U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2017– 5012

Prepared in cooperation with Gwinnett County Department of Water Resources

Hydrology and Water Quality in 13 Watersheds in 

Gwinnett County, Georgia, 2001–15

Turbidity

Yellow River

Sweetwater Creek

No Business Creek

Big Haynes Creek

Brushy Fork Creek

Alcovy River

Wheeler Creek

Apalachee River

Richland Creek

Suwanee Creek

Crooked Creek

North Fork Peachtree Creek

Level Creek

10,000

1,000

100

10

1

0.1

x

x

x

x xx

x

x

x

x

x

o

o

x

x

x

x

o

o

x

x

x

x

x

x

x

x

o

o

x

x

x

x

x

x

x

x

x

x

Turbidity, in formanzin nephelometric units

Monitored watersheds, Gwinnett County

Total suspended solids

x

x

x

x

x

x

x

x

x x

x

x

x

o

x

x

x

x x

x

x

x

x

x

o

x

x

x

x

x

x

o

x

x

x

x

x

x

o

o

x

x

x

x

x

x

x

o

xx

10

100

1,000

10,000

100,000

Concentration, in milligrams per liter

1
Cover. Background image shows multiparameter monitoring station at Richland Creek at Suwanee Dam Road, near Buford, Georgia, looking 

upstream. Photograph by Kerry Caslow, USGS. See figure 26 of this report for details about turbidity and total suspended solids boxplots.
Hydrology and Water Quality in 

13 Watersheds in Gwinnett County, 

Georgia, 2001–15

By Brent T. Aulenbach, John K. Joiner, and Jaime A. Painter

Prepared in cooperation with Gwinnett County Department of Water Resources

Scientific Investigations Report 2017– 5012

U.S. Department of the Interior

U.S. Geological Survey
U.S. Geological Survey

William H. Werkheiser, Acting Director

U.S. Geological Survey, Reston, Virginia: 2017

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as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Aulenbach, B.T., Joiner, J.K., and Painter, J.A., 2017, Hydrology and water quality in 13 watersheds in Gwinnett County,

Georgia, 2001–15: U.S. Geological Survey Scientific Investigations Report 2017–5012, 82 p., https://doi.org/10.3133/

sir20175012. 

ISSN 2328-031X (print) 

ISSN 2328-0328 (online)

ISBN 978-1-4113-4128-9
iii

Contents

Acknowledgments.........................................................................................................................................x

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

Introduction.....................................................................................................................................................2

Purpose and Scope ..............................................................................................................................4

Study Design and Methods..........................................................................................................................4

Description of Study Watersheds ......................................................................................................5

Surface-Water Monitoring..................................................................................................................5

Precipitation...........................................................................................................................................8

Identification of Surface-Water Constituent Concentration Long-Term Trends........................8

Constituent Load Estimation .............................................................................................................10

Load Estimation Error.........................................................................................................................12

Watershed Characteristics ........................................................................................................................13

Basin Characteristics.........................................................................................................................13

Population ............................................................................................................................................14

Land Use and Changes in Impervious Area ...................................................................................20

Hydrologic Budgets.....................................................................................................................................23

Climate and Precipitation ..................................................................................................................23

Runoff....................................................................................................................................................24

Flow Duration Curves.........................................................................................................................27

Base Flow.............................................................................................................................................29

Runoff Ratios........................................................................................................................................29

Flood Frequency...........................................................................................................................................31

Surface-Water Quality ................................................................................................................................35

Continuous Water-Quality Monitoring.............................................................................................35

Base-Flow and Stormflow Water Quality .......................................................................................35

Water-Quality Seasonality and Long-Term Trends ................................................................................41

Seasonality in Water Quality.............................................................................................................41

Long-Term Trends in Water Quality .................................................................................................42

Constituent Loads and Yields.....................................................................................................................64

Load Models ........................................................................................................................................64

Program Area Loads and Trends......................................................................................................64

Watershed Yields................................................................................................................................70

Differences with Previous Load Estimates.....................................................................................74

Uncertainty in Load Estimates..........................................................................................................74

Interpreting Aggregate Effects on Water Quality...................................................................................75

Summary........................................................................................................................................................76

References Cited..........................................................................................................................................78
iv

Figures

1. Map showing location of the study area and the 13 monitored watersheds and

water-quality and precipitation monitoring stations, Gwinnett County, Georgia...............3

2. Photograph showing multiparameter monitoring station at Richland Creek at

Suwanee Dam Road, near Buford, Georgia, looking upstream............................................4

3. Map showing land-surface altitude for Gwinnett County, Georgia ...................................15

4. Graph showing average basin slope of the 13 monitored watersheds in 

Gwinnett County, Georgia..........................................................................................................16

5. Map showing land-surface slope for Gwinnett County, Georgia .......................................17

6. Graphs showing total population by year and population growth for 

Gwinnett County, Georgia, 2001–15 .........................................................................................18

7. Graph showing annual change in population for Gwinnett County, Georgia,

from 2001 to 2015.........................................................................................................................18

8. Map showing population density for Gwinnett County, Georgia, 2013..............................19

9. Graph showing population density for the 13 monitored watersheds in 

Gwinnett County, Georgia, in 2009 and 2013...........................................................................20

10. Graph showing percentage of watershed impervious area from 

transportation and building land use for 13 monitored watersheds

in Gwinnett County, Georgia, 2012 ...........................................................................................21

11. Graphs showing watershed impervious area as related to high-density 

land use and population density for 13 monitored watersheds in Gwinnett County, 

Georgia, 2013 ...............................................................................................................................22

12. Graph showing percentage of impervious area in 13 monitored watersheds

in Gwinnett County, Georgia .....................................................................................................22

13. Graph showing annual precipitation totals for each of the 13 monitored 

watersheds in Gwinnett County, Georgia, for water years 2002–15 ..................................23

14. Graph showing monthly average precipitation for each of the 13 monitored

watersheds in Gwinnett County, Georgia, for water years 2002–15 ..................................24

15. Graph showing annual runoff for each of the 13 monitored watersheds

in Gwinnett County, Georgia, for water years 2002–15.........................................................25

16. Graph showing weekly drought severity in Gwinnett County, Georgia, 

for years 2001–15 as a portion of county area.......................................................................26

17. Graph showing average monthly runoff for each of the 13 monitored

watersheds in Gwinnett County, Georgia, for water years 2002–15 ..................................27

18. Graph showing streamflow duration curves for each of the 13 monitored

watersheds in Gwinnett County, Georgia, for water years 2002–15 ..................................28

19. Graph showing streamflow duration curves for the Yellow River watershed

for wet, average, and dry years. ..............................................................................................28

20. Graphs showing average base-flow runoff versus percentage of impervious area

in 2008, and base-flow index versus percentage of impervious area in 2008 for

13 watersheds in Gwinnett County, Georgia, for water years 2002–15 .............................29

21. Graph showing annual runoff ratios for each of the 13 monitored watersheds

in Gwinnett County, Georgia, for water years 2002–15.........................................................30

22. Graph showing runoff ratios for water years 2002–15 versus percentage

of impervious areas in 2008 for each of the 13 monitored watersheds in

Gwinnett County, Georgia..........................................................................................................31

23. Graph showing example of PeakFQ probability graph of annual peaks, 

fitted frequency curve, and confidence limits for USGS station 02208050,

Alcovy River near Lawrenceville, Georgia .............................................................................34
v

24. Plot of stormflow response of specific conductance to streamflow for

USGS station 02205865, Sweetwater Creek at Club Drive near Lilburn, 

Georgia, December 22–27, 2013 ...............................................................................................35

25. Plots of total suspended solids, suspended sediment, total lead, and 

total zinc sample concentrations versus turbidity for USGS station 02205865, 

Sweetwater Creek at Club Drive near Lilburn, Georgia.......................................................36

26. Boxplots of base-flow and stormflow sample concentrations for specific 

conductance, turbidity, total nitrogen, total ammonia plus organic nitrogen, 

total nitrate plus nitrite, total phosphorus, dissolved phosphorus, total organic

carbon, total suspended solids, suspended sediment, total lead, total zinc, 

and total dissolved solids at 13 monitored watersheds in Gwinnett County ....................38

27. Graphs showing total nitrogen residuals to flow-only load estimation model

versus time for 13 monitored watersheds in Gwinnett County, Georgia...........................44

28. Graphs showing total nitrate plus nitrite residuals to flow-only load estimation

model versus time for 13 monitored watersheds in Gwinnett County, Georgia...............46

29. Graphs showing total phosphorus residuals to flow-only load estimation model

versus time for 13 monitored watersheds in Gwinnett County, Georgia...........................48

30. Graphs showing dissolved phosphorus residuals to flow-only load estimation

model versus time for 13 monitored watersheds in Gwinnett County, Georgia...............50

31. Graphs showing total organic carbon residuals to flow-only load estimation

model versus time for 13 monitored watersheds in Gwinnett County, Georgia...............52

32. Graphs showing total suspended-solid residuals to flow-only load estimation

model versus time for 13 monitored watersheds in Gwinnett County, Georgia...............54

33. Graphs showing suspended-sediment residuals to flow-only load estimation

model versus time for 13 monitored watersheds in Gwinnett County, Georgia...............56

34. Graphs showing total lead residuals to flow-only load estimation model

versus time for 13 monitored watersheds in Gwinnett County, Georgia...........................58

35. Graphs showing total zinc residuals to flow-only load estimation model 

versus time for 13 monitored watersheds in Gwinnett County, Georgia ...........................60

36. Graphs showing total dissolved solids residuals to flow-only load estimation

model versus time for 13 monitored watersheds in Gwinnett County, Georgia ...............62

37. Graphs showing annual loads and annual runoff for water years 2003–15 

for the LTTM program study area in Gwinnett County, Georgia .........................................66

38. Graphs showing annual average concentrations and annual runoff for water

years 2003–15 for the LTTM program study area in Gwinnett County, Georgia...............68

39. Graph showing average suspended-sediment loads and average annual runoff

for years with dry, average, and wet climatic conditions for water years 2003–15

for the LTTM program study area in Gwinnett County, Georgia .........................................70

40. Graph showing average total nitrogen and total nitrate plus nitrite yields for

13 monitored watersheds in Gwinnett County, Georgia, for water years 2003–15..........71

41. Graph showing average total and dissolved phosphorus yields for 13 monitored

watersheds in Gwinnett County, Georgia, for water years 2003–15 ..................................71

42. Graph showing average total organic carbon yields for 13 monitored watersheds

in Gwinnett County, Georgia, for water years 2003–15 ........................................................72

43. Graph showing average total suspended solids and suspended-sediment

yields for 13 monitored watersheds in Gwinnett County, Georgia, for water

years 2003–15..............................................................................................................................73

44. Graph showing average total lead and total zinc yields for 13 monitored 

watersheds in Gwinnett County, Georgia, for water years 2003–15 ..................................73

45. Graph showing average total dissolved solids yields for 13 monitored 

watersheds in Gwinnett County, Georgia, for water years 2003–15 ..................................74
vi

Tables

1. Thirteen U.S. Geological Survey water-quantity and water-quality 

monitoring stations included in the watershed characteristics and

water-quality trends study in Gwinnett County, Georgia, including

dates established and drainage areas for each station ........................................................5

2. Water-quality constituents measured and analyzed for samples

collected in streams in Gwinnett County, Georgia, units of measures,

and predominant laboratory reporting limit .............................................................................7

3. Summary of analytical methods used to quantify water-quality constituent

concentrations of samples in streams in Gwinnett County, Georgia...................................8

4. Precipitation gages included in the study, including dates established

and decommissioned, Gwinnett County, Georgia ...................................................................9

5. Watershed characteristics for 13 watersheds in Gwinnett County, Georgia...................14

6. Land use and watershed impervious area for 13 watersheds in 

Gwinnett County, Georgia, 2012................................................................................................21

7. Average base-flow runoff and base-flow index for 13 watersheds in

Gwinnett County, Georgia, for water years 2002–15.............................................................29

8. Historical terminology associated with flood-frequency estimates

and corresponding annual exceedance probability and P-percent

chance exceedance...................................................................................................................31

9. Annual exceedance probability discharges estimated from 13 stations

in the LTTM program study area, Gwinnett County, Georgia ..............................................32

10. Summary of seasonal variability in 10 water-quality constituents at the

13 LTTM program watersheds in Gwinnett County, Georgia, based on the

seasonality terms in the flow-only load estimation models ................................................41

11. Water-quality constituent trends and statistical significance for sample

 concentrations in 13 monitored watersheds in Gwinnett County, Georgia,

for water years 2001–15.............................................................................................................42

12. Runoff and water-quality constituent load averages, annual minimum, annual

maximum, trends, and trend statistical significance for water years 2003–15

for the LTTM program study area, Gwinnett County, Georgia.............................................65
vii

Conversion Factors

U.S. customary units to International System of Units

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 0.4047 hectare (ha)

acre 0.004047 square kilometer (km2

)

square mile (mi2

) 259.0 hectare (ha)

square mile (mi2

) 2.590 square kilometer (km2) 

Volume

million gallons (Mgal) 3,785 cubic meter (m3

)

Flow rate

cubic foot per second (ft3

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

million gallons per day (Mgal/d) 0.04381 cubic meter per second (m3

/s)

Mass

pound, avoirdupois (lb) 0.4536 kilogram (kg) 

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

°F=(1.8× °C) +32.

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

°C= (°F–32)/1.8.

Datum

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

(NAVD 88).

Altitude, as used in this report, refers to distance above the vertical datum.

Supplemental Information

Specific conductance (SC) 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).

A water year is the period from October 1 to September 30 and is designated by the year in 

which the period ends.
viii

Abbreviations

°F degree Fahrenheit

AEP Annual Exceedance Probability 

AMLE adjusted maximum likelihood estimate

BFI base-flow index 

BMP best management practice

C constituent concentration

CI confidence interval

DP dissolved phosphorus

dtime decimal time

EMA Expected Moments Algorithm 

ET evapotranspiration

EWI equal-width increment 

FNU Formazin Nephelometric Unit

in/yr inch per year

LAD least absolute deviation

lb/acre/yr pound per acre per year

lb/d/yr pound per day per year

lidar light detection and ranging

LRL laboratory reporting limit

LT-MDL long-term method detection level 

LTTM Long-Term Trend Monitoring

MNGWPD Metropolitan North Georgia Water Planning District

NAVD 88 North American Vertical Datum of 1988 

NO3+NO2 total nitrate plus nitrite

NPDES National Pollutant Discharge Elimination System

NWIS National Water Information System 

Pb total lead

PET potential evapotranspiration

R2 coefficient of multiple determination

SC specific conductance

SE standard error

SEP standard error of the prediction

SSC suspended-sediment concentration

TDS total dissolved solids
ix

TKN total ammonia plus organic nitrogen (total Kjeldahl nitrogen)

TMDL total maximum daily load

TN total nitrogen

TOC total organic carbon

TP total phosphorus

TSS total suspended solids

USGS U.S. Geological Survey

WRTDS weighted regression on time, discharge, and season

WY water year

Zn total zinc
x

Acknowledgments

The authors wish to thank personnel of Gwinnett County Department of Water Resources 

for their long-term commitment to improved data and understanding of water quality in the 

watersheds in their area.

This report is possible because of the field data-collection efforts and analysis expertise 

of the U.S. Geological Survey, Gwinnett Project personnel Kerry Caslow, Jonathan Evans, 

and numerous other USGS employees in the data section. Thanks are also extended to 

U.S. Geological Survey employees Jonathan Musser for GIS support, Anthony Gotvald, 

Celeste Journey, and Jimmy Webber for technical reviews.
Hydrology and Water Quality in 13 Watersheds in 

Gwinnett County, Georgia, 2001–15

By Brent T. Aulenbach, John K. Joiner, and Jaime A. Painter

Abstract

The U.S. Geological Survey (USGS), in cooperation 

with Gwinnett County Department of Water Resources, 

established a Long-Term Trend Monitoring (LTTM) program 

in 1996. The LTTM program is a comprehensive, long-term, 

water-quantity and water-quality monitoring program designed 

to document and analyze the hydrologic and water-quality 

conditions of selected watersheds in Gwinnett County, 

Georgia. Water-quality monitoring initially began in six 

watersheds and currently [2016] includes 13 watersheds. 

As part of the LTTM program, streamflow, precipitation, water temperature, specific conductance, and turbidity 

were measured every 15 minutes for water years 2001–15 at 

12 of the 13 watershed monitoring stations and for water years 

2010–15 at the other watershed. In addition, discrete waterquality samples were collected seasonally from May through 

October (summer) and November through April (winter), 

including one base-flow and three stormflow event composite 

samples, during the study period. Samples were analyzed for 

nutrients (nitrogen and phosphorus), total organic carbon, 

trace elements (total lead and total zinc), total dissolved solids, 

and total suspended sediment (total suspended solids and 

suspended-sediment concentrations). The sampling scheme 

was designed to identify variations in water quality both 

hydrologically and seasonally.

The 13 watersheds were characterized for basin slope, 

population density, land use for 2012, and the percentage 

of impervious area from 2000 to 2014. Several droughts 

occurred during the study period—water years 2002, 2007–08, 

and 2011–12. Watersheds with the highest percentage of 

impervious areas had the highest runoff ratios, which is the 

portion of precipitation that occurs as runoff. Watershed 

base-flow indexes, the ratio of base-flow runoff to total runoff, 

were inversely correlated with watershed impervious area. 

Flood-frequency estimates were computed for 

13 streamgages in the study area that have 10 or more 

years of annual peak flow data through water year 2015, 

using the expected moments algorithm to fit a Pearson 

Type III distribution to logarithms of annual peak flows. 

Kendall’s tau nonparametric test was used to determine the 

statistical significance of trends in the annual peak flows, with 

none of the 13 streamgages exhibiting significant trends. 

A comparison of base-flow and stormflow water-quality 

samples indicates that turbidity and concentrations of 

total ammonia plus organic nitrogen, total nitrogen, total 

phosphorus, total organic carbon, total lead, total zinc, total 

suspended solids, and suspended-sediment concentrations 

increased with increasing discharge at all watersheds. Specific 

conductance decreased during stormflow at all watersheds, 

and total dissolved solids concentrations decreased during 

stormflow at a few of the watersheds. Total suspended solids 

and suspended-sediment concentrations typically were two 

orders of magnitude higher in stormflow samples, turbidities 

were about 1.5 orders of magnitude higher, total phosphorus 

and total zinc were about one order of magnitude higher, 

and total ammonia plus organic nitrogen, total nitrogen, total 

organic carbon, and total lead were about twofold higher than 

in base-flow samples.

Seasonality and long-term trends were identified for 

the period water years 2001–15 for 10 constituents—total 

nitrogen, total nitrate plus nitrite, total phosphorus, dissolved 

phosphorus, total organic carbon, total suspended solids, 

suspended-sediment concentration, total lead, total zinc, 

and total dissolved solids. Seasonal patterns were present in 

most watersheds for all constituents except total dissolved 

solids, and the watersheds had fairly similar patterns of higher 

concentrations in the summer and lower concentrations 

in the winter. A linear long-term trend analysis of residual 

concentrations from the flow-only load estimation model 

(without time-trend terms) identified significant trends in 

67 of the 130 constituent-watershed combinations. Seventy 

percent of the significant trends were negative. Total organic 

carbon and total dissolved solids had predominantly positive 

trends. Total phosphorus, total suspended solids, suspendedsediment concentration, total lead, and total zinc had only 

negative trends. The other three constituents exhibited fewer 

trends, both positive and negative.
2 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

Streamwater loads were estimated annually for the 

13-year period water years 2003–15 for the same 10 constituents in the trend analysis. Loads were estimated using a 

regression-model-based approach developed by the USGS for 

the Gwinnett County LTTM program that accommodates the 

use of storm-event composited samples. Concentrations were 

modeled as a function of discharge, base flow, time, season, 

and turbidity to improve model predictions and reduce errors 

in load estimates. Total suspended solids annual loads have 

been identified in Gwinnett County’s Watershed Protection 

Plan for target performance criterion.

Although the amount of annual runoff was the primary 

factor in variations in annual loads, climatic conditions 

(classified as dry, average, or wet) affected annual loads 

beyond what was attributed to climatic-related variations 

in annual runoff. Significant negative trends in loads were 

estimated for the combined area of the watersheds for all 

constituents except dissolved phosphorus, total organic 

carbon, and total dissolved solids. The trend analysis 

indicated that total suspended solids and suspended-sediment 

concentration loads in the study area were decreasing by 

57,000 and 87,000 pounds per day per year, respectively. 

Variations in constituent yields between watersheds 

appeared to be related to various watershed characteristics. 

Suspended sediment (as either total suspended solids or 

suspended-sediment concentrations), along with constituents 

transported predominately in solid phase (total phosphorus, 

total organic carbon, total lead, and total zinc), and total 

dissolved solids typically had higher yields from watersheds 

that had high percentages of impervious areas or high basin 

slope. High total nitrogen yields were also associated with 

watersheds with high percentages of impervious areas. Low 

total nitrogen, total suspended solids, total lead, and total zinc 

yields appeared to be associated with watersheds that had a 

low percentage of high-density development. 

Introduction

Surface-water quantity and quality reflect and integrate 

the effects of watershed characteristics, inputs from point 

and nonpoint source pollutants, and climatic variability. 

Changes in land use alter complex interactions that affect 

many processes within a watershed (MacDonald, 2000). 

Urbanization, with its associated increases in impervious 

area, has been determined to be an important factor affecting 

the rainfall-runoff relations in many studies (for example, 

Hollis, 1975; Ogden and others, 2011), including studies 

of areas in Gwinnett County (Landers and others, 2007). 

Impervious areas affect hydrologic response by (1) decreasing 

rainfall infiltration and groundwater recharge rates, resulting 

in lower stream base flow, and (2) increasing storm runoff, 

peak discharges, and flood flows (Leopold, 1968). High 

peak storm streamflows can affect surface-water quality by 

increasing surface erosion, sediment transport, and pollutant 

loadings and by altering stream-channel stability. Groundwater 

storage and stream base-flow levels can be critically important 

during droughts in providing water for human activities and 

promoting healthy ecosystems (for example, Poff and others, 

1997; Swirepik and others, 2016). Many studies have also 

shown that the effects of urbanization on the flow regime may 

decrease stream biological richness (for example, DeGasperi 

and others, 2009) and that native stream biota is best adapted 

to natural, unimpacted streamflows (Richter and others, 1996, 

1997). Stormwater best management practices and other 

erosion controls are frequently implemented to mitigate the 

effects of urbanization on stream hydrology and minimize 

sediment transport in urbanized areas. Long-term monitoring 

of specific water-quality constituents allows for the identification of trends and the computation of loads and yields. 

Variations and trends in water quality can then be attributed 

to climatic variability, changes in land use and contaminant 

inputs, and potentially lead to implementation of watershed 

management strategies. 

Gwinnett County has undergone rapid population growth 

since 1980, and land use has changed from what was once 

predominantly agriculture and forest to a highly developed 

area. The U.S. Geological Survey (USGS), in cooperation 

with Gwinnett County Department of Water Resources, 

established a comprehensive Long-Term Trend Monitoring 

(LTTM) program in 1996 to monitor, analyze, and quantify 

the magnitudes of pollutants and the effects of urbanization on 

six watersheds. Six additional watersheds were added to the 

LTTM program in 2001, and a 13th watershed was added in 

2010 (fig. 1). These 13 watersheds were continuously monitored for water level (stage), precipitation, water temperature, 

specific conductance (SC), and turbidity at 15-minute intervals. 

Three stormflow-composited samples and one base-flow 

sample were collected in summer (May–October) and winter 

(November–April) of each year for a total of eight samples a 

year; samples were analyzed for nutrients, total recoverable 

trace metals, total dissolved solids, and total suspended 

sediment (total suspended solids and suspended-sediment 

concentration). The sampling scheme was designed to identify 

variations in water quality both hydrologically and seasonally. 

The primary purpose of the monitoring program is to 

collect consistent, high-quality water-quantity and waterquality data and determine the status and trends in both 

stream runoff and water quality. Watershed managers can use 

these data to make informed management decisions in order 

to maintain the designated uses of streams, protect aquatic 

habitats, and optimize the effectiveness of best management 

practices (BMPs). Gwinnett County has adopted a stormwater 

management plan (Gwinnett County Department of Planning 

and Development, 2006) to mitigate the effects of increased 

impervious area on storm runoff and water quality. This plan 

has resulted in the widespread implementation of structural 

BMPs consisting mostly of wet, dry, and dry-extended 

detention ponds and constructed wetlands (Gwinnett County 

Department of Planning and Development, unpub. database, 

2010). Intensive, long-term monitoring of watershed 

characteristics, streamflow, and stream quality are essential 
Introduction 3 

Chattahoochee–

Flint River Basin

Ocmulgee–

Oconee–Altamaha

River Basin

GEORGIA

Ocean Atlantic

Atlanta

GWINNETT

COUNTY

Lake Sidney Lanier

Buford

Dam

Piedmont

Physiographic Province

Monitored watershed boundary

Eastern Continental Divide

USGS water-quality monitoring station,

 with watershed name and identifier

0 1 2 3 4 5 MILES

0

Base modified from U.S. Geological Survey 

1:100,000-scale digital data

31 2 4 5 KILOMETERS

EXPLANATION

Figure 1.

Original Gwinnett County LTTM program

 watersheds, 1996–97

Watersheds added in 2001

N

Watershed added in 2010

USGS precipitation only monitoring station

 and identifier 02205000

02205522

02208050

02208130

02205000 02335000

Wheeler Creek

watershed

02217274

Suwanee Creek

watershed

02334885

Richland Creek

watershed

02334480

Level Creek

watershed

02334578

Crooked Creek

watershed

02335350

North Fork

Peachtree Creek

watershed

02336030

Yellow River

watershed

02207120

No Business Creek

watershed

02207185

Big Haynes Creek

watershed

02207385

Brushy Fork Creek

watershed

02207400

Alcovy River

watershed

02208150

Apalachee River

watershed

02218565

Sweetwater Creek

watershed

02205865

Chattahooch e e River

Figure 1. Location of the study area and the 13 monitored watersheds and water-quality and precipitation monitoring stations, 

Gwinnett County, Georgia.
4 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

to quantifying the effects of specific land uses, point-source 

and nonpoint-source discharges, and management practices 

on surface water (water quantity, flow characteristics, and 

water quality). The LTTM program also fulfills requirements 

outlined in the Gwinnett County Watershed Protection Plan 

(Gwinnett County Department of Public Utilities, 2000) as 

well as requirements for the National Pollutant Discharge 

Elimination System (NPDES) permit and the Metropolitan 

North Georgia Water Planning District (MNGWPD). 

Purpose and Scope

The purpose of this report is to summarize and analyze the 

land-use, hydrologic, and water-quality data collected as part of 

Gwinnett County’s LTTM program for 13 watersheds for the 

period water years (WYs) 2001–15. This report is an update to, 

and continuation of, the reports by Landers and others (2007), 

in which data for the original 6 watersheds were presented and 

analyzed for WYs 1996–2003, and by Joiner and others (2014), 

in which data for 12 watersheds were presented and analyzed 

for WYs 2004–09. The specific goals of this report are to

• Present watershed characteristics for the 13 watersheds 

and discuss changes and trends in population and 

impervious area;

• Report and discuss annual and monthly hydrologic 

inputs and outputs (precipitation and runoff) for 

WYs 2002–15;

• Calculate selected flood-frequency statistics for 

13 streamgages in the study area that have 10 or more 

years of annual peak flow data through WY 2015;

• Summarize water-quality data for WYs 2001–15;

• Determine trends for 10 water-quality constituents 

(total nitrate plus nitrite, total nitrogen, total phosphorus, dissolved phosphorus, total organic carbon, 

total suspended solids, suspended sediment, total lead, 

total zinc, and total dissolved solids) for the period 

WYs 2001–15; and

• Provide annual load and yield estimates for 10 constituents (total nitrate plus nitrite, total nitrogen, total 

phosphorus, dissolved phosphorus, total organic 

carbon, total suspended solids, suspended sediment, 

total lead, total zinc, and total dissolved solids) for 

WYs 2003–15.

Study Design and Methods

Thirteen watersheds were monitored at their outlets for 

stage (water level), discharge, precipitation, and continuous 

water-quality constituents as part of the Gwinnett County 

LTTM program. A typical multiparameter stream monitoring 

station is shown in figure 2. Water-quality samples were 

collected seasonally during base-flow and stormflow 

events and were measured or analyzed for 12 water-quality 

constituents, including field properties, nutrients, trace metals, 

suspended sediment, and total dissolved solids. Long-term 

trends were examined in sample concentrations for 10 waterquality constituents. Annual streamwater loads and yields, and 

their confidence intervals, were estimated for the 10 waterquality constituents.

Figure 2. Multiparameter monitoring station at Richland Creek at Suwanee Dam Road, near Buford, Georgia (USGS 

station 02334480), looking upstream. USGS hydrologic technicians prepare to conduct a discharge measurement. 

Photograph by Kerry Caslow, USGS.
Study Design and Methods 5

Description of Study Watersheds

The 13 watersheds monitored as part of the Gwinnett 

County LTTM program are listed in table 1. Initially, six 

watersheds were monitored when the LTTM program 

started in 1996: (1) Brushy Fork Creek, (2) Alcovy River, 

(3) Big Haynes Creek, (4) Suwanee Creek, (5) Yellow River, 

and (6) Crooked Creek. In 2001, six additional watersheds 

were added to the LTTM program: (7) No Business Creek, 

(8) Wheeler Creek, (9) Apalachee River, (10) Richland Creek, 

(11) Level Creek, and (12) North Fork Peachtree Creek. In 

2010, the (13) Sweetwater Creek watershed was added to 

the LTTM program. The Sweetwater Creek watershed lies 

within the monitored area of the Yellow River watershed and 

is the only “nested” watershed in the study (fig. 1). Program 

watersheds were selected to ensure diverse basin characteristics 

and land use for evaluating streamflow quantity and quality 

characteristics, while ensuring an appropriate spatial coverage 

of the county. The selected watersheds have variable waterquality attainment statuses (whether or not water-quality 

standards are met) and contain a diversity of point-source 

discharges. The specific monitoring station locations were 

determined on the basis of the suitability for hydrologic 

instrumentation and personnel safety. All stage, discharge, 

precipitation, and continuous and sample water-quality data 

used in this analysis are available from the USGS National 

Water Information System (NWIS) web interface at 

https://doi.org/10.5066/F7P55KJN using the USGS station 

numbers listed in tables 1 and 4 (U.S. Geological Survey, 2016).

Surface-Water Monitoring

The LTTM program follows standard USGS protocols 

for measuring stage, making streamflow measurements, 

and computing discharge (Rantz and others, 1982a, 1982b). 

Stage is recorded every 15 minutes to the nearest 0.01 foot 

and is routinely verified to an outside reference gage. This 

reference gage is periodically checked with surveying levels 

and other established reference marks to verify the gage 

vertical datum. Discharge measurements are made over a wide 

range of hydrologic conditions to develop a stage-discharge 

relation (rating curve) at each monitoring station used to 

compute discharge from the stage measurements. Discharge 

measurements were regularly made in order to continually 

refine the stage-discharge relation and to account for any 

temporal changes in the relation due to changes in the shape 

of the streambed. Streamflow for periods of missing stage data 

was estimated from relations in hydrograph data between the 

station with the missing data and nearby basins having similar 

characteristics (Rantz and others, 1982b).

Daily average streamflow data were not available for 

33 high flow days in WYs 2002–03. High flow discharge 

measurements for the North Fork Peachtree Creek watershed 

were insufficient during that period to develop the rating curve 

to relate stage to discharge for high flows. In order to report 

annual and monthly runoff for these two WYs, streamflows 

for these days were estimated using rating curve 2.1 developed 

for WY 2004 that covered the range of stages observed in 

WYs 2002–03.

Table 1. Thirteen U.S. Geological Survey (USGS) water-quantity and water-quality monitoring stations included in the 

watershed characteristics and water-quality trends study in Gwinnett County, Georgia, including dates established and 

drainage areas for each station.

USGS station 

number Station name Date established Drainage area (square miles)

02205865 Sweetwater Creek at Club Drive near Lilburn, Ga. March 2010 21.0

02207120 Yellow River at Ga. Hwy 124 near Lithonia, Ga. April 1996 161.4

02207185 No Business Creek at Lee Road, below Snellville, Ga. March 2001 10.1

02207385 Big Haynes Creek at Lenora Road, near Snellville, Ga. June 1996 17.3

02207400 Brushy Fork Creek at Beaver Road near Loganville, Ga. June 1996 8.18

02208150 Alcovy River at New Hope Road, near Grayson, Ga. June 1997 30.8

02217274 Wheeler Creek at Bill Cheek Road, near Auburn, Ga. June 2001 1.31

02218565 Apalachee River at Fence Road, near Dacula, Ga. July 2001 5.65

02334480 Richland Creek at Suwanee Dam Road, near Buford, Ga. May 2001 9.37

02334578 Level Creek at Suwanee Dam Road, near Suwanee, Ga. May 2001 5.06

02334885 Suwanee Creek at Suwanee, Ga. September 1996 47.1

02335350 Crooked Creek near Norcross, Ga. March 1996 8.87

02336030 North Fork Peachtree Creek at Graves Road, near Doraville, Ga. June 2001 1.53
6 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

Daily average streamflow was separated into base-flow 

and stormflow components for each watershed by hydrograph 

separation. This was done for the purposes of calculating the 

base-flow index of each watershed and for using base flow as 

a predictor variable of constituent concentrations. Hydrograph 

separations were done using the web-based Hydrograph 

Analysis Tool (https://engineering.purdue.edu/~what/, 

accessed May 18, 2016; Lim and others, 2005). The simple 

local minimum method was used, which required no parameters to be fit.

Continuous water-quality monitors were deployed in 

2001 at each monitoring station to measure water temperature, 

SC, and turbidity at 15-minute intervals. These water-quality 

monitors typically are cleaned and their calibration checked 

every 2 weeks and more frequently following hydrologic 

events or after observing abnormal readings, which could be 

associated with fouling, instrument failure, or point sources 

or nonpoint sources of pollution. These water-quality monitors are maintained, and their corresponding sensor records 

are checked using the quality-assurance and quality-control 

procedures outlined in Wagner and others (2006).

All continuously monitored data (stage, precipitation, 

and water quality) are transmitted hourly by way of satellite 

communication and are available to the public from the USGS 

NWIS web interface (https://doi.org/10.5066/F7P55KJN) as 

values and time-series plots, which also can be accessed at 

http://waterdata.usgs.gov/ga/nwis/current/?type=flow&group_

key=basin_cd (accessed January 17, 2017). Due to public 

interest in receiving the data in near real time during periods 

of extreme runoff, each station is designed to send emergency 

transmissions every 15 minutes during these periods. These 

transmissions occur when rainfall intensities exceed 2 inches 

per hour or when there are large rates of change in stage, as 

determined from thresholds defined for each station. The 

real-time data combined with the USGS WaterAlert tool 

(http://maps.waterdata.usgs.gov/mapper/wateralert/, accessed 

January 17, 2017), can be used as a flood warning system for 

emergency managers and the public.

Two discrete base-flow samples and six stormflow 

samples were collected each year at each of the 13 stations for 

water-quality analyses. The year is divided into two seasons, 

summer (May through October) and winter (November 

through April), with one base-flow sample and three stormflow 

samples collected during each season. Base-flow samples 

were collected using a USGS DH-81 manual sampler, using 

depth integrated, equal-width-increment (EWI) integrating 

techniques to ensure a representative sample as outlined in 

the National Field Manual for the Collection of Water-Quality 

Data (U.S. Geological Survey, 2006). When streamflow velocities fell below 1.5 feet per second (ft/s) during these baseflow conditions, these samples were considered multivertical 

grab samples; but at streamflow velocities above 1.5 ft/s, these 

samples were considered true isokinetic samples. Base-flow 

samples were collected after no more than 0.1 inch of 

precipitation had fallen during the previous 72 hours. During 

base-flow sampling, a calibrated water-quality multiparameter 

sonde was used to concurrently measure the field properties of 

SC, pH, water temperature, dissolved oxygen, and turbidity in 

accordance with the Watershed Protection Plan for Gwinnett 

County (Gwinnett County Department of Public Utilities, 2000).

Stormflow sampling protocols required event precipitation to be a minimum of 0.3 inch. Additionally, a minimum 

of 72 hours is required between each event to ensure that 

the events are discrete and that the measured water-quality 

properties are associated with the sampled event. Stormflow 

samples were collected using automatic samplers that 

pump water from a designated point in the stream. The 

sampler is programmed to begin sampling when specified 

precipitation and (or) stage thresholds are reached. A single 

discharge-weighted composite sample is collected for each 

storm for water-quality analysis; samples are assumed to be 

representative of the constituent concentration variations that 

occur during an event. The composite sample is collected 

using the constant volume, time proportional to flow volume 

increment protocol (U.S. Environmental Protection Agency, 

1992), in which a sample of equal volume is collected each 

time a specified volume of water flows by the station. Before 

each storm event, samplers are programmed for the volume of 

water to flow by the station between each sample aliquot, with 

the intent of sampling throughout the duration of the storm. 

This volume of water is determined from the storm’s expected 

runoff response, which is estimated from previous storm 

responses to the predicted amount, duration, and intensity 

of the precipitation event during similar antecedent wetness 

conditions. The samplers were programed to collect either 

14 one-liter or 25 half-liter aliquots during each storm. The 

average streamflow of the aliquots that make up the composite 

sample are stored for each sample record in the USGS 

NWIS database.

The automatic sampler tubing and intake are cleaned 

and prepared prior to each sampling event. The samples 

are refrigerated in the automatic sampler at about 4 degrees 

Celsius and are retrieved from the sampler within 24 hours 

of the end of an event. Sampler cleaning and maintenance 

procedures are further documented in the USGS field methods 

protocol (Wilde, 2004). Because automatic samplers collect 

samples from a single point in the stream cross section, 

periodic concurrent EWI and automatic samples are collected. 

These samples are independently analyzed and compared to 

ensure that the automatic point sample is representative of 

the entire stream cross section.

Base-flow and stormflow samples were processed and 

preserved following USGS field methods (Wilde and others, 

2004). Samples were measured for specific conductance 

(SC) and turbidity and were analyzed for 10 constituents: 

nutrients (total nitrate plus nitrite [NO3+NO2], total ammonia 

plus organic nitrogen [total Kjeldahl nitrogen, TKN], total 

phosphorus [TP], dissolved phosphorus [DP], and total 

organic carbon [TOC]); suspended sediment (total suspended 

solids [TSS] and suspended-sediment concentrations [SSC]); 
Study Design and Methods 7

trace metals (total lead [Pb] and total zinc [Zn]); and total 

dissolved solids (TDS) in USGS laboratories in Denver, 

Colorado; Atlanta, Georgia; Louisville, Kentucky; and other 

USGS-approved laboratories (RTI Laboratories, Livonia, 

Michigan, and TestAmerica Laboratories, Inc., Arvada, 

Colorado). Total nitrogen (TN) was calculated as the sum of 

NO3+NO2 and TKN. Units of measurement and laboratory 

reporting limits (LRLs) for each constituent are listed in 

table 2. A censored value occurs when a concentration is 

below its long-term method detection level (LT-MDL) and 

is indicated by a remark code of “<” with its concentration 

set to the LRL. For inorganic analyses, the LRL is set equal 

to its LT-MDL. For organic analyses, the LRL is set to twice 

the LT-MDL, with concentrations less than the LT-MDL 

indicated as censored values (<), and values between the 

LT-MDL and the LRL indicated as an estimated concentration 

with a remark code of “e” (Childress and others, 1999). The 

LRLs for some constituents have changed over time due to 

changes in approved laboratory methods. The LRLs listed 

in table 2 are the predominant reporting limits observed and 

reflect the analytical methods used during WYs 2001–15. The 

percentages of censored concentrations are summarized by 

constituent in table 2. Analytical methods are listed in table 3. 

Several constituents that were included in the 1996–2003 

period of LTTM program have been discontinued as of 2004: 

total cadmium, total chromium, total copper, biological 

oxygen demand, and chemical oxygen demand. Field waterquality blank and replicate samples were collected in compliance with USGS protocols (U.S. Geological Survey, 2006); 

laboratory analyses include an extensive quality-control and 

quality-assurance program.

Concentrations of suspended organic and inorganic 

particles in surface waters were quantified in this study using 

two different laboratory analytical methods—suspendedsediment concentrations (SSC) and total suspended solids 

(TSS; Gray and others, 2000). The SSC analytical method 

involves measuring the dry weight of the sediment in an entire 

sample of a known volume, and the TSS method involves 

measuring the dry weight of the sediment in a subsample of 

the available sample volume, rather than the entire sample 

(Landers, 2013). Whole-water samples were analyzed for 

SSC at the USGS Georgia Sediment Laboratory, in Atlanta, 

Georgia, and the USGS Kentucky Sediment Laboratory, 

in Louisville, Kentucky. Methods for processing SSC are 

described in Knott and others (1993) and Shreve and Downs 

(2005). The SSC analytical method is considered to produce 

more consistent results than the TSS method; however, the 

TSS method is often the method adopted for regulatory 

monitoring. Annual yield of TSS is the primary performance 

criterion for suspended sediment in Gwinnett County’s 

Watershed Protection Plan (Gwinnett County Department of 

Public Utilities, 2000). Generally, a bias in relation to SSC 

and TSS is observed when sand-sized material are greater 

than 25 percent of the sediment dry weight, such that the 

SSC values tend to exceed their paired TSS values (Gray and 

others, 2000). This bias indicated TSS was a poor measure 

of suspended particles in stormwater when the dominant 

fraction was sand size or coarser.

Table 2. Water-quality constituents measured and analyzed for samples collected in streams in Gwinnett County, 

Georgia, units of measures, and predominant laboratory reporting limit.

[USGS, U.S. Geological Survey; na, not applicable. Units: °C, degrees Celsius; FNU, Formazin Nephelometric Units; mg/L, milligram per liter; 

μg/L, microgram per liter; μS/cm, microsiemen per centimeter; C, carbon; N, nitrogen; P, phosphorus] 

USGS 

parameter 

code

Constituent Constituent

abbreviation Units 

Laboratory

reporting

limit

Percentage 

of censored 

samples

00095 Specific conductance SC μS/cm at 25 °C na na

63680 Turbidity na FNU na na

00630 Total nitrate plusnitrite NO3

 +NO2 mg/L as N 0.019 0.1

00625 Total ammonia plus organic nitrogen TKN mg/L as N 0.20 14.3

00600 Total nitrogen TN mg/L as N 0.22 14.3

00665 Total phosphorus TP mg/L as P 1

0.02 13.4

00666 Dissolved phosphorus DP mg/L as P 1

0.02 34.7

00680 Total organic carbon TOC mg/L 1.0 0.3

00530 Total suspended solids TSS mg/L 1.0 4.2

80154 Suspended sediment SSC mg/L 1.0 0.0

01051 Total lead Pb μg/L 1.0 26.4

01092 Total zinc Zn μg/L 2.0 3.1

70300 Total dissolved solids TDS mg/L 5.0 0.0

1

 0.02 mg/L as P prior to and including July 2004; typically <0.05 mg/L as P August 2004–December 2006; typically <0.005 mg/L as P after 

December 2006.
8 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

Precipitation

Precipitation was measured at each station and was 

recorded at 15-minute intervals, using self-calibrating 

tipping bucket rain gages that measure precipitation in 

0.01-inch increments. The rain gages were routinely cleaned 

and calibrated as outlined in the Surface-Water QualityAssurance Plan for the USGS Georgia Water Science Center 

(Gotvald, 2010).

Watershed precipitation estimates are based on the 

13 USGS rain gages that are co-located with the stream gages 

at the watershed outlets and the 5 additional USGS gages 

within the study area (fig. 1; table 4). These precipitation 

gages are fairly well distributed across and along the margins 

of the study area and should reasonably represent the rainfall 

within the 13 watersheds. Daily precipitation within each 

watershed was approximated by averaging all available 

daily precipitation values, each weighted by the inverse 

distance squared between centroid of the watershed and the 

precipitation gage. Precipitation data were not available at all 

18 gages for every day of the study period; some precipitation 

stations were added later in the study period and occasional 

equipment malfunction or poor data required that some data 

had to be discarded. Data coverage was generally good, with 

99.2 percent of the days having data for 11 or more precipitation gages. 

Identification of Surface-Water Constituent 

Concentration Long-Term Trends

Assessing whether water quality is improving, degrading, 

or unchanging is an important objective of this monitoring 

study, because the assessment provides Gwinnett County 

with information that allows them to be more proactive in 

managing their water resources and assessing the effectiveness of their implementation of best management practices 

for improving water quality. Identification of long-term 

temporal trends in water quality often requires a decade or 

more of monitoring because (1) natural climatic and seasonal 

variability can obscure or mimic trends in water quality, 

(2) degradation or improvement in water-quality conditions 

Table 3. Summary of analytical methods used to quantify water-quality constituent concentrations of samples in streams 

in Gwinnett County, Georgia.

[EPA, U.S. Environmental Protection Agency; °C, degree Celsius]

Constituent Method Reference

Total nitrate plus nitrite EPA Method 353.2: Determination of nitrate-nitrite 

nitrogen by automated colorimetry

U.S. Environmental Protection Agency 

(1993a)

Total ammonia plus 

organic nitrogen

EPA Method 351.2: Determination of total Kejeldahl 

nitrogen by semi-automated colorimetry

U.S. Environmental Protection Agency 

(1993b)

Total phosphorus

Dissolved phosphorus

EPA Method 365.2: Determination of phosphorus 

by manual colorimetry

U.S. Environmental Protection Agency 

(1971)

Total organic carbon Standard Method1

 SM5310B: High-temperature 

combustion method

American Public Health Association, 

American Water Works Association, 

and Water Pollution Control Federation 

(1995)

Total lead

Total zinc

EPA Method 200.8: Trace elements in waters 

and wastes by inductively coupled plasma-mass 

spectrometry

U.S. Environmental Protection Agency 

(1994)

Total dissolved solids 1

Standard Method 2540 C: Gravimetric total 

dissolved solids dried at 180 °C

American Public Health Association, 

American Water Works Association, 

and Water Pollution Control Federation 

(1995)

Total suspended solids 1

Standard Method 2540 D: Gravimetric total 

suspended solids dried at 103–105 °C

American Public Health Association, 

American Water Works Association, 

and Water Pollution Control Federation 

(1995)

Suspended sediment 2

Standard Test Method D3977-97 (2002) American Society for Testing and 

Materials (2000)

1

Standard methods for the examination of water and wastewater.

2

Standard test methods for determing sediment concentration in water samples.
Study Design and Methods 9

can be delayed after watershed changes, and (3) effects on 

water-quality condition from multiple activities within a 

watershed can offset each other (Landers and others, 2007). 

Water quality varies in response to many natural processes that 

can be dominant factors on different or multiple time scales, 

for example, event-driven and seasonal hydrologic changes 

in sources and flow paths, seasonal changes in terrestrial and 

in-stream biogeochemical processes, and year-to-year climatic 

variations. These natural processes make it difficult to attribute 

the effects of human activity to changes in water quality. It 

is therefore imperative to remove as much of the effects of 

natural variation from water-quality data as possible. For 

water-quality constituents that vary strongly with discharge, 

streamflow can be the most influential effect on water quality 

to remove due to variations in discharge with climate. For 

long-term temporal trends, the natural effects of seasonal 

variability on water quality are less of an issue because of its 

relatively short-term, recurring nature. 

Long-term trends in concentrations were evaluated for 

12 watersheds for the 15-year period WYs 2001–15, and 

for the Sweetwater Creek watershed for the 6-year period 

WYs 2010–15, where monitoring was initiated later (table 1). 

To remove the effects of discharge and other natural processes 

on water-quality concentrations, trends were evaluated from 

residual concentrations of a flow-only regression model 

plotted versus time for 10 constituents: TN, NO3+NO2, TP, DP, 

TOC, TSS, SSC, Pb, Zn, and TDS. This follows the approach 

presented in Hirsch and others (1991) and Helsel and Hirsch 

(1992). Residuals are defined as the observed concentration 

minus the model predicted concentration. The flow-only 

regression models used to estimate loads (described later) 

were used for the trend evaluations, because the additional 

explanatory variables used in the flow-turbidity models can 

capture human-induced changes in water quality that we seek 

to evaluate in the trend analysis. Similarly, any significant 

time and time-squared trend variables were excluded from 

the regression model, as the trend evaluations need to be 

independent of any trends fitted by the regression models. 

The long-term trend analysis was performed on the waterquality concentration data rather than load estimates because 

it is more straightforward to remove the effects of climatic 

variability related to discharge.

Base-flow and stormflow samples were evaluated 

together. Residuals were plotted and trends were evaluated in 

logarithmic space, because the regression models were based 

on log-transformed concentration. This also made it easier to 

compare the magnitudes of the slope of the trend of different 

water-quality constituents. A linear trend line was fitted to the 

residual concentrations in logarithmic space to help illustrate 

any underlying trends and to evaluate its significance. A trend 

was considered significant if the fitted trend line had a p-value 

of ≤0.05. The trends did not necessarily vary linearly, and 

more detailed temporal variations in trends can be discerned 

from the temporal pattern in the residual plots.

Table 4. Precipitation gages included in the study, including dates established and decommissioned, 

Gwinnett County, Georgia.

Station

number Station name Start date End date

02205000 Wildcat Creek near Lawrenceville, Ga. 10/4/2001 Active

02205522 Pew Creek at Patterson Rd, near Lawrenceville, Ga. 3/28/2003 7/28/2014

02205865 Sweetwater Creek at Club Drive near Lilburn, Ga. 2/26/2010 Active

02207120 Yellow River at Ga. 124, near Lithonia, Ga. 4/26/1996 Active

02207185 No Business Creek at Lee Road, below Snellville, Ga. 3/1/2001 Active

02207385 Big Haynes Creek at Lenora Road, nr Snellville, Ga. 10/5/1998 Active

02207400 Brushy Fork Creek at Beaver Road, nr Loganville, Ga. 11/11/1998 Active

02208050 Alcovy River near Lawrenceville, Ga. 8/6/2003 Active

02208130 Shoal Creek at Paper Mill Rd, nr Lawrenceville, Ga. 10/1/2005 7/28/2014

02208150 Alcovy River at New Hope Road, near Grayson, Ga. 1/1/1999 9/15/2015

02217274 Wheeler Creek at Bill Cheek Road, near Auburn, Ga. 6/30/2001 Active

02218565 Apalachee River at Fence Road, near Dacula, Ga. 8/22/2001 Active

02334480 Richland Creek at Suwanee Dam Road, near Buford, Ga. 5/17/2001 Active

02334578 Level Creek at Suwanee Dam Road, near Suwanee, Ga. 5/10/2001 5/16/2015

02334885 Suwanee Creek at Suwanee, Ga. 10/1/1996 Active

02335000 Chattahoochee River near Norcross, Ga. 6/29/2002 Active

02335350 Crooked Creek near Norcross, Ga. 3/23/2001 Active

02336030 N.F. Peachtree Creek at Graves Rd, nr Doraville, Ga. 6/9/2001 Active
10 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

Constituent Load Estimation

Constituent load, often referred to as mass flux, is the 

mass of chemical constituent or sediment transported at a 

point in a stream during a specific period. Load serves as 

an integrated measure of all processes within the watershed 

that affect water quality (Semkin and others, 1994). With 

increased emphasis on watershed-based strategies for the 

control of nonpoint-source pollutants, reliable, temporal 

measures of loads are needed to address whether water quality 

is improving or degrading within a reasonably short period 

of time. In the United States, stream reaches that do not 

meet U.S. Environmental Protection Agency water-quality 

standards (U.S. Environmental Protection Agency, 2000a) are 

subject to waste-load allocation schemes that are based on the 

total maximum daily load (TMDL). A TMDL is defined as the 

maximum amount of a pollutant that a water body can receive 

and still meet water-quality standards (U.S. Environmental 

Protection Agency, 2000b). 

Constituent load (L) is the product of constituent concentration (C) and discharge (Q) integrated over time (t):

L = ∫ C(t) Q(t) dt (1)

Load estimation using the integral in equation 1 requires 

a continuous record of both concentration and discharge. 

Although discharge can readily be measured in a nearly 

continuous manner, constituent concentration typically is 

measured less frequently because of the effort and expense of 

collecting and analyzing samples for water quality. Various 

techniques have been developed to estimate loads using discrete 

concentration observations. These techniques can be categorized 

into four classes: (1) averaging methods, (2) period-weighted 

approaches (for example, Likens and others, 1977; Larson and 

others, 1995), (3) regression-model (or rating-curve) methods, 

and (4) ratio estimators (for example, Dann and others, 1986; 

Preston and others, 1989). The moderate to strong concentration-discharge relations for most of the constituents used 

herein along with the relatively infrequent sampling in this 

study indicate that a regression-model method is most appropriate (Aulenbach and others, 2016) and is used herein to 

estimate loads. In this approach, C(t) is estimated continuously using a regression model that relates concentration to 

continuously measured variables, such as discharge and day of 

year (Johnson, 1979; Crawford, 1991; Cohn and others, 1992), 

thus enabling a direct calculation of equation 1. The regression 

model predicts the average concentration response for the set 

conditions, such as discharge and season.

Loads were estimated for WYs 2003–15 for 12 watersheds and for WYs 2011–15 for the Sweetwater Creek 

watershed. Loads were estimated for 10 constituents: TN, 

NO3+NO2, TP, DP, TOC, TSS, SSC, Pb, Zn, and TDS. Loads 

were reported on an annual basis. The load estimation methods 

used herein are similar to those used in the previous two 

reports, Landers and others (2007; WYs 1996–2003) and 

Joiner and others (2014; WYs 2004–09). The only change 

in approach from the second report was the addition of several 

regression model terms to model climatic variability and 

improve model fit. These terms were daily base flow, streamflow during base-flow conditions, and allowing the use of both 

the turbidity during all flow conditions and turbidity during 

stormflow conditions terms within the same model.

The regression models were developed and load 

estimates were estimated using the USGS LOAD ESTimator 

software (LOADEST; Runkel and others, 2004), using the 

adjusted maximum likelihood estimates (AMLE) algorithm 

(Cohn and others, 1989, 1992). This algorithm applies a 

correction factor to account for retransformation bias of a 

logarithmic model transformed back to linear space (Ferguson, 

1986) and can appropriately handle censored water-quality 

data—concentrations that are below the analytical detection 

limit. The TIBCO Spotfire S+ statistical software (version 8.1) 

computing platform version of LOADEST software was used 

in this analysis.

The typical regression-model load estimation method 

required modification to handle the use of storm composite 

samples. When estimating the loads, it is inappropriate to 

apply the concentration-discharge relation developed from 

the storm composite samples to the instantaneous discharges, 

because the average storm concentration versus average 

storm-discharge relation differs from the instantaneous 

concentration-discharge relation. Thus, this study used the 

approach developed by Landers and others (2007) in which 

loads were estimated from average discharge on the basis 

of a predetermined time step instead of from instantaneous 

discharge. This time step is related to the average duration 

of storms sampled for each watershed. Each watershed was 

analyzed to determine the optimal time step to use for stormflow load estimation based on the median duration of sampled 

storms and ranged from 4 to 24 hours. LOADEST can 

estimate loads at time intervals as short as 1 hour. The time 

step is longer for larger watersheds because of the integration 

of runoff over a larger area with varied and longer travel times 

to the watershed outlet as well as the attenuation of the storm 

hydrograph as the runoff travels downstream, which reduces 

peak flows and spreads out the hydrograph over time. This 

average-discharge load estimation approach can likewise 

be reasonably applied to the instantaneous concentrationdischarge relation developed from base-flow samples as 

well, because during base-flow conditions, discharge does 

not change rapidly, and instantaneous and average discharges 

would not be significantly different.

A 12-parameter regression model was developed for 

estimating loads in LOADEST and has the form of natural 

logarithm of load as a function of variables including 

discharge, base flow, season, time, and turbidity (from the 

continuous water-quality sensors): 

ln(L) = a0

 + a1S + a2lnQ + a3lnQ2 + a4(1–S)lnQ + a5lnQb

+ a6lnT + a7SlnT + a8sin(2πdtime)

+ a9cos(2πdtime) + a10dtime + a11dtime2 (2)
Study Design and Methods 11

where:

L is load, the product of concentration and 

discharge, in pounds per day;

S is an indicator variable that indicates the 

flow condition, 0 for base flow and 

1 for stormflow;

Q is streamflow, centered, in cubic feet 

per second;

Qb is daily base flow, in cubic feet per second;

T is turbidity, in Formazin Nephelometric Units;

dtime is decimal time, centered, in years; and

a0 ... a11 are regression model coefficients.

Although equation 2 is a function of load, to make the details 

given below more understandable, we refer to the relations as 

a function of concentration. This is just a matter of semantics, 

as equation 2 could be written as a function of concentration, 

and predicted concentrations could then be multiplied by 

streamflow resulting in the same load estimates as if it were 

a function of load.

The daily base-flow variable, Qb, was included to 

capture the climatic status of the watershed. Although base 

flow generally varies seasonally, it should better reflect the 

observed climatic condition than the seasonal terms that fit 

a set pattern for all years. The variable Qb was calculated by 

hydrograph separation of daily average streamflow as previously described.

Turbidity, T, is a surrogate variable in which predicted 

concentrations are modeled as a function of this chemically 

related, continuously available optical variable. Surrogate 

variables are powerful for capturing variations in concentrations and in the resulting loads, because the predicted 

concentrations can better reflect variations through time 

based on the relation with observed changes in the continuous 

surrogate. The turbidity surrogate typically is useful for 

constituents that have a substantial particulate component, as 

increases in turbidity are related to less light transmission due 

to solid particles in the water column blocking light. Most 

of the other variables in equation 2 are static throughout the 

model calibration time period, such that they always predict 

the average response for the period. The exception to this 

is the time and time-squared terms, which can adjust the 

model intercept over time in a fixed second-order polynomial 

pattern. Other approaches and methods allow for variations in 

variable relations over time, such as concentration-discharge 

relations, to improve incorporation of variability in estimated 

loads. These methods include using a shorter moving-window 

calibration time period (Yochum, 2000; Aulenbach and others, 

2007) or using weighted regression on time, discharge, and 

season (WRTDS; Hirsch and others, 2010), but these methods 

generally require more frequent sampling to adequately define 

the changes over time. 

The model form was designed with the flexibility to fit 

base-flow and composite storm samples simultaneously in 

order to expedite the model fitting process by reducing the 

number of models to be fit in half. The samples not only differ 

in whether the samples represent instantaneous or average 

concentrations, they also represent two distinct flow regimes 

that may have distinct concentration relations. In some cases 

these two types of samples follow a single concentration 

relation, while in other cases they have separate relations. 

The model terms were formulated to allow for differences 

in the relations for an offset, streamflow and turbidity while 

fitting a single relation for season and long-term trends. 

The “indicator variable” S was used in the regression model 

equation to indicate whether hydrologic conditions of the 

sample were during base flow (S=0) or stormflow (S=1). The 

indicator variable controls the absence (multiplied by zero) or 

presence (multiplied by one) of the use of a variable within 

the model equation. The variable S was used to adjust for any 

offset in load of the stormflow samples relative to the overall 

model intercept a0

. The load-streamflow relation was fit for 

both flow conditions with a second-order polynomial, lnQ and 

lnQ2, while any differences in this relation during base-flow 

conditions were adjusted using the (S –1)lnQ term. The loadturbidity relation was fit using the terms lnT and SlnT, where 

inclusion of lnT indicated a single turbidity relation for all 

flow conditions, SlnT indicated a turbidity relation only during 

stormflow conditions, and both terms indicated different 

turbidity relations for base-flow and stormflow conditions.

Two load models were fitted for each constituentwatershed combination, a flow-turbidity model that included 

at least one of the two turbidity terms and a flow-only model 

that was fit excluding the turbidity terms. The flow-only model 

was used as a fallback to the flow-turbidity model, to estimate 

loads when there were missing data in the continuous turbidity 

record. Gaps are to be expected in data from continuous waterquality sensors due to their high maintenance requirements 

and penchant for malfunctions. The flow-turbidity model 

typically outperformed the flow-only model. Flow-turbidity 

models were not created for all constituent-watershed combinations because model turbidity terms were sometimes not 

significant, as was often the case for dissolved constituents.

During the model parameter fitting process, if a parameter was not significant (p-value >0.05), the term was excluded 

from the regression model to avoid overparameterization, 

which is when models include variables that do not explain 

much variation in the concentrations. Initially, all model 

terms were included. Model terms were then removed in a 

backward-stepwise fashion by incrementally removing the 

term with the highest p-values until all remaining terms had 

p-values <0.05. The seasonal sine and cosine model terms 

worked as a single variable and were both included in the 

model as long as one term had a p-value <0.05. In the rare 

case where the linear time term had a p-value >0.05 and the 

time-squared term had a p-value <0.05, both terms were 

included in the model. Occasionally the backward-stepwise 

fashion approach was exempted when a better model was 

found to explain more variance and exhibited better model 

residual behavior. Model variables Q and dtime were 
12 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

“centered”—had their central value subtracted from them—

to remove correlation between their linear and squared terms 

such that the terms were independent of each other. Correlation between model terms can affect tests of significance of 

model terms.

To ensure that regression models fit the observed relations well, outliers that could affect model predictions were 

removed from the model, and model residuals were inspected 

to ensure that model predictions would not be biased. Outliers 

can have an undue influence on the model fit due to the 

minimization of the squared errors fitting process that gives 

outliers greater weights. Before fitting the regression models, 

concentration data were checked for outliers by plotting 

concentrations versus variables used in the regression models: 

date, day-of-year, streamflow, and turbidity. Concentrations 

were also plotted versus other constituents where relations 

might be expected to exist: dissolved constituents were 

plotted versus the other dissolved constituents and specific 

conductance, total constituents were plotted versus the other 

total constituents, and nutrients were plotted versus the other 

nutrients. When a concentration was observed well outside 

any relation between concentration and variables used in the 

regression models and would affect the model fit, the concentration was identified as an outlier. Consideration for outlier 

identification was also given to concentrations that did not 

fit overall patterns with other constituents, but this required 

multiple observations of evidence to ensure that the concentration was the outlier and not the constituent concentration it 

was plotted against. Identified outliers were not necessarily in 

error, but may represent some condition that occurs so rarely 

that it would be inappropriate to adjust the model toward such 

an ephemeral condition; such as from a transient point-source 

pollutant. All 245 outliers are listed in Aulenbach and Joiner 

(2017; table 1, water-quality outliers excluded from load 

model calibrations) and represent 1.5 percent of the total 

number of concentrations considered for the load calculations.

 Model residuals also were examined to ensure that 

they were identically (randomly) distributed. In some cases, 

small patterns were observed but were not great enough to 

have considerable effect on biasing the load estimates. More 

often, the residuals did not follow a normal distribution. In 

this case, the AMLE approach may not provide optimal load 

estimates for censored data (Runkel and others, 2004). An 

alternative load estimation approach that does not require 

residuals to be distributed normally is available in LOADEST. 

Unfortunately, this alternative was not applicable, because the 

cases where residuals were not normal typically had censored 

values, and the least absolute deviation (LAD) approach 

implementation in LOADEST does not allow censored data. 

Models that did not have normally distributed residuals can be 

identified as having a Turnbull-Weiss normality test statistic 

p-value of <0.05. This test statistic is reported for all models 

in Aulenbach and Joiner (2017; table 3, load estimation 

regression models). For models where residuals were not 

distributed normally, load estimates may be less accurate 

than their confidence intervals indicate.

Although load estimates are reported beginning in 

WY 2003, the calibration datasets used to fit the regression 

models extend back to WY 2001 when data were available. 

During model prediction, time periods in the estimation file 

are set to zero during base-flow conditions and one during 

stormflow conditions. Hydrologic conditions were based 

on levels of turbidity or discharge instead of determined 

from the dynamics of the hydrograph, which requires a 

hydrograph separation. For the streams in this study, turbidities greater than 20 Formazin Nephelometric Units (FNU) 

were determined to be indicative of stormflow, because only 

about 6.7 percent of base-flow samples had turbidities ≤20 

FNU and only about 11.2 percent of stormflow samples had 

turbidities ≥20 FNU. Therefore, conditions were determined 

as stormflow when turbidity from the continuous water-quality 

sensor was greater than or equal to this 20 FNU threshold. 

If turbidity data were missing, then discharge was used to 

determine stormflow conditions—stormflow was defined to 

have occurred when discharge was greater than the average of 

the 50th and 75th percentiles of discharge for that watershed.

Load is highly dependent on the amount of runoff; hence, 

large watersheds typically will transport high loads. To better 

compare load from different sized watersheds, load was 

divided by the watershed area to determine yield, which is 

the load per unit area.

Load Estimation Error

Quantifying the errors in the load estimates is important 

in order to understand whether a difference in load represents 

a significant difference or change in load, or whether the 

difference is within the errors of the estimates. The error 

bounds are also a guide to the magnitude of change that is 

required for a trend to be detected. There are many sources 

of errors, including streamflow measurements, water-quality 

sample representativeness, laboratory analytical measurements, and load estimation calculations. Horowitz (2003) 

indicated that suspended-sediment load errors of less than 

or equal to ±15–20 percent should be considered relatively 

accurate for small to large rivers and for load estimates 

reported for quarterly to greater time frames.

Error estimates were calculated using the LOADEST 

software and reported for all the annual loads and yields. 

For three watersheds (No Business Creek, Apalachee River, 

and Crooked Creek), annual error estimates could not 

be calculated for Pb because of technical issues with the 

software. LOADEST calculates both the standard error (SE) 

and the standard error of the prediction (SEP) of the mean 

load estimate. The SE represents the variability attributed to 

parameter uncertainty of the model calibration, while the SEP 

also includes the effects of random error. Therefore, SEP tends 

to be larger than SE, but SEP provides a better estimate of the 

errors between the load estimates and the actual loads. SEP 

calculations assume that the errors are independent in time 

(no serial correlation), because underlying serial correlation 

can result in SEPs underestimating the actual uncertainty in 
Watershed Characteristics 13

the load estimates (Aulenbach, 2013). The size of error for 

any given year depends on the actual streamflow and turbidity 

observed for that year.

Error estimates are complicated by the fact that some 

loads estimates are combined from one model that includes 

turbidity and one that does not. The annual error in loads of 

the combined models was estimated by weighting the annual 

errors of the two models by the fraction of annual runoff 

each of the models contributed to the annual load. The lower 

(CI95%Lower) and upper (CI95%Upper) 95 percent confidence 

intervals are estimated using equations 3 and 4, respectively.

CI95%Lower=LTotal– [(LQT –CI95%Lower(QT)) *RFQT

+ (LQ –CI95%Lower(Q) )*RFQ] (3)

CI95%Upper=LTotal+(CI95%Upper(QT) –LQT) *RFQT

 +(CIQ95%Upper(Q) –LQ)*RFQ (4)

where

LTotal is the total annual load;

L is annual load;

RF is the annual runoff fraction for that model’s 

load; and

QT and Q represent the flow-turbidity and flow-only 

models, respectively.

Confidence intervals for yields were then calculated from the 

load estimate confidence intervals.

Errors for plots showing annual loads for the combined 

LTTM program study area and for individual watersheds for 

the entire study period were calculated by combining the 

errors from the annual loads. These errors were combined by 

adding them in quadrature (that is, squared, added, and then 

square rooted), which is a common approach for combining 

error when summing multiple values (Kirchner, 2001).

Watershed Characteristics

Watershed characteristics were determined for the 

13 watersheds in the LTTM program, including basin characteristics, population, land use, and percentage of impervious 

area. Basin characteristics, such as geology, drainage systems, 

altitudes, and basin slopes can all affect surface-water quantity 

and quality. Population growth and the resulting land-use 

changes can make watershed management more challenging, 

because increases in impervious areas typically are associated 

with increased storm runoff and decreased base flow, and 

population growth increases the demands on the water supply 

and for wastewater treatment.

Basin Characteristics

Gwinnett County is located in north-central Georgia, 

about 15 miles northeast of Atlanta (fig. 1). The county, 

which encompasses 436.8 square miles (mi2), is located in 

the Piedmont physiographic province. The geology of the 

county is a mixture of complex and varied metamorphic rocks 

(U.S. Geological Survey, 2014). Gwinnett County is composed 

predominantly of headwater streams that drain into one of 

three major rivers: the Chattahoochee, the Ocmulgee, and the 

Oconee. The Eastern Continental Divide, which separates 

drainages that flow into the Gulf of Mexico from those that 

flow into the Atlantic Ocean, runs approximately northeastsouthwest across the northwestern portion of the county. Five 

study watersheds (Richland Creek, Level Creek, Suwanee 

Creek, Crooked Creek, and North Fork Peachtree Creek) 

are northwest of the divide and lie within the ApalachicolaChattahoochee-Flint River Basin, which flows into the Gulf of 

Mexico. The remaining eight study watersheds (Sweetwater 

Creek, Yellow River, Alcovy River, Big Haynes Creek, 

Brushy Fork Creek, No Business Creek, Wheeler Creek, and 

Apalachee River) lie within the Ocmulgee-Oconee-Altamaha 

Load estimation error conventions

In this report, errors are based on the SEP and are expressed as 95 percent lower and upper confidence intervals. 

Errors are reported in a probabilistic manner, meaning that the range of values should be reliably within the range 

of the errors a defined percentage of the time. Two standards are commonly reported and users should be careful to 

note which standard is being used because the 95 percent confidence interval standard has error bounds almost twice 

as large as the plus or minus one standard error standard even though both represent the same amount of error. The 

95 percent confidence interval standard represents the range of values that should likely be expected about 95 percent 

of the time and can be thought of as error bounds that are “rarely exceeded.” The plus or minus one standard error 

method represents the range of values that should be within about 68.3 percent of the time and can be thought of as 

error bounds that are “likely to be within more often than not.” The errors reported are assumed to follow a normal 

probability distribution, and as load models were created in logarithmic space, the upper 95 percent confidence interval 

will be larger than the lower 95 percent confidence interval. Landers and others (2007) previously reported LTTM 

program watershed load errors using the plus or minus one standard error method.
14 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

River Basin, which flows into the Atlantic Ocean (fig. 1). 

The watersheds cover an area of 306.6 mi2, 300.2 of which 

lie within Gwinnett County and compose 68.7 percent of 

the county (table 5). The watershed sizes range across two 

orders of magnitude, from the smallest at 1.31 mi2 (Wheeler 

Creek) to the largest at 161.4 mi2 (Yellow River; covering 

52.65 percent of the study area). 

Land-surface altitudes in Gwinnett County range from 

297 to 1,293 feet above North American Vertical Datum 

of 1988 (NAVD 88; fig. 3; table 5). Altitudes generally are 

highest in the northern portion of the county and lowest in 

the southern and eastern portions of the county. Note that the 

Yellow River watershed contains a deep quarry in the eastern 

portion of the watershed, resulting in artificially low altitudes 

within the basin, although the watershed outlet is at an 

altitude of about 730 feet.

The mean land-surface slope within the area of the LTTM 

program watersheds is 11.4 percent, with mean basin slopes 

ranging from 7.4 percent (Brushy Fork Creek watershed) to 

16.7 percent (Richland Creek; table 5; fig. 4). Higher landsurface slopes are observed more commonly in the northern 

portion of the county within the Apalachicola-ChattahoocheeFlint River Basin (fig. 5). Landers and others (2007) showed 

that higher basin slopes in Gwinnett County were significantly 

correlated to lower base-flow yield and higher annual TP yields. 

Note that mean basin slopes in this study differ slightly from 

those published in Landers and others (2007) and Joiner and 

others (2014), because the slopes provided herein are derived 

from more recent, higher quality altitude data from Gwinnett 

County Department of Public Utilities (unpub. data, 2014).

Population

Gwinnett County is a densely populated, primarily 

suburban to urban county of the Atlanta metropolitan area. 

The population in Gwinnett County in 2015 was about 

919,000 (fig. 6), about 2,100 people per square mile; whereas, 

the national average in 2010 was 87.4 people per square mile 

(U.S. Census Bureau, 2011). Gwinnett County has undergone 

rapid population growth from about 1980 through 2015, and 

land use has changed from what was once predominantly 

agriculture and forest to a highly developed area. Population 

in Gwinnett County increased by about 269,000 people from 

2001 to 2015, an average of about 21,100 people per year and 

a 47 percent increase since 2001 (figs. 6 and 7). Population 

growth was fairly steady during this period except from 

2008 to 2011, when growth was slower and even declined 

in one year. This downturn appears to be associated with the 

“Great Recession” economic downturn that occurred between 

December 2007 and June 2009.

The population density is greater in the western portion 

of the county and along the major roadways (fig. 8). Average 

watershed population densities ranged from about 880 (2009) 

and 940 (2013) people per square mile in the Wheeler Creek 

watershed to about 4,200 (2009) and 4,300 (2013) people 

per square mile in the North Fork Peachtree Creek watershed 

(fig. 9). Population densities increased in all watersheds 

between 2009 and 2013, but increases varied greatly between 

watersheds, ranging in annual rates from a low of 0.3 people 

per square mile per year (Richland Creek) to a high of 

44 people per square mile per year (Big Haynes Creek).

Table 5. Watershed characteristics for 13 watersheds in Gwinnett County, Georgia.

[NAVD 88, North American Vertical Datum of 1988]

Station 

number Watershed name

Drainage area Altitude (feet above NAVD 88) Mean

basin slope 

(percent)

(square 

miles)

Percentage 

of study

area

Minimum Maximum Range Mean

02205865 Sweetwater Creek 21.0 6.84 854 1,177 323 985 11.3

02207120 Yellow River 161.4 52.65 297 1,215 918 968 11.1

02207185 No Business Creek 10.1 3.28 738 1,202 464 930 10.5

02207385 Big Haynes Creek 17.3 5.63 843 1,126 283 976 9.1

02207400 Brushy Fork Creek 8.18 2.67 885 1,101 216 985 7.4

02208150 Alcovy River 30.8 10.05 787 1,213 426 1,014 11.6

02217274 Wheeler Creek 1.31 0.43 884 1,105 221 992 11.0

02218565 Apalachee River 5.65 1.84 928 1,201 273 1,064 11.3

02334480 Richland Creek 9.37 3.06 924 1,284 360 1,085 16.7

02334578 Level Creek 5.06 1.65 956 1,185 228 1,066 12.3

02334885 Suwanee Creek 47.1 15.36 910 1,293 383 1,095 12.8

02335350 Crooked Creek 8.87 2.89 872 1,176 304 996 12.1

02336030 North Fork Peachtree Creek 1.53 0.50 931 1,100 169 1,020 10.2

LTTM program study area 306.6 100 297 1,293 996 1,000 11.4
Watershed Characteristics 15

Chattahooche e River

0 21 3 4 5 MILES

0

Base modified from U.S. Geological Survey 

1:100,000-scale digital data

21 3 4 5 KILOMETERS

Figure 3. 

EXPLANATION

Monitored watershed boundary

Eastern Continental Divide

USGS water-quality monitoring station, 

 identifier, and watershed

02207185

Land-surface altitude, in feet 

 above NAVD 88 (2014)

High: 1,293

Low: 297

N

Wheeler Creek

watershed

02217274

Suwanee Creek

watershed

02334885

Richland Creek

watershed

02334480

Level Creek

watershed

02334578

Crooked Creek

watershed

02335350

North Fork

Peachtree Creek

watershed

02336030

Yellow River

watershed

02207120

No Business Creek

watershed

02207185

Big Haynes Creek

watershed

02207385

Brushy Fork Creek

watershed

02207400

Alcovy River

watershed

02208150

Apalachee River

watershed

02218565

Figure 3. Land-surface altitude for Gwinnett County, Georgia. Altitude data from aerial light detection and ranging 

(lidar; laser-radar) survey from Gwinnett County Department of Public Utilities, unpub. data, 2014.
16 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

0 

Figure 4.

2 

4 

6 

8 

10 

12 

14 

16 

18 

Sweetwater Creek 

Yellow River 

No Business Creek 

Big Haynes Creek 

Brushy Fork Creek 

Alcovy River 

Wheeler Creek 

Apalachee River 

Richland Creek 

Level Creek 

Suwanee Creek 

Crooked Creek 

North Fork Peachtree

 Creek 

Average basin slope, in percent 

Monitored watersheds, Gwinnett County 

Figure 4. Average basin slope of the 13 monitored watersheds in Gwinnett County, Georgia. 

Derived from altitude data from Gwinnett County Department of Public Utilities, unpub. data, 2014.
Watershed Characteristics 17

Slope, in percent (2014)

0 to 6

>6 to 12

>12 to 18

>18

0 21 3 4 5 MILES

0

Base modified from U.S. Geological Survey 

1:100,000-scale digital data

21 3 4 5 KILOMETERS

Figure 5

EXPLANATION

Monitored watershed boundary

Eastern Continental Divide

USGS water-quality monitoring station, 

 identifier, and watershed

02207185

N

Wheeler Creek

watershed

02217274

Suwanee Creek

watershed

02334885

Richland Creek

watershed

02334480

Level Creek

watershed

02334578

Crooked Creek

watershed

02335350

North Fork

Peachtree Creek

watershed

02336030

Yellow River

watershed

02207120

No Business Creek

watershed

02207185

Big Haynes Creek

watershed

02207385

Brushy Fork Creek

watershed

02207400

Alcovy River

watershed

02208150

Apalachee River

watershed

02218565

Sweetwater Creek

watershed

02205865

Chattahooch e e River

Figure 5. Land-surface slope for Gwinnett County, Georgia. Derived from altitude data from Gwinnett County 

Department of Public Utilities, unpub. data, 2014.
18 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

Figure 6.

Total population

0 

100,000 

200,000 

300,000 

400,000 

500,000 

600,000 

700,000 

800,000 

900,000 

1,000,000 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

110 

1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 1996 

Year

2007 2008 2009 2010 2011 2012 2013 2014 2015 

Cumulative population growth, in percent 

A

B

Figure 7.

2001–02 2002–03 2003–04 2004–05 2005–06 2006–07 2007–08 2008–09

Years

2009–10 2010–11 2011–12 2012–13 2013–14 2014–15

Annual change in population

–10,000

0

10,000

20,000

30,000

40,000

Figure 6. (A) Total population by year and (B) population growth for Gwinnett County, Georgia, 2001–15. Data from U.S. 

Census Bureau (2004, 2011) and Gwinnett County, Georgia (2004, 2005, 2006, 2007, 2008, 2009, 2010, 2012, 2013, 2014, 2015, 2016).

Figure 7. Annual change in population for Gwinnett County, Georgia, from 2001 to 2015.
Watershed Characteristics 19

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

Buford

Dacula

Duluth

Grayson

Lawrenceville

Lilburn

Norcross

Suwanee

Sugar

Hill

Snellville

316

141

140

317

324

124

120

20

13

23

29

78

0 21 3 4 5 MILES

0

Base modified from U.S. Geological Survey 

1:100,000-scale digital data

21 3 4 5 KILOMETERS

Figure 8

EXPLANATION

Monitored watershed boundary

Eastern Continental Divide

USGS water-quality monitoring station, 

 identifier, and watershed

02207185

N

Population density during

 2013, per square mile

396 to 1,000

1,001 to 3,000

3,001 to 5,000

5,001 to 6,000

6,001 to 23,331

85

985

Wheeler Creek

watershed

02217274

Suwanee Creek

watershed

02334885

Richland Creek

watershed

02334480

Level Creek

watershed

02334578

Crooked Creek

watershed

02335350

North Fork

Peachtree Creek

watershed

02336030

Yellow River

watershed

02207120

No Business Creek

watershed

02207185

Big Haynes Creek

watershed

02207385

Brushy Fork Creek

watershed

02207400

Alcovy River

watershed

02208150

Apalachee River

watershed

02218565

Sweetwater Creek

watershed

02205865

Chattahooch e e River

Figure 8. Population density for Gwinnett County, Georgia, 2013. Data from American community survey 5-year estimates 

of 2009–13 block group data, U.S. Census Bureau, 2014.
20 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

0

1,000

2,000

3,000

4,000

5,000

2009

EXPLANATION

2013

Figure 9.

Population density, per square mile 

Sweetwater Creek 

Yellow River 

No Business Creek 

Big Haynes Creek 

Brushy Fork Creek 

Alcovy River 

Wheeler Creek 

Apalachee River 

Richland Creek 

Level Creek 

Suwanee Creek 

Crooked Creek 

North Fork Peachtree

 Creek 

Monitored watersheds, Gwinnett County 

Land Use and Changes in Impervious Area

Land use and impervious area are summarized for the 

13 LTTM program watersheds in table 6; data are based on 

the Atlanta Regional Commission 2012 land-use dataset 

(2012). Individual land-use categories have been grouped 

into six categories: high density, low density, estate/park, 

transportation/utilities, undeveloped/water, and other. Land 

use varies greatly among watersheds. For example, North Fork 

Peachtree Creek and Crooked Creek watersheds have much 

higher high-density land use, while No Business Creek, Big 

Haynes Creek, and Apalachee River watersheds have higher 

low-density land use, relative to the other watersheds.

Impervious areas have been shown to have substantial 

effects on rainfall-runoff relations. Impervious areas reduce 

infiltration and, hence, groundwater recharge, resulting in 

lower base flows. Impervious areas also increase runoff rates, 

resulting in a “flashy” hydrologic response where discharge 

rapidly increases and decreases during a storm, along with 

higher peak storm discharges that may increase both erosion 

and transport of land surface, tributary, and stream sediments. 

The percentage of impervious area in the 13 study watersheds 

in 2012 ranged from 12.71 percent in the Richland Creek 

watershed to 52.43 percent in the North Fork Peachtree Creek 

watershed (table 6; fig. 10). Watershed impervious area is 

categorized into (1) transportation elements, which include 

land uses such as roads, parking lots, and driveways, and 

(2) building elements. On average, about two-thirds of the 

total impervious area in the watersheds is from transportation 

elements. High-density land use typically contains a large 

proportion of impervious area, hence, the strong association 

between these characteristics. The percentage of impervious 

area in the watersheds is associated more with high-density 

land use than with population density (fig. 11).

Figure 12 illustrates how the percentage of impervious 

area has changed over the time period 2000–14 in the 

13 watersheds. Data were available for years 2000, 2005, 2006 

and annually for 2008 to 2014 (Gwinnett County Department 

of Public Utilities, unpub. data, 2000–14). All 13 watersheds 

show increases in impervious area over the period 2000–14. 

The increases in impervious area for the LTTM program 

study area weighted by drainage area were 1.0 percent of the 

drainage area per year from 2000 to 2005 and 0.31 percent 

of the drainage area per year from 2005 to 2014. Although 

these increases are likely related to development within the 

watersheds associated with population growth (fig. 7), the 

decrease in the rate observed in the later 9 years did not match 

the pattern in population growth. The two watersheds with the 

highest percentage of impervious areas, North Fork Peachtree 

Creek and Crooked Creek, exhibited the lowest increases 

in impervious areas over the 14-year period, 0.05 and 

0.14 percent per year, respectively. The Wheeler Creek 

watershed, which had the lowest percentage of impervious 

area (4.3 percent) of the 13 watersheds in 2000, exhibited 

the highest rate of increase over the period (0.86 percent 

per year), indicating rapid development. Sweetwater Creek, 

Yellow River, Alcovy River, and Apalachee Creek watersheds 

also exhibited large increases in percentage of impervious 

areas over this same period, ranging from 0.64 to 0.70 percent 

per year.

Figure 9. Population density for the 13 monitored watersheds in Gwinnett County, Georgia, in 2009 and 2013. Derived from 

American community survey 5-year estimates of 2006–10 and 2009–13 block group data, U.S. Census Bureau, 2012 and 2014, 

respectively.
Watershed Characteristics 21

Table 6. Land use and watershed impervious area for 13 watersheds in Gwinnett County, Georgia, 2012.

[Land-use data from Atlanta Regional Commission, 2012]

Station 

number Watershed name

Drainage 

area

(square 

miles)

Land use, percent Watershed impervious area 

(percent)

High density1

Low density2

Estate/park3

Transportation/

utilities4

Undeveloped/

water5

Other

Transportation

Building

Total

02205865 Sweetwater Creek 21.0 35.92 40.05 16.34 3.50 3.09 1.09 10.11 21.89 31.99

02207120 Yellow River 161.4 25.93 56.36 12.13 1.18 2.88 1.92 8.82 16.82 25.64

02207185 No Business Creek 10.1 14.40 64.01 19.25 0.10 1.48 0.75 6.07 12.69 18.75

02207385 Big Haynes Creek 17.3 12.27 64.35 19.95 0.00 2.06 1.36 6.47 10.26 16.73

02207400 Brushy Fork Creek 8.18 17.63 41.13 30.55 0.00 6.56 4.12 5.22 9.51 14.73

02208150 Alcovy River 30.8 17.43 40.20 28.83 4.03 8.49 1.04 5.92 11.45 17.38

02217274 Wheeler Creek 1.31 23.60 46.92 20.01 4.97 4.49 0.00 5.84 10.06 15.91

02218565 Apalachee River 5.65 6.94 64.43 25.74 0.00 2.88 0.00 7.19 10.32 17.51

02334480 Richland Creek 9.37 12.71 37.72 27.97 2.33 19.18 0.09 4.37 8.34 12.71

02334578 Level Creek 5.06 17.96 48.00 30.66 0.00 3.38 0.00 6.36 10.36 16.72

02334885 Suwanee Creek 47.1 22.37 35.53 30.27 3.24 7.50 1.09 6.49 12.94 19.42

02335350 Crooked Creek 8.87 45.00 37.98 8.23 4.67 3.88 0.23 12.73 24.41 37.14

02336030 North Fork Peachtree 

Creek

1.53 60.59 31.80 1.13 5.51 0.96 0.00 20.02 32.41 52.43

LTTM program study area 306.6 22.99 50.58 18.67 1.78 4.70 1.50 7.80 14.77 22.57

1

High-density land use includes commercial, industrial, schools, religions, quarries,and residential lots with less than 1/4 acre.

2

Low-density land use includes residential lot sizes greater than or equal to 1/4 acre and less than 5 acres.

3

Estate/park use includes public and private parks, golf courses, agricultural lands, and forests.

4

Transportation/utilities land use includes pervious and impervious areas within the right-of-way for roads, railroads, communications, and utilities.

5

Undeveloped land use includes landfills, waste dumps, water control structures, and recently cleared transitional land. Water land use includes reservoirs 

and wetlands.

0

10

20

30

40

50

60

Watershed impervious area, in percent

Building

Transportation

EXPLANATION

Figure 10.

Sweetwater Creek 

Yellow River 

No Business Creek 

Big Haynes Creek 

Brushy Fork Creek 

Alcovy River 

Wheeler Creek 

Apalachee River 

Richland Creek 

Level Creek 

Suwanee Creek 

Crooked Creek 

North Fork Peachtree

 Creek 

Monitored watersheds, Gwinnett County 

Figure 10. Percentage 

of watershed impervious 

area from transportation 

and building land use for 

13 monitored watersheds 

in Gwinnett County, 

Georgia, 2012. Data from 

Gwinnett County land 

coverage data, Gwinnett 

County Department of 

Public Utilities, unpub. 

data, 2012.
22 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

0

10

20

30

40

50

60

0 10 20 30 40 50 60 70

High-density land use, in percent

Figure 11.

Watershed impervious area, in percent

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

Population density, per square mile

0

10

20

30

40

50

60

2000 2002 2004 2006 2008 2010 2012 2014

Year

Percentage of impervious area

Sweetwater Creek 

EXPLANATION

Figure 12

Yellow River 

No Business Creek 

Big Haynes Creek 

Brushy Fork Creek 

Alcovy River 

Wheeler Creek 

Apalachee River 

Richland Creek 

Level Creek 

Suwanee Creek 

Crooked Creek 

North Fork Peachtree Creek 

LTTM weighted average 

Figure 11. Watershed impervious area as related to (A) high-density land use and (B) population density 

for 13 monitored watersheds in Gwinnett County, Georgia, 2013. Impervious area from Gwinnett County landcoverage data, Gwinnett County Department of Public Utilities, unpub. data 2013; population data derived from 

American community survey 5-year estimates of 2009–13 block group data, U.S. Census Bureau, 2014.

Figure 12. Percentage of impervious area in 13 monitored watersheds in Gwinnett County, Georgia. The LTTM 

program weighted average represents the spatial average of the area of the monitored watersheds. Data from 

Gwinnett County land-cover data, Gwinnett County Department of Public Utilities, unpub. data, 2000–14.
Hydrologic Budgets 23

Hydrologic Budgets

The components of water budgets include precipitation, 

stream runoff, evapotranspiration (ET), and groundwater 

inputs, outputs, and storage; of which precipitation and runoff 

are measured as part of the LTTM program. Annual and 

monthly average precipitation and runoff were computed for 

the 13 watersheds in the study area using the measured data. 

The measured precipitation and runoff data were also used to 

compute flow duration curves, base flow, and runoff ratios for 

the 13 watersheds. Variations in runoff between the watersheds 

can be the result of differences in precipitation and watershed 

characteristics. Droughts and wet conditions also have large 

effects on water budgets, and the effects of these conditions on 

runoff, groundwater storage, and runoff ratios are discussed. 

Climate and Precipitation

Gwinnett County has a humid, subtropical climate 

characterized by warm, humid summers and cool, wet winters. 

Monthly average air temperatures range from 42.2 degrees 

Fahrenheit (°F) in January to 79.8 °F in July (1981–2010 

thirty-year average for Atlanta Dekalb Peachtree Airport station 

USW00053863, National Oceanic and Atmospheric Administration, 2014; nearest air temperature station, in adjacent Dekalb 

County). Mean annual precipitation is 54.7 inches (1981–2010 

thirty-year average for Norcross station USC00096407, 

National Oceanic and Atmospheric Administration, 2014; in 

western portion of Gwinnett County). Precipitation is fairly 

evenly distributed throughout the year with the fall having the 

lowest amount of precipitation. Winter rainstorms are characterized by long duration, evenly distributed, and typically 

low-intensity frontal systems. In contrast, spring and summer 

rainstorms are characterized by short duration, unevenly 

distributed, intense convective thunderstorms. Despite their 

short duration, the intensity of these rainstorms often result in 

higher peak streamflows in headwater watersheds than those 

observed for longer-duration, low-intensity storms. Higher 

peak streamflows may result in increased erosion and washoff 

of constituents. Mean annual ET is estimated to account 

for about 50 to 60 percent of the annual precipitation in the 

study area (Sanford and Selnick, 2013). Evapotranspiration 

is highest in the summer as the result of temperature and 

solar radiation being at their peak. The seasonal pattern of 

ET results in declining base flows and groundwater storage 

throughout the growing season (April–September) as well as 

progressively increasing base flows and groundwater storage 

from event recharge during the dormant season.

The annual precipitation totals for WYs 2002–15 

for the 13 study watersheds are shown in figure 13. The 

WYs 2002–15 annual average for the LTTM program 

0 

10 

20 

30 

40 

50 

60 

70 

80 

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Average 

Annual precipitation, in inches 

Water year 

Figure 13

LTTM Program average 

EXPLANATION

Monitoring stations

Yellow River

Sweetwater Creek

No Business Creek

Big Haynes Creek

Brushy Fork Creek

Alcovy River

Wheeler Creek

Apalachee River

Richland Creek

Level Creek Suwanee Creek

Crooked Creek

North Fork Peachtree 

 Creek

Figure 13. Annual precipitation totals for each of the 13 monitored 

watersheds in Gwinnett County, Georgia, for water years 2002–15. 

The LTTM program average represents the spatial average of the 

area of the monitored watersheds.
24 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

study area was 50.2 inches, which was below the long-term 

average of 54.7 inches reported at Norcross, Georgia. Annual 

precipitation ranged from a low of 35.7 inches (WY 2007) to 

a high of 72.7 inches (WY 2003). Five years were dry, with 

precipitation <40 inches (WYs 2002, 2007, 2008, 2011, and 

2012), and 5 years were fairly to very wet, with precipitation 

>59 inches (WYs 2003, 2005, 2009, 2010, and 2013).

Variability in spatial patterns in precipitation can be due 

to factors such as topography (McCrary, 2011), the variable 

distribution of precipitation from convective thunderstorms, 

and proximity and orientation to urban heat islands (Bornstein 

and Lin, 2000; Dixon and Mote, 2003; Mote and others, 

2007; Shem and Shepherd, 2009) such as nearby Atlanta. 

Precipitation for WYs 2002–15 was highest in watersheds 

in the northern part of the county where altitudes are the 

highest (fig. 3; Alcovy River, Richland Creek, and Level 

Creek watersheds; 52.1, 52.4, and 54.5 inches per year [in/yr], 

respectively) and was lowest in watersheds in the southern 

part of the county where altitudes are lower (No Business 

Creek, Big Haynes Creek, and Brushy Fork Creek watersheds; 

48.3, 48.1, and 47.0 in/yr, respectively). It is important to 

consider the variations in precipitation among the 13 LTTM 

program watersheds when comparing the resulting stream 

runoff among watersheds. Nevertheless, spatial variability in 

precipitation observed between watersheds is much smaller 

than the temporal variations between years.

Monthly average precipitation for WYs 2002–15 

ranged from 3.3 inches (April) to 5.4 inches (July; fig. 14). 

Precipitation did not vary strongly with seasons. The lowest 

values were observed in April, October, and November 

(3.3, 3.5, and 3.7 inches, respectively), and the highest values 

were observed in March, July, and December (4.6, 5.4, and 

4.7 inches, respectively).

Runoff

In this report, runoff (the volume of streamflow) was 

divided by the area of each watershed and was expressed as a 

depth in units of inches. This convention allows comparison 

of runoff values between watersheds with different sized 

drainage areas and accommodates direct comparison with 

precipitation and is similar to the distinction in use of load 

versus yield with respect to contaminants. Runoff “depth” 

herein is referred to as specific discharge. Runoff for the 

period WYs 2002–15 averaged 21.5 in/yr for the LTTM 

program study area (fig. 15). Annual runoff varied greatly, 

from a low of 11.1 inches (WY 2012) to a high of 38.7 inches 

0 

1 

2 

3 

4 

5 

6 

January February March April May June July August September October November December 

Monthly average precipitation, in inches 

Month 

Figure 14

LTTM Program average 

EXPLANATION

Monitoring stations

Yellow River

Sweetwater Creek

No Business Creek

Big Haynes Creek

Brushy Fork Creek

Alcovy River

Wheeler Creek

Apalachee River

Richland Creek

Level Creek Suwanee Creek

Crooked Creek

North Fork Peachtree 

 Creek

Figure 14. Monthly average precipitation for each of the 13 monitored 

watersheds in Gwinnett County, Georgia, for water years 2002–15. The 

LTTM program average represents the spatial average of the area of 

the monitored watersheds.
Hydrologic Budgets 25

(WY 2003), a range of 27.6 inches. Runoffs during the period 

were lowest in the No Business Creek, Big Haynes Creek, 

and Brushy Fork Creek watersheds (19.0, 19.1, and 19.1 in/yr, 

respectively), which was at least partially due to the lower 

amounts of precipitation in this part of the county (fig. 13). 

Runoffs were highest in the Crooked Creek and North Fork 

Peachtree Creek watersheds (24.8 and 29.3 in/yr, respectively). The variability in annual runoff between years was 

greater than the variability in annual runoff between individual 

watersheds for a given year, indicating that the variability 

in precipitation and possibly other components of the water 

budget were more important in controlling annual variability 

in runoff than differences in watershed characteristics. Hence, 

when determining the relations between runoff and watershed 

characteristics, it is important to make these comparisons 

for the same period, and also to take into account any spatial 

variations in precipitation.

The year-to-year variations in annual runoff (fig. 15) 

follows the overall pattern for annual precipitation (fig. 13), 

but the variability in runoff is more pronounced as indicated 

by a higher relative standard error for runoff (0.44) than for 

precipitation (0.23). Relative standard error is a statistic that 

can be used to measure the variability of a variable with 

respect to its mean value and is calculated as its standard 

deviation divided by its mean. This higher variability in annual 

runoff reflects the effects of other components of the water 

budget, ET and storage.

Although groundwater storage was not quantified in this 

study, the effects of variations in groundwater storage can 

be observed in the patterns in annual runoff. The effect of 

prior groundwater storage level on current runoff is referred 

to as watershed memory (Nippgen and others, 2016). For 

example, during the drought of WYs 2011–12, while the 

LTTM program study area annual precipitation were similar 

in both years (39.4 and 39.2 inches in WYs 2011 and 2012, 

respectively), annual runoff declined from 15.1 inches 

(WY 2011) to 11.1 inches (WY 2012). Much of this difference 

is likely the result of a smaller contribution of stream base 

flow from depleted groundwater storage in WY 2012. The 

effects of storage on runoff can also be observed in WYs 2005 

and 2009 for the LTTM program study area. While these years 

had similar amounts of precipitation, 59.6 (WY 2005) and 

59.1 (WY 2009) inches, WY 2009 had much lower runoff than 

WY 2005, 23.5 versus 32.9 inches, respectively. Much of this 

apparent discrepancy was likely the result of a larger portion 

of precipitation recharging (replenishing) groundwater storage 

in WY 2009, while a larger portion of precipitation resulted 

in runoff in WY 2005. This occurred because groundwater 

0 

5 

10 

15 

20 

25 

30 

35 

40 

45 

50 

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Average 

Annual runoff, in inches 

Water year 

Figure 15

LTTM Program average 

EXPLANATION

Monitoring stations

Yellow River

Sweetwater Creek

No Business Creek

Big Haynes Creek

Brushy Fork Creek

Alcovy River

Wheeler Creek

Apalachee River

Richland Creek

Level Creek Suwanee Creek

Crooked Creek

North Fork Peachtree 

 Creek

Figure 15. Annual runoff for each of the 13 monitored watersheds 

in Gwinnett County, Georgia, for water years 2002–15. The LTTM 

program average represents the spatial average of the area of the 

monitored watersheds. Sweetwater Creek watershed runoff data 

are available only for water years 2011–15.
26 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

storage started out lower in WY 2009, following a 2-year 

drought in WYs 2007–08, compared to WY 2005, which 

followed a year with more average hydrologic conditions.

The LTTM program study area annual runoff was used 

to categorize annual watershed wetness conditions as dry, 

average, or wet, which are then related to variations in waterquality loads and yields later in the report. Although annual 

patterns in both precipitation (fig. 13) and runoff (fig. 15) were 

related to periods of drought, runoff was selected for analysis 

because it incorporates the effects of groundwater storage 

conditions, which is an important aspect of drought. Five WYs 

were categorized as dry (WYs 2002, 2007–08, and 2011–12; 

runoff 11.1–15.9 inches), six as average (WYs 2004, 2006, 

2009, and 2013–15; runoff 17.6–25.4 inches), and three as 

wet (WYs 2003, 2005, and 2010; runoff 32.3–38.7 inches). 

The five WYs categorized as dry corresponded well with 

weekly estimates of drought severity in Gwinnett County 

as classified by the U.S. Drought Monitor (2014 and 2016; 

fig. 16). Droughts were persistent in all five of these WYs, 

with moderate to extreme drought conditions observed in 

WYs 2002 and 2011–12 (annual runoff 11.1–15.8 inches) 

and moderate to exceptional drought conditions observed in 

WYs 2007–08 (annual runoff 12.0–13.4 inches).

Monthly average runoff for the LTTM program study 

area for the period of study was lowest in August (1.2 inches) 

and highest in March (2.6 inches; fig. 17). Patterns in monthly 

average runoff reflect the effects of seasonal patterns in 

groundwater storage and ET components of the water budget. 

Monthly average runoff increased sequentially from October 

to March, with the exception of a slight decline between 

December and January. This period corresponds to the 

dormant season when potential evapotranspiration (PET) is 

low and precipitation typically exceeds PET, with some of this 

excess resulting in groundwater recharge. Potential ET represents the theoretical amount of ET that occurs when surface 

soil conditions are saturated (wet). Actual ET is less than PET 

when there is not enough water available to evapotranspire. 

Potential ET varies seasonally and is highest in summer due 

to higher air temperatures and inputs of solar radiation that 

provide the energy for evapotranspiration. The progressive 

increase in runoff observed during the dormant season was the 

result of a combination of increases in stream base flow and 

higher storm runoff, in response to increasing groundwater 

storage and wetter shallow soil moisture conditions. 

Monthly average runoff decreased rapidly from March 

to April and then exhibited a less rapid, but overall decline 

through the summer and into October. This period represents 

the growing season, when high rates of PET typically exceed 

precipitation, resulting in precipitation being evapotranspired 

from shallow soils and precluding much groundwater 

recharge. The rapid decline in runoff from March to April is 

likely due to a combination of (1) low precipitation in April 

(fig. 14) that limited storm runoff, (2) decreases in storm 

runoff due to drier soil moisture conditions, and (3) rapid 

decreases in base flow. The rapid decreases in observed base 

flow were due to a combination of declines in groundwater 

storage, attributed to losses from base flow and ET, and the 

lack of groundwater recharge from the combination of low 

April precipitation and high PET.

Continued declines in monthly average runoff during 

the remainder of the growing season were associated with the 

same processes mentioned above for the March to April runoff 

decline. Runoff declines were slower because releases of 

stream base flow from storage become less rapid as groundwater levels decline. Furthermore, as groundwater storage 

continues to decline, ET can become limited by the availability of shallower groundwater that can be evapotranspired, 

thereby limiting the amount of losses of groundwater storage 

from ET and the rate of subsequent base-flow declines later 

in the season. Some of the variability observed in the overall 

declines in monthly average runoff was associated with higher 

amounts of precipitation that likely temporarily exceeded PET 

Drought severity

EXPLANATION

Figure 16.

None Abnormally dry Moderate drought Severe drought Extreme drought Exceptional drought

0

Portion of county area, in percent 20

40

60

80

100

Time, in years

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Figure 16. Weekly drought severity in Gwinnett County, Georgia, for years 2001–15 as a portion of county area. Modified from 

U.S. Drought Monitor, 2016.
Hydrologic Budgets 27

and resulted in groundwater recharge and increased runoff. For 

example, July had the highest monthly average precipitation 

observed (5.4 inches; fig. 14) and also had a higher monthly 

average runoff (1.7 inches) than its adjacent months. A 

similar increase in monthly average runoff was observed in 

September, which had both higher monthly average precipitation (4.5 inches) and runoff (1.6 inches) than its adjacent 

months. Although precipitation was less in September than in 

July, September generated a similar amount of runoff likely 

because PET was also lower in September than in July. In 

summary, in the dormant season, precipitation in excess of 

PET results in recharge of groundwater storage, increased base 

flow, and more storm runoff, while in the growing season, a 

larger portion of precipitation results in ET.

Flow Duration Curves

The streamflow duration curves were compared across 

watersheds by plotting the cumulative percentile of streamflow 

for each watershed for WYs 2002–15 (fig. 18). These curves 

were generated from the daily average streamflows. This 

graph allows for watershed comparisons of not only the range 

of streamflows observed, but also the cumulative temporal 

frequency at which a particular flow occurs. For example, 

the streamflow with a cumulative frequency of 40 percent 

indicates that streamflow occurred at this discharge (flow 

rate) or lower for 40 percent of the period. Streamflows 

below cumulative percentiles of approximately 80 percent 

generally represent days with base-flow conditions, while 

streamflows above 80 percent generally represent days with 

storm conditions.

The flow duration curves were fairly similar for most 

watersheds in the 20 to 85 percent cumulative percentile 

range. Distributions exhibited more variability between watersheds below the 20th percentile than above the 85th percentile. 

Comparisons with the Sweetwater Creek watershed were not 

considered because its distribution represents a shorter period 

(WYs 2011–15) during which the station was monitored that 

had drier conditions than the longer period (WYs 2002–15) for 

the other watersheds. No Business Creek, Brushy Fork Creek, 

and Wheeler Creek watersheds had the lowest base flows. 

Lower base flows in the No Business Creek and Brushy Fork 

Creek watersheds may be related to lower amounts of precipitation that were observed in that region of the county (fig. 13) 

and to lower basin slopes (figs. 4 and 5). Yellow River, Big 

Haynes Creek, and Richland Creek had the highest base flows. 

The Richland Creek watershed, which had the highest base 

0.0 

0.5 

1.0 

1.5 

2.0 

2.5 

3.0 

3.5 

Monthly average runoff, in inches 

January February March April May June July August September October November December 

Month 

Figure 17

LTTM Program average 

EXPLANATION

Monitoring stations

Yellow River

Sweetwater Creek

No Business Creek

Big Haynes Creek

Brushy Fork Creek

Alcovy River

Wheeler Creek

Apalachee River

Richland Creek

Level Creek Suwanee Creek

Crooked Creek

North Fork Peachtree 

 Creek

Figure 17. Average monthly runoff for each of the 13 monitored 

watersheds in Gwinnett County, Georgia, for water years 2002–15. 

The LTTM program average represents the spatial average of the 

area of the monitored watersheds. Sweetwater Creek watershed 

averages are for water years 2011–15.
28 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

flows of all the watersheds, also was the watershed with the 

highest basin slope, the lowest percentage of impervious area 

(fig. 10), and the second highest amount of precipitation. No 

Business Creek, Big Haynes Creek, and Brushy Fork Creek 

watersheds had the three lowest stormflows. These watersheds 

were in the region of the county with the lowest precipitation 

and also had three of the four lowest basin slopes. The Brushy 

Fork watershed also had the second lowest percentage of 

impervious area. Apalachee River and North Fork Peachtree 

Creek watersheds had the highest stormflows; North Fork 

Peachtree Creek watershed had the highest percentage of 

watershed impervious area.

Flow duration curves are affected by hydrologic 

conditions, as illustrated in figure 19 for the Yellow River 

watershed. The three curves represent various years with the 

three hydrologic conditions (wet, average, and dry) as previously defined from annual runoff. The three curves generally 

parallel each other, with higher flows for the same cumulative 

percentiles as hydrologic conditions become wetter. Similar 

patterns with hydrologic conditions are observed seasonally. 

This illustrates the importance of comparing distributions for 

periods that experience the same climatic conditions.

0.0001

0.001

0.01

0.1

1

10

Streamflow, in inches per day

0 20 40 60 80 100

Cummulative percentile, in percent

Figure 18

Sweetwater Creek 

EXPLANATION

Yellow River 

No Business Creek 

Big Haynes Creek 

Brushy Fork Creek 

Alcovy River 

Wheeler Creek 

Apalachee River 

Richland Creek 

Level Creek 

Suwanee Creek 

Crooked Creek 

North Fork Peachtree Creek 

0 20 40 60 80 100

Cummulative percentile, in percent

0.001

0.01

0.1

1

10

Streamflow, in inches per day

Wet

EXPLANATION

Average

Dry

Figure 19

Figure 18. Streamflow duration curves for each of the 13 monitored watersheds in Gwinnett County, 

Georgia, for water years 2002–15. Cumulative percentiles plotted on a probability scale. Sweetwater 

Creek watershed runoff data are available only for water years 2011–15.

Figure 19. Streamflow duration curves for the Yellow River 

watershed for wet (water years 2003, 2005, and 2010), 

average (water years 2004, 2006, 2009, and 2013–15), and 

dry (water years 2002, 2007–08, and 2011–12) years. 

Cumulative percentiles are plotted on a probability scale.
Hydrologic Budgets 29

Base Flow

The results of the hydrograph separation into base-flow 

and stormflow components tell a somewhat different story 

than the flow duration curve plots (table 7). Sweetwater Creek, 

Crooked Creek, and North Fork Peachtree Creek watersheds 

had the lowest base-flow runoff of the 13 watersheds. Note 

that Sweetwater Creek was reported for a shorter drier period, 

WYs 2011–15. Base-flow runoff was highest at Richland 

Creek, the watershed with the steepest basin slope. Base-flow 

index (BFI), calculated as base-flow runoff divided by total 

runoff, was lowest for the North Fork Peachtree Creek watershed (0.257), which had the highest stormflows as indicated by 

the flow duration curves plot (fig. 18) and also had the highest 

percentage of watershed impervious area (table 6). The highest 

BFI was for the Richland Creek watershed. There is a strong 

relation of base-flow runoff and BFI with the percentage 

of impervious area in a watershed (R2

s of 0.62 and 0.89, 

respectively; fig. 20), with a stronger relation for BFI, which 

indicates that impervious area not only affects the amount 

of base flow but also the amount of base flow relative 

to stormflow. Increases in impervious area are known to 

decrease base flows while also increasing stormflows and 

this has been shown to occur in urbanized areas in the nearby 

Atlanta, Ga. area (Rose and Peters, 2001; Calhoun and others, 

2003). Increases in base-flow concentrations can result from 

reductions in base-flow runoff while stream point-source and 

waste-load allocation contributions remain steady due to the 

reductions in runoff volume diluting these inputs.

Runoff Ratios

Runoff ratios (also known as water yields) represent the 

portion of precipitation that results in runoff as opposed to 

being evapotranspired. The runoff ratios were calculated as 

the percentage of runoff relative to precipitation (fig. 21). The 

runoff ratio removes much of the variability in runoff due to 

variations in the amount of precipitation among years, months, 

and watersheds. Variability in the runoff ratio can be the result 

of differences in the amounts of stream base flow and storm 

runoff, groundwater storage, and ET and can reflect differences in both hydrologic conditions and watershed characteristics. The runoff ratio for WYs 2002–15 was 42.8 percent for 

the LTTM program study area. Assuming that the effects of 

changes in groundwater storage on the water budget become 

negligible over longer periods of time, this runoff ratio 

indicates that ET represents 57.2 percent of the long-term 

water budget. Annual runoff ratios varied from a minimum 

of 28.4 percent (WY 2012) to a maximum of 55.1 percent 

(WY 2005). Annual runoff ratios were low in 6 years 

(≤40.0 percent; WYs 2002, 2007–08, 2011–12, and 2015) 

and high in 3 years (53.2–55.1 percent; WYs 2003, 2005, and 

2010). These low and high years generally correspond with the 

dry and wet year hydrologic conditions previously categorized 

using annual runoff. 

Table 7. Average base-flow runoff and base-flow index (baseflow runoff divided by total runoff) for 13 watersheds in Gwinnett 

County, Georgia, for water years 2002–15.

Station 

number Watershed Base-flow runoff(inches per day)

Base-flow 

index

02205865 Sweetwater Creek1 0.019 0.356

02207120 Yellow River 0.030 0.503

02207185 No Business Creek 0.030 0.571

02207385 Big Haynes Creek 0.032 0.608

02207400 Brushy Fork Creek 0.029 0.563

02208150 Alcovy River 0.030 0.553

02217274 Wheeler Creek 0.030 0.536

02218565 Apalachee River 0.029 0.505

02334480 Richland Creek 0.039 0.624

02334578 Level Creek 0.028 0.513

02334885 Suwanee Creek 0.029 0.501

02335350 Crooked Creek 0.023 0.338

02336030 North Fork Peachtree

Creek

0.021 0.257

1

Sweetwater Creek watershed for water years 2011–15.

 R2=0.62 

0 

0.01 

0.02 

0.03 

0.04 

0.05 

Base-flow runoff, in inches per day 

 R2=0.89 

0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0 10 20 30 40 50 60 

Base-flow index 

Impervious area in 2008, in percent 

A

B

Figure 20 Figure 20. (A) Average base-flow runoff versus percentage 

of impervious area in 2008, and (B) base-flow index (base-flow 

runoff divided by total runoff) versus percentage of impervious 

area in 2008 for 13 watersheds in Gwinnett County, Georgia, for 

the period water years 2002–15. Sweetwater Creek watershed 

data are for water years 2011–15.
30 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

Runoff ratios for WYs 2002–15 were highest for the 

North Fork Peachtree Creek (57.5 percent), Crooked Creek 

(50.8 percent), Yellow River (44.2 percent), and Richland 

Creek (44.0 percent) watersheds and were lowest in the 

Level Creek (36.2 percent) and Alcovy River watersheds 

(37.5 percent; fig. 21). High runoff ratios in the North Fork 

Peachtree Creek, Crooked Creek, and Yellow River watersheds can likely be attributed to impervious areas (fig. 10; 

table 6), because these three watersheds had three of the four 

highest percentages of impervious areas of the 13 watersheds; 

53.00, 36.84, and 23.37 percent of their watershed areas in 

2008 (middle of study period), respectively. The high runoff 

ratio for the Richland Creek watershed is likely to be at least 

partially related to having the highest basin slope of any of 

the 13 watersheds (16.7 percent; fig. 5; table 5). Interestingly, annual runoff ratios for the Richland Creek watershed 

tended to be more above average with respect to the other 

watersheds in years when conditions became drier and less 

notably different in years when conditions became wetter. 

This pattern suggests that the Richland Creek watershed may 

have a larger dynamic groundwater storage component that 

provides more base flow during droughts compared to the 

other watersheds.

Watershed runoff ratios for the period WYs 2002–15 

were positively related to the percentage of impervious 

areas within each watershed (R2=0.73; fig. 22). The runoff 

ratios for the entire period were used to average the effects 

of climatic variations. The impervious areas were plotted for 

2008, which represents the middle of the study period. This 

relation was evident despite implementation of BMPs to 

mitigate the effects of impervious areas on runoff within the 

watersheds. The relation would presumably be stronger, and 

runoff ratios would be higher, if it were not for the extensive 

implementation of BMPs. 

0 

10 

20 

30 

40 

50 

60 

70 

80 

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2002–15 

Annual runoff ratio, in percent 

Water year 

Figure 21

LTTM Program average 

EXPLANATION

Monitoring stations

Yellow River

Sweetwater Creek

No Business Creek

Big Haynes Creek

Brushy Fork Creek

Alcovy River

Wheeler Creek

Apalachee River

Richland Creek

Level Creek Suwanee Creek

Crooked Creek

North Fork Peachtree Creek

Figure 21. Annual runoff ratios for each of the 13 monitored 

watersheds in Gwinnett County, Georgia, for water years 2002–15. 

The LTTM program average represents the spatial average of the 

area of the monitored watersheds. Sweetwater Creek watershed 

runoff ratios were available only for water years 2011–15.
Flood Frequency 31

Flood Frequency

Reliable flood-frequency estimates are particularly 

important in densely populated urban areas. Urbanization 

changes a basin’s response to precipitation. The most common 

effects are reduced infiltration and decreased lag time, which 

substantially increase peak flows (U.S. Department of Agriculture, 1986). Engineers and planners often need to consider the 

potential effects on peak flow of urban development scenarios 

in their design and planning efforts. Because urbanization 

can produce substantial changes in flood-frequency characteristics of streams, rural basin flood-frequency relations 

are not always applicable to urban streams. Historically, 

flood-frequency terminology was associated with a potentially 

misleading recurrence interval. To help alleviate any potential 

confusion with nomenclature and to further educate users, the 

recurrence interval terminology for flood events was adapted 

to further reflect the statistical meaning of the flood event, 

Annual Exceedance Probability (AEP; table 8).

The AEPs, 0.6667, 0.5, 0.2, 0.1, 0.04, 0.02, and 0.01, 

were computed using the long-term annual peak flow 

data from 13 streamgages in Gwinnett County, Ga., with 

10 or more water years of annual peak flow record. Twelve of 

the 13 streamgages are part of the LTTM program. The other 

streamgage (USGS station 02208050) is a crest-stage gage, 

which records only peak flows. The annual peak flow data for 

these 13 streamgages ranged from 11 to 30 years. The annual 

peak flows for each station are fitted using a log-Pearson 

Type III distribution and Expected Moments Algorithm 

(EMA; Cohn and others, 1997) with a multiple Gubbs-Beck 

test (Cohn and others, 2013), which identifies potentially 

influential low flow outliers. 

The estimated AEPs were computed using the USGS 

computer program PEAKFQ, which automates many of 

the procedures listed in Bulletin 17B, Guidelines for the 

Interagency Advisory Committee on Water Data (Veilleux 

and others, 2014). The PeakFQ results provided estimates 

of flood magnitudes, fitted frequency curves with 95 percent 

confidence intervals, and the mean square error (fig. 23) 

based on the annual peaks for each of the 13 streamgages. In 

comparing the results of the flood-frequency magnitudes in 

each watershed, having a longer length of record and more 

annual peaks improved the range of the 95 percent confidence 

interval. Table 9 provides the AEPs, corresponding estimates 

for discharge, and confidence intervals for each station. 

Model settings used to estimate AEP using PEAKFQ are 

listed in Aulenbach and Joiner (2017; table 2, flood frequency 

PEAKFQ input parameters).

Kendall’s tau nonparametric test (Kendall, 1938) was 

used to determine statistical significance of monotonic trends 

in annual peak flows with time (Helsel and Hirsch, 1992). A 

trend was considered statistically significant if the Kendall’s 

tau value significantly differed from zero using a probability 

value (p-value) of ≤0.05. None of the 13 streamgages showed 

a significant trend in the annual peak flow data. Increasing 

trends in annual peak flows would indiciate the likelihood of 

more frequent, higher magnitude flooding along with associated increases in bank erosion and sediment transport.

R2=0.73

35

40

45

50

55

60

0 10 20 30 40 50 60

Impervious area in 2008, in percent

Runoff ratio, in percent

Figure 22

Table 8. Historical terminology associated with flood-frequency 

estimates and corresponding annual exceedance probability and 

P-percent chance exceedance.

[From Feaster and others, 2014]

Recurrence

interval (yr)

Annual exceedance 

probability (AEP) P-percent AEP

1.5 0.6667 67

2 0.5 50

5 0.2 20

10 0.1 10

25 0.04 4

50 0.02 2

100 0.01 1

Figure 22. Runoff ratios for water years 2002–15 versus 

percentage of impervious areas in 2008 for each of the 

13 monitored watersheds in Gwinnett County, Georgia. 

Sweetwater Creek watershed runoff ratio was calculated 

for water years 2011–15.
32 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

P-percent

AEP AEP Discharge

(ft3/s)

95% confidence intervals

Lower Upper

02207120 Yellow River at GA 124, near Lithonia, GA

Period of record: 2002–14

Number of years analyzed: 13 years

67 0.667 3,190 2,320 4,750

50 0.5 4,040 2,780 6,250

20 0.2 6,920 4,520 14,200

10 0.1 9,580 6,160 28,100

4 0.04 14,060 8,430 69,100

2 0.02 18,400 10,200 124,000

1 0.01 23,690 12,100 230,000

02207185 No Business Creek at Lee Road, below Snellville, GA

Period of record: 2001–14

Number of years analyzed: 14 years

67 0.667 420 270 620

50 0.5 560 380 820

20 0.2 950 660 1,540

10 0.1 1,250 860 2,460

4 0.04 1,650 1,110 4,530

2 0.02 1,960 1,270 6,870

1 0.01 2,300 1,400 10,100

02207385 Big Haynes Creek at Lenora Road, near Snellville, GA

Period of record: 2001–14

Number of years analyzed: 14 years

67 0.6667 630 560 800

50 0.5 700 890 920

20 0.2 940 740 1,460

10 0.1 1,160 900 2,180

4 0.04 1,520 1,100 4,020

2 0.02 1,850 1,250 6,270

1 0.01 2,260 1,400 10,100

02207400 Brushy Fork Creek at Beaver Road, near Loganville, GA

Period of record: 1997–2014

Number of years analyzed: 17 years

67 0.6667 270 210 360

50 0.5 330 260 440

20 0.2 510 390 740

10 0.1 640 480 1,110

4 0.04 830 600 2,200

2 0.02 990 690 3,200

1 0.01 1,160 770 4,670

P-percent

AEP AEP Discharge

(ft3/s)

95% confidence intervals

Lower Upper

02208050 Alcovy River near Lawrenceville, GA

Period of record: 1965–2014

Number of years analyzed: 29 years

67 0.6667 560 440 700

50 0.5 710 570 890

20 0.2 1,140 910 1,510

10 0.1 1,480 1,150 2,110

4 0.04 1,950 1,450 3,240

2 0.02 2,350 1,670 4,520

1 0.01 2,780 1,890 6,320

02208150 Alcovy River at New Hope Road, near Grayson, GA

Period of record: 2001–14

Number of years analyzed: 14 years

67 0.6667 1,500 980 2,350

50 0.5 2,040 1,300 3,320

20 0.2 3,960 2,450 8,520

10 0.1 5,780 3,500 17,900

4 0.04 8,910 5,020 52,100

2 0.02 12,000 6,280 97,200

1 0.01 15,700 7,650 183,000

02217274 Wheeler Creek at Bill Cheek Road, near Auburn, Ga

Period of record: 1997–2014

Number of years analyzed: 18 years

67 0.6667 140 80 240

50 0.5 220 130 380

20 0.2 520 310 1,050

10 0.1 810 470 2,160

4 0.04 1,310 720 5,920

2 0.02 1,790 920 12,900

1 0.01 2,370 1,120 23,800

02218565 Apalachee River at Fence Road, near Dacula, GA

Period of record: 1994–2014

Number of years analyzed: 21 years

67 0.6667 350 230 500

50 0.5 480 340 680

20 0.2 860 620 1,270

10 0.1 1,150 820 1,960

4 0.04 1,530 1,070 3,320

2 0.02 1,830 1,220 4,660

1 0.01 2,040 1,340 6,390

Table 9. Annual exceedance probability (AEP) discharges estimated from 13 stations in the LTTM program study area, Gwinnett 

County, Georgia.

[AEP discharges estimated using Expected Moments Algorithm (EMA). ft 3/s, cubic foot per second; %, percent]
Flood Frequency 33

P-percent

AEP AEP Discharge

(ft3/s)

95% confidence intervals

Lower Upper

02334480 Richland Creek at Suwanee Dam Road, near Buford, GA

Period of record: 1995 –2014

Number of years analyzed: 20 years

67 0.667 770 500 1,120

50 0.5 1,070 730 1,540

20 0.2 1,950 1,370 2,950

10 0.1 2,620 1,830 4,650

4 0.04 3,540 2,430 8,520

2 0.02 4,260 2,810 13,000

1 0.01 5,020 3,130 19,400

02334578 Level Creek at Suwanee Dam Road, near Suwanee, GA

Period of record: 2003–14

Number of years analyzed: 12 years

67 0.6667 510 290 880

50 0.5 730 420 1,310

20 0.2 1,530 870 3,620

10 0.1 2,290 1,280 7,810

4 0.04 3,580 1,880 24,700

2 0.02 4,820 2,370 45,300

1 0.01 6,330 2,890 83,000

02334885 Suwanee Creek at Suwanee, GA

Period of record: 1985–2014

Number of years analyzed: 30 years

67 0.667 1,350 1,010 1,760

50 0.5 1,810 1,380 2,360

20 0.2 3,150 2,420 4,350

10 0.1 4,200 3,180 6,470

4 0.04 5,680 4,150 10,800

2 0.02 6,890 4,840 15,600

1 0.01 8,090 5,460 22,100

P-percent

AEP AEP Discharge

(ft3/s)

95% confidence intervals

Lower Upper

02335350 Crooked Creek near Norcross, GA

Period of record: 2001–14

Number of years analyzed: 14 years

67 0.6667 960 810 1,230

50 0.5 1,090 880 1,460

20 0.2 1,540 1,180 2,490

10 0.1 1,910 1,440 3,890

4 0.04 2,500 1,770 7,210

2 0.02 3,040 2,010 11,200

1 0.01 3,660 2,250 18,000

02336030 N.F. Peachtree Creek at Graves Road, near Doraville, GA

Period of record: 2004–14

Number of years analyzed: 11 years

67 0.6667 850 560 1,203

50 0.5 1,075 730 1,590

20 0.2 1,730 1,190 3,000

10 0.1 2,220 1,520 4,850

4 0.04 2,920 1,930 9,560

2 0.02 3,480 2,220 14,100

1 0.01 4,090 2,490 20,000

Table 9. Annual exceedance probability (AEP) discharges estimated from 13 stations in the LTTM program study area, Gwinnett 

County, Georgia.—Continued

[AEP discharges estimated using Expected Moments Algorithm (EMA). ft 3/s, cubic foot per second; %, percent]
34 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

10

100

1,000

10,000

100,000

Annual peak discharge, in cubic feet per second

99.5 98 90 75 60 40

Annual exceedance probability, in percent

20 5 1 0.2

Fitted frequency

Confidence limit

Urban or Reg peak

Systematic peak

EXPLANATION

Figure 23

Peakfq v 7.1 run 1/12/2016 2:09:25 PM

EMA using Station Skew option

0.148=Skew (G)

0.16=Mean Sq Error (MSE sub G)

0 Zeroes not displayed

0 Peaks below PILO (LO) Threshold

Multiple Grubbs-Beck

Figure 23. Example of PeakFQ probability graph of annual peaks, fitted frequency curve, and confidence 

limits for USGS station 02208050, Alcovy River near Lawrenceville, Georgia. [Reg, regulated]
Surface-Water Quality 35

Surface-Water Quality

Patterns in continuous water-quality monitoring data 

for specific conductance (SC) and turbidity and their relation 

to other water-quality constituents were assessed. Statistical 

summaries of 13 measured or calculated constituents are 

provided for each of the 13 watersheds. Comparisons are made 

between base-flow and storm-composited concentrations.

Continuous Water-Quality Monitoring

Water-quality monitors were installed at all gages to 

continuously measure water temperature, SC, and turbidity. 

These continuous water-quality data are used to assess relations with constituents analyzed from base-flow and stormflow 

samples. For example, SC is related to the type and concentration of dissolved constituents in water and, thus, is a useful 

surrogate for total dissolved solids or other dissolved constituents. Specific conductance typically varies inversely with 

discharge—a process referred to as dilution—at the 13 LTTM 

program stations (fig. 24). Dilution is a commonly observed 

relation that occurs for many dissolved constituents when, 

during stormflow, there is a larger proportion of dilute water 

from precipitation runoff and shallow flow paths, compared to 

base-flow concentrations, contributing to streamflow. This is 

not always the case, because some dissolved constituents are 

mobilized and transported during higher discharges.

Turbidity is an optical water-quality property related to 

the amount of light scattered or absorbed by suspended and 

dissolved matter and often is a good surrogate for suspended 

sediment. Turbidity can also be a surrogate for a wide range 

of other constituents, because certain pollutants, such as heavy 

metals, pesticides, and fecal coliform, adhere to suspendedsediment particles. The U.S. Environmental Protection Agency 

(2010) lists sediment and other solid-phase constituents as 

some of the most common sources of impairment in Georgia 

streams. Strong relations were observed during this study 

between turbidity and many of the water-quality constituents 

measured during base-flow and stormflow sampling. For 

example, relations for TSS, SSC, Pb, and Zn are shown for 

the Sweetwater Creek watershed (fig. 25). Because turbidity 

is continuously monitored and is highly related to many of the 

water-quality constituents analyzed as part of this program, 

turbidity was included as a possible explanatory variable for 

the concentration models used to estimate loads.

Base-Flow and Stormflow Water Quality

Statistical summaries of the water-quality sample 

concentrations for 13 field and laboratory constituents 

(table 2) are provided in Aulenbach and Joiner (2017; table 4, 

water-quality constituent statistics). Graphical summaries for 

the 13 constituents in table 2 for both base-flow and stormflow 

conditions are shown in figure 26.

Specific conductance typically varied between about 

50 and 200 microsiemens per centimeter (μS/cm) at 

25 degrees Celsius (fig. 26A). The term “typical” used in this 

discussion refers to the approximate range of values observed 

in concentrations within the box-and-whisker plots and not the 

range of values of the outside and detached concentrations. 

Specific conductance was significantly lower during stormflow 

than base flow at 12 of the 13 watersheds, indicating that 

dissolved constituents generally were diluted during stormflow 

conditions. Specific conductance typically was highest for 

Yellow River and Suwannee Creek watersheds. Turbidity 

typically varied between about 2 and 20 FNU for base-flow 

samples and between about 20 and 2,000 FNU for stormflow 

samples (fig. 26B). Turbidity values were significantly higher 

in stormflow samples, with stormflow turbidities generally 

1 to 2 orders of magnitude higher than base-flow turbidities, 

reflecting the transport of sediment during these conditions.

Nitrogen concentrations typically varied between about 

0.2 and 5 milligrams per liter as nitrogen (mg/L as N) for total 

nitrogen (TN; fig. 26C ), between about <0.25 and 5 mg/L as 

N for total ammonia plus organic nitrogen (TKN; fig. 26D), 

and between about <0.27 and 5 mg/L as N for total nitrate plus 

nitrite (NO3+NO2; fig. 26E ). Differences between base-flow 

and stormflow NO3+NO2 concentrations varied across the 

watersheds, with significantly lower concentrations observed 

during stormflow at Yellow River, No Business Creek, 

Big Haynes Creek, Alcovy River, Wheeler Creek, Richland 

Creek, and Suwanee Creek watersheds, significantly higher 

concentrations observed during stormflow at Apalachee Creek 

and Level Creek watersheds, and similar ranges in concentrations observed at the other four watersheds. Total ammonia 

plus organic nitrogen concentrations in stormflow samples 

were significantly higher than concentrations in base-flow 

samples at all the watersheds, and TN concentrations in stormflow samples were significantly higher than concentrations 

in base-flow samples at all but two watersheds. Stormflow 

sample concentrations typically were about twice as high 

0

100

200

300

400

0

20

40

60

80

100

22 23 24 25 26 27

December 2013

Figure 24

Specific conductivity, in microsiemens

per centimeter at 25 degrees Celsius

Streamflow, in cubic feet per second

Specific conductivity

Streamflow

Figure 24. Stormflow response of specific conductance to 

streamflow for USGS station 02205865, Sweetwater Creek at 

Club Drive near Lilburn, Georgia, December 22–27, 2013.
36 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15 Total suspended solids, in milligrams per liter Suspended-sediment concentration, in milligrams per liter Total lead, in micrograms per liter Total zinc, in micrograms per liter

Turbidity, in formazin nephelometric units

2 5 10 20 50 100 200 2 5 10 20 50 100 200

A. Total suspended solids B. Suspended sediment

C. Total lead

Figure 25

D. Total zinc

2

5

10

20

50

100

200

500

1,000

1 5

10

20

50

100

200

500

1,000

2,000

2

5

10

20

50

100

2

5

10

20

1

0.2

0.5

as base-flow sample concentrations. Both Yellow River and 

No Business Creek watersheds have similar TN concentrations 

for base-flow and stormflow samples, and the increase in 

TN concentrations during stormflow observed for Suwannee 

Creek watershed was less than observed at the other watersheds that had significant increases. 

Total phosphorus concentrations typically varied between 

about <0.005 and 1 mg/L as phosphorus (P) (fig. 26F). 

Stormflow TP concentrations were significantly higher than 

base-flow concentrations by about an order of magnitude, 

indicating that the majority of transport occurred during 

storm events. Total phosphorus concentrations were often 

>0.1 mg/L in stormflow samples, which is the threshold 

at which TP concentrations are considered to be able to 

support nuisance levels of algal production in flowing waters 

(U.S. Environmental Protection Agency, 2000a). Dissolved 

phosphorus concentrations typically were less than 0.1 mg/L 

as P and exhibited no particular pattern of differences 

between base-flow and stormflow conditions, though a few 

differences were significant (fig. 26G). A comparison of mean 

DP and TP concentrations indicates that about two-thirds 

to three-quarters of the phosphorus exists in solid form; 

therefore, it is not unexpected that TP concentrations would be 

higher when discharge and suspended-sediment concentrations 

are higher. Richland Creek watershed had the highest mean 

stormflow TP concentration, 0.235 mg/L as P, which may be 

the result of high runoffs (fig. 15) and runoff ratios (fig. 21) 

associated with the watershed having the highest basin slope 

of the 13 watersheds (fig. 5; table 5). Sweetwater Creek had the 

lowest mean stormflow TP concentration of 0.059 mg/L as P.

Total organic carbon (TOC) typically varied between about 

0.5 and 4 mg/L as carbon in base-flow samples and between 

about 2 and 20 mg/L as carbon in stormflow samples (fig. 26H). 

Stormflow TOC concentrations were significantly higher than 

during base flow by about half an order of magnitude, but 

comparisons varied by watershed. Stormflow TOC concentrations were fairly similar among all 13 watersheds, whereas individual watersheds had more distinct base-flow concentrations.

Total suspended solids (TSS) and suspended-sediment 

concentrations (SSC) typically varied between about 

<1 and 5,000 mg/L (figs. 26I and 26J). Total suspended solids 

and SSC concentrations are similar, with the same pattern 

Figure 25. (A) Total suspended solids, (B) suspended sediment, (C) total lead, and (D) total zinc sample concentrations 

versus turbidity for USGS station 02205865, Sweetwater Creek at Club Drive near Lilburn, Georgia.
Surface-Water Quality 37

Interpreting box-and-whisker plots

The box-and-whisker plots in figure 26 illustrate the range and distribution of sample concentrations for each 

watershed and sampling flow condition (Helsel and Hirsch, 1992). The top and bottom of the “box” (rectangle) 

represents the 75th and 25th quartiles of sample concentrations, respectively, and the line within the box represents 

the 50th percentile (median concentration). The “whiskers” (lines extending above and below the box) represent the 

range of sample concentrations within one step above or below the interquartile range represented by the box and are 

referred to as “adjacent” values. A step is defined as 1.5 times the interquartile range (the height of the box). Concentrations between one and two steps from the box in either direction are referred to as “outside” values and are expected 

to occur fewer than one in 100 times for data that follow a normal distribution. Concentrations greater than two steps 

from the box are referred to as either “detached” or “far-out” values. Outside values are plotted as X symbols, and 

detached values are plotted as circles. Concentrations were plotted on a log scale because they exhibited a normal 

distribution in log space. The magnitude of a censored value (its laboratory reporting limit [LRL]; table 2) or an 

estimated value (a concentration above its long-term method detection level [LT-MDL] but below its LRL) was used to 

plot its concentration in the box-and-whisker plots. All sample concentrations were plotted, including values that were 

excluded as outliers for the development of the load estimation models. Significant differences between individual 

watershed base-flow and stormflow sample concentration distributions were determined using the Mann-Whitney 

U test (also called the Wilcox rank-sum test) with a significance level of p-value ≤0.05.

of relative difference between watersheds observed for both 

constituents that is especially apparent in the stormflow 

concentrations. Suspended-sediment concentrations typically 

were slightly higher than TSS concentrations during both 

base-flow and stormflow conditions. Stormflow TSS and 

SSC concentrations are significantly higher than base-flow 

concentrations by about two orders of magnitude, emphasizing 

the relation between discharge and sediment mobilization and 

transport. Richland Creek watershed, which had the highest 

basin slope of any of the 13 watersheds (fig. 5; table 5), had 

the highest median TSS concentration, 194 mg/L (Aulenbach 

and Joiner, 2017; table 4, water-quality constituent statistics). 

The two orders of magnitude increase of TSS concentrations 

from base flow to stormflow compared to the one order of 

magnitude increase for TP concentrations suggests that the 

suspended sediment transported during storm events has a 

disproportionately smaller ratio of TP compared to base-flow 

conditions. Total suspended solids concentrations are about 

three orders of magnitude greater than TP in storm event 

samples, indicating that TP is only a small portion of TSS.

In a previous analysis of the Gwinnett County LTTM 

program watersheds for the period 1998–2003, Landers 

and others (2007) found concentrations (and loads) of TSS 

typically were less than concentrations (and loads) of SSC for 

six watersheds. The comparison of TSS to SSC was reevaluated using data from 12 watersheds for the period 2005–09 

(Joiner and others, 2014), and measured TSS concentrations 

continued to be less than SSC concentrations. This difference is 

more pronounced for samples in which the sediment contains 

more than 25 percent sand (the coarser portion of the sediment).

Total lead (Pb) concentrations typically varied between 

<1 and 10 micrograms per liter (μg/L) in base-flow samples 

and between <1 and 120 μg/L in stormflow samples (fig. 26K). 

Total lead concentrations sometimes exceeded the national 

recommended freshwater aquatic life critera for particulate 

lead of 65 μg/L for acute conditions and 2.5 μg/L for 

chronic conditions (U.S. Environmental Protection Agency, 

2017). Base-flow Pb concentrations had a large range at 

6 of the 13 watersheds, with about one-and-a-half orders of 

magnitude range between the 25th and 75th percentiles of 

concentrations. Stormflow Pb concentrations were about half 

an order of magnitude higher than base-flow concentrations.

Total zinc (Zn) concentrations typically varied between 

<2 and 850 μg/L (fig. 26L). Total zinc concentrations sometimes exceeded the national recommended freshwater aquatic 

life critera for particulate zinc of 120 μg/L for both acute and 

chronic conditions (U.S. Environmental Protection Agency, 

2017). Stormflow Zn concentrations were about one order of 

magnitude higher than base-flow concentrations, but comparisons varied by watershed. Apalachee River, Richland Creek, 

Suwanee Creek, and Crooked Creek watersheds exhibited the 

largest order of magnitude increases between base-flow and 

stormflow Zn concentrations. Alcovy River, Apalachee River, 

Richland Creek, Suwanee Creek, Crooked Creek, and North 

Fork Peachtree Creek watersheds had the highest stormflow 

Zn and Pb concentrations. Some of these high concentrations 

may be related to the high basin slope at Richland Creek 

(fig. 5; table 5) and the high percentage of impervious areas 

for Crooked Creek and North Fork Peachtree Creek (fig. 10; 

table 6).
38 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

E. Total nitrate plus nitrite

Figure 26

F. Total phosphorus

A. Specific conductivity B. Turbidity

C. Total nitrogen D. Total ammonia plus organic nitrogen

Yellow River

Sweetwater Creek

No Business Creek

Big Haynes Creek

Brushy Fork Creek

Alcovy River

Wheeler Creek

Apalachee River

Richland Creek

Suwanee Creek

Crooked Creek

North Fork Peachtree Creek

Level Creek

Yellow River

Sweetwater Creek

No Business Creek

Big Haynes Creek

Brushy Fork Creek

Alcovy River

Wheeler Creek

Apalachee River

Richland Creek

Suwanee Creek

Crooked Creek

North Fork Peachtree Creek

Level Creek

Monitored watersheds, Gwinnett County

x

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Conductance, in microsiemens per centimeter

at 25 degrees Celsius

1,000

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Concentration, in milligrams per liter as P

11 39 76 120 58 102 79 99 1 99 68 61 59 56 57 79 94 93 92 86 99 116 59 102 54 99 11 39 77 118 58 102 115 119 97 121 77 60 58 59 57 103 113 101 95 93 99 130 85 122 57 100

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Concentration, in milligrams per liter as N

11 39 78 106 57 87 96 106 97 102 78 59 56 57 57 74 100 88 77 85 82 109 73 96 57 88 11 39 78 106 57 87 96 106 97 102 78 59 56 57 57 82 100 88 77 85 82 114 73 96 57 88

11 39 78 106 57 87 96 106 97 102 78 59 56 57 57 95 100 88 77 85 82 115 87 105 57 88 10 39 77 105 57 86 95104 96102 78 59 56 58 57 94 99 87 77 84 81 114 88 104 57 87

Figure 26. Base-flow and stormflow sample concentrations for (A) specific conductance, (B) turbidity, (C) total nitrogen, (D) total 

ammonia plus organic nitrogen, (E ) total nitrate plus nitrite, and (F ) total phosphorus at 13 monitored watersheds in Gwinnett County, 

Georgia. Significant difference between base-flow and stormflow sample concentration distributions determined from Mann-Whitney

U test with a p-value of ≤0.05. Data-collection period ranges from 2001 to 2015.
Figure 26—Continued

Yellow River

Sweetwater Creek

No Business Creek

Big Haynes Creek

Brushy Fork Creek

Alcovy River

Wheeler Creek

Apalachee River

Richland Creek

Suwanee Creek

Crooked Creek

North Fork Peachtree Creek

Level Creek

Yellow River

Sweetwater Creek

No Business Creek

Big Haynes Creek

Brushy Fork Creek

Alcovy River

Wheeler Creek

Apalachee River

Richland Creek

Suwanee Creek

Crooked Creek

North Fork Peachtree Creek

Level Creek

Monitored watersheds, Gwinnett County

G. Dissolved phosphorus

J. Suspended sediment

H. Total organic carbon

I. Total suspended solids

K. Total lead L. Total zinc

x

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Concentration, in milligrams per liter as C

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11 39 78 105 57 86 96 105 97 102 75 55 56 58 57 91 98 77 77 84 81 105 89 104 57 87 11 39 33 53 53 84 51 64 35 64 35 57 54 56 55 47 59 80 76 82 79 64 43 61 5584

0 39 15 74 24 77 17 68 16 75 19 23 24 25 24 22 61 75 65 73 74 83 17 60 24 76

11 39 77 106 40 67 94 105 96 102 73 39 38 39 39 78 99 69 60 6911 38 78 105 40 67 96 102 97 102 74 39 38 39 39 79 96 68 56 69 66 115 78 101 38 69 66 115 77 105 38 69

11 39 77 106 57 87 96 107 97 105 76 59 57 58 57 88 101 88 80 85 82 115 88 108 57 88

Figure 26. Base-flow and stormflow sample concentrations for (G) dissolved phosphorus, (H ) total organic carbon, (I) total 

suspended solids, (J) suspended sediment, (K) total lead, and (L) total zinc at 13 monitored watersheds in Gwinnett County. 

Significant difference between base-flow and stormflow sample concentration distributions determined from Mann-Whitney 

U test with a p-value of ≤0.05. Data-collection period ranges from 2001 to 2015.—Continued

Surface-Water Quality 39
40 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

Figure 26—Continued

Yellow River

Sweetwater Creek

No Business Creek

Big Haynes Creek

Brushy Fork Creek

Alcovy River

Wheeler Creek

Apalachee River

Richland Creek

Suwanee Creek

Crooked Creek

North Fork Peachtree Creek

Level Creek

Monitored watersheds, Gwinnett County

M. Total dissolved solids

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11 39 78 106 57 85 96 105 97 102 76 59 56 58 57 77 100 88 76 86 82 115 77 105 57 88

10

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Concentration, in milligrams per liter

Darker color indicates significantly

 higher distribution (p-value ≤0.05)

EXPLANATION

x

x

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30

75th percentile

Upper adjacent

Lower adjacent

Upper outside

Lower outside

Upper detached

Lower detached

Number of samples 

25th percentile

Median

Base

flow

Stormflow

Total dissolved solids (TDS) concentrations represent 

the amount of inorganic and organic constituents that are 

dissolved in water; concentrations in the LTTM program 

watersheds typically varied between 20 and 200 mg/L 

(fig. 26M). The U.S. Environmental Protection Agency has 

established TDS as a secondary drinking water standard with 

a maximum contaminant level of 500 mg/L, because high 

concentrations of TDS can be an indicator of elevated levels 

of other pollutants (U.S. Environmental Protection Agency, 

2011). Seven of the watersheds—Sweetwater Creek, Yellow 

River, No Business Creek, Big Haynes Creek, Suwanee Creek, 

Crooked Creek, and North Fork Peachtree Creek—exhibited 

dilution in TDS concentrations during storm events. Four of 

these watersheds—Yellow River, Suwanee Creek, Crooked 

Creek, and North Fork Peachtree Creek—had the four 

highest percentages of impervious area (fig. 10; table 6). The 

Yellow River watershed had the highest median TDS concentrations of 91 mg/L (Aulenbach and Joiner, 2017; table 4, 

water-quality constituent statistics) of the 13 watersheds. 

Two of the watersheds—Brushy Fork Creek and Richland 

Creek—had significantly higher stormflow TDS. Richland 

Creek watershed also had the highest basin slope of any of the 

13 watersheds (fig. 5; table 5). For the remaining three watersheds, base-flow and stormflow TDS concentrations varied in 

similar, insignificantly different overlapping ranges. Variations 

in TDS are difficult to interpret because TDS comprises all 

the dissolved constituents, each with their own particular 

relations, into a single measure.

Figure 26. Base-flow and stormflow sample concentrations for (M) total dissolved solids at 

13 monitored watersheds in Gwinnett County. Significant difference between base-flow and 

stormflow sample concentration distributions determined from Mann-Whitney U test with a 

p-value of ≤0.05. Data-collection period ranges from 2001 to 2015.—Continued
Water-Quality Seasonality and Long-Term Trends 41

Water-Quality Seasonality and 

Long-Term Trends

Seasonal patterns and long-term trends for 10 waterquality constituents were examined in the streamwater 

samples. These patterns and trends can be the result of both 

natural variability and human activity. The effects of variations in discharge, for which a strong relation was observed 

for most of the water-quality constituents in this study, have 

been removed by performing the trend analyses on residuals 

to flow-only load estimation models such that the data are 

flow adjusted.

Seasonality in Water Quality

Seasonality was determined from the sine and cosine 

model terms (eq. 2) in the flow-only load estimation models 

(Aulenbach and Joiner, 2017; table 3, load estimation regression models). Seasonality terms were significant for 92 of the 

130 models (71 percent; table 10). Seasonality terms were 

included in almost all the models for TOC, TSS, SSC, Pb, and 

Zn. Seasonality terms were significant in slightly more than 

half of the models (7 to 9 of 13 models) for TN, NO3

+NO2, 

TP, and DP. Only three models had seasonality terms for TDS, 

which also had the lowest average seasonal range of any of the 

constituents. The largest seasonal ranges observed were for TP, 

DP, TSS, SSC, and Pb, though the relative importance of the 

seasonality on total load was less for TSS and SSC because of 

their much greater loads. The No Business Creek watershed 

exhibited the highest amount of seasonality for TSS, SSC, 

and Pb. The timing of seasonality varied somewhat by 

constituent. All constituents, except TDS, exhibited minimums 

in the winter and maximums in the summer. Although the 

approach for assessing seasonality herein differed from that 

in Joiner and others (2014), these results are consistent in 

both magnitude and timing for the five constituents (TN, TP, 

TSS, Zn, and TDS) and 12 of the watersheds assessed in that 

report. Joiner and others (2014) found seasonality terms to be 

significant for all regression models fit to stormflow concentration. In the current study, seasonality terms were not included 

for many models. Some of these differences are likely due to 

(1) fitting both base-flow and stormflow samples to a single 

seasonality model, (2) the use of a higher p-value of <0.10 in 

the previous study, and (3) including additional variables in 

this study, such as base flow, which may have more effectively 

modeled seasonality.

Seasonal variations in nutrients—nitrogen, phosphorus, 

and carbon—can be the result of seasonal variations in 

biogeochemical cycling in both the terrestrial and aquatic 

environments as well as the result of seasonal human practices 

such as agriculture and fertilizer application. Seasonal patterns 

in suspended sediment and related particulate constituents can 

be the result of seasonal differences in the types of weather 

systems (storms) and seasonal variability in human practices. 

Higher suspended-sediment loads observed in the summer in 

this study might be related to intense summer convective thunderstorms that can result in greater storm-related erosion due 

to higher peak streamflows and increased transport of surficial 

sediments. Increased summer suspended-sediment loads may 

also be related to seasonal land-disturbing activities, which 

can result in increased washoff of sediments.

Table 10. Summary of seasonal variability in 10 water-quality constituents at the 13 LTTM program watersheds in 

Gwinnett County, Georgia, based on the seasonality terms in the flow-only load estimation models.

Constituent

Number of 

models with 

seasonal terms

Average

seasonal range

(± natural log

pounds per day)

Typical

minimum month(s)

Typical

maximum month(s)

Total nitrogen 9 0.16 November–December June

Total nitrate plus nitrite 9 0.15 October-December May–July

Total phosphorus 7 0.38 January–February July–September

Dissolved phosphorus 7 0.34 February-April August–September

Total organic carbon 12 0.17 January–February July–August

Total suspended solids 12 0.39 December-January June-July

Suspended sediments 11 0.41 December-January June-July

Total lead 11 0.45 January–February July–August

Total zinc 11 0.25 November–February May-August

Total dissolved solids 3 0.09 Variable Variable
42 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

Long-Term Trends in Water Quality

Long-term trends were determined from a linear fit of 

the relation of residuals of the flow-only load estimation 

models (with time terms excluded) versus time. These trends 

were determined from the same water-quality datasets used 

for estimating loads, with outliers removed. Significant 

trends in concentrations (p-value <0.05) were identified in 

67 of the 130 constituent-watershed combinations (52 percent 

of combinations; table 11) for WYs 2001–15 (15 years). 

There were 20 postitive trends (30 percent) and 47 negative 

trends (70 percent). Constituents exhibiting predominantly 

positive trends included TOC (5 watersheds) and TDS 

(9 of the 10 trends were positive). Constituents exhibiting 

Table 11. Water-quality constituent trends and statistical significance for sample concentrations in 13 monitored watersheds in 

Gwinnett County, Georgia, for water years 2001–15.

[Bold values are significant (p-value ≤0.05)]

Site

number Watershed Total nitrogen Total nitrate plus nitrite Total phosphorus Dissolved phosphorus Total organic carbon

Slope p–value Slope p–value Slope p–value Slope p–value Slope p–value

02205865 Sweetwater Creek1 0.012 0.774 –0.026 0.391 –0.17 0.015 0.040 0.496 –0.026 0.261

02207120 Yellow River –0.012 0.080 –0.030 0.000 –0.025 0.052 0.011 0.336 0.008 0.385

02207185 No Business Creek –0.042 0.000 –0.083 0.000 –0.020 0.161 0.009 0.475 0.018 0.001

02207385 Big Haynes Creek –0.017 0.003 –0.018 0.001 –0.098 0.000 –0.047 0.001 0.022 0.022

02207400 Brushy Fork Creek –0.011 0.141 –0.010 0.088 –0.041 0.000 0.018 0.099 0.033 0.000

02208150 Alcovy River 0.009 0.211 –0.007 0.150 –0.047 0.004 0.020 0.162 0.028 0.004

02217274 Wheeler Creek 0.010 0.205 0.008 0.155 –0.058 0.001 0.004 0.778 0.013 0.483

02218565 Apalachee River 0.006 0.451 –0.007 0.210 –0.010 0.555 0.038 0.006 0.013 0.066

02334480 Richland Creek 0.005 0.589 0.013 0.019 –0.067 0.002 0.028 0.011 0.007 0.391

02334578 Level Creek 0.002 0.806 –0.010 0.153 –0.049 0.008 –0.005 0.703 –0.008 0.394

02334885 Suwanee Creek 0.025 0.001 0.048 0.000 –0.103 0.000 0.007 0.500 0.000 0.988

02335350 Crooked Creek –0.013 0.215 –0.010 0.139 –0.061 0.000 0.022 0.031 0.031 0.005

02336030 North Fork Peachtree 

Creek

0.005 0.541 –0.019 0.005 –0.053 0.006 –0.005 0.779 –0.013 0.249

Site

number Watershed

Total

suspended solids Suspended sediment Total lead Total zinc Totaldissolved solids

Slope p–value Slope p–value Slope p–value Slope p–value Slope p–value

02205865 Sweetwater Creek1 0.041 0.458 0.113 0.068 0.089 0.587 0.044 0.166 –0.021 0.161

02207120 Yellow River –0.017 0.038 0.022 0.185 –0.029 0.007 –0.021 0.009 –0.001 0.881

02207185 No Business Creek –0.037 0.013 –0.063 0.002 –0.070 0.003 –0.028 0.145 –0.019 0.000

02207385 Big Haynes Creek –0.031 0.016 –0.017 0.506 –0.011 0.485 –0.008 0.511 0.013 0.003

02207400 Brushy Fork Creek –0.031 0.009 –0.028 0.086 –0.031 0.002 0.007 0.466 0.028 0.000

02208150 Alcovy River –0.042 0.000 –0.041 0.028 –0.024 0.083 0.020 0.180 0.021 0.000

02217274 Wheeler Creek –0.093 0.000 –0.102 0.000 –0.100 0.000 –0.042 0.007 0.023 0.000

02218565 Apalachee River –0.022 0.174 –0.013 0.500 –0.036 0.239 –0.024 0.145 0.025 0.000

02334480 Richland Creek –0.066 0.001 –0.053 0.020 –0.075 0.000 –0.039 0.025 0.009 0.141

02334578 Level Creek –0.021 0.229 –0.050 0.022 –0.025 0.177 –0.027 0.148 0.022 0.000

02334885 Suwanee Creek –0.066 0.000 –0.018 0.281 –0.065 0.000 –0.021 0.047 0.022 0.000

02335350 Crooked Creek –0.026 0.052 –0.041 0.025 –0.042 0.000 –0.011 0.148 0.018 0.001

02336030 North Fork Peachtree 

Creek

–0.050 0.002 –0.068 0.001 –0.067 0.001 –0.056 0.001 0.018 0.001

1

Sweetwater Creek trends for period water years 2011–15.
Water-Quality Seasonality and Long-Term Trends 43

only negative trends included TP (10 watersheds), TSS 

(9 watersheds), SSC (7 watersheds), Pb (8 watersheds), and Zn 

(5 watersheds). The other constituents exhibited both positive 

and negative trends: TN (1 positive, 2 negative), NO3+NO2

(2 positive, 4 negative), and DP (3 positive, 1 negative). 

Seven of the watersheds had four or more negative 

trends (Yellow River, No Business Creek, Big Haynes 

Creek, Wheeler Creek, Richland Creek, Suwanee Creek, 

and North Fork Peachtree Creek). Sweetwater Creek, 

Apalachee River, and Level Creek watersheds had the fewest 

trends, one, two, and three trends, respectively. Sweetwater 

Creek watershed trends were based only on 5 1/2 years of 

monitoring, which makes it more difficult to detect long-term 

trends. Suwanee Creek and Crooked Creek watersheds 

had the most positive trends, three each. Trends observed 

herein were fairly consistent with trends identified for the 

period beginning as early as WY 1997 (for watersheds with 

monitoring data available) through WY 2011 in Joiner and 

others (2014) for the five constituents (TN, TP, TSS, Zn, and 

TDS) and 12 of the watersheds assessed in that report. Of 

the 60 constituent-watershed combinations evaluated in that 

report, 45 of the trends were the same (75 percent), and only 

one trend changed to the opposite direction (the TSS trend at 

North Fork Peachtree Creek watershed changed from positive 

to negative). Some differences in trends are to be expected 

when evaluating different time periods. Some differences were 

also likely because the earlier report used a higher p-value of 

<0.1 in determining the significance of the trends.

The Suwanee Creek watershed had the largest positive 

trends in TN (fig. 27; table 11) and NO3+NO2 (fig. 28) while 

the No Business Creek watershed had the largest negative 

trends of the 13 study watersheds. These trends did not 

appear linear, with the trends at Suwanee Creek watershed 

increasing more in the later part of the time period and most 

of the declines at No Business Creek watershed occurring 

before 2007 when trends began leveling off. Yellow River, 

Big Haynes Creek, and North Fork Peachtree Creek watersheds also exhibited a larger negative trend in NO3

+NO2.

Trends in TP were consistently negative across the 

county, with the largest rates of decline observed in the 

Sweetwater Creek, Big Haynes Creek, and Suwanee Creek 

watersheds (fig. 29; table 11). Dissolved phosphorus had fewer 

trends, with positive trends observed for Apalachee River, 

Richland Creek, and Crooked Creek watersheds (fig. 30). 

Only the Big Haynes Creek watershed exhibited a negative 

trend in DP. The lack of consistency in trends between TP and 

DP indicates that the declines observed in TP are from the 

particulate portion of the constituent.

Five of the 13 watersheds exhibited positive trends in 

TOC (fig. 31; table 11). The largest increases were observed 

for the Brushy Fork Creek, Alcovy River, and Crooked 

Creek watersheds. 

Both TSS (fig. 32; table 11) and SSC (fig. 33) were 

consistent in having only negative trends. The negative trends 

in suspended sediment were consistent with overall negative 

trends in several of the other predominantly particulate 

constituents—TP, Pb, and Zn. The negative trends in TSS were 

largest for Wheeler Creek, Richland Creek, Suwanee Creek, 

and North Fork Peachtree Creek watersheds. The negative 

trends in SSC were largest for No Business Creek, Wheeler 

Creek, and North Fork Peachtree Creek watersheds. The 

trends between TSS and SSC were somewhat inconsistent. For 

example, Suwanee Creek had one of the larger negative trends 

for TSS but had no significant trend for SSC (table 11).

The large negative trends in TSS and SSC at Wheeler 

Creek are in contrast with the highest increases in impervious areas observed at any of these watersheds during the 

timeframe of the trend analysis (fig. 12). Of the other four 

watersheds that exhibited large increases in impervious 

areas (Sweetwater Creek, Yellow River, Alcovy Creek, 

and Apalachee River), Yellow River had a negative trend 

in TSS, and Alcovy Creek had negative trends in TSS and 

SSC. Gwinnett County has long implemented detention 

pond BMP requirements to reduce and mitigate the effects 

of impervious areas on sediment transport. The fact that 

suspended sediment is either staying the same or declining 

at the watersheds that are experiencing the highest increases 

in impervious areas suggests that the BMP implementation 

within these watersheds during this period is maintaining 

(within the ability to detect trends) and, in some cases, 

improving water quality. It is also still possible that climatic 

patterns may be playing a role in the TSS trends, even though 

no corresponding significant trends were found for annual 

runoff. Suspended-sediment concentrations and transport are 

controlled particularly by the magnitude of storm runoff rather 

than the amount of annual runoff. Any trends in the variations 

of the frequency, magnitude, and duration of storm runoff have 

not been assessed in this study.

Eight of the watersheds exhibited negative trends in 

Pb (fig. 34; table 11), and five of the watersheds exhibited 

negative trends in Zn (fig. 35). The largest decreases in Pb 

and Zn were observed for the Wheeler Creek and North 

Fork Peachtree Creek watersheds, respectively. Other larger 

decreases in Pb were observed for No Business Creek, 

Richland Creek, Suwanee Creek and North Fork Peachtree 

Creek watersheds. Other larger decreases in Zn were observed 

for Wheeler Creek and Richland Creek watersheds.

Nine of the 13 watersheds exhibited positive trends in 

TDS, whereas one watershed (No Business Creek) had a 

negative trend (fig. 36; table 11). The Brushy Fork Creek 

watershed had the highest positive trend, while the other 

watersheds with positive trends had similar increases. 

Trends in TDS were not detected for the Sweetwater Creek, 

Yellow River, and Richland Creek watersheds.
44 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

02205865 Sweetwater Creek (p-value=0.774)

Year

Logarithm of total nitrogen residuals, in milligrams per liter as nitrogen

02207120 Yellow River (p-value=0.080)

02207185 No Business Creek (p-value=0.000) 02207358 Big Haynes Creek (p-value=0.003)

02207400 Brushy Fork Creek (p-value=0.141) 02208150 Alcovy River (p-value=0.211)

02217274 Wheeler Creek (p-value=0.205)

Figure 27

02218565 Apalachee River (p-value=0.451)

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

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0

0.5

1.0

1.5

2000 2005 2010 2015 2000 2005 2010 2015

–1.5

–1.0

–0.5

0.0

0.5

1.0

1.5
Water-Quality Seasonality and Long-Term Trends 45

Figure 27—Continued

Logarithm of total nitrogen residuals, in milligrams per liter as nitrogen

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

Year

Year

2000 2005 2010 2015

2000 2005 2010 2015

02334480 Richland Creek (p-value=0.589) 02334578 Level Creek (p-value=0.806)

02334885 Suwanee Creek (p-value=0.001) 02335350 Crooked Creek (p-value=0.215)

02336030 North Fork Peachtree Creek

(p-value=0.541)

Figure 27 (pages 44 and 45). Total nitrogen residuals to flow-only load estimation model (excluding time terms) 

versus time for 13 monitored watersheds in Gwinnett County, Georgia. Line represents linear fit of relation between 

residuals and time. 
46 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

02205865 Sweetwater Creek (p-value=0.391)

Year

02207120 Yellow River (p-value=0.000)

02207185 No Business Creek (p-value=0.000) 02207358 Big Haynes Creek (p-value=0.001)

02207400 Brushy Fork Creek (p-value=0.088) 02208150 Alcovy River (p-value=0.150)

02217274 Wheeler Creek (p-value=0.155)

Figure 28

02218565 Apalachee River (p-value=0.210)

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

2000 2005 2010 2015 2000 2005 2010 2015

–1.5

–1.0

–0.5

0.0

0.5

1.0

1.5

Logarithm of total nitrite plus nitrate residuals, in milligrams per liter as nitrogen
Water-Quality Seasonality and Long-Term Trends 47

Figure 28—Continued

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

–1.5

–1.0

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0

0.5

1.0

1.5

Year

Year

2000 2005 2010 2015

2000 2005 2010 2015

02334480 Richland Creek (p-value=0.019) 02334578 Level Creek (p-value=0.153)

02334885 Suwanee Creek (p-value=0.000) 02335350 Crooked Creek (p-value=0.139)

02336030 North Fork Peachtree Creek

(p-value=0.005)

Logarithm of total nitrite plus nitrate residuals, in milligrams per liter as nitrogen

Figure 28 (pages 46 and 47). Total nitrate plus nitrite residuals to flow-only load estimation model (excluding time 

terms) versus time for 13 monitored watersheds in Gwinnett County, Georgia. Line represents linear fit of relation between 

residuals and time.
48 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

02205865 Sweetwater Creek (p-value=0.015)

Year

02207120 Yellow River (p-value=0.052)

02207185 No Business Creek (p-value=0.161) 02207358 Big Haynes Creek (p-value=0.000)

02207400 Brushy Fork Creek (p-value=0.000) 02208150 Alcovy River (p-value=0.004)

02217274 Wheeler Creek (p-value=0.001)

Figure 29

02218565 Apalachee River (p-value=0.555)

2000 2005 2010 2015 2000 2005 2010 2015

–4

–2

–3

–1

0

2

4

1

3

–4

–2

–3

–1

0

2

4

1

3

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–1

0

2

4

1

3

–4

–2

–3

–1

0

2

4

1

3

Logarithm of total phosphorus residuals, in milligrams per liter as phosphorus
Water-Quality Seasonality and Long-Term Trends 49

Figure 29—Continued

Year

Year

2000 2005 2010 2015

2000 2005 2010 2015

02334480 Richland Creek (p-value=0.002) 02334578 Level Creek (p-value=0.008)

02334885 Suwanee Creek (p-value=0.000) 02335350 Crooked Creek (p-value=0.000)

02336030 North Fork Peachtree Creek

(p-value=0.006)

–4

–2

–3

–1

0

2

4

1

3

–4

–2

–3

–1

0

2

4

1

3

–4

–2

–3

–1

0

2

4

1

3

Logarithm of total phosphorus residuals, in milligrams per liter as phosphorus

Figure 29 (pages 48 and 49). Total phosphorus residuals to flow-only load estimation model (excluding time terms) 

versus time for 13 monitored watersheds in Gwinnett County, Georgia. Line represents linear fit of relation between 

residuals and time.
50 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

02205865 Sweetwater Creek (p-value=0.496)

Year

02207120 Yellow River (p-value=0.336)

02207185 No Business Creek (p-value=0.475) 02207358 Big Haynes Creek (p-value=0.001)

02207400 Brushy Fork Creek (p-value=0.099) 02208150 Alcovy River (p-value=0.162)

02217274 Wheeler Creek (p-value=0.778)

Figure 30

02218565 Apalachee River (p-value=0.006)

2000 2005 2010 2015 2000 2005 2010 2015

–2

–3

–1

0

2

4

1

3

–2

–3

–1

0

2

4

1

3

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0

2

4

1

3

–2

–3

–1

0

2

4

1

3

Logarithm of dissolved phosphorus residuals, in milligrams per liter as phosphorus
Water-Quality Seasonality and Long-Term Trends 51

Figure 30—Continued

Year

Year

2000 2005 2010 2015

2000 2005 2010 2015

02334480 Richland Creek (p-value=0.011) 02334578 Level Creek (p-value=0.703)

02334885 Suwanee Creek (p-value=0.500) 02335350 Crooked Creek (p-value=0.031)

02336030 North Fork Peachtree Creek

(p-value=0.779)

–2

–3

–1

0

2

4

1

3

–2

–3

–1

0

2

4

1

3

–2

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–1

0

2

4

1

3

Logarithm of dissolved phosphorus residuals, in milligrams per liter as phosphorus

Figure 30 (pages 50 and 51). Dissolved phosphorus residuals to flow-only load estimation model (excluding time terms) 

versus time for 13 monitored watersheds in Gwinnett County, Georgia. Line represents linear fit of relation between 

residuals and time.
52 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

02205865 Sweetwater Creek (p-value=0.261)

Year

02207185 No Business Creek (p-value=0.001) 02207358 Big Haynes Creek (p-value=0.022)

02207400 Brushy Fork Creek (p-value=0.000) 02208150 Alcovy River (p-value=0.004)

02217274 Wheeler Creek (p-value=0.483)

Figure 31

02218565 Apalachee River (p-value=0.066)

2000 2005 2010 2015 2000 2005 2010 2015

–2.0

–1.5

–0.5

0.5

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0

2.0

1.5

1.0

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0

2.0

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1.0

Logarithm of total organic carbon residuals, in milligrams per liter as carbon

02207120 Yellow River (p-value=0.385)
Water-Quality Seasonality and Long-Term Trends 53

Figure 31—Continued

Year

Year

2000 2005 2010 2015

2000 2005 2010 2015

02334480 Richland Creek (p-value=0.391) 02334578 Level Creek (p-value=0.394)

02334885 Suwanee Creek (p-value=0.988) 02335350 Crooked Creek (p-value=0.005)

02336030 North Fork Peachtree Creek

(p-value=0.249)

–2.0

–1.5

–0.5

0.5

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0

0

0

2.0

1.5

1.0

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2.0

1.5

1.0

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–1.5

–0.5

0.5

–1.0

2.0

1.5

1.0

Logarithm of total organic carbon residuals, in milligrams per liter as carbon

Figure 31 (pages 52 and 53). Total organic carbon residuals to flow-only load estimation model (excluding time terms) 

versus time for 13 monitored watersheds in Gwinnett County, Georgia. Line represents linear fit of relation between 

residuals and time. 
54 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

02205865 Sweetwater Creek (p-value=0.458)

Year

02207120 Yellow River (p-value=0.038)

02207185 No Business Creek (p-value=0.013) 02207358 Big Haynes Creek (p-value=0.016)

02207400 Brushy Fork Creek (p-value=0.009) 02208150 Alcovy River (p-value=0.000)

02217274 Wheeler Creek (p-value=0.000)

Figure 32

02218565 Apalachee River (p-value=0.174)

2000 2005 2010 2015 2000 2005 2010 2015

–4

–2

–3

–1

0

2

4

1

3

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1

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0

2

4

1

3

Logarithm of total suspended solids residuals, in milligrams per liter
Water-Quality Seasonality and Long-Term Trends 55

Figure 32—Continued

Year

Year

2000 2005 2010 2015

2000 2005 2010 2015

02334480 Richland Creek (p-value=0.001) 02334578 Level Creek (p-value=0.229)

02334885 Suwanee Creek (p-value=0.000) 02335350 Crooked Creek (p-value=0.052)

02336030 North Fork Peachtree Creek

(p-value=0.002)

–4

–2

–3

–1

0

2

4

1

3

–4

–2

–3

–1

0

2

4

1

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–1

0

2

4

1

3

Logarithm of total suspended solids residuals, in milligrams per liter

Figure 32 (pages 54 and 55). Total suspended-solid residuals to flow-only load estimation model (excluding time terms) 

versus time for 13 monitored watersheds in Gwinnett County, Georgia. Line represents linear fit of relation between 

residuals and time. 
56 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

02205865 Sweetwater Creek (p-value=0.068)

Year

02207120 Yellow River (p-value=0.185)

02207185 No Business Creek (p-value=0.002) 02207358 Big Haynes Creek (p-value=0.506)

02207400 Brushy Fork Creek (p-value=0.086) 02208150 Alcovy River (p-value=0.028)

02217274 Wheeler Creek (p-value=0.000)

Figure 33

02218565 Apalachee River (p-value=0.500)

2000 2005 2010 2015 2000 2005 2010 2015

–4

–2

–3

–1

0

2

4

1

3

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0

2

4

1

3

Logarithm of suspended-sediment residuals, in milligrams per liter
Water-Quality Seasonality and Long-Term Trends 57

Figure 33—Continued

Year

Year

2000 2005 2010 2015

2000 2005 2010 2015

02334480 Richland Creek (p-value=0.020) 02334578 Level Creek (p-value=0.022)

02334885 Suwanee Creek (p-value=0.281) 02335350 Crooked Creek (p-value=0.025)

02336030 North Fork Peachtree Creek

(p-value=0.001)

–4

–2

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–1

0

2

4

1

3

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–2

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–1

0

2

4

1

3

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0

2

4

1

3

Logarithm of suspended-sediment residuals, in milligrams per liter

Figure 33 (pages 56 and 57). Suspended-sediment residuals to flow-only load estimation model (excluding time terms) 

versus time for 13 monitored watersheds in Gwinnett County, Georgia. Line represents linear fit of relation between 

residuals and time.
58 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

02205865 Sweetwater Creek (p-value=0.587)

Year

02207120 Yellow River (p-value=0.007)

02207185 No Business Creek (p-value=0.003) 02207358 Big Haynes Creek (p-value=0.485)

02207400 Brushy Fork Creek (p-value=0.002) 02208150 Alcovy River (p-value=0.083)

02217274 Wheeler Creek (p-value=0.000)

Figure 34

02218565 Apalachee River (p-value=0.239)

2000 2005 2010 2015 2000 2005 2010 2015

–4

–2

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–1

0

2

4

1

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1

3

Logarithm of total lead residuals, in micrograms per liter

–8

–4

–6

–2

0

4

2
Water-Quality Seasonality and Long-Term Trends 59

Figure 34—Continued

Year

Year

2000 2005 2010 2015

2000 2005 2010 2015

02334480 Richland Creek (p-value=0.000) 02334578 Level Creek (p-value=0.177)

02334885 Suwanee Creek (p-value=0.000) 02335350 Crooked Creek (p-value=0.000)

02336030 North Fork Peachtree Creek

(p-value=0.001)

–4

–2

–3

–1

0

2

4

1

3

–4

–2

–3

–1

0

2

4

1

3

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0

2

4

1

3

Logarithm of total lead residuals, in micrograms per liter

Figure 34 (pages 58 and 59). Total lead residuals to flow-only load estimation model (excluding time terms) versus 

time for 13 monitored watersheds in Gwinnett County, Georgia. Line represents linear fit of relation between 

residuals and time.
60 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

02205865 Sweetwater Creek (p-value=0.166)

Year

02207120 Yellow River (p-value=0.009)

02207185 No Business Creek (p-value=0.145) 02207358 Big Haynes Creek (p-value=0.511)

02207400 Brushy Fork Creek (p-value=0.466) 02208150 Alcovy River (p-value=0.180)

02217274 Wheeler Creek (p-value=0.007)

Figure 35

02218565 Apalachee River (p-value=0.145)

2000 2005 2010 2015 2000 2005 2010 2015

–5

–4

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–1

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0

4

5

2

3

1

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2

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1

Logarithm of total zinc residuals, in micrograms per liter
Water-Quality Seasonality and Long-Term Trends 61

Figure 35—Continued

Year

Year

2000 2005 2010 2015

2000 2005 2010 2015

02334480 Richland Creek (p-value=0.025) 02334578 Level Creek (p-value=0.148)

02334885 Suwanee Creek (p-value=0.047) 02335350 Crooked Creek (p-value=0.148)

02336030 North Fork Peachtree Creek

(p-value=0.001)

–5

–4

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–1

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0

4

5

2

3

1

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0

4

5

2

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1

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–1

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0

4

5

2

3

1

Logarithm of total zinc residuals, in micrograms per liter

Figure 35 (pages 60 and 61). Total zinc residuals to flow-only load estimation model (excluding time terms) versus 

time for 13 monitored watersheds in Gwinnett County, Georgia. Line represents linear fit of relation between 

residuals and time.
62 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

02205865 Sweetwater Creek (p-value=0.161)

Year

02207120 Yellow River (p-value=0.881)

02207185 No Business Creek (p-value=0.000) 02207358 Big Haynes Creek (p-value=0.003)

02207400 Brushy Fork Creek (p-value=0.000) 02208150 Alcovy River (p-value=0.000)

02217274 Wheeler Creek (p-value=0.000)

Figure 36

02218565 Apalachee River (p-value=0.000)

–1.5

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0

0.5

1.0

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0

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2000 2005 2010 2015 2000 2005 2010 2015

–1.5

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–0.5

0.0

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1.0

1.5

Logarithm of total dissolved solids residuals, in milligrams per liter
Water-Quality Seasonality and Long-Term Trends 63

Figure 36—Continued

–1.5

–1.0

–0.5

0

0.5

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1.5

Year

Year

2000 2005 2010 2015

2000 2005 2010 2015

02334480 Richland Creek (p-value=0.141) 02334578 Level Creek (p-value=0.000)

02334885 Suwanee Creek (p-value=0.000) 02335350 Crooked Creek (p-value=0.001)

02336030 North Fork Peachtree Creek

(p-value=0.001)

Logarithm of total dissolved solids residuals, in milligrams per liter

Figure 36 (pages 62 and 63). Total dissolved solids residuals to flow-only load estimation model (excluding time terms) 

versus time for 13 monitored watersheds in Gwinnett County, Georgia. Line represents linear fit of relation between 

residuals and time.
64 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

Constituent Loads and Yields

Streamwater constituent load is the mass of chemical 

solutes or sediment transported at a point in a stream during a 

set period of time. The load estimation procedure essentially 

flow weights variations in concentrations during the estimation 

period, thereby integrating the effects of all processes within 

the watershed that temporally affect water quality. Yields are 

defined as loads per unit area and are used to allow easier 

comparison of the magnitude of loads among watersheds 

with different drainage areas.

Load Models

A total of 240 regression models were fit for estimating 

loads. A summary of each model is contained in Aulenbach 

and Joiner (2017; table 3, load estimation regression models) 

and includes information about the calibration dataset used, 

model statistics, and model parameter estimates. The coefficient of determination, R2

, model statistic can be used to assess 

the amount of variance in the data that the model explains, 

with the R2 indicating the fractional amount of variance 

explained. Note that the R2s are reported for models fit to loads 

and are inflated relative to models that are fit to concentrations, 

because stream discharge is contained in both sides of load 

equations resulting in an increase in variance explained. There 

were 20 constituent-watershed combinations where turbidity 

terms were not significant and no flow-turbidity model was fit. 

This was typically the situation for the dissolved constituents 

DP and TDS because turbidity is a surrogate for particulates. 

The model calibration datasets averaged about 124 samples. 

About 10 percent of the sample concentrations were censored, 

indicating that the concentrations were below the analytical 

detection limits. Overall, 246 outliers were removed from 

the calibration datasets, 1.5 percent of the total number of 

concentrations.

The terms included in the models can be informative in 

understanding which variables were useful in explaining the 

variations in the sample concentrations. The storm intercept 

term was significant in 43 percent of the models, indicating 

that there was often a different intercept between base-flow 

and stormflow concentrations. The discharge-squared, 

discharge during base-flow conditions, and base-flow terms 

were each significant in about one-third of the models. The 

turbidity terms in the flow-turbidity models were about 

equally split between fitting the model for all flow conditions 

or just the stormflow condition. Generally only one of the 

turbidity terms was significant with the exception of five 

models. Seasonal terms were included in 68 percent of the 

models. Long-term time terms were included in 60 percent of 

the models for the linear term and in 33 percent of the models 

for the squared term. The time trend terms were reasonably 

consistent in significance and in direction as the long-term 

trends identified in the model residuals presented above.

Program Area Loads and Trends

Constituent loads were summarized for the entire 

monitoring study area for the period WYs 2003–15 (table 12). 

These loads are dominated by the three largest watersheds 

in the study area (table 5), Yellow River, Alcovy River, and 

Suwanee Creek, which made up 78.1 percent of the drainage 

area. The annual loads had a large range in magnitudes. The 

most important factor related to the variability in annual 

loads for all 10 constituents is the amount of annual runoff 

(fig. 37). Annual runoff and all loads were lowest in WY 2012 

and highest in WY 2003. Annual average concentrations 

were calculated in order to normalize the loads for the 

amount of annual runoff and determine how concentrations 

change temporally. These concentrations were calculated 

by dividing the annual load by the annual runoff (fig. 38). 

For constituents where concentrations become more dilute 

at higher streamflow—TN, NO3+NO2, and TDS—higher 

annual average concentrations were observed for years with 

lower annual runoff. Total phosphorus, TOC, TSS, SSC, Pb, 

and Zn concentrations increased with higher streamflow, and 

annual average concentrations were higher for years with 

higher annual runoff. Annual average DP concentrations did 

not show any pattern associated with annual runoff.
Constituent Loads and Yields 65

Table 12. Runoff and water-quality constituent load averages, annual minimum, annual maximum, trends, and trend statistical 

significance for water years 2003–15 for the LTTM program study area, Gwinnett County, Georgia.

[Bold values are significant (p-value ≤0.05)]

Variable Units Average Annual

minimum

Annual

maximum

Trend

(per year) p-value

Runoff Inches 21.9 11.1 38.7 –0.96 0.139

Total nitrogen Pounds per day nitrogen 4,240 2,340 7,760 –220 0.049

Total nitrate plus nitrite Pounds per day nitrogen 2,550 1,700 3,950 –120 0.005

Total phosphorus Pounds per day phosphorus 187 55.1 552 –24 0.016

Dissolved phosphorus Pounds per day phosphorus 35.3 19.9 56.7 –0.87 0.369

Total organic carbon Pounds per day carbon 9,370 4,270 18,700 –490 0.149

Total suspended solids Pounds per day 420,000 95,100 1,320,000 –57,000 0.028

Suspended sediment Pounds per day 742,000 185,000 2,000,000 –87,000 0.040

Total lead Pounds per day 15.7 3.34 55.0 –2.1 0.024

Total zinc Pounds per day 81.2 26.8 197 –8.3 0.030

Total dissolved solids Pounds per day 200,000 123,000 303,000 –4,900 0.289
66 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

0

2,000

4,000

6,000

8,000

10,000

0

10

20

30

40

Annual load, in pounds per day

Water year

0

1,000

2,000

3,000

4,000

5,000

0

10

20

30

40

Annual runoff, in inches

Total nitrogen

Fiugre 37

Total nitrate plus nitrite

Annual load, in pounds per day

Annual runoff, in inches

0

200

400

600

800

0

10

20

30

40

Total phosphorus Dissolved phosphorus

Total organic carbon

0

50

100

150

0

10

20

30

40

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Water year

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

0

5,000

10,000

15,000

20,000

0

10

20

30

40

Load

Runoff

EXPLANATION

Upper 95 percent

Lower 95 percent

Confidence interval

Figure 37. Annual loads and annual runoff for water years 2003–15 for the LTTM program study area in 

Gwinnett County, Georgia. 
Constituent Loads and Yields 67

Fiugre 37—Continued

Water year

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Water year

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Annual load, in pounds per day

Annual runoff, in inches

Total suspended solids Suspended sediment

Annual load, in pounds per day

Annual runoff, in inches

Total lead Total zinc

Total dissolved solids

0

500,000

1,000,000

1,500,000

2,000,000

0

10

20

30

40

0

1,000,000

2,000,000

3,000,000

4,000,000

0

10

20

30

40

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50

100

150

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250

0

10

20

30

40

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100,000

200,000

300,000

400,000

0

10

20

30

40

0

20

40

60

80

0

10

20

30

40

Load

Runoff

EXPLANATION

Upper 95 percent

Lower 95 percent

Confidence interval

Figure 37. Annual loads and annual runoff for water years 2003–15 for the LTTM program study area in 

Gwinnett County, Georgia.—Continued
68 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

Water year

Total nitrogen

Fiugre 38

Total nitrate plus nitrite

Total phosphorus Dissolved phosphorus

Total organic carbon

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Water year

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

0

Annual average concentration, in milligrams per liter

Annual runoff, in inches

Annual average concentration, in milligrams per liter

Annual runoff, in inches

0

2

4

6

0

10

20

30

40

0

1

2

3

4

5

0

10

20

30

40

0

0.01

0.02

0.03

0.04

0.05

10

20

30

40

0

0.01

0.02

0.03

0.04

0.05

0

10

20

30

40

5

10

15

0

10

20

30

40

Runoff

Figure 38. Annual average concentrations and annual runoff for water years 2003–15 for the LTTM program study 

area in Gwinnett County, Georgia. 
Constituent Loads and Yields 69

Fiugre 38—Continued

Water year

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Water year

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Total suspended solids Suspended sediment

Total lead Total zinc

Annual runoff, in inches

Annual runoff, in inches

 Annual average concentration,

in micrograms per liter

Annual average concentration,

in milligrams per liter

 Annual average concentration,

in micrograms per liter

Annual average concentration,

in milligrams per liter

Annual average concentration,

in milligrams per liter

0

200

400

600

800

1,000

0

10

20

30

40

0

500

1,000

1,500

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300

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Runoff

Total dissolved solids

Figure 38. Annual average concentrations and annual runoff for water years 2003–15 for the LTTM program study 

area in Gwinnett County, Georgia.—Continued
70 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

Dry Average Wet

0

400,000

800,000

1,200,000

1,600,000

0

10

20

30

40

Average suspended-sediment load, in pounds per day

Average annual runoff, in inches

Climatic condition

Total suspended solids

Suspended-sediment

 concentraton

Runoff

EXPLANATION

Figure 39

It is not unusual for a few of the largest storms in a year 

to transport the majority of the annual sediment load and is the 

result of the nearly two orders of magnitude increase in sediment concentrations between base flow and average stormflow 

observed at the 13 watersheds (fig. 26I and 26J ). During years 

with wet climatic conditions, runoff within the LTTM program 

study area was 61.5 percent higher than during years with an 

average climatic condition, while average TSS and SSC loads 

were 156 and 120 percent higher than average, respectively 

(fig. 39). Conversely, during drier years, little sediment may 

be transported. During years with dry climatic conditions, 

average annual runoff was 39.8 percent below average, while 

average TSS and SSC loads were 66.1 and 69.9 percent below 

average, respectively. There are fewer storms during drier 

years, and the storms tend to have low peak flows because 

base-flow discharges are also low, which then results in less 

sediment transported than might be expected on the basis of 

annual runoff alone.

Due to the strong relation between loads and runoff, 

trends in runoff can easily result in trends in loads, hence 

the reason for adopting the approach of identifying trends in 

concentrations herein where it is easy to remove the effect of 

variations in streamflow. Still, it is informative to examine 

trends in load to illustrate how these trends affect constituent 

transport. Annual runoff exhibited an overall decline during 

WYs 2003–15, the period for which loads were estimated, but 

the trend of this decline was not significant (p-value=0.139; 

table 12). Significant negative trends in loads were detected 

from linear trend lines fit between annual loads and time for 

7 of the 10 constituents, all but DP, TOC, and TDS. These 

trends are readily discernable in the average annual concentration plots for TP, TSS, SSC, Pb, and Zn (figs. 29 and 32–35) 

because concentrations were lower later in the years with 

similar annual runoff. These trends in loads were fairly 

consistent with the trends identified in the load model residual 

concentrations for the larger watersheds. The insignificant 

decrease in runoff may still be having some effect on the 

load trends. Possible evidence of this effect includes many 

watersheds that had significantly positive trends in TOC and 

TDS residuals, while having negative, albeit insignificant, 

trends in loads.

The trend analysis indicated that TSS loads were 

decreasing by 57,000 pounds per day per year (lb/d/yr) and 

SSC loads were decreasing by 87,000 lb/d/yr over the 13-year 

period. This decrease is substantial when considering that 

TSS loads averaged 420,000 lb/d and SSC loads 742,000 lb/d 

during this period.

Watershed Yields

Annual yields and confidence intervals for all 13 watersheds are compiled in Aulenbach and Joiner (2017; table 5, 

streamwater constituent yields). Average annual TN yields 

for WYs 2003–15 were highest for the North Fork Peachtree 

Creek and Yellow River watersheds (11.0 and 10.2 pounds per 

acre per year (lb/acre/yr) as N, respectively; fig. 40). Average 

annual NO3

+NO2 yields were highest for the Yellow River, 

Suwannee Creek, and North Fork Peachtree Creek watersheds 

(6.36, 3.96, and 4.03 lb/acre/yr as N). Yellow River and North 

Fork Peachtree Creek have the fourth and first highest percentages of impervious area (table 6; fig. 10). Sweetwater Creek, 

Brushy Fork Creek, Alcovy River, and Level Creek watersheds had the four lowest average annual TN and NO3

+NO2

yields for the period. The Sweetwater Creek watershed, which 

is nested in the Yellow River watershed, had low nitrogen 

yields even though the Yellow River watershed had some of 

the highest nitrogen yields. This contrast illustrates how variable different parts of a larger watershed such as Yellow River 

can be. Overall, the NO3+NO2 fraction makes up 60.1 percent 

of TN loads.

Average annual TP and DP yields for WYs 2003–15 

were highest for the North Fork Peachtree Creek watershed, 

0.966 and 0.286 lb/acre/yr as P, respectively (fig. 41). Wheeler 

Creek, Richland Creek, Level Creek, and Crooked Creek 

watersheds also had higher TP and DP yields. The fact that 

these five watersheds had both higher TP and DP yields 

may indicate that TP and DP have similar sources, even 

though DP makes up a small portion of TP. Overall, the DP 

fraction makes up 18.9 percent of TP loads. High TP and 

DP yields appear to be related to high TSS yields, because 

these five watersheds also had the five highest average 

annual TSS yields. Richland Creek watershed had the highest 

basin slope of any of the 13 watersheds (fig. 5; table 5), 

while the Crooked Creek and North Fork Peachtree Creek 

watersheds had the two highest percentages of impervious 

area, 37.14 and 52.43 percent, respectively (fig. 10; table 6). 

Figure 39. Average suspended-sediment loads and average 

annual runoff for years with dry (water years 2007, 2008, 2011, 

2012), average (water years 2004, 2006, 2009, 2013–15), and wet 

(water years 2003, 2005, 2010) climatic conditions for water years 

2003–15 for the LTTM program study area in Gwinnett County, 

Georgia. 
Constituent Loads and Yields 71

0

2

4

6

8

10

12

14

Average nitrogen yield, in pounds per acre per year as N

Total nitrogen 

EXPLANATION

Figure 40

Total nitrate

 plus nitrite 

Upper 95 percent

Lower 95 percent

Sweetwater Creek 

Yellow River 

No Business Creek 

Big Haynes Creek 

Brushy Fork Creek 

Alcovy River 

Wheeler Creek 

Apalachee River 

Richland Creek 

Level Creek 

Suwanee Creek 

Crooked Creek 

North Fork Peachtree

 Creek 

Monitored watersheds, Gwinnett County 

Confidence interval

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Average phosphorus yield, in pounds per acre per year as P

Total phosphorus

EXPLANATION

Dissolved phosphorus

Figure 41

Sweetwater Creek 

Yellow River 

No Business Creek 

Big Haynes Creek 

Brushy Fork Creek 

Alcovy River 

Wheeler Creek 

Apalachee River 

Richland Creek 

Level Creek 

Suwanee Creek 

Crooked Creek 

North Fork Peachtree

 Creek 

Monitored watersheds, Gwinnett County 

Upper 95 percent

Lower 95 percent

Confidence interval

Figure 40. Average total nitrogen and total nitrate plus nitrite yields for 13 monitored watersheds in 

Gwinnett County, Georgia, for the period water years 2003–15. Sweetwater Creek watershed yields 

are for water years 2011–15.

Figure 41. Average total and dissolved phosphorus yields for 13 monitored watersheds in Gwinnett County, 

Georgia, for water years 2003–15. Sweetwater Creek watershed yields are for water years 2011–15.
72 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

The Sweetwater Creek watershed had the lowest average 

annual TP yields (0.175 lb/acre/yr as P) while the Alcovy 

River watershed had the lowest average annual DP yields 

(0.048 lb/acre/yr as P).

Average annual TOC yields for WYs 2003–15 were 

highest for Yellow River, Crooked Creek, and North Fork 

Peachtree Creek watersheds (18.9, 20.9 and 36.5 lb/acre/yr 

as carbon, respectively; fig. 42). These three watersheds 

had three of the four highest percentages of impervious area 

(fig. 5; table 5). The Alcovy River watershed has the lowest 

average annual TOC yield, 12.3 lb/acre/yr as carbon. All the 

other watersheds had fairly similar TOC yields.

Average annual TSS yields for WYs 2003–15 were 

highest for the Richland Creek and Crooked Creek watersheds 

(2,720 and 1,790 lb/acre/yr, respectively; fig. 43). Richland 

Creek watershed had the highest basin slope (fig. 5; table 5), 

and Crooked Creek watershed had the second highest 

percentage of impervious area of the 13 watersheds (fig. 10; 

table 6). No Business Creek watershed had the lowest average 

annual TSS yield (434 lb/acre/yr) and had the fourth lowest 

percentage of high-density development (table 6).

Average annual SSC yields were highest for Apalachee 

River, Richland Creek, and Level Creek watersheds 

(6,980, 6,190, and 4,410 lb/acre/yr, respectively) and lowest 

for the Suwanee Creek watershed (716 lb/acre/yr; fig. 43). 

0

5

10

15

20

25

30

35

40

45

Average total organic carbon yield, in pounds per acre per year

EXPLANATION

Figure 42

Sweetwater Creek 

Yellow River 

No Business Creek 

Big Haynes Creek 

Brushy Fork Creek 

Alcovy River 

Wheeler Creek 

Apalachee River 

Richland Creek 

Level Creek 

Suwanee Creek 

Crooked Creek 

North Fork Peachtree

 Creek 

Monitored watersheds, Gwinnett County 

Upper 95 percent

Lower 95 percent

Confidence interval

There was some inconsistency between which watersheds 

had the highest TSS versus SSC yields. Suspended-sediment 

concentration yields were notably higher than TSS yields. The 

ratio of SSC to TSS loads for all watersheds combined was 

about 1.77, but this ratio varied widely by watershed: Suwanee 

Creek had the lowest ratio of 1.04, and Apalachee River had 

the highest ratio of 6.55. The watersheds with higher TSS and 

SSC yields generally had higher SSC to TSS ratios. Landers 

(2013) indicated that watersheds that have a higher percentage 

of sand-sized particles in stormflow runoff tend to have a 

greater discrepancy between SSC and TSS. The watersheds 

with the highest SSC yields had large confidence intervals, 

indicating that there was a lot of uncertainty in these estimates. 

Despite the differences between SSC and TSS, annual yields 

of TSS were used as the primary performance criterion for 

suspended sediment in the Gwinnett County Watershed 

Protection Plan (Gwinnett County Department of Public 

Utilities, 2000).

Average annual Pb yields for WYs 2003–15 were highest 

for Alcovy River and North Fork Peachtree Creek watersheds 

(0.073 and 0.069 lb/acre/yr, respectively), and average 

annual Zn yields were highest for North Fork Peachtree 

Creek, Crooked Creek, and Richland Creek watersheds 

(0.780, 0.395, and 0.291 lb/acre/yr, respectively, fig. 44). 

These three watersheds also had three of the four highest 

Figure 42. Average total organic carbon yields for 13 monitored watersheds in 

Gwinnett County, Georgia, for water years 2003–15. Sweetwater Creek watershed 

yields are for water years 2011–15.
Constituent Loads and Yields 73

0

5,000

10,000

15,000

20,000

25,000

Average sediment yield, in pounds per acre per year

Total suspended solids

EXPLANATION

Suspended sediment

 concentration

Figure 43

Sweetwater Creek 

Yellow River 

No Business Creek 

Big Haynes Creek 

Brushy Fork Creek 

Alcovy River 

Wheeler Creek 

Apalachee River 

Richland Creek 

Level Creek 

Suwanee Creek 

Crooked Creek 

North Fork Peachtree

 Creek 

Monitored watersheds, Gwinnett County 

Upper 95 percent

Lower 95 percent

Confidence interval

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Average yield, in pounds per acre per year

Total lead

EXPLANATION

Total zinc

Figure 44

Sweetwater Creek 

Yellow River 

No Business Creek 

Big Haynes Creek 

Brushy Fork Creek 

Alcovy River 

Wheeler Creek 

Apalachee River 

Richland Creek 

Level Creek 

Suwanee Creek 

Crooked Creek 

North Fork Peachtree

 Creek 

Monitored watersheds, Gwinnett County 

Upper 95 percent

Lower 95 percent

Confidence interval

Figure 43. Average total suspended solids and suspended-sediment yields for 13 monitored watersheds 

in Gwinnett County, Georgia, for water years 2003–15. Sweetwater Creek watershed yields are for water 

years 2011–15.

Figure 44. Average total lead and total zinc yields for 13 monitored watersheds in Gwinnett County, 

Georgia, for water years 2003–15. Sweetwater Creek watershed yields are for water years 2011–15.
74 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

average annual TSS yields. Richland Creek watershed had the 

highest basin slope of any of the 13 watersheds (fig. 5; table 5), 

whereas Crooked Creek and North Fork Peachtree Creek 

had the two highest percentages of impervious area (fig. 10; 

table 6). The Brushy Fork Creek watershed had the lowest 

average annual Pb and Zn yields of 0.015 and 0.067 lb/acre/yr, 

respectively. The Brushy Fork watershed had the lowest mean 

basin slope, the second lowest percentage of impervious area, 

and the second lowest average annual TSS yields.

Average annual TDS yields for WYs 2003–15 were 

highest for North Fork Peachtree Creek and Yellow River 

watersheds (791 and 433 lb/acre/yr, respectively; fig. 45). 

North Fork Peachtree Creek and Yellow River watersheds had 

the first and fourth highest percentages of impervious area, 

respectively (fig. 10; table 6). Brushy Fork Creek watershed 

had the lowest average annual TDS yield of 196 lb/acre/yr 

and had the lowest mean basin slope (fig. 5; table 5) and the 

second lowest percentage of impervious area.

Differences with Previous Load Estimates

Loads are estimated in this report for WYs 2003–15. 

These loads are different than the loads estimated for 

WYs 2004–09 in Joiner and others (2014) for several reasons. 

0

200

400

600

800

1,000

1,200

Average total dissolved solids yield, in pounds per acre per year

Figure 45

Sweetwater Creek 

Yellow River 

No Business Creek 

Big Haynes Creek 

Brushy Fork Creek 

Alcovy River 

Wheeler Creek 

Apalachee River 

Richland Creek 

Level Creek 

Suwanee Creek 

Crooked Creek 

North Fork Peachtree

 Creek 

Monitored watersheds, Gwinnett County 

EXPLANATION

Upper 95 percent

Lower 95 percent

Confidence interval

The calibration dataset used to fit the load estimation models 

in this report represented samples from WYs 2001–15, 

whereas the previous report used samples from WYs 1996–11. 

These periods likely had different average concentration 

responses. The load estimation regression models (eq. 2) 

were improved by including a term for base-flow discharge 

and a term to allow for a separate slope for discharge during 

base-flow conditions.

Uncertainty in Load Estimates

Confidence intervals for load estimates are provided 

for all annual loads. Errors are estimated on the basis of how 

well the load estimation regression model fits the observed 

data applied to the conditions observed during the estimation 

period. These confidence intervals are useful for determining 

whether differences between loads are meaningful and for 

assessing the magnitude of change that can be detected as a 

trend. Still, confidence intervals should be used as a guide, 

knowing errors could be larger, as there can be quite a bit of 

uncertainty in estimating errors.

 The sources of error in the load estimation process 

can be numerous. Measurement errors include stream stage, 

the stage-discharge relation, the representativeness of the 

water-quality sampling, and the laboratory analytical analyses. 

Figure 45. Average total dissolved solids yields for 13 monitored watersheds in 

Gwinnett County, Georgia, for water years 2003–15. Sweetwater Creek watershed 

yields are for water years 2011–15.
Interpreting Aggregate Effects on Water Quality 75

The precision of these measurements results in model uncertainty, which is then incorporated into the errors estimated by 

the load models. Still, biases in any of these measurements 

would result in biases in the load estimates. The regressionmodel approach uses the inherent assumption that the variability in the model relations are due to errors in the predicted 

variables and that there are no errors in the explanatory 

variables. It is not possible for the regression model to accurately predict the confidence intervals for conditions observed 

in the estimation period that are not included in the calibration 

dataset. The calibration datasets used to model loads herein 

were sufficiently large that they represented the full range of 

flow conditions observed across all seasons. Although care 

was taken to ensure that the models were of sufficient quality 

such that residuals were identically distributed, any residual 

patterns could result in biases over particular ranges of flow 

or turbidity. Error estimates assume that deviations from 

the model are temporally random, but serial correlations are 

common, resulting in errors to be underestimated at shorter 

reporting periods (Aulenbach, 2013). The use of turbidity as a 

surrogate for many of the constituents may effectively remove 

serial correlation and alleviate its effects on error estimates 

for these models.

Load estimation errors were also introduced with the 

application of estimating loads on a time-step basis, which 

was a necessary accommodation to estimate loads from storm 

composite samples. Although the purpose of the time step was 

to capture the average storm streamflow (and turbidity) for 

which the storm composite samples are represented, the time 

step does not always function properly. Time-step average 

streamflows during storms can be lower than average event 

streamflows represented by storm composite samples due to 

(1) the time step splitting a single storm into two time periods, 

depending on the timing of the storm, and (2) storms having a 

duration shorter that the time step, thereby including adjacent 

base-flow streamflows. Time-step average streamflows that are 

higher than average event streamflows can likewise result if a 

storm with a duration greater than the length of the time step 

was split such that the time step represented the peak of the 

event. These differences in streamflows are important when 

the relation between concentration and discharge is not near 

linear, as the load estimates would be different depending on 

how the averages in the time steps compare to the observed 

average storm discharge. As the concentration-discharge 

relations were fit in logarithmic space, the fit is generally 

not linear, and models that contain the flow-squared term 

are likely to be more nonlinear. The effect of splitting events 

by time steps on load estimation is likely dependent on the 

particular model relations, and possible effects of this relation 

have not been fully assessed at this time. Despite this issue, 

the approach was superior to applying storm composite sample 

average concentrations to instantaneous discharges.

Interpreting Aggregate Effects on 

Water Quality

The water quality of streams in a watershed reflects and 

integrates the effects of watershed characteristics, hydrologic 

processes and variability, biogeochemical processes, and 

human influences. Although watershed characteristics 

including land use can indicate possible explanations for 

variations in water quality among watersheds, relations rarely 

reflect straightforward cause and effect. In some cases, there 

can be spurious relations where two unrelated variables appear 

related due to some other common response variable. For 

example, BMP implementation and constituent inputs could 

both be increasing in watersheds with higher development 

rates. Several factors can result in similar water-quality 

patterns; for example, in this study high impervious area and 

steep basin slope both appear to be related to greater transport 

and higher yields of TSS and SSC. A specific watershed characteristic may not always relate to a particular water-quality 

pattern in all cases because water quality is integrating all the 

processes within the watershed; for example, in this study the 

watersheds with the lowest Zn yields also had low percentages of high-density development, yet not all the watersheds 

with low percentages of high-density development had low 

Zn yields. There may be specific, unaccounted for human 

activities present within individual watersheds that result in 

point-source pollutants that affect water-quality constituents. 

Many watershed characteristics are also related to each other, 

whereas individual characteristics may be better at explaining 

particular relations in water quality. For example, the 

percentage area of high-density development and impervious 

area are correlated with each other, but high-density development may be more related to specific trace elements that might 

be the result of pollutants, while impervious area may be more 

related to the transport of TSS and SCC. Furthermore, Gwinnett County has long required BMPs to control and mitigate 

the effects of increases in impervious areas. The effectiveness 

of these measures is difficult to parse out because more BMPs 

are implemented in watersheds that have higher percentages 

of impervious area.

The effects of variations in climate can have large 

effects on water-quality concentrations, loads, and yields. 

These effects occur for any water-quality constituent that 

exhibits concentration variations with discharge or season, 

because climatic variations in the amount and distribution of 

runoff annually and seasonally will affect the concentrations 

observed. This is especially notable for constituents that are 

transported during storms that exhibit a strong relation of 

increasing concentration with discharge, such as sediment 

(TSS and SSC) and other sediment-related constituents 

(TP, Zn, and Pb). Dry years have less runoff (lower average 
76 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

annual discharges) with generally smaller hydrologic 

responses to storms (due to reduced base flow), which 

result in less opportunity for storm transport. Similarly, wet 

years have more runoff and greater hydrologic responses 

to storms, resulting in more transport. For constituents that 

exhibit dilution at higher discharge—TN, NO3+NO2, and 

TDS—annual average concentrations will be higher in dry 

years and lower in wet years (fig. 38). Although the amount 

of annual runoff is often the most important controlling 

factor determining the magnitude of annual loads, variations 

in concentrations related to variations in discharge can be 

as or more important, as observed for suspended-sediment 

loads for the LTTM program study area (fig. 39). Increases 

in average TSS and SSC loads between average and wet 

climatic conditions were 154 and 95 percent more than the 

percentage increase observed in runoff, respectively. Similarly, 

decreases in average TSS and SSC loads between average 

and dry climate conditions were 66 and 76 percent more than 

the percentage decrease observed in runoff, respectively. 

Removing the effects of climate can be further complicated 

if the concentration-discharge relation is altered by the 

climatic conditions. For example, some constituents can be 

retained during droughts and flushed when conditions return 

to average. It can be difficult to determine whether a change in 

the concentration-discharge relation over time is the result of 

climatic variability or human activity. Nevertheless, patterns 

in model residual concentrations (figs. 27–36) did not appear 

to vary with the multiple climatic cycles observed during this 

study period (fig. 16).

Summary

The U.S. Geological Survey (USGS), in cooperation 

with Gwinnett County Department of Water Resources, 

established a comprehensive Long-Term Trend Monitoring 

(LTTM) program in 1996 to monitor, analyze, and quantify 

the magnitudes of pollutants and the effects of urbanization 

on six watersheds. In 2001, six additional watersheds were 

added to the LTTM program. A thirteenth watershed was 

added in 2010. These 13 watersheds were continuously 

monitored for water level (stage), discharge, precipitation, 

and water-quality properties (water temperature, specific 

conductance [SC], and turbidity). For each season, summer 

(May– October) and winter (November–April), three stormcomposited samples were collected during storm events, and 

one sample was collected during base-flow conditions for a 

total of eight samples per year. Samples were analyzed for 

SC, turbidity, nutrients (total nitrate plus nitrite [NO3

+NO2], 

total ammonia plus organic nitrogen [TKN], total phosphorus 

[TP], dissolved phosphorus [DP], and total organic carbon 

[TOC]), suspended sediment (total suspended solids [TSS] 

and suspended-sediment concentration [SSC]), trace metals 

(total lead [Pb] and total zinc [Zn]), and total dissolved solids 

(TDS). The sampling scheme was designed to identify variations in water quality both hydrologically and seasonally. 

Gwinnett County, an urban county of the Atlanta 

metropolitan area, is composed predominantly of headwater 

watersheds along the Eastern Continental Divide. Watershed 

mean basin slopes range from 7.4 to 16.7 percent. Gwinnett 

County has undergone rapid population growth from about 

1980 to 2015, though growth had slowed somewhat during 

2008–11. Land use has become more highly developed over 

time. Land use is summarized for each watershed for 2012. 

The percentage of impervious area has a large influence on 

hydrology and water quality. Changes in the percentage of 

impervious area are documented for 2000–14, with increases 

of varying extent observed within most watersheds.

Gwinnett County has a humid, subtropical climate with a 

mean annual precipitation of 54.7 inches and high evapotranspiration rates during the growing season. Several droughts occurred 

during the study period—water years (WY) 2002, 2007–08, and 

2011–12. Study area runoff ratios, the percentage of precipitation

that occurs as runoff, ranged from 28.4 to 55.1 percent for 

WYs 2002–15, with lower runoff ratios in low precipitation years. 

The watersheds with the highest percentage of impervious areas 

had the highest runoff ratios. Watershed base-flow indexes, the 

ratio of base-flow runoff to total runoff, were inversely correlated 

with watershed impervious area. 

Selected flood-frequency statistics were calculated for 

13 streamgages in the study area that have 10 or more years 

of annual peak flow data through WY 2015. Low-outlier 

and historic information were incorporated into the floodfrequency analysis. A Kendall’s tau nonparametric test indicated that none of these streamgages exhibited a significant 

trend in the annual peak flow data. A comparison of base-flow 

and stormflow water quality indicate that turbidity, TKN, total 

nitrogen (TN), TP, TOC, TSS, SSC, Pb, and Zn concentrations 

increased with increasing discharge at all watersheds. Specific 

conductance decreased during stormflow at all watersheds, 

and TDS exhibited dilution at a few of the watersheds. Total 

suspended solids and SSC typically were two orders of magnitude higher in stormflow samples than in base-flow samples, 

turbidities were about 1.5 orders of magnitude higher, TP and 

Zn were about one order of magnitude higher, and TKN, TN, 

TOC, and Pb were about twofold higher. 

Seasonality and long-term trends were determined for 

10 constituents (TN, NO3

+NO2, TP, DP, TOC, TSS, SSC, 

Pb, Zn, and TDS) for WYs 2001–15. Seasonal terms were 

significant in most load estimation models for TOC, TSS, 

SSC, Pb, and Zn, and in slightly more than half of the models 

for TN, NO3+NO2, TP, and DP. Seasonal patterns for these 

constituents were all fairly similar, with higher concentrations 

in the summer and lower concentrations in the winter. A linear 

long-term trend analysis of residual concentrations from the 
Summary 77

flow-only load estimation model (without time-trend terms) 

identified significant trends in 67 of the 130 constituentwatershed combinations. Seventy percent of the significant 

trends were negative. Constituents exhibiting predominantly 

positive trends included TOC (5 watersheds) and TDS 

(9 of the 10 trends were positive). Constituents exhibiting 

only negative trends included TP (10 watersheds), TSS 

(9 watersheds), SSC (7 watersheds), Pb (8 watersheds), and 

Zn (5 watersheds). The other constituents exhibited fewer 

trends, both positive and negative: TN (1 positive, 2 negative), 

NO3+NO2 (2 positive, 4 negative), and DP (3 positive, 

1 negative). Of the five watersheds that exhibited large 

increases in impervious areas during WYs 2000–14 (Sweetwater Creek, Yellow River, Alcovy Creek, Wheeler Creek, and 

Apalachee River), Yellow River had a negative trend in TSS 

while Alcovy Creek and Wheeler Creek had negative trends in 

TSS and SSC. The fact that transport of suspended sediment 

is either remaining the same or declining at the watersheds 

that are experiencing the highest increases in impervious 

areas suggests that the implementation of best management 

practices (BMP) within these watersheds during this period is 

maintaining (within the ability to detect trends) and, in some 

cases, improving water quality.

Streamwater constituent loads were estimated for 

10 water-quality constituents for the 13-year period 

WYs 2003–15. These estimates represent the cumulative 

effects of watershed characteristics, hydrologic processes and 

variability, biogeochemical processes, and human influences 

on watershed water quality. Yields, in load per unit area, were 

used to compare loads from watersheds with different sizes. 

For the Gwinnett County LTTM program, storm samples 

were collected as a composite so the load estimation approach 

developed and used for the Gwinnett County LTTM program 

for 1998–2003 was adopted with some minor modifications. 

This approach employs the commonly used regression-model 

method, but employs a time-step estimation approach to simulate storm average discharges and allows for separate relations 

for base-flow and storm-based average concentrations versus 

discharge and turbidity model terms.

Although the amount of annual runoff was the primary 

factor in variations in annual loads, changes in constituent 

average annual concentrations that were related to climatic 

conditions were also important. This was especially evident 

for TSS and SSC loads in which increases in loads were 

attributed about equally to increases in concentration and 

runoff during wet years. Significant negative trends in 

loads were detected from linear trend lines fit between 

annual loads and time for 7 of the 10 constituents—TN, 

NO3

+NO2, TP, TSS, SSC, Pb, and Zn. The trend analysis 

indicated that TSS and SSC loads were decreasing by a rate 

of 57,000 and 87,000 pounds per day per year, respectively, 

which is substantial when considering that TSS and SSC loads 

averaged 420,000 and 742,000 pounds per day, respectively, 

during this same period.

Variations in constituent yields between watersheds 

appeared to be related to various watershed characteristics 

though relations rarely reflect straightforward cause and effect. 

Suspended sediment (TSS and SSC) along with constituents 

transported predominately in solid phase (TP, TOC, Pb, and 

Zn), and TDS typically had higher yields from watersheds 

that had high percentages of impervious areas (Yellow River, 

Crooked Creek, and North Fork Peachtree Creek) or high 

basin slope (Richland Creek) than other watersheds. High 

TN yields, which have a significant dissolved component, 

were also associated with watersheds with high percentages of 

impervious areas. High percentages of impervious area results 

in increased runoff, flood magnitudes, and the “flashiness” 

of flooding (how rapidly streamflow responds to rainfall), 

which can result in increased land-surface erosion and stream 

transport of sediments. Low TN, TSS, Pb, and Zn yields 

appear to be associated with watersheds that have a low 

percentage of high-density development, but this characteristic 

did not presuppose high yields. Watershed Pb and Zn yields 

were related to TSS yields. Total suspended solids and 

SSC yields differed, which is possibly the result of low-biased 

TSS concentrations for samples with sand fractions of greater 

than 25 percent.

Long-term, comprehensive records of climatic, hydrologic, and water-quality conditions can be used by watershed 

managers to evaluate, protect, and enhance rivers and streams. 

The monitoring stations used in this study represent a range 

of watershed conditions, including water-quality attainment 

status, point-source discharges, and other physical characteristics. The computation and analysis of constituent loads is a 

critical element and useful tool in the management and protection of the diverse watersheds in Gwinnett County, Georgia. 

The annual load of total suspended solids has been identified 

in the Gwinnett County Watershed Protection Plan as a target 

performance criterion. The Gwinnett County Department 

of Water Resources uses these computations along with the 

assessment of trends in imperviousness for the management 

and protection of aquatic habitat. 
78 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

References Cited

American Public Health Association, American Water Works 

Association, and Water Pollution Control Federation, 1995, 

Standard methods for the examination of water and wastewater (19th ed.): Washington, D.C., American Public Health 

Association, variously paged.

American Society for Testing and Materials (ASTM), 2000, 

Standard test methods for determining sediment concentration in water samples: D3977–97, v. 11.02 Water (II), 

p. 395–400.

Atlanta Regional Commission, 2012, 2012 Land use GIS 

dataset: Atlanta Regional Commission web page, accessed 

January 18, 2017, at http://www.atlantaregional.com/infocenter/gis-data-maps/gis-data.

Aulenbach, B.T., 2013, Improving regression-model-based 

streamwater constituent load estimates derived from serially 

correlated data: Journal of Hydrology, v. 503, p. 55–66.

Aulenbach, B.T., Burns, D.A., Shanley, J.B., Yanai, R.D., 

Bae, Kikang, Wild, A.D., Yang, Yang, and Yi, Dong, 2016, 

Approaches to stream solute load estimation for solutes 

with varying dynamics from five diverse small watersheds: 

Ecosphere, v. 7, no. 6, 22 p.

Aulenbach, B.T., Buxton, H.T., Battaglin, W.A., and Coupe, R.H.,

2007, Streamflow and nutrient fluxes of the MississippiAtchafalaya River Basin and subbasins for the period of 

record through 2005: U.S. Geological Survey Open-File 

Report 2007–1080, accessed January 17, 2017, at 

https://pubs.usgs.gov/of/2007/1080/.

Aulenbach, B.T., and Joiner, J.K., 2017, Stream water-quality 

summary statistics and outliers, streamwater load models 

and yield estimates, and peak flow modeling parameters for 

13 watersheds in Gwinnett County, Georgia: U.S. Geological

Survey data release, https://doi.org/10.5066/F7639MXG.

Bornstein, Robert, and Lin, Qinglu, 2000, Urban heat islands 

and summertime convective thunderstorms in Atlanta—

Three case studies: Atmospheric Environment, v. 34, no. 3, 

p. 507–516.

Calhoun, D.L., Frick, E.A., and Buell, G.R., 2003, Effects 

of urban development on nutrient loads and streamflow, 

Upper Chattahoochee River Basin, Georgia, 1976–2001: 

Proceedings of the 2003 Georgia Water Resources Conference, held April 23–24, 2003, at the University of Georgia, 

Kathyrn J. Hatcher, editor, Institute of Ecology, The 

Univerisity of Georgia, Athens, Georgia.

Childress, C.J.O., Foreman, W.T., Connor, B.F., and Maloney, T.J.,

1999, New reporting procedures based on long-term method 

detection levels and some considerations for interpretations

of water-quality data provided by the U.S. Geological 

Survey National Water Quality Laboratory: U.S. Geological 

Survey Open-File Report 99–193, 19 p. 

Cohn, T.A., Caulder, D.L., Gilroy, E.J., Zynjuk, L.D., and 

Summers, R.M., 1992, The validity of a simple statistical 

model for estimating fluvial constituent loads—An empirical study involving nutrient loads entering Chesapeake 

Bay: Water Resources Research, v. 28, no. 9, p. 2353–2363.

Cohn, T.A., DeLong, L.L., Gilroy, E.J., Hirsch, R.M., and 

Wells, D.K., 1989, Estimating constituent loads: Water 

Resources Research, v. 25, no. 5, p. 937–942.

Cohn, T.A., England, J.F., Berenbrock, C.E., Mason, R.R., 

Stedinger, J.R., and Lamontagne, J.R., 2013, A generalized 

Grubbs-Beck test for detecting multiple potentially influential low outliers in flood series: Water Resources Research, 

v. 49, no. 8, p. 5047–5058.

Cohn, T.A., Lane, W.L., and Baier, W.G., 1997, An algorithm 

for computing moments-based flood quantile estimates 

when historical flood information is available: Water 

Resources Research, v. 33, no. 9, p. 2089–2096.

Crawford, C.G., 1991, Estimation of suspended-sediment 

rating curves and mean suspended-sediment loads: Journal 

of Hydrology, v. 129, p. 331–348.

Dann, M.S., Lynch, J.A., and Corbett, E.S., 1986, Comparison 

of methods for estimating sulfate export from a forested 

watershed: Journal of Environmental Quality, v. 15, no. 2, 

p. 140–145.

DeGasperi, C.L., Berge, H.B., Whiting, K.R., Burkey, J.J.,

Cassin, J.L., and Fuerstenberg, R.R., 2009, Linking 

hydrologic alteration to biological impairment in urbanizing 

streams of the Puget Lowland, Washington, USA: Journal 

of the American Water Resources Association, v. 45, no. 2, 

p. 512–533.

Dixon, P.G., and Mote, T.L., 2003, Patterns and causes

of Atlanta’s urban heat island—Initiated precipitation: 

Journal of Applied Meteorology and Climatology, v. 42, 

p. 1273–1284, accessed February 3, 2017, at https://doi.org/

10.1175/1520-0450(2003)042<1273:PACOAU>2.0.CO;2.

Feaster, T.D., Gotvald, A.J., and Weaver, J.C., 2014, Methods 

for estimating the magnitude and frequency of floods for 

urban and small, rural streams in Georgia, South Carolina, 

and North Carolina, 2011: U.S. Geological Survey Scientific Investigations Report 2014–5030, 104 p., accessed 

January 17, 2017, at https://pubs.usgs.gov/sir/2014/5030/. 

Ferguson, R.I., 1986, River loads underestimated by rating 

curves: Water Resources Research: v. 22, no. 1, p. 74–76.

Gotvald, A.J., 2010, Surface-water quality-assurance plan for the

U.S. Geological Survey Georgia Water Science Center, 2010: 

U.S. Geological Survey Open-File Report 2010–1263, 43 p.

Gray, J.R., Glysson, G.D., Turcios, L.M., and Schwarz, G.E., 

2000, Comparability of suspended-sediment concentration 

and total suspended solids data: U.S. Geological Survey 

Water-Resources Report 2000–4191, 20 p.
References Cited 79

Gwinnett County Department of Planning and Development, 

2006, Gwinnett County, Georgia, Stormwater systems and 

facilities installation standards and specifications: Gwinnett County, Georgia, last revised July 28, 2006, accessed 

May 31, 2016, at https://www.gwinnettcounty.com/static/

departments/planning/pdf/stormwater_review_stormwater_systems_design_manual.pdf.

Gwinnett County Department of Public Utilities, 2000, 

Gwinnett County watershed protection plan: Atlanta, Ga., 

CH2MHill, prepared in June 2000 for Gwinnett County, 

Georgia, 52 p., accessed May 31, 2016, at http://www.

gwinnettcounty.com/static/departments/publicutilities/

pdf/Gwinnett-WPP.pdf.

Gwinnett County, Georgia, 2004, State of the county 04 

Gwinnett County: Gwinnett County, 4 p., accessed 

January 31, 2014, at http://www.gwinnettcounty.com/

static/departments/boc/pdf/2004_state_county.pdf. 

Gwinnett County, Georgia, 2005, State of the county 05 

Gwinnett County: Gwinnett County, 4 p., accessed 

January 31, 2014, at http://www.gwinnettcounty.com/

static/departments/boc/pdf/2005_state_county.pdf. 

Gwinnett County, Georgia, 2006, State of the county 06 

Gwinnett County: Gwinnett County, 4 p., accessed 

January 31, 2014, at http://www.gwinnettcounty.com/

static/departments/boc/pdf/2006_SOC.pdf. 

Gwinnett County, Georgia, 2007, 07 state of the county: 

Gwinnett County, 4 p., accessed January 31, 2014, at 

http://www.gwinnettcounty.com/static/departments/boc/

pdf/2007_state_of_the_county.pdf. 

Gwinnett County, Georgia, 2008, 08 state of the county: 

Gwinnett County, 4 p., accessed January 31, 2014, at 

http://www.gwinnettcounty.com/static/departments/boc/

pdf/2008_SOC_handout.pdf. 

Gwinnett County, Georgia, 2009, Gwinnett County state of 

the county, January 2009: Gwinnett County, 4 p., accessed 

January 31, 2014, at http://www.gwinnettcounty.com/static/

departments/boc/pdf/09_SOC_Handout.pdf. 

Gwinnett County, Georgia, 2010, Gwinnett County state of 

the county, February 2010: Gwinnett County, 2 p., accessed 

January 31, 2014, at http://www.gwinnettcounty.com/static/

departments/boc/pdf/2010_SOC_Handout.pdf. 

Gwinnett County, Georgia, 2012, State of the county, February 

2012: Gwinnett County, 2 p., accessed January 31, 2014, 

at http://www.gwinnettcounty.com/static/pdf/2012_SOC_

Handout.pdf. 

Gwinnett County, Georgia, 2013, State of the county, January 2013: Gwinnett County, 2 p., accessed January 31, 2014,

 at http://www.gwinnettcounty.com/static/departments/boc/

pdf/2013_SOC_Handout_FINAL.PDF.

Gwinnett County, Georgia, 2014, State of the county, January 2014: Gwinnett County, 2 p., accessed January 31, 2014, 

at http://www.gwinnettcounty.com/static/departments/boc/

pdf/2014_SOC_Handout.pdf. 

Gwinnett County, Georgia, 2015, 2015 state of the county: 

Gwinnett County, 2 p., accessed January 17, 2017, at 

https://www.gwinnettcounty.com/static/departments/boc/

pdf/State_of_the_County_2015_WEB.pdf. 

Gwinnett County, Georgia, 2016, State of the county 2016: 

Gwinnett County, 2 p., accessed January 17, 2017, at 

https://www.gwinnettcounty.com/static/departments/boc/

pdf/State_of_the_County_2016_Handout.pdf. 

Helsel, D.R., and Hirsch, R.M., 1992, Statistical methods in 

water resources—Studies in Environmental Science 49: 

Amsterdam, The Netherlands, Elsevier Science, 529 p.

Hirsch, R.M., Alexander, R.B., and Smith, R.A., 1991, 

Selection of methods for the detection and estimation of 

trends in water quality: Water Resources Research, v. 27, 

no. 5, p. 803–813.

Hirsch, R.M., Moyer, D.L., and Archfield, S.A., 2010, Weighted

regressions on time, discharge, and season (WRTDS), with 

an application to Chesapeake Bay river inputs: Journal of 

the American Water Resources Association, v. 46, no. 5, 

p. 857–880, accessed January 17, 2017, at https://doi.org/

10.1111/j.1752-1688.2010.00482.x.

Hollis, G.E., 1975, The effect of urbanization on floods of 

different recurrence interval: Water Resources Research, 

v. 11, no. 3, p. 431–435.

Horowitz, A.J., 2003, An evaluation of sediment rating curves 

for estimating suspended sediment concentrations for 

subsequent flux calculations: Hydrological Processes, v. 17, 

no. 17, p. 3387–3409.

Johnson, A.H., 1979, Estimating solute transport in streams 

from grab samples: Water Resources Research, v. 15, no. 5, 

p. 1224–1228.

Joiner, J.K., Aulenbach, B.T., Landers, M.N., 2014, Watershed

characteristics and water-quality trends and loads in 12 watersheds in Gwinnett County, Georgia: U.S. Geological Survey 

Scientific Investigations Report 2014–5141, 79 p. [Also 

available at https://doi.org/10.3133/sir20145141.]

Kendall, M., 1938, A new measure of rank correlation: Biometrika,

v. 30, no. 1–2, p. 81–93, accessed January 17, 2017, at 

https://doi.org/10.1093/biomet/30.1-2.81.

Kirchner, James, 2001, Data analysis toolkit #5—Uncertainty 

analysis and error propagation: Analysis of Environmental 

Data Course, University of California, Berkeley, Calif., 8 p., 

accessed January 17, 2017, at http://seismo.berkeley.edu/

~kirchner/eps_120/Toolkits/Toolkit_05.pdf.
80 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

Knott, J.M., Glysson, G.D., Malo, B.A., and Schroeder, L.J.,

1993, Quality assurance plan for the collection and 

processing of sediment data by the U.S. Geological Survey, 

Water Resources Division: U.S. Geological Survey 

Open-File Report 92–499, 18 p. [Also available 

at http://pubs.er.usgs.gov/pubs/ofr/ofr92499.]

Landers, M.N., 2013, Total suspended solids concentrations 

and yields for water-quality monitoring stations in Gwinnett 

County, Georgia, 1996–2009: U.S. Geological Survey 

Open-File Report 2013–1145, 10 p., accessed July 21, 2014, 

at http://pubs.usgs.gov/of/2013/1145/. 

Landers, M.N., Ankcorn, P.D., and McFadden, K.W., 2007, 

Watershed effects on streamflow quantity and quality in six 

watersheds of Gwinnett County, Georgia: U.S. Geological 

Survey Scientific Investigations Report 2007–5132, 62 p.,

accessed January 17, 2017, at http://pubs.usgs.gov/sir/

2007/5132/.

Larson, S.J., Capel, P.D., Goolsby, D.A., Zaugg, S.D., and 

Sandstrom, M.W., 1995, Relations between pesticide use 

and riverine flux in the Mississippi River basin: Chemosphere, v. 31, no. 5, p. 3305–3321.

Leopold, L.B., 1968, Hydrology for urban land planning—

A guidebook on the hydrologic effects of land use: 

U.S. Geological Survey Circular 554, 18 p.

Likens, G.E., Bormann, F.H., Pierce, R.S., Eaton, J.S., and 

Johnson, N.M., 1977, Biogeochemistry of a forested 

ecosystem: New York, Springer-Verlag, 146 p.

Lim, K.J., Engel, B.A., Tang, Zhenxu, Choi, Joongdae, 

Kim, K.-S., Muthukrishnan, Suresh, and Tripathy, Dibyajyoti, 2005, Automated web GIS based hydrograph analysis 

tool, WHAT: Journal of the American Water Resources 

Association, v. 41, no. 6, p. 1407–1416.

MacDonald, L.H., 2000, Evaluating and managing cumulative 

effects—Process and constraints: Environmental Management, v. 26, no. 3, p. 299–315.

McCrary, Matt, 2011, Effects of topography on North Georgia 

(Atlanta, GA), Severe weather and tornadoes: theweather

prediction.com, accessed July 12, 2011, at http://www.

theweatherprediction.com/weatherpapers/122/index.html.

Mote, T.L., Lacke, M.C., and Shepherd, J.M., 2007, Radar

signatures of the urban effect on precipitation distribution:

A case study for Atlanta, Georgia: Geophysical Research

Letters, v. 34, L20710, accessed February 3, 2017, at

https://doi.org/10.1029/2007GL031903.

National Oceanic and Atmospheric Administration, National 

Climatic Data Center, 2014, 1981–2010 U.S. climate normals,

accessed May 1, 2014, at http://www.ncdc.noaa.gov/oa/

ncdc.html.

Nippgen, Fabian, McGlynn, B.L., Emanuel, R.E., and 

Vose, J.M., 2016, Watershed memory at the Coweeta 

Hydrologic Laboratory—The effect of past precipitation 

and storage on hydrologic response: Water Resources 

Research, v. 52, p. 1673–1695.

Ogden, F.L., Pradhan, N.R., Downer, C.W., and Zahner, J.A., 

2011, Relative importance of impervious area, drainage 

density, width function, and subsurface storm drainage on 

flood runoff from an urbanized catchment: Water Resources 

Research, v. 47, 12 p.

Poff, N.L., Allan, J.D., Bain, M.B., Karr, J.R., Prestegaard, K.L.,

Richter, B.D., Sparks, R.E., and Stromberg, J.C., 1997, The 

natural flow regime—A paradigm for river conservation and 

restoration: BioScience, v. 47, no. 11, p. 769–784.

Preston, S.D., Bierman, V.J., Jr., and Silliman, S.E., 1989, An 

evaluation of methods for the estimation of tributary mass 

loads: Water Resources Research, v. 25, no. 6, p. 1379–1389.

Rantz, S.E., and others, 1982a, Measurement and computation

of streamflow—Volume 1, Measurement of stage and 

discharge: U.S. Geological Survey Water-Supply Paper 2175,

313 p. 

Rantz, S.E., and others, 1982b, Measurement and computation 

of streamflow—Volume 2, Computation of discharge: 

U.S. Geological Survey Water-Supply Paper 2175, 373 p.

Richter, B.D., Baumgartner, J.V., Powell, Jennifer, and 

Braun, D.P., 1996, A method for assessing hydrologic 

alteration within ecosystems: Conservation Biology, v. 10, 

no. 4, p. 1163–1174.

Richter, B.D., Baumgartner, Jeffery, Wigington, Robert, and 

Braun, David, 1997, How much water does a River need?: 

Freshwater Biology, v. 37, no. 1, p. 231–249.

Rose, Seth, and Peters, N.E., 2001, Effects of urbanization 

on streamflow in the Atlanta area (Georgia, USA)—A 

comparative hydrological approach: Hydrological Processes,

v. 15, p. 1441–1457, accessed February 3, 2017, at

https://doi.org/10.1002/hyp.218.

Runkel, R.L., Crawford, C.G., and Cohn, T.A., 2004, 

Load estimator (LOADEST)—A FORTRAN program

for estimating constituent loads in streams and rivers:

U.S. Geological Survey Techniques and Methods, book 4, 

chap. A5, 69 p.

Sanford, W.E., and Selnick, D.L., 2013, Estimation of evapotranspiration across the conterminous United States using a 

regression with climate and land-cover data: Journal of the 

American Resources Association, v. 49, no. 1, p. 217–230.

Semkin, R.G., Jefferies, D.S., and Clair, T.A., 1994, Hydrochemical methods and relationships for study of stream 

output from small catchments, Chap. 7 of Moldan, B., and 

Cerny, J., eds. Biogeochemistry of small catchments—

A tool for environmental research: New York, John Wiley 

and Sons, p. 163–187.
References Cited 81

Shem, Willis, and Shepherd, Marshall, 2009, On the impact 

of urbanization on summertime thunderstorms in Atlanta—

Two numerical model case studies: Atmospheric Research,

v. 92, no. 2, p. 172–189, accessed February 3, 2017, at

https://doi.org/10.1016/j.atmosres.2008.09.013.

Shreve, E.A., and Downs, A.C., 2005, Quality-assurance plan 

for the analysis of fluvial sediment by the U.S. Geological 

Survey Kentucky Water Science Center Sediment Laboratory:

U.S. Geological Survey Open-File Report 2005–1230, 28 p.

Swirepik, J.L., Burns, I.C., Dyer, F.J., Neave, I.A., O’Brien, M.G.,

Pryde, G.M., and Thompson, R.M., 2016, Establishing 

environmental water requirements for the Murray–Darling 

basin, Australia’s largest developed river system: River 

Research and Applications, v. 32, no. 6, p. 1153–1165.

U.S. Census Bureau, 2004, U.S. Census Bureau website 

accessed December 31, 2004, at http://www.census.gov.

U.S. Census Bureau, 2011, Population of Gwinnett County, 

Georgia, accessed January 18, 2017, at http://www.census.gov/

quickfacts/table/PST045215/13135.

U.S. Census Bureau, 2012, American community survey 

5-year estimates of 2006–2010 block group data: 

U.S. Census web page, accessed January 26, 2014, at 

http://www.census.gov/geo/maps-data/data/tiger-data.html.

U.S. Census Bureau, 2014, American community survey 

5-year estimates of 2009–2013 block group data: 

U.S. Census web page, accessed January 17, 2017, at 

http://www.census.gov/geo/maps-data/data/tiger-data.html.

U.S. Department of Agriculture, 1986, Urban hydrology for 

small watersheds (2d ed.): U.S. Department of Agriculture, 

Natural Resources Conservation Service, Conservation 

Engineering Division, Technical Release 55 [TR–55], 164 p.

U.S. Drought Monitor, 2014, U.S. drought monitor classification scheme, drought severity classification: The 

National Drought Mitigation Center web page, accessed 

April 28, 2014, at http://droughtmonitor.unl.edu/AboutUs/

ClassificationScheme.aspx.

U.S. Drought Monitor, 2016, U.S. drought monitor statistics 

graph for Gwinnett County, Georgia: The National Drought 

Mitigation Center web page, accessed February 23, 2016, at 

http://droughtmonitor.unl.edu/MapsAndData/Graph.aspx.

U.S. Environmental Protection Agency, 1971, Method 365.2—

Phosphorous, all forms (colorometric, ascorbic acid, single 

reagent): U.S. Environmental Protection Agency, 7 p.

U.S. Environmental Protection Agency, 1992, NPDES storm 

water sampling guidance document: U.S. Environmental 

Protection Agency, EPA 833–8–92–001, 123 p.

U.S. Environmental Protection Agency, 1993a, Method 353.2, 

Revision 2.0—Determination of nitrate-nitrite nitrogen 

by automated colorimetry: U.S. Environmental Protection 

Agency, 14 p.

U.S. Environmental Protection Agency, 1993b, Method 351.2, 

Revision 2.0—Determination of total Kjeldahl nitrogen by 

semi-automated colorimetry: U.S. Environmental Protection 

Agency, 15 p.

U.S. Environmental Protection Agency, 1994, Method 200.8, 

Revision 5.4—Determination of trace elements in waters 

and wastes by inductively coupled plasma–mass spectrometry: U.S. Environmental Protection Agency, 57 p.

U.S. Environmental Protection Agency, 2000a, Criteria 

development guidance—Rivers and streams, in

U.S. Environmental Protection Agency, Nutrient criteria 

technical guidance manual—Rivers and streams, 

EPA–822–B–00–002, accessed June 21, 2012, at 

http://water.epa.gov/scitech/swguidance/standards/

criteria/nutrients/rivers/index.cfm.

U.S. Environmental Protection Agency, 2000b, Overview 

of current total maximum daily load—TMDL—Program 

and regulations: U.S. Environmental Protection Agency, 

EPA 841–F–00–009, 4 p.

U.S. Environmental Protection Agency, 2010, Georgia water 

quality assessment report, Georgia cumulative TMDLs 

by pollutant: U.S. Environmental Protection Agency web 

page, accessed August 31, 2011, at http://ofmpub.epa.gov/

tmdl_waters10/attains_state.control?p_state=GA.

U.S. Environmental Protection Agency, 2011, Secondary Drinking

Water Regulations—Guidance for nuisance chemicals: 

U.S. Environmental Protection Agency 816–F–10–079, 

accessed October 26, 2011, at http://water.epa.gov/drink/

contaminants/secondarystandards.cfm.

U.S. Environmental Protection Agency, 2017, National 

recommended water quality criteria – aquatic life criteria 

table, accessed January 30, 2017, at https://www.epa.gov/

wqc/national-recommended-water-quality-criteria-aquaticlife-criteria-table.

U.S. Geological Survey, 2006, Collection of water samples

(ver. 2.0): U.S. Geological Survey Techniques of WaterResources Investigations, book 9, chap. A4, September 2006,

accessed July 21, 2014, at http://pubs.water.usgs.gov/twri9A4/.

U.S. Geological Survey, 2014, Geologic units in Gwinnett 

County, Georgia: U.S. Geological Survey mineral resources 

online spatial data, accessed May 16, 2014, at http://mrdata.

usgs.gov/geology/state/fips-unit.php?code=f13135.

U.S. Geological Survey, 2016, National Water Information System—Web interface, accessed April, 2016, at 

https://doi.org/10.5066/F7P55KJN.
82 Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15

Veilleux, A.G., Cohn, T.A., Flynn, K.M., Mason, R.R., and 

Hummel, P.R., 2014, PeakFQ Version 7.01, Flood frequency 

analysis based on Bulletin 17B and recommendations of 

the Advisory Committee on Water (ACWI) Subcommittee 

on Hydrology (SOH) Hydrologic Frequency Work Group 

(HFAWG), accessed March 18, 2016, at http://water.usgs.gov/

software/PeakFQ/.

Wagner, R.J., Boulger, R.W., Jr., Oblinger, C.J., and Smith, B.A.,

2006, Guidelines and standard procedures for continuous 

water-quality monitors—Station operation, record computation, and data reporting: U.S. Geological Survey Techniques 

and Methods 1–D3, 51 p., 8 attachments. 

Wilde, F.D., ed., 2004, Cleaning of equipment for water 

sampling (ver. 2.0): U.S. Geological Survey Techniques 

of Water-Resources Investigations, book 9, chap. A3, 

April 2004, accessed July 21, 2014, at http://pubs.water.

usgs.gov/twri9A3/.

Wilde, F.D., Radtke, D.B., Gibs, Jacob, and Iwatsubo, R.T., 

eds., 2004 with updates through 2009, Processing of water 

samples (ver. 2.2): U.S. Geological Survey Techniques 

of Water-Resources Investigations, book 9, chap. A5, 

April 2004, accessed July 21, 2014, at http://pubs.water.

usgs.gov/twri9A5/.

Yochum, S.E., 2000, A revised load estimation procedure for 

the Susquehanna, Potomac, Patuxent, and Choptank Rivers: 

U.S. Geological Survey Water-Resources Investigations, 

Report 00–4156, 49 p.
Manuscript approved February 8, 2017

Prepared by the USGS Science Publishing Network

Reston Publishing Service Center

Edited by Kay P. Naugle

Illustrations and layout by Caryl J. Wipperfurth

For additional information regarding this publication, 

please contact:

Director, South Atlantic Water Science Center

U.S. Geological Survey

720 Gracern Road

Stephenson Center, Suite 129

Columbia, SC 29210

(803) 750-6100

Or visit the South Atlantic Water Science Center 

website at

http://www.usgs.gov/water/southatlantic/
Aulenbach and others—Hydrology and Water Quality in 13 Watersheds in Gwinnett County, Georgia, 2001–15—Scientific Investigations Report 2017–5012

ISSN 2328-031X (print) 

ISSN 2328-0328 (online)

https://doi.org/10.3133/sir20175012 Printed on recycled paper