Sharpe

Narrow valley, multi-thread

channels, Bad River delta

Variable valley and channel width,

single-thread channel, variable

sinuosity, riverine & lacustrine

Poor restoration potential in lacustrine

reaches downstream from river mile (RM)

1050; moderate potential within Bad River

delta, RM 1050 - 1065; poor RM 1065 - 1072

Wide valley, narrow single-thread

channel, Lake Sharpe

Wide, multi-thread channel, low

sinuosity, Lewis and Clark delta

Narrow, multi-thread channel, 

high sinuosity, upstream of Niobrara

Low-medium valley and channel

width, single-thread channel,

variable sinuosity, riverine

Wide valley, wide single-thread

channel, low sinuosity, Lewis & 

Clark Lake

IMPLICATIONS FOR

COTTONWOOD

RESTORATION

...

...

Poor restoration potential in lake portion.

Moderate restoration potential in Bad River 

delta reaches.

Poor restoration potential in lake portion.

Poor restoration potential because of incision,

small amounts of land at flooded elevations.

Poor restoration potential in lake portion.

Moderate restoration in multi-thread,

dynamic reaches.

Moderate restoration in multi-thread,

dynamic reaches; may be limited by high

sedimentation, high soil saturation.

Figure 29. Schematic summary of classification of the lower segments and reaches of the Middle Missouri River, with interpreted 

implications for cottonwood restoration.
Conclusions 35

of elevations associated with address points along the river 

and do not show absolute area. Moreover, some of the current 

land uses in the valley bottom at appropriate elevations may 

be incompatible with cottonwood restoration activities; much 

of the wider flood plain of the Sharpe segment is occupied by 

intensive, irrigated agriculture (fig. 2). Also, areas where valley-bottom lands are relatively close to the floodwater-surface 

elevations may too wet for successful cottonwood restoration. 

Excessively wet conditions may apply to flood plain bordering 

the Lewis and Clark delta where high groundwater levels may 

limit cottonwood growth and survival.

The reach-scale channel classification presented in this 

report provides guidance for evaluating where natural cottonwood restoration may be most successful. Clusters with multithread and highly sinuous channels (cluster 3 in the Sharpe 

segment and clusters 3 and 4 in the Fort Randall segment) 

are likely to be associated with dynamic channel migration 

and deposition of fresh, bare sediment conducive to cottonwood germination (fig. 29). Channel migration and sediment 

deposition rates are likely reduced from natural conditions that 

existed prior to flow regulation. Nevertheless, ongoing channel 

migration in these reaches is expected to continue to create 

new surfaces for germination, although less extensive and at a 

slower rate (Elliott and Jacobson, 2006). 

It is less clear whether the current flow regime would be 

conducive for regeneration on these new surfaces. In particular, pulsed discharges would need to be large enough to 

deposit seeds at elevations where seedlings will survive winter 

ice, and groundwater-recession rates would need to be fast 

enough to encourage deep root growth but not so fast as to 

create water stress. Lack of groundwater recession may be a 

limiting factor in deltaic segments affected by lake levels or in 

riverine reaches affected by unnaturally high discharges during 

summer months. 

The classification presented in this report is not definitive about potential for cottonwood restoration in the Sharpe 

and Fort Randall segments but does provide a filter to prioritize and evaluate reaches within these segments. Increased 

accuracy in classifying restoration potential could be achieved 

by acquiring improved topographic data that would resolve 

bathymetry and low-elevation surfaces near the water surface 

and by developing hydraulic models to provide high-resolution flood profiles. This would provide data sufficient to replicate the LCPI used in the Lower Missouri River (Jacobson 

and others, 2007). Development of channel migration models 

would provide another useful tool for evaluating the quality 

and extent of cottonwood regeneration under naturally occurring conditions without mechanical disturbance, seeding, and 

irrigation. The classification presented in this report could be 

used to target these science investments in specific reaches or 

segments that would be considered to have the highest restoration potential. 

Conclusions

The objective of this study was to develop a spatially 

explicit river and flood-plain classification to apply to evaluation of the potential for cottonwood restoration along the 

Sharpe and Fort Randall segments of the Middle Missouri 

River. Two subobjectives were (1) to evaluate whether existing topographic, water-surface elevation, and soils data were 

sufficient to create an index classification similar to the LCPI 

of Jacobson and others (2007), and (2) if an LCPI-like classification was not possible, to develop a geomorphically based 

classification that would provide a framework for evaluating 

restoration potential.

Assessment of topographic, water-surface elevation, and 

soils data available for the Middle Missouri River indicated 

that they were not sufficient to replicate the LCPI. Relatively 

high-resolution topographic data from the 1/3-arc-second 

NED largely delineated most of the topographic complexity 

and produced cumulative frequency distributions similar to 

the high-resolution 5-m topographic dataset developed for the 

Lower Missouri River. However, because the 1/3-arc-second 

NED does not include bathymetry, topographic data are only 

available for areas that were subaerially exposed when the 

original data were collected. This presents a potentially critical 

bias in evaluation of frequently flooded surfaces close to the 

river.

We explored the idea that soil data alone would be sufficient to provide most or all of the information content of the 

LCPI. Comparison reaches were identified along the Lower 

Missouri River where high-resolution LCPI data were available to delineate areas presently at or below the 5-year return 

interval flood elevation. The comparison reaches of the Lower 

Missouri River also were characterized by a range of channel incision. In these reaches, soil characteristics from the 

SSURGO database correctly classified only 2 to 98 percent 

of the area at or below the 5-year return interval flood stage, 

depending on reach and soil parameters used. The incongruence between mapped soil characteristics and present-day 

hydrology over much of the flood plain is explained by the 

relatively rapid geomorphic evolution of the Lower Missouri 

River. Like many impounded rivers, the regulated segments 

of the Missouri River have been variably affected by incision, 

thereby altering the relation between present-day hydrology, geomorphology, and flood-plain soils. We concluded 

that it was unlikely soils would be useful as a sole classification guide in the reservoir and interreservoir segments of the 

Middle Missouri River.

The most sparsely available data in the Middle Missouri 

River were water-surface elevations. Compared to the Lower 

Missouri River where water-surface elevations for 2- to 500-

year return interval floods were modeled every 1.6 km along 

the river, water-surface elevations in the Middle Missouri 

River had to be obtained from current streamflow gaging stations spaced at 3–116 km intervals. Although water surfaces 

for some of the segments are very flat because of reservoir 
36 Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration

lake levels, lack of high-resolution data in riverine sections 

precludes development of an LCPI-like intersection of topography with water-surface elevations to map indices of wetness.

An hierarchical river classification framework provides 

structure for combining deductive and inductive, statistical 

classifications at a range of scales. The segment-scale classification presented in this report is deductive and based on 

the concept that dams, significant tributaries, and geological 

(and engineered) channel constraints present primary controls 

on the biophysical capacity of the river system, including flow 

regime, material fluxes, and channel dynamics. 

An inductive reach-scale classification is nested within 

the segment scale. The reach-scale classification is based on 

multivariate statistical clustering of geomorphic data collected at 500-m intervals along the Sharpe and Fort Randall 

segments. Separate, four-cluster classifications for the two 

segments delineate reaches of the river with similar channel 

flood-plain geomorphology, and presumably, similar geomorphic and hydrologic processes that would affect cottonwood restoration in similar ways. The dominant variables 

in the clustering process were channel width (Fort Randall) 

and valley width (Sharpe), followed by braiding index (both 

segments). 

The classification provides a useful tool for evaluating 

cottonwood restoration potential. Clusters with multithread 

and highly sinuous channels are likely to be associated with 

dynamic channel migration and deposition of fresh, bare sediment conducive to natural cottonwood germination. However, 

restoration potential within these reaches is likely to be mitigated by the highly regulated flow regime and lake levels that 

may be out of phase with cottonwood life cycles. In particular, 

multithread reaches in the Lewis and Clark delta probably 

have high sedimentation rates and high groundwater levels 

that will limit growth and survival. Steady summer-fall flows 

in riverine reaches may not produce groundwater-recession 

rates sufficient to support adequate root growth.

 The reach-scale classification of the Sharpe and Fort 

Randall segments provides a tool to prioritize and evaluate 

reaches for cottonwood restoration. Development of highresolution topographic data to resolve bathymetry and lowelevation surfaces near the water surface, and development 

of hydraulic models to provide high-resolution flood profiles, 

would provide data sufficient to replicate the LCPI used in the 

Lower Missouri River (Jacobson and others, 2007). In addition, channel migration models would provide a useful tool 

for evaluating the quality and extent of natural cottonwood 

regeneration. The reach-scale classification presented in this 

report provides a framework to focus science and management 

investments in reaches with the highest restoration potential. 

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 R.B. Jacobson and others—Development of a Channel Classification to Evaluate Potential for Cottonwood Restoration—Scientific Investigations Report 2010–5208