U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2006–5066

In cooperation with the U.S. Environmental Protection Agency

Present and Reference Concentrations and Yields 

of Suspended Sediment in Streams in the Great 

Lakes Region and Adjacent Areas 
Present and Reference Concentrations 

and Yields of Suspended Sediment in 

Streams in the Great Lakes Region and 

Adjacent Areas

By Dale M. Robertson, David A. Saad, and Dennis M. Heisey

In cooperation with the U.S. Environmental Protection Agency

Scientific Investigations Report 2006–5066

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

P. Patrick Leahy, Acting Director

U.S. Geological Survey, Reston, Virginia: 2006

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Cover photo:

Bad River discharging sediment into Lake Superior near Odanah, Wisconsin 

(U.S. Department of Agriculture, 2005)

Inset photo:

Confluence of the White River and Bad River (U.S. Department of Agriculture, 2005)
iii

Contents

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

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

Methods.................................................................................................................................................. 3

Suspended Sediment Data......................................................................................................... 3

Streamflow Data .......................................................................................................................... 4

Load, Yield, and Volumetrically Weighted Concentration Computations ........................... 4

Basin Boundaries and Environmental Characteristics ......................................................... 5

Statistical Methods...................................................................................................................... 5

Normalization....................................................................................................................... 5

Simultaneous Partial-Residualization Approach........................................................... 5

Correlations.......................................................................................................................... 8

Spatial Regression-Tree Analysis (SPARTA).................................................................. 8

Regional Patterns and Relations to Environmental Factors .......................................................... 8

Distribution in Water Quality...................................................................................................... 8

Median TSS Concentrations.................................................................................................... 10

Relation with Environmental Factors............................................................................. 10

Simultaneous Partial Residualization................................................................... 10

Effects of Multiple Environmental Variables ....................................................... 10

SPARTA Regionalization and Response to Changes in Land Use ............................ 14

TSS Yields.................................................................................................................................... 16

Relation with Environmental Factors............................................................................. 16

SPARTA Regionalization and Response to Changes in Land Use ............................ 21

Volumetrically Weighted TSS Concentrations...................................................................... 25

Relation with Environmental Factors............................................................................. 25

SPARTA Regionalization and Response to Changes in Land Use ............................ 25

Comparisons between Concentrations and Yields ....................................................................... 30

Comparisons of Factors Related to Concentrations and Yields......................................... 30

Comparisons of Reference Concentrations and Yields....................................................... 31

Ranking Sites and Prioritizing Rehabilitation Efforts .................................................................... 31

Summary and Conclusions................................................................................................................ 33

Literature Cited.................................................................................................................................... 34
iv

Figures

1–3. Maps showing:

 1. Major land-use/land-cover categories and national nutrient ecoregions

in the study area ........................................................................................................... 3

 2. Distributions of (A) median total suspended sediment/solids (TSS)

concentrations, (B) median annual TSS yields, and (C) median annual

volumetrically weighted TSS concentrations in the study area........................... 9

 3. Distributions of median logarithmically transformed (A) total suspended

sediment/solids (TSS) concentrations, (B) predicted TSS concentrations 

on the basis of the agricultural and urban land use in the basin, and (C) 

land-use-residualized TSS concentrations in study-area basins ...................... 11

4. Diagram showing regression-tree analysis results for logarithmically 

transformed (A) median total suspended sediment/solids (TSS) concentrations 

and (B) land-use-residualized TSS concentrations..................................................... 13

5. Map showing environmental total suspended sediment/solids (TSS) 

concentration (TSSC) zones in the study area ............................................................. 15

6. Graph showing response curves for total suspended sediment/solids (TSS) 

concentrations as a function of the percentage of agriculture in the basin, 

by environmental TSS concentration (TSSC) zone. ..................................................... 17

7. Map showing distributions of land-use-residualized logarithmically 

transformed annual total suspended sediment/solids (TSS) yields in 

study-area basins .............................................................................................................. 19

8. Diagram showing regression-tree results for logarithmically transformed 

(A) land-use-residualized total suspended sediment/solids (TSS) yields 

(TSSRes yield) with only land-use-residualized characteristics for the entire 

basin and (B) TSSRes yield with land-use-residualized characteristics for 

the entire basin and the land-use-residualized characteristics of the 

undammed areas ............................................................................................................... 20

9. Map showing environmental total suspended sediment/solids yield (TSSY) 

zones in the study area delineated based on regression-tree results using 

(A) land-use-residualized characteristics for the entire basin and (B) landuse-residualized characteristics for the entire basin and the land-useresidualized characteristics of the undammed areas................................................. 22

10. Graph showing response curves for total suspended sediment/solids 

(TSS) yields as a function of the percentage of agriculture in the basin, by 

environmental TSS yield (TSSY) zone ............................................................................ 24

11. Map showing distributions of land-use-residualized logarithmically 

transformed volumetrically weighted (VW) total suspended sediment/solids 

(TSS) concentrations in study-area basins................................................................... 27

12. Diagram showing regression-tree results for land-use-residualized 

volumetrically weighted total suspended sediment/solids (TSSRes) 

concentrations ................................................................................................................... 28

13. Map showing environmental volumetrically weighted total suspended 

sediment/solids concentrations (TSSV) zones in the study area.............................. 29
Tables

1. Summary statistics for total suspended sediment/solids and environmental

characteristics examined in the Great Lakes Region and adjacent areas................ 6

2. Pearson correlation coefficients (r) between logarithmically transformed

median total suspended sediment/solids (TSS) concentrations, urban area,

total agricultural area, and various environmental characteristics, and

between land-use-residualized TSS concentrations and land-useresidualized environmental characteristics.................................................................. 12

3. Reference median total suspended sediment/solids concentrations (TSSC)

and percentiles of all data in various TSSC zones....................................................... 16

4. Pearson correlation coefficients (r) between logarithmically transformed

total suspended sediment/solids (TSS) yields, urban area, agricultural

area, and various environmental characteristics for the entire basin and

characteristics of the undammed area of the basin.................................................... 18

5. Pearson correlation coefficients (r) between selected environmental

characteristics of the entire basin for sites with total suspended

sediment/solids yields....................................................................................................... 21

6. Reference median annual total suspended sediment/solids yields (TSSY) 

and percentiles of all data in various TSSY zones ....................................................... 23

7. Pearson correlation coefficients (r) between logarithmically transformed 

volumetrically weighted (VW) total suspended sediment/solids (TSS) 

concentration, total agricultural area, and various environmental 

characteristics and between land-use-residualized VW TSS concentration 

and land-use-residualized environmental characteristics for the entire basin...... 26

8. Reference median annual volumetrically weighted (VW) total suspended

sediment/solids concentrations (TSSV) and percentiles of all data in various 

TSSV zones. ........................................................................................................................ 27

Figures (cont.)

14. Graph showing response curves for volumetrically weighted (VW) total 

suspended sediment/solids (TSS) concentrations as a function of the 

percentage of agriculture in the basin, by VW TSS concentration 

(TSSV) zone......................................................................................................................... 30

15. Map showing distributions of (A) median total suspended sediment/solids 

(TSS) concentrations, (B) TSS yields, and (C) volumetrically weighted TSS 

concentrations exceeding the upper 95th percentile for predicted reference 

conditions............................................................................................................................ 32
vi

Conversion Factors and Abbreviated Units of 

Measurement

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square kilometer (km2) 247.1 acre

square kilometer (km2) 0.3861 square mile (mi2)

Rate

centimeter per hour (cm/hr) 0.03281 foot per hour (ft/hr)

centimeter per year (cm/yr) 0.03281 foot per year (ft/yr)

kilogram per square kilometer 

(kg/km2) 5.711 pound per square mile (lb/mi2

)

Mass

kilogram (kg) 2.205 pound avoirdupois (lb)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as 

follows:

°F=(1.8×°C)+32

Concentrations of chemical constituents in water are given either in milligrams per liter 

(mg/L) or micrograms per liter (μg/L).
vii

Acknowledgments

Technical Reviewers

Walter (Pete) Redmon, Biologist, U.S. Environmental Protection Agency, Chicago, Ill. 

Gregory E. Schwarz, Economist, U.S. Geological Survey, Reston, Va.

Editorial and Graphics

Michael Eberle, Technical Publications Editor, U.S. Geological Survey, Columbus, Ohio

Jennifer L. Bruce, Geographer, U.S. Geological Survey, Middleton, Wis.

Michelle M. Greenwood, Publications Unit Chief, U.S. Geological Survey, Middleton, Wis.

Approving Official 

Dorothy H. Tepper, Reports Improvement Advisor, U.S. Geological Survey, Reston, Va.
viii
Present and Reference Concentrations and Yields of 

Suspended Sediment in Streams in the Great Lakes 

Region and Adjacent Areas

By Dale M. Robertson, David A. Saad, and Dennis M. Heisey1

Abstract

In-stream suspended sediment and siltation and 

downstream sedimentation are common problems in 

surface waters throughout the United States. The most 

effective way to improve surface waters impaired by sediments is to reduce the contributions from human activities 

rather than try to reduce loadings from natural sources. 

Total suspended sediment/solids (TSS) concentration data 

were obtained from 964 streams in the Great Lakes, Ohio, 

Upper Mississippi, and Souris-Red-Rainy River Basins 

from 1951 to 2002. These data were used to estimate 

median concentrations, loads, yields, and volumetrically 

(flow) weighted (VW) concentrations where streamflow 

data were available. SPAtial Regression-Tree Analysis 

(SPARTA) was applied to land-use-adjusted (residualized) 

TSS data and environmental-characteristic data to determine the natural factors that best described the distribution 

of median and VW TSS concentrations and yields and to 

delineate zones with similar natural factors affecting TSS, 

enabling reference or natural concentrations and yields to 

be estimated.

Soil properties (clay and organic-matter content, 

erodibility, and permeability), basin slope, and land use 

(percentage of agriculture) were the factors most strongly 

related to the distribution of median and VW TSS concentrations. TSS yields were most strongly related to amount 

of precipitation and the resulting runoff, and secondarily to 

the factors related to high TSS concentrations. Reference 

median TSS concentrations ranged from 5 to 26 milligrams per liter (mg/L), reference median annual VW TSS 

concentrations ranged from 10 to 168 mg/L, and reference 

TSS yields ranged from about 980 to 90,000 kilograms per 

square kilometer per year.

1U.S. Geological Survey, National Wildlife Health Center, Madison, Wis.

Independent streams (streams with no overlapping 

drainage areas) with TSS data were ranked by how much 

their water quality exceeded reference concentrations 

and yields. Most streams exceeding reference conditions 

were in the central part of the study area, where agricultural activities are the most intensive; however, other sites 

exceeding reference conditions were identified outside 

of this area. Whether concentrations or yields should be 

considered in guiding rehabilitation efforts depends on 

whether in-stream or downstream effects are more important. Although this study attempted to obtain all available 

water-quality data for the study area, any actual prioritization of sites for remediation would need to rely on more 

extensive data collection or numerical models that can 

accurately simulate the effects of various human activities 

in a range of environmental settings.

Introduction

Suspended sediment and siltation are the most common stressors affecting streams throughout the United 

States (U.S. Environmental Protection Agency, 1998). Suspended sediment reduces the clarity in streams and affects 

sight-feeding fish; it also interferes with water-treatment 

processes and recreational uses of streams. Excessive siltation can bury and suffocate fish eggs and bottom-dwelling organisms. In addition to in-stream effects, excessive 

sediment loading causes sedimentation problems in many 

downstream lakes and harbors and water-clarity problems 

in nearshore areas.

The source of much of the sediment and associated 

nutrients in streams is from upland and streambank erosion. High erosion rates are usually thought to be associated with agricultural activities; however, studies have 

shown erosion rates to be strongly related to the type of 
soil and the slope of the terrain in the basin (for example, 

Monteith and Sonzogni, 1981; Robertson, 1997). Therefore, suspended sediment concentrations in streams are 

expected to be a function of the soil type, slope of the 

terrain, and land use in the basin. The load of sediment 

transported in streams is a function of the concentrations 

of suspended sediment and the volume of water moving 

through the stream; therefore, in addition to the factors 

affecting sediment concentration, precipitation and the 

resulting runoff also are expected to be important factors.

The most effective way to attain the designated uses 

of streams impaired by sediments is to reduce the contributions from human activities rather than try to reduce natural loadings. Natural or reference concentrations and loads 

and their response to human activities (such as agriculture) 

are expected to vary because of the regional differences in 

the factors affecting concentrations and loads of suspended 

sediment. By quantifying reference water quality, basins 

that are substantially affected by human activities could 

be more appropriately identified and, therefore, remedial 

actions could be prioritized.

Several approaches are used to estimate present and 

reference water quality in streams and describe how water 

quality responds to various natural and anthropogenic factors. One common approach is to use empirical relations 

between explanatory factors and specific water-quality 

characteristics. In this approach, the explanatory factors 

expected to be related to the distribution of a specific 

constituent for each monitored stream are defined or 

quantified and are used to develop empirical equations 

by use of linear- or nonlinear-regression techniques. This 

approach has commonly been used to estimate streamflow 

and chemical concentrations and loads for unmonitored 

streams (for example, Larson and Gilliom, 2001). Dodds 

and Oakes (2004) extended this approach to estimate reference water quality by developing multiple linear-regression models relating water quality to various anthropogenic factors such as the percentage of agriculture and 

percentage of urban area in the basin. The concentration of 

a constituent occurring in the absence of human activities 

(for example, 0 percent agriculture and urban areas) then 

represents the reference concentration. These relations can 

also be used to place confidence intervals on the estimated 

reference concentrations. This approach can be used to 

estimate reference conditions for specific sites or for 

broader areas having similar environmental characteristics, 

such as ecoregions (Omernik, 1995).

Various approaches are used to subdivide large areas 

into regions with similar environmental characteristics that 

should contain streams with similar reference or natural 

water quality and that should respond similarly to various 

factors, such as changes in land use in the basin. For many 

applications, such as establishing reference conditions, it is 

preferable to delineate these regions on the basis of physical characteristics that are not affected by human activities. 

Nevertheless, many approaches, such as ecoregion classifications, often rely on land use to delineate the regions or 

have difficulties compensating for the effects of land use. 

Land use not only directly affects water quality but it also 

is typically correlated with the factors used to define the 

regions. For example, Robertson and others (2006) have 

shown land use was the main factor in delineating a set of 

national nutrient ecoregions in the Midwest (fig. 1; U.S. 

Environmental Protection Agency, 1998). To remove the 

effects of land use and delineate zones with similar natural 

factors affecting water quality, Robertson and others (2006) 

developed SPAtial Regression-Tree Analysis (SPARTA). In 

this approach, land-use-adjusted (residualized) water-quality and environmental characteristics are first computed for 

each site (described in more detail later). Regression-tree 

analysis (described in more detail later) is then applied to 

the land-use-residualized data to determine the most statistically important environmental characteristics describing the distribution of a specific water-quality constituent. 

Geographic information describing the most important 

environmental characteristics of small basins throughout a 

study area is then used to subdivide a large area into relatively homogeneous environmental water-quality zones. 

SPARTA was used to delineate zones of similar reference 

suspended sediment and total phosphorus concentrations in 

streams throughout the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy Basins (Robertson and others, 

2006). These zones were shown to describe the differences 

in reference concentrations better than the national nutrient 

ecoregions that were delineated primarily by the distributions of different types of land use.

For each area with relatively similar environmental 

conditions, several approaches can be used to define its 

reference water quality and how its water quality responds 

to changes in land use. Robertson and others (2006) used 

the regression approach (Dodds and Oakes, 2004) to 

define reference concentrations for suspended sediment 

for streams in the various zones in the Great Lakes, Ohio, 

Upper Mississippi, and Souris-Red-Rainy Basins delineated 

with SPARTA; however, they did not examine loads, yields, 

and volumetrically (or flow) weighted (VW) concentrations 

of suspended sediment. The U.S. Environmental Protection Agency (USEPA) has suggested using the frequency 

distribution of data available for a specific region to define 

reference concentrations (U.S. Environmental Protection 

 Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
Agency, 2000). The concentration indicative of reference 

conditions has been suggested to be the lower 25th percentile of all the data or the upper 75th percentile of a subset 

of streams thought to be the least affected or impacted by 

human activity within a defined area.

In 2003, the U.S. Geological Survey (USGS) and the 

USEPA began a cooperative study in which suspended 

sediment and suspended solids data collected from 1951 

to 2002 in 964 streams throughout the Great Lakes, Ohio, 

Upper Mississippi, and Souris-Red-Rainy River Basins 

(Great Lakes region and adjacent areas) of the United 

States were used to describe the distribution of median 

concentrations, annual yields (annual load per unit area 

of the basin), and VW concentrations (total annual load 

divided total annual flow) of suspended sediment. Spatial regression-tree analyses were used to determine the 

environmental factors that were most important in describing the distribution of the concentrations and yields of 

suspended sediment and to delineate zones with similar 

reference or background median and VW concentrations 

and yields. A multiple regression approach was used to 

quantify reference conditions in the various zones and 

describe how the concentrations and yields in the various 

zones respond to human activities (changes in the amount 

of agriculture). The sites were then ranked on the basis of 

their anthropogenic sources of sediment. The results of this 

study are summarized in this report.

Methods

Suspended Sediment Data

Water-quality data for this analysis were limited to 

total suspended sediment and suspended solids concentrations measured in 964 streams in the study area for 

which sufficient data were available from 1951 to 2002 

(described below). For purpose of analysis, total suspended 

sediment and total suspended solids data were combined 

into one constituent, TSS. Gray and others (2000) found 

6

6

8

8

8

9

7

7

7

11

11

11

0

0

250 500 KILOMETERS

125 250 MILES

Cropland

Cropland and pasture

Forest

Wetland

Urban

Water

EXPLANATION

Nutrient ecoregion

Corn Belt and Northern Great Plains

Mostly Glaciated Dairy Region

Glaciated Upper Midwest and Northeast

Southeast Temperate Forested Plains

Central/Eastern Forested Uplands

Land use/land cover

6

7

8

9

11

102° 84°

45°

36°

Base from USGS digital line graphs

Figure 1. Major land-use/land-cover categories and national nutrient ecoregions (U.S. Environmental Protection Agency, 1998) 

in the study area.

Methods 
that total suspended solids were generally less than total 

suspended sediment by about 25 to 34 percent; however, there was considerable variability in this relation 

among different areas of the United States. The difference 

between total suspended sediment and total suspended 

solids is small compared to spatial differences that exist 

throughout the study area; therefore, no adjustment factor 

was applied. These data were assembled from data collected by the USGS and the major sampling agency(s) in 

each state. USGS data were retrieved from the National 

Water Information System (NWIS), the National WaterQuality Assessment Program’s Data Warehouse, and the 

Upper Mississippi Basin Loading database. All of the 

state-agency data were obtained from USEPA legacy and 

modernized STORET databases, except data from Illinois 

(collected by the Illinois Environmental Protection Agency 

and contained in NWIS), Indiana (collected by the Indiana 

Department of Environmental Management; C. Bell, 

written commun., 2002) and Wisconsin (collected by the 

Wisconsin Department of Natural Resources; J. Ruppel, 

written commun., 2004).

The number of samples collected at each site was 

highly variable, and the period of record ranged from 2 

years to decades. To compute temporally unbiased median 

concentrations for each site, the data were subsampled to 

include only one record per constituent per month per year. 

The record included in the statistical summaries was the 

one collected closest to the middle of the month (midmonthly sample). All data reported at less than the detection limit were set to one-half of the detection limit. A 

selection requirement for a site to be included for a median 

concentration was that it had at least 15 midmonthly 

samples. A median concentration was used because medians have been used to establish criteria for streams (U.S. 

Environmental Protection Agency, 2000), and a median 

value reduces the effects of outliers and values reported at 

less than a detection limit. Only independent streams (675 

streams) were used in the statistical analyses. Independent 

streams are those with completely different (nonoverlapping) drainage basins. Additional larger, nonindependent 

streams were used for graphical purposes.

A selection requirement for a site to be used to compute median annual loads and VW concentrations was that 

it had at least 25 samples over a period of at least 2 years 

and at least 5 years of complete daily streamflow records; 

therefore, a site needed at least 5 years of complete 

estimated loads to compute a median value. An annual 

load was computed only if there were no missing daily 

flows for that year. The requirement of having 5 years of 

estimated load data to compute median values was used 

to reduce the potentially large effects of natural climatic 

variability. Annual VW concentrations were computed by 

dividing the estimated annual loads by the annual flows. 

Again, only independent streams were used in the statistical analyses; additional larger streams were used for 

graphical purposes.

Streamflow Data

An attempt was made to locate a nearby streamflow 

gage for each potential load site. A nearby gage is defined 

as a gage on the same stream or a nearby stream with a 

drainage-area ratio (water-quality station area divided 

by gaged area) between about 0.3 and 2.0. Nearby gages 

with long periods of record were selected over gages with 

shorter periods of record. In all, 550 sites had sufficient 

data to compute loads, and 367 of those sites were classified as independent and used for statistical analyses.

Load, Yield, and Volumetrically Weighted 

Concentration Computations

Annual loads (calculated by summing daily loads) 

were estimated by a regression approach by use of the 

Fluxmaster program (Schwarz and others, 2006) that 

implements the minimum variance unbiased estimator procedure developed by Cohn and others (1989). In this study, 

estimated daily loads (L) were computed on the basis of 

relations between constituent load (in kilograms) and three 

variables: streamflow (Q, in cubic meters per day), time of 

the year (T, in radians), and DECTIME (years in decimal 

format, used to adjust for temporal trends at specific sites). 

The general form of the model was

ln(L) = a + b[ln(Q) - c] + d[sin(T)] + 

e[cos(T)] + f [DECTIME] (1)

Values for the regression coefficients (a, b, c, d, e, and f ) 

were computed for each site by the use of multiple-regression analyses between daily loads (daily average streamflows multiplied by instantaneously measured concentrations, in milligrams per liter) and Q, T, and DECTIME. All 

TSS data from 1951 to 2002 with available corresponding 

daily streamflows were used in the analysis to estimate the 

regression coefficients. Daily loads were then estimated for 

each site from 1971 to 2002. Because a natural logarithmic 

transformation was used in equation 1, daily loads were 

adjusted to account for a retransformation bias by use of 

the minimum variance unbiased estimate procedure (Cohn 

and others, 1989). Total annual loads were then computed 

 Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
for all years that had no missing daily values (no missing 

daily flows). Median annual loads, yields, and VW concentrations (total annual load divided by total annual flow) 

were then computed for each site.

The accuracy of the regression approach to estimate 

annual loads based on sparse temporal data, as was the 

case for many of the sites in this study, was evaluated by 

Robertson (2003). It was found that using this method with 

more than 2­­­­­­­­–3 years of data resulted in median annual 

loads with standard errors of about 50 percent. Spatial 

variability in yield and VW concentration data was several 

orders of magnitude; therefore, the effects of the errors 

in estimating yields and VW concentrations should be 

minimal.

Basin Boundaries and Environmental 

Characteristics

Boundaries for most large basins (greater than about 

500 km2) were delineated with a geographic information 

system (GIS) using the USEPA River Reach file (Alexander and others, 1999) and 1-km digital elevation data for 

North America (Nolan and others, 2002). Boundaries for 

most small basins were manually digitized from 7.5-minute USGS topographic quadrangle maps or 1:100,000-

scale digital coverage of the USGS Hydrologic Unit maps 

(Seaber and others, 1987) refined with streams included 

in the National Hydrography Dataset (U.S. Geological 

Survey, 1999a). The environmental characteristics thought 

to affect or be related to the TSS in the streams used in 

this study included land use/land cover (U.S. Geological 

Survey, 2000), thickness of quaternary deposits (Soller and 

Packard, 1998), soil characteristics (from the USSOILS 

digital coverage of the State Soil Geographic (STATSGO) 

database; Schwarz and Alexander, 1995), types of surficial 

deposits (Fullerton and others, 2003), annual air temperature and precipitation (National Climatic Data Center, 

2002), annual evaporation (Farnsworth and others, 1982), 

mean land-surface slope (based on 30-m digital elevation 

model data resampled to 100 m; U.S. Geological Survey, 

1999b), and average annual runoff (Gebert and others, 

1987). All characteristics were compiled in digital form by 

use of a GIS and used to compute the average or percentage of each environmental characteristic for each of the 

964 basins. A summary of the environmental characteristics 

for all of the basins used in this study is given in table 1.

In the independent basins with yield data, areas 

upstream of dams were manually delineated with a GIS 

based on the locations of dams identified in the “National 

Inventory of Dams” (U.S. Army Corps of Engineers, 2005) 

and “Major Dams of the United States” (U.S. Geological 

Survey, 1999c) and the stream networks included in the 

National Hydrography Dataset (U.S. Geological Survey, 

1999a). Where multiple dams were present within a basin, 

the most-downstream dam on the main stem or tributary 

was used to delineate the dammed part of the basin. Dams 

that were on small headwater streams, and those that were 

poorly located or poorly attributed were not included in the 

delineations.

Statistical Methods

The SAS statistical software package (SAS Institute, 

Inc., 1989) was used for all statistical analyses except for 

the regression-tree analyses, which were done by use of 

the S-PLUS statistical software package (Insightful Corporation, 2001).

Normalization

Before statistical analyses, all TSS concentrations 

and yields were logarithmically transformed (natural 

logarithm). This transformation improved the normality of 

the data, although not always to the 5-percent significance 

level (Shapiro-Wilk normality test; Royston, 1982).

Simultaneous Partial-Residualization 

Approach

A simultaneous partial-residualization approach, 

related to partial correlation, was used to remove the 

agricultural and urban effects from the TSS concentrations 

and yields and each of the environmental characteristics 

because land use not only directly affects water quality but 

it also is typically correlated with the environmental characteristics used to define regions of similar water quality. 

In simple regression, the relationship between a dependent 

variable Y (for example, TSS concentration) and a predictor X1 (for example, clay content of the soil in the basin) 

is measured by the sample correlation rYX1. If variable X1 

is regressed on the variable X2 (for example, percentage of 

agriculture in the basin), then the regression equation is Xˆ1 

= β

ˆ

0

 + β

ˆ

1

X2. To adjust X1 for the effects of X2, a “residualized X1”, X1Res, is created by computing X1Res = X1 – Xˆ1. 

In a manner similar to simple correlation, the strength of 

the relation between X1 and Y adjusted for X2 is obtained 

by the correlation between the residuals for Y on X2 (YRes) 

and the residuals for X1 on X2 (X1Res). The resulting correlation is the partial correlation of Y and X1 adjusted for 

X2; that is, the strength of the relation between Y and X1 

Methods 
Table 1. Summary statistics for total suspended sediment/solids and environmental characteristics examined in the Great Lakes Region and adjacent areas. Summary statistics 

for land use, surficial deposits, soil properties, and basin characteristics are given for only the independent basins (basins with no overlapping drainage areas).[TSS, total suspended sediment/solids; VW, volumetrically weighted; N, number of sites; mg/L, milligram per liter; kg/km2, kilogram per square kilometer; %, percent; m, meter; cm/cm, centimeter per centimeter; --, no data or not applicable; cm/hr, centimeter per hour; C, Celsius; cm, centimeter; cm/yr, centimeter per year; km2, square kilometer; mm, millimeter; study area shown in figure 1]

Characteristic Units

TSS concentration data set TSS yield and VW concentration data sets

Median Mean Standard deviation Minimum Maximum Median Mean Standard deviation Minimum Maximum

Water quality

TSS concentration (mg/L)

All sites (N = 964) mg/L 24.0 112 496 0.3 7,060 -- -- -- -- --Independent basins (N = 675) mg/L 21.0 120 566 0.3 7,060 -- -- -- -- --

TSS annual yields (kg/km2/yr)

All sites (N = 550) kg/km2 -- -- -- -- -- 35,400 85,100 226,000 22 3,373,000Independent basins (N = 367) kg/km2 -- -- -- -- -- 34,700 89,500 258,000 73 3,373,000

TSS annual volumetrically weighted (VW) concentration (mg/L)

All sites (N = 550) mg/L -- -- -- -- -- 107 248 654 2.1 9,300Independent basins (N = 367) mg/L -- -- -- -- -- 98.5 267 768 2.1 9,300

Land use

Total forest

% 26.8 38.0 32.8 0.0 100.0 22.4 33.1 29.6 0.0 100.0

Total agriculture

% 57.0 51.1 34.0 0.0 98.9 66.5 55.7 32.3 0.0 98.9

Total wetland

% 0.8 3.8 7.6 0.0 51.6 1.1 4.5 8.5 0.0 51.3

Transitional area

% 0.0 0.2 0.5 0.0 6.2 0.0 0.1 0.4 0.0 3.1

Grassland

% 0.0 1.0 3.2 0.0 42.6 0.0 1.0 3.0 0.0 33.5

Barren

% 0.0 0.3 1.0 0.0 13.7 0.0 1.7 5.2 0.0 6.3

Urban

% 1.1 4.7 12.7 0.0 96.7 1.3 4.2 10.7 0.0 81.4

Surficial deposits

Mean thickness m 14.4 30.3 34.8 7.6 249.9 17.9 31.3 32.3 7.6 227.3

Clay

% 0.0 13.0 29.5 0.0 100.0 0.0 13.8 29.0 0.0 100.0

Weathered bedrock

% 0.0 30.2 44.3 0.0 100.0 0.0 24.5 40.7 0.0 100.0

Mixed

% 46.4 46.2 42.4 0.0 100.0 53.6 50.0 40.5 0.0 100.0

Organic

% 0.0 0.6 3.3 0.0 39.9 0.0 0.8 3.8 0.0 39.9

Sand and gravel

% 0.0 10.0 20.4 0.0 100.0 0.4 10.9 19.3 0.0 100.0

 Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
Characteristic Units

TSS concentration data set TSS yield and VW concentration data sets

Median Mean Standard deviation Minimum Maximum Median Mean Standard deviation Minimum Maximum

Soil properties

Available water capacity cm/cm 0.14 0.14 0.03 0.07 0.23 0.15 0.15 0.03 0.07 0.23Soil erodibility -- 0.30 0.30 0.08 0.10 0.47 0.31 0.31 0.08 0.11 0.47

Clay content1 % 24.03 23.13 8.07 3.78 53.10 25.07 23.75 7.95 3.78 44.94Organic-matter content2 % 1.00 2.74 3.96 0.20 24.34 1.10 2.82 3.92 0.20 21.86

Permeability cm/hr 4.82 6.86 5.82 0.76 29.48 4.27 6.28 5.45 0.90 29.96

Soil slope % 5.93 11.78 12.48 0.64 50.90 5.37 9.30 9.83 0.64 50.90

Basin characteristics

Air temperature degrees C 9.39 9.14 2.61 2.72 14.61 9.44 9.05 -15.13 2.72 14.60

Precipitation cm 96.48 97.05 17.89 39.79 167.31 96.54 96.42 17.43 43.48 167.21Evaporation cm 83.82 84.57 9.95 92.83 106.68 83.82 85.38 9.75 68.58 106.68

Precipication minus evaporation cm 9.23 12.48 21.60 -48.66 101.17 9.11 11.04 20.43 -45.42 78.31

Basin slope degrees 1.45 3.57 4.38 0.18 20.67 1.30 2.73 3.50 0.18 19.98Runoff cm/yr 30.97 33.34 15.14 0.69 96.35 11.95 12.67 5.75 0.27 37.93

Watershed area km2 241 515 1,630 1.00 3,040 477 1,140 2,530 1.1 30,000Undammed area km2 -- -- -- -- -- 342 630 1,330 0.5 21,200Undammed fraction -- -- -- -- -- -- 0.95 0.78 0.31 0.00 1.00

1 Percentage of soil particles less than 2 mm in size.2 Percentage by weight.

Table 1. Summary statistics for total suspended sediment/solids and environmental characteristics examined in the Great Lakes Region and adjacent areas. Summary statistics for land use, surficial deposits, soil properties, and basin characteristics are given for only the independent basins (basins with no overlapping drainage areas)—Continued.[TSS, total suspended sediment/solids; VW, volumetrically weighted; N, number of sites; mg/L, milligram per liter; kg/km2, kilogram per square kilometer; %, percent; m, meter; cm/cm, centimeter per centimeter; --, no data or not applicable; cm/hr, centimeter per hour; C, Celsius; cm, centimeter; cm/yr, centimeter per year; km2, square kilometer; mm, millimeter; study area shown in figure 1]

Methods 
adjusting for the effects of X2. This approach is easily 

extended to control for more than one variable; X2 can be 

replaced by an arbitrary set of variables. In this study, the 

water-quality constituents and environmental characteristics were adjusted for the percentage of agriculture and 

urban areas in the basin. 

Correlations

Pearson correlation analyses were done to determine 

the direction and magnitude of linear relations between 

each logarithmically transformed water-quality characteristic and the environmental factors. Correlations were done 

with the original data and with the land-use-residualized 

data.

Spatial Regression-Tree Analysis (SPARTA)

In traditional linear-regression analysis, a continuous 

response variable is assumed to be a linear function of a 

set of explanatory variables. This assumption is often unrealistic, and departures from linearity can result in underestimating—or completely discounting—the importance of 

key explanatory variables. Regression-tree analysis (Breiman and others, 1984) avoids these problems by requiring 

no assumptions about the type of relations between the 

explanatory variables and response variable. Instead of 

assuming linear relations, regression-tree analysis sequentially partitions the values of each explanatory variable 

into two groups, computes mean values of the response 

variable for each group, and then computes square errors 

for each partition. At each step, all of response variables 

are scanned, and the response variable and its breakpoint 

that minimize the least-square-error criterion are chosen. 

The least-square-error criterion seeks breakpoints that 

maximize the variance of interpartition means relative 

to intrapartition variance. This approach partitions the 

independent variable space into increasingly homogeneous 

regions. The end result of this sequential process is a 

branching diagram.

Regression-tree analyses were done with the original 

data and the corresponding environmental characteristics 

data and also with the land-use-residualized water-quality 

data and the corresponding land-use-residualized environmental characteristic data to determine the most statistically significant environmental characteristics and their 

breakpoints to describe the distribution of TSS concentrations and yields. In the analysis, the minimum number of 

observations used to define a subgroup was set to 50 to 

avoid small outlier groups.

Regression-tree results not only identify the environmental characteristics most strongly related to water 

quality; of additional importance is that the values used 

to define the branches can be used to guide the spatial 

delineation of regions or zones with similar environmental 

characteristics. Spatially delineating the regions (SPA) 

from the results of the regression-tree analyses (RTA) 

led to the process referred to as SPARTA (Robertson and 

Saad, 2003). To delineate these zones, the study area was 

first subdivided into approximately 10,000 small drainage 

basins (mean area of 144 km2) using the USEPA River 

Reach file (Alexander and others, 1999) and 1-km digital 

elevation data for North America (Nolan and others, 2002) 

and the environmental characteristics of each basin were 

computed with a GIS. Then, each of the 10,000 basins was 

classified into a specific environmental water-quality zone 

on the basis of the regression-tree results.

In this study, the residualization process was used 

to adjust all of the non-land-use variables for the average 

effects of agriculture and urban areas in the basin. The 

resulting land-use-residualized concentrations (or yields) 

of the response variable reflect differences from what 

would be expected given the land use in the basins. These 

differences can be associated with differences in the reference concentrations (where the percentages of agriculture 

and urban areas in the basin are zero) or differences in the 

response throughout a range in land uses (percentages of 

agriculture and urban areas) in the study area. Therefore, 

applying SPARTA to the land-use-residualized data should 

result in areas of different reference concentrations and 

(or) areas with different responses to changes in land use. 

Because factors influencing median TSS concentrations, 

yields, and VW concentrations may differ, the zones of 

similar environmental characteristics for each constituent 

also may differ.

Regional Patterns and Relations to 

Environmental Factors

Distribution in Water Quality

Median midmonthly TSS concentrations ranged from 

0.3 to 7,060 mg/L (table 1). The overall median and mean 

were 24.0 and 112 mg/L, respectively (21.0 and 120 mg/L, 

for independent basins). Highest concentrations were in 

the western, southwestern, and south-central parts of the 

study area (fig. 2A). Lowest concentrations were mostly 

in northern areas of Wisconsin and Michigan and in the 

northeastern part of the study area.

 Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
A. Median TSS concentrations

0.3 to 7.5

Concentration,

in milligrams per liter

7.6 to 18.0

18.1 to 34.0

34.1 to 65.0

65.1 to 7,060

B. Median annual TSS yields

22 to 7,925

Yield, in kilograms 

per square kilometer per year

7,926 to 24,000

24,001 to 47,900

47,901 to 93,300

93,301 to 3,370,000

C. Median annual volumetrically

weighted TSS concentrations

2.1 to 28.8

Concentration,

in milligrams per liter

28.9 to 75.0

75.1 to 140

141 to 262

263 to 9,300

Figure 2. Distributions of (A) median total suspended sediment/solids (TSS) concentrations, (B) median annual TSS yields, and 

(C) median annual volumetrically weighted TSS concentrations in the study area.

Regional Patterns And Relations To Environmental Factors 
Median annual TSS yields ranged from 22 to 

3,373,000 kg/km2 (table 1). The overall median and mean 

were 35,400 and 85,100 kg/km2, respectively (34,700 to 

89,500 kg/km2 for independent basins). Highest yields 

were throughout the southern half of the study area (fig. 

2B). Lowest yields were throughout the northern half of 

the study area. The major difference in the distributions 

in concentrations and yields was in the northwestern part 

of the study area, which had high concentrations but low 

yields.

Median annual VW TSS concentrations ranged from 

2.1 to 9,300 mg/L (table 1). The overall median and mean 

were 107 and 248 mg/L, respectively (98.5 to 267 mg/L 

for independent basins). The distribution in VW concentrations was similar to that for median concentrations, 

with the highest concentrations along the western side of 

the study area (North Dakota, southern Minnesota) and 

through the central part of the study area (fig. 2C). The 

highest VW concentrations shifted a little south compared 

to the median concentrations. Lowest concentrations were 

in northern areas of Minnesota, Wisconsin, and Michigan 

and in the northeastern part of the study area.

Patterns in median and VW concentrations closely 

resembled those of the agricultural areas in the study area 

(fig. 1), with highest concentrations corresponding to areas 

with either cropland or cropland and pasture. The major 

difference was in the southeastern part of the study area, 

where agriculture is not widespread, yet relatively high 

concentrations were measured. TSS yields were much less 

related to agriculture than were concentrations. Low yields 

were found in the northwestern part of the study area, 

which is mostly cropland, and high yields were found in 

the southeast part, which is mostly forested.

Median TSS Concentrations

Relation with Environmental Factors

Pearson correlation coefficients (r values) between 

logarithmically transformed median TSS concentrations 

(fig. 3A) and each environmental factor are listed in table 2 

(based on data for 675 independent streams only). Concentrations were significantly correlated with many environmental variables; however, they were most highly correlated with factors describing the basin’s soil properties, 

evaporation, runoff, and percentage of agriculture. Many 

of the environmental characteristics (evaporation, precipitation, basin slope, and several surficial-deposit characteristics and soil properties) were strongly correlated with 

land use, mainly the percentage of agriculture and forest in 

the basin. For example, evaporation had an r value of 0.66 

with the percentage of agriculture (table 2). Therefore, 

even if the land-use characteristics were not used in further 

statistical analyses, their effects could be incorporated into 

the final results by using variables such as evaporation.

Simultaneous Partial Residualization

To remove the effects of land use from median TSS 

concentrations and from each environmental characteristic, a simultaneous partial-residualization analysis was 

done with the percentages of agriculture (Ag%) and urban 

(Urb%) areas in the basin. Land-use-residualized TSS 

concentrations (TSSRes) were obtained with

ln TSSRes = lnTSSMeasured – lnTSSPredicted (2)

where ln TSSPredicted = 2.273 + 0.015Ag% + 

0.015 Urb% (r2 = 0.12)

The distribution of predicted median TSS concentrations (fig. 3B) closely resembled the land-use patterns in 

figure 1. After the relations with agriculture and urban land 

use were removed, high TSSRes were found throughout the 

entire study area but were still primarily in the western and 

southern parts (fig. 3C). Residual transformations were 

also done on all of the environmental characteristics.

Pearson correlation coefficients between TSSRes concentrations and the land-use-residualized environmental 

characteristics are listed in table 2. TSSRes concentrations 

were still strongly correlated with many land-use-residualized soil properties and basin slope. Soil erodibility 

remained the variable most strongly correlated with TSS 

concentrations. Several characteristics that were strongly 

correlated to median concentrations were much less correlated to land-use-residualized concentrations; for example, 

evaporation and runoff.

Effects of Multiple Environmental Variables

Regression-tree analyses were done with all environmental characteristics except characteristics describing 

land use to try to determine which natural environmental characteristics were most statistically significant in 

describing the distribution of median TSS concentrations 

(fig. 4A). Soil erodibility was the independent variable 

chosen for the first subdivision. In that first subdivision, 

the two groups were sites with erodibility less than (<) 

0.26 or greater than or equal to (≥) 0.26. The subgroup 

with erodibility < 0.26 was further subdivided on the 

basis of whether the soil clay content was < 14.8 percent 

10 Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
A. Measured TSS concentrations

-1.2 to 1.8

ln (concentration, 

in milligrams per liter)

1.9 to 2.8

2.9 to 3.4

3.5 to 4.0

4.1 to 8.9

B. Predicted TSS concentrations

-1.2 to 1.8

ln (concentration, 

in milligrams per liter)

1.9 to 2.8

2.9 to 3.4

3.5 to 4.0

4.1 to 8.9

C. Land-use-residualized

TSS (TSSRes ) concentrations

-4.25 to -1.04

Residual ln (concentration, 

in milligrams per liter)

-1.03 to -0.41

-0.40 to 0.13

0.14 to 0.72

0.73 to 5.43

Figure 3. Distributions of median logarithmically transformed (natural logarithm) (A) total suspended sediment/solids (TSS) 

concentrations, (B) predicted TSS concentrations on the basis of the agricultural and urban land use in the basin, and (C) landuse-residualized TSS concentrations in study-area basins.

Regional Patterns And Relations To Environmental Factors 11
Table 2. Pearson correlation coefficients (r) between logarithmically transformed median total suspended sediment/solids 

(TSS) concentrations, urban area, total agricultural area, and various environmental characteristics, and between land-useresidualized TSS concentrations and land-use-residualized environmental characteristics.

[r values with an absolute value greater than 0.08 are significant at p less than 0.05 with 675 sites; --, not applicable; ln, natural logarithm; r values with 

an absolute value greater than 0.25 are in bold]

Basin characteristic ln

(TSS concentrations) Urban area Totalagricultural area

Residualized TSS concentrations 

with residual variables

Land use

Total forest -0.29 -0.22 -0.89 --

Total agriculture 0.33 -0.16 1.00 --

Total wetland -0.27 -0.06 -0.27 --

Transitional area -0.16 -0.08 -0.38 --

Grassland 0.04 0.05 0.02 --

Barren 0.00 0.01 -0.28 --

Urban 0.08 1.00 -0.16 --

Surficial deposits

Mean thickness 0.00 0.07 0.17 -0.07

Clay 0.11 0.27 0.16 0.01

Weathered bedrock -0.05 -0.15 -0.51 0.19

Mixed 0.10 -0.08 0.49 -0.07

Organic -0.15 -0.06 -0.19 -0.09

Sand and gravel -0.22 0.10 -0.09 -0.22

Soil properties

Available water capacity 0.31 0.01 0.62 0.13

Soil erodibility 0.42 0.02 0.54 0.30

Clay content 0.37 0.11 0.36 0.27

Organic-matter content -0.26 0.02 -0.16 -0.23

Permeability -0.36 0.02 -0.41 -0.26

Soil slope -0.09 -0.16 -0.65 0.23

Basin characteristics

Air temperature 0.15 0.03 -0.03 0.17

Precipitation -0.04 -0.06 -0.32 0.09

Evaporation 0.38 0.11 0.66 0.21

Precipitation minus evaporation -0.21 -0.10 -0.57 0.01

Basin slope -0.07 -0.17 -0.63 0.26

Runoff -0.27 -0.12 -0.61 -0.06

Watershed area – ln transformed 0.00 -0.19 0.17 -0.03

12  Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
Regional Patterns And Relations To Environmental Factors 13

A. TSS concentration without land-use characteristicsAll ln TSS(675 sites) Erodibility < 0.26

Evaporation < 93.8 centimeters per year No (469 sites)

Yes (206 sites)

Group 1: 1.87Group 2: 2.43Group 3: 2.90Group 4: 3.48Group 5: 4.10

B. Land-use-residualized TSS (TSSRes

) concentration with land-use-residualized environmental characteristics

No (102 sites)Yes (104 sites)No (159 sites)Yes (310 sites)

No (159 sites)Yes (151 sites)

Clay content < 14.8 percent

Erodibility < 0.32

All ln TSSRes(675 sites) Clay contentRes < -4.4 percent

RunoffRes < 3.3 centimeters per year

No (495 sites)Yes (180 sites)

Group 1: -0.90Group 2: -0.47Group 3: 0.22Group 4: 0.87Group 5: -0.27

No (66 sites)Yes (114 sites)No (128 sites)Yes (367 sites)

No (135 sites)Yes (232 sites)

Clay contentRes < -8.0 percent

Basin slopeRes < 0.76 degrees

Group: mean ln

(concentration)

Group: mean ln

(residual concentration)

Figure 4. Regression-tree analysis results for logarithmically transformed (

A) median total suspended sediment/solids (TSS) concentrations (land-use characteristics were 

excluded from the analysis) and (

B) land-use-residualized TSS concentrations. Final groups are color-coded on the basis of mean concentrations or mean land-use-residualized 

concentrations from green (lowest) to red (highest). 
(group 1, with 104 sites and the lowest mean ln concentration of 1.87) or ≥ 14.8 percent (group 2, with 102 sites and 

second lowest mean concentration). The subgroup with 

erodibility ≥ 0.26 was further subdivided into sites with 

evaporation < 93.8 cm/yr or ≥ 93.8 cm/yr (group 5, with 

159 sites and the highest mean ln concentration of 4.10). 

The subgroup with evaporation < 93.8 cm/yr was further 

subdivided into sites with erodibility < 0.32 (group 3, with 

151 sites) and ≥ 0.32 (group 4, with 159 sites and second 

highest mean concentration).

Although land-use characteristics were not directly 

included in the regression-tree analysis, they may have 

indirectly affected the results because of their strong correlations with evaporation. Therefore, to remove the effects 

of the correlations with land use, regression-tree analysis 

was applied with the TSSRes data and land-use-residualized 

environmental characteristic data (fig. 4B). Residualized 

soil clay content (clayRes) was now the independent variable 

chosen for the first subdivision. In that first subdivision, the 

two groups were sites with clayRes < or ≥ -4.4 percent. The 

subgroup with clayRes < -4.4 percent was further subdivided 

on the basis of whether clayRes was < -8.0 percent (group 1, 

with 114 sites and residuals representing the lowest mean 

TSSRes concentration) or ≥ -8.0 percent (group 2, with 66 

sites and residuals representing the second lowest concentration). The subgroup with clayRes ≥ -4.4 percent was 

further subdivided on the basis of whether the residualized 

runoff (runoffRes) was < or ≥ 3.3 cm/yr (group 5, with 128 

sites). Sites with runoffRes < 3.3 cm/yr were further subdivided into those with residualized basin slope (slopeRes) 

< 0.76 degrees (group 3, with 232 sites) or slopeRes ≥ 0.76 

degrees (group 4, with 135 sites and residuals representing the highest mean concentration). Differences between 

groups were compared with the Kruskal-Wallis test followed by the Tukey multiple-comparison test. Residual 

concentrations in group 4 were significantly higher 

(p < 0.05) than those in group 3, which in turn were significantly higher than those in groups 2 and 5 (which were 

not significantly different from one another), which in turn 

were significantly higher than those in group 1. Therefore, 

although the drainage basins of streams in groups 2 and 5 

had different environmental characteristics, their overall 

effects on TSSRes were similar.

Correlations and regression-tree results indicate that 

soil properties are the primary natural factors related to 

the distribution of median TSS concentrations; however, 

land-use effects may influence the apparent importance of 

secondary factors such as evaporation. However, simply 

omitting factors describing land use and reanalyzing the 

data may or may not give a true indication of secondary factors affecting TSS concentrations because of the 

correlations of some of these factors with land use. For 

example, if land-use characteristics are omitted from the 

analysis, evaporation remains an important factor related 

to the median TSS distribution. The reason for this is 

probably not that evaporation directly affects TSS but 

that a specific evaporation value occurred near the border 

between mixed cropland and mostly forested areas. After 

removing the correlations with land use, residualized 

evaporation was still correlated with residualized TSS, but 

not as strongly correlated as many other factors. The primary natural factors influencing the distribution of median 

TSS concentrations were several soil properties (primarily 

the clay content of the soil) and basin slope.

SPARTA Regionalization and Response to 

Changes in Land Use

Results of the regression-tree analysis for TSSRes

concentrations were used to classify each of the approximately 10,000 basins in the study area into five specific 

environmental TSS concentration (TSSC) zones (fig. 5) 

based on the land-use-residualized characteristics of each 

basin and the breakpoints defined in figure 4B. Each TSSC 

zone represents an area having characteristics similar to 

one of the groups in figure 4B. For example, TSSC zone 

1 represents streams in areas with soils having the lowest 

clay content. By applying SPARTA to the land-use-residualized data, each zone delineates streams that should have 

similar minimally impacted conditions (similar reference 

conditions with no agriculture and no urban land) and (or) 

a similar response to changes in land use.

To define reference or background concentrations and 

describe the responses or changes in concentration as a 

function of land use in each zone, a multiple linear-regression model relating water quality to the percentage of 

agriculture and urban areas was used (similar to that used 

by Dodds and Oakes, 2004):

 ln (median TSS concentration) = aAg% + bUrb% + c (3)

where a, b, and c are regression coefficients determined 

for each zone. With this approach, the concentration of a 

constituent occurring in the absence of human activities 

(no agriculture and no urban land) represents the reference 

concentration. The reference concentration for a zone was 

estimated as ec, where “e” is the base of the natural system 

of logarithms. A bias correction is typically applied when 

logarithmic regression is used; however, it was not used 

here because the goal was to estimate median reference 

14  Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
concentrations rather than mean reference concentrations. 

All estimated median reference concentrations, standard 

errors, and 95-percent confidence intervals for the median 

concentrations are given in table 3.

Median reference TSS concentrations based on the 

regression approach ranged from about 4 to 6 mg/L in 

TSSC zones 1 and 2 to 17.2 mg/L in TSSC zones 3 and 4. 

The upper 95-percent confidence intervals of these estimates ranged from 5.1 mg/L in zone 1 to about 26 mg/L 

in zones 3 and 4. Lowest reference concentrations (green 

and light green) were found in central Minnesota, Wisconsin (except for the southeast and southwest parts of these 

states), Michigan, and the eastern part of the study area 

(fig. 5). The five zones can be combined into three main 

categories: TSSC zones 1 and 2 with a reference concentration of about 5 mg/L, zone 5 with a reference concentration of about 9 mg/L, and zones 3 and 4 with a reference 

concentration of about 17 mg/L. The 95-percent confidence 

limits for the reference concentrations of zone 5 slightly 

overlapped with those of the other two main categories.

The USEPA has suggested using the frequency 

distribution of data available for a specific area to define 

a reference concentration (U.S. Environmental Protection 

Agency, 2000). On the basis of the 25th percentile of all of 

the data in each zone, reference TSS concentrations range 

from 4 mg/L in TSSC zone 1 to 19 mg/L in zone 3 (table 

3). Robertson and others (2006) showed that the effects 

of the differences in land use within various areas can 

strongly affect the results of the percentile approach for 

constituents correlated with land use, such as TSS. Therefore, it is difficult to determine whether the differences 

between zones 3 and 4 found using the 25th-percentile 

approach are real or simply an effect of more agriculture 

in zone 3. Therefore, the values based on the regression 

approach are considered the more accurate estimates of 

reference concentrations.

TSS concentration (TSSC)

zones

1

2

3

4

5

0 100 200 MILES

0 100 200 KILOMETERS

Base from USGS digital line graphs

102° 84°

45°

36°

Figure 5. Environmental total suspended sediment/solids (TSS) concentration (TSSC) zones in the study area. Each zone was 

delineated on the basis of the results in figure 4B and the land-use-residualized basin characteristics of approximately 10,000

small basins.

Regional Patterns And Relations To Environmental Factors 15
The multiple regression equations (eq. 3) used to 

estimate reference concentrations can also be used to 

show how changes in land use affect water quality in the 

different TSSC zones in the study area by adjusting the 

percentage of agriculture from 0 to 100 percent (fig. 6). 

The percentage of urban area in a basin was held at zero 

during the computations. Similar results were obtained 

when the variable describing the percentage of urban area 

was completely omitted from the analysis. In general, 

median TSS concentrations increased as the percentage of 

agriculture increased in all zones; of substantial interest is 

the relative difference in how these changes occurred. The 

major difference is that concentrations in zone 4 increased 

at a much faster rate than those in the other zones. This 

indicates that streams in areas dominated by clay soils with 

relatively low runoff and steep basin slopes have the highest reference TSS concentrations and that increasing agricultural use in this type of environmental setting results in 

the most rapid increase in concentration. Changes in TSS 

concentrations as a function of the percentage of agriculture in the other zones were relatively similar. The 90-percent confidence limits for coefficient a (coefficient with 

Ag%) in equation 3 for zone 4 slightly overlapped with 

those of all of the other zones.

TSS Yields

Relation with Environmental Factors

Pearson correlation coefficients between ln-transformed TSS yields and each environmental factor are 

listed in table 4 (based on data for the 367 independent 

streams only). Annual yields were most highly correlated 

with soil properties in the basin, air temperature, precipitation, evaporation, and the percentage of wetlands in the 

basin. Unlike TSS concentrations, yields were only weakly 

correlated with the percentage of agriculture in the basin; 

however, many characteristics (such as evaporation, runoff, 

and several soil properties) were strongly correlated with 

the percentage of agriculture in the basin.

To remove the effects of land use from TSS yields 

and from each environmental characteristic, a simultaneous partial-residualization analysis was done with the percentages of agriculture and urban areas in the basin. The 

residualization process had a smaller effect on TSS yields 

than it had on concentrations because of the weaker correlations with the percentage of agriculture. The distribution 

of land-use-residualized yields for the independent basins 

(fig. 7) was similar to the untransformed data. TSSRes yields

were strongly correlated with most soil properties, air 

temperature, precipitation, runoff, and basin slope (table 

4). The major difference in the correlations with the landuse-residualized variables from the original data was the 

increase in the r values between TSSRes yields and runoff 

and with precipitation minus evaporation.

Reservoirs and other impoundments can greatly 

reduce the amount of sediment transported down a stream. 

Therefore, it was hypothesized that the characteristics 

of the area downstream from impoundments should be 

more strongly correlated with TSS yields than the characteristics of the entire basin. To test this hypothesis, the 

environmental characteristics of the area downstream 

from the impoundments also were determined and examined. Correlation coefficients between logarithmically 

transformed TSS yields and each environmental factor 

computed just for the area downstream from the impoundments (undammed area) also are given in table 4. These 

results were similar to those based on the entire basin 

for both the original yields and the land-use-residualized 

yields. This similarity was a result of the environmental 

Table 3. Reference median total suspended sediment/solids concentrations (TSSC) and percentiles of all data in various TSSC 

zones.

[%, percent; CI, confidence interval; data are concentrations in milligrams per liter]

TSSC zone

Number 

of sites

Reference concentration from 

multiple linear regression Percentiles of all data for each zone

Lower

5% CI Median Upper95% CI

Standard 

error

0 10 25 50 75 90 100

1 114 3.3 4.1 5.1 0.5 2.0 3.0 4.0 6.0 13.0 19.5 76.0

2 66 3.4 5.6 9.1 1.6 2.0 4.0 7.0 16.5 28.0 49.0 470

3 232 11.6 17.2 25.4 3.7 1.5 10.5 19.0 31.0 57.5 95.0 7,060

4 135 11.2 17.2 26.3 4.1 1.5 5.0 11.5 32.0 115 695 6,800

5 128 6.3 9.4 14.1 2.1 0.3 3.0 6.5 17.0 39.0 67.5 2,230

16  Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
characteristics of the undammed areas of each basin being 

strongly correlated with the environmental characteristics of the entire basin. The smallest Pearson correlation 

coefficient between the characteristics of the dammed and 

corresponding characteristic of the undammed areas was 

0.93; most coefficients were larger than 0.95.

Although land-use characteristics were not as strongly 

correlated with yields as they were with concentrations, 

many correlations were statistically significant; therefore, 

the land-use-residualized data were used in the regression-tree analysis. Runoff was not included in the analysis 

because yields are computed as a product of streamflow 

and concentration and total annual streamflow is approximately equal to runoff, so runoff was not considered an 

independent explanatory variable. The results of the first 

analysis with characteristics for the entire basin are given 

in figure 8A. Residualized precipitation (PPTRes) was the 

independent variable chosen for the first subdivision. In 

that first subdivision, the two groups were sites with PPTRes

< -11.8 cm/yr (group 1, with 84 sites and residuals representing the lowest yields) or PPTRes ≥ -11.8 cm/yr. The subgroup with PPTRes ≥ -11.8 cm/yr was further subdivided on 

the basis of whether the residualized percentage of organicmatter content of the soil (OMRes) was < -0.32 percent or 

≥ -0.32 percent (group 5, with 86 sites and second lowest 

residualized yields). The subgroup with OMRes < -0.32 

percent was further subdivided on the basis of whether 

the residualized permeability (permRes) was < -4.1 cm/hr 

(group 2, with 50 sites and highest residualized yields) or 

≥ -4.1 cm/hr. Sites with permRes ≥ -4.1 cm/yr were further 

subdivided into those with the residualized percentage of 

organic surficial deposits (ODRes) < 0.00 percent (group 3, 

with 64 sites) or ODRes ≥ 0.00 percent (group 4, with 83 

sites and second highest residualized yields). Residualized 

yields in group 2 were higher than those in group 4 (not 

significantly different at p < 0.05 but significantly different 

at p < 0.1) and were both significantly higher than those in 

group 3, which in turn were significantly higher than those 

in group 5, which in turn were significantly higher than 

those in group 1. Therefore, highest yields were in streams 

in areas with high precipitation and soils with little organic 

matter and low permeability.

Regression-tree analysis was then used to examine the 

relative importance of the environmental characteristics of 

the entire basin and the undammed areas by including all 

of the land-use-residualized variables in one analysis (fig. 

8B). PPTRes was again the independent variable chosen for 

the first subdivision. In that division, the two groups were 

sites with PPTRes < -11.8 cm/yr (group 1, with 84 sites and 

lowest residualized yields) or PPTRes ≥ -11.8 cm/yr. The 

0

20

40

60

80

100

120

140

160

0 10 20 30 40 50 60 70 80 90 100

PERCENTAGE OF AGRICULTURE IN BASIN

 MEDIAN TSS CONCENTRATION,

IN MILLIGRAMS PER LITER

TSSC zone 1

TSSC zone 2

TSSC zone 3

TSSC zone 4

TSSC zone 5

All data

Figure 6. Response curves for total suspended sediment/solids (TSS) concentrations as a function of the percentage of 

agriculture in the basin, by environmental TSS concentration (TSSC) zone.

Regional Patterns And Relations To Environmental Factors 17
Table 4. Pearson correlation coefficients (r) between logarithmically transformed total suspended sediment/solids (TSS) yields, 

urban area, agricultural area, and various environmental characteristics for the entire basin and the undammed area of the basin, 

and between land-use-residualized TSS yields and land-use-residualized evironmental characteristics for the entire basin and 

characteristics of the undammed area of the basin.

[r values with an absolute value greater than 0.1 are significant at p less than 0.05 with 367 sites; r values with an absolute value greater than or equal to 

0.4 for non-land-use characteristics are in bold; ln, natural logarithm; --, not applicable]

Basin characteristic

Entire basin area Undammed area

ln

(TSS yield)

Agricultural 

area

Residualized TSS 

yield with residual 

variables

ln

(TSS yield)

Agricultural 

area

Residualized TSS 

yield with residual 

variables

Land use

Total forest -0.07 -0.89 -- -0.04 -0.86 --

Total agriculture 0.21 1.00 -- 0.16 0.96 --

Total wetland -0.45 -0.41 -- -0.41 -0.39 --

Transitional area -0.07 -0.38 -- -0.05 -0.27 --

Grassland -0.29 0.04 -- -0.28 0.05 --

Barren 0.01 -0.29 -- 0.06 -0.25 --

Urban 0.09 -0.15 -- 0.07 -0.13 --

Surficial deposits

Mean thickness -0.17 0.15 -0.22 -0.15 0.18 -0.20

Clay 0.12 0.13 0.07 0.09 0.08 0.06

Weathered bedrock 0.14 -0.43 0.29 0.14 -0.42 0.27

Mixed -0.01 0.44 -0.12 0.00 0.44 -0.09

Organic -0.24 -0.24 -0.19 -0.21 -0.22 -0.17

Sand and gravel -0.39 -0.16 -0.38 -0.37 -0.11 -0.37

Soil properties

Available water capacity 0.05 0.55 -0.08 0.06 0.55 -0.06

Soil erodibility 0.51 0.54 0.47 0.50 0.53 0.48

Clay content 0.48 0.41 0.43 0.45 0.35 0.41

Organic-matter content -0.47 -0.29 -0.43 -0.45 -0.25 -0.43

Permeability -0.47 -0.42 -0.43 -0.45 -0.39 -0.41

Soil slope 0.10 -0.60 0.34 0.12 -0.57 0.33

Basin characteristics

Air temperature 0.55 0.14 0.53 0.55 0.13 0.53

Precipitation 0.44 -0.22 0.51 0.45 -0.21 0.52

Evaporation 0.30 0.65 0.19 0.28 0.65 0.20

Precipitation minus

evaporation

0.23 -0.50 0.42 0.25 -0.49 0.42

Basin slope 0.13 -0.57 0.36 0.14 -0.56 0.35

Runoff 0.18 -0.57 0.40 0.20 -0.56 0.39

Watershed area – ln 

transformed

-0.12 0.00 -0.10 -0.12 0.00 -0.10

Undammed ratio -- -- -- 0.16 0.11 0.15

18  Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
subgroup with PPTRes ≥ -11.8 cm/yr was further subdivided 

on the basis of whether the residualized erodibility of the 

undammed area (UD erodRes) was < -0.01 (group 2, with 

84 sites and second lowest residualized yields) or ≥ -0.01. 

The subgroup with UD erodRes ≥ -0.01 was further subdivided on the basis of whether the undammed OMRes (UD 

OMRes) was < -1.17 percent or ≥ -1.17 percent (group 5, 

with 75 sites). Sites with UD OMRes < -1.17 percent were 

further subdivided into those with undammed permRes (UD 

permRes) < 3.9 cm/hr (group 3, with 51 sites and highest 

residualized yields) and ≥ 3.9 cm/hr (group 4, with 73 

sites and second highest residualized yields). Residualized 

yields in group 3 were higher than those in group 4, which 

were higher than those in group 5, which were higher than 

those in group 2, which were higher than those in group 1 

(all significant at p < 0.05). Therefore, highest yields were 

in streams in areas with high precipitation and soils in the 

areas downstream from the impoundments with high erodibility, low organic-matter content, and low permeability.

Correlations and regression-tree results indicate that 

precipitation and several soil properties are the major 

factors related to the distribution of TSS yields. Highest 

yields were found in streams in areas with high precipitation, especially in areas with highly erodible soils with 

low permeability and low organic-matter content. Air 

temperature was the variable most strongly correlated with 

TSS yields; however, air temperatures were also strongly 

correlated with precipitation (r = 0.75) and with most of 

the soil properties that were strongly correlated with yields 

(r > 0.5). Therefore, it is difficult to interpret the relation between air temperature and TSS yield. The relation 

between runoff and TSS yields was expected because 

runoff is part of the yield. TSS yields were most strongly 

correlated with the types of soils in the undammed areas of 

the basin, but these soil characteristics are strongly correlated with the characteristics of the entire basin. Therefore, 

separating these effects is difficult.

Land-use-residualized ln

(yields, in kilograms per 

square kilometer per year)

-5.86 to -1.25

-1.24 to -0.28

-0.27 to 0.54

0.55 to 1.15

1.16 to 4.57

0 100 200 MILES

0 100 200 KILOMETERS

Base from USGS digital line graphs

102° 84°

45°

36°

Figure 7. Distributions of land-use-residualized logarithmically transformed (natural logarithm) annual total suspended 

sediment/solids (TSS) yields in study-area basins.

Regional Patterns And Relations To Environmental Factors 19
A. Land-use-residualized TSS yields with land-use-residualized characteristics for the entire basin

All ln 

TSSResYield

(367 sites)

All ln 

TSSResYield

(367 sites)

PrecipitationRes < -11.8 centimeters per year

Organic-matter contentRes < -0.32 percent

No (283 sites)

Yes (84 sites) Group 1: -1.43

Group 2: 1.29Group 5: -0.44

B. Land-use-residualized TSS yields with land-use-residualized characteristics for the entire basinand the land-use-residualized characteristics of the undammed areas

No (86 sites)Yes (197 sites)

No (147 sites)Yes (50 sites)

No (83 sites)Yes (64 sites) PermeabilityRes < 4.1 centimeters per hour

Organic depositsRes < 0.0 percent

Group 3: 0.43

PrecipitationRes < -11.8 centimeters per year

UD erodibilityRes < -0.01 percent No (283 sites)

Yes (84 sites) Group 5: 0.39

Group 4: 0.86Group 1: -1.43

No (199 sites)Yes (84 sites)

No (75 sites)Yes (124 sites)

No (73 sites)Yes (51 sites)

UD organic-matter contentRes < -1.17 percent

UD permeabilityRes < 3.9 centimeters per hour

Group: mean ln

Group 3: 1.35

(residual concentration)

Group: mean ln

(residual concentration)

Group 4: 0.80Group 2: -0.48

Figure 8. Regression-tree results for logarithmically transformed (

A) land-use-residualized total suspended sediment/solids (TSS) yields (TSSRes yield) with only land-useresidualized characteristics for the entire basin and (

B) TSSRes yield with land-use-residualized characteristics for the entire basin and the land-use-residualized characteristics 

of the undammed areas. Final groups are color-coded on the basis of mean land-use-residualized yields from green (lowest) to red (highest).

20 Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
SPARTA Regionalization and Response to 

Changes in Land Use

Regression-tree results for land-use-residualized TSS 

yields based on the characteristics for the entire basin (fig. 

8A) and for the entire basin characteristics and the characteristics of the undammed areas (fig. 8B) were used to 

subdivide the study area into five environmental TSS yield 

(TSSY) zones (fig. 9). Results of these analyses were used 

to classify each of the approximately 10,000 basins into 

specific TSSY zones based on the land-use-residualized 

characteristics of the entire basin. In the second delineation 

process, it was assumed that the environmental characteristics upstream from all of the impoundments were similar 

to those downstream from the impoundments. Most of the 

characteristics used in the delineation were the variables 

most strongly correlated with land-use-residualized TSS 

yields (table 4). Although the two regionalization schemes 

used different characteristics to delineate the different 

zones, the final delineation was relatively similar. Both 

schemes selected residualized precipitation as the most 

important variable. The reason that the two schemes used 

different variables in further subdivisions and yet resulted 

in relatively similar delineation was the strong correlations 

between the other important explanatory variables (table 

5). For example, the second subdivision in one analysis 

selected on residualized organic-matter content, whereas 

the other selected on residualized erodibility (r = -0.63 

between the nonresidualized data). Organic-matter content, 

permeability, and erodibility were all strongly correlated 

with one another.

To define reference yields and describe the responses 

in yields as a function of the percentage of agriculture in 

each zone, the multiple linear-regression model (eq. 3) 

was applied to land-use-residualized TSS yields for each 

regionalization scheme. All estimated median annual reference yields, standard errors, and the 95-percent confidence 

intervals are given in table 6. Each regionalization scheme 

for TSS yields (TSSY) is discussed separately, and the 

results are then compared.

On the basis of results found using the entire basin 

characteristics and the regression approach, median reference yields range from 785 kg/km2

/yr in TSSY zone 5 

to 108,000 kg/km2/yr in zone 4. The upper 95-percent 

confidence intervals range from 2,610 kg/km2/yr in zone 

5 to 341,000 kg/km2/yr in zone 4. The lowest reference 

yields occur in the central part of the study area (fig. 9A). 

The five zones can be combined into four main categories: 

TSSY zone 5 with a reference yield of about 1,000 

kg/km2/yr; zone 1 with a reference yield of about 5,000 

kg/km2/yr; zones 2 and 3 with a reference yield of about 

40,000 kg/km2/yr; and zone 4 with a reference yield of 

about 100,000 kg/km2/yr. The 95-percent confidence limits 

for the reference yields of TSSY zones 2, 3, and 4 overlapped with one another.

On the basis of results from the regression approach 

using both the entire basin characteristics and the characteristics of the undammed areas, median reference yields 

ranged from 657 kg/km2/yr in TSSY zone 5 to 47,400 

kg/km2/yr in zone 3 (table 6). The upper 95-percent confidence intervals ranged from 980 kg/km2

/yr in zone 5 to 

89,300 kg/km2/yr in zone 3. The standard errors and 

Table 5. Pearson correlation coefficients (r) between selected environmental characteristics of the entire basin for sites with 

total suspended sediment/solids yields.

[r values with an absolute value greater than 0.1 are significant at p less than 0.05 with 367 sites; r values with an absolute value greater than 0.5 are in 

bold]

Basin characteristic Organic 

deposits

Soil 

erodibility

Clay

content

Organicmatter

content

Permeability Air 

temperature Precipitation

Precipitation 

minus 

evaporation

Organic deposits 1.00

Soil erodibility -0.35 1.00

Clay content -0.27 0.63 1.00

Organic-matter content 0.65 -0.63 -0.55 1.00

Permeability 0.32 -0.78 -0.77 0.52 1.00

Air temperature -0.34 0.51 0.58 -0.61 -0.44 1.00

Precipitation -0.17 0.22 0.25 -0.37 -0.21 0.75 1.00

Precipitation minus 

evaporation

-0.03 -0.02 0.01 -0.14 0.00 0.47 0.88 1.00

Regional Patterns And Relations To Environmental Factors  21
TSS yield (TSSY) zones

1

2

3

4

5

TSS yield (TSSY) zones

A. Zones based on characteristics for the entire basin

B. Zones based on characteristics for the entire basin and the characteristics of the undammed areas

1

2

3

4

5

0 100 200 MILES

0 100 200 KILOMETERS

Base from USGS digital line graphs

102° 84°

45°

36°

0 100 200 MILES

0 100 200 KILOMETERS

Base from USGS digital line graphs

102° 84°

45°

36°

Figure 9. Environmental total suspended sediment/solids yield (TSSY) zones in the study area delineated based on regressiontree results using (A) land-use-residualized characteristics for the entire basin and (B) land-use-residualized characteristics for 

the entire basin and the land-use-residualized characteristics of the undammed areas. Each zone was delineated on the basis of 

the results in figure 8 and the residualized basin characteristics of approximately 10,000 small basins.

22  Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
Table 6. Reference median annual total suspended sediment/solids yields (TSSY) and percentiles of all data in various TSSY zones.

[%, percent; kg/km2/yr, kilogram per square kilometer per year; CI, confidence interval]

TSSY zone

Number 

of sites

Reference yield from 

multiple linear regression Percentiles

Lower

5% CI Median Upper 95% CI Standard error 0 10 25 50 75 90 100

Total suspended sediment/solids yield (kg/km2/yr) – based on entire basin characteristics only

1 84 2,860 4,720 7,800 1,350 73 1,210 2,350 5,320 12,000 30,900 110,0002 50 25,500 49,200 94,800 19,100 4,680 16,500 36,300 59,900 147,000 519,000 2,040,0003 68 15,700 27,600 48,200 8,900 1,140 4,920 15,500 33,200 58,100 185,000 1,070,000

4 79 34,400 108,000 341,000 84,000 2,810 20,500 56,400 82,500 132,000 220,000 3,370,0005 86 237 785 2,610 646 1,190 3,400 12,500 31,600 54,200 90,200 447,000

Total suspended sediment/solids yield (kg/km2/yr) – based on entire basin characteristics and the characteristics of the undammed areas

1 84 2,860 4,720 7,800 1,350 73 1,210 2,350 5,320 12,000 30,900 110,000

2 84 5,810 11,500 22,600 4,640 1,190 3,260 8,100 21,400 47,100 89,300 447,0003 51 25,100 47,400 89,300 17,700 4,680 18,600 39,700 73,900 158,000 474,000 2,040,000

4 73 16,600 26,100 41,000 6,630 3,870 12,100 25,100 57,000 158,000 226,000 1,170,0005 75 440 657 980 146 1,140 25,100 34,500 66,200 91,100 156,000 3,370,000

Regional Patterns And Relations To Environmental Factors  23
confidence intervals were smaller than those for the 

regionalization scheme based on characteristics for the 

entire basin; therefore, the combination approach is considered the better delineation process for reference yields. 

Again, the five zones can be combined into four main categories, TSSY zone 5 with a reference yield of about 700 

kg/km2/yr, zone 1 with a reference yield of about 5,000 

kg/km2/yr, zones 2 and 4 with a reference yield of about 

11,000–26,000 kg/km2/yr, and zone 3 with a reference 

yield of about 45,000 kg/km2/yr. The 95-percent confidence limits for the reference yields of zones 2, 3, and 4 

overlapped with one another. Lowest reference yields were 

again found in the central part of the study area (fig. 9B).

On the basis of the 25th percentile of all of the data 

in each zone for both regionalization schemes, reference 

TSS yields range from about 2,300 to 56,000 kg/km2/yr 

(table 6). Large differences in land use within the various 

areas probably affected these results; therefore, the values 

based on the regression approach are considered the more 

accurate estimates of reference yields.

Multiple regression equations (eq. 3) were used to 

describe how changes in the percentage of agriculture in 

the basin affect the yields in the different TSSY zones (fig. 

10). The response curves are shown only for the regionalization scheme based on results from characteristics from 

both the entire basin characteristics and the undammed 

areas (fig. 9B) because the combination approach was 

considered the better delineation process. TSS yields 

increased as the percentage of agriculture increased in all 

zones. The major difference in the responses among zones 

is that streams in zone 5 had the lowest reference yields, 

but yields increased at a much faster rate than in the other 

zones. Zones 3 and 4 had the highest reference yields, 

but yields increased at moderate rates. Zones 1 and 2 had 

some of the lowest reference yields, and yields increased 

at the slowest rates. Streams in zone 5 (fig. 9A), based on 

results with only the entire basin characteristics used in 

the regression-tree analysis, also had the lowest reference 

yields, and they responded most dramatically to increases 

in agricultural use. Streams in both zone 5s (fig. 9) have 

relatively high precipitation and high organic-matter content. Streams in both zone 1s have the lowest precipitation 

and some of the lowest reference yields, and they increased 

at the slowest rates. The 90-percent confidence limits for 

coefficient a in equation 3 (agriculture coefficient) for all 

of the zones except zone 5 overlapped with those of all of 

the other zones, an indication that many of these differences in response were not statistically significant and 

that differences in soil properties were more important in 

affecting yields than the differences in land use.

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

0 10 20 30 40 50 60 70 80 90 100

PERCENTAGE OF AGRICULTURE IN BASIN

TSS YIELD,

IN KILOGRAMS PER SQUARE KILOMETER PER YEAR

TSSY zone 1

TSSY zone 2

TSSY zone 3

TSSY zone 4

TSSY zone 5

All data

Figure 10. Response curves for total suspended sediment/solids (TSS) yields as a function of the percentage of agriculture in 

the basin, by environmental TSS yield (TSSY) zone. The zones were delineated on the basis of regression-tree results using landuse-residualized characteristics of the entire basin and the land-use-residualized characteristics of the undammed areas.

24  Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
Volumetrically Weighted TSS Concentrations

Relation with Environmental Factors

Pearson correlation coefficients between logarithmically transformed VW TSS concentrations and each environmental factor are listed in table 7 (based on data for the 

367 independent streams only). VW concentrations were 

most highly correlated with many of the same factors correlated with median concentrations and yields: soil properties, air temperature, evaporation, and the percentages of 

agriculture and wetlands in the basin. Unlike median TSS 

concentrations, VW concentrations were highly correlated 

with air temperature. Unlike TSS yields, VW concentrations were not highly correlated with runoff or precipitation. VW concentration and many of its correlates were 

highly correlated with the percentage of agriculture in the 

basin.

Simultaneous partial residualization was done to 

remove the land-use effects from the analysis. After the 

relations with land use were removed, high VW TSSRes concentrations were found throughout the study area; however, 

most high concentrations were in the southern half (fig. 

11). VW TSSRes were most strongly correlated with several 

soil properties, percentage of sand-and-gravel deposits, 

and air temperature (table 7). The major difference in the 

correlations with the land-use-residualized variables was 

the increase in the r values between VW TSSRes concentrations and soil slope, precipitation, and basin slope.

Only land-use-residualized VW TSS concentration data were used in the regression-tree analysis (fig. 

12). Residualized clay content (clayRes) was the explanatory variable chosen for the first subdivision. In that first 

subdivision, the two groups were sites with clayRes < -4.65 

percent (group 1, with 95 sites and residuals representing the lowest concentrations) or clayRes ≥ -4.65 percent. 

The subgroup with clayRes ≥ -4.65 percent was further 

subdivided on the basis of whether permRes was < -4.24 

cm/hr (group 2, with 50 sites and residuals representing 

the highest concentrations) or ≥ -4.24 cm/hr. The subgroup 

with permRes ≥ -4.24 cm/hr was further subdivided on the 

basis of whether erodRes was < -0.03 cm/hr (group 3, with 

53 sites and second lowest residualized concentrations) or 

≥ -0.03. Sites with erodRes ≥ -0.03 were further subdivided 

into those with the residualized basin slope (slopeRes) 

< 0.41 degrees (group 4, with 108 sites) or ≥ 0.41 degrees 

(group 5, with 61 sites and second highest residualized 

concentrations). The residualized VW concentrations in 

group 2 were higher than those in group 5 (not significantly different at p < 0.05, but significantly different at 

p < 0.1) and were both significantly higher than those 

in group 4, which in turn were significantly higher than 

those in group 3, which in turn were significantly higher 

than those in group 1. Therefore, highest VW concentrations were in streams in areas with soils having high clay 

content and low permeability or in areas with soils having 

high clay content, low permeability, and high erodibility, 

and steep basin slopes.

Correlations and regression-tree results indicate that 

soil properties and land use are the major factors related 

to the distribution of VW TSS concentrations. Highest 

concentrations were in areas with soils having high clay 

content, low permeability, and high erodibility, and steep 

basin slopes. Concentrations were especially high if agriculture took place in these areas.

SPARTA Regionalization and Response to 

Changes in Land Use

Results of the regression-tree analysis for VW TSSRes

concentrations (fig. 12) were used to subdivide the study 

area into five VW TSS concentration (TSSV) zones (fig. 

13). Most of the characteristics used for delineation were 

the variables most strongly correlated with VW TSSRes

concentrations. The regionalization had features found for 

either median TSS concentrations or TSS yields.

Equation 3 was used to define reference median 

annual VW TSS concentrations and describe the responses 

in concentrations as a function of land use in each zone. 

Estimated reference VW concentrations, standard errors, 

and 95-percent confidence intervals are given in table 8. 

Reference median VW concentrations ranged from 7.1 

mg/L in TSSV zone 1 to 88.1 mg/L in zone 2. The upper 

95-percent confidence intervals ranged from 10.2 mg/L in 

zone 1 to 168 mg/L in zone 2. The lowest reference VW 

concentrations were found throughout most of Wisconsin 

and Michigan and the highest VW concentrations were 

in the southeastern part of the study area. The five zones 

can be combined into three main categories: TSSV zone 1 

with a reference VW concentration of about 7 mg/L, zones 

3, 4, and 5 with a reference concentration of about 30–60 

mg/L, and zone 2 with a reference concentration of about 

90 mg/L. The 95-percent confidence limits for the reference concentrations of zones 3, 4, and 5 overlap with those 

of zone 2.

On the basis of the 25th percentile of all of the data 

in each zone, reference VW TSS concentrations also 

range from about 8 to 88 mg/L. However, there were large 

differences in land use within the various areas that may 

have affected the results; therefore, the values based on 

Regional Patterns And Relations To Environmental Factors  25
Table 7. Pearson correlation coefficients (r) between logarithmically transformed volumetrically weighted (VW) total suspended 

sediment/solids (TSS) concentration, total agricultural area, and various environmental characteristics and between land-useresidualized VW TSS concentration and land-use-residualized environmental characteristics for the entire basin.

[r values with an absolute value greater than 0.1 are significant at p less than 0.05 with 367 sites; ln, natural logarithm; r values with an absolute value 

greater than 0.4 are in bold]

Basin characteristic ln 

(VW TSS concentration)

Agricultural

area

Residualized VW TSS concentration 

with residual variables

Land use

Total forest -0.30 -0.89 --

Total agriculture 0.41 1.00 --

Total wetland -0.45 -0.41 --

Transitional area -0.15 -0.38 --

Grassland -0.03 0.04 --

Barren -0.02 -0.29 --

Urban 0.04 -0.15 --

Surficial deposits

Mean thickness -0.07 0.15 -0.16

Clay 0.12 0.13 0.05

Weathered bedrock 0.01 -0.43 0.25

Mixed 0.14 0.44 -0.05

Organic -0.24 -0.24 -0.15

Sand and gravel -0.43 -0.15 -0.41

Soil properties

Available water capacity 0.26 0.55 0.04

Soil erodibility 0.57 0.54 0.44

Clay content 0.55 0.41 0.45

Organic-matter content -0.48 -0.29 -0.41

Permeability -0.54 -0.42 -0.44

Soil slope -0.04 -0.60 0.33

Basin characteristics

Air temperature 0.41 0.14 0.39

Precipitation 0.15 -0.22 0.27

Evaporation 0.47 0.65 0.28

Precipitation minus evaporation -0.10 -0.50 0.15

Basin slope -0.01 -0.57 0.34

Runoff -0.15 -0.57 0.13

Watershed area – ln 

transformed

-0.04 0.00 -0.03

Undammed ratio 0.14 0.11 0.11

26  Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
-3.69 to -1.14

-1.13 to -0.32

-0.31 to 0.27

0.28 to 0.99

1.00 to 4.63

Land-use-residualized ln

(volumetrically weighted concentration, 

in milligrams per liter)

0 100 200 MILES

0 100 200 KILOMETERS

Base from USGS digital line graphs

102° 84°

45°

36°

Figure 11. Distributions of land-use-residualized logarithmically transformed (natural logarithm) volumetrically weighted (VW) 

total suspended sediment/solids (TSS) concentrations in study-area basins.

Table 8. Reference median annual volumetrically weighted (VW) total suspended sediment/solids concentrations (TSSV) and 

percentiles of all data in various TSSV zones.

[%, percent; mg/L, milligram per liter; CI, confidence interval; concentrations are shown in milligrams per liter]

TSSV zone

Number 

of sites

Reference concentration with multiple 

linear regression Percentiles

Lower

5% CI Median Upper95% CI

Standard 

error

0 10 25 50 75 90 100

1 95 4.9 7.1 10.2 1.4 2.1 5.1 8.3 20.5 56.7 140 376

2 50 46.2 88.1 168 33.6 5.2 33.5 75.1 128 335 989 6,250

3 53 12.3 28.7 67.1 15.2 4.7 19.3 32.7 86.6 195 520 2,230

4 108 34.7 59.3 101 18.2 9.3 47.4 88.1 161 259 370 1,710

5 61 27.9 50.4 91.0 17.3 6.2 21.5 61.3 148 414 940 9,300

Regional Patterns And Relations To Environmental Factors  27
All ln 

VW TSSRes

(367 sites)

Clay contentRes < -4.65 percent

PermeabilityRes < -4.24 centimeters per hour No (272 sites)

No (222 sites)

Yes (95 sites)

Yes (50 sites)

ErodibilityRes < -0.03

No (169 sites)

Yes (53 sites)

Basin slopeRes < 0.41 degrees

No (61 sites)

Yes (108 sites)

Group: mean ln

Group 5: 0.74

(residual concentration)

Group 1: -1.10Group 2: 1.11Group 3: -0.29Group 4: 0.17

Figure 12. Regression-tree results for land-use-residualized volumetrically weighted total suspended sediment/solids (VW TSSRes) concentrations. Final groups are color-coded 

on the basis of mean land-use-residualized concentrations from green (lowest) to red (highest).

28  Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
Volumetrically weighted 

TSS concentration 

(TSSV) zones

1

2

3

4

5

0 100 200 MILES

0 100 200 KILOMETERS

Base from USGS digital line graphs

102° 84°

45°

36°

Figure 13. Environmental volumetrically weighted total suspended sediment/solids concentrations (TSSV) zones in the study 

area. Each zone was delineated on the basis of the results in figure 12 and the land-use-residualized basin characteristics of 

approximately 10,000 small basins.

Regional Patterns And Relations To Environmental Factors  29
the regression approach are considered the more accurate 

estimates of reference concentrations.

Multiple regression equations (eq. 3) were used to 

demonstrate how changes in the percentage of agriculture 

in the basin affect VW TSS concentrations in the different 

TSSV zones (fig. 14). VW TSS concentrations increase as 

the percentage of agriculture increases in all of the zones. 

The major difference in the responses is that the rate of 

increase in zone 4 was a little less than for the other zones, 

although none of the rates were significantly different 

from one another (the 90-percent confidence limits for 

coefficient a in eq. 3 for all zones overlapped). Reference 

concentrations of VW TSS concentrations in zones 2 and 

5 were among the highest, which resulted in higher VW 

concentrations at high percentages of agriculture than in 

the other zones.

Comparisons between 

Concentrations and Yields

Comparisons of Factors Related to 

Concentrations and Yields

Soil properties and land use were the factors most 

strongly related to the distribution of median and VW 

TSS concentrations. Highest concentrations occurred in 

streams in areas where soils have high clay content, high 

erodibility, low organic-matter content, low permeability, 

and steep slopes. Concentrations were especially high if 

agricultural practices are extensive in these areas. TSS 

yields were related most strongly to climatic variables 

(precipitation and runoff) and secondarily related to the 

factors associated with high TSS concentrations. Highest 

yields occurred where both precipitation and TSS concentrations were high.

0

100

200

300

400

500

600

700

0 10 20 30 40 50 60 70 80 90 100

PERCENTAGE OF AGRICULTURE IN BASIN

VOLUMETRICALLY WEIGHTED TSS CONCENTRATION,

IN MILLIGRAMS PER LITER

TSSV zone 1

TSSV zone 2

TSSV zone 3

TSSV zone 4

TSSV zone 5

All data

Figure 14. Response curves for volumetrically weighted (VW) total suspended sediment/solids (TSS) concentrations as a 

function of the percentage of agriculture in the basin, by VW TSS concentration (TSSV) zone.

30 Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
The percentage of agricultural land was strongly 

correlated with median and VW TSS concentrations and 

less strongly (but significantly) correlated with TSS yields. 

The residualization process was important in removing the 

effects of land use in order that the importance of other 

factors could be determined. The residualization process 

was found to be even more important in determining factors influencing constituents that are more strongly related 

to land use than TSS, such as total phosphorus (Robertson 

and others, 2006).

It was hypothesized that reservoirs and impoundments 

may reduce the TSS being transported down a stream. If 

this was true, then the characteristics downstream from the 

impoundments may be more influential than the characteristics upstream from the impoundments. TSS yields 

were found to be most strongly correlated with the types of 

soils in the undammed areas of the basins. However, these 

soil characteristics were strongly correlated with similar 

characteristics computed for the entire basin; therefore, 

separating these effects was difficult and appeared to have 

only a small effect on the land-use-regionalization process. 

Additional information about impoundments, such as their 

morphometry, age, and retention time, may aid in understanding their effects.

Yields in small streams have been shown to be higher 

than those in large rivers even if the environmental characteristics of the two basins are similar (Richards, 1989). 

Because of this finding, delivery coefficients have been 

added to basin models to describe the loss in sedi­ment and 

nutrient loads with increasing basin size (Smith and others, 

1997). Results of this study indicated that this conclusion 

is valid; however, the effects of basin size appear to be 

small. Basin size was only weakly correlated with TSS 

yields and VW concentrations (r = -0.1) and not correlated 

with median concentrations.

Comparisons of Reference Concentrations 

and Yields

Lowest reference median and VW TSS concentrations were found for streams in areas having low-clay-content soils, which occur throughout most of Wisconsin and 

Michigan. Lowest reference yields were found for streams 

in areas with low precipitation, which occur throughout 

the northwestern half of the study area, where the lowest 

reference TSS concentrations also were found. Highest 

reference median concentrations were found for streams in 

areas with high-clay-content soils, which occur throughout 

the western and central parts of the study area. Highest 

reference VW concentrations and yields were found for 

streams in the southern and southeastern areas with high 

precipitation on steep slopes with low-permeability clay 

soils.

Median TSS concentrations in streams in areas with 

high-clay-content soils and steep basin slopes were more 

responsive to increases in the percentage of agriculture 

than streams in other areas. However, the responses in VW 

concentrations and yields to increases in the percentage of 

agriculture were relatively similar among zones except in 

areas with gentle basin slopes, which were less responsive.

Ranking Sites and Prioritizing 

Rehabilitation Efforts

The most effective way to attain the designated uses 

of surface waters that are currently impaired by sediments 

is to reduce the contributions from human activities rather 

than try to reduce natural concentrations and loadings. 

Prioritization of such efforts could be based on how much 

the concentration or yield exceeds reference conditions. 

To determine this, the reference concentrations and yields 

estimated in this study were subtracted from the measured data at the independent sites, and the sites were then 

ranked. Although this study attempted to remove all the 

natural variability in reference conditions by subdividing 

the study area into zones of similar important environmental characteristics, considerable variability in other factors 

still exists as demonstrated by the magnitude of the 95-percent confidence limits. Therefore, rather than subtracting 

the median reference conditions from the measured data, 

the values at the upper 95-percent confidence limit (tables 

3, 6, and 8) were subtracted from the measured data to 

reduce the potential for identifying sites as impacted when, 

in fact, their concentrations may reflect natural variability. 

The sites were then subdivided into five categories: sites 

not significantly above reference conditions; sites above 

reference conditions but lower than the 50th percentile of 

all sites; sites between the 50th and 75th percentiles; sites 

between the 75th and 90th percentiles; and sites in the 

upper 10th percentile (fig. 15).

For median and VW TSS concentrations and TSS 

yields, most of the sites with concentrations or yields 

exceeding the upper 75th-percentile values were in the 

central part of the study area; specifically, in eastern Iowa 

and western Illinois. For median and VW concentrations, 

many sites exceeding background concentrations were in 

southern Wisconsin, the southern half of Minnesota, and 

Ranking Sites And Prioritizing Rehabilitation Efforts  31
A. Median TSS concentrations

-24.8 to 0.0

Concentration,

in milligrams per liter

0.1 to 3.6

3.7 to 26.4

26.5 to 83.7

83.8 to 7,030

B. Median annual TSS yields

-84,000 to 0

Yield, in kilograms per

square kilometers per year

1 to 6,765

6,766 to 58,400

58,401 to 132,000

132,001 to 3,370,000

C. Median annual volumetrically

weighted TSS concentrations

-163 to 0

Concentration,

in milligrams per liter

1 to 24

25 to 138

139 to 340

341 to 9,210

Figure 15. Distributions of (A) median total suspended sediment/solids (TSS) concentrations, (B) TSS yields, and (C) 

volumetrically weighted TSS concentrations exceeding the upper 95th percentile for predicted reference conditions. Sites are 

subdivided into five categories: not significantly above reference conditions (light blue), significantly above reference conditions 

but lower than the 50th percentile of all data (gray), between the 50th and 75th percentiles (pink), between the 75th and 90th 

percentiles (red), and the upper 10 percent of all sites (maroon).

32  Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region
North Dakota, whereas for yields, few sites exceeding 

background conditions were found in central Minnesota 

and North Dakota. These differences in the distributions 

in concentrations and yields again demonstrate that land 

use (agricultural areas) affects concentrations more than 

it does yields. These maps also identify several basins 

significantly exceeding reference conditions in areas normally considered relatively pristine, such as in the Upper 

Peninsula of Michigan. These areas may be affected by 

other land-use practices, such as logging activities.

All rankings indicate that the streams in the central 

part of the study area are most affected by anthropogenic 

factors. However, when using this type of information to 

guide specific rehabilitative efforts, two questions must 

be asked: “Which of these data summaries is most important?” and “How do unsampled streams compare to the 

sampled streams?”

The summary statistics which should be considered 

depends on how the data will be used. If one is most concerned about the biology in the stream, then the concentrations that typically occur in the stream may be most important, and one may want to consider median concentrations. 

Median concentrations primarily reflect the concentrations 

during low flows because low flows occur more frequently 

than high flows. The amount of TSS being transported 

down the stream (yield) may be most important if one is 

concerned about sedimentation in downstream harbors or 

lakes. Yields would also be very important for nutrients, 

especially if one was interested in controlling downstream 

productivity. However, if one is interested in minimizing or 

controlling the anthropogenic effects on water quality, the 

most important variable should be the VW concentrations. 

VW concentrations represent a combination of concentrations during low and high flows. VW concentrations do not 

reflect the amount of water transported down the stream, 

which is primarily a function of the amount of precipitation minus evaporation and the type of soils in the basin. 

Reference and measured VW concentrations were much 

higher than median concentrations because VW concentrations are more influenced by the higher TSS concentrations that occur during high flows when most of the TSS is 

transported. Reference median concentrations ranged from 

5 to 26 mg/L compared to 10 to 168 mg/L for reference 

VW concentrations.

Answering the question about unsampled streams 

compared to the sampled streams is difficult. Although 

there are distinctive patterns in concentrations and yields, 

there is also considerable local variability. All three maps 

in figure 2 show a few sites with relatively low values next 

to sites with very high values. The relatively low values 

may be used to test the reference values determined in this 

study, although even these sites may be impacted. The 

very high values in some areas suggest that there could 

also be other sites with very high concentrations and (or) 

yields that were simply not sampled. Therefore, any actual 

prioritization of all sites would need to rely on extensive 

data collection or numerical models that can accurately 

simulate the effects of various human activities in a range 

of environmental settings.

Summary and Conclusions

In-stream suspended sediment and siltation and 

downstream sedimentation are common problems in surface waters throughout the United States. The most effective way to improve surface waters impaired by suspended 

sediments/solids (TSS) is to reduce the contributions from 

human activities rather than try to reduce loadings from 

natural sources. Natural or reference TSS concentrations 

and yields (loads) and their response to human activities 

vary because of regional differences in the environmental 

factors affecting their distribution. The main goals of this 

study, which was performed by the U.S. Geological Survey 

in cooperation with the U.S. Environmental Protection 

Agency, were to understand the factors influencing TSS 

concentrations and yields, and then use this information to delineate zones with similar reference conditions 

and similar response to changes in land use in the basin. 

SPAtial Regression-Tree Analysis (SPARTA) was applied 

to land-use-adjusted (residualized) TSS data and environmental characteristics to determine the natural factors 

affecting median and volumetrically (flow) weighted (VW) 

TSS concentrations and yields and to delineate zones with 

similar natural factors affecting TSS. Reference concentrations and yields were then determined for each zone and 

used to determine which of the monitored basins were 

significantly affected by human activities.

Soil properties and land use were the factors most 

strongly related to the distribution of median and VW 

TSS concentrations. Highest concentrations were in areas 

with soils having high clay content, high erodibility, low 

organic-matter content, and low permeability. Concentrations were especially high if agriculture was extensive in 

the basins in these areas. TSS yields were most strongly 

related to precipitation and the resulting runoff and 

secondarily to the factors related to high TSS concentrations. Yields were highest where precipitation was highest, 

especially in areas with high TSS concentrations.

Summary And Conclusions  33
Before analysis, all of the data were land-use-adjusted 

(residualized) to remove the direct and indirect effects of 

agriculture and urbanization. Regression-tree analysis was 

then used to determine which natural factors best described 

the variability in TSS. Breakpoints from the analysis were 

then used to delineate zones with similar natural factors affecting TSS. These zones were expected to have 

relatively similar reference conditions and (or) different 

responses to changes in land use. Because the most important factors influencing TSS concentrations and yields 

differed, their respective regionalization also differed.

Reference median TSS concentrations ranged from 

5 to 26 mg/L, reference median annual VW TSS concentrations ranged from 10 to 168 mg/L, and reference TSS 

yields ranged from about 980 to 90,000 kg/km2/yr. Lowest 

reference median and VW TSS concentrations were in 

areas with low-clay-content soils. Lowest reference yields 

were in areas with the lowest precipitation, which included 

the areas with lowest TSS concentrations. Highest reference concentrations were in areas with high-clay-content 

soils in the western and central parts of the study area, 

whereas the highest VW concentrations and yields were in 

areas with high precipitation on steep slopes with lowpermeability clay soils. Median TSS concentrations were 

most responsive to changes in the percentage of agriculture in areas with high-clay-content soils and steep basin 

slopes. Responses in VW concentrations and yields in 

most areas were similar except in areas with gentle basin 

slopes, which were less responsive.

With this information, the independent streams (no 

overlapping drainage areas) were ranked on the basis of 

how much their water quality exceeded reference conditions. Most streams significantly exceeding reference 

conditions were in the central part of the study area, where 

agricultural activities are the most intensive; however, 

many sites exceeding reference conditions were also outside of this area. The ranking that should be considered in 

guiding rehabilitation efforts depends on what is considered most important. If in-stream biology is most important, then median concentrations should be considered. If 

downstream sedimentation and downstream productivity is 

most important, then yields should be considered. If one is 

interested in minimizing or controlling the anthropogenic 

effects on water quality, then VW concentrations should 

be considered. Although this study attempted to obtain all 

available water-quality data for the study area, any actual 

prioritization for remediation of all sites would need to 

rely on more extensive data collection or numerical models 

that can accurately simulate the effects of various human 

activities in a range of environmental settings.

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