U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2009–5058

Prepared for the Missouri River Recovery–Integrated Science Program

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

Hydrodynamic Simulations of Physical Aquatic Habitat 

Availability for Pallid Sturgeon in the Lower Missouri River, 

at Yankton, South Dakota, Kenslers Bend, Nebraska, Little 

Sioux, Iowa, and Miami, Missouri, 2006–07

EXPLANATION

8 percent

0

EXPLANATION

0.25

0.0

Froude Number Velocity Slope

0.25 8 percent

0 0
Front cover: Examples of derivative hydraulic habitat metrics at Little Sioux reach: Froude number and velocity slope.
Hydrodynamic Simulations of Physical 

Aquatic Habitat Availability for Pallid 

Sturgeon in the Lower Missouri River, at 

Yankton, South Dakota, Kenslers Bend, 

Nebraska, Little Sioux, Iowa, and Miami, 

Missouri, 2006–07

By Robert B. Jacobson, Harold E. Johnson, III, and Benjamin J. Dietsch

Scientific Investigations Report 2009–5058

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2009

For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, 

natural hazards, and the environment, visit http://www.usgs.gov or call 1-888-ASK-USGS

For an overview of USGS information products, including maps, imagery, and publications, 

visit http://www.usgs.gov/pubprod

To order this and other USGS information products, visit http://store.usgs.gov

Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

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., Johnson, H.E., III, and Dietsch, B.J., 2009, Hydrodynamic simulations of physical aquatic habitat 

availability for pallid sturgeon in the Lower Missouri River, at Yankton, South Dakota, Kenslers Bend, Nebraska, Little 

Sioux, Iowa, and Miami, Missouri, 2006–07: U.S. Geological Survey, Scientific Investigations Report 2009–5058, 67 p.
iii

Contents

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

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

Background............................................................................................................................................3

Aquatic Habitat Assessment ..............................................................................................................4

Study Areas............................................................................................................................................5

Purpose and Scope ..............................................................................................................................5

Methods...........................................................................................................................................................6

Data Collection......................................................................................................................................6

Hydrographic and Topographic Surveying..............................................................................6

Hydrographic Surveying....................................................................................................6

Discharges...........................................................................................................................7

Terrestrial Topographic Surveying...................................................................................7

Acoustic Doppler Current Velocity Measurements ......................................................7

Substrate and Sediment ....................................................................................................7

Hydrodynamic Modeling......................................................................................................................7

Model Development ..................................................................................................................10

Modeled Flows..................................................................................................................10

Water Stage.......................................................................................................................10

Terrain Models ..................................................................................................................10

Sediment Classification and Hydraulic Roughness ....................................................10

Computational Grid...........................................................................................................10

Boundary Conditions........................................................................................................23

Calibration..........................................................................................................................23

Model Assessment...........................................................................................................23

Hydraulic Definitions of Habitat .......................................................................................................26

Selection of Habitat Parameters.............................................................................................26

Depth-Velocity Fields .......................................................................................................29

Hydraulic Parameters and Spatial Variation................................................................26

Reach-Scale Integrative Metrics...................................................................................31

Sensitivity Analysis.............................................................................................................................31

Sensitivity to Drag Coefficient and Lateral Eddy Viscosity.................................................31

Sensitivity to Changes in Channel Morphology....................................................................32

Modeling Results .........................................................................................................................................32

Depth and Velocity Fields ..................................................................................................................32

Hydraulic Parameters and Spatial Variation..................................................................................39

Low Froude Number Areas ......................................................................................................39

Convergent Flow Areas ............................................................................................................39

Energy Dissipation Areas: Kinetic Energy Variance, Vorticity, Strain, and Wakes........39

Reach-Scale Integrative Measures ................................................................................................45

Benthic Terrain Mapping Classes...........................................................................................45

Habitat Patch Statistics ............................................................................................................45

Residence Time..........................................................................................................................54

Hydraulic Strain..........................................................................................................................54
iv

Sensitivity of Habitat Functions to Pulsed Flow Modifications...................................................54

Model Information Content and Ecological Understanding .................................................................60

Model Capabilities..............................................................................................................................60

Models as Exploration Tools .............................................................................................................61

Reference Conditions.........................................................................................................................61

Summary and Conclusions.........................................................................................................................61

Acknowledgments..............................................................................................................................62

References Cited..........................................................................................................................................63

Figures

1. Map showing locations of hydrodynamic modeling reaches used in this study...............2

2. Graphs showing duration hydrographs showing variation in 25–75 percent flow 

exceedances, current water control plan and natural flow regime (run of the river)......4

3. Discharge hydrographs for four hydrodynamic modeling reaches showing dates of 

compilation and calibration surveys .........................................................................................8

4. Examples from Yankton, South Dakota modeling reach showing types and density of 

data: (A) Elevation points from three sources, (B) Close up showing elevation points 

and nodes of 5-m computational grid nodes............................................................................9

5–7. Maps showing:

5. Elevation of the hydrodynamic modeling reach at Miami, Missouri, 2006 

bathymetry ...................................................................................................................................19

6. Elevation of the hydrodynamic modeling reach at Little Sioux, Iowa, 2006 

bathymetry ...................................................................................................................................20

7. Elevation of the hydrodynamic modeling reach at Kenslers Bend, Nebraska, 2006 

bathymetry ...................................................................................................................................21

8. Elevation of the hydrodynamic modeling reach at Yankton, South Dakota, 

2006 bathymetry ..........................................................................................................................22

9–10. Graphs showing:

9. Predicted and observed water-surface elevations ..............................................................24

10. Comparisons between modeled and measured water velocities at selected transects, 

Miami, Missouri and Little Sioux, Iowa, Kenslers Bend, Nebraska and Yankton, South 

Dakota reaches, 2006 and 2007 ................................................................................................27

11–14. Maps showing:

11. Elevation, Miami, Missouri reach, 2006, 2007, and elevation difference between the 

two years......................................................................................................................................35

12. Elevation, Little Sioux, Iowa reach, 2006, 2007, and elevation difference between the 

two years......................................................................................................................................36

13. Elevation, Kenslers Bend reach, 2006, 2007, and elevation difference between the 

two years......................................................................................................................................37

14. Elevation, Yankton, South Dakota reach, 2006, 2007, and elevation difference 

between the two years ..............................................................................................................38

15. Map showing depth-velocity field patch structure at the Miami, Missouri reach..........40

16–17. Graphs showing showing:

16. Depth-velocity patch area as a function of discharge at Miami, Missouri and 

Yankton, South Dakota...............................................................................................................41
v

17. Depth-velocity habitat patch areas as a function of discharge at four modeling 

reaches.........................................................................................................................................42

18. Map showing areas with Froude numbers less than 0.05 at Miami, Missouri and 

Little Sioux, Iowa reaches.........................................................................................................43

19. Graphs showing wake and low Froude number patch areas as a function of 

discharge at four modeling reaches .......................................................................................44

20. Maps showing examples of convergent flow classes at Yankton, South Dakota and 

Miami, Missouri ..........................................................................................................................46

21. Graphs showing convergent flow class areas as a function of discharge at four 

modeling reaches .......................................................................................................................47

22–25. Maps showing:

22. Examples of derivative hydraulic metrics at Little Sioux, Iowa reach: velocity, depth, 

Froude number and velocity slope...........................................................................................48

23. Examples of derivative hydraulic metrics at Little Sioux, Iowa reach: standard 

deviation of kinetic energy, vorticity, strain index, and wakes ...........................................49

24. Examples of wake habitat unit distributions at Kenslers Bend, Nebraska and Little 

Sioux, Iowa reaches...................................................................................................................50

25. Examples of benthic terrain map (BTM) units at Kenslers Bend, Nebraska....................51

26–28. Graphs showing:

26. Areas of benthic terrain map (BTM) classes as a function of discharge for the four 

modeling reaches .......................................................................................................................52

27. Patch and edge density values as functions of discharge for the four modeling 

reaches.........................................................................................................................................53

28. Simpson’s diversity index (SDI) for habitat patches as functions of discharge for the 

four modeling reaches...............................................................................................................55

29. Maps showing depth-velocity field patch maps at Little Sioux, Iowa for discharges of 

800 cubic meters per second, 2006 and 2007.........................................................................56

30–32. Graphs showing:

30. Interspersion and juxtaposition index (IJI) and mean nearest neighbor (MNN) 

between like patches as functions of discharge for the four modeling reaches ............57

31. Mean residence time of water in hours per kilometer of reach length, as functions 

of discharge for the four modeling reaches...........................................................................58

32. Total strain per unit channel length (TSUL) for the modeling reaches, 2006 and 

2007 ...............................................................................................................................................59

Tables

1. Characteristics of modeling reaches ........................................................................................6

2. Discharges and hydraulic parameter values for calibration and solution runs for four 

hydrodynamic modeling reaches.............................................................................................11

3. Average bedform dimensions at modeling reaches over period of study ........................23

4. Depth and velocity combination fields and class codes used in habitat patch 

analysis.........................................................................................................................................29

5. Sensitivity analysis results from varying drag coefficient and lateral eddy viscosity 

coefficient at Little Sioux reach, 2006 topography, 768 cubic meters per second ..........33
vi

Conversion Factors

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

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd) 

Area

hectare (ha) 2.471 acre

square kilometer (km2) 247.1 acre

square meter (m2) 10.76 square foot (ft2) 

hectare (ha) 0.003861 square mile (mi2) 

square kilometer (km2) 0.3861 square mile (mi2)

Volume

cubic meter (m3) 264.2 gallon (gal) 

cubic meter (m3) 35.31 cubic foot (ft3)

cubic meter (m3) 1.308 cubic yard (yd3) 

cubic kilometer (km3) 0.2399 cubic mile (mi3) 

Flow rate

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

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

Mass

megagram, metric ton (Mg) 1.102 ton, short (2,000 lb)

Megagram, metric ton (Mg) 0.9842 ton, long (2,240 lb)

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.

Horizontal coordinate information is referenced to the World Geodetic System of 1984 (WGS 84). 

Elevation data are referenced to the North American Vertical Datum of 1988 (NAVD88).
Abstract

The objective of this study was to assess the sensitivity 

of habitat availability in the Lower Missouri River to discharge variation, with emphasis on habitats that might support 

spawning of the endangered pallid sturgeon. We constructed 

computational hydrodynamic models for four reaches that 

were selected because of evidence that sturgeon have spawned 

in them. The reaches are located at Miami, Missouri (river 

mile 259.6–263.5), Little Sioux, Iowa (river mile 669.6–

673.5), Kenslers Bend, Nebraska (river mile 743.9–748.1), 

and Yankton, South Dakota reach (river mile 804.8–808.4). 

The models were calibrated for a range of measured flow 

conditions, and run for a range of discharges that might be 

affected by flow modifications from Gavins Point Dam. Model 

performance was assessed by comparing modeled and measured water velocities. 

A selection of derived habitat units was assessed for 

sensitivity to hydraulic input parameters (drag coefficient and 

lateral eddy viscosity). Overall, model results were minimally sensitive to varying eddy viscosity; varying lateral 

eddy viscosity by 20 percent resulted in maximum change in 

habitat units of 5.4 percent. Shallow-water habitat units were 

most sensitive to variation in drag coefficient with 42 percent 

change in unit area resulting from 20 percent change in the 

parameter value; however, no habitat unit value changed more 

than 10 percent for a 10 percent variation in drag coefficient. 

Sensitivity analysis provides guidance for selecting habitat 

metrics that maximize information content while minimizing 

model uncertainties.

To assess model sensitivities arising from topographic 

variation from sediment transport on an annual time scale, we 

constructed separate models from two complete independent 

surveys in 2006 and 2007. The net topographic change was 

minimal at each site; the ratio of net topographic change to 

water volume in the reaches at 95 percent exceedance flow 

was less than 5 percent, indicating that on a reach-average 

basis, annual topographic change contributed little to habitat 

area variation. Net erosion occurred at Yankton (the upstream 

reach) and because erosion was distributed uniformly, there 

was little affect on many habitat metrics. Topographic change 

was spatially nonuniform at Little Sioux and Kenslers Bend 

reaches. Shallow water habitat units and some reach-scale 

patch statistics (edge density, patch density, and Simpson’s 

Diversity Index) were affected by these changes. Erosion 

dominated at the downstream reach but habitat metrics did not 

vary substantially from 2006 to 2007.

Among habitat metrics that were explored, zones of 

convergent flow were identified as areas that most closely 

correspond to spawning habitats of other sturgeon species, as 

identified in the scientific literature, and that are consistent 

with sparse data on pallid sturgeon spawning locations in the 

Lower Missouri River. Areas of convergent zone habitat varied little with discharges that would be associated with spring 

pulsed flows, and relations with discharge changed negligibly 

between 2006 and 2007. 

Other habitat measures show how physical habitat varies with discharge and among the four reaches. Wake habitats defined by velocity gradients seem to correspond with 

migration pathways of adult pallid sturgeon. Habitats with 

low Froude-number correspond to low energy areas that may 

accumulate passively transporting particles, organic matter, and larval fish. Among the modeled reaches, Yankton 

had substantially longer water residence time for equivalent 

flow exceedances than the other three modeled reaches. 

Longer residence times result from greater flow resistance in 

the relatively wide, shallow channel and may be associated 

with longer residence times of passively transported particulate materials.

Introduction

This report addresses sensitivity of physical aquatic habitat for pallid sturgeon (Scaphirhynchus albus) in the Lower 

Missouri River (fig. 1) using multidimensional hydrodynamic 

Hydrodynamic Simulations of Physical Aquatic Habitat 

Availability for Pallid Sturgeon in the Lower Missouri 

River, at Yankton, South Dakota, Kenslers Bend, Nebraska, 

Little Sioux, Iowa, and Miami, Missouri, 2006–07

By Robert B. Jacobson, Harold E. Johnson, III, and Benjamin J. Dietsch
2 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

!

!

!

!

!

B

B

B

B

"

#

#

#

#

#

#

#

M iss o u r i

R i v e r

b a si n

Little Sioux River

100

200

700

800

600

400

300

500

0

900

Kenslers Bend

Little Sioux

Yankton

Miami

Hermann

Kansas City

i Nebraska C ty

Map area

#

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 H

D A K O T A

Sioux City

Waverly

Decatur

GavinsPointDam

0 50 100 KILOMETERS0 50 100 MILES

ux

#

B

!

E

X

P

L

A

N

ATIO

N

90° 94° 92° 96° 98°

42°

40°

38°

Missouri River Basin

GavinsPoncaBig SioPlatteKansasGrandOsage

Lower Missouri River segmentsMissouri RiverMissouri River tributaryU.S. Geological Survey gaging station Omaha

Miami

500

Study reachRiver-mile marker and number—

Number is distance upstream from 

 the junction with the Mississippi River,  in miles

Base data from the National Atlas, 2006Universal Transverse Mercator projectionZone 15

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

Omaha

Figure 1. Locations of hydrodynamic modeling reaches used in this study.
Introduction 3

modeling. The modeling approach is used to quantify habitat 

availability as it changes with seasonal or annual variations 

in discharge and channel morphology. Emphasis is placed on 

assessing sensitivity for a range of discharges that might be 

amenable to pulsed flow modification, or a “spring rise.” The 

modeling approach provides a systematic basis for defining 

physical habitat variables thought to be important for various 

life stages of pallid sturgeon (and other Missouri River fishes) 

and for exploring alternative definitions of important habitats.

Background

The Missouri River drains 1,371,000 square kilometers 

(km2) of North America and hosts to the nation’s largest reservoir system with 91 cubic kilometers (km3

) of storage (Galat 

and others, 2005b). The mainstem system of six reservoirs 

impounds 53 percent of the drainage basin upstream from 

the junction with the Mississippi River at St. Louis (fig. 1). 

Downstream from Gavins Point Dam the river is known as 

the Lower Missouri River (LMOR). Constructed between 

1937 and 1963, the reservoir system has substantially altered 

the flow regime, including reductions in intra-annual flow 

variability, generally decreased spring pulses, and generally 

increased summer flows. The intensity of hydrologic alteration 

diminishes somewhat downstream from the dams as tributaries enter the Missouri River (fig. 2). The 590 km downstream 

from the Kansas River confluence (at Kansas City, Missouri) 

retains substantial intra-annual variability including springsummer flow pulses (fig. 2).

The sediment regime of the river also has been substantially altered as a result of reservoir operations, decreasing 

from 326 million metric tons per year (Mg/y) to 55 million 

Mg/y as measured at Hermann, Missouri (Jacobson and 

others, U.S. Geological Survey, written commun., 2009). 

The decrease in sediment load also has been associated with 

decreases in turbidity that are thought to affect native fish 

fauna (Galat and others, 2005a; Blevins, 2006).

From St. Louis, Missouri to Sioux City, Iowa, the LMOR 

also has been engineered for bank stabilization and navigation. Engineering of the LMOR channel began in the 1830s 

with clearing of large woody debris and bank stabilization to 

improve conditions for steamboat navigation; channelization 

intensified considerably from 1930 to 1970 (Ferrell, 1996). 

Wing dikes and revetments now stabilize 1,200 km of riverbanks. Dikes and other navigation structures serve to focus 

the flow in the thalweg to maintain a narrow, swift, and selfdredging navigation channel in what was historically a shallow 

river characterized by interspersed braided, anabranched, and 

single-channel reaches. River engineering resulted in the loss 

of as much as 400 km2 of river-corridor habitats (Funk and 

Robinson, 1974; Hesse and Sheets, 1993; National Research 

Council, 2002; U.S. Army Corps of Engineers, 2004; Galat 

and others, 2005b). 

Changes to flow regime, sediment regime, and channel 

morphology on the LMOR have been associated with declines 

of native species (National Research Council, 2002; U.S. Fish 

and Wildlife Service, 2003). Biological information to support this inference includes the general decline of native fish 

species populations, commercial fish catches (Hesse, 1987; 

Pflieger and Grace, 1987; Hesse and others, 1989; Hesse and 

Sheets, 1993; Galat and others, 2005b), and sandbar nesting 

birds (U.S. Fish and Wildlife Service, 2000) in the LMOR 

since regulation and channelization. In 2000, the U.S. Fish and 

Wildlife Service issued a Biological Opinion, subsequently 

amended in 2003, that indicated management actions that 

threatened three listed species, the pallid sturgeon, interior 

least tern (Sternula antillarum athalassos), and the piping 

plover (Charadrius melodus) (U.S. Fish and Wildlife Service, 

2000; 2003). Among other remedies, the amended Biological Opinion (U.S. Fish and Wildlife Service, 2003) prescribed 

reasonable and prudent alternatives related to management 

of flow regime and channel morphology to provide habitat 

for reproduction and survival of these species. Specifically, 

the Biological Opinion called for naturalization of the flow 

regime to:

• build sandbars in 95 km of the LMOR downstream 

from Gavins Point Dam to support nesting of the least 

tern and piping plover;

• connect the main channel to the flood plain seasonally, to augment nutrient and energy exchange, and to 

provide fish access to overbank habitats;

• maintain nursery habitat for larval and juvenile pallid sturgeon by achieving seasonal low flows in late 

summer;

• provide an environmental spawning cue for the pallid 

sturgeon through some combination of discharge and 

discharge-related variables like temperature, turbidity, 

and water velocity;

• provide access to spawning habitat or “conditioning” 

of spawning habitat for pallid sturgeon by flushing fine 

sediment from coarse substrate.

The amended Biological Opinion also called for restoration of a portion of the shallow-water aquatic habitat lost from 

river engineering. Shallow-water habitat (SWH) on the LMOR 

has been defined as 0–1.5 meters (m) depth and 0–0.6 meters 

per second (m/s) current velocity, and is thought to be important for rearing of larval and juvenile pallid sturgeon and for 

other native fishes (U.S. Fish and Wildlife Service, 2003; U.S. 

Army Corps of Engineers, 2004). The Amended Biological 

Opinion requires restoration of 4,800–7,900 hectares of lost 

aquatic habitat (assumed to be SWH) by the year 2020 and 

allows this habitat to substitute for achievement of SWH by 

flow manipulation (item 3, above). SWH is being created by a 

variety of mechanisms, including excavation of side-channel 

chutes, dike notching, bank notching, and construction of 

chevrons.

In addition to changes in channel morphology that result 

from purposeful re-engineering of channel, there are complex 
4 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

readjustments of channel morphology that take place as a 

result of influxes or effluxes of sediment related to seasonal 

patterns of sediment transport, tributary flows, and large flood 

events (Elliott and others, 2009) . These factors can result in 

background variability in the quality and quantity of habitat 

availability to river organisms.

Aquatic Habitat Assessment

There are three fundamental challenges in assessments 

of aquatic habitat availability. The first challenge is to account 

for variable discharge so rivers or reaches can be assessed on 

an equivalent-flow basis. The standard approach, adopted in 

this study, is to use hydraulic modeling to assess how discharge affects distributions of depths and velocities in the 

aquatic environment (Bovee, 1982; Bovee and others, 1998; 

Jacobson and Galat, 2006; Johnson and others, 2006). Hydrodynamic modeling is especially useful for understanding the 

spatial and temporal organization of habitat patches that may 

determine reproductive success at the reach scale (Coutant, 

2004; Jacobson and Galat, 2006; Johnson and others, 2006). 

The use of two or three dimensional hydrodynamic models 

allows quantification of spatial attributes of habitat, including diversity (Reuter and others, 2003; Pasternack and others, 

2004), gradients between habitat units (Crowder and Diplas, 

2006; Johnson and others, 2006), patch dynamics (Bowen and 

others, 2003), and patch persistence (Bovee and others, 2004). 

The second fundamental challenge is to incorporate 

dynamic geomorphic adjustments of the channel in hydraulic 

assessments of habitat. Habitat simulation studies generally 

have been limited in performance because they have assumed 

a fixed bed, and typical habitat models lack the ability to 

model sediment transport and channel evolution. For lowDISCHARGE, IN CUBIC METERS PER SECOND

4,000

3,000

2,000

1,000

0

4,000

3,000

2,000

1,000

0

5,000

4,000

3,000

2,000

1,000

0

Sioux City,

Iowa

Nebraska City,

Nebraska

Kansas City,

Missouri

EXPLANATION

Current water-control plan

Natural flow regime

Hermann,

Missouri

Jan. Mar. May July Sept. Nov. Jan. Mar. May July Sept. Nov.

MONTH

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

Data from the U.S. Army Corps of Engineers Daily

Routing Model (U.S. Army Corps of Engineers, 1998) 

Figure 2. Duration hydrographs showing variation in 25–75 percent flow exceedances, current water control plan and natural 

flow regime (run of the river).
Introduction 5

flow studies or studies on rivers with immobile beds this is a 

minor problem; however, for studies that consider the ecological effects of flows capable of transporting bed material, 

this has been a substantial limitation because the models 

do not account for changing channel boundary conditions 

(geometry and flow resistance) during individual flow events 

or over seasons. New understanding of sediment transport 

at scales relevant to habitat (Schmeeckle and Nelson, 2003) 

and hydrodynamic modeling code that can simulate bed 

evolution (McDonald and others, 2005) are contributing to 

progress toward relaxing the assumption of a fixed bed. For 

the purposes of this study, we evaluate effects of bed changes 

by evaluating habitat based on two bed geometries surveyed 

a year apart, and by reference to surveys of within-year bed 

changes (Elliott and others, 2009).

The third, and greatest, challenge is estimating habitat 

functions for aquatic biota. Fish habitat is conventionally 

defined as the place or a set of places where a fish, a fish 

population, or a fish assemblage finds suitable environmental 

features to survive and reproduce (Orth and White, 1999). 

Because Missouri River sturgeon migrate long distances 

during their lives (DeLonay and others, 2007), habitat assessments need to consider a broad suite of locations within the 

river system. A more restricted definition of physical habitat 

is the three-dimensional structure in which riverine organisms live; time (frequency, duration, sequence, rate of change) 

adds a critical fourth dimension (Gordon and others, 1992). 

Water depth, flow velocity, and substrate are the three main 

characteristics of physical habitat that are usually evaluated. 

Vegetation is also often measured to the extent that it operates 

to provide substrate or cover to aquatic organisms, or provides 

shading that alters water temperatures. Water temperature and 

turbidity typically are also strongly associated with depth and 

flow velocity, depending on the geomorphology and hydrology 

of the system. All of these factors can contribute to ecological 

functions of a habitat patch.

A primary uncertainty associated with habitat simulations, and a frequent source of criticisms, is the degree to 

which the assessed habitat availability relates to population 

processes, including the occurrence of limiting conditions 

or ecological bottlenecks (Gutreuter, 2004; Bergman and 

others, 2008). Data are rarely available to link population 

demographic processes, such as reproductive success, directly 

to hydraulic parameters. We address habitat requirements 

through the results of coordinated habitat-use studies for Missouri River sturgeon (Korschgen, 2007; Reuter and others, 

U.S. Geological Survey, written commun., 2009), from other 

literature on sturgeon habitat use (Bramblett and White, 2001; 

Braaten and others, 2008), and through inference from general 

understanding of sturgeon life histories (Wildhaber and others, 

2007).

Study Areas

This study assesses habitat availability in four reaches 

of the LMOR: Miami, Missouri (river mile1 259.6–263.5), 

Little Sioux, Iowa (river mile 669.6–673.5), Kenslers Bend, 

Nebraska (river mile 743.9–748.1), and Yankton, South 

Dakota (river mile 804.8–808.4; fig. 1). Hydrologic and 

hydraulic statistics for these reaches are summarized in 

table 1. The reaches were selected based on two criteria: each 

reach (1) has patches of hard substrate with particle size ranging from gravel to boulder, thought to be preferred spawning 

substrate for sturgeon (Laustrup and others, 2007) or (2) was 

the upstream apex of movement of a female shovelnose sturgeon (Scaphirhynchus platorynchus) that completely spawned 

in 2005 (DeLonay and others, 2007), or both. Complete 

spawning indicates that the fish was verified to have dropped 

her eggs, but successful fertilization, hatch, or recruitment 

was not verified. For three of the four reaches, evidence is 

strong that spawning occurred somewhere within the reach. 

The Yankton reach lacked data confirming sturgeon spawning activity in 2005. However, the reach was selected for 

study because has the most extensive deposit of gravel-cobble 

substrate identified on the Lower Missouri River (Laustrup 

and others, 2007); spawning-like activity of sturgeon was 

confirmed in this reach during 2006 (Gerald Mestl, Nebraska 

Game and Parks Commission, oral commun., 2006). Each 

reach was delineated to be approximately 6 km in length to 

encompass two or more bend-crossover units to ensure that a 

representative range of habitat patches was present.

Purpose and Scope

Models of natural systems generally are classified 

into two categories: those used to predict and those used to 

increase understanding of the system (Kirkby, 1996). The 

focus of this report is the use of hydrodynamic models to 

increase understanding of what is suitable spawning and 

early-life-stage habitat for the Missouri River sturgeon and 

to address the sensitivity of habitat to flow variation. The 

modeling effort is exploratory and intended to elucidate relations between sturgeon and their environment. This work is a 

component of a suite of collaborative studies being carried out 

by the U.S. Geological Survey as part of the Comprehensive 

Sturgeon Research Program (Korschgen, 2007). The work is 

cooperatively funded through the U.S. Army Corps of Engineers Missouri River Recovery–Integrated Science Program 

and the U.S. Geological Survey.

The scope of the study was chosen to encompass a 

representative range of the hydrologic and geomorphic variability of the LMOR. The Miami reach is characterized by 

a navigation channel typical of the downstream 735 miles 

of the Missouri River and a flow regime that has recovered 

1

River miles are the customary units of longitudinal measurement on the 

Lower Missouri River and are measured upstream from St. Louis, Missouri. 

These river miles correspond to the channel position in 1960.
6 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

substantial amounts of inter- and intra-annual variability. The 

next upstream reach at Little Sioux has a typical navigation 

channel and a substantially altered flow regime. The Little 

Sioux and Miami reaches have been included in recent lowintensity restoration activities, including dike notching and 

unrooting dikes from the bank. Restoration activities in these 

two reaches varied minimally during the period of this study. 

The reach at Kenslers Bend reach has a simplified, engineered 

channel geometry and a highly altered flow regime. The most 

upstream reach at Yankton has a complex channel geometry 

and highly altered flow regime representative of the unchannelized segment of the LMOR river mile 753–811. 

Methods

Our general approach was to use multi-dimensional 

hydrodynamic modeling to characterize and inventory hydraulic habitats over a range of discharges. Hydraulic models are 

useful in habitat studies because they can explicitly account 

for changing discharge conditions, and thereby allow for 

assessments of discharge effects or for comparisons among 

sites at a constant discharge or flow exceedance. Hydrodynamic models, however, generally are limited in their ability to 

capture dynamic conditions associated with sediment transport and geomorphic change, factors that can alter hydraulic 

roughness and how water is conveyed through a model reach. 

To assess how channel dynamics may contribute to modeling 

errors, we model each of the four reaches during each of two 

years, based on two complete, high-resolution topographic 

surveys. In addition, coordinated studies of geomorphic 

dynamics at the four reaches (Elliott and others, 2009) provide 

contextual understanding of how channel dynamics at bedform 

to reach scales may contribute to model errors and habitat 

dynamics. 

Data Collection 

Topographic data for the hydrodynamic models were 

obtained by combining existing elevation data sets (photogrammetrically derived digital elevation data and airborne 

Light Detection and Ranging [LIDAR] data) with new 

hydroacoustic surveys. LIDAR elevation data also were used 

to supplement topographic information in terrestrial locations 

that underwent minimal geomorphic change during 2006 and 

2007. All elevation data were collected in or converted to the 

North American Vertical Datum 1988 (NAVD88) for consistency among datasets. Conversions used the VERTCON 

program (Mulcare, 2004).

Hydrographic and Topographic Surveying

Two general types of surveys were employed to develop 

topographic data for the hydrodynamic models. For each 

modeling reach in 2006 and 2007, complete compilation 

surveys were performed, including hydroacoustic surveys in 

the channel and real-time kinematic global positioning system 

(RTK-GPS) surveys of the banks and engineering structures. 

At Yankton and Kenslers Bend these data were supplemented 

with LIDAR data; at Little Sioux and Miami, they were supplemented with photogrammetric elevation data. Compilation 

surveys were completed during relatively high water periods 

when depths allowed efficient collection of hydroacoustic data 

by boat (fig. 3).

Each reach was surveyed an additional three to four times 

during 2006 and 2007 to develop data for model calibration and assessment (fig. 3, table 2). The calibration surveys 

included longitudinal profiles and approximately 30 transects 

that were randomly selected from the total set of cross-sectional transects. The randomly selected transects were used 

in a coordinated study to evaluate geomorphic changes on 

seasonal timeframes (Elliott and others, 2009), and they were 

used to provide water-surface elevation and velocity data for 

this study.

Hydrographic Surveying

Compilation hydrographic surveys consisted of 

300 transects spaced at 20 m intervals, plus two or three longprofile surveys. Identical transect locations were used in 2006 

and 2007. Transects were terminated laterally when the depth 

became too shallow for the boat to survey, at approximately 

0.30 m. Depths were measured with a 200 kilohertz (kHz) 

single beam echo sounder with an 8o transducer. The echo 

Table 1. Characteristics of modeling reaches.

[m, meters]

Reach River miles Nearest stream gage1 Length2 (m) Average bankfull 

width (m)

Reach slope 

(m/m)

Miami, Missouri 259.6 – 263.5 Waverly, Missouri 6,320 345 0.00015

Little Sioux, Iowa 669.6 – 673.5 Decatur, Nebraska 6,250 223 0.00021

Kenslers Bend, Nebraska 743.9 – 748.1 Sioux City, Iowa 6,450 243 0.00024

Yankton, South Dakota 804.8 – 808.4 Yankton and Gavins Point, South Dakota 5,580 436 0.00012

1

 Hydrologic record from this stream gage used to calculate flow statistics for the modeling reach.

2

 Length refers to the habitat-inventory portion of the reach, which is slightly shorter than the modeled length in order to avoid edge effects.
Methods 7

sounder was calibrated for draft and sound velocity using a bar 

check before each survey. Hypack® surveying software was 

used for navigation and to collect, process, and store sounding information. Depth readings were monitored in real time, 

and adjustments were made to equipment settings, such as 

gain and tracking gates, to minimize false bottom readings. 

The data were edited in the office to remove false bottom 

identifications, spikes, and other anomalies not addressed 

during surveying. 

Longitudinal profiles of depths, bottom elevations, and 

water-surface elevations were collected in the channel using 

similar equipment and collection protocols. Water-surface 

elevation profiles along the thalweg were collected for use in 

model calibration

Hydrographic data were georeferenced using surveygrade 12-channel real-time kinematic global positioning 

systems (RTK-GPS). Local base stations were established 

using static GPS surveys for elevation control and were used 

throughout the study. A repeater radio was used in some cases 

to improve radio signal strength during the hydrographic 

surveys. The GPS data were collected at 200 millisecond 

(ms) intervals, resulting in positions approximately every 

0.30 m along each transect at typical boat speeds of 2–4 knots 

(1–2 m/s) during data collection. The RTK-GPS gives x, y, and 

elevation positions to 0.05 m-scale accuracy. Pitch and heave 

were not compensated during hydroacoustic data collection; 

however, survey boats were not operated under conditions 

with significant waves. The precision of the echosounder data 

is 0.03 m. Bar check results indicate that, under favorable bottom conditions, the depth and elevation accuracy are approximately 0.07 m.

Discharges

A separate protocol was used for determining discharge 

during surveys. For each survey date, a transect or a set of 

closely-spaced transects, oriented perpendicular to flow, was 

selected in an area with relatively low turbulence and relatively consistent depth and velocity. Representative discharges 

were measured by collecting location information using 

RTK-GPS or differential global positioning system (DGPS) 

equipment, depth readings using a 200 kHz single-beam echo 

sounder, and velocity data using a Teledyne RDI Rio Grand 

1200 kHz acoustic Doppler current profiler (ADCP) unit. The 

selected transect or set of transects was traversed four times, 

twice in each direction. WinRiver software (version 10.06) 

was used to integrate, display, and store measurement data, 

and to compute a total discharge for each of the four passes. 

An average of the four discharges was used as the representative discharge for the survey. 

Terrestrial Topographic Surveying

Topographic surveys were conducted to provide accurate elevations along channel banks and engineered control 

structures, such as wing dikes and spur dikes, and in depths 

too shallow for bathymetric surveying. Pole-mounted RTKGPS units were used to collect ground elevation points along 

survey transects and to define key topographic features that 

would not be adequately characterized by transect data or 

existing data sets. The combination of hydrographic survey, 

topographic survey, and supplementary data points produced 

a high-resolution topographic dataset for characterizing the 

modeled reaches (fig. 4A).

Acoustic Doppler Current Velocity Measurements

Acoustic Doppler velocity profiles were collected concurrently with hydrographic surveys. A 1200 kHz ADCP was used 

in conjunction with WinRiver software to collect, process, and 

store velocity profiles. These velocity data were processed to 

compute vertically-averaged velocity vectors for each vertical profile, or “ensemble.” A selection of vertically-averaged 

velocity vectors was used to assess modeled velocities. 

Substrate and Sediment

Substrate data sets were collected with RoxAnn instrumentation (Sonavision, Aberdeen, Scotland) during compilation surveys. RoxAnn seabed classification instruments 

analyze the return signals from the echo sounder and generate 

two parameters, E1 and E2. E1 is based on the shape of the 

first return and E2 is based on the shape of the second return 

(Hamilton, 2001; Elliott and others, 2009). Statistical processing of RoxAnn data generally followed procedures established 

by other seabed classification studies (Cholwek and others, 

2000; Brown and others, 2005), although methods had to be 

optimized for the turbid and high-velocity conditions of the 

LMOR. Specific methods are described by Reuter (Joanna 

Reuter, U.S. Geological Survey, written commun., 2008) 

Substrate classes were verified through qualitative sediment 

sampling, side-scan sonar surveys of selected areas, and repeat 

substrate mapping. 

The substrate classification verified that the bed of the 

Missouri River is dominated by sand with localized areas of 

sandy mud and mud in slack-water areas associated with wing 

dikes and tributary mouths. Coarse substrate exists in patches 

in the Yankton reach and is associated with wing dikes and 

revetments in all reaches. 

Hydrodynamic Modeling

We selected the Multi-Dimensional Surface Water 

Modeling System (MD_SWMS) as the hydrodynamic model 

for this study. MD_SWMS (McDonald and others, 2005) is a 

modular, public-domain two-dimensional hydraulic modeling 

code and graphic user interface (GUI) developed by the U.S. 

Geological Survey (McDonald and others, 2005). FaSTMECH 

is the computational model used within the MD_SWMS 

modeling system (Nelson and others, 2003). FaSTMECH is 

an implementation of the Reynolds equations of fluid motion 

using an eddy viscosity turbulence closure, depth averaging, and an assumption of hydrostatic pressure distribution 
8 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

0

300

600

900

1,200

1,500

0

300

600

900

1,200

1,500

0

300

600

900

1,200

1,500

U.S. Geological Survey gage 06601200, Decatur, Nebraska

Little Sioux Reach Surveys

Jan. Mar. May July Sept. Nov. Jan. Mar. May July Sept. Nov.

0

2.000

4,000

6,000

8,000

10,000

U.S. Geological Survey gage 06895500, Waverly, Missouri

Miami Reach Surveys

DISCHARGE, IN CUBIC METERS PER SECOND

2006 2007

DATE

Gavins Point Dam, United States Army Corps of Engineers Data

Yankton Reach Surveys

Discharge

Calibration survey

Compilation survey

U.S. Geological Survey gage 06486000, Sioux City, Iowa

Kenslers Bend Reach Surveys

.

Figure 3. Discharge hydrographs for four hydrodynamic modeling reaches showing dates of compilation and calibration 

surveys.
Methods 9

97°24'40" 97°24'30" 97°24'20"

42°52'10"

42°52'

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 14

0 50 100 METERS

0 200 400 FEET

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 14

0 20 40 METERS

0 75 150 FEET

A

B

Area B, below

EXPLANATION

Elevations mapped onto 5-meter

 computational grid

Light detection and ranging (LiDAR) 

 elevation points used where available

Hydroacoustic and land real-time

 kinematic global positioning system

 elevation points

EXPLANATION

Elevations mapped onto 5-meter

 computational grid

Light detection and ranging (LiDAR) 

 elevation points used where available

Hydroacoustic and land real-time

 kinematic global positioning system

 elevation points

Figure 4. Examples from Yankton, South Dakota modeling reach showing types and density of data: (A) Elevation points from 

three sources, (B) Close up showing elevation points and nodes of 5-m computational grid nodes. 
10 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

in open-channel flow. The last assumption is key because it 

neglects vertical accelerations that may occur where there are 

rapid changes in bed elevation, such as those associated with 

wing dikes. 

We implemented FaSTMECH as steady flow simulations on a streamwise computational coordinate system with 

5-m node spacing (fig. 4B). Inputs to the model were reach 

topography, upstream discharge, downstream water-surface 

elevation, and estimates of drag coefficient and lateral 

eddy viscosity.

Model Development 

Eight computational hydrodynamic model geometries 

were constructed using the MD_SWMS modeling system. 

For each of the four sites, one model was generated using 

discharge and elevation data collected in 2006, and a second model was created using discharge and elevation data 

collected in 2007. Parts of the channel and bank that did 

not experience erosion or deposition during 2006–08 were 

used in both models at each site. Each of the eight models 

was calibrated for several flow conditions: four flow conditions for 2006 models and three or four flow conditions for 

2007 models. 

Modeled Flows

The discharges observed during the hydrographic surveys 

were the basis for calibration conditions. At each site, hydrographic surveys were completed for four flow conditions in 

2006 and three flow conditions in 2007. Each of these flow 

conditions was used in model calibration. 

To capture habitat conditions for discharges likely to be 

encountered during spring pulsed flows, our objective was 

to simulate flows over a nominal range of 25–90 percent 

flow exceedance. Because of a lack of available flows and 

scheduling, we were unable to measure hydraulic conditions 

over the intended range of discharges at Kenslers Bend and 

Yankton. Calibration data were collected for 7–98 percent flow 

exceedance at Miami, 15–93 percent flow exceedance at Little 

Sioux, 64–99 percent flow exceedance at Kenslers Bend, and 

56–94 percent flow exceedance at Yankton (table 2).

Water Stage

Water-surface elevations used in the calibrations were 

determined from hydrographic survey data. For each model 

and flow condition, an observed water-surface elevation nearest the downstream end of the modeling grid was used as the 

downstream water-surface elevation. Linear or power-law 

functions were fit to the values of water-surface elevation and 

discharge, and the statistical models were used to estimate 

water-surface elevations for nonmeasured conditions.

Terrain Models

We developed terrain models for each of the eight compilation surveys by computing a triangular irregular network 

for the hydrographic, terrestrial, and supporting elevation data. 

The model extended from the thalweg to the top of the bank 

for a corridor nominally 20 m outside the bank. The modeled 

corridor included some embayments where small tributaries 

entered the Missouri River (figs. 5–8). 

Sediment Classification and Hydraulic Roughness

Hydraulic roughness in MD_SWMS is parameterized by 

the drag coefficient, a dimensionless number that integrates 

frictional resistance from the bed, including roughness arising 

from bed particles and bedforms. Because of uncertainties in 

how various factors integrate to produce frictional resistance, 

the standard practice in MD_SWMS modeling is to calibrate 

the model to measured water-surface elevations by varying 

the drag coefficient. Knowledge of bed sediment can provide 

useful guidance to setting and varying the drag coefficient but 

is not necessary for model calibration. Bed-sediment classification data from the modeling reaches were used to confirm 

general sediment types but were not used to set drag coefficient values. 

Based on general uniformity of the sediment classes 

in the modeling reaches, we also elected to calibrate the 

models to a single drag coefficient value for each reach and 

discharge, rather than varying the coefficient spatially within 

the reach. Use of a spatially varied drag coefficient would add 

unnecessary complexity to the calibration process and was 

not supported by the sediment classification data (Reuter and 

others, U.S. Geological Survey, written commun., 2008), success in modeling velocity distributions (see section on model 

assessment following), or sensitivity analysis (see section on 

sensitivity analysis following).

Frictional resistance can arise from form roughness 

associated with sedimentary bedforms. Dynamic adjustments 

of bedforms to depth, velocity, and water temperature impose 

substantive challenges to calibrating hydrodynamic models in 

sandbed rivers. Typical dimensions of bedforms measured in 

long profiles (table 3, from Elliott and others, 2009) indicate that dune-size bedforms are prominent in these reaches. 

Because of the dynamic variation of these bedforms during the 

period of study (Elliott and others, 2009) we did not attempt 

to model bedform contributions to flow resistance explicitly. 

Bedform roughness is implicit in calibrated drag coefficients 

and dynamic changes in bedform roughness probably contribute substantially to overall modeling error. Drag coefficients 

for simulation runs were interpolated and (to a limited extent) 

extrapolated from the relations developed between drag coefficient and discharge for the calibration runs. 

Computational Grid

Curvilinear, orthogonal computational grids were developed for each of the eight models (fig. 4B). Each grid was 
Methods 11

Table 2. Discharges and hydraulic parameter values for calibration and solution runs for four hydrodynamic modeling reaches.

[m3/s, cubic meters per second; m, meters; m2/s, square meters per second; na, not applicable]

Reach Run Date Discharge 

(m3/s)

Approximate flow 

exceedance 

(percent)

Downstream 

stage 

(m)

Drag 

coefficient 

(dimensionless)

Lateral eddy 

viscosity 

coefficient 

(m2/s)

Water 

surface 

prediction, root 

mean 

square 

error (m)

Miami, Missouri - 2006 Calibration 11/6/2006 588 98 189.46 0.0041 0.026 0.039

Calibration 3/23/2006 633 96 189.68 0.0054 0.027 0.044

Calibration 7/25/2006 944 83 190.60 0.0046 0.036 0.029

Calibration 5/22/2006 1,166 68 191.10 0.0047 0.042 0.029

Calibration 4/19/2006 1,376 53 191.55 0.0044 0.048 0.036

Calibration 4/30/2007 2,472 14 193.36 0.0035 0.079 0.029

Calibration 5/18/2007 3,136 7 194.92 0.0040 0.098 0.026

Solution 2006 550 98 189.27 0.0048 0.025 na

Solution 2006 600 97 189.52 0.0048 0.026 na

Solution 2006 650 95 189.73 0.0047 0.027 na

Solution 2006 700 93 189.88 0.0047 0.029 na

Solution 2006 750 91 190.03 0.0047 0.030 na

Solution 2006 800 89 190.17 0.0047 0.032 na

Solution 2006 850 86 190.32 0.0047 0.033 na

Solution 2006 900 85 190.47 0.0046 0.035 na

Solution 2006 950 83 190.61 0.0046 0.036 na

Solution 2006 1,000 80 190.73 0.0046 0.038 na

Solution 2006 1,050 77 190.84 0.0046 0.039 na

Solution 2006 1,100 74 190.95 0.0046 0.040 na

Solution 2006 1,150 70 191.06 0.0045 0.042 na

Solution 2006 1,200 65 191.18 0.0045 0.043 na

Solution 2006 1,250 61 191.28 0.0045 0.044 na

Solution 2006 1,300 58 191.39 0.0045 0.046 na

Solution 2006 1,350 55 191.49 0.0045 0.047 na

Solution 2006 1,400 52 191.59 0.0044 0.049 na

Solution 2006 1,450 49 191.67 0.0044 0.050 na

Solution 2006 1,500 46 191.75 0.0044 0.051 na

Solution 2006 1,550 44 191.84 0.0044 0.053 na

Solution 2006 1,600 41 191.92 0.0044 0.054 na

Solution 2006 1,650 39 192.00 0.0043 0.056 na

Solution 2006 1,700 36 192.08 0.0043 0.057 na
12 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

Table 2. Discharges and hydraulic parameter values for calibration and solution runs for four hydrodynamic modeling reaches.—

Continued

[m3/s, cubic meters per second; m, meters; m2/s, square meters per second; na, not applicable]

Reach Run Date Discharge 

(m3/s)

Approximate flow 

exceedance 

(percent)

Downstream 

stage 

(m)

Drag 

coefficient 

(dimensionless)

Lateral eddy 

viscosity 

coefficient 

(m2/s)

Water 

surface 

prediction, root 

mean 

square 

error (m)

Miami, Missouri - 2006 Solution 2006 1,750 34 192.17 0.0043 0.059 na

Solution 2006 1,800 32 192.25 0.0043 0.060 na

Solution 2006 1,850 30 192.33 0.0043 0.061 na

Solution 2006 1,900 28 192.41 0.0042 0.063 na

Solution 2006 1,950 27 192.50 0.0042 0.064 na

Solution 2006 2,000 25 192.58 0.0042 0.066 na

Solution 2006 2,050 23 192.66 0.0042 0.067 na

Solution 2006 2,100 22 192.74 0.0042 0.068 na

Solution 2006 2,150 21 192.83 0.0041 0.070 na

Solution 2006 2,200 19 192.91 0.0041 0.071 na

Solution 2006 2,250 18 192.99 0.0041 0.073 na

Solution 2006 2,300 17 193.08 0.0041 0.074 na

Solution 2006 2,350 16 193.16 0.0041 0.076 na

Solution 2006 2,400 15 193.24 0.0040 0.077 na

Solution 2006 2,450 15 193.32 0.0040 0.078 na

Solution 2006 2,500 14 193.41 0.0040 0.080 na

Solution 2006 2,550 13 193.54 0.0040 0.081 na

Solution 2006 2,600 12 193.66 0.0040 0.083 na

Solution 2006 2,650 12 193.78 0.0039 0.084 na

Solution 2006 2,700 11 193.89 0.0039 0.086 na

Solution 2006 2,750 11 194.01 0.0039 0.087 na

Solution 2006 2,800 10 194.13 0.0039 0.088 na

Solution 2006 2,850 10 194.25 0.0039 0.090 na

Solution 2006 2,900 9 194.36 0.0038 0.091 na

Solution 2006 2,950 9 194.48 0.0038 0.093 na

Solution 2006 3,000 8 194.60 0.0038 0.094 na

Solution 2006 3,050 8 194.72 0.0038 0.096 na

Solution 2006 3,100 8 194.83 0.0038 0.097 na

Solution 2006 3,150 7 194.95 0.0037 0.098 na
Methods 13

Table 2. Discharges and hydraulic parameter values for calibration and solution runs for four hydrodynamic modeling reaches.—

Continued

[m3/s, cubic meters per second; m, meters; m2/s, square meters per second; na, not applicable]

Reach Run Date Discharge 

(m3/s)

Approximate flow 

exceedance 

(percent)

Downstream 

stage 

(m)

Drag 

coefficient 

(dimensionless)

Lateral eddy 

viscosity 

coefficient 

(m2/s)

Water 

surface 

prediction, root 

mean 

square 

error (m)

Miami, Missouri - 2007 Calibration 11/27/2007 743 91 190.18 0.0052 0.030 0.0362

Calibration 8/15/2007 1,384 53 191.70 0.0049 0.048 0.0388

Calibration 3/26/2007 1,473 47 191.95 0.0053 0.051 0.0194

Calibration 4/30/2007 2,472 14 193.36 0.0035 0.079 0.0281

Calibration 5/18/2007 3,136 7 194.92 0.0041 0.098 0.0073

Solution 2007 550 98 189.72 0.0054 0.025 na

Solution 2007 600 97 189.84 0.0054 0.026 na

Solution 2007 650 95 189.96 0.0053 0.027 na

Solution 2007 700 93 190.08 0.0053 0.029 na

Solution 2007 750 91 190.20 0.0053 0.030 na

Solution 2007 800 89 190.32 0.0052 0.032 na

Solution 2007 850 86 190.43 0.0052 0.033 na

Solution 2007 900 85 190.55 0.0052 0.034 na

Solution 2007 950 83 190.67 0.0051 0.036 na

Solution 2007 1,000 80 190.79 0.0051 0.037 na

Solution 2007 1,050 77 190.91 0.0051 0.039 na

Solution 2007 1,100 74 191.03 0.0050 0.040 na

Solution 2007 1,150 70 191.14 0.0050 0.041 na

Solution 2007 1,200 65 191.26 0.0050 0.043 na

Solution 2007 1,250 61 191.38 0.0049 0.044 na

Solution 2007 1,300 58 191.50 0.0049 0.046 na

Solution 2007 1,350 55 191.62 0.0049 0.047 na

Solution 2007 1,400 52 191.74 0.0048 0.049 na

Solution 2007 1,450 49 191.88 0.0048 0.050 na

Solution 2007 1,500 46 191.99 0.0048 0.052 na

Solution 2007 1,550 44 192.06 0.0047 0.053 na

Solution 2007 1,600 41 192.13 0.0047 0.055 na

Solution 2007 1,650 39 192.20 0.0046 0.056 na

Solution 2007 1,700 36 192.27 0.0046 0.057 na

Solution 2007 1,750 34 192.34 0.0046 0.059 na
14 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

Table 2. Discharges and hydraulic parameter values for calibration and solution runs for four hydrodynamic modeling reaches.—

Continued

[m3/s, cubic meters per second; m, meters; m2/s, square meters per second; na, not applicable]

Reach Run Date Discharge 

(m3/s)

Approximate flow 

exceedance 

(percent)

Downstream 

stage 

(m)

Drag 

coefficient 

(dimensionless)

Lateral eddy 

viscosity 

coefficient 

(m2/s)

Water 

surface 

prediction, root 

mean 

square 

error (m)

Miami, Missouri - 2007 Solution 2007 1,800 32 192.41 0.0045 0.060 na

Solution 2007 1,850 30 192.48 0.0045 0.062 na

Solution 2007 1,900 28 192.55 0.0045 0.063 na

Solution 2007 1,950 27 192.62 0.0044 0.064 na

Solution 2007 2,000 25 192.69 0.0044 0.066 na

Solution 2007 2,050 23 192.76 0.0044 0.067 na

Solution 2007 2,100 22 192.83 0.0043 0.069 na

Solution 2007 2,150 21 192.91 0.0043 0.070 na

Solution 2007 2,200 19 192.98 0.0043 0.071 na

Solution 2007 2,250 18 193.05 0.0042 0.073 na

Solution 2007 2,300 17 193.12 0.0042 0.074 na

Solution 2007 2,350 16 193.19 0.0042 0.076 na

Solution 2007 2,400 15 193.26 0.0041 0.077 na

Solution 2007 2,450 15 193.33 0.0041 0.078 na

Solution 2007 2,500 14 193.40 0.0041 0.080 na

Solution 2007 2,550 13 193.54 0.0040 0.081 na

Solution 2007 2,600 12 193.66 0.0040 0.083 na

Solution 2007 2,650 12 193.78 0.0039 0.084 na

Solution 2007 2,700 11 193.89 0.0039 0.086 na

Solution 2007 2,750 11 194.01 0.0039 0.087 na

Solution 2007 2,800 10 194.13 0.0038 0.088 na

Solution 2007 2,850 10 194.25 0.0038 0.090 na

Solution 2007 2,900 9 194.36 0.0038 0.091 na

Solution 2007 2,950 9 194.48 0.0037 0.093 na

Solution 2007 3,000 8 194.60 0.0037 0.094 na

Solution 2007 3,050 8 194.72 0.0037 0.096 na

Solution 2007 3,100 8 194.83 0.0036 0.097 na

Solution 2007 3,150 7 194.95 0.0036 0.098 na
Methods 15

Table 2. Discharges and hydraulic parameter values for calibration and solution runs for four hydrodynamic modeling reaches.—

Continued

[m3/s, cubic meters per second; m, meters; m2/s, square meters per second; na, not applicable]

Reach Run Date Discharge 

(m3/s)

Approximate flow 

exceedance 

(percent)

Downstream 

stage 

(m)

Drag 

coefficient 

(dimensionless)

Lateral eddy 

viscosity 

coefficient 

(m2/s)

Water 

surface 

prediction, root 

mean 

square 

error (m)

Little Sioux, Iowa - 2006 Calibration 10/24/2006 350 93 306.28 0.0048 0.023 0.0479

Calibration 3/14/2006 383 90 306.39 0.0055 0.022 0.0575

Calibration 7/18/2006 768 55 307.81 0.0067 0.040 0.0476

Calibration 5/16/2006 884 37 308.51 0.0063 0.044 0.0299

Calibration 3/16/2007 1,150 15 309.42 0.0060 0.056 0.0368

Solution 2006 250 98 305.95 0.0052 0.017 na

Solution 2006 300 97 306.12 0.0053 0.020 na

Solution 2006 350 93 306.28 0.0053 0.022 na

Solution 2006 400 88 306.45 0.0054 0.024 na

Solution 2006 450 80 306.64 0.0055 0.026 na

Solution 2006 500 76 306.82 0.0056 0.028 na

Solution 2006 550 74 307.01 0.0056 0.030 na

Solution 2006 600 71 307.19 0.0057 0.032 na

Solution 2006 650 69 307.37 0.0058 0.034 na

Solution 2006 700 67 307.56 0.0059 0.036 na

Solution 2006 750 60 307.74 0.0059 0.038 na

Solution 2006 800 51 308.00 0.0060 0.041 na

Solution 2006 850 42 308.31 0.0061 0.043 na

Solution 2006 900 34 308.57 0.0062 0.045 na

Solution 2006 950 25 308.74 0.0062 0.047 na

Solution 2006 1,000 21 308.91 0.0063 0.049 na

Solution 2006 1,050 18 309.08 0.0064 0.051 na

Solution 2006 1,100 16 309.25 0.0065 0.053 na

Solution 2006 1,150 15 309.42 0.0065 0.055 na

Little Sioux, Iowa - 2007 Calibration 10/31/2007 435 83 306.96 0.0072 0.025 0.0288

Calibration 4/10/2007 673 68 307.82 0.0080 0.035 0.0671

Calibration 7/20/2007 700 67 307.80 0.0078 0.037 0.0594

Calibration 3/16/2007 1,150 15 309.42 0.0071 0.056 0.0352

Solution 2007 250 98 306.29 0.0078 0.017 na
16 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

Table 2. Discharges and hydraulic parameter values for calibration and solution runs for four hydrodynamic modeling reaches.—

Continued

[m3/s, cubic meters per second; m, meters; m2/s, square meters per second; na, not applicable]

Reach Run Date Discharge 

(m3/s)

Approximate flow 

exceedance 

(percent)

Downstream 

stage 

(m)

Drag 

coefficient 

(dimensionless)

Lateral eddy 

viscosity 

coefficient 

(m2/s)

Water 

surface 

prediction, root 

mean 

square 

error (m)

Little Sioux, Iowa - 2007 Solution 2007 300 97 306.47 0.0078 0.019 na

Solution 2007 350 93 306.65 0.0077 0.021 na

Solution 2007 400 88 306.83 0.0077 0.024 na

Solution 2007 450 80 307.01 0.0077 0.026 na

Solution 2007 500 76 307.19 0.0077 0.028 na

Solution 2007 550 74 307.38 0.0076 0.030 na

Solution 2007 600 71 307.56 0.0076 0.032 na

Solution 2007 650 69 307.74 0.0076 0.034 na

Solution 2007 700 67 307.80 0.0076 0.037 na

Solution 2007 750 60 307.98 0.0075 0.039 na

Solution 2007 800 51 308.16 0.0075 0.041 na

Solution 2007 850 42 308.34 0.0075 0.043 na

Solution 2007 900 34 308.52 0.0075 0.045 na

Solution 2007 950 25 308.70 0.0074 0.048 na

Solution 2007 1,000 21 308.88 0.0074 0.050 na

Solution 2007 1,050 18 309.06 0.0074 0.052 na

Solution 2007 1,100 16 309.24 0.0074 0.054 na

Solution 2007 1,150 15 309.42 0.0073 0.056 na

Kenslers Bend, Nebraska - 2006 Calibration 3/10/2006 295 99 320.15 0.0042 0.017 0.072

Calibration 3/30/2006 592 76 330.35 0.0072 0.028 0.031

Calibration 5/16/2006 754 64 330.65 0.0062 0.034 0.045

Solution 2006 250 100 328.91 0.0040 0.016 na

Solution 2006 300 98 329.22 0.0042 0.017 na

Solution 2006 350 96 329.49 0.0048 0.019 na

Solution 2006 400 92 329.71 0.0053 0.021 na

Solution 2006 450 88 329.92 0.0059 0.023 na

Solution 2006 500 84 330.10 0.0064 0.025 na

Solution 2006 550 79 330.26 0.0070 0.026 na

Solution 2006 600 76 330.41 0.0073 0.028 na

Solution 2006 650 74 330.55 0.0071 0.030 na

Solution 2006 700 69 330.67 0.0069 0.032 na
Methods 17

Table 2. Discharges and hydraulic parameter values for calibration and solution runs for four hydrodynamic modeling reaches.—

Continued

[m3/s, cubic meters per second; m, meters; m2/s, square meters per second; na, not applicable]

Reach Run Date Discharge 

(m3/s)

Approximate flow 

exceedance 

(percent)

Downstream 

stage 

(m)

Drag 

coefficient 

(dimensionless)

Lateral eddy 

viscosity 

coefficient 

(m2/s)

Water 

surface 

prediction, root 

mean 

square 

error (m)

Kenslers Bend, Nebraska - 2006 Solution 2006 750 64 330.79 0.0067 0.034 na

Solution 2006 800 59 330.90 0.0065 0.035 na

Solution 2006 850 54 331.01 0.0063 0.037 na

Solution 2006 900 43 331.11 0.0061 0.039 na

Solution 2006 950 34 331.20 0.0060 0.041 na

Kenslers Bend, Nebraska - 2007 Calibration 4/12/2007 469 86 329.90 0.0064 0.023 0.0540

Calibration 3/15/2007 624 75 330.39 0.0060 0.029 0.0360

Calibration 7/25/2007 629 75 330.57 0.0070 0.029 0.0322

Solution 2007 250 100 328.91 0.0040 0.016 na

Solution 2007 300 98 329.22 0.0042 0.017 na

Solution 2007 350 96 329.49 0.0048 0.019 na

Solution 2007 400 92 329.71 0.0053 0.021 na

Solution 2007 450 88 329.92 0.0059 0.023 na

Solution 2007 500 84 330.10 0.0064 0.025 na

Solution 2007 550 79 330.26 0.0070 0.026 na

Solution 2007 600 76 330.41 0.0073 0.028 na

Solution 2007 650 74 330.55 0.0071 0.030 na

Solution 2007 700 69 330.67 0.0069 0.032 na

Solution 2007 750 64 330.79 0.0067 0.034 na

Solution 2007 800 59 330.90 0.0065 0.035 na

Solution 2007 850 54 331.01 0.0063 0.037 na

Solution 2007 900 43 331.11 0.0061 0.039 na

Solution 2007 950 34 331.20 0.0060 0.041 na

Yankton, South Dakota - 2006 Calibration 3/7/2006 292 90 350.46 0.0045 0.012 0.029

Calibration 3/30/2006 577 66 351.07 0.0041 0.020 0.023

Calibration 5/15/2006 680 56 351.19 0.0047 0.023 0.026

Solution 2006 200 99 350.27 0.0046 0.010 na

Solution 2006 250 94 350.37 0.0046 0.011 na

Solution 2006 300 90 350.46 0.0045 0.013 na

Solution 2006 350 87 350.56 0.0044 0.014 na
18 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

Table 2. Discharges and hydraulic parameter values for calibration and solution runs for four hydrodynamic modeling reaches.—

Continued

[m3/s, cubic meters per second; m, meters; m2/s, square meters per second; na, not applicable]

Reach Run Date Discharge 

(m3/s)

Approximate flow 

exceedance 

(percent)

Downstream 

stage 

(m)

Drag 

coefficient 

(dimensionless)

Lateral eddy 

viscosity 

coefficient 

(m2/s)

Water 

surface 

prediction, root 

mean 

square 

error (m)

Yankton, South Dakota - 2006 Solution 2006 400 83 350.65 0.0043 0.016 na

Solution 2006 450 79 350.75 0.0043 0.017 na

Solution 2006 500 73 350.84 0.0042 0.018 na

Solution 2006 550 69 350.94 0.0041 0.020 na

Solution 2006 600 64 351.03 0.0042 0.021 na

Solution 2006 650 61 351.13 0.0044 0.023 na

Solution 2006 700 54 351.22 0.0047 0.024 na

Solution 2006 750 48 351.32 0.0047 0.025 na

Solution 2006 800 42 351.41 0.0047 0.027 na

Solution 2006 850 32 351.51 0.0047 0.028 na

Solution 2006 900 27 351.60 0.0047 0.030 na

Solution 2009 950 20 351.70 0.0047 0.031 na

Yankton, South Dakota - 2007 Calibration 3/9/2007 255 94 350.40 0.0055 0.011 0.029

Calibration 3/13/2007 459 77 350.72 0.0048 0.017 0.022

Calibration 6/28/2007 497 73 350.84 0.0042 0.018 0.023

Solution 2007 200 99 350.27 0.0057 0.010 na

Solution 2007 250 94 350.37 0.0055 0.011 na

Solution 2007 300 90 350.46 0.0054 0.013 na

Solution 2007 350 87 350.56 0.0052 0.014 na

Solution 2007 400 83 350.65 0.0050 0.016 na

Solution 2007 450 79 350.75 0.0048 0.017 na

Solution 2007 500 73 350.84 0.0042 0.018 na

Solution 2007 550 69 350.94 0.0043 0.020 na

Solution 2007 600 64 351.03 0.0043 0.021 na

Solution 2007 650 61 351.13 0.0044 0.023 na

Solution 2007 700 54 351.22 0.0045 0.024 na

Solution 2007 750 48 351.32 0.0045 0.025 na

Solution 2007 800 42 351.41 0.0046 0.027 na

Solution 2007 850 32 351.51 0.0047 0.028 na

Solution 2007 900 27 351.60 0.0047 0.030 na

Solution 2007 950 20 351.70 0.0048 0.031 na
Methods 19

#

#

#

262

261

260

#

263

93°12' 93°13' 93°14'

39°22'

39°21'

39°20'

EXPLANATION

0 200 400 METERS0 1,000 2,000 FEET

B

Velocity assessment transect

B

A

260

181 

River miles from the U.S. Army Corps of Engineers, 1960 Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projectionZone 15

 Miami, Missouri

2006

River-mile marker and number—

Number is distance upstream from 

Elevation, in meters

 the junction with the Mississippi  River, in miles

—

Vertical datum is NAVD 88

207

Figure 5. Elevation of the hydrodynamic modeling reach at Miami, Missouri, 2006 bathymetry. 
20 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

#

#

#

#

670

671

672

673

96°04' 96°05' 96°06' 96°07'

41°50'41°49'

0 200 400 METERS0 1,000 2,000 FEET

A

B

River miles from the U.S. Army Corps of Engineers, 1960 Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projectionZone 15

Little Sioux, Iowa

2006

B

670

Velocity assessment transect

River-mile marker and number—

Number is distance upstream from 

 the junction with the Mississippi  River, in miles

315298 

E

X

P

L

A

N

ATIO

N

Elevation, in meters

—

Vertical datum is NAVD 88

Figure 6. Elevation of the hydrodynamic modeling reach at Little Sioux, Iowa, 2006 bathymetry. 
Methods 21

#

#

#

#

#

746

747

748

744

745

743

96°35' 96°36' 96°37' 96°38' 96°39'

42°33'42°32'

42°31'

42°30'

0 200 400 METERS0 1,000 2,000 FEET

B

Velocity assessment transect

A

B

745

River miles from the U.S. Army Corps of Engineers, 1960 Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projectionZone 15

Kenslers Bend, Nebraska

2006

E

341320 

XPLANATION

Elevation, in meters—

Vertical datum is NAVD 88

River-mile marker and number—

Number is distance upstream from 

 the junction with the Mississippi  River, in miles

Figure 7. Elevation of the hydrodynamic modeling reach at Kenslers Bend, Nebraska. 2006 bathymetry. 
22 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

#

#

#

#

808

807

805

806

97°23' 97°24' 97°25' 97°26'

42°52'

42°51'

0 200 400 METERS0 1,000 2,000 FEET

Yankton, South Dakota

2006

B

A

B

Velocity assessment transect

805 River-mile marker and number—

Number is distance upstream from 

 the junction with the Mississippi  River, in miles

River miles from the U.S. Army Corps of Engineers, 1960 Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projectionZone 15

391342 

E

X

P

L

A

N

ATIO

N

Elevation, in meters

—

Vertical datum is NAVD 88

Figure 8. Elevation of the hydrodynamic modeling reach at Yankton, South Dakota, 2006 bathymetry. 
Methods 23

created by drawing a centerline from the upstream end of the 

modeling reach to the downstream end. Around the centerline, 

the computation grid was expanded in the cross-stream direction to extend to elevations higher than the highest expected 

water-surface elevations in each model. Grid incrementing 

was adjusted in the streamwise and cross-stream directions 

such that a 5 m by 5 m grid was produced. Elevations then 

were assigned to grid nodes from the underlying terrain model 

(fig. 4B).

Boundary Conditions

MD_SWMS uses discharge and downstream water-surface elevation as boundary conditions for each hydrodynamic 

simulation (table 2). The discharge was maintained constant 

for each model run and downstream stage was assumed to be 

constant in the cross-stream direction. For selected models, 

simplified channel extensions were added to the upstream 

or downstream ends of the reach to allow for stabilization of 

flow. Models were run through sufficient number of iterations 

to converge with discharge errors of less than 2 percent in 

all cases.

Calibration

Each model was calibrated for a given flow by iteratively changing the drag coefficient to match the modeled and 

observed water-surface elevation profiles (U.S. Army Corps 

of Engineers, 1993). A measure of calibration performance 

was computed by taking the root-mean squared error (RMSE) 

value of the differences of the modeled and observed watersurface elevations. The roughness coefficient with the lowest 

RMSE value of water-surface elevation differences and least 

difference in overall water-surface slope was chosen for that 

discharge model run (table 2, fig. 9). Multiple calibrations 

over a range of discharges produced a functional relation 

between discharge and drag coefficient for each reach, each 

year. Drag coefficients for non-calibration solutions were 

interpolated, and to a limited extent, extrapolated from these 

relations. Calibration to water-surface elevation assumes that 

if a model replicates energy loss (water-surface slope as an 

approximation of energy slope) it will accurately represent 

depths and velocities. Use of interpolated drag coefficients to 

model non-calibration flows assumes that the relation between 

discharge and drag coefficient does not change significantly 

over time from variation in parameters other than discharge. 

The first assumption can be evaluated by assessing how well 

modeled and measured velocities correspond, as discussed in 

the next section. Variation in boundary roughness because of 

effects of water temperature, sediment transport, and bedform 

changes are examples of factors not captured in the calibrated 

models that might change the relation between discharge and 

drag coefficient, and contribute to model error (Fenwick, 

1969; U.S. Army Corps of Engineers, 1993). 

The lateral eddy viscosity (LEV) coefficient is also an 

adjustable parameter in MD_SWMS. LEV parameterizes turbulent momentum transfer and has a strong effect on modeled 

velocities. In our model implementation, we did not adjust 

LEV by calibrating modeled velocity distributions to match 

measured data; instead, we computed LEV for each simulation 

using the relation:

0.01* * LEV v d = avg avg (1)

where

LEV is lateral eddy viscosity coefficient, in square 

meters per second

vavg is reach-average velocity, in meters per 

second

davvg is reach-average depth, in meters 

Average velocity and average depth were calculated from 

discharge measurement data collected during surveys and 

modeled from nearby streamflow gaging stations. Because we 

did not use LEV in calibration, predicted velocity distributions 

provide an internal test of model performance.

Model Assessment

Assessment of the adequacy of model performance ultimately depends on how the model results will be used (U.S. 

Army Corps of Engineers, 1993; Bates and Anderson, 2001; 

Lane and Richards, 2001). We present two approaches to 

model assessment, an internal evaluation of velocity distributions and an analysis of the sensitivity of predicted habitat to 

variation in the model parameters. The second of these relates 

directly to performance of the model for its intended use—

assessment of habitat variation with changes in discharge—

and is discussed in a later section of this report on sensitivity 

analysis. Evaluation of velocity distributions is presented in 

this section.

In the velocity assessment, we evaluated how well the 

calibrated model predicts velocity distributions in typical cross 

sections. This is an internal test of how well the model predicts 

depth-average velocities in a cross section without adjusting LEV, a parameter that strongly affects the cross sectional 

velocity distribution. The cross sections were selected to represent a broad range of velocity conditions within each reach 

Table 3. Average bedform dimensions at modeling reaches 

over period of study. From Elliott and others (2009).

[m3/s, cubic meters per second; m, meters]

Reach Discharge 

range (m3/s)

Average 

bedform 

amplitude 

(m)

Average 

bedform 

wavelength 

(m)

Miami, Missouri 700–3,300 0.84 14.2

Little Sioux, Iowa 330–1,140 0.38 3.8

Kenslers Bend, 

Nebraska

350–830 0.35 4.0

Yankton, South 

Dakota

250–710 0.40 15.1
24 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River Figure 9. Predicted and observed water-surface elevations. Predicted Observed EXPLANATION

WATER-SURFACE ELEVATION, IN METERS ABOVE NAVD 88

Miami, Missouri,

2006

Little Sioux, Iowa

2006

Miami, Missouri,

2007

Little Sioux, Iowa

2007

DISTANCE DOWNSTREAM, IN METERS

197196195194193192191190189

312311310309308307306312311310309308307

197196195194193192191190189

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000

306
Methods 25

Figure 9. Predicted and observed water-surface elevations.—Continued

PredictedObserved

EXPLANATION

Kenslers Bend, Nebraska

2006

Yankton, South Dakota

2006

Kenslers Bend, Nebraska

2007

WATER-SURFACE ELEVATION, IN METERS ABOVE NAVD 88 Yankton, South Dakota 2007

DISTANCE DOWNSTREAM, IN METERS

335334333332331330329335334333332331330329

353352351350349353352351350349

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 0 1,000 2,000 3,000 4,000 5,000 6,000
26 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

(figs. 5–8, 10). In the downstream three reaches, this included 

cross sections that extended from the main channel into lowvelocity areas downstream from wing dikes. Modeled velocities were extracted from grids of the velocity field at locations 

where current velocities were measured with ADCP during 

surveys. Depth-averaged velocity magnitudes were computed 

from the ADCP data by vector averaging velocities in depth 

increments (bins) within the ADCP ensemble. 

Comparison plots were examined for bias and accuracy 

(fig. 10). We considered general conformity of the modeled 

cross sectional velocity distribution to measured values to support model performance. No broad departures from measured 

data were noticed in the representative sample selected for 

velocity assessment, although small departures were common. 

In particular, the model captures the cross-channel velocity 

gradients that figure prominently in habitat definitions. Comparisons of modeled velocities to ADCP-measured velocities 

are capable of indicating gross model errors, but are inherently 

limited because ADCP data capture relatively high-frequency 

turbulent variation in the flow field and the model presents 

steady flow at a 5-m grid scale (Lane and Richards, 2001). The 

agreement between measured and modeled velocities indicates 

that calibration to a spatially uniform drag coefficient captures 

the salient features of velocity distributions in this river.

Hydraulic Definitions of Habitat

The concept of habitat can be treated as an extremely 

complex entity, encompassing all physical, chemical, and biological factors that interact to support occupancy of an organism (Hall and others, 1997; Bergman and others, 2008). For 

the purposes of this report, however, we define aquatic habitat 

more narrowly as the joint spatial and temporal distribution 

of depth, velocity, substrate, turbidity, temperature, and other 

related variables. At the reach scale, physical habitat typically 

is described even more narrowly as measures of depth, velocity, and substrate (Gordon and others, 1992; Reuter and others, 

2003). This definition of habitat encompasses all spaces and 

is not organism specific, thereby allowing consideration of 

spaces for which the biological importance is unknown or 

only suspected. 

Aquatic habitat can be considered a static volume of 

water with particular characteristics, or as a temporally and 

spatially varying entity. The seasonal occurrence, sequence, 

and durations of habitat availability may be critical for 

survival of some aquatic organisms because of life-stagespecific habitat requirements (Maddock, 1999; Doyle and 

others, 2005). Using two- or three-dimensional hydrodynamic 

models allows quantification of spatial attributes of habitat, 

including diversity (Reuter and others, 2003; Pasternack and 

others, 2004), gradients between habitat units (Crowder and 

Diplas, 2000; 2002; 2006; Johnson and others, 2006; Nestler 

and others, 2008), patch dynamics (Bowen and others, 2003), 

and patch persistence (Bovee and others, 2004). Advances 

also are being made in statistical methods for quantifying and 

analyzing habitat (Legleiter and Goodchild, 2005; AhmadiNedushan and others, 2006), which may improve understanding of habitat-biota links.

Selection of Habitat Metrics

In order to explore functions and sensitivities of a wide 

range of potential habitat types, we use selected habitat metrics in three broad categories: simple depth/velocity combinations, complex hydraulic metrics, and reach-scale integrative 

metrics. Ultimately, habitat metrics used to help manage a 

particular species should be based on identification of specific 

habitat functions or limitations. We present and explore a wide 

range of habitat metrics in this report in order to characterize 

multiple, potential indicators of ecological function. Although 

direct links to pallid sturgeon reproduction or survival may 

be poorly defined at this time, the metrics are all fundamental 

descriptors of the hydrodynamics of the river in which the 

fish resides. The intent is to provide some new approaches 

to habitat characterization that may motivate testing of new 

hypotheses about ecological functions of habitats, whether 

those habitats relate directly to pallid sturgeon reproduction 

and survival or indirectly through food sources, competition, 

or predation (Wildhaber and others, 2007). 

For the purposes of this study, we standardized on a minimum resolution of 5x5 m cells as a reasonable compromise 

between detail and cost. Many fundamental hydrodynamic 

and ecological processes are realized at a finer scale, including 

a range of turbulent flow features that may determine energy 

refugia and food distributions. Because of the resolution of 

the hydrodynamic model and the simplification of three-dimensional hydraulics into two dimensions, our habitat metrics 

should not be considered explicit descriptors of the processes. 

Rather, the habitat metrics are intended to be used as indicators of the variety of scales and processes occurring within the 

5x5 m resolution. 

Depth-Velocity Fields

Results from the hydrodynamic models were gridded 

into maps with 5-m square cells. The continuous velocity and 

depth grids then were used to place each cell into a depthvelocity category (table 4). The categories span the range of 

depth and velocity computed in the hydrodynamic models. 

The boundaries between categories are defined to provide sufficient resolution of depth and velocity bins without creating 

so many categories that trends are obscured. These depth-velocity fields provide potential habitat definitions with minimal 

functional interpretation. They are amenable to general exploration of trends in the modeled datasets and for calculation of 

measures of diversity (discussed below). 

Available information in the scientific literature supports 

the notion that adult pallid sturgeon do not select strongly for 

depth. For example, Mississippi River pallid sturgeon were 

found in a wide range of depths, 1.8–19.1 m (Hurley, 1999) 

and Upper Missouri and Yellowstone River pallid sturgeon 
Methods 27

500 600 700 800 900

 0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

500 600 700 800 900

 0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

500 600 700 800 900

500 600 700 800 900

0 50 100 150 200 250 300

 0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0 50 100 150 200 250 300 0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0 50 100 150 200 250 300

0 50 100 150 200 250 300

WATER VELOCITY, IN METERS PER SECOND

DISTANCE ALONG TRANSECTS,IN METERS

Miami, Missouri, Transect A, 2006

944 cubic metes per second

Miami, Missouri, Transect A, 2007

1,384 cubic metes per second

Miami, Missouri, Transect B, 2006

944 cubic meters per second

Miami, Missouri, Transect B, 2007

1,384 cubic meters per second

Little Sioux, Iowa, Transect A, 2006

768 cubic metes per second

Little Sioux, Iowa, Transect A, 2007

700 cubic metes per second

Little Sioux, Iowa, Transect B, 2006

768 cubic meters per second

Little Sioux, Iowa, Transect B, 2007

700 cubic meters per second

EXPLANATION

[Section locations are shown on figures 5 and 6;

cross sections are oriented looking downstream]

Modeled velocity

Observed velocity

Figure 10. Comparisons between modeled and measured water velocities at selected transects, Miami, Missouri 

and Little Sioux, Iowa, Kenslers Bend, Nebraska and Yankton, South Dakota reaches, 2006 and 2007. 
28 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

DISTANCE ALONG TRANSECTS, IN METERS

0 50 100 150 200 250 300 0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0 50 100 150 200 250 0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0 50 100 150 200 250 300 0 50 100 150 200 250 300 0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0 50 100 150 200 250 0 50 100 150 200 250 0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

WATER VELOCITY, IN METERS PER SECOND

Kenslers Bend, Nebraska, Transect A, 2006

752 cubic metes per second

Kenslers Bend, Nebraska, Transect B, 2006

752 cubic meters per second

Yankton, South Dakota, Transect A, 2006

662 cubic metes per second

Yankton, South Dakota, Transect B, 2006

662 cubic meters per second

0 50 100 150 200 250 300

0 50 100 150 200 250

Kenslers Bend, Nebraska, Transect B, 2007

629 cubic meters per second

Kenslers Bend, Nebraska, Transect A, 2007

629 cubic metes per second

Yankton, South Dakota, Transect B, 2007

497 cubic meters per second

Yankton, South Dakota, Transect A, 2007

497 cubic metes per second

EXPLANATION

[Section locations are shown on figures 7 and 8;

cross sections are oriented looking downstream]

Modeled velocity

Observed velocity

Figure 10. Comparisons between modeled and measured water velocities at selected transects, Miami, Missouri 

and Little Sioux, Iowa, Kenslers Bend, Nebraska and Yankton, South Dakota reaches, 2006 and 2007.—Continued
Methods 29

have been captured at 0.6–14.5 m depth (Bramblett and White, 

2001). Lack of strong depth selection has also been supported 

by telemetry studies in the LMOR, in which adult sturgeon 

were located at depths symmetrically distributed around the 

mean depth available in many reaches (Jacobson and others, 

2007). Depth selection of other fishes is often thought to relate 

strongly to provision of cover for predator avoidance (Rabeni 

and Jacobson, 1999), a factor that would be minimized in the 

turbid LMOR. Accordingly, for the purposes of this study, 

depths were arbitrarily divided into four classes: 0-1.5 m, 

1.5–3.0 m, 3.0–4.5 m, and greater than 4.5 m. (table 4). 

Pallid sturgeon selection for velocity appears stronger 

than for depth. Adult pallid sturgeon have been found at velocities 0–1.37 m/s in the Upper Missouri and Yellowstone Rivers 

(Bramblett and White, 2001) and 0.17–0.97 m/s in the Platte 

River (Snook and others, 2002). Selection of smaller than 

reach-average current velocity by adult pallid sturgeon has 

been documented in telemetry studies on the LMOR (Jacobson 

and others, 2007). Because of the key role of current velocity 

in energetics of many fishes, however, we included additional 

potential velocity classes in this analysis. As several smallbodied fishes (for example, sicklefin chub [Macrhybopsis 

meeki] and sturgeon chub [Macrhybopsis gelida]) are thought 

to be preferred food items for pallid sturgeon (Gerrity and others, 2006), and because swimming speed generally decreases 

with decreasing body size (Bainbridge, 1958; Weihs, 1973), 

we divided velocities into five classes, including a very slow 

current velocity 0–0.3 m/s (table 4). The remaining velocity classes were 0.3–0.6 m/s, 0.6–1.2 m/s, 1.2–1.8 m/s, and 

greater than 1.8 m/s. 

Hydraulic Parameters and Spatial Variation

Several habitat metrics were explored based on derivative 

hydraulic metrics or spatial variation (rate of change) of depth 

and velocity. Depth and velocity grids can be used to compute 

Froude number, a common dimensionless number relating 

inertial forces to gravity forces:

V F

gD = (2)

where, V is current velocity, D is depth, and g is the gravitational constant. The Froude number has been investigated by 

other researchers for its usefulness in describing the combined 

effect of velocity and depth on habitat suitability (Jowett, 

1993; Quinn and Hickey, 1994; Yu and Peters, 1997; Reuter 

and others, 2003). The range of Froude number that might be 

designated good or bad sturgeon habitat is unknown, although 

deep, slow areas around wing dikes have been identified as 

potential overwintering habitat for pallid sturgeon (Grady and 

others, 2001). In addition, low Froude numbers have been 

associated with deep, slow areas of the LMOR that are inhabited by invasive Asian carp (Kolar and others, 2005). For the 

purposes of this report, we selected low values of the Froude 

number to delineate deep, slow habitat areas, typically downstream from wing dikes, that could be accumulation areas for 

drifting larval fish or particulate organic matter. These areas 

coincide generally with Froude numbers less than 0.05. 

Identifying and quantifying pallid sturgeon spawning 

habitat is of particular interest to pallid sturgeon researchers 

(Quist and others, 2004; Bergman and others, 2008). Currently 

(February, 2009), spawning of pallid sturgeon in the wild has 

not been documented directly by observation. Telemetry studies have tracked migrating, gravid female pallid and shovelnose sturgeon to discrete reaches (including three of the four 

reaches in this study) and, in some cases, recaptures of the fish 

have confirmed that eggs had been released (DeLonay and 

others, 2007; U.S. Geological Survey, 2007). These studies 

and unpublished data from telemetry tracking during 2008 

(A. DeLonay, oral commun., U.S. Geological Survey, January, 2009) trace spawning to reaches (100’s of meters length) 

and in some cases patches (10’s of meters length), but have 

not identified consistent spawning locations or characteristics of locations. Spawning potential within the modeling 

reaches, therefore, is explored largely through reference to 

known characteristics of spawning locations used by other 

sturgeon species.

Observations of white sturgeon (Acipenser transmontanus) egg deposition indicate that areas of convergent flow 

downstream from rapids or riffles generally are selected for 

spawning (Paragamian and others, 2001; Paragamian and 

others, 2002; McDonald and others, 2006). This tendency has 

been supported by observations of Chinese sturgeon spawning (Fu and others, 2007). Descriptions of spawning habitats 

of shortnose sturgeon emphasize areas of coarse substrate and 

well-developed turbulence (Kynard, 1997). Presumably, high 

turbulence acts to keep the coarse substrate free of fine sediment and may act as a physiological cue for conditions that 

will disperse eggs (Coutant, 2004). Based on these descriptions, we explored several measures of flow complexity that 

relate to convergence/divergence and turbulence.

We used variation in unit discharge as an indicator of 

flow convergence. Unit discharge is the discharge in a cell 

expressed per unit width, thereby indicating how much of 

a cross-sectional discharge passes through a particular cell. 

Areas of high flow convergence, Cn, were identified as those 

with unit discharges (discharge per unit channel width) 

Table 4. Depth and velocity combination fields and class 

codes used in habitat patch analysis.

[m, meters; m/s, meters per second]

Velocity class 

(m/s)

Depth class (m)

0-1.5 1.5-3.0 3.0-4.5 4.5+

0–0.03 11 21 31 41

0.3–0.6 12 22 32 42

0.6–1.2 13 23 33 43

1.2–1.8 14 24 34 44

1.8+ 15 25 35 45
30 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

greater than the mean plus one standard deviation at a given 

discharge. Areas of high Cn are consistent with three pallid sturgeon spawning patches identified by high-intensity 

telemetry tracking efforts during 2008; these three patches 

were all on outside revetted bends between river mile 230 and 

370 (A. DeLonay, oral commun., U.S. Geological Survey, 

January, 2009).

The emerging data on catch and telemetry locations of 

migrating adult shovelnose and pallid sturgeon indicate selection for areas of high velocity gradients (Johnson and others, 

2006; DeLonay and others, 2007; Jacobson and others, 2007; 

Reuter and others, U.S. Geological Survey, written commun., 

2009). In smaller rivers, areas of recirculation and flow deformation have been associated with energy refugia for fishes 

and benthic invertebrates (Crowder and Diplas, 2002). This 

information has led us to explore additional hydraulic metrics 

related to spatial variability of velocity and flow deformation 

with the intent of defining metrics that combine ecological 

function and simplicity. Johnson and others (2006) defined a 

wake metric, Wk, that was intended to be indicative of flow 

separation and eddy shedding zones that occur downstream 

from flow obstructions and along sandbars. The turbulent vortices that exist in these zones are three-dimensional features 

ranging in size from millimeter scale to 10’s of meters; such 

features are not captured at the scale or complexity of our twodimensional models. Nevertheless, the velocity gradients that 

are coincident with the wake zones can be extracted from the 

hydrodynamic models. Wk habitats were defined as those with 

velocity standard deviations in a 50-m radius around a cell 

greater than 0.16 m/s and depth standard deviations between 

0.5 and 1.5 m (Johnson and others, 2006). Pending additional 

information on sturgeon habitat selection, we simplified the 

definition of Wk to areas with greater than 2 percent variation 

in velocity per meter, calculated along the steepest velocity 

slope among eight neighboring cells. This equates to a threshold of 10 percent variation over the 5-m cell resolution of the 

models.

We also explored three derivative hydraulic metrics 

proposed by Crowder and Diplas (2002) and one proposed 

by Nestler and others (2008) for their information content in 

defining habitat functionality. Although the biological significance of these metrics is not known, we included them to 

illustrate approaches to measuring hydraulic complexity in 

the system that might be correlated with emerging biological 

datasets. The four derivative metrics were: kinetic energy gradient, vorticity, circulation in the total reach, and total distortion. Kinetic energy gradient (KEG) was originally proposed 

as the gradient of kinetic energy (proportional to v2/2) between 

two points, requiring a predetermination of the direction of the 

gradient:

2

2

2

2

v

v

x KEG ∂

∂

= (3)

where, v = velocity magnitude. In our implementation of this 

concept we calculated kinetic energy at each cell as the velocity magnitude squared, divided by two times the gravitational 

constant, and assessed the spatial standard deviation of kinetic 

energy over a 3x3 cell (225 m2) window. Using spatial standard deviation avoids the requirement to stipulate a direction 

of gradient and provides a more general assessment of where 

kinetic energy is being expended at the reach scale.

Vorticity, ξ, in a two-dimensional flow is twice the rate 

at which a fluid element rotates about a vertical axis, and is 

expressed as:

k v u

x y

  ∂ ∂ ξ = −     ∂ ∂

(4)

where, v is depth averaged velocity in the x direction, u is 

depth averaged velocity in the y direction, and k is the unit 

vector in the vertical dimension (Crowder and Diplas, 2006). 

We calculated ξ on the cell-based model output using orthogonal streamwise and stream-normal velocities, calculated 

between four points around the computational point. The 

average then was used as a measure of vorticity at the center 

point. Measures of vorticity at this scale are intended to assess 

rotation of water parcels on the order of 15 m diameter and, 

therefore, do not attempt to resolve rotation at smaller scales 

of turbulence.

Vorticity can be scaled to broader areas using the concept 

of circulation (Crowder and Diplas, 2002) by integrating ξ, 

over an area of interest. In discrete form, circulation is:

Γ = ξ∆ ∑ A (5)

where ΔA is a unit area. In summing through an area of 

arbitrary size, positive and negative values of xi may cancel 

one another. Hence, another metric proposed to measure total 

strength of circulation in an area (absolute circulation) by 

summing the absolute value of vorticity (Crowder and Diplas, 

2002):

Γ = ξ∆ abs ∑ A (6)

The spatial density or strength of circulation in an area 

can be calculated by dividing the Гabs by the total defined area. 

The area under consideration could be the entire reach or subsections within the reach.

A metric measuring total distortion of the flow field, Sl, 

originally was calculated as total hydraulic strain for highresolution, three-dimensional velocity fields (Nestler and others, 2008). Sl

 was calculated as the sum of absolute values of 

all spatial velocity gradients and therefore included measures 

of linear deformation, rotation, and angular deformation. We 

simplified Sl for the two-dimensional case as:
Methods 31

l

u u v v S

x y x y

∂ ∂ ∂ ∂

= + + +

∂ ∂ ∂ ∂ (7)

Sl was calculated from each of the four corners of a 3x3 cell 

window relative to the center cell and the average value was 

assigned to the center cell.

Reach-Scale Integrative Metrics

Habitat metrics at the reach scale serve to illustrate 

integrated responses of habitat to discharge variation. We use 

benthic terrain mapping (BTM) to integrate habitat into landforms based on depth classification (Weiss, 2001; Lundblad 

and others, 2006; Jacobson and others, 2007). This calculation 

uses local concavity/convexity and slope of channel topography to map out landforms units that correspond roughly to bar 

complexes, the thalweg, and crossovers.

We assessed habitat patch complexity using spatial 

statistical tools available in Fragstats (McGarigal and Marks, 

1995). For selected habitat classes based on depth and velocity 

fields (table 4), we calculated patch characteristic statistics 

including:

• Total patch area (hectares);

• Patch density (number per hectare);

• Edge density (meters of edges between patches per 

hectare);

• Simpsons Diversity Index (the probability that any two 

patches selected at random in the reach will be of different types, ranging 0-1);

• Reach-scale nearest neighbor (edge-to-edge distance 

from a patch to its nearest neighbor of the same class, 

in meters); and

• Interspersion and Juxtaposition Index (an index that 

measures adjacency of patches, such that maximum 

adjacency measures 100 percent).

At the reach scale, we also calculated mean residence 

time of water in the reach as the total volume of water divided 

by discharge, and total hydraulic strain by summing hydraulic 

strain through the reach. Mean residence time gives an integrated measure of ability of the reach to retain water or passively transporting particles (for example: invertebrate drift, 

particulate organic matter, or larval fish). Water residence time 

in rivers has been linked to diverse measures of habitat function including temperature (Webb, 1996), water quality (Roos 

and Pieterse, 1994; Kelly, 1997; James and others, 2008), and 

larvae retention (Mion and others, 1998). Mean residence time 

is inversely related to mean current velocity.

Habitat measures were computed within a georeferenced 

framework. Processing steps that did not require human judgment were automated with Python scripts (Python Software 

Foundation, Hampton, New Hampshire) and Perl scripts 

(Practical Extraction and Report Language, ActiveState 

Corporation, Vancouver, British Columbia), utilizing ArcGIS processing tools (version 9.2, Environmental Systems 

Research Institute, Inc., Redlands, California). All derivative 

maps were generated with 5-m grid cells and are stored in the 

Environmental Systems Research Institute (ESRI) grid format. 

Maps for the Miami reach were projected to UTM Zone 15N, 

NAD83 datum. Maps for Little Sioux, Kenslers Bend, and 

Yankton reaches were projected to UTM Zone 14N, NAD83.

Sensitivity Analysis

Systematic errors in the underlying hydrodynamic 

model can arise from incomplete specification of the hydraulic parameters (drag coefficient and lateral eddy viscosity 

coefficient), or from unmeasured variation in channel morphology. In turn, these errors may limit the ultimate value of 

information derived from the modeling process. A sensitivity 

analysis addresses how model outputs may vary for a range of 

potential errors in inputs and serves as an assessment of model 

performance.

Sensitivity to Drag Coefficient and Lateral 

Eddy Viscosity

We addressed sensitivity of modeled habitat variables to 

the drag coefficient and the lateral eddy viscosity coefficient 

by systematically varying values for one model from -20 to 

+20 percent of the calibrated or computed value (table 5). We 

evaluated variation in the RMSE of water surface elevation 

as a standard hydraulic modeling metric. We also calculated 

all the derivative habitat variables in order to understand 

propagated sensitivity of habitat variables to variation in drag 

coefficient and lateral eddy viscosity. Sensitivity is shown as 

reach-scale sums of selected variables and as percentage of the 

calibrated value.

Water-surface elevation RMSE is relatively sensitive 

to variation of the drag coefficient. A 20 percent variation of 

drag coefficient can result in as much as 0.08 m of increase in 

RMSE. This sensitivity propagates through to relatively high 

sensitivity of shallow, slow current velocity classes (classes 

11 plus 12), because the shallow-water areas are inherently 

sensitive to variation in water-surface elevation. A 20 percent decrease in drag coefficient, for example, resulted in a 

42 percent decrease in estimated area of the shallow water 

habitat class. Residence time and total wetted area were relatively insensitive to drag coefficient variation, as was area of 

convergent zones. Wake zones and low-Froude number zones 

were moderately sensitive, changing as much as 10 percent in 

area with a 20 percent variation in drag coefficient. Benthic 

terrain map classes and reach-scale patch statistics were relatively insensitive to drag coefficient variation. Mean nearest 

neighbor and patch density varied as much as 5 percent with 

variation of drag coefficient by 20 percent. 
32 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

Most metrics were insensitive to 20 percent variation 

in lateral eddy viscosity. The largest change associated with 

20 percent variation of lateral eddy viscosity was a 5.4 percent 

change in area of slow, deep water (class 41). Although sensitive to changes in lateral eddy viscosity, this habitat class did 

not change systematically as eddy viscosity was increased 

and decreased.

Sensitivity to Changes in Channel Morphology

Some of the LMOR bed is in motion almost all the time 

(Gaeuman and Jacobson, 2006; 2007b; 2007a). Changes in 

channel morphology at the reach and bedform scale can alter 

flow resistance, hydraulic connections, and consequently, 

habitat quality and availability. We assessed how morphologic 

changes at the reach scale may affect hydrodynamic modeling 

results by constructing individual models for 2006 and 2007 

bed morphologies. Analysis of within-year and year-to-year 

morphologic variation in the modeling reaches is presented in 

a companion report (Elliott and others, 2009). Comparisons 

of channel morphology surveyed in 2006 and 2007 are shown 

in figures 11–14 and sensitivity of habitats on a year-to-year 

basis is discussed in the results section. 

Year-to-year variability in channel morphology among 

the four sites relates to the flows experienced during the time 

period and the background sediment transport dynamics. The 

average annual suspended-sediment flux varies considerably 

from upstream to downstream on the LMOR. At Yankton, 

modern annual suspended-sediment flux is 0.24 x106 Mg/y 

(millions of megagrams per year) and it increases to 7.3 at 

Sioux City, 18.6 at Omaha, and 41.9 x106 Mg/y at Kansas 

City (Jacobson and others, U.S. Geological Survey, written 

commun., 2009). Hence, much more sediment is available for 

redistribution at the downstream reaches. 

The Miami reach experienced the highest relative discharges among the four reaches, and the complete topographic 

survey used to evaluate change took place after the highest 

flows in 2007 (figs. 3, 11). The net effect of the high discharge 

was to accentuate existing topography, eroding sediment 

from the thalweg and depositing sediment on marginal bars. 

During this period Miami experienced net erosion of 48,000 

m3 (cubic meters; Elliott and others, 2009). Little Sioux and 

Kenslers Bend experienced relatively high peak flows during 

March 2006 (fig. 3) and both reaches experienced considerable 

redistribution of sediment (figs. 12, 13). In both, morphologic 

change included substantial deposition in the thalweg and 

erosion of bars. Net change at Little Sioux was 134,000 m3

of erosion and Kenslers experienced 36,000 m3 of deposition. Yankton experienced much diminished flows over the 

time period and had the least amount of morphologic change. 

Nonetheless, Yankton had a net 87,000 m3 of erosion.

Modeling Results

The hydrodynamic modeling results for the four reaches 

for 2006 and 2007 provide insight into how physical habitat 

varies by segment of the LMOR, how it varies with discharge, and how it varies between years as a result of channel 

dynamics. The following sections address habitat variations 

principally through graphical analysis of habitat functions, 

that is, plots of habitat metrics by discharge. The plots are 

arranged to show variation by reach, variation with discharge, 

and variation during 2006 and 2007. Discharge is normalized 

by median daily discharge at the nearest streamflow gaging station in order to provide a basis for comparison among 

the reaches.

Depth and Velocity Fields

Depth-velocity fields form a mosaic of physical habitat 

patches. Generally, habitat patches are relatively small and 

highly interspersed at low discharges. As discharges increase 

in the channelized river, deep, swift water in the navigation 

channel increasingly dominates the patch structure (fig. 15). 

Patches in the unchannelized river (Yankton, not shown) generally are larger and less interspersed.

Depth-velocity classes exhibit characteristic responses 

to increasing discharge (habitat functions) in the channelized 

river (fig. 16). Slow and shallow water classes (classes 11, 12, 

and 22) are maximized at lowest discharges, whereas other 

classes that are intermediate depth and velocity (for example, 

class 33) increase in area from small to intermediate discharges and then decrease. Deep and swift classes (class 44) 

increase at a high rate as discharge increases (fig. 16A). The 

relation of classes to discharge in the unchannelized river is 

different, reflecting the smaller size of the river and the wider, 

shallower channel morphology (fig. 16B). Shallow-water 

classes at Yankton are proportionately larger and, although 

variable, do not decrease monotonically with increasing 

discharge. The deepest and fastest classes are relatively poorly 

represented at Yankton over all discharges.

Shallow, slow-current-velocity habitats are substantially diminished in the LMOR relative to historical conditions (Funk and Robinson, 1974; Jacobson and Galat, 2006). 

Although the functional relation of shallow, slow-currentvelocity habitat to reproduction and survival of pallid sturgeon 

has not been documented, these habitats are thought to be 

important for rearing of larval and juvenile sturgeon and for 

supporting populations of prey fish for sturgeon and shorebirds 

(U.S. Fish and Wildlife Service, 2000; 2003). Two shallow, 

slow-water classes (11 and 12; table 4, fig. 17) characteristically decrease in area with increasing discharge in the channelized river. These habitat functions reflect drowning out 

of marginal, shallow water habitat as discharge increases 

within the banks (Jacobson and Galat, 2006; Johnson and 

others, 2006). The sum of classes 11 and 12 is equivalent to 

the shallow-water habitat unit defined in the Missouri River 
Modeling Results 33

Table 5. Sensitivity analysis results from varying drag coefficient and lateral eddy viscosity coefficient at Little Sioux reach, 2006 

topography, 768 cubic meters per second.

[m2/s, square meters per second; m, meters; h, hour; km, kilometer; ha, hectare]

Drag coefficient 

(dimensionless)

Eddy 

viscosity 

(m2/s)

Reach-scale metrics Habitat patch areas

Watersurface 

elevation root 

mean 

square 

error (m)

Rotation 

(m2/s)

Total 

strain 

(m2/s)

Residence 

time 

(h/km)

Total 

area (ha)

Habitat 

classes 

11 and 12 

(ha)

Habitat 

class 41 

(ha)

Convergent 

zones 

(ha)

Wake 

zones 

(ha)

LowFroude 

number 

zones 

(ha)

0.0054 0.40 0.12 1,180.2 3,896.3 0.26 132.4 13.1 0.3 19.8 25.9 8.1

0.0060 0.40 0.07 1,107.4 3,611.6 0.26 132.6 21.3 0.3 19.8 25.3 7.8

0.0067 0.40 0.05 1,113.5 3,653.4 0.26 132.7 22.6 0.3 19.7 24.7 8.0

0.0074 0.40 0.13 1,198.6 3,809.8 0.27 132.8 26.2 0.2 19.7 23.4 7.3

0.0080 0.40 0.08 1,174.1 3,767.0 0.27 132.8 24.5 0.2 19.8 24.0 7.2

0.0067 0.32 0.05 1,114.2 3,685.2 0.26 132.7 22.5 0.3 19.7 24.6 7.8

0.0067 0.36 0.05 1,085.7 3,535.5 0.26 132.7 22.6 0.3 19.8 24.8 8.0

0.0067 0.40 0.05 1,113.5 3,653.4 0.26 132.7 22.6 0.3 19.7 24.7 8.0

0.0067 0.44 0.05 1,092.5 3,569.5 0.26 132.7 22.7 0.3 19.8 24.5 7.8

0.0067 0.48 0.05 1,087.4 3,559.0 0.27 132.7 22.7 0.3 19.8 24.7 7.9

-19% 0% 142% 6.0% 6.6% -2.9% -0.3% -42.2% 3.6% 0.2% 5.0% 1.1%

-10% 0% 45% -0.6% -1.1% -1.5% -0.1% -5.9% -1.8% 0.4% 2.7% -3.4%

0% 0% 0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

10% 0% 165% 7.6% 4.3% 2.8% 0.1% 15.9% -17.1% -0.2% -5.2% -9.8%

20% 0% 71% 5.4% 3.1% 1.5% 0.1% 8.3% -11.7% 0.2% -2.9% -10.6%

0% -20% 0% 0.1% 0.9% 0.0% -0.0% -0.6% -5.4% -0.1% -0.5% -2.6%

0% -10% 0% -2.5% -3.2% 0.0% -0.0% 0.0% 4.5% 0.1% 0.5% -1.1%

0% 0% 0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

0% 10% 0% -1.9% -2.3% 0.0% -0.0% 0.1% 4.5% 0.1% -0.5% -2.8%

0% 20% 0% -2.3% -2.6% 0.0% -0.0% 0.2% -2.7% 0.1% 0.3% -1.4%
34 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

Table 5. Sensitivity analysis results from varying drag coefficient and lateral eddy viscosity coefficient at Little Sioux reach, 2006 

topography, 768 cubic meters per second.—Continued

[m2/s, square meters per second; m, meters; h, hour; km, kilometer; ha, hectare]

Drag coefficient 

(dimensionless)

Eddy 

viscosity 

(m2/s)

Benthic terrain map classes Reach-scale patch statistics

Depression area 

(ha)

Ridge 

area (ha)

Planarflat area 

(ha)

Planarsteep 

area (ha)

Mean 

nearest 

neighbor 

(m)

Interspersion 

juxtaposition 

index 

(percent)

Edge 

density 

(m/ha)

Patch 

density 

(number/

ha)

Simpkins 

diversity 

index (dimensionless)

0.0054 0.40 46.3 44.8 37.4 3.9 25.2 67.8 755 12.1 0.862

0.0060 0.40 46.5 44.7 37.5 3.9 26.4 68.5 757 11.3 0.859

0.0067 0.40 46.7 44.7 37.5 3.9 26.4 67.6 748 11.3 0.847

0.0074 0.40 46.8 44.6 37.5 3.9 27.7 65.1 723 12.0 0.826

0.0080 0.40 46.8 44.6 37.5 3.9 27.8 66.2 697 11.3 0.808

0.0067 0.32 46.7 44.7 37.5 3.9 26.2 67.7 746 11.3 0.847

0.0067 0.36 46.7 44.7 37.5 3.9 26.8 67.5 748 11.3 0.847

0.0067 0.40 46.7 44.7 37.5 3.9 26.4 67.6 748 11.3 0.847

0.0067 0.44 46.7 44.7 37.5 3.9 26.7 67.4 748 11.3 0.846

0.0067 0.48 46.7 44.7 37.5 3.9 26.9 67.6 745 11.3 0.846

-20% 0% -0.8% 0.2% -0.2% -0.6% -4.7% 0.2% 0.9% 7.0% 1.8%

-10% 0% -0.4% 0.1% -0.1% -0.3% -0.2% 1.4% 1.1% 0.1% 1.5%

0% 0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

10% 0% 0.3% -0.1% -0.0% 0.3% 4.9% -3.6% -3.5% 6.6% -2.5%

20% 0% 0.2% -0.1% -0.0% 0.6% 5.0% -2.1% -6.8% -0.1% -4.5%

0% -20% 0.0% -0.0% 0.0% -0.1% -0.9% 0.1% -0.3% 0.0% 0.0%

0% -10% -0.0% 0.0% -0.0% 0.0% 1.2% -0.1% -0.1% 0.0% -0.0%

0% 0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

0% 10% 0.0% -0.0% -0.0% -0.1% 0.8% -0.3% -0.1% 0.0% -0.1%

0% 20% -0.0% 0.0% 0.0% 0.0% 1.6% -0.1% -0.4% 0.0% -0.0%
Modeling Results 35

!

!

!P

!P

!P

!P

P

!P

!P

263

262

261

260

93°12' 93°13' 93°14'

!

!

!

!

!

!P

!P

!P

!P !

!P

263

262

261

260

93°12' 93°13' 93°14'

!

!

!

!

!

!

!

!

!

!P

!P

!P

!P ! 263

262

261

260

93°12' 93°13' 93°14'

39°21'45"

39°21'

39°20'15"

39°19'30"

0 500 1,000 METERS

0 3,100 6,200 FEET

2006 2007 Difference

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006Universal Transverse Mercator projectionZone 14

River miles from the U.S. Army Corps of Engineers, 1960

Elevation, in meters—

Vertical datum is NAVD 88

191.9180.2

EXPLANATION

-7 to < -3-3 to < -2-2 to < -1-1 to < -0.3-0.3 to 00 to < 0.30.3 to < 11 to < 22 to < 33 to < 44 to < 6

ErosionDeposition

Difference 2006–07, in meters; < less than River-mile marker and number—Number is 

 distance upstream from the junction with the  Mississippi River, in miles

263

Figure 11. Elevation, Miami, Missouri reach, 2006, 2007, and elevation difference between the two years.
36 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

!P

!P

!P

!P

670

671

672

673

670

671

672

673

!

!P

!P

!P

!P

670

671

672

673 41°50'15"

41°49'30"41°48'45"

0 500 1,000 METERS

0 2,600 5,200 FEET

2006 2007 Difference

-7 to < -4-4 to < -3-3 to < -2-2 to < -1-1 to < -0.3-0.3 to < 00 to < 0.30.3 to < 11 to < 22 to < 33 to < 4

ErosionDeposition

Difference 2006–07, in meters; < less than

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006Universal Transverse Mercator projectionZone 15

River miles from the U.S. Army Corps of Engineers, 1960

308.8297.2

96°04'30" 96°05'15" 96°05'15" 96°06' 96°06' 96°06'45" 96°06'45" 96°04'30" 96°05'15" 96°06' 96°06'45" 96°04'30"

P!River-mile marker and number—Number is distance upstream from the junction with the 

 Mississippi River, in miles

671

EXPLANATION

Elevation, in meters

—

Vertical datum is NAVD 88

Figure 12. Elevation, Little Sioux, Iowa reach, 2006, 2007, and elevation difference between the two years.
Modeling Results 37 Figure 13. Elevation, Kenslers Bend reach, 2006, 2007, and elevation difference between the two years.

!P

!P

!P

!P

!P

746

747

748

744

745

96°36' 96°37' 96°38'

!P

!P

!P

!P

!P

746

747

748

744

745

96°36' 96°37' 96°38'

!P

!P

!P

!P

!P

746

747

748

744

745

96°36' 96°37' 96°38'

42°33'

42°32'15"42°31'30"42°30'45"

0 500 1,000 METERS

0 3,000 6,000 FEET

2006 2007 Difference

!

-6 to < -3-3 to < -2-2 to < -1-1 to < -0.3-0.3 to < 00 to < 0.30.3 to < 11 to < 22 to < 33 to < 4

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006Universal Transverse Mercator projectionZone 14

River miles from the U.S. Army Corps of Engineers, 1960

ErosionDeposition

Difference 2006–07, in meters; < less than

308.8297.2

P!River-mile marker and number—Number is distance upstream from the junction with the 

 Mississippi River, in miles

671

EXPLANATION

Elevation, in meters—

Vertical datum is NAVD 88
38 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

#

#

#

#

808

807

805

806

42°51'45"

42°51'

#

#

#

#

808

807

805

806 42°52'

42°51'30"

42°51'

#

#

#

#

808

807

805

805

806

97°25'30" 97°24'45" 97°24' 97°23'15" 97°22'30"

42°52'

42°51'30"

42°51'

0 200 400 METERS

0 750 1,500 FEET

2006

2007

Difference

Deposition

Erosion

-5 to < -4

-4 to < -3

-3 to < -2

-2 to < -1

-1 to < -0.3

-0.3 to < 0

0 to < 0.3

0.3 to < 1

1 to < 2

2 to < 3

3 to < 4

351.9

341.1

EXPLANATION

Difference 2006–07, 

 in meters; < less than

Base from U.S. Department of Agriculture 

National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 15

River miles from the U.S. Army Corps of Engineers, 1960

River-mile marker and 

 number—Number is distance 

 upstream from the junction 

 with the Mississippi River, 

 in miles

Elevation, in meters—

 Vertical datum is NAVD 88

Figure 14. Elevation, Yankton, South Dakota reach, 2006, 2007, and elevation difference between the two years.
Modeling Results 39

Biological Opinion (U.S. Fish and Wildlife Service, 2000; 

2003). In the Kenslers Bend reach, the shallow-water habitat classes increase near 0.75 of the median discharge. This 

increase reflects water overtopping mid-channel and marginal 

bars in the Kenslers Bend reach and also overtopping bars in 

an anomalously widened reach near river mile 745.5 (fig. 7). 

Shallow-water habitat classes in the Yankton reach have 

substantially different functional relations with increasing 

discharge (fig. 17). The generally wider channel accommodates more shallow-water habitat classes over a wider range 

of discharge.

Deep, slow depth-velocity patches are exemplified by 

class 41 (table 3). The upstream reaches at Yankton and 

Kenslers Bend have negligible areas of this habitat class. Area 

of class 41 increases with increasing size of wing-dikes in 

the Little Sioux and Miami reaches (fig. 17). Wing dikes are 

responsible for deep scours that have slow, recirculating water 

at low to moderate discharges. 

Hydraulic Parameters and Spatial Variation

Hydraulic parameters provide derivative measures of 

depth and velocity fields that may be useful indicators of ecological functions. Remapping depth and velocity fields from 

the model results allows for inventory of total area of habitat 

units and for assessment of the spatial patterns.

Low Froude Number Areas

Because Froude number is calculated from the ratio of 

velocity and depth, habitat analysis by Froude number yields 

slightly different results from analysis by depth and velocity fields. Shallow and deep water can have similar Froude 

numbers if the velocities scale similarly. Froude number has 

been used by other researchers to quantify habitat suitability 

(Jowett, 1993; Quinn and Hickey, 1994; Yu and Peters, 1997; 

Reuter and others, 2003). On the Missouri River, areas of 

low Froude number seem to be indicative of habitat for Asian 

carps that inhabit areas of slack water upstream and downstream from wing dikes (Kolar and others, 2005). These areas 

also may retain passively floating particles of organic matter, 

plankton, and larval fish.

We selected areas of Froude number less than 0.05 as a 

preliminary indicator of the slack water areas. Areas of low 

Froude number are mapped on insides of bends, at tributary 

mouths, and upstream and downstream from wing dikes 

(fig. 18). In the Miami reach, the relatively large and stable 

area of low Froude-number habitat results from slow, deep 

water associated with wing dikes. The area decreases with 

increasing discharge as wing dikes are overtopped, then 

increases sharply as water flows over the banks onto low-lying 

flood plain areas (fig. 19). Overbank flows at all modeling 

reaches would be expected to have similar low Froude numbers, but the Miami reach is the only one with calibrated flows 

that achieved near bankfull stage. Areas of low Froude number 

are limited and relatively insensitive to discharge variation in 

the Little Sioux and Kenslers reaches because of short wing 

dikes. Areas of low Froude number decrease with increasing discharge in the Yankton reach because the channel lacks 

structures to retard flow.

Convergent Flow Areas

Areas of convergent flow are of special interest because 

of indications they are associated with spawning of some 

sturgeon species (Paragamian and others, 2001; Paragamian 

and others, 2002; McDonald and others, 2006; Fu and others, 

2007). In addition, three gravid female pallid sturgeon were 

tracked during 2008 to convergent-flow areas on outside bends 

where they were inferred to have spawned (A. DeLonay, oral 

commun., U.S. Geological Survey, January, 2009). To derive 

convergent-flow areas from the model results, we used cells 

with unit discharges greater than the reach-average plus one 

standard deviation. This criterion maps areas on outsides of 

bends and in areas downstream from mid-channel bars where 

flows converge (fig. 20). Total areas of convergent flow are 

stable over years and relatively insensitive to discharge variation although the location may change (fig. 21). 

Spawning habitat probably also requires the presence 

of coarse, hard substrate (Bemis and Kynard, 1997; Kynard, 

1997; Paragamian and others, 2001). Many of the convergentflow areas identified occur on the outside of revetted bends in 

the Miami, Little Sioux, and Kenslers Bend sites; such sites 

are associated with riprap consisting of coarse, hard rock. In 

some areas (Yankton, fig. 20) there are convergent-flow areas 

that exist away from revetted banks. Coarse substrate exists in 

some of these areas (Reuter and others, 2008) but has not been 

uniformly confirmed. 

Energy-Dissipation Zones: Kinetic Energy 

Variance, Vorticity, Strain, and Wakes

Energy-dissipation zones are areas of fluid shear, flow 

separation, and turbulence. Such zones have been associated 

with fish foraging strategies (Orth and White, 1999) and fish 

migration (Nestler and others, 2008). Recent telemetry studies 

in the LMOR confirm that migrating adult pallid and shovelnose sturgeon are found disproportionately on the edges of the 

channel in areas of high energy dissipation (Jacobson and others, 2007; Reuter and others, U.S. Geological Survey, written 

commun., 2009). 

Maps of kinetic energy dissipation spatial standard 

deviation, vorticity, total strain, and velocity gradient show 

similar patterns (figs. 22, 23). Similarity indicates that at the 

resolution of our models, deformation of the flow field can be 

captured in a variety of ways. Although, the simplest indicator is the slope of the velocity map (velocity gradient), the 

other metrics serve to illustrate related hydrodynamic phenomena that may be better correlated with some ecological 

functions. As a simple indicator, we identified areas of high 
40 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

264 263

262

261

260

93°15' 93°14' 93°13' 93°12' 93°11'

39°22'

39°21'

39°20'

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 15

0 400 800 METERS

0 2,000 4,000 FEET

Discharge 550 cubic meters per second

EXPLANATION

Miami, Missouri, 2006

River-mile marker and number—Number is distance 

 upstream from the junction with the Mississippi River, 

 in miles

264

264 263

262

261

260

93°15' 93°14' 93°13' 93°12' 93°11'

39°22'

39°21'

39°20'

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 15

River miles from U.S. Army Corps 

of Engineers, 1960

0 400 800 METERS

0 2,000 4,000 FEET

11–Shallow, very slow

12–Shallow slow

13–Shallow, medium

14–Shallow, fast

21–Medium, very slow

22–Medium, slow

23–Medium, medium

24–Medium, fast

25–Medium, very fast

31–Deep, very slow

32–Deep, slow

33–Deep, medium

34–Deep, fast

35–Deep, very fast

41–Very deep, very slow

42–Very deep, slow

43–Very deep, medium

44–Very deep, fast

45–Very deep, very fast

11–Shallow, very slow

12–Shallow slow

13–Shallow, medium

14–Shallow, fast

21–Medium, very slow

22–Medium, slow

23–Medium, medium

24–Medium, fast

25–Medium, very fast

31–Deep, very slow

32–Deep, slow

33–Deep, medium

34–Deep, fast

35–Deep, very fast

41–Very deep, very slow

42–Very deep, slow

43–Very deep, medium

44–Very deep, fast

45–Very deep, very fast

Discharge 3,150 cubic meters per second

EXPLANATION

Miami, Missouri, 2006

River-mile marker and number—Number is distance 

 upstream from the junction with the Mississippi River, 

 in miles

264

Figure 15. Depth-velocity field patch structure at the Miami, Missouri reach. 
Modeling Results 41

0 0.5 1.0 1.5 2.0 2.5

0

20

40

60

80

100

120

140

160

Class 11

Class 12

Class 22

Class 23

Class 33

Class 34

Class 44

Class 45

DISCHARGE/MEDIAN DISCHARGE

0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

HABITAT 

AREA, IN HECTARES

0

20

40

60

80

100

Class 11

Class 12

Class 22

Class 23

Class 33

Class 34

Class 44

Class 45

Miami, Missouri

Yankton, South Dakota

Note: Vertical scale differs

between the two graphs.

A

B

Figure 16. Depth-velocity patch area as a function of discharge at Miami, Missouri and Yankton, South 

Dakota.
42 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

Miami, Missouri

0 0.5 1.0 1.5 2.0 2.5

HABITAT AREA, IN HECTARES

0

10

20

30

40

50

Little Sioux, Iowa

0

10

20

30

40

50

Kenslers Bend, Nebraska

0

10

20

30

40

50

Yankton, South Dakota

DISCHARGE/MEDIAN DISCHARGE

0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

0

20

40

60

80

100

Sum Classes 11 and 12, 2006

Sum Classes 11 and 12, 2007

Class 11, 2006

Class 11, 2007

Class 12, 2006

Class 12, 2007

Class 41, 2006

Class 41, 2007

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Figure 17. Depth-velocity habitat patch areas as a function of discharge at four modeling reaches. 
Modeling Results 43

93°15' 93°14' 93°13' 93°12' 93°11'

39°22'

39°21'

39°20'

0 400 800 METERS

0 2,000 4,000 FEET

EXPLANATION

96°08' 96°07' 96°06' 96°05' 96°04' 96°03'

41°50'

41°49'

0 400 800 METERS

0 2,000 4,000 FEET

Discharge 1,400 cubic meters per second

Little Sioux, Iowa, 2006

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 15

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 15

Miami, Missouri, 2006

 Froude number < 0.05

EXPLANATION

Discharge 800 cubic meters per second

 Froude number < 0.05

Figure 18. Areas with Froude numbers less than 0.05 at Miami, Missouri and Little Sioux, Iowa reaches. 
44 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

Miami, Missouri

0 0.5 1.0 1.5 2.0 2.5

0

10

20

30

40

50

Little Sioux, Iowa

0

10

20

30

40

50

Kenslers Bend, Nebraska

0

10

20

30

40

50

Yankton, South Dakota

DISCHARGE/MEDIAN DISCHARGE

0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

0

10

20

30

40

50

Wake class, 2006

Froude number < 0.05, 2006

Wake class, 2007

Froude number < 0.05, 2007

HABITAT AREA, IN HECTARES

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Figure 19. Wake and low Froude number patch areas as a function of discharge at four modeling 

reaches. 
Modeling Results 45

velocity slope (hereafter referred to as wakes) as those cells 

with greater than a 2 percent change in velocity over a meter, 

or 10 percent change over a 5-m cell. These wake areas occur 

preferentially along the margins of banks, bars, and wing dikes 

(figs. 23, 24). 

Wakes comprise greater area in the downstream modeled 

reaches than at Yankton, indicating the effect of river size, and 

perhaps engineering structures, on increasing flow deformation (fig. 19). There are somewhat higher areas of wake habitat 

at low flows in 2006 compared to 2007 at the Little Sioux and 

Kenslers Bend reaches, probably as a result of high flows in 

early spring 2007 that decreased sandbar-margin areas. Wakes 

area generally increases with increasing discharge (fig. 19).

Reach-Scale Integrative Measures

Several measures of hydraulic habitat can be aggregated 

at the reach scale to indicate broad trends along the LMOR. 

These include benthic terrain mapping classes (BTM), spatial 

statistics that assess relations among habitat patches, residence 

time of water in the reach, and a measure of total fluid strain.

Benthic Terrain Mapping Classes

Benthic terrain mapping (BTM) classes are defined using 

measures of local topography derived from depth grids. The 

approach classifies cells into ridges, depressions, planar areas 

with low slope, and planar areas with high slope (Weiss, 2001; 

Lundblad and others, 2006; Jacobson and others, 2007). The 

units derived depend on scaling parameters that determine the 

area to be evaluated for local concavity/convexity, the elevation threshold for determining that a cell is above or below 

neighboring cells, and a slope steepness threshold (Jacobson 

and others, 2007). We used one set of parameter values for 

all four modeling reaches. The parameters were selected to 

resolve landforms of consistent size along the river rather 

than scaling the landforms relative to the size of the channel, 

although either approach could be justified. The parameter 

values generally resolve sandbar complexes (ridge areas), the 

thalweg (depression areas), steep banks (planar and steep), and 

flat areas like crossovers and sand-bar margins (planar and not 

steep). Examples are shown in figure 25. 

BTM depression and ridge areas are relatively insensitive 

to discharge variation over the range of discharges evaluated 

and did not change much from 2006 to 2007 (fig. 26). Because 

of this, BTM classes may be useful in evaluating relevant 

landforms without the complication of varying discharge. 

Because BTM classes resolve landforms rather than patches, 

they serve to delineate habitat complexes that may host a suite 

of ecological functions. For example, the ridge BTM class 

generally maps shallow sandbar complexes marginal to the 

navigation channel. This class may be an effective descriptor 

of the suite of habitats intended to be restored under the SWH 

program on the LMOR. 

Habitat Patch Statistics

Patch density (number of depth-velocity patches per 

100 hectares) is highest in the Little Sioux and Kenslers Bend 

reaches, indicating greater complexity in those two reaches 

relative to Yankton and Miami (fig. 27). Conversely, mean 

patch size (not shown) is larger in Yankton and Miami than 

the other two reaches. Edge density tends to co-vary with 

patch density, as increased patchiness generally increases 

edges between patches. Patch and edge density at Little Sioux 

decreased from 2006 to 2007. High flows during March 2007, 

arising from the Vermillion, James, and Big Sioux tributaries 

to the Missouri River, had a peak discharge approximately 

1.4 times the median daily discharge (fig. 3). Substantial 

sediment transport and geomorphic change during this period 

(Elliott and others, 2009) resulted in simplification of habitat 

patches as shown by the decrease in patch and edge density. At 

Kenslers Bend, which was subjected to the same flood event 

but at a lower relative magnitude, density and edge density 

increased during the same time period. The Yankton reach 

was not affected by these high discharges, and patch and edge 

density remained relatively constant. The Miami reach was 

affected by even greater relative discharge during May, 2007 

(5.6 times median discharge), but topography and patch structure were not simplified or changes did not persist (fig. 11).

Diversity of habitat patches can be quantified by the 

Simpsons Diversity Index (SDI, fig. 28) which represents 

the probability that any two patches selected at random from 

the reach will be different (McGarigal and Marks, 1995); 

high values of the index indicate a more diverse reach. The 

four reaches have four different functional relations between 

discharge and SDI. In the Miami reach, SDI increases with 

increasing discharge to approximately 0.9 times the median 

discharge, after which it falls. Simplification of patch structure with increasing discharge also is evident from the maps 

in figure 15. There is little difference between SDI in 2006 

and 2007 at Miami. The increase in diversity as discharge 

increases to approximately median discharge coincides with 

flows that emerge from the thalweg and flow over sandbars. As 

discharges increase past the median, patch structure is increasingly dominated by depth-velocity classes in the main channel. 

In the Little Sioux reach SDI also increased from 

low flow up to approximately median discharge and then 

decreased; however, the 2007 SDI was uniformly lower 

(indicative of patch simplification) and it was less sensitive to 

discharge variation than in 2006 (figs. 28 and 29). 

At Kenslers Bend, the SDI relation to discharge was 

similar in 2006 and 2007. In each year, SDI decreased as 

discharge increased from 0.25 to 0.75 times median discharge 

and then increased up to the maximum modeled discharge 

at approximately 1.1 times median discharge. The relatively 

high SDI at low discharge probably indicates high habitat 

complexity within the low-flow channel in this minimally 

engineered reach. Diversity decreases as the thalweg fills, 

then increases as flows interact with channel-marginal bars. 
46 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

97°26' 97°25' 97°24' 97°23'

42°52'

42°51'

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 14

0 400 800 METERS

0 2,000 4,000 FEET

93°15' 93°14' 93°13' 93°12' 93°11'

39°22'

39°21'

39°20'

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 15

0 400 800 METERS

0 2,000 4,000 FEET

Convergent flow area

Discharge 750 cubic meters per second

EXPLANATION Yankton, South Dakota

Convergent flow area

Discharge 1,450 cubic meters per second

EXPLANATION Miami, Missouri

Figure 20. Examples of convergent flow classes at Yankton, South Dakota and Miami, Missouri.
Modeling Results 47

Miami, Missouri

0 0.5 1.0 1.5 2.0 2.5

0

10

20

30

40

50

Little Sioux, Iowa

0

10

20

30

40

50

Kenslers Bend, Nebraska

0

10

20

30

40

50

Yankton, South Dakota

DISCHARGE/MEDIAN DISCHARGE

0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

0

10

20

30

40

50

Convergent flow class, 2006

Convergent flow class, 2007

HABITAT AREA, IN HECTARES

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Figure 21. Convergent flow class areas as a function of discharge at four modeling reaches. 
48 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

96°06'45" 96°06'30" 96°06'15"

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 14

0 100 200 METERS

0 500 1,000 FEET

96°06'45" 96°06'30" 96°06'15"

41°50'

41°49'45"

41°49'30"

41°50'

41°49'45"

41°49'30"

96°06'45" 96°06'30" 96°06'15"

41°50'

41°49'45"

41°49'30"

41°50'

41°49'45"

41°49'30"

96°06'45" 96°06'30" 96°06'15"

EXPLANATION

8 percent

0

EXPLANATION

0.25

0.0

Current Velocity Depth

Froude Number Velocity Slope

EXPLANATION EXPLANATION

1.35 meters per

 second

13 meters

0.25 8 percent

0

0 0

0

Figure 22. Examples of derivative hydraulic metrics at Little Sioux, Iowa reach: velocity, depth, Froude number and velocity slope.
Modeling Results 49

Figure 23. Examples of derivative hydraulic metrics at Little Sioux, Iowa reach: standard deviation of kinetic energy, vorticity, 

strain index, and wakes.

96°06'45" 96°06'30" 96°06'15"

96°06'45" 96°06'30" 96°06'15"

41°50'

41°49'45"

41°49'30"

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 14

0 100 200 METERS

0 500 1,000 FEET

96°06'45" 96°06'30" 96°06'15"

96°06'45" 96°06'30" 96°06'15"

41°50'

41°49'45"

41°49'30"

41°50'

41°49'45"

41°49'30"

41°50'

41°49'45"

41°49'30"

EXPLANATION

EXPLANATION

+2.2

-2.6

STD Kinetic Energy Vorticity

Strain Index Wake Unit

EXPLANATION

0.22 square meter

 per second

 squared

+2.2 per second

8.6 per second EXPLANATION

Wake unit

0 -2.6

0
50 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

96°40' 96°39' 96°38' 96°37' 96°36' 96°35' 96°34'

42°33'

42°32'

42°31'

42°30'

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 14

0 400 800 METERS

0 2,000 4,000 FEET

96°08' 96°07' 96°06' 96°05' 96°04' 96°03'

41°50'

41°49'

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 14

0 400 800 METERS

0 2,000 4,000 FEET

Wake habitat area

Discharge 350 cubic meters per second

Kenslers Bend, Nebraska EXPLANATION

Wake habitat area

Discharge 350 cubic meters per second

Little Sioux, Iowa EXPLANATION

Figure 24. Examples of wake habitat unit distributions at Kenslers Bend, Nebraska and Little Sioux, Iowa reaches.
Modeling Results 51

96°38' 96°37'45" 96°37'30"

42°31'30"

42°31'15"

42°31'

42°30'45"

Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006

Universal Transverse Mercator projection

Zone 15

0 100 200METERS

0 500 1,000 FEET

96°38' 96°37'45" 96°37'30"

42°31'30"

42°31'15"

42°31'

42°30'45"

96°38' 96°37'45" 96°37'30"

42°31'30"

42°31'15"

42°31'

96°38' 96°37'45" 96°37'30"

42°31'30"

42°31'15"

42°31'

 

250 cubic meters per second 450 cubic meters per second

650 cubic meters per second 850 cubic meters per second

Planar-flat landform

Planar-steep landform

Ridge landform

Depression landform

EXPLANATION

Figure 25. Examples of benthic terrain map (BTM) units at Kenslers Bend, Nebraska.
52 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

Miami, Missouri

0 0.5 1.0 1.5 2.0 2.5

0

20

40

60

80

100

120

140

Little Sioux, Iowa

0

20

40

60

80

Kenslers Bend, Nebraska

0

20

40

60

80

Yankton, South Dakota

DISCHARGE/MEDIAN DISCHARGE

0 0.25 0.50 0.75 1.00 1.25 1.50

0

20

40

60

80

Depression BTM class, 2007

Ridge BTM class, 2007

Depression BTM class, 2006

Ridge BTM class, 2007

HABITAT AREA, IN HECTARES

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Figure 26. Areas of benthic terrain map (BTM) classes as a function of discharge for the four 

modeling reaches. 
Modeling Results 53

Miami, Missouri

0 0.5 1.0 1.5 2.0 2.5

0

500

1,000

1,500

2,000

Little Sioux, Iowa

0

500

1,000

1,500

2,000

Kenslers Bend, Nebraska

0

500

1,000

1,500

2,000

Yankton, South Dakota

DISCHARGE/MEDIAN DISCHARGE

0 0.25 0.50 0.75 1.00 1.25 1.50

0

200

400

600

800

1,000

PATCH DENSITY, NUMBER PER 100 HECTARES

EDGE DENSITY, METERS PER HECTARE

Patch density, 2007

Edge density, 2007

Patch density, 2006

Edge density, 2006

Potential range of 

pulsed flow modifications

Potential range of 

pulsed flow modifications

Potential range of 

pulsed flow modifications

Potential range of 

pulsed flow modifications

Figure 27. Patch and edge density values as functions of discharge for the four modeling reaches. 
54 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

At Yankton, SDI is relatively high and relatively insensitive to 

discharge variation.

 The interspersion and juxtaposition index (IJI) measures 

how interspersed different patch classes are relative to a maximum of 100 percent. In highly interspersed reaches, dissimilar 

patches tend to occur close together. The degree of interspersion would affect how easily organisms, energy, and materials 

can be transferred among similar or dissimilar habitat patches. 

IJI is relatively insensitive to discharge or annual variability 

at all sites. Decreases in IJI from 2006 to 2007, at Little Sioux 

and Kenslers Bend, at discharges slightly below the median 

probably result from patch simplification from the geomorphically effective flood of March 2007.

The mean nearest neighbor metric (MNN) measures 

mean distance between edges of like patches, and indicates 

distance an organism may need to travel to find another patch 

of suitable habitat. IJI and MNN are insensitive to discharge 

in the four modeling reaches (fig. 30). Decreases in MNN at 

Kenslers Bend and Yankton at low discharges probably result 

from expansion of shallow, low-velocity patches (fig. 17, 30). 

Residence Time

Mean residence time of water in a reach is indicative of 

the ability of a reach to retain water and relates inversely to 

mean reach velocity. Longer residence time is associated with 

higher probability of retaining passively transporting, buoyant particles (organic matter, drifting invertebrates, and larval 

fish), and greater opportunity for biogeochemical interactions. 

As such, mean residence time of water may provide a useful 

integrative indicator of some ecological functions and how 

those functions are altered by restoration activities. 

All four modeling reaches have mean residence times 

(measured as hours per kilometer of reach length) that 

decrease steeply with increasing discharge, then level off after 

reaching median discharge (fig. 31). The three downstream 

reaches have very similar residence time functions. The 

Yankton reach stands out with substantially longer residence 

times for given discharges and with a substantially steeper rate 

of decline as discharge increases from 0.25 to about 0.6 times 

median. Longer mean residence time at Yankton results from 

the wide, shallow channel and the effects of side channels on 

slowing and retaining water. The steeply decreasing residence 

time with increasing discharge indicates that pulsed flow 

modifications could substantially alter retention of organic 

matter, invertebrate drift, or larval fish in this reach. 

Hydraulic Strain

An index of hydraulic strain was calculated by summing 

unit two-dimensional strain associated with each computational cell in the models and dividing by reach length to produce a total strain per unit length (TSUL) with units of m3

/s/

m2, or m/s. As hydraulic strain is coupled with turbulence, and 

turbulence may strongly influence fish migration pathways 

(Nestler and others, 2008), energetics (Enders and others, 

2003; Liao, 2004; Standen and others, 2004; Liao, 2006), 

spawning site selection (Buckley and Kynard, 1981; 1985; 

Paragamian and others, 2001; Paragamian and others, 2002), 

and feeding strategies (Lupandin and Pavlov, 1996), differences in total strain among river reaches may be important to 

reproduction and survival of the pallid sturgeon. At present, 

however, ecological mechanisms are unknown.

TSUL in the Miami reach is substantially higher for all 

discharges than that of Little Sioux and Kenslers Bend reaches 

(fig. 32). Greater TSUL strain is probably associated with the 

longer wing dikes that exist in the Miami reach. TSUL varies 

considerably with discharge at Yankton and attains values substantially higher than Little Sioux or Kenslers Bend. Higher 

values of TSUL at Yankton are probably attributed to greater 

flow resistance in the wide, shallow channel, and to flow 

through side channels. TSUL was also substantially lower in 

2007 than 2006 at Yankton. We speculate that this difference 

results from high sensitivity of TSUL to how flow is divided 

between the main channel and the side channels. Small topographic changes that limit flow down a side channel would 

substantially diminish total hydraulic strain in the reach. 

Sensitivity of Habitat Functions to Pulsed 

Flow Modifications

Pulsed flow modifications during the spring have been 

hypothesized to affect reproduction and survival of pallid 

sturgeon by (1) providing an environmental/behavioral cue, 

(2) altering habitat through sediment transport, and (3) altering habitat availability (U.S. Fish and Wildlife Service, 2000; 

2003; Jacobson and Galat, 2008). We assessed the potential 

for proposed spring flow modifications to alter habitat availability by looking at changes in habitat functions over a range 

discharges that might be affected by flow modifications. The 

ranges of flows modeled also were constrained by confidence 

in extrapolating flows beyond calibrated limits. We defined 

a reference for potential flow modifications by calculating 

median flow for the nearest streamgaging station for the period 

April 1–May 30 under a modeled flow scenario with no spring 

rise (the water control plan prior to 2005) and adding/subtracting one half of a potential pulse of 15,000 ft3

/s (414 m3/s). 

This range of discharges serves as a reference for the potential 

effects of pulsed flow modifications on habitat availability 

(figs. 17, 19, 21, 26–28, 30–32). At the three upstream reaches, 

this interval extends somewhat beyond the upper limit of modeled discharges that was chosen to encompass 25–90 percent 

flow exceedances based on the entire year.

Sensitivity of suspected spawning habitat to flow modifications is of special interest because pulsed flows have been 

hypothesized to improve spawning success of sturgeon on the 

LMOR (U.S. Fish and Wildlife Service, 2000; 2003; Quist 

and others, 2004; Bergman and others, 2008). With the present 

level of understanding (2008) provided by scientific literature 

on other species and emerging telemetry data from the LMOR, 
Modeling Results 55

Miami, Missouri

0 0.5 1.0 1.5 2.0 2.5

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

Little Sioux, Iowa

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

Kenslers Bend, Nebraska

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

Yankton, South Dakota

DISCHARGE/MEDIAN DISCHARGE

0 0.25 0.50 0.75 1.00 1.25 1.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

SIMPSON’S DIVERSITY INDEX

Simpkins Diversity Index, 2007

Simpkins Diversity Index, 2006

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Figure 28. Simpson’s diversity index (SDI) for habitat patches as functions of discharge for the four 

modeling reaches. 
56 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River Figure 29. Depth-velocity field patch maps at Little Sioux, Iowa for discharges of 800 cubic meters per second, 2006 and 2007. 670 671 672 673 96°05' 96°06' 41°50' 41°49' 670 671 672 673 96°05' 96°06' 41°50' 41°49' Base from U.S. Department of Agriculture National Agriculture Imagery Program, digital data, 2006 Universal Transverse Mercator projection Zone 14 River miles from U.S. Army Corps of Engineers, 1960 EXPLANATION 11–Shallow, very slow 12–Shallow slow 13–Shallow, medium 14–Shallow, fast 21–Medium, very slow 22–Medium, slow 23–Medium, medium 24–Medium, fast 25–Medium, very fast 31–Deep, very slow 32–Deep, slow 33–Deep, medium 34–Deep, fast 35–Deep, very fast 41–Very deep, very slow 42–Very deep, slow 43–Very deep, medium 44–Very deep, fast 45–Very deep, very fast

0 400 800 METERS

0 2,000 4,000 FEET

Little Sioux, Iowa, 2006

Discharge 800 cubic meters per second

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

672

Little Sioux, Iowa, 2007

Discharge 800 cubic meters per second

Class
Modeling Results 57

Miami, Missouri

0 0.5 1.0 1.5 2.0 2.5

0

20

40

60

80

Little Sioux, Iowa

0

20

40

60

80

Kenslers Bend, Nebraska

0

20

40

60

80

Yankton, South Dakota

DISCHARGE/MEDIAN DISCHARGE

0 0.25 0.50 0.75 1.00 1.25 1.50

0

20

40

60

80

Interspersion and juxtaposition index, 2007

Mean nearest neighbor, 2007

Interspersion and juxtaposition index, 2006

Mean nearest neighbor, 2007

INTERSPERSION AND JUXTAPOSITION INDEX, PERCENT

MEAN NEAREST NEIGHBOR, IN 

METERS

Potential range

of pulsed flow

modifications

Potential range

of pulsed flow

modifications

Potential range of pulsed flow

modifications

 

Potential range of pulsed flow

modifications

Figure 30. Interspersion and juxtaposition index (IJI) and mean nearest neighbor (MNN) between like 

patches as functions of discharge for the four modeling reaches. 
58 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

convergent flow zones over coarse substrate appear to have 

the most potential for spawning habitat for LMOR sturgeon 

(Paragamian and Kruse, 2001; Paragamian and others, 2001; 

Paragamian and others, 2002; McDonald and others, 2006; 

Fu and others, 2007). Availability of convergent zones is not 

sensitive to discharges modeled in this study in the four modeling reaches (fig. 21); however, this result does not address 

the quality of substrate in convergent zones. Many studies 

on sturgeon reproductive ecology support the understanding 

that sturgeon spawn over clean, coarse, hard substrate like 

gravel, cobble, and bedrock (June, 1977; Parsley and others, 

1993; Kynard, 1997; Fox and others, 2000; Paragamian and 

others, 2001; Hatin and others, 2002; Kolman and Aarkua, 

2002; Manny and Kennedy, 2002). The substantial quantity 

of sediment transport that occurred in the Kenslers Bend and 

Little Sioux reaches between 2006 and 2007 surveys indicates 

that modest flows have the potential to alter substrate surface 

characteristics in some reaches (figs. 12 and 13). Sediment 

transport and habitat dynamics at the modeling reaches are 

addressed in more detail in Elliott and others (2009).

Low-flow discharge modifications also have been proposed as a mechanism to provide rearing habitat for larval 

and juvenile sturgeon, and foraging habitat for other native 

species including sturgeon prey fishes (U.S. Fish and Wildlife 

Service, 2000; 2003; Quist and others, 2004; Bergman and 

others, 2008). These habitat functions have been ascribed to 

shallow, slow-current velocity habitats (shallow-water habitat, 

SWH), which are assessed in this report as classes 11 and 12. 

Shallow-water habitat availability decreases with increasing 

discharge at the three downstream reaches to about 0.8 time 

median discharge. This is consistent with previous work has 

concluded that much of the increase in shallow-water habitat 

in the channelized section of the LMOR occurs at discharges 

that are too low to support other river uses, especially navigation (Jacobson and Galat, 2006; Jacobson and Galat, 2008). 

Areas of these habitat classes varied little over the pulsed-flow 

range and would not be affected by proposed pulsed flow 

modifications. Shallow-water habitat classes at Yankton are 

more extensive than the downstream reaches, and decrease at a 

slower rate with increasing discharge through the pulsed flow 

range, compared to the other reaches.

Deep, slow-current velocity habitat like those delineated 

by Froude numbers less than 0.05, or depth-velocity class 41, 

decrease by less than 20 percent with increasing discharge 

through the pulsed flow range at the Miami and Yankton 

reaches (figs. 17, 19). These habitat classes increase no more 

than 10 percent through the pulsed flow range at Little Sioux 

and Kenslers Bend. Low sensitivity of these habitat classes 

indicates that water retention and depth-cover functions of 

these habitats would not be highly affected by pulsed flow 

modifications. 

Landform units delineated by BTM classes also are 

relatively insensitive to discharge variation, making them 

potentially useful as discharge-independent indices of habitat 

DISCHARGE/MEDIAN DISCHARGE

0 0.5 1.0 1.5 2.0 2.5

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Miami, Missouri, 2006

Miami, Missouri, 2007

Little Sioux, Iowa, 2006

Little Sioux, Iowa, 2007

Kenslers Bend, Nebraska, 2006

Kenslers Bend, Nebraska, 2007

Yankton, South Dakota, 2006

Yankton, South Dakota, 2007

REACH MEAN RESIDENCE TIME,

HOUR PER KILOMETER

Figure 31. Mean residence time of water in hours per kilometer of reach length, as functions of discharge 

for the four modeling reaches.
Modeling Results 59

0 0.5 1.0 1.5 2.0 2.5

0

0.5

1.0

1.5

2.0

2006

2007

DISCHARGE/MEDIAN DISCHARGE

0 0.25 0.50 0.75 1.00 1.25 1.50

0

0.5

1.0

1.5

2.0

2006

2007

0

0.5

1.0

1.5

2.0

2006

2007

0

0.5

1.0

1.5

2.0

2006

2007

TOTAL STRAIN PER UNIT LENGTH, IN 

METERS PER SECOND

Potential range of

pulsed flow

modifications

Potential range of

pulsed flow

modifications

Potential range of

pulsed flow

modifications

Potential range of

pulsed flow

modifications

Miami, Missouri

Little Sioux, Iowa

Kenslers Bend, Nebraska

Yankton, South Dakota

Figure 32. Total strain per unit channel length (TSUL) for the modeling reaches, 2006 and 2007. 
60 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

complexity (fig. 26). BTM class area increases no more than 

10 percent with increasing discharge through the pulsed-flow 

range at Miami, Little Sioux, and Kenslers Bend as the thalweg fills with water. Area of the ridge class, however, keeps 

pace by adding small quantities of area on the channel margins 

so total ridge area changes very little. For the Yankton reach, 

depression and ridge classes increase in area 3 to 8 percent 

through the pulsed-flow range. 

Measures of patch structure like patch density and edge 

density, interspersion and juxtaposition index, and mean nearest neighbor vary little with discharge over the pulsed-flow 

range (figs. 27, 30). In contrast, SDI based on habitat patches 

varies substantially with discharge in the three downstream 

reaches over this range (fig. 28). Patch diversity decreases with 

increasing discharge at Miami as patch structure is increasingly dominated by the fast, deep units in the thalweg. The 

patch diversity function at Little Sioux was similar to Miami 

in 2006, but topographic changes in March 2007 resulted in 

overall decrease of SDI and a change in functional relation 

with SDI increasing from 1 to 1.25 times median discharge. 

We interpret this as a result of erosion and simplification of the 

main channel accompanied by deposition and corresponding 

increased numbers of patches at higher elevations. The SDI 

functional relation at Kenslers Bend shows a similar effect 

with SDI increasing as flow emerges from the thalweg and 

creates more diversity as it inundates marginal bars. SDI varies little over the assessment range at Yankton. These results 

show that habitat diversity is a sensitive function of discharge 

in the assessment range, although variation among sites precludes generalization for the river as a whole.

 Although the mean residence time of water in the 

Yankton reach is substantially higher than the downstream 

reaches at all discharge exceedances, residence times at all 

four reaches fall at the same rate with increasing discharge, 

for discharges that would be affected by pulsed flow modifications. These relations indicate that pulsed flow modifications 

would be associated with decreases in mean residence time of 

0.05 h/km or less.

Model Information Content and 

Ecological Understanding

Hydrodynamic modeling was used in this study as a tool 

to explore relations among discharge, channel morphology, 

and ecological functions with emphasis on habitat requirements for reproduction and survival of the endangered pallid 

sturgeon. As is common in many hydroecological studies, 

specific functional relations between physical processes and 

ecological functions (reproduction, larval drift, for example) 

are lacking (Jacobson and Galat, 2008). Although we cannot 

yet link specific biological outcomes to physical processes 

or management actions, modeling of physical processes 

provides useful information to constrain understanding of 

LMOR ecology. 

Model Capabilities

The information content of hydrodynamic models may 

be limited by implicit assumptions in the coding of hydraulic 

equations. In this study, the hydrodynamic models are limited to depth-averaged, two-dimensional views of what are 

actually complex three-dimensional flow fields. In addition, 

model information content can be limited by the resolution of 

the numerical grid, errors or omission of input data, or errors 

arising from factors not addressed in the models, such as 

deformation of the bed because of sediment transport, watertemperature effects, and vegetation effects. Of these factors, 

dynamic changes to bed topography on seasonal to multiyear 

timeframes are probably the most significant in altering model 

predictions. Sensitivity analyses indicate that model parameter 

estimation errors do not propagate to substantial variation 

in many habitat measures (table 5). Channel morphological 

changes from 2006 to 2007 produced changes in some habitat 

measures by 10 percent or more. Changes from 2006 to 2007 

were greatest for patch and edge density at Little Sioux and 

Kenslers Bend (fig. 27). Change in SDI was substantial at 

Little Sioux from 2006 to 2007 (fig. 28). 

Morphological changes at Little Sioux and Kenslers Bend 

substantially altered locations of specific habitats as portions 

of the thalweg were filled in and marginal bars were scoured. 

On a reach-average basis, however, deposition was largely 

balanced by erosion, thereby minimizing reach-average habitat 

variation measured in terms of total area of habitat classes 

(figs. 11–14, see Elliott and others, 2009). Movement of habitat patches from year to year may not be a hardship for fish 

species if they can relocate to functionally equivalent habitats 

in different locations. Alternatively, change in habitats related 

to shape, size, or patchiness may be sensitive to such geomorphic change and may be ecologically important.

Although two-dimensional hydrodynamic models provide 

greater resolution and realism than one-dimensional models 

(Nelson and others, 2003), they do not provide the full threedimensional flow field experienced by fish. Two-dimensional 

models, however, may be a cost-effective compromise in 

modeling complexity in cases where biological responses are 

largely uncertain or immeasurable. In such cases, two-dimensional results may provide indicators of underlying threedimensional processes at a resolution commensurate with prevailing biological knowledge. For example, two-dimensional 

measures of velocity gradient and flow deformation do not 

capture the full three-dimensional, multiscale turbulence that 

occurs in shear zones; however, the low-resolution indicators 

may be perfectly adequate to address questions about habitat 

requirements at the resolution of biological understanding.

Sensitivity analysis at the scale of habitat units provided 

insight into how model requirements may vary with the 

specific need for biological understanding. For example, areas 

of the shallowest habitat classes (classes 11 and 12) were relatively sensitive to selection of drag coefficient, although other 

classes were not. If biological understanding supports a critical 

role of the shallowest habitat classes in ecosystem function or 
Summary and Conclusions 61

pallid sturgeon reproduction and survival, then hydrodynamic 

modeling should put greater emphasis on improving modeling 

of these classes. Conversely, if ecological functions are found 

to correlate better with less-sensitive, integrative measures (for 

example, BTM units or areas of convergent flow), then lower 

standards for hydrodynamic habitat modeling could apply.

Models as Exploration Tools

In addition to evaluating habitat sensitivity to pulsed flow 

modifications, the hydrodynamic models presented in this 

report are intended to provide a framework for exploration 

of physical, chemical, and biological understanding on the 

LMOR. The models present a systematic approach to understanding how physical processes vary along the river and, 

therefore, how chemical and biological responses may also 

vary. For example, one of the most visible results presented in 

this report is the much greater mean residence of water in the 

unchannelized Yankton reach compared to the downstream 

reaches. This observation may be a basis for hypothesizing 

how short residence times constrain nutrient processing or 

larval retention in the channelized river. Similarly, identification of convergent flow areas as probable spawning locations 

at the reach scale provides guidance for sampling designs for 

spawning adults, eggs, and larvae. 

Modeling also can be used to evaluate information 

content of ecosystem indicators. For example, assessments of 

habitat restoration projects that involve reconfiguration of the 

channel require measures that account for varying discharge 

(Jacobson and others, 2004). This can be accomplished by carrying out assessments at all sites at common flow exceedance 

or by using ecological metrics that are insensitive to discharge. 

The BTM units presented in this report have potential as 

robust indicators of ecological structure. BTM classes are 

calculated from depth alone, thereby eliminating the need to 

measure or model velocity. Hydrodynamic modeling indicates 

that BTM classes are relatively insensitive to discharge variation (fig. 26). This property and the fact that BTM units can be 

computed using only depth data, suggest that BTM units could 

be an effective indicator of reach-scale ecosystem structure.

Reference Conditions

Hydrodynamic modeling also can be useful for systematic, quantitative comparison between altered systems and 

reference conditions. Reference conditions provide a standard 

for evaluating the performance of an alteration compared to 

a benchmark state. The benchmark state, however, can be 

defined in a variety of ways, including the minimally disturbed 

condition, the documented historical state, the best attainable 

condition, or the least disturbed condition (Stoddard and others, 2006). The present condition might also be added to this 

list, as performance can be assessed as improvement moving 

away from the present state, as well as improvement moving 

toward a conceptual improved state.

When the contemplated alteration is a change in flow 

regime, as in the case of pulsed flow modifications, a hydrodynamic model provides the utility to assess sensitivity of 

habitat availability to the range of flows under consideration. 

In this case, the implicit reference condition is the present 

state, as habitat changes were assessed as pulses were added 

to the present flow regime. It would also be possible to assess 

performance relative to the historical or minimally disturbed 

condition by assessing how habitat availability varies under 

proposed flow modifications and the natural flow regime. 

Estimates of the natural flow regime are available from models 

of unregulated Missouri River system hydrology (U.S. Army 

Corps of Engineers, 1998); however, spring pulses under a 

natural flow regime were 2 to 6 times the magnitude of those 

contemplated for spring pulsed flow modifications (Jacobson 

and Galat, 2008). The comparison is not possible, because 

flows of this range are far beyond the calibration of the models 

presented here and also would involve substantial sediment 

transport. 

Modeling also provides for explicit comparison to a reference condition when the reference is defined as a place that 

has desirable characteristics. In this case, the Yankton reach 

presents many of the channel morphologic characteristics that 

were present in the pre-engineered Missouri River and could 

serve as a reference to evaluate habitat functions and restoration activities in other reaches. Habitat-function comparisons 

of the engineered reaches to the Yankton reach provide a 

measure of departure of the engineered river from the natural 

state (figs. 17, 19, 21, 26, 27, 28, 30, 31, 32). For example, the 

difference in shallow-water habitat class functions of the channelized river reaches compared to that at Yankton provides 

a measure of departure in the engineered river. Hence, the 

Yankton habitat functions provide a potential reference for 

channel reconfiguration in the lower river (fig. 17). Similarly, 

the interesting correspondence of low-Froude number habitat 

functions at Yankton and Miami suggests that large wing dikes 

in Miami may be providing ecological function that is present 

in the reference condition, although lacking at other engineered reaches (Little Sioux and Kenslers Bend; fig. 19). 

Hydrodynamic modeling also can be used to develop 

historical reference conditions when data are sufficient to 

support modeling of the minimally disturbed condition (Remo 

and Pinter, 2007), as well as to compare present day, restored, 

and historical river conditions (Jacobson and Galat, 2006). 

Although historical data limit the resolution of historical 

reconstructions, such models can still resolve broad ecological 

questions to inform restoration.

Summary and Conclusions

We constructed computational hydrodynamic models for 

four reaches on the Lower Missouri River. Each reach was 

selected on the basis of appearing to have been used by gravid, 

female sturgeon for spawning or was thought to have highly 
62 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

favorable spawning habitat based on spawning requirements 

established for other sturgeon species. The Miami, Missouri, 

reach is the farthest downstream (river mile 259.6–263.5) and 

is typical of the channelized LMOR. The Little Sioux, Iowa, 

reach (river mile 669.6–673.5) is typical of the LMOR navigation channel upstream from the Platte River. The Kenslers 

Bend, Nebraska, reach (river mile 743.9–748.1) is partially 

channelized and revetted but is not used for commercial navigation. The Yankton, South Dakota, reach (river mile 804.8–

808.4) is only 4 km downstream from Gavins Point Dam. The 

Yankton reach is partially stabilized but retains much of the 

geomorphic complexity of the unchannelized river. 

Models were constructed and calibrated for channel conditions as surveyed in 2006 and 2007. Each reach had at least 

three calibration surveys conducted each year. We attempted 

to calibrate the models over a range of flows representing 

25–95 percent flow exceedance, but lack of higher flows at the 

upstream sites prevented calibration over that range. Consequently, we extrapolated calibrated flows using discharge and 

roughness relations determined for the calibrated range.

The model results were analyzed to extract habitat 

metrics that are hypothesized to have high correlation with 

ecological functions based on limited biological datasets from 

the LMOR, ecological theory, or extrapolated from understanding of habitat requirements of other species. Emphasis 

was on understanding sensitivity of spawning habitat to discharge variation of a magnitude that could occur with spring 

pulsed flow modifications (“spring rise”); however, we also 

considered a broad range of derivative habitat metrics in order 

to explore how other habitat measures vary geographically and 

with discharge along the LMOR.

Most of the habitat metrics were insensitive to discharge 

variation over the range of modeled flows and over the range 

of discharges that has been considered for spring pulsed flow 

modifications. Therefore, pulsed flow modifications would 

not be expected to substantively change availability of these 

habitat units. In particular, convergent flow areas, which are 

consistent with limited LMOR biological data on sturgeon 

spawning areas and consistent with descriptions of spawning 

habitats of other sturgeon species, vary little with discharge. 

As with previous modeling studies, these results indicate that 

flow modifications to produce substantially more shallow, 

slow-water habitat in the channelized river would require 

extremely low discharges. Areas of BTM units were relatively 

insensitive to discharge variation and very stable from year to 

year, indicating possible promise as broad indicators of habitat 

complexity along the river. Although relatively insensitive 

to discharge variation, reach-scale patch statistical metrics 

(edge density, patch density) were sensitive to morphological 

changes from 2006 to 2007, especially at the Little Sioux and 

Kenslers Bend reaches. High flow between the two surveys in 

March 2007 simplified patch structure at Little Sioux but had 

the opposite effect at Kenslers Bend. As a habitat complexity 

measure, the SDI was insensitive to morphological changes 

at all but the Little Sioux reach and varied considerably with 

discharge at Miami and Kenslers Bend reaches. Mean water 

residence time at the Yankton reach was substantially greater 

than the other three reaches for a given discharge exceedance. 

Mean water residence time is an integrated measure of of 

the reach’s ability to retain water or passively transporting 

particles and may be useful for its links to understanding temperature, water quality, and larvae retention. 

The hydrodynamic models presented provide understanding of sensitivity of a variety of habitat metrics to discharge 

and to inter-annual morphological changes. While a great 

deal of work remains to establish biological links to physical 

habitat variation, these models provide a framework to support 

systematic assessment of links from management actions to 

habitat enhancement and ecosystem recovery.

Acknowledgments

The authors gratefully acknowledge Mike Anderson, Carrie Elliott, Mikki Johnson, Joanna Reuter, Matt Smith, Chad 

Vishy, Rick Wilson, and Brenda Woodward for their roles 

in collecting and assuring the quality of extensive datasets 

used in this project. We thank Jonathan Nelson and Richard 

McDonald for training and advice in using MD_SWMS. 

The report benefitted from reviews by Paul Grams, Richard 

McDonald, Wayne Stancill, Paul Boyd, Dan Pridal, and John 

Remus. Aaron DeLonay provided access to the Missouri River 

sturgeon tracking database and was a valuable resource for 

understanding of pallid sturgeon reproductive ecology.

References Cited

Ahmadi-Nedushan, B., St-Hilaire, A., Bérubé, M., Robichaud, 

É., Thiémonge, N., and Bobée, B., 2006, A review of statistical methods for the evaluation of aquatic habitat suitability 

for instream flow assessment: River Research and Applications, v. 22, no. 5, p. 503–523.

Bainbridge, R., 1958, The speed of swimming of fish as 

related to size and to the frequency and amplitude of the tail 

beat: Journal of Experimental Biology, v. 35, p. 109–133.

Bates, P.D., and Anderson, M.G., 2001, Validation of hydraulic models, in Anderson, M.G., and Bates, P.D., eds., Model 

validation: Perspectives in hydrological science: Chichester, 

UK, John Wiley and Sons, LTD, p. 325–356.

Bemis, W.E., and Kynard, B., 1997, Sturgeon rivers: An introduction to acipsenseriform biogeography and life history: 

Environmental Biology of Fishes, v. 48, p. 167–183.
References Cited 63

Bergman, H.L., Boelter, A.M., Parady, K., Fleming, C., 

Keevin, T., Latka, D.C., Korschgen, C., Galat, D.L., Hill, 

T., Jordan, G., Krentz, S., Nelson-Stastny, W., Olson, M., 

Mestl, G.E., Rouse, K., and Berkley, J., 2008, Research 

needs and management strategies for pallid sturgeon recovery: William D. Ruckelshaus Institute of Environment and 

Natural Resources, University of Wyoming, Laramie Proceedings of a Workshop held July 31–August 2, 2007, 37 p.

Blevins, D.W., 2006, The response of suspended sediment, turbidity, and velocity to historical alterations of the Missouri 

River: U.S. Geological Survey Circular 1301, 8 p.

Bovee, K.D., 1982, A guide to stream habitat analysis using 

the instream flow incremental methodology: Fish and Wildlife Service Instream Flow Information Paper, 248 p.

Bovee, K.D., Lamb, B.L., Bartholow, J.M., Stalnaker, C.B., 

Taylor, J., and Henriksen, J., 1998, Stream habitat analysis 

using the instream flow incremental methodology: U.S. 

Geological Survey Information and Technology Report 

USGS/BRD-1998-0004, 131 p.

Bovee, K.D., Waddle, T.J., and Jacobson, R.B., 2004, Quantification of habitat patch persistence in rivers affected by 

hydropeaking, in AWRA Spring Specialty Conference: 

GIS and Water Resources III, May 2004, Nashville, Tenn., 

American Water Resources Association, p. 1–10.

Bowen, Z.H., Bovee, K.D., and Waddle, T.J., 2003, Effects 

of flow regulation on shallow-water habitat dynamics and 

floodplain connectivity: Transactions of the American Fisheries Society, v. 132, p. 809–823.

Braaten, P.J., Fuller, D.B., Holte, L.D., Viste, W., Brandt, T.F., 

and Legare, R.G., 2008, Drift dynamics of larval pallid 

sturgeon and shovelnose sturgeon in a natural side channel 

of the Missouri River, Montana: North American Journal of 

Fisheries Management, v. 28, p. 808–826.

Bramblett, R.G., and White, R.G., 2001, Habitat use and 

movements of pallid and shovelnose sturgeon in the Yellowstone and Missouri Rivers in Montana and North Dakota: 

Transactions of the American Fisheries Society, v. 130, 

p. 1,006–1,025.

Brown, C.J., Mitchell, A., Limpenny, D.S., Robertson, M.R., 

Service, M., and Golding, N., 2005, Mapping seabed habitats in the Firth of Lorn off the west coast of Scotland: evaluation and comparison of habitat maps produced using the 

acoustic ground-discrimination system, RoxAnn, and sidescan sonar: Journal of Marine Science, v. 62, p. 790–802.

Buckley, J., and Kynard, B., 1981, Spawning and rearing of 

shortnose sturgeon from the Connecticut River: Progressive 

Fish Culturist, v. 43, no. 2, p. 74–76.

Buckley, J., and Kynard, B., 1985, Habitat and behavior of 

pre-spawning and spawning shortnose sturgeon, Acipenser 

brevirostrum, in the Connecticut River, in Binkowski, F.P., 

and Doroshov, S.I., eds., North American Sturgeons—Biology and Aquaculture Potential: Dordrecht, The Netherlands, 

Junk Publishers, p. 111–117.

Cholwek, G., Bonde, J., Li, X., Richards, C., and Yin, K., 

2000, Processing RoxAnn sonar data to improve its categorization of lake bed surficial substrates: Marine Geophysical 

Researches, v. 21, no. 409–421.

Coutant, C.C., 2004, A riparian habitat hypothesis for successful reproduction of white sturgeon: Review in Fisheries 

Science, v. 12, p. 23–73.

Crowder, D.W., and Diplas, P., 2000, Using two-dimensional 

hydrodynamic models at scales of ecological importance: 

Journal of Hydrology, v. 230, no. 3–4, p. 172–191.

Crowder, D.W., and Diplas, P., 2002, Vorticity and circulation: 

spatial metrics for evaluating flow complexity in stream 

habitats: Canadian Journal of Fisheries and Aquatic Sciences, v. 59, p. 633–645.

Crowder, D.W., and Diplas, P., 2006, Applying spatial hydraulic principles to quantify stream habitat: River Research and 

Applications, v. 22, p. 79–89.

DeLonay, A.J., Papoulias, D.M., Wildhaber, M.L., Mestl, G.E., 

Everitt, D.W., and Chojnacki, K.A., 2007, Movement, habitat use, and reproductive behavior of shovelnose sturgeon 

and pallid sturgeon in the Lower Missouri River, in Korschgen, C., ed., Factors affecting the reproduction, recruitment, 

habitat, and population dynamics of pallid sturgeon and 

shovelnose sturgeon in the Missouri River: U.S. Geological 

Survey Open-File Report 2007–1262, p. 23–102.

Doyle, M.C., Stanley, E.H., Strayer, D.L., Jacobson, R.B., and 

Schmidt, J.C., 2005, Effective discharge analysis of ecological processes in streams: Water Resources Research, v. 41, 

no. W11411, doi:10.1029/2005WR004222, p. 1–16.

Elliott, C.M., Reuter, J.M., and Jacobson, R.B., 2009, Channel morphodynamics in four reaches of the Lower Missouri 

River, 2006–07: U.S. Geological Survey Scientific Investigations Report 2009–5074.

Enders, E.C., Boisclair, D., and Roy, A.G., 2003, The effect 

of turbulence on the cost of swimming for juvenile Atlantic salmon (Salmo salar): Canadian Journal of Fisheries & 

Aquatic Sciences, v. 60, p. 1,149–1,160.

Fenwick, G.B., 1969, Water-temperature effects on stage-discharge relations in large alluvial rivers: Corps of Engineers, 

U.S. Army, Committee on Channel Stabilization Technical 

Report No. 6, 48 p.
64 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

Ferrell, J., 1996, Soundings—100 years of the Missouri River 

navigation project: Omaha, Nebr., U.S. Army Corps of 

Engineers, 171 p.

Fox, D.A., Hightower, J.E., and Parauka, F.M., 2000, Gulf 

sturgeon spawning migration and habitat in the Choctawhatchee River system, Alabama–Florida: Transactions of the 

American Fisheries Society, v. 129, p. 811–826.

Fu, X.-l., Li, D.-m., and Jin, G.-y., 2007, Calculation of flow 

field and analysis of spawning sites for Chinese sturgeon 

in the downstream of Gezhouba Dam: Journal of Hydrodynamics, Ser. B, v. 19, no. 1, p. 78–83.

Funk, J.L., and Robinson, J.W., 1974, Changes in the channel 

of the Lower Missouri River and effects on fish and wildlife: Jefferson City, Mo., Missouri Department of Conservation, 52 p.

Gaeuman, D., and Jacobson, R.B., 2006, Acoustic bed velocity 

and bed load dynamics in a large sand bed river: Journal of 

Geophysical Research, v. 111, p. 1–14.

Gaeuman, D., and Jacobson, R.B., 2007a, Field assessment of 

alternative bedload transport estimators: Journal of Hydraulic Engineering, v. 133, no. 12, p. 1,319–1,328.

Gaeuman, D., and Jacobson, R.B., 2007b, Quantifying fluid 

and bed dynamics for characterizing benthic physical 

habitat in large rivers: Journal of Applied Ichthyology, v. 27, 

p. 359–364.

Galat, D.L., Berry, C.R., Gardner, W.M., Hendrickson, J.C., 

Mestl, G.E., Power, G.J., Stone, C., and Winston, M.R., 

2005a, Spatiotemporal patterns and changes in Missouri 

River fishes, in Rinne, J.N., Hughes, R.M., and Calamusso, 

R., eds., Historical changes in large river fish assemblages 

of the Americas, Bethesda, Maryland, American Fisheries 

Society, Symposium 45, p. 249–291.

Galat, D.L., Berry, C.R., Jr., Peters, E.J., and White, R.G., 

2005b, Missouri River Basin, in Benke, A.C., and Cushing, C.E., eds., Rivers of North America: Oxford, Elsevier, 

p. 427–480.

Gerrity, P.C., Guy, C.S., and Gardner, W.M., 2006, Juvenile 

pallid sturgeon are piscivorous—A call for conserving 

native cyprinids: Transactions of the American Fisheries 

Society, v. 135, p. 604–609.

Gordon, N.D., McMahon, T.A., and Finlayson, B.L., 1992, 

Stream hydrology—an introduction for ecologists: Chichester, England, John Wiley and Sons, 526 p.

Grady, J.M., Mauldin, L., Davison, B., and Milligan, J., 2001, 

Missouri River pallid sturgeon survey route 19 bridge 

replacement project-Hermann, Missouri.: U.S. Fish and 

Wildlife Service, 90 p.

Gutreuter, S., 2004, Challenging the assumption of habitat 

limitation: An example from centrarchid fishes over an 

intermediate spatial scale: River Research and Applications, 

v. 20, p. 413–425.

Hall, L.S., Krausman, P.R., and Morrison, M.L., 1997, The 

habitat concept and a plea for standard terminology: Wildlife Society Bulletin, v. 25, no. 1, p. 173–182.

Hamilton, L.J., 2001, Acoustic seabed classification systems: 

Defence Science and Technology Organisation (Australia), 

66 p.

Hatin, D., Fortin, R., and Caron, F., 2002, Movements and 

aggregation areas of adult Atlantic sturgeon (Acipenser 

oxyrinchus) in the St. Lawrence River estuary, Quebec, 

Canada: Journal of Applied Ichthyology, v. 18, no. 4–6, 

p. 586–594.

Hesse, L.W., 1987, Taming the wild Missouri River—What 

has it cost?: Fisheries, v. 12, p. 2–9.

Hesse, L.W., Schmulbach, J.C., Carr, J.M., Keenlyne, K.D., 

Unkenholz, D.G., Robinson, J.W., and Mestl, G.E., 1989, 

Missouri River fishery resources in relation to past, present, 

and future stresses, in Dodge, D.P., ed., Proceedings of the 

International Large River Symposium: Ottawa, Canadian 

Special Publication of Fisheries and Aquatic Sciences 106, 

p. 353–371.

Hesse, L.W., and Sheets, W., 1993, The Missouri River hydrosystem: Fisheries, v. 18, no. 5, p. 5–14.

Hurley, K.L., 1999, Habitat use, selection, and movements by 

Middle Mississippi River pallid sturgeon and validity of 

pallid sturgeon age estimates from pectoral fin rays: Carbondale, Ill., Southern Illinois University, M.S., 81 p.

Jacobson, R.B., Elliott, C.M., and Johnson, H.E., 2004, 

Assessment of shallow-water habitat availability in modified dike structures, Lower Missouri River, 2004: U.S. 

Geological Survey Open-File Report 2004–1409, 20 p.

Jacobson, R.B., and Galat, D.L., 2006, Flow and form in 

rehabilitation of large-river ecosystems: An example from 

the Lower Missouri River: Geomorphology, v. 77, no. 3–4, 

p. 249–269.

Jacobson, R.B., and Galat, D.L., 2008, Design of a naturalized 

flow regime on the Lower Missouri River: Ecohydrology, 

v. 1, no. 2, p. 81–104.

Jacobson, R.B., Johnson, H.E., Reuter, J.M., and Elliott, 

C.M., 2007, The roles of physical habitat in reproduction 

and survival of pallid sturgeon and shovelnose sturgeon in 

the Lower Missouri River, 2005–06, in Korschgen, C., ed., 

Factors affecting the reproduction, recruitment, habitat, 

and population dynamics of pallid sturgeon and shovelnose 

sturgeon in the Missouri River: U.S. Geological Survey 

Open-File Report 2007–1262, p. 143–212.
References Cited 65

James, W.F., Richardson, W.B., and Soballe, D.M., 2008, 

Effects of residence time on summer nitrate uptake in Mississippi River flow-regulated backwaters: River Research 

and Applications, v. 24, no. 9, p. 1,206–1,217.

Johnson, H.E., Jacobson, R.B., and Delonay, A.J., 2006, 

Hydroecological modeling of the Lower Missouri River, in

Proceedings of the Third Federal Interagency Hydrologic 

Modeling Conference, Reno, Nevada, April 2–6, 2006: 

Subcommittee on Hydrology of the Interagency Advisory 

Committee on Water Information.

Jowett, I., 1993, A method for objectively identifying pool, 

run, and riffle habitats from physical measurements: New 

Zealand Journal of Marine and Freshwater Research, v. 27, 

p. 241–248.

June, F.C., 1977, Reproductive patterns in seventeen species 

of warmwater fishes in a Missouri River reservoir: Environmental Biology of Fishes, v. 2, p. 285–296.

Kelly, V., 1997, Dissolved oxygen in the Tualatin River, 

Orego, during winter flow conditions, 1991 and 1992: U.S. 

Geological Survey Water-Supply Paper 2465–A, 68 p.

Kirkby, M.J., 1996, A role for theoretical models in geomorphology?, in Rhoads, B.L., and Thorne, C.E., eds., The 

scientific nature of geomorphology—Proceedings of the 

27th Annual Binghamton Symposium in Geomorphology: 

New York, John Wiley and Sons, p. 257–272.

Kolar, C.S., Chapman, D.C., Courtenay, W.R.J., C.M., H., 

Williams, J.D., and Jennings, D.P., 2005, Asian carps of the 

genus hypophthalmichthys (Pisces, Cyprinidae)―A biological synopsis and environmental risk assessment: U.S. Fish 

and Wildlife Service, 175 p.

Kolman, R., and Aarkua, Z., 2002, Environmental conditions 

of common sturgeon (Acipenser sturio L.) spawning in 

River Rioni (Georgia): Electronic Journal of Polish Agricultural Universities–Fisheries, v. 5, no. 2, accessed June 2007 

at URL http://www.ejpau.media.pl/series/volume5/issue2/

fisheries/art-01.html.

Korschgen, C.E., 2007, Factors affecting the reproduction, 

recruitment, habitat, and population dynamics of pallid sturgeon and shovelnose sturgeon in the Missouri River: U.S. 

Geological Survey Open-File Report 2007–1262, 280 p.

Kynard, B., 1997, Life history, latitudinal patterns, and status 

of the shortnose sturgeon, Acipenser brevirostru: Environmental Biology of Fishes, v. 48, p. 319–334.

Lane, S.N., and Richards, K.S., 2001, The ‘validation’ of 

hydrodynamic models: some critical perspectives, in Anderson, M.G., and Bates, P.D., eds., Model Validation: Perspectives in Hydrological Science: Chichester, UK, John Wiley 

and Sons, LTD, p. 413–438.

Laustrup, M.S., Jacobson, R.B., and Simpkins, D.G., 2007, 

Distribution of potential spawning habitat for sturgeon in 

the Lower Missouri River: U.S. Geological Survey OpenFile Report 2007–1192, 26 p.

Legleiter, C.J., and Goodchild, M.F., 2005, Alternative representations of in-stream habitat: classification using remote 

sensing, hydraulic modeling, and fuzzy logic: Journal of 

Geographical Information Science, v. 19, no. 1, p. 29–50.

Liao, J.C., 2004, Neuromuscular control of trout swimming in 

a vortex street: implications for energy economy during the 

Karman gait: The Journal of Experimental Biology, v. 207, 

no. 20, p. 3,495–3,506.

Liao, J.C., 2006, The role of the lateral line and vision on body 

kinematics and hydrodynamic preference of rainbow trout 

in turbulent flow: The Journal of Experimental Biology, 

v. 209, no. 20, p. 4,077–4,090.

Lundblad, E.R., Wright, D.J., Miller, J., Larkin, E.M., Rinehart, R., Naar, D.F., Donahue, B.T., Anderson, S.M., and 

Battista, T., 2006, A benthic terrain classification scheme for 

American Samoa: Marine Geodesy, v. 29, no. 2, p. 89–111.

Lupandin, A.J., and Pavlov, D.S., 1996, The effects of starvation on the relation of fish to flows with different intensity in 

turbulence: Journal of Ichthyology, v. 36, no. 5, p. 408–411.

Maddock, I., 1999, The importance of physical habitat assessment for evaluating river health: Freshwater Biology, v. 41, 

no. 2, p. 373–391.

Manny, B.A., and Kennedy, G.W., 2002, Known lake sturgeon 

(Acipenser fulvescens) spawning habitat in the channel 

between lakes Huron and Erie in the Laurentian Great 

Lakes: Journal of Applied Ichthyology, v. 18, p. 486–490.

McDonald, R.R., Barton, G.J., Nelson, J.M., and Paragamian, 

V.L., 2006, Modeling hydraulic and sediment transport processes in white sturgeon spawning habitat on the Kootenai 

River, in Joint 8th Federal Interagency Sedimentation and 

3rd Federal Interagency Hydrologic Modeling Conference, 

p. 8.

McDonald, R.R., Nelson, J.M., and Bennett, J.P., 2005, Multidimensional surface-water modeling system user’s guide: 

U.S. Geological Survey Techniques and Methods 6–B2, 

136 p.

McGarigal, K., and Marks, B.J., 1995, FRAGSTATS—spatial 

pattern analysis program for quantifying landscape structure: Portland, Oregon, U.S. Department of Agriculture, 

Forest Service, General Technical Report PNW–351, 122 p.

Mion, J.B., Stein, R.A., and Marschall, E.A., 1998, River discharge drives survival of larval walleye: Ecological Applications, v. 8, no. 1, p. 88–103.
66 Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon in the Lower Missouri River

Mulcare, D.M., 2004, NGS Toolkit, Part 9: The National Geodetic Survey VERTCON Tool: Professional Surveyor, v. 24, 

no. 3, p. 46–48.

National Research Council, 2002, The Missouri River ecosystem, exploring the prospects for recovery: Washington, 

D.C., National Academy Press, 176 p.

Nelson, J.C., Bennett, J.B., and Wiele, S.M., 2003, Flow and 

sediment-transport modeling, in Kondolf, G.M., and Piégay, 

H., eds., Tools in fluvial geomorphology: Chichester, England, John Wiley and Sons, p. 539–576.

Nestler, J.M., Goodwin, R.A., Smith, D.L., Anderson, J.J., and 

Li, S., 2008, Optimum fish passage and guidance designs 

are based in the hydrogeomorphology of natural rivers: 

River Research and Applications, v. 24, no. 2, p. 148–168.

Orth, D.J., and White, R.J., 1999, Stream habitat management, 

in Kohler, C.C., and Hubert, W.A., eds., Inland fisheries 

management in North America: Bethesda, Md., American 

Fisheries Society, p. 249–284.

Paragamian, V.L., and Kruse, G., 2001, Kootenai River white 

sturgeon spawning migration behavior and a predictive 

model: North American Journal of Fisheries Management, 

v. 21, p. 10–21.

Paragamian, V.L., Kruse, G., and Wakkinen, V., 2001, Spawning habitat of Kootenai River white sturgeon, post-Libby 

Dam: North American Journal of Fisheries Management, 

v. 21, p. 22–33.

Paragamian, V.L., Wakkinen, V.D., and Kruse, G., 2002, 

Spawning locations and movement of Kootenai River white 

sturgeon: Journal of Applied Ichthyology, v. 18, no. 4–6, 

p. 608–616.

Parsley, M.J., Beckham, L.G., and McGabe, G., Jr., 1993, 

White sturgeon spawning and rearing habitat in the Columbia River downstream from McNeary dam: Transactions of 

the American Fisheries Society, v. 122, p. 217–228.

Pasternack, G.B., Wang, C.L., and Merz, J.E., 2004, Application of a 2d hydrodynamic model to design of reachscale spawning gravel replenishment on the Mokelumne 

River, California: River Research and Applications, v. 20, 

p. 205–225.

Pflieger, W.L., and Grace, T.B., 1987, Changes in the fish 

fauna of the lower Missouri River, 1940-1983, in Mathews, 

W., and Heins, D., eds., Community and evolutionary 

ecology of North American stream fishes: Norman, Okla., 

University of Oklahoma Press, p. 166–177.

Quinn, J.M., and Hickey, C., 1994, Hydraulic parameters 

and benthic invertebrate distributions in two gravel-bed 

New Zealand rivers: Freshwater Biology, v. 32, no. 3, 

p. 489–500.

Quist, M.C., Boelter, A.M., Lovato, J.M., Korfanta, N.M., 

Bergman, H.L., Latka, D.C., Korschgen, C., Galat, D.L., 

Krentz, S., Oetker, M., Olson, M., Scott, C.M., and Berkley, 

J., 2004, Research and assessment needs for pallid sturgeon 

recovery in the Missouri River—Final report to the U.S. 

Geological Survey, U.S. Army Corps of Engineers, U.S. 

Fish and Wildlife Service, and U.S. Environmental Protection Agency: Laramie, Wyo., William D. Ruckelshaus 

Institute of Environment and Natural Resources, University 

of Wyoming, 82 p.

Rabeni, C.F., and Jacobson, R.B., 1999, Warmwater streams, 

in Kohler, C.C., and Hubert, W.A., eds., Inland fisheries 

mnagement in North America, second edition: Bethesda, 

Md., American Fisheries Society, p. 505–528.

Remo, J.W.F., and Pinter, N., 2007, Retro-modeling the 

Middle Mississippi River: Journal of Hydrology, v. 337, 

p. 421–435.

Reuter, J.M., Jacobson, R.B., and Elliott, C.M., 2003, Physical stream habitat dynamics in Lower Bear Creek, northern 

Arkansas: U.S. Geological Survey Biological Sciences 

Report 2003–0002, 49 p.

Reuter, J.M., Jacobson, R.B., Elliott, C.M., Johnson, H.E., III, 

and DeLonay, A.J., 2008, Hydraulic and substrate maps of 

reaches used by sturgeon (Genus Scaphirhynchus) in the 

Lower Missouri River, 2005–07: U.S. Geological Survey 

Data Series Report 386, 442 p.

Roos, J., and Pieterse, A.J.H., 1994, Light, temperature and 

flow regimes of the Vaal River at Balkfontein, South Africa: 

Hydrobiologia, v. 277, no. 1, p. 1–15.

Schmeeckle, M.W., and Nelson, J.M., 2003, Direct numerical simulation of bedload transport using a local, dynamic 

boundary condition: Sedimentology, v. 50, p. 279–301.

Snook, V.A., Peters, E.J., and Young, L., 2002, Movements 

and habitat use by hatchery reared pallid sturgeon in the 

Lower Platte River, Nebraska, in Van Winkle, W., Anders, 

P.J., Secor, D.H., and Dixon, D.A., eds., Biology, Management, and Protection of North American Sturgeon: 

Bethesda, Md, American Fisheries Society, p. 161–174.

Standen, E.M., Hinch, S.G., and Rand, P.S., 2004, Influence 

of river speed on path selection by migrating adult sockeye 

salmon (Oncorhynchus nerka): Canadian Journal of Fisheries & Aquatic Sciences, v. 60, p. 905–912.

Stoddard, J.L., Larsen, D.P., Hawkins, C.P., Johnson, R.K., 

and Norris, R.H., 2006, Setting expectations for the ecological condition of streams: the concept of reference condition: 

Ecological Applications, v. 16, no. 4, p. 1,267–1,276.

U.S. Army Corps of Engineers, 1993, River hydraulics: U.S. 

Army Corps of Engineers, Department of the Army Engineer Manual, 176 p.
References Cited 67

U.S. Army Corps of Engineers, 1998, Reservoir regulation 

studies—Daily routing model studies, master water control 

manual Missouri River review and update study: U.S. Army 

Corps of Engineers, Northwest Division, v. 2A, 137 p.

U.S. Army Corps of Engineers, 2004, Summary Missouri 

River final environmental impact statement—Master water 

control manual review and update: U.S. Army Corps of 

Engineers, Northwest Division, 28 p.

U.S. Fish and Wildlife Service, 2000, Biological opinion on 

the operation of the Missouri River main stem reservoir 

system, operation and maintenance of the Missouri River 

bank stabilization and navigation project, and operation of 

the Kansas River reservoir system: U.S. Fish and Wildlife 

Service, 286 p.

U.S. Fish and Wildlife Service, 2003, Amendment to the 2000 

biological opinion on the operation of the Missouri River 

main stem reservoir system, operation and maintenance 

of the Missouri River bank stabilization and navigation 

project, and operation of the Kansas River reservoir system: 

U.S. Fish and Wildlife Service, 308 p.

U.S. Geological Survey, 2007, Sturgeon research update: 

Confirmed pallid sturgeon spawning in the Missouri River 

in 2007: U.S Geological Survey Fact Sheet 2007–3053, 4 p.

Webb, B.W., 1996, Trends in stream and river temperature: 

Hydrological Processes, v. 10, no. 2, p. 205–226.

Weihs, D., 1973, The mechanism of rapid starting of slender 

fish: Biorheology, v. 10, p. 343–350.

Weiss, A., 2001, Topographic position and landforms analysis, 

poster presentation, in Environmental Research Services 

Inc. User Conference, San Diego, California.

Wildhaber, M.L., DeLonay, A.J., Papoulias, D.M., Galat, D.L., 

Jacobson, R.B., Simpkins, D.G., Braaten, P.J., Korschgen, 

C.E., and Mac, M.J., 2007, A conceptual life-history model 

for pallid and shovelnose sturgeon: U.S. Geological Survey 

Circular 1315, 18 p.

Yu, S.L., and Peters, E.J., 1997, Use of Froude number to 

determine habitat selection by fish: Rivers, v. 6, no. 1, 

p. 10–18.
Publishing support provided by:

Rolla Publishing Service Center

For more information concerning this publication, contact:

Director, USGS Columbia Environmental Research Center

4200 New Haven Road

Columbia, MO 65201

(573) 875–5399

Or visit the Columbia Environmental Research Center Web site at:

http://www.cerc.usgs.gov

Jacobson and others—Hydrodynamic Simulations of Physical Aquatic Habitat Availability for Pallid Sturgeon—Scientific Investigations Report 2009–5058