Analysis of Phosphorus Trends and 

Evaluation of Sampling Designs in the 

Quinebaug River Basin, Connecticut

By Elaine C. Todd Trench

U.S. Department of the Interior

U.S. Geological Survey

In cooperation with the Connecticut Department of Environmental Protection

Scientific Investigations Report 2004-5094
U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

Charles G. Groat, Director

U.S. Geological Survey, Reston, Virginia: 2004

For sale by U.S. Geological Survey, Information Services

Box 25286, Denver Federal Center

Denver, CO 80225

For more information about the USGS and its products:

Telephone: 1-888-ASK-USGS

World Wide Web: http://www.usgs.gov/

Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

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

reproduce any copyrighted materials contained within this report.
iii

Contents

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

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

Purpose and Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Description of the Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Phosphorus and Water Quality in the Quinebaug River Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Sources of Phosphorus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Trends in Phosphorus Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Current Challenges Related to Phosphorus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Assessing and Managing Water Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Methods for Time-Series Analysis of Trends and Evaluation of Sampling Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Selection of Data for Trend Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Effects of Method Changes on Historical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Discharge-Related Variability in Concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Differentiating Trends from Other Sources of Concentration Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Application of the Time-Series Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Evaluation of Sampling Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Sensitivity and Efficiency of Sampling Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Acceptable Levels of Probability for Trend Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Variability and Correlation in the Daily Concentration Anomaly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Long-Term Phosphorus Trends in the Quinebaug River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Evaluation of Sampling Designs for Detecting Trends in Total Phosphorus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Size of the Characteristic Trend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Low-Frequency Sampling Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Eight-Sample Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Nine-Sample Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Variability in Optimal Designs Among Stations and Constituents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Effects of Seasonal Changes in the Daily Concentration Anomaly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
iv

Figures

1. Hydrologic features of the Quinebaug River Basin, including major streams, drainage basin

boundaries, and monitoring stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

2. Photograph showing summer algal bloom on the French River in Connecticut, August 20, 2002 . . . . .5

3. Photograph showing Quinebaug River at the Metals Selling Dam, next to the Belden Mill, 

Putnam, Connecticut, March 25, 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

4. Map showing major land uses in the Quinebaug River Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

5–9. Graphs showing: 

5. Daily fluctuations in dissolved-oxygen concentrations and pH associated with algal 

metabolism, Quinebaug River at Cotton Road Bridge near Pomfret Landing, Connecticut, 

August 28 to September 2, 1998 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

6. Annual variability in discharge for (A) Quinebaug River at Quinebaug, Connecticut 

(1980–2001) and (B) Quinebaug River at Jewett City, Connecticut (1971–2001) . . . . . . . . . . . . . . . . .9

7. Trends in flow-adjusted concentrations of total phosphorus for (A) Quinebaug River at 

Quinebaug, Connecticut (1980–2001) and (B) Quinebaug River at Jewett City, Connecticut 

(1971–2001) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

8. Comparisons of sampling schedules and power for detecting trends in total phosphorus

for selected monitoring designs with 8 samples per year for (A) Quinebaug River at 

Quinebaug, Connecticut, and (B) Quinebaug River at Jewett City, Connecticut . . . . . . . . . . . . . . 15

9. Comparisons of sampling schedules and power for detecting trends in total phosphorus

for selected monitoring designs with 9 samples per year for (A) Quinebaug River at 

Quinebaug, Connecticut, and (B) Quinebaug River at Jewett City, Connecticut . . . . . . . . . . . . . . 16

Tables

1. Summary of major land uses in the Quinebaug River Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

2. State of Connecticut criteria for total phosphorus concentrations for lakes . . . . . . . . . . . . . . . . . . . . . . . . .5

3. List of waterbodies in the Quinebaug River Basin not meeting water-quality standards 

because of nutrient-related problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

4. Information for water-quality monitoring stations selected for trend analysis in the 

Quinebaug River Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

5. Summary statistics for concentrations of total phosphorus for stations on the 

Quinebaug River at Quinebaug and Jewett City, Connecticut, 1974–2001 . . . . . . . . . . . . . . . . . . . . . . . . . . 12
v

Conversion Factors and Datums 

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

°F = (1.8 x °C) + 32

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

°C = (°F - 32) / 1.8

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAVD 83).

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius 

(μS/cm at 25°C).

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) or 

micrograms per liter (μg/L).

Multiply By To obtain

Area

square mile (mi2

) 2.590 square kilometer (km2)

Flow rate

inch per year (in/yr) 25.4 millimeter per year (mm/yr)

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

gallon per minute (gal/min) 0.06309 liter per second (L/s)

Analysis of Phosphorus Trends and Evaluation of 

Sampling Designs in the Quinebaug River Basin, 

Connecticut

by Elaine C. Todd Trench

Abstract

A time-series analysis approach developed by the U.S. 

Geological Survey was used to analyze trends in total phosphorus and evaluate optimal sampling designs for future trend 

detection, using long-term data for two water-quality monitoring stations on the Quinebaug River in eastern Connecticut. 

Trend-analysis results for selected periods of record during 

1971–2001 indicate that concentrations of total phosphorus in 

the Quinebaug River have varied over time, but have decreased 

significantly since the 1970s and 1980s. Total phosphorus concentrations at both stations increased in the late 1990s and early 

2000s, but were still substantially lower than historical levels. 

Drainage areas for both stations are primarily forested, but 

water quality at both stations is affected by point discharges 

from municipal wastewater-treatment facilities. 

Various designs with sampling frequencies ranging from 

4 to 11 samples per year were compared to the trend-detection 

power of the monthly (12-sample) design to determine the most 

efficient configuration of months to sample for a given annual 

sampling frequency. Results from this evaluation indicate that 

the current (2004) 8-sample schedule for the two Quinebaug 

stations, with monthly sampling from May to September and bimonthly sampling for the remainder of the year, is not the most 

efficient 8-sample design for future detection of trends in total 

phosphorus. Optimal sampling schedules for the two stations 

differ, but in both cases, trend-detection power generally is 

greater among 8-sample designs that include monthly sampling 

in fall and winter. Sampling designs with fewer than 8 samples 

per year generally provide a low level of probability for detection of trends in total phosphorus. 

Managers may determine an acceptable level of probability for trend detection within the context of the multiple objectives of the state’s water-quality management program and the 

scientific understanding of the watersheds in question. Managers may identify a threshold of probability for trend detection 

that is high enough to justify the agency’s investment in the 

water-quality sampling program. Results from an analysis of 

optimal sampling designs can provide an important component 

of information for the decision-making process in which sampling schedules are periodically reviewed and revised. 

Results from the study described in this report and previous studies indicate that optimal sampling schedules for trend 

detection may differ substantially for different stations and constituents. A more comprehensive statewide evaluation of sampling schedules for key stations and constituents could provide 

useful information for any redesign of the schedule for waterquality monitoring in the Quinebaug River Basin and elsewhere 

in the state.

Introduction

The U.S. Geological Survey (USGS) and the State of Connecticut have worked together since 1955 to monitor and interpret the water quality of Connecticut’s rivers and streams. Management decisions of increasing complexity require a 

monitoring program that provides data for multiple purposes, 

including periodic assessment of trends to detect improvement 

or deterioration in water quality over time. Multi-station trend 

analyses provide opportunities to describe and interpret waterquality conditions by identifying basinwide differences or similarities. Evaluation of optimal monitoring designs for future 

trend analysis provides an important additional interpretive 

step. Managers and scientists can use information on the best 

monitoring designs to ensure that monitoring programs continue to accomplish their purpose and can do so efficiently. 

Water quality in the Quinebaug River in eastern Connecticut (fig. 1) has been monitored through the cooperative monitoring program of the USGS and the Connecticut Department of 

Environmental Protection (CTDEP) since the late 1960s. Significant long-term downward trends in phosphorus concentrations have taken place during the 1970s and 1980s (Trench, 

1996; 2000; Colombo and Trench, 2002). These downward 

trends probably have resulted from reductions in the use of 

phosphate in detergent and from major improvements in wastewater treatment. Despite these water-quality improvements, 

current (2004) concentrations of total phosphorus in the 

Quinebaug River and some of its tributaries are substantially 

higher than those that would be found under natural or nearnatural conditions in Connecticut. Total phosphorus concentrations sometimes exceed concentrations that are likely to cause 
2 Analysis of Phosphorus Trends and Evaluation of Sampling Designs in the Quinebaug River Basin, Connecticut

eutrophication in still water, a common condition along many 

impounded reaches of the Quinebaug River and its major tributaries, particularly during the summer months. Warm weather 

algal blooms occur annually in several river reaches and 

impoundments (fig. 2). Trend analyses will be necessary in the 

future to continue to monitor progress in water-quality 

improvements that result from the implementation of management programs to control nutrients. 

As part of the continuing effort to understand and improve 

water quality in Connecticut, the USGS and the CTDEP began 

a cooperative project in 2002 to analyze long-term trends in 

total phosphorus in the Quinebaug River Basin and evaluate 

optimal sampling designs for monitoring future trends. 

Purpose and Scope

This report presents the results of an analysis of phosphorus trends and an evaluation of sampling designs based on data 

from two water-quality-monitoring stations on the Quinebaug 

River in Connecticut. These stations are part of a network of 

34 monitoring stations throughout Connecticut. The two stations each have more than 20 years of water-quality record, and 

their drainage areas represent a variety of land uses and hydrogeologic conditions. A thorough interpretation of how the 

detected trends relate to hydrogeology, land use, population distribution, hydrologic modifications, and pollution sources is 

beyond the scope of this report. Some supporting information is 

presented to provide perspective on the detected trends and to 

point toward possibilities for further analysis. 

Acknowledgments 

Aldo V. Vecchia of the USGS developed the time-series 

analysis model used for trend detection and sampling design 

evaluation in this study, and made this work possible by providing extensive consultation on applying the time-series techniques and interpreting the results. Eric Thomas, Ernest 

Pizzuto, and Lee Dunbar of the CTDEP reviewed the report. 

Lee Dunbar and Michael Beauchene of the CTDEP and Therese 

Beaudoin of the Massachusetts Department of Environmental 

Protection (MADEP) provided information on state water-quality monitoring, assessment, and management programs. Aldo 

V. Vecchia and Laura Medalie of the USGS provided colleague 

reviews for the report. Barbara Korzendorfer of the USGS provided editorial review and prepared the report for publication. 

Claudia Tamayo of the USGS prepared the maps that appear in 

this report. All of the reviewers provided numerous suggestions 

and insights that have contributed to the improvement of the 

report. 

Description of the Study Area

The Quinebaug River in eastern Connecticut is a major 

tributary of the Shetucket River, which flows into the Thames 

estuary at the eastern end of Long Island Sound (fig. 1). 

Together, the Quinebaug and Shetucket Rivers and their tributaries drain most of eastern Connecticut. The main stem of the 

Quinebaug River originates in south-central Massachusetts, and 

numerous tributaries to the Quinebaug originate in Massachusetts and western Rhode Island. Historically, the Quinebaug 

River and its tributaries played an important role in the industrial development of Connecticut and Massachusetts, with 

mills, dams, and associated structures located throughout the 

watershed (fig. 3). Although industrial use of the Quinebaug has 

waned in recent decades, many of these structures remain. 

Additional dams have been constructed for flood-control purposes, following the severe damage caused by hurricanes in 

1938 and 1955. Consequently, numerous impoundments are 

located along the main stem of the Quinebaug and its major tributaries, and streamflow regulation is part of the hydrologic setting for the watershed. 

The Quinebaug River Basin, with a drainage area of 

739 square miles (mi2), includes undeveloped forested areas, 

agricultural areas, villages, new suburban developments, and 

small urban areas (fig. 4, table 1). Municipal wastewater is discharged in both the Massachusetts and Connecticut parts of the 

watershed; consequently, interstate concerns relate to the quality of water flowing into Connecticut. Water-quality issues in 

the drainage basin include streamflow regulation, accumulation 

and storage of nutrients and other pollutants in sediments, and 

prolonged warm weather algal blooms in several impoundments and river reaches. 

Phosphorus and Water Quality in the 

Quinebaug River Basin

Phosphorus and nitrogen are essential nutrients for plant 

growth. Nitrogen availability rarely limits aquatic plant growth 

in freshwater, whereas phosphorus concentrations in natural or 

near-natural streams are generally low enough to limit plant 

growth. Excessive phosphorus concentrations in freshwater 

promote growth of aquatic algae and eutrophic conditions 

(Hem, 1985, p. 128). When the algae die, they decompose, consuming oxygen in the water and contributing to a condition 

called hypoxia, or low dissolved oxygen. Excessive growth of 

algae, often called an algal bloom, also can affect water quality 

and aquatic habitat by increasing turbidity, limiting light penetration, and altering the composition of the food chain. During 

the day, the photosynthetic activity of aquatic plants, including 

algae, removes carbon dioxide and releases oxygen to the water, 

whereas during the night, as photosynthesis ceases, aquatic 

plants and animals continue to respire, consuming oxygen and 

producing carbon dioxide. Consequently, waterbodies with
Phosphorus and Water Quality in the Quinebaug River Basin 3

41°42'

72 ̊12'

71 ̊54'

41 ̊38'24"N

41 ̊42'0"N

41 ̊45'36"N

41 ̊49'12"N

41 ̊52'48"N

42 ̊

EXPLANATION

72 ̊30' 72 ̊ 71 ̊30'

42 ̊

41 ̊30'

Q uinebaug

River

Quinebaug

Putnam

Danielson

Central 

Village

Jewett 

City

01124000

01127000

Long Island Sound

RHODE

ISLAND CONNECTICUT

MASSACHUSETTS

U.S. Geological Survey surface-water gaging

 station and number

Flood-control dam

Municipal wastewater-treatment facility (WWTF)

Town or village

French River

Moosup River

Pac h aug

River

Connecticut R.

Thames R.

Q uineb aug R. Sh etucket R.

Putnam

WWTF

01127000

MA

CT

CT

RI

Base modified from U.S. 

Geological Survey digital 

line graphs (1980 and 1988)

Aspinook

Pond

West 

Thompson

Lake

Fivemile Ri v er

Little River

Quinebaug

R.

0 5 10 KILOMETERS

0 5 10 MILES

01125520

U.S. Geological Survey surface-water-quality

 monitoring station and number

01125520

Basin boundary

Webster-Dudley 

 WWTF

Southbridge

 WWTF

Figure 1. Hydrologic features of the Quinebaug River Basin, including major streams, drainage basin boundaries, and monitoring stations.
4 Analysis of Phosphorus Trends and Evaluation of Sampling Designs in the Quinebaug River Basin, Connecticut

Table 1. Summary of major land uses in the Quinebaug River Basin.

[Source: National Land Cover Dataset, Vogelmann and others, 2001. Numbers may not total to 100 percent because miscellaneous land-use categories

that total less than 1 percent of basin area have not been included in the table and because of rounding]

Basin name Drainage area 

(in square miles)

Land use (in percent)

Water Wetland Forest Agriculture Urban

Quinebaug River at Quinebaug, Conn.

(USGS station 01124000)

155 3.8 9.3 70.4 7.0 8.8

French River (at mouth downstream from 

West Thompson Lake)

112 5.9 10.0 60.3 8.7 14.3

Little River 

(at mouth near Putnam, Conn.)

39.1 1.3 8.1 60.8 23.8 5.3

Quinebaug River at Putnam, Conn. 

(USGS station 01125500)

328 4.2 9.5 64.8 10.4 10.4

Five Mile River 

(at mouth near Danielson, Conn.)

76.4 3.1 8.8 74.2 7.3 5.9

Moosup River

(at mouth near Central Village, Conn.)

89.1 1.0 6.8 78.6 9.0 4.2

Pachaug River

(at mouth in Jewett City, Conn.)

63.0 4.2 8.0 73.2 9.6 4.4

Quinebaug River at Jewett City, Conn. 

(USGS station 01127000)

713 3.1 8.9 68.0 11.2 8.1

Entire basin to mouth

(confluence with Shetucket River)

739 2.9 9.0 67.8 11.6 7.9

large algal populations may experience large daily fluctuations 

in dissolved oxygen concentrations and pH, affecting habitat 

conditions for other aquatic organisms. 

Sources of Phosphorus

Phosphorus constituents in streams are derived from natural sources and from many human uses of land and water 

resources. Major sources of phosphorus in Connecticut include 

decaying plants, animal wastes (from farm animals, pets, and 

wild animals), fertilizers, detergents, and municipal and

industrial wastewater. Atmospheric deposition contributes 

minor amounts of phosphorus to the land surface. Some forms 

of phosphorus are chemically reactive, adhering to particulate 

materials in water and accumulating in stream sediment. Phosphorus-bearing minerals in rocks and soil are not major sources 

of phosphorus in Connecticut. 

Phosphorus concentrations in the Quinebaug River and 

other streams in Connecticut were historically very high during 

the mid-20th century as a result of untreated or minimally 

treated wastewater discharges and phosphorus in detergents. 

Sediments in streambeds and impoundments continue to constitute a reservoir of nutrients that may be recycled into the water 

column under some conditions. An investigation of water quality, mass of phosphorus in sediment, and seasonal phosphorus 

cycling in West Thompson Lake (fig. 1) is currently (2003–04) 

in progress (John R. Mullaney, U.S. Geological Survey, written 

commun., 2003). 

Trends in Phosphorus Concentrations

Total phosphorus concentrations in many streams in Connecticut, including the Quinebaug River, declined substantially 

between the 1970’s and the 1990’s (Trench, 1996, figs. 30a–b; 

Trench, 2000, table 12; Colombo and Trench, 2002, fig. 18). 

Improvements in wastewater treatment under state water-quality management programs in Massachusetts and Connecticut, 

and the decline in the manufacture and use of detergents containing phosphorus, have probably contributed significantly to 

the detected decreases in phosphorus concentrations in streams. 

Current Challenges Related to Phosphorus

To control eutrophication, the U.S. Environmental Protection Agency (USEPA) has recommended that total phosphorus 

concentrations should not exceed 0.1 milligram per liter (mg/L) 

in flowing waters that do not discharge directly into lakes or 

impoundments, and that concentrations of total phosphates 

should not exceed 0.05 mg/L (as phosphorus) in a stream at a 

point where it enters a lake or reservoir (U.S. Environmental 

Protection Agency, 1986; Mueller and others, 1995, p. 4). 

Despite significant improvements in wastewater treatment and 

significant long-term downward trends in phosphorus concen

trations in the Quinebaug River, current (2004) concentrations of total phosphorus are substantially higher than those that 

would be found under natural or near-natural conditions in Connecticut. More importantly, total phosphorus concentrations 

along some reaches of the Quinebaug River and its tributaries 
Phosphorus and Water Quality in the Quinebaug River Basin 5

sometimes exceed 0.1 mg/L, and concentrations exceeding 

0.05 mg/L are common in several reaches (Ranzau and others, 

2001).

Numerous impoundments along the Quinebaug River and 

its tributaries cause low stream velocities and extensive backwater areas, creating conditions that are favorable for nutrient 

and particulate retention and algal growth. Still water is common along many reaches of the Quinebaug River, particularly 

during the summer months. Consequently, the stream system is 

highly sensitive to phosphorus. Additionally, impoundments 

may store and release nutrients and algae and may function as 

point sources for downstream reaches under some conditions. 

Nuisance algal blooms have been observed by the USGS and 

the CTDEP during late spring, summer, and early fall in various 

locations (M.J. Colombo, U.S. Geological Survey, written commun., 2003) (fig. 2). Continuous water-quality-monitoring data 

for the Quinebaug River Basin show large daily fluctuations in 

pH and dissolved oxygen concentrations during the growing 

season (fig. 5), indicating the importance of algal populations in 

determining water-quality conditions (Davies and others, 1999, 

p. 73–74; Ranzau and others, 2000, p. 82–83). 

Assessing and Managing Water Quality

Provisions of the Federal Clean Water Act (CWA) require 

states to (1) adopt water-quality standards, (2) assess surface 

waters to evaluate compliance with these standards, (3) identify 

waters not currently meeting the standards, and (4) develop 

Total Maximum Daily Load (TMDL) analyses and other management plans to bring waterbodies into compliance with the 

standards (Connecticut Department of Environmental Protection, 2002a, p. 1). The USEPA has developed guidance to assist 

states in assessing nutrient impairment of waterbodies and in 

developing regionally based numeric criteria for river and 

stream systems (U.S. Environmental Protection Agency, 2000). 

The criteria development process is currently (2004) in 

progress. Connecticut currently has narrative criteria for nutrients that describe goals for various water-quality classifications; ranges of nutrient concentrations are established for four 

lake trophic categories (table 2). The state standards require that 

“an assessment of the natural trophic category of the lake, 

absent significant cultural impacts, must be performed to determine which criteria apply” (Connecticut Department of Environmental Protection, 2002b, p. 18). 

Surface-water assessments are reported bi-annually by 

each state as required under Section 305 (b) of the CWA. In 

Connecticut, this document is called the Water-Quality Report 

to Congress. Waterbodies that have been identified as not meeting designated uses also are reported bi-annually in a document 

called the List of Connecticut Water Bodies Not Meeting Water 

Quality Standards, as required under Section 303 (d) of the 

CWA. Several waterbodies or stream reaches in the Quinebaug 

River Basin are on the 303 (d) Lists for Massachusetts and Connecticut because of nutrient-related water-quality impairments 

(table 3) (Connecticut Department of Environmental Protection, 2002a; Kennedy and others, 2002). 

Table 2. State of Connecticut criteria for total phosphorus 

concentrations for lakes.

[Source: Connecticut Department of Environmental Protection, 2002b, 

p. 18–19]

Lake trophic category

Range for total phosphorus 

concentrations

(micrograms per liter, μg/L)

Oligotrophic 0–10

Mesotrophic 10–30

Eutrophic 30–50

Highly eutrophic 50 +

Figure 2. Summer algal bloom on the French River in Connecticut, 

August 20, 2002. (Photograph by S. Lyle Phipps, USGS)

Figure 3. Quinebaug River at the Metals Selling Dam, next to the 

Belden Mill, Putnam, Connecticut, March 25, 2003. 

(Photograph by Elaine C. Todd Trench, USGS)
6 Analysis of Phosphorus Trends and Evaluation of Sampling Designs in the Quinebaug River Basin, Connecticut

0 5 10 KILOMETERS

0 5 10 MILES

01127000

72 ̊12'

71 ̊54'

41 ̊42'

42 ̊0'

EXPLANATION

Basin boundary

U.S. Geological Survey

surface-water gaging

station and number

Land use

Agriculture

Forest

Urban

Water

Wetland

Qu ni ebaug

River

01124000

01127000

MA

CT

CT

RI

Base from U.S. Geological Survey 

digital line graph (1988)

Figure 4. Major land uses in the Quinebaug River Basin. (Data from Vogelmann and others, 2001.)
Phosphorus and Water Quality in the Quinebaug River Basin 7

Table 3. List of waterbodies in the Quinebaug River Basin not meeting water-quality standards because of nutrient-related problems.

[Sources: Connecticut Department of Environmental Protection, 2002a, Appendix A; Kennedy and others, 2002, p. 16–18); Format differs for state 

documents for Connecticut and Massachusetts. Other impairments not specifically related to nutrients may be present but are not listed here. Locations for Connecticut waterbodies are shown in figure 1. DO, dissolved oxygen; WWTF, wastewater treatment facility]

Waterbody segment Impaired 

designated use

Cause (or potential cause) 

Name Location of impairment

French River Basin

Dutton Pond Leicester, Mass. Nutrients

Peter Pond Dudley, Mass. Nutrients, organic enrichment/low DO

Rochdale Pond Leicester, Mass. Nutrients, organic enrichment/low DO

Thayers Pond Oxford, Mass. Nutrients

French River Webster-Dudley, Mass. WWTF to 

Connecticut state line

Nutrients, organic enrichment/low DO

Quinebaug River Basin

Alum Pond Sturbridge, Mass. Organic enrichment/low DO

Glen Echo Lake Charlton, Mass. Organic enrichment/low DO

Quinebaug River Southbridge WWTF, Southbridge, Mass. 

to West Dudley Impoundment, Dudley, 

Mass.

Nutrients

West 

Thompson Lake

Impoundment of Quinebaug River in 

Thompson, Conn.

Aquatic life support; 

primary contact 

recreation

Organic enrichment, low DO, algal growth, 

chlorophyll-a, nutrients

Quinebaug River From confluence with Moosup River 

upstream to Putnam, Conn. WWTF 

Aquatic life support Cause unknown (algal growth, chlorophyll-a, 

flow alteration, organic enrichment, low DO)

Aspinook Pond Impoundment of Quinebaug River in 

Canterbury, Griswold, and Lisbon, Conn.

Primary contact 

recreation

Algal growth, chlorophyll-a, nutrients

Quinebaug River From mouth at Shetucket River upstream 

to outlet of Aspinook Pond, Conn.

Aquatic life support Cause unknown (algal growth, chlorophyll-a, 

nutrients)

Figure 5. Daily fluctuations in dissolved-oxygen concentrations and pH associated with algal metabolism, Quinebaug River at 

Cotton Road Bridge near Pomfret Landing, Connecticut (USGS station 01125520), August 28 to September 2, 1998. 

(From Davies and others, 1999, p. 74.)

2400 1200 2400 1200 2400 1200 2400 1200 2400 1200 2400 1200 2400

28 29 August September 30 31 1 2

1998

6

9

7

8

2400 1200 2400 1200 2400 1200 2400 1200 2400 1200 2400 1200 2400

28 29 August September 30 31 1 2

1998

4

12

6

8

10

pH UNITS MILLIGRAMS PER LITER

Dissolved Oxygen

pH
8 Analysis of Phosphorus Trends and Evaluation of Sampling Designs in the Quinebaug River Basin, Connecticut

The TMDL process results in a watershed plan that provides a framework to restore impaired waters by establishing 

the maximum amount of a pollutant that a waterbody can assimilate without adverse impact to aquatic life, recreation, or other 

public uses (Connecticut Department of Environmental Protection, 2002a, p. 2). Phosphorus trend analyses for the Quinebaug 

River provide information that can be used to evaluate the effectiveness of various phosphorus control measures. Management 

programs can use information on optimal sampling designs to 

improve the effectiveness, efficiency, and value of future waterquality monitoring efforts. 

Methods for Time-Series Analysis of Trends 

and Evaluation of Sampling Designs

A set of observations of a monitored variable, arranged 

chronologically, is called a time series. Time-series analysis is 

a process of fitting a time-series model to a time series of observations, such as water-quality data. Trends were analyzed and 

sampling designs were evaluated in this study using a statistical 

time-series model developed by Vecchia (2000). The methods 

and theoretical background for the time-series model have been 

described in detail by Vecchia (2000) and summarized by 

Trench and Vecchia (2002a). The time-series model can be used 

to evaluate data for linear trends that have one or more changes 

in slope during the period being evaluated, to detect cyclic 

trends, and to evaluate data for combinations of linear trends 

and step trends. This method can be applied to datasets with 

variable sampling frequencies. When carefully applied and 

interpreted, time-series analysis can be used to detect complex 

trends in concentration, and also can be used to evaluate the 

efficiency of various sampling designs for monitoring future 

trends in water quality. Trends analyzed in this report are annual 

trends; that is, the trends may have taken place in any season of 

the year, or in more than one season. 

Selection of Data for Trend Analysis

Data for two water-quality stations on the Quinebaug 

River in eastern Connecticut were analyzed for trends in total 

phosphorus using time-series analysis. Records for monitoring 

stations on the Quinebaug River at Quinebaug and the 

Quinebaug River at Jewett City (table 4; fig. 1) were retrieved 

from the USGS National Water Information System (NWIS) 

and were analyzed for trends in total phosphorus for selected 

periods of record from January 1, 1971 to September 30, 2001. 

Total phosphorus analyses began in 1971 for the Quinebaug 

River at Jewett City and in 1980 for the Quinebaug River at 

Quinebaug.

Effects of Method Changes on Historical Data

Possible effects of changes in field or laboratory methods 

need to be taken into account in analyzing trends and evaluating 

results of trend analysis. Information on dates of important laboratory method changes was used to select dates for linear-trend 

periods or step-trend periods and to evaluate results. A negative 

bias in total phosphorus data has been reported for analyses at 

the USGS National Water Quality Laboratory (NWQL) prior to 

1992 (D.A. Rickert, U.S. Geological Survey, written commun., 

1992). That is, reported total phosphorus concentrations may 

have been less than actual environmental concentrations under 

certain conditions prior to 1992. Possible implications of this 

bias for the analysis and interpretation of phosphorus data for 

Connecticut streams have been discussed by Zimmerman and 

others (1996, p. 29–30) and Trench and Vecchia (2002a, 

p. 8, 28). 

Discharge-Related Variability in Concentration

Concentrations of many chemical constituents are related 

to streamflow conditions. Much of the variability in concentration is caused by variability in streamflow. Identifying and 

removing the streamflow-related variability in concentration 

increases the ability to detect trends (Vecchia, 2003, p. 6). 

Constituent concentrations typically vary seasonally 

because of seasonal variability in discharge. Many Connecticut 

streams that receive wastewater discharges commonly have 

high total phosphorus concentrations during low-flow conditions, because of the phosphorus in wastewater, and low concentrations during medium- and high-flow conditions, because 

of dilution from rainfall and runoff. In streams where wastewater discharges constitute a substantial percentage of the lower 

streamflows, this relation between concentration and discharge 

persists, despite improvements in wastewater treatment. Phosphorus concentrations may increase, however, during storm 

events that are large enough to transport substantial amounts of 

phosphorus-bearing particulates and sediment to streams. In 

general, the relation between total phosphorus concentration 

and streamflow is more complex than the relation between 

many major ions and streamflow (A.V. Vecchia, U.S. Geological Survey, written commun., 2004). 

Streamflow varies from year to year (fig. 6) as well as seasonally, and constituent concentrations vary accordingly. The 

solid lines in figure 6 are 1-year moving averages that represent 

annual variability in discharge at the two Quinebaug stations.

The time-series analysis program is designed to adjust for 

both seasonal and annual variations in streamflow, and all trend 

results in this report are for flow-adjusted total phosphorus concentrations. The process used by the time-series model to relate 

concentration to streamflow is more complex than that of many 

commonly used flow-adjustment techniques, and consequently 

is able to remove more streamflow-related variability from 

concentration (Vecchia, 2003, p. 19).
Methods for Time-Series Analysis of Trends and Evaluation of Sampling Designs 9

1971 1975 1979 1983 1987 1991 1995 1999

0

1

2

3

4

DISCHARGE, AS BASE-10 LOGARITHM

 OF CUBIC FEET PER SECOND

A. Quinebaug River at Quinebaug, CT

1971 1975 1979 1983 1987 1991 1995 1999

1

2

3

4

5

DISCHARGE, AS BASE-10 LOGARITHM 

OF CUBIC FEET PER SECOND

B. Quinebaug River at Jewett City, CT 1-year moving average

1-year moving average

Daily mean discharges, 

3 per month

Daily mean discharges, 

3 per month

Table 4. Information for water-quality monitoring stations selected for trend analysis in the Quinebaug River Basin.

[Station locations are shown in fig. 1]

U.S. Geological Survey water-quality station Drainage area 

(square miles)

Period of waterquality record used 

in this study

Part of current 

(2004) 

Name Number monitoring network

Quinebaug River at Quinebaug, Conn. 01124000 155 1980–2001 yes

Quinebaug River at Jewett City, Conn. 01127000 713 1971–2001 yes

Figure 6. Annual variability in discharge for (A) Quinebaug River at Quinebaug, Connecticut (1980–2001) and (B) Quinebaug River at 

Jewett City, Connecticut (1971–2001). Points indicate daily mean discharges (3 per month). Line indicates 1-year moving average.
10 Analysis of Phosphorus Trends and Evaluation of Sampling Designs in the Quinebaug River Basin, Connecticut

A joint time-series model for daily mean discharge and 

concentration is fitted to historical data for each water-quality 

station and constituent. The time-series model is applied to logtransformed discharge and concentration data. The model is 

used to filter out as much natural, discharge-related variability 

in concentration as possible before analyzing for trends. The 

time-series model separates the concentration data into components of annual variability, seasonal variability, trend, and deviations from the basic conditions. These deviations from basic 

conditions are sometimes referred to as “noise.” Deviations 

from basic conditions also are referred to as the “daily concentration anomaly” (Vecchia, 2003, p. 11, 14), to distinguish this 

short-term variability from the longer-term seasonal and annual 

variability in concentration that constitutes the basic conditions. 

Differentiating Trends from Other Sources of 

Concentration Variability

In the flow-adjustment process, most of the flow-related 

annual and seasonal variability is removed from measured concentration values, as described by Vecchia (2000, p. 41). The 

flow-adjusted concentrations are composed of a constant, plus 

any trend present, plus the daily concentration anomaly (or 

noise). In some statistical-modeling procedures, noise is considered an error term and is not evaluated further. The daily concentration anomaly, however, may have a complex time-series 

structure that is not immediately apparent from simple inspection of the data. The daily concentration anomaly may be serially correlated, or may be cross-correlated with the daily anomaly in discharge data, and the magnitude or direction of the daily 

concentration anomaly and of these correlations may vary seasonally. The statistical significance of the detected trends 

depends on the statistical properties of the daily concentration 

anomaly. A special type of time-series model, called a periodic 

autoregressive moving average (PARMA) model (Vecchia, 

2000, appendix A), is used to detect and filter out the complex 

statistical properties of the daily concentration anomaly. After 

the PARMA model is applied to the daily concentration anomaly, the residuals from the PARMA model represent the unexplained remnant of concentration variability, plus any trends 

that may be present but have not yet been removed by including 

a trend term in the model. The PARMA model provides a convenient way to identify and filter out structure in the daily concentration anomaly and correct for any bias in estimated trends 

and significance levels. The PARMA model also uses information on the statistical properties of the daily concentration 

anomaly to identify months during which constituent sampling 

yields the most information for trend analysis, enabling sampling schedules to be identified that maximize the sensitivity for 

detecting trends. 

Application of the Time-Series Model

The time-series model was initially applied to total phosphorus data at each station with no trend periods specified. 

Because trends are likely in any long-term water-quality 

dataset, it is expected that the residuals from the no-trend model 

will not be randomly distributed. A smooth line was added to 

the plot of PARMA model residuals to aid in identifying central 

patterns in the data. The smooth line indicates trend directions 

and major changes in slope during the period of record. The plot 

of PARMA model residuals from the no-trend model was used 

to select appropriate trend periods for the time-series model. 

Information on dates of laboratory method changes, where 

available, also was used to select dates for linear-trend periods 

or step-trend periods and to evaluate trend results. Several trend 

models were tested for each station using linear-trend periods 

and step-trend periods of varying lengths. Numerical evaluations of the model fit, as well as p-values for the various trend 

periods, were examined and compared to select the model that 

best represented changes in the constituent during the period of 

record. Residual plots for the selected model were examined to 

ensure that residuals met assumptions of random distribution 

with constant variance and no apparent trends. Trend results in 

this report were considered significant if the p-value (attained 

significance level) for the test statistic was less than or equal to 

0.05. A more detailed discussion and illustration of the modeling procedure is provided in Trench and Vecchia (2002a, 

p. 15–16. 

Evaluation of Sampling Designs

The time-series model can be used to evaluate the efficiency and sensitivity of various sampling designs (that is, sampling schedules) for monitoring trends in water quality. Sampling designs can be evaluated for purposes of maintaining a 

sampling frequency that is sufficient for future trend analysis, 

reducing sampling costs by eliminating samples that provide 

redundant information, or shifting the most frequent sampling 

to seasons that provide the greatest gain in information. The theory and mathematical basis for applying the time-series model 

to the analysis of sampling designs have been described in detail 

by Vecchia (2000, appendix A). A brief description of the application of the sampling-design program and related terminology 

is included here. A more detailed discussion and explanation of 

the procedure for identifying and evaluating optimal sampling 

designs is provided in Trench and Vecchia (2002a, p. 17–18). 

Sampling schedules cannot be designed to detect trends 

with absolute certainty or to prove with absolute certainty that 

no trends exist. The size of the trend that can be detected 

depends on the acceptable level of tolerance specified for failing to detect a true trend or incorrectly identifying a trend. In the 

analysis described in this report, the acceptable level of tolerance is controlled by specifying two variables: the probability 

that a true trend is detected when a true trend exists, and the 

probability that a trend is incorrectly detected when no true 
Methods for Time-Series Analysis of Trends and Evaluation of Sampling Designs 11

trend exists. The selection of an efficient sampling design is not 

highly dependent on the values specified for these two probabilities (Vecchia, 2003, p. 39). In this analysis, the probability of 

detecting a trend when a true trend exists has been set at 0.8 

(or 80 percent). The probability of incorrectly detecting a trend 

when none exists (also termed the significance level) has been 

set at 0.05 (or 5 percent). 

In this analysis, monthly sampling is the maximum allowable sampling frequency based on cost considerations, with one 

sample per month collected at approximately the same time of 

month in the 12-sample design. Various designs with sampling 

frequencies ranging from 4 to 11 samples per year are evaluated 

by omitting selected months from the 12-sample design. Lowercost (lower-frequency) designs are compared to the efficiency 

and trend-detection power of the monthly design to determine 

the best configuration of months to sample for a given cost. 

The entire set of mathematically possible sampling designs 

has been reduced to a set of 150 designs (identified by design 

number in this report) that represent the range of reasonable 

possibilities for designs with 4 to 12 samples per year. The 

designs developed in this report assume that trends during all 

times of year are equally important, and samples are allocated 

throughout the year, with at least one sample in each of four seasons, to maximize the capability to detect trends whenever they 

take place. Trends that take place during specific seasons have 

not been investigated in this report. 

Sensitivity and Efficiency of Sampling Designs

Sensitivity and efficiency, two related concepts, are used 

to evaluate sampling designs. Sensitivity measures the ability of 

a design to detect a trend – the smaller the trend that can be 

detected, the more sensitive the design. An efficient design for 

a given water-quality constituent maximizes the sensitivity to 

detect a trend for a fixed sampling cost, which is usually measured in terms of the number of samples per year. The only way 

to increase the sensitivity of an efficient design is to increase the 

cost – that is, to collect samples more frequently. As the number 

of samples is increased, the sensitivity for detecting a given 

trend increases, but the cost also increases. The timing of sample collection during the year is an important consideration. A 

design with fewer well-timed samples may be more efficient for 

trend detection than a more costly design with more frequent 

samples at times that provide redundant information. 

The sensitivity of a sampling design can be evaluated in 

two equivalent ways: (1) in terms of the size of trend that can be 

detected for a given power, or (2) in terms of the power for 

detecting a trend of a given size. In this report, power is defined 

as the probability of detecting a log-linear trend that takes place 

over a specified design period. This probability has been set at 

0.8, as described above, and the design period is 5 years. The 

characteristic trend (or detectable trend) for a design is defined 

as the percentage change in concentration (increase or decrease) 

that can be detected over the 5-year design period with power of 

0.8 (80 percent probability). If the characteristic trend is fixed 

as the size of trend that can be detected with monthly sampling, 

then sampling designs with less than a monthly sampling frequency have less than an 80-percent probability of detecting the 

characteristic trend. The most efficient design for a given sampling frequency is the design with the highest power, because it 

provides the most trend information for a given cost. 

The efficiency of a sampling design is not dependent on 

the number of years selected for the design period. If a particular 6-sample design is the most efficient design for detecting a 

trend that persists for 5 years, that same design generally also 

will be the most efficient 6-sample design for detecting a trend 

that persists for 3 years or 10 years. 

Although the efficiency of a sampling design does not 

depend on the length of the design period, the sensitivity of a 

sampling design does depend on the duration of the trend. As 

the length of the sampling period increases, the sensitivity of a 

given design increases; that is, trends of smaller magnitude can 

be detected as the number of years of sampling increases. 

Acceptable Levels of Probability for Trend Detection

In the process of using the time-series program to evaluate 

sampling designs, other choices could be made for the probability of trend detection, the significance level, or the length of the 

design period. Determining the acceptable level of probability 

for trend detection is a management consideration that can 

incorporate numerous factors, including the goals of the watermanagement program and the scientific understanding of the 

watersheds in question. In general terms, the probability of 

trend detection should be high enough to justify the agency’s 

investment in the water-quality sampling program. In a previous study that used time-series analysis to evaluate trends in 

Connecticut, the acceptable level of trend-detection probability 

was set at 75 percent (power of 0.75) for the purpose of evaluating sampling designs (Trench and Vecchia, 2002a). 

When considering an acceptable threshold of probability 

for trend detection, and comparing the relative efficiency of 

designs with the same sampling frequency, it may be useful to 

consider these probabilities in simpler terms. The time-series 

program is designed so that, with 12 monthly samples, the characteristic trend is detected in four of five cases (80-percent 

probability). If a 75-percent probability is selected as an acceptable threshold of trend-detection sensitivity for the lower sampling frequencies necessitated by budget constraints, then an 

acceptable design detects the characteristic trend in three out of 

four cases. Designs with a 67-percent probability detect the 

characteristic trend in two of three cases, and designs with a 

50-percent probability have similar value to a coin toss in 

detecting the presence of a true trend. 

Differences in design sensitivity need to be considered 

within a management framework that has established reasonable goals or targets for trend-detection probability, so that a 

distinction can be made between a reasonable design and a marginal design for trend detection. However, numerous other fac-
12 Analysis of Phosphorus Trends and Evaluation of Sampling Designs in the Quinebaug River Basin, Connecticut

tors involved in water-quality management may influence the 

choice of sampling design. 

Variability and Correlation in the Daily Concentration 

Anomaly

The time-series program, in its identification of optimal 

sampling designs, quantifies two aspects of the daily concentration anomaly. These two characteristics, termed variability and 

correlation, provide a means of identifying the seasons in which 

more frequent sampling improves the likelihood of trend detection, and the seasons in which more frequent sampling does not 

contribute to improved trend detection. 

Trend detection is more difficult during seasons in which 

there is high variability in the daily concentration anomaly, and 

trends can be detected more easily when variability is low. Conversely, the degree of serial correlation in the daily concentration anomaly indicates the extent to which more frequent sampling may provide redundant information. An increased 

sampling frequency provides better information for trend detection when the serial correlation is low, and provides redundant 

information when the serial correlation is high. Ideally, sampling takes place when the variability in the daily concentration 

anomaly is low, and sampling times are spaced sufficiently far 

apart to avoid high serial correlation. Graphs of variability and 

correlation are not presented in this report; additional discussion of these measures can be found in Vecchia (2000, p. 8–10), 

Trench and Vecchia (2002a, p. 12–14), and Vecchia (2003, 

p. 14). 

Long-Term Phosphorus Trends in the 

Quinebaug River

Concentrations of total phosphorus in the Quinebaug River 

have varied over time, but generally were substantially lower in 

the 1990s and early 2000s than in the 1970s and 1980s (table 5, 

fig. 7). At the Quinebaug River station at Quinebaug, flowadjusted concentrations of total phosphorus decreased steadily 

from 1980 to 1996, with the steepest decrease from 1992 to 

1996 (fig. 7A). At the Jewett City station, flow-adjusted concentrations decreased from 1975 to 1979, increased from 1980 

to 1984, and then decreased steadily from 1985 to 1991 (fig. 

7B). No significant trend was detected at the Jewett City station 

from 1992 to 1997. Flow-adjusted concentrations of total phosphorus at both stations increased during the late 1990s and early 

2000s (fig. 7). 

Long-term downward trends in total phosphorus concentrations in the Quinebaug River probably result from reductions 

in the use of phosphorus in detergents and from improvements 

in wastewater treatment at municipal treatment plants in Massachusetts and Connecticut. The use of phosphorus in detergents 

decreased substantially between the late 1960s and the late 

1990s, as a result of phosphate detergent bans and voluntary 

manufacturing reductions (Litke, 1999, p. 31). Likewise, 

improvements in wastewater treatment have reduced phosphorus concentrations in effluent, although specific treatment-plant 

histories have not been compiled for this report. Reasons for 

increases in phosphorus concentrations in the late 1990s are not 

immediately apparent. Similar increases during the mid- to late1990s have been detected in concentrations of total phosphorus 

in the Quinnipiac and Naugatuck Rivers in central and western 

Connecticut (Trench and Vecchia, 2002a, fig. 12d–e). 

Table 5. Summary statistics for concentrations of total phosphorus for stations on the Quinebaug River at Quinebaug and 

Jewett City, Connecticut, 1974–2001.

[Sources: for 1974–85, Healy and others, 1994, table 35, p. 107; table 41, p. 118; for 2000–01, M.J. Colombo, U.S. Geological Survey, written 

commun., 2003; concentrations are not flow-adjusted; mg/L, milligrams per liter; P, phosphorus]

Period of record Number of 

samples

Standard 

deviation

Total phosphorus concentrations

(mg/L as P)

Minimum Median Maximum

Quinebaug River at Quinebaug, Conn. (USGS station 01124000)

1981–85 57 0.159 0.03 0.210 0.81

2000–01 34 .014 .009 .040 .071

Quinebaug River at Jewett City, Conn. (USGS station 01127000)

1974–85 140 .070 <.01 .100 .36

2000–01 32 .029 .035 .060 .183
Long-Term Phosphorus Trends in the Quinebaug River 13

1971 1975 1979 1983 1987 1991 1995 1999

-2

-1.5

-1

-0.5

0

 CONCENTRATION OF TOTAL PHOSPHORUS

 (AS P), AS BASE-10 LOGARITHM OF 

MILLIGRAMS PER LITER

A. Quinebaug River at Quinebaug, CT

1971 1975 1979 1983 1987 1991 1995 1999

-2

-1.5

-1

-0.5

0

 CONCENTRATION OF TOTAL PHOSPHORUS

 (AS P), AS BASE-10 LOGARITHM OF 

MILLIGRAMS PER LITER

B. Quinebaug River at Jewett City, CT

Trend

Flow-adjusted

data

EXPLANATION

Trend

Flow-adjusted

data

EXPLANATION

Figure 7. Trends in flow-adjusted concentrations of total phosphorus for (A) Quinebaug River at Quinebaug, Connecticut (1980–2001) 

and (B) Quinebaug River at Jewett City, Connecticut (1971–2001).
14 Analysis of Phosphorus Trends and Evaluation of Sampling Designs in the Quinebaug River Basin, Connecticut

When water-quality data are analyzed for time periods that 

cross the 1991–92 time boundary, the potential exists for detecting artificial upward linear trends because of the negative bias 

in some total phosphorus data from the USGS NWQL prior to 

1992. The time-series analysis program can evaluate linear 

trends and step trends simultaneously, making it possible to 

examine the potential effects of laboratory bias within the context of long-term environmental changes. Several combinations 

of step trends and linear trends were examined for both stations 

to evaluate the possible importance of this bias for trend results. 

In the case of the Quinebaug station, the downward linear trend 

during the early- to mid-1990s was highly significant, and there 

was no evidence that the pre-1992 bias had any effect on trend 

results. In the case of the Jewett City station, an upward step 

trend after 1991 was present in some of the trend models tested, 

but was not statistically significant, and the inclusion of the step 

trend in the model did not alter the direction or approximate 

level of significance of the linear trends present. Consequently, 

step trends were not included in the final models selected for 

either station. 

Evaluation of Sampling Designs for 

Detecting Trends in Total Phosphorus

The statistical time-series model was used to evaluate the 

efficiency of various sampling designs for monitoring trends in 

flow-adjusted total phosphorus concentrations. Optimal sampling designs for the two stations on the Quinebaug River were 

identified for each of eight sampling frequencies ranging from 

4 through 11 samples per year, based on a design period of 

5 years, and compared to the trend-detection power of the 

monthly (12-sample) design. 

Size of the Characteristic Trend

In the following discussion, the efficiency of a sampling 

design is evaluated in terms of its power to detect the characteristic trend (defined earlier in the Methods section). In a previous 

study of trends and sampling designs in Connecticut, characteristic trends for total phosphorus were generally much higher 

than the characteristic trends for the major ions chloride and sulfate (Trench and Vecchia, 2002a). Flow-adjusted concentrations of total phosphorus usually have a much higher variance 

than flow-adjusted concentrations for major ions (A.V. Vecchia, U.S. Geological Survey, written commun., 2004). Sources 

of phosphorus may be more spatially variable than for the major 

ions, and the influence of point and nonpoint sources on phosphorus concentrations often varies seasonally. Consequently, 

the relation between phosphorus concentration and streamflow 

often is weaker than the relation between concentrations of 

major ions and streamflow. This high variability in flowadjusted phosphorus concentrations means that a trend must be 

large to be detected. 

With monthly sampling, the characteristic trend for the 

Quinebaug River at Quinebaug is 78 percent. That is, there is an 

80-percent probability of detecting a 78-percent change in the 

concentration of total phosphorus over a design period of 

5 years. The characteristic trend for the Quinebaug River at 

Jewett City is 69 percent. That is, with monthly sampling, there 

is an 80 percent probability of detecting a 69-percent change in 

total phosphorus concentration over 5 years of sampling. If the 

power for all sampling designs is held constant at 0.8 (80-percent probability), then sampling designs with fewer than 12 

monthly samples per year have characteristic trends that are 

larger than the characteristic trends for monthly sampling. As 

the sampling frequency decreases, the size of the trend must 

increase to maintain an 80-percent probability of detection. 

Likewise, if the size of the characteristic trend is held constant 

at 78 percent for the station at Quinebaug and 69 percent for the 

station at Jewett City, then designs with fewer than 12 monthly 

samples have less than an 80-percent probability of detecting 

the characteristic trend over the design period of 5 years. In the 

following discussion, the size of the characteristic trend for each 

station is held constant at the percentage change detectable with 

12 monthly samples, and the probability of trend detection is 

less than 80 percent for designs with fewer than 12 samples per 

year. (This discussion is complicated to some extent by the circumstance that the numerical ranges happen to be similar for the 

probability of trend detection, the size of the characteristic 

trend, and the identifying numbers for some of the sampling 

designs.) 

Low-Frequency Sampling Designs

Sampling designs with a frequency of 4 to 7 samples per 

year (not shown in this report) generally have low power for 

trend detection at both stations. All of the 4- and 5-sample 

designs have less than a 60-percent probability of detecting the 

characteristic trend, and many have less than a 50-percent probability. This is in part because sampling designs for all frequencies are constrained to represent each 3-month season with at 

least one sample. Consequently, with 4- and 5-sample designs, 

insufficient samples are available to represent all seasons and 

also conduct monthly sampling during a season that provides 

the best information for trend analysis. As the number of annual 

samples increases, the potential for a seasonal emphasis of 

monthly sampling becomes more feasible. Trend-detection 

power for 7-sample designs ranges from 0.58 to 0.71 for the 

Quinebaug River at Quinebaug, and from 0.60 to 0.69 for the 

Quinebaug River at Jewett City.

Eight-Sample Designs

Currently (2004), water-quality stations on the Quinebaug 

River at Quinebaug and the Quinebaug River at Jewett City are 

each sampled eight times per year. Consequently, the efficiency 

of 8-sample designs is discussed here in detail. Selected 8-sample designs for both stations are shown in figure 8. The designs 
Evaluation of Sampling Designs for Detecting Trends in Total Phosphorus 15

SAMPLING SCHEDULE

JAN

FEB

MAR

APR

MAY

JUNE

JULY

AUG

SEPT

OCT

NOV

DEC

DESIGN NUMBER

82

71

72

83

84

73

70

66

75

77

89

76

87

88

.4 .6 .8

POWER

INCREASING TREND-DETECTION POWER

A. Quinebaug River at Quinebaug, CT

JAN

FEB

MAR

APR

MAY

JUNE

JULY

AUG

SEPT

OCT

NOV

DEC

DESIGN NUMBER

89

70

71

78

82

84

73

87

75

85

74

86

.4 .6 .8

POWER

INCREASING TREND-DETECTION POWER

SAMPLING SCHEDULE

B. Quinebaug River at Jewett City, CT

with the highest power and designs with lowest power for 

detecting trends in total phosphorus are shown for each station, 

to compare the seasons in which monthly sampling provides the 

most information for trend detection with seasons that provide 

less information. For both stations, the most efficient designs 

provide at least a 70-percent probability of detecting the characteristic trend (fig. 8). Trend-detection power for the best 8-sample designs is somewhat higher for the Quinebaug station than 

for the Jewett City station, and the size of the characteristic 

trend also is larger for the Quinebaug station (78-percent) compared to that for the Jewett City station (69-percent). 

For the station on the Quinebaug River at Quinebaug, the 

most efficient 8-sample designs include monthly sampling from 

fall or early winter through late winter or spring, and bimonthly 

sampling from spring or early summer through fall (fig. 8A). 

The most efficient designs (numbers 66, 75–77, and 87–89 in 

fig. 8A) have a 72- to 74-percent probability of detecting the 

characteristic trend (a 78-percent change in concentration). 

Designs 87 and 88, with power of 0.74, are the best 8-sample 

designs. The least efficient 8-sample designs (numbers 71–73 

and 82–84 in fig. 8A) have a 63- to 66-percent probability of 

detecting the characteristic trend, and include monthly sampling from late spring or summer through fall or early winter. 

The current (2004) sampling schedule corresponds to design 

number 70, which has a 68-percent probability of detecting the 

characteristic trend. This sampling schedule, or a schedule similar to design number 70, has been in effect at both Quinebaug 

stations since 1993. 

For the station at Jewett City, the most efficient 8-sample 

designs include monthly sampling from mid-summer or fall 

through winter, and bimonthly sampling from winter or spring 

to mid-summer or early fall. The most efficient designs (numbers 73–75 and 84–87 in fig. 8B) have a 70- to 73-percent probability of detecting the characteristic trend (a 69-percent change 

in concentration). Designs 74 and 86, with power of 0.73, are 

the best 8-sample designs. The least efficient 8-sample designs 

(numbers 70, 71, 78, 82, and 89 in fig. 8B) have a 65- to 66-percent probability of detecting the characteristic trend, and 

include monthly sampling from winter to late spring or from 

spring or early summer to fall. The current (2004) sampling 

schedule corresponds to design 70, which is among the least 

efficient of the 8-sample designs. 

In a previous study of long-term stream-quality trends and 

optimal sampling designs in Connecticut (Trench and Vecchia, 

2002a), a 75-percent probability of trend detection was selected 

as a reasonable threshold for evaluating the sensitivity of sampling designs. In that study, which did not include any stations 

in the Quinebaug River Basin, some of the 8-sample designs 

provided at least a 75-percent probability of trend detection for 

the stations and constituents evaluated. The sampling design 

analysis in the present study indicates that none of the 8-sample 

designs meet that threshold. 

Figure 8. Comparisons of sampling schedules and power for detecting trends in total phosphorus for selected monitoring designs with 

8 samples per year for (A) Quinebaug River at Quinebaug, Connecticut, and (B) Quinebaug River at Jewett City, Connecticut.

[Power is defined as the probability of detecting the characteristic trend after 5 years of sampling; the characteristic trend is 78 percent 

for (A) and 69 percent for (B).]
16 Analysis of Phosphorus Trends and Evaluation of Sampling Designs in the Quinebaug River Basin, Connecticut

SAMPLING SCHEDULE

JAN

FEB

MAR

APR

MAY

JUNE

JULY

AUG

SEPT

OCT

NOV

DEC

DESIGN NUMBER

90

101

110

113

99

100

111

112

.4 .6 .8

POWER

A. Quinebaug River at Quinebaug, CT

SAMPLING SCHEDULE

JAN

FEB

MAR

APR

MAY

JUNE

JULY

AUG

SEPT

OCT

NOV

DEC

DESIGN NUMBER

96

97

98

109

110

111

.4 .6 .8

POWER

B. Quinebaug River at Jewett City, CT

INCREASING TREND-DETECTION POWER

Nine-Sample Designs

The majority of the 9-sample designs for both Quinebaug 

stations provide at least a 72-percent probability for detecting 

the characteristic trend. Many of the 10- and 11-sample designs 

(not shown in this report) provide trend-detection power close 

to that of the monthly (12-sample) design. 

For the station on the Quinebaug River at Quinebaug, eight 

of the twenty-four 9-sample designs have a 75-percent or higher 

probability of detecting the characteristic trend (fig. 9A). The 

two most efficient designs (numbers 111 and 112 in fig. 9A) 

have a 77-percent probability of detecting the characteristic 

trend. The best monthly sampling season for these two designs 

begins in October or November and continues through May or 

June. 

For the station at Jewett City, none of the 9-sample designs 

have a 75-percent or higher probability of detecting the characteristic trend. Six of the designs have a 74-percent probability of 

detecting the characteristic trend (fig. 9B). The addition of the 

ninth sample, however, does not substantially improve trenddetection power beyond that provided by the two most efficient 

8-sample designs (73-percent probability) (fig. 8B). 

Variability in Optimal Designs Among Stations and 

Constituents

Optimal sampling designs for trend detection may differ 

for different stations and constituents, as indicated by results 

from previous studies of long-term trends and sampling designs 

in Connecticut (Trench and Vecchia, 2002a, 2002b). For two of 

the stations evaluated in previous studies, some of the most efficient 8-sample designs for detecting trends in total phosphorus 

included monthly sampling during the period from April to 

October, which corresponds roughly with the sampling schedule followed under the current (2004) cooperative monitoring 

program between the USGS and the CTDEP. Monthly sampling 

during the low-flow period from summer to fall has been considered necessary for compliance-monitoring purposes on 

streams that receive wastewater discharges. This schedule, 

however, is not optimal for trend detection for the two 

Quinebaug River stations, according to the design results from 

this study. 

Effects of Seasonal Changes in the Daily 

Concentration Anomaly

Analyses of seasonal variability and correlation in the 

daily concentration anomaly (described in the Methods section 

of this report) help to identify the seasons in which more frequent sampling improves the likelihood of trend detection, and 

the seasons in which more frequent sampling does not contribute to improved trend detection. Plots of variability and correlation (not shown in this report) indicate that, although there are 

seasonal changes in the magnitude of these two measures at

Figure 9. Comparisons of sampling schedules and power for detecting trends in total phosphorus for selected monitoring designs with 

9 samples per year for (A) Quinebaug River at Quinebaug, Connecticut, and (B) Quinebaug River at Jewett City, Connecticut. 

[Power is defined as the probability of detecting the characteristic trend after 5 years of sampling; the characteristic trend is 78 percent 

for (A) and 69 percent for (B).]
Summary and Conclusions 17

both Quinebaug stations, the seasonal differences are not large. 

Consequently, the differences in trend-detection power between 

the most efficient and least efficient 8-sample designs also are 

not large. 

Correlation results for the Quinebaug River at Quinebaug 

show that serial correlation in the daily concentration anomaly, 

although not strong in any season, is highest from July to October, indicating the potential for redundant information from frequent sampling during these months. Consequently, designs 

with monthly sampling during this period have less trend-detection power (fig. 8A). The magnitude of variability in the daily 

concentration anomaly does not change markedly from season 

to season, and may not be as important a factor as correlation in 

identifying optimal sampling designs for this station. 

For the Quinebaug River at Jewett City, the magnitude of 

seasonal changes in variability and serial correlation is not 

large, and peak periods of variability and correlation in the daily 

concentration anomaly are distributed among several months. 

Consequently, both the most efficient and the least efficient 

8-sample designs for the Quinebaug River at Jewett City have 

monthly sampling periods that collectively cover a large part of 

the year (fig. 8B). 

Summary and Conclusions

A time-series analysis approach developed by the U.S. 

Geological Survey (USGS) was used to analyze trends in total 

phosphorus and evaluate optimal sampling designs for future 

trend detection using long-term data for two stations on the 

Quinebaug River in eastern Connecticut. Trend-analysis results 

from the two stations indicate that total phosphorus concentrations in the Quinebaug River have decreased significantly since 

the 1970s and 1980s. Downward trends in total phosphorus concentrations are more pronounced and persistent at the upstream 

station, the Quinebaug River at Quinebaug, whereas results for 

the downstream station, the Quinebaug River at Jewett City, 

include an upward trend during 1980–84 and a period of no 

trend during 1992–97. Data for both stations show small 

upward trends in the late 1990s and early 2000s; additional 

investigation would be necessary to determine possible causes 

for these increases. 

Optimal sampling designs are based on water-quality data 

that reflect the unique combination of processes affecting a specific location on a river. Design results are based on an analysis 

of annual, seasonal, and short-term variability in discharge and 

concentration, and may provide clues to understanding constituent sources and watershed processes. A complex analysis of 

the characteristics of short-term variability in concentration 

(termed the daily concentration anomaly), assists in identifying 

the months or seasons that contain the best information for 

future trend analysis. The analysis identifies the months or seasons in which the trend signal is most readily differentiated 

from other sources of variability, and also identifies the months 

or seasons where more frequent sampling provides redundant 

information. 

Various designs with sampling frequencies ranging from 

4 to 11 samples per year were compared to the trend-detection 

power of the monthly (12-sample) design to determine the most 

efficient configuration of months to sample for a given annual 

sampling frequency. Results of this evaluation indicate that the 

current (2004) 8-sample schedule for the two Quinebaug stations, with monthly sampling from May to September and bimonthly sampling for the remainder of the year, does not provide the optimal information for future trend detection among 

the 8-sample designs. For the Quinebaug River at Quinebaug, 

the 8-sample designs with the highest power for detecting 

trends in total phosphorus have monthly sampling within the 

period from October to May. The 8-sample designs with the 

lowest trend detection power have monthly sampling within the 

period from May to December. For the Jewett City station, the 

8-sample designs with the highest power for detecting trends in 

total phosphorus have monthly sampling starting within the 

period from July to October and ending within the period from 

December to March. The 8-sample designs with the lowest 

trend-detection power have monthly sampling from winter to 

late spring or from spring or early summer to fall. The current 

(2004) sampling schedule is among the least efficient of the 

8-sample designs for the Jewett City station. Trend-detection 

probability for 8-sample designs ranges from 63 to 74 percent 

for the Quinebaug station and from 65 to 73 percent for the Jewett City station. Sampling designs with fewer than 8 samples per 

year generally provide a low level of probability for detection 

of trends in total phosphorus. 

Determining the acceptable level of probability for trend 

detection is a management consideration that can incorporate 

numerous factors related to the goals of the water-management 

program and the scientific understanding of the watersheds in 

question. Managers may identify a threshold of probability for 

trend detection that is high enough to justify the agency’s 

investment in the water-quality sampling program. Numerous 

water-quality management considerations in addition to trend 

detection are likely to be involved in determining an appropriate 

sampling schedule. Monthly sampling during the low-flow 

period from summer to fall has been considered necessary for 

compliance-monitoring purposes on streams that receive wastewater discharges, and this factor has influenced the redesign of 

sampling schedules over a period of years in which financial 

constraints have necessitated less frequent sampling. Results 

from the analysis of optimal sampling designs can provide an 

important component of information for the decision-making 

process in which sampling schedules are periodically reviewed 

and revised. 

The time-series analysis program used in this study is 

designed to evaluate long-term annual trends; that is, trends 

may originate in any season of the year. Trends that have taken 

place primarily during specific seasons have not been evaluated 

in this study. Seasonally variable sources of nutrients, such as 

agricultural areas or wastewater-treatment facilities with seasonal phosphorus removal, and the seasonal processes related to 
18 Analysis of Phosphorus Trends and Evaluation of Sampling Designs in the Quinebaug River Basin, Connecticut

algal blooms and decay, are important aspects of phosphorus 

dynamics in the Quinebaug River Basin. Consequently, an analysis of seasonal trends in total phosphorus may improve understanding of seasonally variable phosphorus sources and processes in the watershed. 

Modifications to the schedule for water-quality sampling, 

in the Quinebaug River Basin and elsewhere in the state, may 

be considered in the context of the multiple objectives of the 

state’s water-quality management program. Results from the 

current study and previous studies indicate that optimal sampling schedules for trend detection may differ substantially for 

different stations and constituents. This suggests that a more 

comprehensive statewide evaluation of sampling schedules for 

key stations and constituents could provide useful information 

and insights for any redesign of the schedule for water-quality 

monitoring. 

References 

Colombo, M.J., and Trench, E.C.T., 2002, Trends in surfacewater quality in Connecticut: U.S. Geological Survey WaterResources Investigations Report 02–4012, 39 p.

Connecticut Department of Environmental Protection, 2002a, 

2002 List of Connecticut water bodies not meeting water 

quality standards: Hartford, Connecticut, variously 

paginated. 

Connecticut Department of Environmental Protection, 2002b, 

Surface water quality standards: Hartford, Connecticut, variously paginated.

Davies, B.S., 3rd, Morrison, Jonathan, Norris, J.R., and 

Organek, J.A., 1999, Water resources data, Connecticut, 

water year 1998: U.S. Geological Survey Water-Data Report 

98–1, 340 p.

Healy, D.F., Pizzuto, E.J., and Hoffman, G.F., 1994, Waterquality data for selected rivers and harbors in Connecticut, 

1973–85: Connecticut Water Resources Bulletin 44, 293 p.

Hem, J.D., 1985, Study and interpretation of the chemical characteristics of natural water (3d ed.): U.S. Geological Survey 

Water-Supply Paper 2254, 263 p.

Kennedy, L.E., Kiras, Stella, and McVoy, Richard, 2002, 

French & Quinebaug River Watersheds 2001 water quality 

assessment report: Massachusetts Department of Environmental Protection, Division of Watershed Management, 

Report Number 41/42–AC–1: Worcester, Massachusetts, 

March 2002, variously paginated. 

Litke, D.W., 1999, Review of phosphorus control measures in 

the United States and their effects on water quality: U.S. Geological Survey Water-Resources Investigations Report 

99–4007, 38 p. 

Mueller, D.K., Hamilton, P.A., Helsel, D.R., Hitt, K.J., and 

Ruddy, B.C., 1995, Nutrients in ground water and surface 

water of the United States—An analysis of data through 

1992: U.S. Geological Survey Water-Resources Investigations Report 95–4031, 74 p. 

Ranzau, C.E., Jr., Davies, B.S. 3rd, Frick, T.W., and Organek, 

J.A., 2001, Water resources data, Connecticut, water year 

2000: U.S. Geological Survey Water-Resources Data Report 

CT–00–1, 333 p.

Ranzau, C.E., Jr., Frick, T.W., Norris, J.R., and Martin, J.W., 

2000, Water resources data, Connecticut, water year 1999: 

U.S. Geological Survey Water-Resources Data Report 

CT–99–1, 351 p.

Trench, E.C.T., 1996, Trends in surface-water quality in 

Connecticut,1969–88: U.S. Geological Survey WaterResources Investigations Report 96–4161, 176 p.

Trench, E.C.T., 2000, Nutrient sources and loads in the 

Connecticut, Housatonic, and Thames River Basins: U.S. 

Geological Survey Water-Resources Investigations Report 

99–4236, 66 p.

Trench, E.C.T., and Vecchia, A.V., 2002a, Water-quality trend 

analysis and sampling design for streams in Connecticut, 

1968–98: U.S. Geological Survey Water-Resources Investigations Report 02–4011, 94 p.

Trench, E.C.T., and Vecchia, A.V., 2002b, Evaluation of sampling designs for stream water-quality trend analysis in 

Connecticut: National Water-Quality Monitoring Council 

Conference, Madison, Wisconsin, May 2002.

U.S. Environmental Protection Agency, 1986, Quality criteria 

for water 1986: Washington, D.C., U.S. Environmental 

Protection Agency Report 440/5–86–001, Office of Water, 

variously paginated. 

U.S. Environmental Protection Agency, 2000, Nutrient criteria 

technical guidance manual—Rivers and streams: Office of 

Water, U.S. Environmental Protection Agency, EPA 822–B–

00–002, variously paginated. 

Vecchia, A.V., 2000, Water-quality trend analysis and sampling design for the Souris River, Saskatchewan, North 

Dakota, and Manitoba: U.S. Geological Survey WaterResources Investigations Report 00–4019, 77 p. 

Vecchia, A.V., 2003, Water-quality trend analysis and sampling design for streams in North Dakota, 1971–2000: U.S. 

Geological Survey Water-Resources Investigations Report 

03–4094, 73 p. 

Vogelmann, J.E., Howard, S.M., Yang, L., Larson, C.R., Wylie, 

B.K., Van Driel, N., 2001, Completion of the 1990s National 

Land Cover Dataset for the conterminous Untied States from 

Landsat thematic mapper data and ancillary data sources: 

Photogrammetric Engineering and Remote Sensing, v. 67, 

p. 650–652. 

Zimmerman, M.J., Grady, S.J., Trench, E.C.T., Flanagan, S.M., 

and Nielsen, M.G., 1996, Water-quality assessment of the 

Connecticut, Housatonic, and Thames River Basins study 

unit—Analysis of available data on nutrients, suspended 

sediments, and pesticides, 1972–92: U.S. Geological Survey 

Water-Resources Investigations Report 95–4203, 162 p.