Long Term Resource Monitoring Program

Technical Report

96-T006

Surficial Sediment Characteristics in

Pools 4 and 8, Upper Mississippi River

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November 1996
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Technical Center

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RESEARCH DIRECTOR

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Surficial Sediment Characteristics in

Pools 4 and 8, Upper Mississippi River

by 

James T. Rogala

U.S. Geological Survey

Environmental Management Technical Center

575 Lester Avenue

Onalaska, Wisconsin 54650

November 1996
Additional copies of this report may be obtained from the National Technical Information Service, 5285

Port Royal Road, Springfield, Virginia 22161 (1-800-553-6847 or 703-487-4650).

This report may be cited:

Rogala, J. T. 1996. Surficial sediment characteristics in Pools 4 and 8, Upper Mississippi River. U.S.

Geological Survey, Environmental Management Technical Center, Onalaska, Wisconsin, November

1996. LTRMP 96-T006. 22 pp.
iii

Contents

Page

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

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

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

Methods .................................................1

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Penetrometer Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Sediment Characteristics in 

Pool 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Sediment Characteristics in 

Pool 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Predictive Success With the Penetrometer

..................................................5

Sediment Characteristics of Pools 4 and 8 . . . . . . . . . . . . . . . . . . . . . . . . . 5

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Acknowledgments ...........................................7

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Tables

 Table 1 Descriptions of the sampling strata and distribution of sampling among stata

for Pools 8 and 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Table 2. Stepwise regression equations for moisture content, bulk density, and 

organic content in Pools 4 and 8 . . . . . . . . . . . . . . . . . . . . . . . . . 10

Table 3. Significant differences (P = 0.05) in sediment characteristics among strata 

as detected by using Tukey's multicomparison procedure . . . . . . . . . 11

Figures

Figure 1. Map of the sampling strata used to allocate sampling across Pool 8. . . . 12

Figure 2. Locations of sampling sites used in the sediment survey of Pool 8 in 

1994 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Figure 3. Illustration of the penetrometer and the size and weight of the cones . . 14
iv

Figure 4. Map of the sampling strata used to allocate sampling across Pool 4 . . . 15

Figure 5. Locations of sampling sites used in the sediment survey of Pool 4 in 

1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Figure 6. Means and standard deviations (SD) for moisture, density, and organic 

content for the nine sampling strata in Pool 8 . . . . . . . . . . . . . . . . . 17

Figure 7. Frequency distribution for moisture content, bulk density, and organic 

content for Pool 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Figure 8. Map of sediment moisture in Pool 8 created by interpolating between 

sample points 19

Figure 9. Means and standard deviations (SD) for moisture, density, and organic 

content for the five sampling strata in Pool 4 . . . . . . . . . . . . . . . . . 20

Figure 10. Frequency distribution for moisture content, bulk density, and organic 

content for Pool 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Figure 11. Map of sediment moisture in Pool 4 created by interpolating between 

sample points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
v

Preface

The Long Term Resource Monitoring Program (LTRMP) was authorized under the

Water Resources Development Act of 1986 (Public Law 99-662) as an element of the U.S.

Army Corps of Engineers' Environmental Management Program. The LTRMP is being

implemented by the Environmental Management Technical Center, a U.S. Geological

Survey science center, in cooperation with the five Upper Mississippi River System

(UMRS) States of Illinois, Iowa, Minnesota, Missouri, and Wisconsin. The U.S. Army

Corps of Engineers provides guidance and has overall Program responsibility. The mode

of operation and respective roles of the agencies are outlined in a 1988 Memorandum of

Agreement.

The UMRS encompasses the commercially navigable reaches of the Upper Mississippi

River, as well as the Illinois River and navigable portions of the Kaskaskia, Black,

St. Croix, and Minnesota Rivers. Congress has declared the UMRS to be both a nationally significant ecosystem and a nationally significant commercial navigation system. The

mission of the LTRMP is to provide decision makers with information for maintaining the

UMRS as a sustainable large river ecosystem given its multiple-use character. The longterm goals of the Program are to understand the system, determine resource trends and

effects, develop management alternatives, manage information, and develop useful

products.

This report was prepared under Task 1.2.1.3, Establish Experimental Design, Work

Unit B, Sediment Characterization in LTRMP Study Pools: Empirical Model Testing, and

Task 2.2.5.3., Obtain Historical and Present-Day Monitoring Data, Work Unit C,

Acquiring Sediment Type and Distribution Data, of the Operating Plan (USFWS 1993).

This report was developed with funding provided by the Long Term Resource Monitoring

Program. Additional funding was provided by the Navigation Studies being conducted by

the U.S. Army Corps of Engineers.
Surficial Sediment Characteristics in

Pools 4 and 8, Upper Mississippi River

By James T. Rogala

Abstract

Moisture content, bulk density, and organic content of surficial sediments were estimated with a penetrometer

in Navigation Pools 4 and 8 of the Upper Mississippi River during 1994 and 1995. Mean moisture content

of sediment was low in both Pool 4 (39%, SD = 15.0%) and Pool 8 (34%, SD = 13.7%), suggesting that soft,

fine sediments are uncommon in these pools. Sediment in much of the off-channel habitat was found to have

similarly low moisture content. Sediment in small backwaters was particularly low in moisture content,

although areas with sediment moisture content greater than 70% were found in small backwaters. Sediment

in the large backwaters of Pool 4 was similar to sediment in the small backwaters of Pools 4 and 8, while in

Pool 8 the large backwaters areas had sediment with a higher mean moisture content of 57% (SD = 14.8%).

Only deep, dredged areas in Pool 8 were dominated by sediment with a high moisture content; here the mean

moisture content was 67% (SD = 21.1%). The large, open water off-channel area in lower Pool 8 formed by

impoundment had sediments similar to those of channel habitats. As a whole, sediment in off-channel habitats

in Pools 4 and 8 had low moisture content, which suggests either small inputs of fine sediment or efficient

transport of fine sediment through the off-channel habitats.

Introduction

River scientists and managers can use

information on sediment characteristics for a

variety of studies related to sedimentation, as well

as for other investigations. Data on sediment

characteristics have been used to delineate zones

of sediment transport in lakes (Hakanson 1986)

and may serve similarly as an indicator of the

extent of soft sediment accretion in river systems.

By monitoring sediment characteristics, we can

also detect shifts from firm to flocculent sediments

or nutrient-poor to nutrient-rich sediments. These

changes in sediment characteristics may be

important to a variety of biota. In addition,

generating maps of sediment characteristics may

be useful in habitat mapping and spatial analysis

of sediment types. Therefore, it is desirable to

collect data at a spatial density that may be

suitable for map generation through spatial

interpolation between sampling locations.

To this end it is important to develop methods

for rapidly measuring sediment characteristics,

providing data that might otherwise be difficult or

expensive to obtain. Hakanson and Jansson

(1983) developed the penetrometer, a device that

measures the penetration of objects into the

sediment, to rapidly delineate zones of sediment

transport. Gaugush (1994) used a modification of

such a device in selected areas of limited spatial

extent in the Upper Mississippi River System

(UMRS). I collected data with the penetrometer

in Pools 4 and 8 of the UMRS in 1994 and 1995

to (1) further assess the utility of the penetrometer

over a wide range of sediment conditions, (2)

develop techniques that may be used to create

spatial sediment databases for maps, and (3) begin

to build a database of the physical properties of

sediments in the UMRS.

Methods

I used a stratified random sampling design to

select sampling sites. Initially, I selected strata

using the aquatic areas classification for the

UMRS developed by Wilcox (1993). This

classification was enhanced to include a greater

breakdown of backwaters and side channels to

provide a more efficient sampling of areas

expected to have greater heterogeneity of sediment

characteristics. I used several factors for

stratification of sampling areas in Pool 8: distance

to channels to create buffer areas, water depth,

presence of vegetation in 1989 to delineate

channels, and size or width of unique areas.
2

Table 1 shows a simplified description of the

strata and summarizes the targeted allocation of

sampling effort among the strata. Figure 1

provides a map of the sampling strata for Pool 8.

I performed a random selection of 1,000

sample sites in Pool 8 with the geographic

information system (GIS) software ARC/INFO.

Field maps of the sample sites were produced with

land–water and bathymetry plotted as a

background, which were used to locate most sites

during sampling. Technicians used a Global

Positioning System (GPS) device with 100-m

accuracy to locate sites in large, open-water areas.

We sampled 783 of the 1,000 selected sites (Fig.

2) between June 21 and September 21, 1994, on

31 sampling dates in Pool 8. Most of the sites not

sampled were in the main channel under deep,

high-velocity conditions.

The technicians measured the penetration of

cones into the sediment at all sampling sites with

an in situ penetrometer (Fig. 3). Hakanson (1986)

described the penetrometer and its use. The

penetrometer has three cones (L1, L2, and L3) of

various weights and sizes that penetrate sediments

to varying depths. The L1 cone is the largest and

the lightest, the L3 cone is the smallest cone and

the heaviest, and the L2 cone is between L1 and

L3 in both size and weight. The cones are

attached to rods that are held in place on the body

of the penetrometer. At shallow water sites, we

lowered the penetrometer onto the sediments by

using an attached pole. The penetrometer was

deployed in deep water by a "free-fall" method of

lowering the device by the strings holding the rods

in place. When the penetrometer is resting on the

sediments, the rods are released, which allow the

cones to penetrate the sediments. We then lock

the rods in place with the strings, retrieve the

device, and read the depth of penetration.

We deployed the penetrometer three times at

each site to obtain triplicate penetration

measurements for each of the three cones. The

triplicate deployment was used to minimize the

effects of altered penetration due to objects in the

sediments. I further hoped to minimize the effects

of inconsistent penetration of cones by

investigating various methods of reducing the

triplicate values for each cone. The three

measurements for each cone were reduced to a

single value by using five methods: averaging, by

using maximum values, using minimum values,

averaging the two least deviating values, and

averaging the two greatest values.

 Technicians collected surficial sediment (top

10 cm) at a randomly selected subset of 80 sites

(approximately 10%) of the total sample sites in

Pool 8 with a Wildco Sediment Core Sampler

(Wildco Wildlife Supply, Saginaw, Michigan)

having a 2-inch-diameter core liner. In the

laboratory, the sediment was analyzed for

moisture content (percent weight loss upon drying

at 105 EC), bulk density (dry mass divided by the

volume of sample), and organic content (percent

weight loss upon ignition at 550 EC). I used the

laboratory analysis data from the sediment cores

to develop the relation between the penetration of

cones and actual sediment characteristics.

To predict sediment moisture content, bulk

density, and organic content, I investigated the use

of all three cone types as independent variables

with step-wise multiple regression analysis. I

performed the regression analysis for all five

triplicate reducing methods described previously,

and selected the best-fitting regression equation

(highest R2

) for each sediment characteristic.

Transformations were used when needed to

linearize relations between cone penetration and

sediment characteristics. I predicted moisture

content, bulk density, and organic content with the

regression equations for those sites where

sediment cores for laboratory analysis were not

obtained.

In 1995, I used similar sampling methods and

data analysis for Pool 4. Lake Pepin, a large,

natural lake in Pool 4, was not sampled. On the

basis of my results from Pool 8, technicians

sampled no sites in the main channel and a

reduced number of sites in the main channel

border. This, along with the smaller amount of

backwater area in Pool 4, resulted in the selection

of 500 potential sample sites providing a sampling

density about equal to that of Pool 8. Fewer strata
3

were used to distribute sampling in Pool 4 because

buffer strata were not used and no impounded

area or deep backwater areas exist in Pool 4. I

sampled 478 of the 500 randomly selected sites

between May 18 and August 9, 1995, on 20

sampling dates. I located all sites with a GPS

receiver with 15-m accuracy. Table 1 summarizes

the targeted and actual sampling for Pool 4; maps

show sampling strata (Fig. 4) and sampling

locations (Fig. 5). A subset of 92 sites (about

20%) were randomly selected for sediment core

collection and analysis. I chose the increase in the

percentage of the subsetted sites to ensure

adequate core data for developing the relations

between penetration and sediment characteristics.

I calibrated the penetrometer at Pool 4

independently from the Pool 8 calibration.

I created a GIS database for sediment

characteristics by merging the estimated sediment

characteristics for each sampling point with the

GIS point coverage generated from the random

selection of sampling points. To create a

continuous surface of data, I used GIS data for

moisture content of sampling locations to

interpolate values spatially between sampling

locations. I evaluated several surfacing

algorithms and a number of options for search

radius (sample) and weighing of data based on

distance (power) before selecting an interpolation

method. I selected an inverse distance weighted

interpolation with a power of one and a sample of

nine. My interpolation method also included

restricting interpolation to within sampling strata

found to be significantly different from other

sampling strata. I then combined interpolated

surfaces for each stratum, or groups of strata, to

create a pool coverage. Restricting interpolation

to within strata provided an interpolation barrier

so that data from nearby areas of a different

stratum, which were shown to have different

sediments, did not influence the interpolation. I

did not interpolate data in strata where I collected

no data or a greatly reduced set of data.

Results

Penetrometer Calibration

Using the laboratory data from the sediment

core samples, I calibrated the penetrometer at

Pools 4 and 8 to estimate moisture content, bulk

density, and organic content using regression

equations as described previously. The sample

size of sediment cores available to develop the

regression equations varied, with 45 samples for

bulk density in Pool 8, 80 samples for water

content and organic content in Pool 8, and 92

samples for all parameters in Pool 4. In all

instances, at least two of the three cones

contributed significantly to the model in the

presence of the other variables. Table 2 gives the

regression equations for Pools 4 and 8 for

moisture content, bulk density, and organic

content.

The strongest correlations between cone

penetration (x, the independent variable) and

sediment characteristics (y, the dependent

variable) were found in sediment moisture data.

The highest R2

 (0.79) for Pool 8 sediment

moisture was obtained with y2

 transformed data

using the maximum two values of the triplicate for

each cone. The best fit in Pool 4 was lower (R2

 =

0.67) and obtained with y2

 transformed data using

the maximum value of the triplicate for each cone.

I identified a group of sites in Pool 4, within a

single backwater bay adjoining the north end of

Lake Pepin near Bay City, Wisconsin, as fitting

the regression line poorly. Using a homogeneity

of slopes linear regression model, I found the

relation between moisture content and penetration

to have different slopes (P = 0.05) for the sites

within this bay and other sites in Pool 4. When I

removed all five coring sites within that backwater

bay, I obtained a higher R2

 (0.75) with

transformed data using the maximum value of the

triplicate for each cone.

I also found sediment density to be highly

correlated to cone penetration. In Pool 8, I

obtained the highest R2

 (0.70) with a square root

of y transformation using the two least-deviating

penetration values for each cone. A square root

of y transformation was also used in Pool 4, but

the maximum penetration value for each cone

provided the best fit (R2

 = 0.65). As with the

data for moisture content, omitting the Lake Pepin

bay data increased the correlation (R2

 = 0.73).

Using cone penetration, I found organic

content to be the least predictable of the three

characteristics. The strongest correlation between
4

cone penetration and organic content in Pool 8

was obtained by using the average of the two

greatest values of the triplicate for each cone (R2

= 0.59). I obtained the best fit for all data in

Pool 4 by using the maximum penetration value

for each cone (R2

 = 0.55), which increased to R2

= 0.61 with the Lake Pepin bay sites removed.

Sediment Characteristics in 

Pool 8

Patterns in water content, bulk density, and

organic content are often similar in aquatic

sediment because these physical properties are

related. However, the similarity of these physical

properties of sediment in my results may be

enhanced because all three properties were

predicted from penetrometer measurements.

Sediment in deep backwaters of Pool 8 had the

highest mean moisture content (66.1%), highest

mean organic content (12.6%), and lowest mean

bulk density (0.48 g/mL). Sediment in large

backwaters were second highest in mean moisture

content (57.2%) and mean organic content

(8.32%) and had the second lowest mean bulk

density (0.69 g/mL). The small, shallow

backwaters were next with a mean moisture

content of 43.0%, mean organic content of

5.96%, and mean bulk density of 0.95 g/mL.

Sediment in the other five strata were similar in

mean moisture content (ranging from 25.2% to

31.4%), mean organic content (ranging from

2.51% to 3.12%), and mean bulk density (ranging

from 1.25 to 1.33 g/mL). Figure 6 shows means

and standard deviations of water content, bulk

density, and organic content for sediment in each

sampling stratum.

I used a Tukey's multicomparison procedure to

determine differences in sediment characteristics

among strata. The water content, bulk density,

and organic content of sediment in each of the

three backwater strata (deep backwater lakes,

large backwater lakes, and small shallow

backwaters) differed significantly (P = 0.05)

from the other five strata (Table 3). All three

sediment characteristics were significantly

different between small, shallow backwaters and

the other two backwater types. I also detected

significant differences in organic content between

sediment in large backwater lakes and sediment in

deep backwater lakes, but found no significant

differences between these two backwater types for

sediment moisture content and sediment bulk

density. No significant differences were found

among other strata.

Sediment characteristics in Pool 8 were

determined by weighing sediment characteristics

at each sample site on the basis of the area of each

stratum and the number of samples in each

stratum. Because no sites in the main channel

strata were sampled and only a third of the main

channel border sites were sampled, these areas

were not included in the summary for Pool 8. For

sediment in Pool 8, the mean moisture content

was 34.0%, mean bulk density was 1.17 g/mL,

and mean organic content was 3.89% (Fig. 6).

The frequency distributions for moisture content,

bulk density, and organic content for sediment in

Pool 8, by strata, are illustrated in Figure 7. A

map of Pool 8 sediment moisture content was

generated through interpolation between data

locations (Fig. 8).

Sediment Characteristics in 

Pool 4

Patterns in water content, bulk density, and

organic content of sediment in Pool 4 differed

somewhat from the same strata types in Pool 8.

Sediment in the small shallow backwaters had the

highest mean moisture content (45.2%), highest

mean organic content (5.80%), and lowest mean

bulk density (0.83 g/mL). Large backwaters

contained the stratum with the second highest

mean moisture content (41.9%) and mean organic

content (5.07%) and had the second lowest mean

bulk density (0.89 g/mL). Sediment in the three

channel strata (main channel border, large side

channels, and small side channels) were similar in

mean moisture content (ranging from 24.2% to

28.5%), mean organic content (ranging from

1.69% to 2.54%), and mean bulk density (ranging

from 1.16 to 1.25 g/mL). Figure 9 shows means

and standard deviations of water content, bulk

density, and organic content for sediment in each

sampling stratum.
5

Using a Tukey's multicomparison procedure,

I detected significant differences (P = 0.05) for

moisture content, bulk density, and organic

content between sediment in the two backwater

strata (large backwater lakes and shallow

backwaters) and sediment in the channel strata

(Table 3). I found significant differences between

sediment in the two backwater strata, but no

significant differences among sediment in channel

strata (main channel border, large side channels,

and small side channels).

I determined sediment characteristics for Pool

4 using the same methods as for Pool 8. The

summary data for Pool 4 does not include Lake

Pepin, the Lake Pepin bay described previously,

the main navigation channel, or the main channel

border. For sediment in Pool 4, the mean

moisture content was 39.9%, mean bulk density

was 0.93 g/mL, and mean organic content was

4.75 (Fig. 9). The frequency distributions for

moisture content, bulk density, and organic

content for sediment in Pool 4, by strata, are

illustrated in Figure 10. A map of Pool 4

sediment moisture content was generated through

interpolation between data locations (Fig. 11).

Discussion

Predictive Success With the

Penetrometer

Better relations than obtained here between the

penetrometer and sediment characteristics may be

obtained in less dynamic systems. In the UMRS,

areas of sediment accumulation, transport, or

erosion may shift through time as a result of the

highly variable river flow. Therefore, fine and

coarse sediments are probably layered. The

layering will alter the sediment characteristics

estimated with a penetrometer because a coarse

sediment layer on the surface will impede

penetration to buried, high-moisture content

sediments. Similary, the surficial (top 10-cm)

sediments may or may not differ from subsurface

(>10-cm) sediments; thus, penetration to a great

depth may not indicate softer sediments at the

surface than found in penetration to a lesser depth.

For example, penetration at two sites may be 20

cm and 40 cm, but the difference may reflect

subsurface sediment differences and not surficial

sediment differences. I found the L3 cone often

penetrates beyond the surficial sediments and, in

some instances, penetrated to depths greater than

40 cm.

Another consideration derived from the Pool 8

sampling is a limitation in using the penetrometer

in deep, high-velocity habitats. As mentioned

earlier, I did not sample some of the main channel

border sites nor all of the main channels sites

because of the difficulty in deploying the

penetrometer. Shallow, high-velocity habitats

were sampled by using the pole on the

penetrometer to assure proper deployment on the

bottom, and deep, low-velocity habitats were

sampled with a "free-fall" penetrometer.

However, in deep, high-velocity areas I could not

confirm that the penetrometer had been properly

deployed. I noted highly variable readings in the

penetration, which probably indicated that the

penetrometer was not vertically aligned or perhaps

it was placed on a surface with deep dunes.

Sediment Characteristics of

Pools 4 and 8

Because all three characteristics have similar

patterns, I limit discussions here to moisture

content of the sediments. Hakanson and Jansson

(1983) established an ability to predict areas of

fine sediment accumulation from the moisture

content of sediment in lakes. Gunkel et al. (1984)

suggested that this relation also exists in

reservoirs. Therefore, data presented here on the

moisture content of sediments in the Upper

Mississippi River may provide estimates of the

amount of area where fine sediment is

accumulating.

In general, the moisture content of sediments in

Pools 4 and 8 was lower than would be expected

in a system undergoing fine sediment accretion.

In lakes, Hakanson and Jansson (1983) suggested

that areas with greater than 70% moisture content

are probably depositional areas for fine sediments.

Means of 39.2% moisture content for sediment in
6

Pool 4 and 34.0% moisture content for sediment

in Pool 8 for all areas excluding the main channel

and the main channel border do not suggest

extensive areas of fine sediment accretion in the

pools as a whole. Only 2.5% (77 ha) of the study

area of Pool 4 and 2.8% (198 ha) of the study

area of Pool 8 are estimated to have sediment with

a moisture content greater than 70% (Figs. 7 and

10). Even with all channel areas and the

impounded area in Pool 8 excluded, the

percentage of the backwater area having sediment

moisture content greater than 70% is only 3.3%

for Pool 4 and 11.0% for Pool 8.

The poolwide mean for the moisture content of

sediment in Pool 8 is strongly influenced by the

impounded area that makes up 44% of its surface

area. The impounded area is often thought of as

an aging reservoir with much soft sediment

accretion (Fremling and Claflin 1984; Nielsen et

al. 1984). However, mean moisture content for

sediment in the impounded area was only 30.2%

in this survey, which suggests that little fine

sediment accretion is presently occurring in this

area. Only a small fraction of this area is made

up of high-moisture content sediments (Fig. 7).

Coarse sediment deposition in deltalike areas near

the main channel may contribute to the low mean

moisture content of sediment in the impounded

area.

Other backwater types also exhibited

characteristics unlike areas with soft sediment

accumulation. Sediment in the small, shallow

backwater strata in both Pools 4 and 8, although

significantly differing from channel areas, as

found to contain low-moisture content sediment,

with means of 45.2% in Pool 4 and 43.0% in Pool

8. The low-moisture content sediment in small

backwaters may reflect the dominance of river

flows during high water, which may transport

previously deposited fine sediments or deposit

coarse sediments. Therefore, in a dynamic river

system such as the UMRS, fine sediment

deposition over long periods may be limited to a

small portion of the small, highly connected offchannel area. This is evident in the small fraction

of these backwaters with high-moisture content

sediments (Figs. 7 and 10).

 The mean moisture content was low for

sediment in large backwaters as well, although

some individual large backwaters had sediment

with high-moisture content. Sediment in large

backwaters in Pool 4 had particularly lowmoisture content (41.9%) compared with that in

Pool 8 (57.2%). This lower moisture content may

largely be because coarse sediments are deposited

in the large lakes below the Chippewa River

located in the middle of Pool 4 (Fremling and

Claflin 1984). Those lakes are also likely to have

little fine sediment delivered from the Mississippi

River because of the high trapping efficiency of

Lake Pepin (Maurer et al. 1995), located just

upstream of the mouth of the Chippewa River.

The lower sediment moisture content may also

reflect the flow-through nature of most large

backwaters in Pool 4 not found in Pool 8.

Only sediment in the deep backwater stratum in

Pool 8 had a mean moisture content near the 70%

that Hakanson and Jansson (1983) suggested as an

indicator of fine sediment accretion in lakes. All

these backwaters are actually borrow pits, deep

areas created by dredging. Thus, the data suggest

that the only areas of uniform fine sediment

deposition are in human-made sediment traps, but

these areas compose only a small fraction of the

total backwater area in Pool 8 (Fig. 7). Other

backwaters, whether existing before impoundment

or created by impoundment, seem to have low

trapping efficiency.

Two issues related to the previous discussions

should be clarified: First, because coarse

sediments are periodically scoured and deposited,

the variability in accretion over time can influence

the detection of accreted fines. For example, I

sampled in Pool 8 just 1 yr after a high-discharge

year and in Pool 4 just 2 yr after a high-discharge

year. The high river flows associated with the

high-discharge period may have removed recently

deposited fines that had not compacted; the flow

may also have buried previously deposited fine

sediments with coarse sediment during high

discharge. Such patterns in sedimentation were

suggested in the sediment cores obtained. As

previously described, buried fine sediment would

not be detected by the methods used in this

survey. Second, compaction of accumulated fines

can result in low-moisture sediments. The low

fetch in small backwaters of the UMR may

enhance sediment compaction, although shallow

depths may provide for sediment resuspension
7

despite the low fetch. Compaction is also greatly

accelerated if sediments are dewatered and

allowed to dry. However, it is unlikely

dewatering is a factor in the present survey

because water level records indicated that none of

the sites sampled had been dewatered in the past

30 yr.

I used the buffer strata in Pool 8 to investigate

the sediment type gradient between off-channel

strata and channel strata. Data did not suggest

that the buffer strata were transitional areas

between low-moisture and high-moisture

sediments. I would expect variability of sediment

character to be higher in a transitional area, but

standard deviations were less for buffer areas as

a whole. This may be a result of (1) poor

classification of boundaries between channel and

off-channel types, (2) inadequate size in the buffer

distance to catch the transition zone, or (3) slight

gradients between channel and nonchannel areas.

The moisture content of sediment in the buffer

areas was similar to channel types, suggesting that

a slight gradient may exist, and high-moisture

content sediments are found only at a great

distance from the channels.

The maps created through interpolation for

sediment moisture content can be created for bulk

density and organic content as well. However, all

these maps should be used with caution and must

only be used with knowledge of the location of

sample points and an understanding of

interpolation algorithms. Even the rather

extensive sampling in this study provides sparse

data compared with the data needed to reasonably

estimate spatial sediment characteristics. General

trends can be observed with the maps, but

characteristics interpolated within individual

backwater areas may be based on limited or no

data. Also, the use of strata to restrict

interpolation between strata presents a

misconception that sediment characteristics change

sharply at those boundaries. As previously

discussed, these sharp transitions do not occur for

most backwaters.

Conclusions

Areas with high water content, high organic

content, and low-bulk density sediments were

uncommon in both Pools 4 and 8. The estimated

area of high-moisture sediments (>70% moisture)

in both Pools 4 and 8 was less than 3% of the total

area of the pool, excluding the main channel.

Only human-made dredge holes in Pool 8 had a

high mean moisture content (66.1%). With the

exception of the impounded stratum in Pool 8,

sediment types of backwater strata differed

significantly from channel strata.

The summary data provided by this survey can

be used to establish a baseline of sediment

conditions that can be used to detect large-scale

changes or differences among pooled reaches. In

the future, I may be able to detect shifts of

sediment character as a result of reservoir aging

or changes in resource management with

replication of this sampling design. The ability to

detect differences, whether spatially or

temporally, would however be dependent on the

sample size and heterogeneity of the sediment

character. The significant differences detected

among some strata types suggest that the sampling

in this survey was sufficient for detecting some

differences.

Acknowledgments

Pete Boma served as crew leader during most

field work; Steve Lounsbury, Eric Olsen, Randy

Poelma, and other LTRMP technicians assisted

during data collection and data entry. Bob

Gaugush provided the penetrometer design and

technical assistance for this study. Nani

Bhowmik, Bob Gaugush, Bill James, and Dan

Wilcox provided review of a draft of this report

and John Barko provided final review of this

report.

References

Fremling, C. R., and T. O. Claflin. 1984.

Ecological history of the Upper Mississippi

River. Pages 5–24 in J. G. Wiener, R. V.

Anderson, and D. R. McConville, editors.

Contaminants in the Upper Mississippi River.

Butterworth Publishers, Stoneham,

Massachusetts.
8

Gaugush, R. F. 1994. Sediment surveys of Lake

Onalaska, Pool 7, Upper Mississippi River.

U.S. Fish and Wildlife Service, Environmental

Management Technical Center, Onalaska,

Wisconsin. (Unpublished data)

Gunkel, R. C., R. F. Gaugush, R. H. Kennedy,

G. E. Saul, J. H. Carroll, and J. E. Gauthey.

1984. A comparative study of sediment quality

in four reservoirs. U.S. Army Engineer

Waterways Experiment Station. E-84-2.

Hakanson, L., and M. Jansson. 1983. Principles

of lake sedimentology. Springer-Verlag,

Berlin. 316 pp. 

Hakanson, L. 1986. A sediment penetrometer

for in situ determination of sediment type and

potential bottom dynamic conditions.

Hydrobiologia 71:851–858.

Maurer, W. R., T. O. Claflin, R. G. Rada, and J.

T. Rogala. 1995. Volume loss and mass

balance for selected physiochemical

constituents in Lake Pepin, Upper Mississippi

River, USA. Regulated Rivers 11:175–184.

Nielsen, D. N., R. G. Rada, and M. M. Smart.

1984. Sediments of the Upper Mississippi:

Their sources, distribution, and characteristics.

Pages 67–98 in J. G. Wiener, R. V. Anderson,

and D. R. McConville, editors. Contaminants

in the Upper Mississippi River. Butterworth

Publishers, Stoneham, Massachusetts.

U.S. Fish and Wildlife Service. 1993. Operating

Plan for the Upper Mississippi River System

Long Term Resource Monitoring Program.

Environmental Management Technical Center,

Onalaska, Wisconsin, Revised September

1993. EMTC 91-P002R. 179 pp. (NTIS

#PB94-160199)

Wilcox, D. B. 1993. An aquatic habitat

classification system for the Upper Mississippi

River System. U.S. Fish and Wildlife Service,

Environmental Management Technical Center,

Onalaska, Wisconsin, May 1993. EMTC

93-T003. 9 pp. + Appendix A. (NTIS

#PB93-208981)
9

Sampling strata Description

Main navigation channel Area for navigation as defined by channel buoys and markers

Main channel borders Area between the main channel shore and the navigation channel

Large side channels Unbraided channels >100 m wide

Small side channels Braided channels or unbraided channels <100 m wide

Buffer between channels and backwaters Area 40 m wide between channels and backwaters

Small shallow backwaters Backwaters <25 ha or backwaters with >1 outlet and <100 ha

Deep backwaters Areas in backwaters dredged to a depth of >3 m

Large backwater lakes Backwater lakes >100 ha or lakes with one outlet and >25 ha

Buffer between impounded and channels Area 40 m wide between channels and the impounded area

Impounded backwater areas Area of large fetch upstream of the dam

 Sampling strata Percentage of 

total area

Target 

percentage of 

total samples

Actual 

number of 

sites sampled

Actual 

percentage of 

sampled sites

Density of 

sampling 

(points per 

100 ha)

Pool 8

Main navigation channel 7.4 5 0 0.0 0.0

Main channel borders 7.4 10 30 3.8 4.9

Large side channels 6.2 8 74 9.5 14.4

Small side channels 5.4 8 64 8.2 14.3

Buffer between channels and backwaters 4.9 10 98 12.5 27.0

Small shallow backwaters 16.2 27 230 29.4 17.2

Deep backwaters 0.4 1 8 1.0 23.4

Large backwater lakes 4.7 8 44 5.6 11.3

Buffer between impounded and channels 3.1 5 39 5.0 15.1

Impounded backwater areas 44.1 20 196 25.0 5.4

Total 783 10.3

Pool 4 

 Main channel borders 15.1 5 18 3.7 3.2

Large side channels 9.4 10 49 10.3 14.0

Small side channels 9.8 10 48 10.0 13.1

Small shallow backwaters 24.1 35 170 35.6 19.0

Large backwater lakes 41.6 40 193 40.4 12.5

Total 478 12.8

Table 1. Descriptions of the sampling strata and distribution of sampling among stata for Pools 8 and 4.
10

Pool Sediment characteristic Regression equation

4

Moisture content (%) (-29.68 - 52.74 * L1 + 193.13 * L2 + 65.26 * L3) 0.75

Bulk density (g/mL) (1.211 - 0.0238 * L2 - 0.0107 * L3) 0.73

Organic content (%) (0.00875 + 0.4793 * L2 + 0.149 * L3) 0.61

8

Moisture content (%) (-693.08 + 176.16 * L2 + 137.6 * L3) 0.79

Bulk density (g/mL) (1.267 + 0.0406 * L1 - 0.021 * L2 - 0.0231 * L3) 0.70

Organic content (%) (-0.471 + 0.2565 * L2 + 0.3309 * L3) 0.59

2

2

R

2

Table 2. Stepwise regression equations for moisture content, bulk density, and organic content in Pools

4 and 8. Cone penetration (L1, L2, and L3) is measured in centimeters and the penetration value is

obtained through reduction of triplicate data.
11

Sampling 

strata MCB SSC LSC BCB SBW DBW LBL BCI IMP

MCB 0 0 0 *** *** *** 0 0

SSC 0 0 0 *** *** *** 0 0

LSC 0 0 0 *** *** *** 0 0

BCB 0 0 0 *** *** *** 0 0

SBW *** *** *** *** *** *** *** ***

DBW *** *** *** *** *** 0 *** ***

LBL *** *** *** *** *** 0 *** ***

BCI 0 0 0 0 *** *** *** 0

IMP 0 0 0 0 *** *** *** 0 

Sampling 

strata

MCB SSC LSC SBW LBL

MCB 0 0 *** ***

SSC 0 0 *** ***

LSC 0 0 *** ***

SBW *** *** *** ***

LBL *** *** *** *** 

Sampling 

strata MCB SSC LSC BCB SBW DBW LBL BCI IMP

MCB 0 0 0 *** *** *** 0 0

SSC 0 0 0 *** *** *** 0 0

LSC 0 0 0 *** *** *** 0 0

BCB 0 0 0 *** *** *** 0 0

SBW *** *** *** *** *** *** *** ***

DBW *** *** *** *** *** *** *** ***

LBL *** *** *** *** *** *** *** ***

BCI 0 0 0 0 *** *** *** 0

IMP 0 0 0 0 *** *** *** 0 

(a)

MCB = Main channel borders BCB = Buffer between channels and backwaters LBL = Large backwater lakes 

SSC = Small side channels SBW = Small shallow backwaters BCI = Buffer between impounded areas and channels 

LSC = Large side channels DBW = Deep backwaters IMP = Impounded backwater areas

(c)

(b)

Table 3. Significant differences (P = 0.05) in sediment characteristics among strata as detected by using

Tukey's multicomparison procedure. Significant differences are indicated by *** for Pool 8 moisture

content and bulk density (a), Pool 8 organic content (b), and Pool 4 moisture content, bulk density, and

organic content (c).
12

Figure 1. Map of the sampling strata used to allocate sampling across Pool 8.
13

Figure 2. Locations of sampling sites used in the sediment survey of Pool 8 in 1994.
14

Figure 3. Illustration of the penetrometer and the size and weight of the cones.
15

Figure 4. Map of the sampling strata used to allocate sampling across Pool 4.
16

Figure 5. Locations of sampling sites used in the sediment survey of Pool 4 in 1995.
17

Figure 6. Means and standard deviations (SD) for moisture, density, and organic content for the nine

sampling strata in Pool 8. Included are weighted means (WM) for each sediment characteristic for Pool 8,

excluding the main channel and main channel border.
18

Figure 7. Frequency distribution for moisture content, bulk density, and organic content for Pool 8. The

bars for each strata are weighted on the basis of the area of each stratum, and the pool frequency

distribution is the total of the stacked bars.
19

Figure 8. Map of sediment moisture in Pool 8 created by interpolating between sample points. Note that

values were not interpolated in the main channel and main channel border because those strata were only

partly sampled.
20

Figure 9. Means and standard deviations (SD) for moisture, density, and organic content for the five

sampling strata in Pool 4. Included are weighted means (WM) for each sediment characteristic for Pool 4,

excluding Lake Pepin, the main channel, and the main channel border.
21

Figure 10. Frequency distribution for moisture content, bulk density, and organic content for Pool 4. The

bars for each strata are weighted on the basis of the area of each stratum, and the pool frequency

distribution is the total of the stacked bars.
22

Figure 11. Map of sediment moisture in Pool 4 created by interpolating between sample points. Note that

values were not interpolated in the main channel, main channel border, and Lake Pepin because those

strata were only partly sampled.
REPORT DOCUMENTATION PAGE Form Approved

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Project (0704-0188), Washington, D.C. 20503

1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE

November 1996

3. REPORT TYPE AND DATES COVERED

4. TITLE AND SUBTITLE

 Surficial sediment characteristics in Pools 4 and 8, Upper Mississippi River

5. FUNDING NUMBERS

6. AUTHOR(S)

James T. Rogala

7. PERFORMING ORGANIZATION NAME AND ADDRESS

U.S. Geological Survey 

Environmental Management Technical Center

575 Lester Avenue

Onalaska, Wisconsin 54650

8. PERFORMING ORGANIZATION

 REPORT NUMBER

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

U.S. Geological Survey 

Environmental Management Technical Center

575 Lester Avenue

Onalaska, Wisconsin 54650

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 AGENCY REPORT NUMBER

96-T006

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Release unlimited. Available from National Technical Information Service, 5285 Port Royal Road, Springfield, VA 22161 

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12b. DISTRIBUTION CODE

13. ABSTRACT (Maximum 200 words)

Moisture content, bulk density, and organic content of surficial sediments were estimated with a penetrometer in Navigation Pools 4 and 8 of the Upper Mississippi

River during 1994 and 1995. Mean moisture content of sediment was low in both Pool 4 (39%, SD = 15.0%) and Pool 8 (34%, SD = 13.7%), suggesting that soft,

fine sediments are uncommon in these pools. Sediment in much of the off-channel habitat was found to have similarly low moisture content. Sediment in small

backwaters was particularly low in moisture content, although areas with sediment moisture content greater than 70% were found in small backwaters. Sediment in the

large backwaters of Pool 4 was similar to sediment in the small backwaters of Pools 4 and 8, while in Pool 8 the large backwaters areas had sediment with a higher

mean moisture content of 57% (SD = 14.8%). Only deep, dredged areas in Pool 8 were dominated by sediment with a high moisture content; here the mean moisture

content was 67% (SD = 21.1%). The large, open water off-channel area in lower Pool 8 formed by impoundment had sediments similar to those of channel habitats. 

As a whole, sediment in off-channel habitats in Pools 4 and 8 had low moisture content, which suggests either small inputs of fine sediment or efficient transport of fine

sediment through the off-channel habitats.

14. SUBJECT TERMS

Mississippi River, moisture content, penetrometer, sediment, sediment mapping

15. NUMBER OF PAGES

22 pp.

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20. LIMITATION OF ABSTRACT
The Long Term Resource Monitoring Program (LTRMP) for the Upper Mississippi

River System was authorized under the Water Resources Development Act of 1986

as an element of the Environmental Management Program. The mission of the 

LTRMP is to provide river managers with information for maintaining the Upper 

Mississippi River System as a sustainable large river ecosystem given its 

multiple-use character. The LTRMP is a cooperative effort by the U.S. Geological 

Survey, the U.S. Army Corps of Engineers, and the States of Illinois, Iowa, 

Minnesota, Missouri, and Wisconsin.