Prepared in cooperation with the North Carolina Department of Environment and

Natural Resources, Division of Water Quality

Relation of Watershed Setting and Stream Nutrient Yields

at Selected Sites in Central and Eastern North Carolina, 

1997–2008 

Scientific Investigations Report 2013–5007

U.S. Department of the Interior

U.S. Geological Survey
Cover: Neuse River at Highway 70, Kinston, North Carolina, April 2, 2013. Photograph by 

Stephen L. Harden.
Relation of Watershed Setting and Stream 

Nutrient Yields at Selected Sites in Central 

and Eastern North Carolina, 1997–2008 

By Stephen L. Harden, Thomas F. Cuffney, Silvia Terziotti, and Katharine R. Kolb

Prepared in cooperation with the North Carolina Department of Environment and 

Natural Resources, Division of Water Quality

Scientific Investigations Report 2013–5007

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

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

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

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

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

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

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

U.S. Government.

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

reproduce any copyrighted materials contained within this report.

Suggested citation:

Harden, S.L., Cuffney, T.F., Terziotti, Silvia, and Kolb, K.R., 2013, Relation of watershed setting and stream nutrient 

yields at selected sites in central and eastern North Carolina, 1997–2008: U.S. Geological Survey Scientific 

Investigations Report 2013–5007, 47 p., http://pubs.usgs.gov/sir/2013/5007/.
iii

Acknowledgments

The research described in this report was partially funded by the U.S. Environmental 

Protection Agency’s 319 grant program that is administered by the North Carolina Department 

of Environment and Natural Resources, Division of Water Quality. The authors thank Evan Kane, 

Michael Tutwiler, and Keith Larick of the North Carolina Department of Environment and Natural 

Resources, Division of Water Quality, Aquifer Protection Section, in Raleigh for their help and 

support in this project. 

Thanks also to Brian Pointer, U.S. Geological Survey, for his assistance in compiling 

data during the study. Gratitude is extended to Douglas Harned and Timothy Spruill (retired), 

U.S. Geological Survey, for providing valuable technical discussions throughout the study. 

Finally, many thanks to all of the reviewers of this report who made many helpful comments and 

suggestions.

v

Contents

Acknowledgments........................................................................................................................................iii

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

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

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

Description of the Study Area ............................................................................................................3

Description of the Stream Monitoring Network ..............................................................................3

Methods of Data Compilation and Analysis ............................................................................................15

Stream Nutrient Loads.......................................................................................................................15

Nutrient Data ..............................................................................................................................15

Streamflow Data ........................................................................................................................16

Load Estimation..........................................................................................................................17

Watershed Setting.......................................................................................................................................17

Physical and Climatic Factors ..........................................................................................................20

Land Cover..................................................................................................................................20

Hydrologic Soil Groups .............................................................................................................23

Precipitation................................................................................................................................23

Anthropogenic Factors ......................................................................................................................23

NPDES Wastewater Discharge Facilities..............................................................................23

CAFOs...........................................................................................................................................24

Statistical Analysis of Nutrient Yield Data......................................................................................24

Relation of Streamflow and Nutrient Loads ............................................................................................25

Selection of Nutrient Yield Data for Statistical Evaluation ...................................................................25

Relation of Watershed Setting and Stream Nutrient Yields .................................................................30

Comparison of Stream Nutrient Yields by Land-Use Category ...................................................30

Identification of Watershed Environmental Variables Influencing Stream

Nutrient Yields........................................................................................................................34

Regression Tree Model 1..........................................................................................................34

Regression Tree Model 2..........................................................................................................37

Regression Tree Model 3..........................................................................................................39

Regression Tree Model 4..........................................................................................................40

Summary and Application of the Regression Tree Model Results....................................42

Summary and Conclusions.........................................................................................................................43

References Cited..........................................................................................................................................45

Figures

1. Map showing study area, major river basins, and site locations examined in

Central and Eastern North Carolina...........................................................................................7

2. Map showing location of stream study sites 1 and 2 in the Roanoke River basin.............8

3. Map showing location of stream study site 3 in the Chowan River Basin and

study sites 4–8 in the Tar-Pamlico River basin ........................................................................9

4. Maps showing location of stream study sites 9–26 in the Neuse River basin.................10
vi

5. Maps showing location of stream study sites 27–44 in the

Cape Fear River basin ................................................................................................................12

6. Map showing location of stream study sites 45–48 in the

Lumber River basin.....................................................................................................................14

7. Graph showing relation between drainage area and median annual

streamflow for the study sites, 1997–2008..............................................................................26

8. Graph showing relation between drainage area and median annual

nutrient loads for the study sites, 1997–2008 .........................................................................26

9. Graph showing relation between median annual streamflow and median

annual nutrient loads for the study sites, 1997–2008 ............................................................27

10. Graphs showing comparison of median annual yields for nitrate, total nitrogen,

and total phosphorus for 1997–2008 and 2002–2008 .............................................................28

11. Graphs showing annual data for total nitrogen yields and streamflow and

point-source flow yields for study site 36, 1997–2008...........................................................29

12. Graphs showing distributions of median annual yields for nitrate, total nitrogen,

and total phosphorus for study sites based on land-use category....................................32

13. Graphs showing annual data for nutrient yields and streamflow yields for

study site 29, and annual data for nutrient yields and streamflow and point-source 

flow yields for study site 30, 2001–2008...................................................................................33

14. Regression tree for Model 1 identifying those predictor (environmental) variables

that best explain observed variations in median annual yields of nitrate, 

total nitrogen, and total phosphorus for all 48 study sites...................................................36

15. Regression tree for Model 2 identifying those predictor (environmental) variables

that best explain observed variations in median annual yields of total

nitrogen and total phosphorus for the 42 study sites with point-source

flow contributions less than or equal to 10 percent .............................................................38

16. Regression tree for Model 3 identifying those predictor (environmental) variables

that best explain observed variations in median annual yields of total nitrogen

and total phosphorus for the 33 study sites with drainage areas less than or

equal to 1,000 square miles and point-source flow contributions less than or

equal to 10 percent.....................................................................................................................39

17. Regression tree for Model 4 identifying those predictor (environmental) variables

that best explain observed variations in median annual yields of total

nitrogen and total phosphorus for the 17 study sites with drainage areas

less than or equal to 100 square miles and point-source flow contributions

less than or equal to 10 percent...............................................................................................41

Tables

 1. Stream study-site network with paired U.S. Geological Survey and Division

of Water Quality stations used for evaluation of nutrient loads ...........................................4

2. Median annual nutrient loads and yields during 1997 to 2008 ............................................18

3. Median annual nutrient loads and yields during 2002 to 2008 ............................................19

4. Criteria used for assigning land-use categories to the study sites....................................21

5. Designated land-use categories and land-cover class percentages for

the study sites .............................................................................................................................22

6. Summary of number of sites and median drainage area, annual nutrient yields, and 

land-cover class percentages by land-use category...........................................................31

7. Summary results of the ANOVA and Tukey tests of median annual nutrient yields for 

sites compiled by land-use category ......................................................................................31
vii

8. Regression tree Model 1 results for all 48 study sites..........................................................35

9. Regression tree Model 2 results for the 42 study sites with point-source flow

contributions less than or equal to 10 percent......................................................................37

10. Regression tree Model 3 results for the 33 study sites with drainage areas less

than or equal to 1,000 square miles and point-source flow contributions less

than or equal to 10 percent.......................................................................................................39

11. Regression tree Model 4 results for the 17 study sites with drainage areas less

than or equal to 100 square miles and point-source flow contributions less than 

or equal to 10 percent................................................................................................................40

Appendixes 1–4 (available at http://pubs.usgs.gov/sir/2013/5007/)

1. Water quality, streamflow, and nutrient load data by basin and site ......................MS Excel file

2. Watershed land cover, stream buffer land cover, hydrologic soil groups, and

precipitation data....................................................................................................MS Excel file

3. NPDES point-source data and CAFO data ...................................................................MS Excel file

4. Dataset used for regression tree analyses..................................................................MS Excel file

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 4,047 square meter (m2

)

square mile (mi2

) 2.590 square kilometer (km2)

Volume

gallon (gal) 3.785 liter (L)

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

Flow rate

cubic foot per second (ft3

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

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

Mass

ounce, avoirdupois (oz) 28.35 gram (g)

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

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

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm at 25 °C).

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

micrograms per liter (μg/L).
Relation of Watershed Setting and Stream Nutrient Yields 

at Selected Sites in Central and Eastern North Carolina, 

1997–2008 

By Stephen L. Harden, Thomas F. Cuffney, Silvia Terziotti, and Katharine R. Kolb

Abstract

Data collected between 1997 and 2008 at 48 stream sites 

were used to characterize relations between watershed settings 

and stream nutrient yields throughout central and eastern North 

Carolina. The focus of the investigation was to identify environmental variables in watersheds that influence nutrient export for 

supporting the development and prioritization of management 

strategies for restoring nutrient-impaired streams.

Nutrient concentration data and streamflow data compiled for 

the 1997 to 2008 study period were used to compute stream yields 

of nitrate, total nitrogen (N), and total phosphorus (P) for each 

study site. Compiled environmental data (including variables for 

land cover, hydrologic soil groups, base-flow index, streamflows, 

wastewater treatment facilities, and concentrated animal feeding 

operations) were used to characterize the watershed settings for the 

study sites. Data for the environmental variables were analyzed in 

combination with the stream nutrient yields to explore relations 

based on watershed characteristics and to evaluate whether 

particular variables were useful indicators of watersheds having 

relatively higher or lower potential for exporting nutrients.

Data evaluations included an examination of median annual 

nutrient yields based on a watershed land-use classification 

scheme developed as part of the study. An initial examination of 

the data indicated that the highest median annual nutrient yields 

occurred at both agricultural and urban sites, especially for urban 

sites having large percentages of point-source flow contributions 

to the streams. The results of statistical testing identified significant 

differences in annual nutrient yields when sites were analyzed 

on the basis of watershed land-use category. When statistical 

differences in median annual yields were noted, the results for 

nitrate, total N, and total P were similar in that highly urbanized 

watersheds (greater than 30 percent developed land use) and (or) 

watersheds with greater than 10 percent point-source flow contributions to streamflow had higher yields relative to undeveloped 

watersheds (having less than 10 and 15 percent developed and 

agricultural land uses, respectively) and watersheds with relatively 

low agricultural land use (between 15 and 30 percent). The statistical tests further indicated that the median annual yields for total 

P were statistically higher for watersheds with high agricultural 

land use (greater than 30 percent) compared to the undeveloped 

watersheds and watersheds with low agricultural land use. The 

total P yields also were higher for watersheds with low urban land 

use (between 10 and 30 percent developed land) compared to the 

undeveloped watersheds. The study data indicate that grouping 

and examining stream nutrient yields based on the land-use 

classifications used in this report can be useful for characterizing 

relations between watershed settings and nutrient yields in streams 

located throughout central and eastern North Carolina.

Compiled study data also were analyzed with four regression 

tree models as a means of determining which watershed environmental variables or combination of variables result in basins 

that are likely to have high or low nutrient yields. The regression 

tree analyses indicated that some of the environmental variables 

examined in this study were useful for predicting yields of nitrate, 

total N, and total P. When the median annual nutrient yields for all 

48 sites were evaluated as a group (Model 1), annual point-source 

flow yields had the greatest influence on nitrate and total N yields 

observed in streams, and annual streamflow yields had the greatest 

influence on yields of total P. The Model 1 results indicated that 

watersheds with higher annual point-source flow yields had higher 

annual yields of nitrate and total N, and watersheds with higher 

annual streamflow yields had higher annual yields of total P.

When sites with high point-source flows (greater than 

10 percent of total streamflow) were excluded from the regression 

tree analyses (Models 2–4), the percentage of forested land in the 

watersheds was identified as the primary environmental variable 

influencing stream yields for both total N and total P. Models 2, 3 

and 4 did not identify any watershed environmental variables that 

could adequately explain the observed variability in the nitrate 

yields among the set of sites examined by each of these models. 

The results for Models 2, 3, and 4 indicated that watersheds with 

higher percentages of forested land had lower annual total N and 

total P yields compared to watersheds with lower percentages of 

forested land, which had higher median annual total N and total P 
2 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

yields. Additional environmental variables determined to further 

influence the stream nutrient yields included median annual 

percentage of point-source flow contributions to the streams, 

variables of land cover (percentage of forested land, agricultural 

land, and (or) forested land plus wetlands) in the watershed 

and (or) in the stream buffer, and drainage area. The regression 

tree models can serve as a tool for relating differences in select 

watershed attributes to differences in stream yields of nitrate, 

total N, and total P, which can provide beneficial information for 

improving nutrient management in streams throughout North 

Carolina and for reducing nutrient loads to coastal waters.

Introduction

Excessive nutrient loadings in eastern North Carolina 

have contributed to the degradation of surface-water quality 

in the Neuse and Tar-Pamlico River basins, particularly in the 

estuaries (Gilliam and others, 1997; Spruill and others, 1998; 

Luettich and others, 2000; Burkholder and others, 2006). 

Water-quality concerns related to overenrichment of nutrients 

in the estuaries include eutrophication, excess algal blooms, 

fish kills, and outbreaks of toxic dinoflagellates (Burkholder 

and others, 1995; Burkholder and Glasgow, 1997; Stow and 

others, 2001; Paerl and others, 2004). In response to these 

concerns, the North Carolina Department of Environment 

and Natural Resources Division of Water Quality (DWQ) has 

implemented nutrient sensitive waters (NSW) management 

strategies for the Neuse River basin (North Carolina Division 

of Water Quality, 2009, chap. 24) and the Tar-Pamlico River 

basin (North Carolina Division of Water Quality, 2011, 

chap. 6) to reduce nutrient loadings to the Neuse and Pamlico 

estuaries.

The NSW management strategies include total maximum 

daily loads (TMDLs), point-source discharge requirements, 

agricultural loading reduction requirements, stormwater management rules, riparian buffer protection rules, and other rules 

intended to reduce nutrient contributions from point sources 

(such as municipal and industrial wastewater dischargers) and 

nonpoint sources (such as urban runoff and agriculture) to 

the Neuse and Tar-Pamlico Rivers. The TMDL for the Neuse 

River aims to reduce total nitrogen (N) loads by 30 percent 

from 1991 to 1995 baseline conditions at the compliance point 

at Fort Barnwell, North Carolina (North Carolina Division of 

Water Quality, 2009). The TMDL for the Tar-Pamlico River 

aims to reduce total N loads by 30 percent and to control total 

phosphorus (P) loads at or below 1991 levels at the compliance point at Washington, North Carolina (North Carolina 

Division of Water Quality, 2011). All of the rules for the Neuse 

and Tar-Pamlico NSW strategies were fully implemented by 

2003 and 2006, respectively.

According to the 2009 Neuse River and 2010 Tar-Pamlico basinwide water-quality plans (North Carolina Division 

of Water Quality, 2009 and 2011, respectively), individual 

categories of nutrient sources (including point-source 

discharges and agriculture) have met or exceeded their respective goals for nutrient reductions; however, evaluations of 

nutrient monitoring data at the compliance points in each basin 

by DWQ staff indicated that significant reductions in stream 

nutrient loads to the estuaries had not yet been achieved. Some 

important considerations are that (1) the monitoring data 

used in the evaluation of loads only included several years of 

data collected following full implementation of the nutrient 

reduction rules, and (2) DWQ staff indicated that additional 

years of data collection are likely needed before potential 

improvements in stream water quality may be identified and 

to more fully assess the effectiveness of the NSW strategies. 

Also, the DWQ evaluated potential limitations to the current 

strategies and identified additional information needed to 

support further development and implementation of the NSW 

strategies for the Neuse River basin (North Carolina Division 

of Water Quality, 2009, chap. 24) and the Tar-Pamlico River 

basin (North Carolina Division of Water Quality, 2011, 

chap. 6). One of the primary needs identified was to obtain 

more detailed analysis of available data to document nutrient 

loadings to the Neuse and Tar-Pamlico Rivers with a focus on 

smaller watersheds, having dominant land-use types (such as 

urban and agricultural), located upstream from the estuaries.

In a study of N and P concentrations in the AlbemarlePamlico drainage basin in North Carolina and Virginia, 

McMahon and Harned (1998) indicated that understanding 

the relation between water-quality characteristics and 

environmental settings, such as land use and soil drainage, is 

an important consideration in developing watershed management plans. In a more recent study, Rothenberger and others 

(2009) examined land-use data and nutrient concentrations in 

26 subbasins throughout the Neuse River basin and modeled 

specific land-use characteristics that influenced surface-water 

quality among the study sites. Contributions of N and P to 

streams in the Upper Neuse basin were found to be highly 

influenced by wastewater dischargers in urban subbasins, 

whereas in the Lower Neuse basin, agricultural subbasins with 

intensive swine production were the most important contributors of N and P to receiving streams. In 2009, the DWQ and 

U.S. Geological Survey (USGS) initiated a collaborative study 

to better understand the relations between various watershed 

settings and nutrient yields in streams throughout central and 

eastern North Carolina. Results of this study will provide 

needed information to assist DWQ in the development and 

implementation of NSW management strategies for reducing 

N and P loadings in central and eastern North Carolina.

Purpose and Scope

The primary purpose of this report is to summarize and 

synthesize nutrient yield data compiled for 48 stream sites 

located in central and eastern North Carolina. Data on land 

cover and other watershed variables also were included in 

the synthesis to examine potential influences of watershed 

attributes on nitrate, total N, and total P yields within and 
Introduction 3

among the stream study sites. The scope of work included 

a compilation of existing nutrient concentration data and 

streamflow data obtained between 1997 and 2008 for the 

48 stream sites. The nutrient and streamflow data were 

used for developing model estimates of nitrate, total N, and 

total P loads at each study location. Annual nutrient yields 

were determined by dividing annual nutrient loads by the 

drainage area of the watershed for each site. Information 

on attributes such as land cover, point sources, and concentrated animal feeding operations (CAFOs) were compiled 

for each stream watershed. The nutrient yield data, which 

normalize the effects of drainage area and streamflow 

differences among the sites, were used in the final analysis 

to explore relations between watershed attributes and 

stream nutrient export. The study results are intended to 

increase our understanding of environmental variables in 

watersheds that influence stream export of nutrients. This 

information can assist water-resource managers and policy 

makers in their efforts to protect and improve stream water 

quality throughout North Carolina.

Description of the Study Area

The stream sites examined in this study are located in 

six river basins within central and eastern North Carolina 

(fig. 1). These river basins include, from north to south, 

the Roanoke, Chowan, Tar-Pamlico, Neuse, Cape Fear, 

and Lumber River basins. The Roanoke basin originates 

in the Blue Ridge Mountains of Virginia and ends in the 

northeastern Coastal Plain of North Carolina at Albemarle 

Sound. Approximately 36 percent (3,503 square miles 

[mi2

]) of the basin area is located within the Piedmont and 

Coastal Plain regions of North Carolina (North Carolina 

Division of Water Quality, 2006). The Chowan River 

basin originates in the Coastal Plain of Virginia and flows 

through the northeastern Coastal Plain of North Carolina, 

representing about 25 percent (1,373 mi2

) of the basin area, 

where it empties into Albemarle Sound (North Carolina 

Division of Water Quality, 2007).

The Tar-Pamlico (6,148 mi2

), Neuse (6,235 mi2), and 

Cape Fear River basins (9,149 mi2

) lie entirely within 

North Carolina. The Tar-Pamlico River and Neuse River 

basins represent the fourth and third largest basins in the 

State, respectively. These large basins originate in the 

Piedmont region in the north-central part of North Carolina 

and flow southeastward through the Coastal Plain where 

they empty into the Pamlico Sound (North Carolina Division of Water Quality, 2009, 2011). The Cape Fear River 

basin, the largest in the State, originates in the Piedmont 

Province in the central part of the State and flows through 

the Sandhills and Coastal Plain regions before emptying 

into the Atlantic Ocean (North Carolina Division of Water 

Quality, 2005).

The Lumber River basin in North Carolina comprises 

four subbasins where three of the subbasins ultimately 

flow to the Pee Dee River basin in South Carolina and 

one subbasin drains to the Atlantic Ocean in southeastern 

North Carolina (North Carolina Division of Water Quality, 

2010). The upper reaches of the Lumber River basin originate 

in the Sandhills region and the rest of the basin is located 

in the Coastal Plain. Detailed information for each basin, 

including population and land-cover characteristics, impaired 

water bodies, water-quality and ecological concerns, and point 

and nonpoint sources (NPS), are presented by DWQ within 

the basinwide water-quality plans referenced above.

Description of the Stream Monitoring Network

Nutrient concentration data for 48 stream-monitoring 

stations were compiled from existing databases for the period 

1997 through 2008. The monitoring stations were selected 

on the basis of the availability of both USGS continuous 

streamflow data and nutrient concentration data collected as 

part of the DWQ Ambient Monitoring System (AMS) and 

(or) through USGS water-quality studies. The USGS and 

co-located DWQ AMS stations included in this study are listed 

in table 1 by river basin and associated subbasins, which are 

based on the 8-digit hydrologic unit codes (HUCs).

The network of 48 study sites are distributed throughout 

the Roanoke, Chowan, Tar-Pamlico, Neuse, Cape Fear, and 

Lumber River basins (table 1), which cover the Piedmont, 

Sandhills, and Coastal Plain regions of North Carolina (fig. 1). 

The North Carolina part of the Roanoke River basin includes 

study sites 1 and 2 in the Upper Dan River subbasin (fig. 2). 

The North Carolina part of the Chowan River basin includes 

study site 3 in the Meherrin River subbasin (fig. 3). Five study 

sites are located in the Tar-Pamlico basin, including site 4 

in the Upper Tar River subbasin, site 5 in the Fishing Creek 

subbasin, sites 6 and 7 in the Lower Tar River subbasin, and 

site 8 in the Pamlico River subbasin (fig. 3; table 1). 

Most of the study sites (36 of 48) are located in the Neuse 

River and Cape Fear River basins, with each having 18 sites 

(table 1). Study sites in the Neuse River basin include sites 

9–19 in the Upper Neuse River subbasin, sites 20–22 and 25 

in the Middle Neuse River subbasin, sites 23 and 24 in the 

Contentnea Creek subbasin, and site 26 in the Lower Neuse 

River subbasin (fig. 4). Study sites in the Cape Fear River 

basin include sites 28–39 in the Haw River subbasin, site 40 

in the Deep River subbasin, sites 41 and 42 in the Upper Cape 

Fear River subbasin, site 43 in the Lower Cape Fear subbasin, 

site 44 in the Black River subbasin, and site 27 in the New 

River subbasin (fig. 5). Although the New River subbasin is 

included in the Cape Fear River basin as part of this study, 

the New River subbasin drains directly to the Atlantic Ocean 

and not the Cape Fear River. The North Carolina part of the 

Lumber River basin includes sites 45–47 in the Lumber River 

subbasin and site 48 in the Waccamaw River subbasin (fig. 6). 

As discussed later, the study sites represent various drainage 

areas and mixtures of urban, agricultural, and forested lands.
4 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 Table 1. Stream study-site network with paired U.S. Geological Survey and Division of Water Quality stations used for evaluation of nutrient loads. [USGS, U.S. Geological Survey; HUC, hydrologic unit code; DWQ, North Carolina Department of Environment and Natural Resources Division of Water Quality; AMS, ambient monitoring system; LNBA, Lower Neuse Basin Association; UCFRBA, Upper Cape Fear River Basin Association; MCFRBA, Middle Cape Fear River Basin Association; NC, North Carolina; na, not applicable; Rd, road] Study site number (see figs. 1–6) River basin Subbasin 8- digit HUC Subbasin name USGS station number USGS station name Decimal latitude Decimal longitude DWQ AMS station number Source of data for AMS station DWQ AMS station name

1 Roanoke 03010103 Upper Dan River 02068500 Dan River near Francisco, NC 36.5150 –80.3030 N0150000 DWQ Dan River at NC704 near 

Francisco

2 Roanoke 03010103 Upper Dan River 02071000 Dan River near Wentworth, NC 36.4125 –79.8261 N2300000 DWQ Dan River at SR2150 near 

Wentworth

3 Chowan 03010204 Meherrin River 02053200 Potecasi Creek near Union, NC 36.3708 –77.0255 D4150000 DWQ Potecasi Creek at NC11 

near Union

4 Tar Pamlico 03020101 Upper Tar River 02081500 Tar River near Tar River, NC 36.1941 –78.5830 O0100000 DWQ Tar River at NC96 near Tar 

River

5 TarPamlico 03020102 Fishing Creek 02083000 Fishing Creek near Enfield, NC 36.1505 –77.6930 O4680000 DWQ Fishing Creek at US301 

near Enfield

6 Tar Pamlico 03020103 Lower Tar River 02083500 Tar River at Tarboro, NC 35.8944 –77.5330 O5250000 DWQ Tar River at NC33 and 

US64 Bus at Tarboro

7 Tar Pamlico 03020103 Lower Tar River 02084160 Chicod Creek at SR1760 near Simpson, NC 35.5616 –77.2308 O6450000 DWQ Chicod Creek at SR1760 

near Simpson

8 Tar Pamlico 03020104 Pamlico River 02084557 Van Swamp near Hoke, NC 35.7308 –76.7461 O9755000 DWQ Van Swamp at NC32 near 

Hoke

9 Neuse 03020201 Upper Neuse River 02085000 Eno River at Hillsborough, NC 36.0711 –79.0955 na

10 Neuse 03020201 Upper Neuse River 02085070 Eno River near Durham, NC 36.0722 –78.9077 J0770000 DWQ Eno River at US501 near 

Durham

11 Neuse 03020201 Upper Neuse River 0208521324 Little River at SR1461 near Orange Factory, 

NC

36.1416 –78.9191 J0820000 DWQ Little River at SR1461 near 

Orange Factory

12 Neuse 03020201 Upper Neuse River 0208524090 Mountain Creek at SR1617 near Bahama, NC 36.1497 –78.8966 na13 Neuse 03020201 Upper Neuse River 0208524975 Little River below Little River Trib at 

Fairntosh, NC

36.1133 –78.8597 na

14 Neuse 03020201 Upper Neuse River 02085500 Flat River at Bahama, NC 36.1827 –78.8788 na15 Neuse 03020201 Upper Neuse River 02086500 Flat River at dam near Bahama, NC 36.1486 –78.8288 J1100000 DWQ Flat River at SR1004 near 

Willardsville

16 Neuse 03020201 Upper Neuse River 0208650112 Flat River Trib near Willardville, NC 36.1319 –78.8333 na17 Neuse 03020201 Upper Neuse River 02087183 Neuse River near Falls, NC 35.9400 –78.5808 J1890000 DWQ Neuse River at SR2000 

near Falls

18 Neuse 03020201 Upper Neuse River 0208726005 Crabtree Creek at Ebenezer Church Rd near 

Raleigh, NC

35.8452 –78.7244 J3000000 DWQ Crabtree Creek at SR1649 

near Raleigh

19 Neuse 03020201 Upper Neuse River 02087580 Swift Creek near Apex, NC 35.7188 –78.7522 na20 Neuse 03020202 Middle Neuse 

River

02089000 Neuse River near Goldsboro, NC 35.3375 –77.9975 J5970000 DWQ Neuse River at SR1915 

near Goldsboro

21 Neuse 03020202 Middle Neuse 

River

0208925200 Bear Creek at Mays Store Rd, NC 35.2747 –77.7944 J6044500 LNBA Bear Creek at SR1311 Bear 

Creek Rd near Kinston

22 Neuse 03020202 Middle Neuse 

River

02089500 Neuse River at Kinston, NC 35.2577 –77.5855 J6150000 DWQ Neuse River at NC11 at 

Kinston
Introduction 

5

Table 1. Stream study-site network with paired U.S. Geological Survey and Division of Water Quality stations used for evaluation of nutrient loads.—Continued[USGS, U.S. Geological Survey; HUC, hydrologic unit code; DWQ, North Carolina Department of Environment and Natural Resources Division of Water Quality; AMS, ambient monitoring system; LNBA, Lower Neuse Basin Association; UCFRBA, Upper Cape Fear River Basin Association; MCFRBA, Middle Cape Fear River Basin Association; NC, North Carolina; na, not applicable; Rd, road]

Study site 

number 

(see figs. 

1–6)

River 

basin

Subbasin 

8- digit 

HUC

Subbasin name USGS sta- tion number USGS station name Decimal latitude Decimal longitude DWQ AMS station number Source of data for AMS station DWQ AMS station name

23 Neuse 03020203 Contentnea Creek 02090380 Contentnea Creek near 

Lucama, NC

35.6911 –78.1097 J6740000 DWQ Contentnea Creek at NC581 near Lucama

24 Neuse 03020203 Contentnea Creek 02091500 Contentnea Creek at 

Hookerton, NC

35.4288 –77.5825 J7450000 DWQ Contentnea Creek at NC123 at Hookerton

25 Neuse 03020202 Middle Neuse 

River

02091814 Neuse River near Fort 

Barnwell, NC

35.3138 –77.3027 J7850000 DWQ Neuse River at SR1470 near Fort Barnwell

26 Neuse 03020204 Lower Neuse 

River

02092500 Trent River near Trenton, NC 35.0641 –77.4613 J8690000 DWQ Trent River at SR1129 near Trenton

27 Cape Fear 03020302 New Rver 02093000 New River near Gum Branch, 

NC

34.8491 –77.5194 P0600000 DWQ New River at SR1314 near Gum Branch

28 Cape Fear 03030002 Haw River 02094500 Reedy Fork near Gibsonville, 

NC

36.1730 –79.6141 B0400000 UCFRBA Reedy Fork at SR 2719 High Rock Rd near 

Monticello

29 Cape Fear 03030002 Haw River 02095000 South Buffalo Creek near 

Greensboro, NC

36.0600 –79.7258 B0670000 UCFRBA South Buffalo Creek at SR3000 McConnell Rd near Greensboro

30 Cape Fear 03030002 Haw River 02095500 North Buffalo Creek near 

Greensboro, NC

36.1205 –79.7080 B0540000 DWQ North Buffalo Creek at SR2832 near 

Greensboro

31 Cape Fear 03030002 Haw River 0209553650 Buffalo Creek at SR2819 

near McLeansville, NC

36.1280 –79.6616 B0540050 UCFRBA North Buffalo Creek at SR2770 Hufine 

Mill Rd near McLeansville

32 Cape Fear 03030002 Haw River 02096500 Haw River at Haw River, NC 36.0872 –79.3661 B1140000 DWQ Haw River at NC 49N at Haw River33 Cape Fear 03030002 Haw River 02096846 Cane Creek near Orange 

Grove, NC

35.9872 –79.2061 na

34 Cape Fear 03030002 Haw River 02096960 Haw River near Bynum, NC 35.7652 –79.1358 B2100000 DWQ Haw River at SR1713 near Bynum

35 Cape Fear 03030002 Haw River 02097314 New Hope Creek near 

Blands, NC

35.8850 –78.9652 B3040000 DWQ New Hope Creek at SR1107 near Blands

36 Cape Fear 03030002 Haw River 0209741955 Northeast Creek at SR1100 

near Genlee, NC

35.8722 –78.9130 B3660000 DWQ Northeast Creek at SR1100 near Nelson

37 Cape Fear 03030002 Haw River 02097464 Morgan Creek near White 

Cross, NC

35.9236 –79.1150 na

38 Cape Fear 03030002 Haw River 02097517 Morgan Creek near 

Chapel Hill, NC

35.8933 –79.0197 B3899180 UCFRBA Morgan Creek at Mason Farm WWTP 

entrance at Chapel Hill

39 Cape Fear 03030002 Haw River 0209782609 White Oak Creek at mouth 

near Green Level, NC

35.7602 –78.9202 na

40 Cape Fear 03030003 Deep River 02099000 East Fork Deep River near 

High Point, NC

36.0372 –79.9455 B4240000 DWQ East Fork Deep River at SR1541 near High 

Point

41 Cape Fear 03030004 Upper Cape Fear 

River

02102500 Cape Fear River at 

Lillington, NC

35.4061 –78.8133 B6370000 DWQ Cape Fear River at US401 at Lillington

42 Cape Fear 03030004 Upper Cape Fear 

River

02104220 Rockfish Creek at 

Raeford, NC

34.9997 –79.2147 B7679300 MCFRBA Rockfish Creek at US 401 bypass near 

Raeford
6 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 Table 1. Stream study-site network with paired U.S. Geological Survey and Division of Water Quality stations used for evaluation of nutrient loads.—Continued [USGS, U.S. Geological Survey; HUC, hydrologic unit code; DWQ, North Carolina Department of Environment and Natural Resources Division of Water Quality; AMS, ambient monitoring system; LNBA, Lower Neuse Basin Association; UCFRBA, Upper Cape Fear River Basin Association; MCFRBA, Middle Cape Fear River Basin Association; NC, North Carolina; na, not applicable; Rd, road] Study site number (see figs. 1–6) River basin Subbasin 8- digit HUC Subbasin name USGS sta- tion number USGS station name Decimal latitude Decimal longitude DWQ AMS station number Source of data for AMS station DWQ AMS station name 43 Cape Fear 03030005 Lower Cape Fear River 02105769 Cape Fear River at lock 1 nr Kelly, NC 34.4044 –78.2936 B8350000 DWQ Cape Fear River at Lock 1 near Kelly 44 Cape Fear 03030006 Black River 02106500 Black River near Tomahawk, NC 34.7550 –78.2886 B8750000 DWQ Black River at NC411 near Tomahawk 45 Lumber 03040203 Lumber River 02133500 Drowning Creek near Hoffman, NC 35.0611 –79.4938 I2090000 DWQ Drowning Creek at US1 near Hoffman 46 Lumber 03040203 Lumber River 02133624 Lumber River near Maxton, NC 34.7727 –79.3319 I2750000 DWQ Lumber River at SR1303 near Maxton 47 Lumber 03040203 Lumber River 02134500 Lumber River at Boardman, NC 34.4425 –78.9602 I5690000 DWQ Lumber River at US74 at Boardman 48 Lumber 03040206 Waccamaw River 02109500 Waccamaw River at Freeland, NC 34.0950 –78.5483 I8970000 DWQ Waccamaw River at NC130 at Freeland
Introduction 7

#

# #

# #

#

#

#

# #

###

###

#

#

#

#

# #

#

#

#

#

#

#

#

## #

#

#

## ##

#

#

#

#

#

#

#

#

#

#

9

8

7

6

5

4

2 3

1

48

47

46

45

44

43

42

41

40

39

38

37

35

34

33

32

31

30

29

28

27

26

25

24

23

21 22

20

19

18

17

16

15

14

10

36

13

12

11

Piedmont

Blue

Ridge

SOUTH CAROLINA

NORTH CAROLINA

VIRGINIA

Coastal

Plain

Sand

Hills

80° 79° 78° 77° 76°

37°

36°

35°

34°

NC

SC

GA

TN

KY

WV

VA

OH MD

Location of study area and physiographic

province lines in Virginia, North Carolina,

and South Carolina

0 12.5 25 50 MILES

0 25 50 KILOMETERS 12.5

Streams data from 1:100,000 scale National Hydrography Dataset, USGS

Major river basins from 1:100,000 scale National Hydrography Dataset, USGS

County and city boundaries from 1:100,000 scale Bureau of Census

ATLANTIC OCEAN

Roanoke

Chowan

Tar-Pamlico

Neuse

Cape Fear

Lumber

Basin boundary

Physiographic province boundary

Study site and number

EXPLANATION

River Basins

Pamlico Sound

Albemarle Sound

48#

Figure 1. Study area, major river basins, and site locations examined in Central and Eastern North Carolina.
8 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

#

#

#

SURRY

PATRICK

VIRGINIA

NORTH CAROLINA

CARROLL

STOKES

HENRY

GUILFORD FORSYTH

ROCKINGHAM

PITTSYLVANIA

FLOYD

FRANKLIN

CASWELL

DANVILLE

Galax Martinsville

03010103

03010104

03010101 03010105

Danville

79°30' 79°45' 80° 80°15' 80°30' 80°45'

36°45'36°30'36°15'

Streams data from 1:100,000 scale National Hydrography Dataset, USGSMajor river basins from 1:100,000 scale National Hydrography Dataset, USGSCounty and city boundaries from 1:100,000 scale Bureau of Census

2

1

0 10 20 MILES 5

0 10 20 KILOMETERS 5

Location of study area in North Carolina

and Virginia

Dan

River

EXPLANATION

Basin boundarySubbasin boundary and 8-digit hydrologic unit codeStudy site and number

#1

03010103

VA

NC

Figure 2. Location of stream study sites 1 and 2 in the Roanoke River basin.
Introduction 

9

#

#

#

#

#

#

#

PITT

BERTIE

HALIFAX

NASH

MARTIN

FRANKLIN

WARREN

GRANVILLE

WILSON

BEAUFORT

HERTFORD

VANCE

NORTHAMPTON

EDGECOMBE

GATES

PERSON

SUFFOLK GREENSVILLE SOUTHAMPTON

BRUNSWICK

WASHINGTON

CHOWAN

03020101

03020103

03020102

03010203

03010204

03020104

03010201

8

7

6

5

4

3

76°45' 77° 77°15' 77°30' 77°45' 78° 78°15' 78°45'

36°30'36°15'

36°

35°45'35°30'

Streams data from 1:100,000 scale National Hydrography Dataset, USGSMajor river basins from 1:100,000 scale National Hydrography Dataset, USGSCounty and city boundaries from 1:100,000 scale Bureau of Census

78°30'

EXPLANATION

Chowan River basinTar-Pamlico River basinBasin boundarySubbasin boundary and 8-digit hydrologic unit codeStudy site and number #4

0 10 20 MILES 5

0 10 20 KILOMETERS 5

03020101

Rocky

Mount

Tarboro

Greenville

Tar

River

River

Chowan

VIRGINIA

NORTH CAROLINA

Location of study area in North Carolina

and Virginia

VA

NC

Figure 3. Location of stream study site 3 in the Chowan River Basin and study sites 4–8 in the Tar-Pamlico River basin.
10 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

#

#

#

#

#

#

#

#

#

#

#

#

WAKE

NASH

EDGECOMBE

FRANKLIN

WILSON

ORANGE

GRANVILLE

JOHNSTON

DURHAM

PERSON

WAYNE

GREENE

03020201

03020203

9

19

18

17

16

14

13

12 11

10

15

23

Durham

Raleigh

FALLS

LAKE

Location of study area in North Carolina

77°45' 78° 78°15' 78°30' 78°45' 79° 79°15'

35°45'

36°

36°15'

Streams data from 1:100,000 scale National Hydrography Dataset, USGSMajor river basins from 1:100,000 scale National Hydrography Dataset, USGSCounty and city boundaries from 1:100,000 scale Bureau of Census

0 10 20 MILES 5

0 10 20 KILOMETERS 5

EXPLANATION

Basin boundarySubbasin boundary and 8-digit hydrologic unit codeStudy site and number

#

03020203

17

Wilson

River

Neuse

Figure 4. Location of stream study sites 9–26 in the Neuse River basin.
Introduction 11 0 10 20 MILES 5 0 10 20 KILOMETERS 5

#

#

#

#

#

#

#

#

#

PITT

JOHNSTON

WAYNE

SAMPSON

LENOIR

JONES

WILSON

WAKE

GREENE

CRAVEN

ONSLOW

EDGECOMBE

NASH

03020201

03020203

03020202

03020204

26

25

24

23

22 21

20

19

77°15' 77°30' 77°45' 78° 78°15' 78°30' 78°45'

35°45'35°30'35°15'

35°

Location of study area in North Carolina

Streams data from 1:100,000 scale National Hydrography Dataset, USGSMajor river basins from 1:100,000 scale National Hydrography Dataset, USGSCounty and city boundaries from 1:100,000 scale Bureau of Census

EXPLANATION

Basin boundarySubbasin boundary and 8-digit hydrologic unit codeStudy site and number #

03020201

21

Wilson

Goldsboro

Kinston

River

Neuse

Figure 4. Location of stream study sites 9–26 in the Neuse River basin.—Continued
12 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

#

#

#

#

#

#

#

#

#

#

#

#

#

#

WAKE

MOORE

GUILFORD

RANDOLPH

HARNETT

CHATHAM

LEE

FORSYTH

ORANGE

ALAMANCE

MONTGOMERY

DURHAM

JOHNSTON

CASWELL

ROCKINGHAM

SAMPSON CUMBERLAND

03030002

03030003

03030004

03030006

41

40

39

38

37

36

35

34

33

32

31

30

29

28

Raleigh

High

Point

Greensboro

78°30' 79° 79°30' 80° 80°30'

36°

35°30'

Location of study area in North Carolina,

Streams data from 1:100,000 scale National Hydrography Dataset, USGSMajor river basins from 1:100,000 scale National Hydrography Dataset, USGSCounty and city boundaries from 1:100,000 scale Bureau of Census

0 10 20 MILES 5

0 10 20 KILOMETERS 5

Durham

EXPLANATION

Basin boundarySubbasin boundary and 8-digit hydrologic unit codeStudy site and number

#

03030004

27

River

River

Haw

Deep

River

Cape

Fear

JORDAN

LAKE

Northeast

Creek

Creek

Buffalo

South 

Creek

Buffalo

North 

Figure 5. Location of stream study sites 27–44 in the Cape Fear River basin.
Introduction 13

# #

#

#

#

#

#

DUPLIN

BLADEN

PENDER

ROBESON

SAMPSON

COLUMBUS

ONSLOW

HOKE

CUMBERLAND JONES

LENOIR

BRUNSWICK

WAYNE HARNETT MOORE

NEW

HANOVER

JOHNSTON

Location of study area in North Carolina,

03030007

03030006

03030004

03030005

03020302

44

43

42

27

77°30' 78° 78°30' 79°

35°

34°30'

Streams data from 1:100,000 scale National Hydrography Dataset, USGSMajor river basins from 1:100,000 scale National Hydrography Dataset, USGSCounty and city boundaries from 1:100,000 scale Bureau of Census

0 10 20 MILES 5

0 10 20 KILOMETERS 5

ATLANTIC OCEAN

EXPLANATION

Basin boundarySubbasin boundary and 8-digit hydrologic unit codeStudy site and number #

03030005

44 Wilmington

Fayetteville

Jacksonville

River

Cape

Fear

River

New

Clinton

Lumberton River

Black

Figure 5. Location of stream study sites 27–44 in the Cape Fear River basin.—Continued
14 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

#

#

#

#

BLADEN ROBESON

NORTH

CAROLINA

SOUTH

CAROLINA

COLUMBUS

HORRY

HOKE

DILLON

BRUNSWICK

MOORE

MARION

RICHMOND

MARLBORO

SCOTLAND

MONTGOMERY

Waccamaw

River

03040203

03040204

03040206

03040208

48

47

46

45

78° 78°15' 78°30' 78°45' 79° 79°15' 79°30' 79°45' 80°

35°15'

35°

34°45'34°30'34°15'

34°

Streams data from 1:100,000 scale National Hydrography Dataset, USGSMajor river basins from 1:100,000 scale National Hydrography Dataset, USGSCounty and city boundaries from 1:100,000 scale Bureau of Census

0 10 20 MILES 5

0 10 20 KILOMETERS 5

Location of study area in North Carolina

and South Carolina

NC

SC

EXPLANATION

Basin boundarySubbasin boundary and 8-digit hydrologic unit codeStudy site and number

#

03040206

45

River

Lumber Lumberton

Figure 6. Location of stream study sites 45–48 in the Lumber River basin.
Methods of Data Compilation and Analysis 15

Methods of Data Compilation and 

Analysis

This section provides a discussion of the approach and 

methods used to compile datasets for determining nutrient 

loads and for characterizing watershed conditions at the study 

sites. In addition to stream nutrient load data, information 

on watershed attributes, including land cover, soil drainage, 

precipitation, permitted point-source dischargers, and 

permitted CAFOs, was compiled for the contributing drainage 

areas of each stream watershed. Statistical methods used to 

evaluate potential relations between stream nutrient yields and 

watershed attributes within and among the study sites also are 

presented.

Stream Nutrient Loads

At each of the 48 study sites, available nutrient concentration data and streamflow data were used to compute stream 

loadings of nitrate, total N, and total P for the 1997 to 2008 

study period, based on calendar years. The methods used to 

compile the nutrient datasets (for model calibrations) and the 

streamflow datasets (for model predictions) needed for computing stream nutrient loads with the USGS Load Estimator 

(LOADEST; Runkel and others, 2004) statistical program are 

presented in the following sections.

Nutrient Data

The water-quality datasets (including laboratory analysis 

of nutrients, field measurements of water temperature, specific 

conductance, dissolved oxygen, and pH, and streamflow) 

compiled for the USGS monitoring stations were based on 

data for surface-water samples collected during the 12-year 

study period from 1997 to 2008. Many of the sites had nutrient 

and streamflow data for the entire 12-year period of record 

(POR). For some sites, the first year that nutrient and (or) 

streamflow data were available varied between 1998 and 2004; 

thus, the POR for these sites ranged between 5 and 11 years.

Most of the nutrient concentration data used in the 

analysis were obtained from DWQ AMS stations co-located 

with the USGS monitoring stations (table 1). The DWQ 

AMS stations were operated and sampled by either DWQ or 

coalitions of National Pollutant Discharge Elimination System 

(NPDES) dischargers that partner with DWQ to acquire waterquality information within specific watersheds. The 39 DWQ 

AMS stations co-located with the USGS stations (table 1) 

included 33 stations operated by DWQ and 6 stations operated 

by coalition partners—Lower Neuse Basin Assocation, Upper 

Cape Fear River Basin Association, and Middle Cape Fear 

River Basin Association.

Water-quality data based on USGS sample collections 

were analyzed by the USGS National Water Quality Laboratory (NWQL) in Lakewood, Colorado. The USGS data were 

retrieved from the USGS National Water Information System 

(NWIS) database (http://waterdata.usgs.gov/nc/nwis). Samples 

collected by DWQ were analyzed by the DWQ Central 

Laboratory in Raleigh, North Carolina, and samples collected 

by the individual monitoring associations were analyzed by 

independent contract laboratories. All of the water-quality data 

for the DWQ AMS stations were obtained through the U.S. 

Environmental Protection Agency (USEPA) STOrage and 

RETrieval (STORET) database (http://www.epa.gov/storet/) 

using retrieval procedures provided by DWQ (see 

http://portal.ncdenr.org/web/wq/ess/eco/ams and associated 

links in the Data and Results section). All nutrient concentration data included any reported laboratory remark codes, such 

as less than (<) censored values, associated with analytical 

results. Concentrations of the nitrogen species are reported in 

milligrams per liter as N and concentrations of P are reported 

in milligrams per liter as P. Depending on the individual study 

site, nutrient data were compiled from one or more sources 

(DWQ, coalition partners, and USGS) for use in computing 

stream nutrient loads.

The following describes differences in the nutrient 

concentration data obtained for the DWQ AMS stations and 

the USGS stations, and steps taken to prepare the data for 

subsequent use in developing the nutrient data calibration 

files for the LOADEST program. Some data from DWQ AMS 

stations had sample collection dates without associated sample 

collection times. Because both a collection date and time for 

each sample are needed for LOADEST calibrations, estimated 

sample times were assigned to these samples based on 

examination of sampling times associated with other samples.

The analytical results retrieved for DWQ AMS stations 

were based on unfiltered samples and included concentrations 

for total ammonia plus organic N (also referred to as total 

Kjeldahl nitrogen (TKN)), total nitrate plus nitrite, and total P. 

The total concentrations reported for total nitrate plus nitrite 

are assumed to be equivalent to dissolved concentrations of 

nitrate plus nitrite because these constituents are present in 

water only in dissolved form. Values of total N for DWQ AMS 

data were computed by summing the reported concentrations 

for total ammonia plus organic N and total nitrate plus nitrite. 

The analytical results retrieved for USGS samples included 

concentrations for total ammonia plus organic N, dissolved 

nitrate plus nitrite, and total P. For some USGS samples, the 

analytical results included direct laboratory measurement of 

total N. If samples did not include direct measurement of total 

N, then values of total N were computed by summing the 

reported concentrations for total ammonia plus organic N and 

dissolved nitrate plus nitrite. 

Left-censored (“less-than”) values for nitrogen fractions 

were handled as follows for computing total N. If concentrations for both total ammonia plus organic N and dissolved 

nitrate plus nitrite were censored, then the < remark code was 

carried forward with the computed total N value. If only one 

of the constituents was censored and its value represented 

approximately half or more of the computed total N value, 

then the < remark code also was assigned to the total N value.
16 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

For each study site, the nutrient analytical results and 

associated laboratory remarks were reviewed to identify 

questionable sample results and obvious outliers, which 

were then excluded from use in developing the nutrient 

data LOADEST model calibration files. Some of the 

excluded data included analytical results generated by the 

DWQ laboratory during March through July 2001. During 

this period the DWQ laboratory implemented changes to 

internal quality-assurance practices and analytical methods 

that resulted in substantial, but temporary, increases in 

analytical reporting levels for nutrient constituents (see 

http://portal.ncdenr.org/web/wq/ess/eco/ams and associated 

link to AMS Data Explanations). Consequently, nutrient 

concentrations analyzed by the DWQ laboratory during this 

period were excluded from consideration. Any dates for which 

samples were collected and the associated daily mean value of 

streamflow was equal to zero, or no flow, also were excluded 

from consideration because the nutrient concentrations need to 

be associated with actual streamflow in order to calibrate the 

load estimation models. 

The final step taken in preparing the compiled nutrient 

concentration data involved subsetting the data to represent 

a similar sampling frequency throughout the POR for each 

study site. The nutrient data retrieved from the STORET and 

NWIS databases represented sample collection frequencies 

(including daily, weekly, biweekly, monthly, and bimonthly) 

that typically varied throughout the POR for an individual site 

and among sites. At some sites for example, the collection 

frequency during the POR shifted from initial daily collections 

to weekly and biweekly collections in the latter years. At 

other sites, the collection frequency changed from weekly to 

monthly sampling. For each study site, the sampling frequency 

of compiled data was reviewed and, where appropriate, 

the data were subsetted such that the nutrient data used in 

the load calibration files were more uniformily distributed 

throughout the POR. This was done to avoid potential bias in 

the nutrient calibration data by overweighting values during 

those years that had higher sampling frequencies. For most 

sites, the compiled nutrient data consisted of samples having 

a frequency of collection between about 3 to 5 weeks. The 

POR for some sites had substantial gaps in nutrient data 

where samples were not collected over periods of months 

or, in limited cases, a couple of years. Larger gaps generally 

occurred near the middle part of the POR, and the data were 

still considered appropriate for use in estimating loads because 

sufficient nutrient data were available before and after the gaps 

for calibrating the load models. 

The final water-quality datasets compiled for estimating 

nutrient loads for each study site are presented in appendix 1; 

data files are arranged first by river basin and then by individual study site. The calibration files include the USGS 

station number, sample dates and times, the daily mean value 

of streamflow, and laboratory remark codes and concentrations 

for nitrate, total N, and total P. The nitrate concentrations 

presented in the calibration files actually represent the reported 

concentrations of nitrate plus nitrite, but because nitrite 

typically represents a small fraction of the total concentration, 

the concentrations and subsequent loads computed with the 

LOADEST program are presented and discussed as nitrate. 

Streamflow Data

For each study site, daily mean streamflow values were 

retrieved through the USGS NWIS database. The compiled 

streamflow data primarily were used in combination with the 

nutrient calibration data for predicting stream nutrient loads 

with the LOADEST program (Runkel and others, 2004). On 

occasion, the retrieved daily mean value of streamflow (in 

cubic feet per second) for some sites was reported as zero (no 

streamflow). This is problematic because the LOADEST computations rely on log-transformed streamflow values and will 

not function when the streamflow input file contains values of 

zero. The dates with zero flow values can be removed from the 

streamflow input file and then daily loads can be estimated for 

only those dates having actual flow. Cumulative sums of loads 

on an annual, seasonal, or monthly basis, however, would not 

be computed because cumulative loads are only computed for 

time periods with complete data (that is, no missing values). 

For this reason, dates with reported daily mean values of zero 

streamflow were arbitrarily assigned a value of 0.001 cubic 

foot per second (ft3

/s) for the streamflow prediction files in 

order to permit computation of annual, seasonal, and monthly 

loads. Use of this extremely low value has negligible effects 

on the estimated loads. The streamflow data prediction files 

compiled for estimating nutrient loads are presented in appendix 1. If 0.001 ft3

/s was substituted for zero streamflow on one 

or more dates, this was noted within the site’s prediction file 

(for example, site 11 in the Neuse River basin, appendix 1).

The compiled streamflow data also were used to 

determine streamflow characteristics (annual streamflows, 

annual streamflow yields, and base-flow index [BFI]) for 

use in statistical evaluations of the nutrient yields among 

the study sites. Annual streamflow yields were computed by 

dividing annual streamflow by watershed drainage area to 

normalize the effects of large differences in drainage areas 

among the study sites. For sites not known to be influenced by 

controlled releases from surface-water reservoirs, streamflow 

hydrograph separations were performed using the streamflow 

data to determine the BFI, or percentage contribution of the 

annual streamflow derived from base flow, or groundwater 

discharge. For example, a computed BFI of 0.386 indicates 

that the mean annual contribution of groundwater to the total 

streamflow during the POR was 38.6 percent with the balance 

(61.4 percent) derived from surface-water runoff. Hydrograph 

separations were performed using the Web-based hydrograph 

analysis tool (WHAT) from Purdue University (http://cobweb.

ecn.purdue.edu/~what/). The WHAT program, described by 

Lim and others (2005), uses the local minimum and digital 

filtering methods to separate base flow from daily streamflow 

datasets. For consistency, the local minimum method was used 

to determine the average annual BFI for the POR of each site. 
Watershed Setting 17

Load Estimation

Stream nutrient loads were computed using the USGS 

LOADEST program (Runkel and others, 2004). Specifically, 

the S-LOADEST program, which is a USGS plug in version of 

LOADEST within the S-Plus software suite (by TIBCO Software 

Inc.), was used for estimating stream loads of nitrate, total N, 

and total P. The S-LOADEST software and documentation are 

publically available and can be downloaded from the USGS Web 

page at http://water.usgs.gov/software/loadest/. 

For each study site, the nutrient data calibration files were 

input into S-LOADEST for use in selecting and calibrating one 

of nine predefined models that specify the form of the regression 

equation (Runkel and others, 2004) used to estimate stream loads 

of each constituent (nitrate, total N, and total P). The regression 

models include explanatory terms to address variability in 

constituent concentrations resulting from variability in discharge, 

time, and seasonality. The S-LOADEST progam allows the user 

to select any of the regression models for load estimation. The 

program also provides an automated option that identifies and 

selects the best model fit, from the list of predefined models, 

for the calibration data based on the Akaike information criteria 

(Akaike, 1974; Cohn and others, 1989; Gilroy and others, 1990; 

Cohn and others, 1992). In this study, the automated option was 

used to select the best regression model for each constituent at 

each site.

The calibration and estimation procedures used with the 

selected regression models within S-LOADEST were based on 

the Adjusted Maximum Likelihood Estimation (AMLE) statistical 

estimation method (Cohn, 2005). The AMLE method is appropriate when the model calibration errors are normally distributed 

and is the method of choice when the calibration datasets contain 

censored data; most calibration data compiled for the study 

sites typically contain censored data. The streamflow prediction 

files compiled for the study sites then were combined with the 

calibrated regression models in S-LOADEST to estimate annual, 

seasonal, monthly, and daily loads (in tons) for nitrate, total N, 

and total P for each study site. Seasonal loads were computed for 

four periods during the year, including January through March, 

April through June, July through September, and October through 

December.

The S-LOADEST program output results generated for 

each constituent and time period (including, regression model 

used, daily mean flux [in tons per day], variance of the flux, the 

lower and upper 95 percent confidence intervals of the flux, the 

standard error of prediction [SEP] of the flux, the number of 

days in the period, and the total estimated load for the period [in 

tons]) are compiled in appendix 1. The SEP for the load estimates 

(Runkel and others, 2004) incorporates both variability attributed 

to the model calibration (parameter uncertainty) and unexplained 

variability about the model (random error). The SEP indicates 

how closely estimated loads correspond to actual loads and is 

used to develop the 95 percent confidence intervals for each load 

estimate. The upper and lower 95 percent confidence intervals for 

the total load for the time period can be computed by multiplying 

the lower and upper 95 percent confidence limits of the daily flux 

by the number of days in the period. The SEP indicates how closely 

estimated loads correspond to actual loads and is used by the model 

to develop the 95 percent confidence intervals for the daily flux 

estimates (appendix 1). Multiplying the lower and upper 95 percent 

confidence intervals of the daily mean flux by the number of days 

in the period will yield the upper and lower 95 percent confidence 

intervals of the total load computed for the time period.

Data evaluations for this report focused on the computed 

annual loads of nitrate, total N, and total P to better understand 

relations between stream nutrient yields and watershed characteristics of the study sites. For each site, annual nutrient loads were 

normalized by drainage area to compute annual yields of nitrate, 

total N, and total P, in tons per square mile (appendix 1). Although 

not discussed in this report, the computed seasonal, monthly, and 

daily load results also are included in appendix 1 to serve as an 

additional resource to support DWQ’s ongoing development and 

implementation of NSW management strategies in North Carolina.

The annual nutrient load and yield data for the study sites 

were used to compute median loads and yields for two time 

periods, the first time period included all 48 sites having either full 

or partial PORs during 1997 to 2008 (table 2) and the second time 

period included just those sites having complete data during 2002 

to 2008 (table 3). For 1997 to 2008, median loads and yields were 

determined for 35 sites having data for all 12 years and 13 sites 

with annual data ranging from 5 to 11 years (table 2). The 13 sites 

with partial PORs were included to provide additional spatial 

coverage and representation of different watershed attributes in the 

study basins. The 7-year period from 2002 to 2008 was chosen to 

maximize the number of study sites for which median annual loads 

and yields could be determined for the same period of time (table 

3). A total of 45 of the 48 study sites had all 7 years of data; the 

remaining three sites (15, 19, and 42) had less than 7 years of data 

and were not included in the 2002 to 2008 time period.

Watershed Setting

Previous water-quality studies conducted in North Carolina 

have indicated that many integrated anthropogenic and natural 

environmental factors influence the delivery of nutrients to surfacewater bodies and, ultimately, the mass of nutrients exported by 

watersheds. These factors include, but are not limited to, differences 

in nutrient inputs by point and nonpoint sources (Glasgow and 

Burkholder, 2000; Mallin and others, 2005; Burkholder and others, 

2006), hydrogeologic and geochemical processes affecting nutrient 

fate and transport within watersheds (Stow and others, 2001; 

Spruill and others, 2005; Harden and Spruill, 2008), subsurface 

tile drainage (Harden and Spruill, 2004), and hydrologic and 

land-use conditions (Bales and others, 1999, 2000; Rothenberger 

and others, 2009). One of the issues for water-resource managers is 

that although many environmental variables are known to influence 

nutrient transport in streams, there is a lack of readily available 

data for all the variables to characterize their relative effects on 

nutrient yields in watersheds throughout a region such as 

central and eastern North Carolina.
18 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

Table 2. Median annual nutrient loads and yields during 1997 to 2008.

[mi2, square miles; N, nitrogen; P, phosphorus]

Study site 

number 

(see figs. 

1–6)

Site drainage area 

(mi2

)

Period of record Median annual nutrient loads and yields

Start date End date Number of 

years

Median 

nitrate 

load 

(ton)

Median 

nitrate 

yield 

(ton/mi2

)

Median 

total N 

load 

(ton)

Median 

total N 

yield 

(ton/mi2

)

Median 

total P 

load 

(ton)

Median 

total P 

yield 

(ton/mi2

)

1 123.5 Jan. 1997 Dec. 2008 12 62.4 0.51 115.8 0.94 8.5 0.07

2 1,043 Jan. 1997 Dec. 2008 12 275.5 0.26 508.2 0.49 153.7 0.15

3 224.8 Jan. 1997 Dec. 2008 12 20.9 0.09 144.0 0.64 22.4 0.10

4 166.7 Jan. 1997 Dec. 2008 12 17.9 0.11 113.0 0.68 12.1 0.07

5 529.8 Jan. 1997 Dec. 2008 12 59.4 0.11 232.8 0.44 28.2 0.05

6 2,210 Jan. 1997 Dec. 2008 12 550.5 0.25 1,526 0.69 195.5 0.09

7 43.3 Jan. 1997 Dec. 2008 12 26.7 0.62 56.6 1.31 12.1 0.28

8 26.3 Jan. 1997 Dec. 2008 12 15.3 0.58 34.0 1.29 0.44 0.02

9 66.2 Jan. 1997 Dec. 2008 12 13.4 0.20 41.3 0.62 4.0 0.06

10 141.7 Jan. 1997 Dec. 2008 12 42.2 0.30 76.2 0.54 14.2 0.10

11 78.3 Jan. 1997 Dec. 2008 12 25.0 0.32 50.6 0.65 5.8 0.07

12 8.0 Jan. 1997 Dec. 2008 12 1.9 0.24 5.6 0.69 0.82 0.10

13 98.3 Jan. 1997 Dec. 2008 12 13.8 0.14 40.1 0.41 3.2 0.03

14 148.7 Jan. 1997 Dec. 2008 12 33.7 0.23 97.7 0.66 10.1 0.07

15 167.8 Jan. 2003 Dec. 2008 6 23.9 0.14 72.4 0.43 6.2 0.04

16 1.1 Jan. 1997 Dec. 2008 12 0.03 0.03 0.27 0.24 0.03 0.03

17 771.3 Jan. 1997 Dec. 2008 12 43.6 0.06 272.1 0.35 21.0 0.03

18 76.4 Jan. 1998 Dec. 2008 11 29.2 0.38 96.3 1.26 17.5 0.23

19 21.0 Jan. 2003 Dec. 2008 6 5.0 0.24 22.9 1.09 3.3 0.16

20 2,398 Jan. 1997 Dec. 2008 12 752.4 0.31 1,976 0.82 258.9 0.11

21 59.4 Jan. 1997 Dec. 2008 12 142.2 2.40 186.5 3.14 10.0 0.17

22 2,706 Jan. 1997 Dec. 2008 12 1,245 0.46 2,709 1.00 263.2 0.10

23 159.3 Jan. 1997 Dec. 2008 12 17.3 0.11 97.1 0.61 7.4 0.05

24 730.6 Jan. 1997 Dec. 2008 12 373.6 0.51 766.9 1.05 74.3 0.10

25 3,939 Jan. 1997 Dec. 2008 12 1,834 0.47 3,825 0.97 364.7 0.09

26 166.2 Jan. 2001 Dec. 2008 8 85.4 0.51 178.9 1.08 11.6 0.07

27 85.1 Jan. 1997 Dec. 2008 12 101.9 1.20 191.9 2.25 37.8 0.44

28 131.5 Jan. 2002 Dec. 2008 7 10.3 0.08 36.7 0.28 3.0 0.02

29 33.9 Jan. 2001 Dec. 2008 8 38.4 1.13 66.3 1.96 6.1 0.18

30 37.2 Jan. 1999 Dec. 2008 10 242.9 6.53 327.0 8.79 33.2 0.89

31 89.0 Jan. 2001 Dec. 2008 8 599.3 6.74 734.9 8.26 47.4 0.53

32 603.1 Jan. 1997 Dec. 2008 12 621.2 1.03 931.4 1.54 123.8 0.21

33 7.6 Jan. 1997 Dec. 2008 12 2.8 0.38 5.5 0.73 0.77 0.10

34 1,273 Jan. 1997 Dec. 2008 12 771.9 0.61 1,393 1.09 180.6 0.14

35 76.5 Jan. 1997 Dec. 2008 12 102.1 1.33 154.5 2.02 19.2 0.25

36 21.5 Jan. 1997 Dec. 2008 12 155.9 7.24 111.5 5.18 8.7 0.40

37 8.3 Jan. 1997 Dec. 2008 12 3.8 0.45 5.4 0.65 0.64 0.08

38 40.8 Jan. 2001 Dec. 2008 8 14.1 0.35 35.8 0.88 4.4 0.11

39 12.0 Jan. 2000 Dec. 2008 9 0.76 0.06 8.8 0.73 0.97 0.08

40 14.6 Jan. 1998 Dec. 2008 11 6.2 0.43 15.7 1.07 3.5 0.24

41 3,471 Jan. 1997 Dec. 2008 12 1,453 0.42 2,768 0.80 369.1 0.11

42 92.8 Jan. 2004 Dec. 2008 5 6.5 0.07 29.4 0.32 1.9 0.02

43 5,261 Jan. 1997 Dec. 2008 12 2,781 0.53 5,106 0.97 705.2 0.13

44 678.5 Jan. 1997 Dec. 2008 12 386.5 0.57 756.1 1.11 71.8 0.11

45 182.8 Jan. 1997 Dec. 2008 12 32.4 0.18 87.4 0.48 3.5 0.02

46 361.5 Jan. 2000 Dec. 2008 9 199.0 0.55 363.8 1.01 42.4 0.12

47 1,234 Jan. 1997 Dec. 2008 12 171.6 0.14 936.6 0.76 89.0 0.07

48 711.3 Jan. 1997 Dec. 2008 12 23.6 0.03 639.8 0.90 20.6 0.03
Methods of Data Compilation and Analysis 19

Table 3. Median annual nutrient loads and yields during 2002 to 2008.

[mi2, square miles; N, nitrogen; P, phosphorus]

Study site 

number 

(see figs. 

1–6)

Site drainage area 

(mi2

)

Period of record Median annual nutrient loads and yields

Start date End date Number of 

years

Median 

nitrate 

load 

(ton)

Median 

nitrate 

yield 

(ton/mi2

)

Median 

total N 

load 

(ton)

Median 

total N 

yield 

(ton/mi2

)

Median 

total P 

load 

(ton)

Median 

total P 

yield 

(ton/mi2

)

1 123.5 Jan. 2002 Dec. 2008 7 65.8 0.53 124.3 1.01 14.8 0.12

2 1,043 Jan. 2002 Dec. 2008 7 312.7 0.30 545.3 0.52 224.6 0.22

3 224.8 Jan. 2002 Dec. 2008 7 11.2 0.05 142.4 0.63 18.1 0.08

4 166.7 Jan. 2002 Dec. 2008 7 13.8 0.08 114.5 0.69 13.2 0.08

5 529.8 Jan. 2002 Dec. 2008 7 51.9 0.10 207.9 0.39 28.7 0.05

6 2,210 Jan. 2002 Dec. 2008 7 522.7 0.24 1,452 0.66 191.3 0.09

7 43.3 Jan. 2002 Dec. 2008 7 21.3 0.49 45.9 1.06 11.0 0.25

8 26.3 Jan. 2002 Dec. 2008 7 13.7 0.52 42.6 1.62 0.8 0.03

9 66.2 Jan. 2002 Dec. 2008 7 11.6 0.18 33.1 0.50 3.6 0.05

10 141.7 Jan. 2002 Dec. 2008 7 35.0 0.25 73.0 0.52 11.6 0.08

11 78.3 Jan. 2002 Dec. 2008 7 22.5 0.29 52.6 0.67 5.9 0.08

12 8.0 Jan. 2002 Dec. 2008 7 1.7 0.22 5.6 0.69 0.9 0.12

13 98.3 Jan. 2002 Dec. 2008 7 13.1 0.13 35.5 0.36 3.3 0.03

14 148.7 Jan. 2002 Dec. 2008 7 32.5 0.22 97.0 0.65 11.4 0.08

16 1.1 Jan. 2002 Dec. 2008 7 0.02 0.02 0.28 0.24 0.03 0.03

17 771.3 Jan. 2002 Dec. 2008 7 38.4 0.05 266.9 0.35 20.5 0.03

18 76.4 Jan. 2002 Dec. 2008 7 29.2 0.38 98.0 1.28 22.1 0.29

20 2,398 Jan. 2002 Dec. 2008 7 680.2 0.28 1,721 0.72 271.5 0.11

21 59.4 Jan. 2002 Dec. 2008 7 141.9 2.39 188.4 3.17 10.6 0.18

22 2,706 Jan. 2002 Dec. 2008 7 1,095 0.40 2,234 0.83 248.1 0.09

23 159.3 Jan. 2002 Dec. 2008 7 16.5 0.10 95.9 0.60 7.1 0.04

24 730.6 Jan. 2002 Dec. 2008 7 345.9 0.47 722.5 0.99 63.4 0.09

25 3,939 Jan. 2002 Dec. 2008 7 1,630 0.41 3,205 0.81 320.2 0.08

26 166.2 Jan. 2002 Dec. 2008 7 121.4 0.73 257.1 1.55 16.6 0.10

27 85.1 Jan. 2002 Dec. 2008 7 132.0 1.55 237.6 2.79 59.3 0.70

28 131.5 Jan. 2002 Dec. 2008 7 10.3 0.08 36.7 0.28 3.0 0.02

29 33.9 Jan. 2002 Dec. 2008 7 33.0 0.97 69.4 2.05 6.9 0.20

30 37.2 Jan. 2002 Dec. 2008 7 240.9 6.48 298.0 8.01 21.8 0.59

31 89.0 Jan. 2002 Dec. 2008 7 604.6 6.80 705.1 7.93 41.1 0.46

32 603.1 Jan. 2002 Dec. 2008 7 609.1 1.01 900.5 1.49 100.2 0.17

33 7.6 Jan. 2002 Dec. 2008 7 2.7 0.36 5.4 0.71 0.7 0.10

34 1,273 Jan. 2002 Dec. 2008 7 627.6 0.49 1,190 0.94 143.5 0.11

35 76.5 Jan. 2002 Dec. 2008 7 108.9 1.42 162.2 2.12 20.9 0.27

36 21.5 Jan. 2002 Dec. 2008 7 77.2 3.59 93.4 4.34 8.5 0.40

37 8.3 Jan. 2002 Dec. 2008 7 3.1 0.38 4.1 0.50 0.6 0.07

38 40.8 Jan. 2002 Dec. 2008 7 12.9 0.32 35.4 0.87 4.2 0.10

39 12.0 Jan. 2002 Dec. 2008 7 0.79 0.07 9.0 0.75 1.1 0.09

40 14.6 Jan. 2002 Dec. 2008 7 5.1 0.35 13.6 0.93 2.5 0.17

41 3,471 Jan. 2002 Dec. 2008 7 1,419 0.41 2,884 0.83 379.5 0.11

43 5,261 Jan. 2002 Dec. 2008 7 2,558 0.49 5,339 1.01 619.8 0.12

44 678.5 Jan. 2002 Dec. 2008 7 333.0 0.49 758.3 1.12 75.9 0.11

45 182.8 Jan. 2002 Dec. 2008 7 27.2 0.15 86.1 0.47 3.4 0.02

46 361.5 Jan. 2002 Dec. 2008 7 195.3 0.54 374.7 1.04 52.7 0.15

47 1,234 Jan. 2002 Dec. 2008 7 167.3 0.14 880.0 0.71 84.8 0.07

48 711.3 Jan. 2002 Dec. 2008 7 22.3 0.03 631.2 0.89 19.2 0.03
20 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

In this study, readily available data were compiled for 

selected environmental variables to characterize watershed 

conditions for the study sites. Data for the environmental 

variables were compared to the annual nutrient yields to 

evaluate whether particular variables may be useful indicators 

of watersheds having relatively higher or lower potential for 

exporting nutrients. Available information on land cover, 

hydrologic soil groups (HSGs), precipitation, wastewater treatment facilities, and CAFOs were compiled for the contributing 

watershed drainages of all study sites by using geographic 

information system (GIS) processes.

Physical and Climatic Factors

Physical and climatic data compiled for the watershed 

study sites include land cover, hydrologic soil drainage groups, 

and precipitation. As part of the GIS compilations, the contributing drainage area for each site first was determined using 

light detection and ranging (LiDAR)-derived digitial elevation 

models (North Carolina Floodplain Mapping Program, 2012). 

The elevation data also were used to determine the mean basin 

slope (in degrees) within the drainage area of each site. The 

watershed-attribute data layers were combined with the site 

drainage basin delinations in a GIS database for compiling the 

land cover, soil drainage, and precipitation data within each of 

the watershed sites.

Land Cover

Land-cover datasets were compiled for the entire 

watershed drainage area of each site and for a 50-foot buffer 

zone bounding both sides of the stream channel within the 

watershed of each study site. The areal extent and relative 

percentage of 15 individual land-cover classes compiled for 

the watersheds using both the 2001 National Land Cover Data 

(NLCD) (Homer and others, 2004) and 2006 NLCD (Fry and 

others, 2011) are presented in appendix tables A2-1 and A2-2, 

respectively. The 15 individual land-cover classes include 

open water, developed – open space, developed – low intensity, developed – medium intensity, developed – high intensity, 

barren land, deciduous forest, evergreen forest, mixed forest, 

shrub/scrub, grassland/herbaceous, pasture/hay, cultivated 

crops, woody wetlands, and emergent herbaceous wetlands. 

Aggregated land-cover classes: Twelve of the 15 

land-cover classes were aggregated into four primary classes 

(developed, forested, agriculture, and wetlands) for evaluating 

relations between watershed land cover and nutrient yields. 

Compiled data for the aggregated classes are presented in 

appendix tables A2-1 and A2-2. The four aggregated classes 

used in this study consist of those individual land-cover 

classes that are considered to best represent differences in 

watershed settings that may promote or inhibit the supply of 

nutrients to the streams. The developed class represents the 

sum of all four individual developed land-cover classes. The 

forested class includes the three individual forest classes plus 

shrub/scrub. The agriculture class includes the pasture/hay and 

cultivated crops classes. The wetlands class includes both the 

woody wetlands and emergent herbaceous wetlands classes. 

For most of the sites, these four aggregated classes accounted 

for more than 90 percent of the total land cover for the study 

watersheds (appendix tables A2-1 and A2-2). The individual 

land-cover classes for open water, barren land, and grassland/

herbaceous typically represented a minimal percentage of the 

total land cover for the study sites and were not included as 

part of the aggregated classes.

The 2001 and 2006 NLCD were used to determine 

whether land-cover conditions within the study sites changed 

substantially or remained fairly uniform during the 1997 to 

2008 study period. The data were compared to examine the 

relative percentage increase or decrease in the four aggregated 

land-cover classes between 2001 and 2006 for each study site 

(appendix table A2-3). In the majority of cases, the percentage 

change in watershed land cover from 2001 to 2006 typically 

was less than 1 to 2 percent; therefore, land-cover conditions 

at the study sites are considered to have remained fairly 

uniform during the study period. Evaluations between the 

aggregated land-cover classes and nutrient yields were based 

on the land-cover data derived from the NLCD for 2001.

Stream buffer zones: For the stream buffer land cover, 

GIS processes first were used to establish a buffer zone 

extending outward 50-feet in both directions of each 1:24,000-

scale National Hydrography Dataset (NHD) stream center 

line or waterbody within the watershed of each study site. The 

areal extent and relative percentage of the same individual and 

aggregated land-cover classes used for the watersheds were 

then determined within the 50-foot buffer zones established 

for each stream study site. The stream buffer land-cover data 

compiled for each site, based on the NLCD for 2001, are 

presented in appendix table A2-4.

Principal land-use categories: As part of a study of 

nutrient concentrations and loads in the northeastern United 

States, Trench and others (2012) used land-cover data to 

assign study basins to one of five principal land-use categories 

that were used to examine relations between nutrient yields 

and predominant land uses. The land-use categories were 

assigned on the basis of the relative percentages of urban and 

agricultural land cover present within the basins. A modified 

version of the approach taken by Trench and others (2012) was 

used in this investigation for assigning predominant land-use 

categories to the study sites.

For this study, the aggregated land-cover data (developed, 

agriculture, forested, and wetlands) were used to assign each 

site to one of six land-use categories based on the relative 

percentages of developed and agricultural lands within the 

watershed. As will be discussed later, nutrient yields are 

observed to be strongly influenced by high point-source flow 

contributions to the streams. Thus, an additional land-use 

category was used to designate any site having high annual 

point-source flow contributions to the stream (greater than 

(>) 10 percent), regardless of the amount of developed or 

agricultural lands within the watershed.
Watershed Setting 21

Data compilations for annual point-source flow contributions 

to the stream sites are discussed later in the section on NPDES 

Wastewater Discharge Facilities.

The seven land-use categories developed for this study 

are summarized in table 4 and include undeveloped (UN), 

low agricultural (LAG), high agricultural (HAG), low urban 

(LUR), high urban (HUR), mixed (MIX), and high pointsource flow (HPS) land uses. These categories are based on 

a priori divisions that are intended to reflect changes in the 

predominant land use(s) that influence nutrient yields among 

the stream sites. Based on the classification criteria (table 4), 

the land-use categories were assigned to the study sites 

(table 5). Sites designated as UN land use are not intended 

to reflect pristine background conditions but rather to reflect 

watersheds with substantially larger amounts of undeveloped 

forested land and wetlands compared to developed and 

Table 4. Criteria used for assigning land-use categories to the study sites.

[≤, less than or equal to; >, greater than; na, not applicable]

Land-use category Land-use 

code

Percentage developed in 

watershed

Percentage agriculture 

in watershed

Percentage 

point-source 

flow to 

streamflow in 

watershed

Number 

of sites

Undeveloped UN ≤ 10 ≤ 15 0 4

Low agricultural LAG ≤ 10 > 15 and ≤ 30 ≤ 10 14

High agricultural HAG ≤ 10 > 30 ≤ 10 8

Low urban LUR > 10 and ≤ 30 ≤ 15 ≤ 10 2

High urban HUR > 30 ≤ 15 ≤ 10 4

Mixed MIX > 10 > 15 ≤ 10 10

High point-source flow HPS na na > 10 6

agricultural lands that are more likely to contribute nutrients 

to streams. The LAG and HAG categories reflect watersheds 

with minimal developed land relative to agricultural land; 

conversely, the LUR and HUR categories reflect watersheds 

with minimal agricultural land relative to developed land 

(table 4). The MIX category reflects watersheds that likely are 

influenced by both developed and agricultural lands. The HPS 

category is used to separate those sites with high point-source 

flow contributions to examine nutrient yields among sites 

based on the above land-use categories that have low pointsource flow contributions (<10 percent) to the streams. Half of 

the study sites are classified as either LAG (14 sites) or MIX 

(10 sites) land uses (tables 4 and 5). Eight sites are classified 

as HAG sites, and six sites are classified as HPS sites. Fewer 

sites are classified as UN (4), HUR (4), or LUR (2).
22 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

Table 5. Designated land-use categories and land-cover class percentages for the study sites.

Study site 

number 

(see figs. 

1–6)

Site designated 

land-use

category

Land-use 

code

Land cover class percentages within watershed

Developed Agriculture Forested Wetlands

Undeveloped

(equals Forested plus

Wetlands)

1 Low agricultural LAG 4.5 20.9 72.7 0.1 72.8

2 Low agricultural LAG 6.2 18.0 70.3 0.4 70.7

3 High agricultural HAG 6.5 31.9 44.9 15.9 60.9

4 Low agricultural LAG 4.7 23.3 60.7 2.0 62.7

5 Low agricultural LAG 5.0 17.0 65.6 6.0 71.6

6 Low agricultural LAG 7.6 27.1 48.8 9.8 58.6

7 High agricultural HAG 4.3 41.3 29.4 21.7 51.1

8 Undeveloped UN 3.1 5.5 43.6 44.5 88.1

9 Mixed MIX 11.8 25.1 58.1 0.7 58.8

10 Mixed MIX 17.1 17.6 61.0 0.5 61.6

11 Low agricultural LAG 5.9 27.5 61.5 1.0 62.5

12 High agricultural HAG 9.3 31.8 54.1 0.3 54.4

13 Low agricultural LAG 7.6 27.0 59.8 0.8 60.6

14 Low agricultural LAG 6.3 28.7 59.1 0.8 59.9

15 Low agricultural LAG 6.1 27.9 59.5 0.9 60.4

16 Undeveloped UN 1.4 0.3 97.1 0.0 97.1

17 Mixed MIX 13.0 17.6 59.5 2.1 61.6

18 High urban HUR 47.5 5.4 40.5 1.3 41.9

19 High urban HUR 74.0 2.2 20.5 0.7 21.2

20 Mixed MIX 16.9 25.8 40.5 8.8 49.3

21 High agricultural HAG 8.6 56.0 19.0 12.6 31.6

22 Mixed MIX 16.4 27.8 38.4 9.9 48.3

23 High agricultural HAG 8.6 33.4 43.1 8.1 51.2

24 High agricultural HAG 9.2 44.0 24.5 16.4 40.9

25 Mixed MIX 13.9 32.6 34.2 12.5 46.7

26 Low agricultural LAG 3.3 25.8 44.6 22.9 67.5

27 High agricultural HAG 4.0 31.7 39.7 21.9 61.6

28 Mixed MIX 28.5 22.4 41.6 1.7 43.3

29 High urban HUR 86.8 2.9 9.1 0.5 9.6

30 High point-source flow HPS 83.7 3.3 12.0 0.1 12.1

31 High point-source flow HPS 72.4 7.4 18.5 0.5 19.0

32 High point-source flow HPS 24.1 28.8 41.0 1.6 42.6

33 Low agricultural LAG 4.5 17.0 74.7 0.1 74.8

34 Mixed MIX 17.8 28.9 47.4 0.9 48.3

35 High point-source flow HPS 39.5 5.5 47.3 5.3 52.6

36 High point-source flow HPS 55.4 2.2 35.4 3.7 39.0

37 Low agricultural LAG 5.3 19.2 70.7 0.2 70.9

38 High point-source flow HPS 19.4 11.2 65.2 0.2 65.3

39 Low urban LUR 27.3 12.1 48.4 5.9 54.4

40 High urban HUR 62.0 14.9 21.5 0.1 21.6

41 Mixed MIX 14.7 22.0 55.1 1.4 56.6

42 Undeveloped UN 8.6 2.2 67.1 9.9 77.1

43 Mixed MIX 13.7 19.8 51.4 6.5 57.9

44 High agricultural HAG 6.9 39.8 33.9 17.1 51.0

45 Undeveloped UN 7.4 11.8 52.8 11.6 64.4

46 Low urban LUR 10.8 12.1 49.3 15.1 64.4

47 Low agricultural LAG 7.9 29.3 32.5 24.0 56.5

48 Low agricultural LAG 3.7 17.0 45.0 26.7 71.7
Watershed Setting 23

Hydrologic Soil Groups

The study sites not only represent basins with different 

land-cover mixtures but also hydrologic soil groups (HSGs) 

with varying degrees of soil drainage capacity. Data used to 

characterize the distribution of HSGs within the study sites 

were obtained through the U.S. Department of Agriculture 

Soil Survey Geographic Database (Soil Survey Staff, Natural 

Resources Conservation Service, n.d.). The areal extent and 

relative percentage for the four major HSGs (A, B, C, and D) 

were determined within the watershed drainages of each study 

site. For this study, dually classified HSGs A/D, B/D, and C/D 

were combined with their respective major HSGs (A, B, and 

C). In order to reduce the number of variables included in the 

data analysis, two aggregated HSGs were compiled for use in 

examining relations to nutrient yields. Soils classified as HSG 

A and (or) B were combined as HSGs A+B to represent the 

areal percentage of soil that is excessively to moderately well 

drained. Soils classified as HSG C and (or) D were combined 

as HSGs C+D to represent the areal percentage of soil that is 

somewhat poorly drained to very poorly drained. Compiled 

data on the individual and aggregated HSGs for each study site 

are presented in appendix table A2-5.

Precipitation

Stream nutrient loads for the study sites are largely 

contingent on the amounts of streamflow within each of the 

watersheds. Although various factors influence streamflows 

(such as basin size and slope, land cover, geology, and watersupply uses), streamflow amounts are determined primarily 

by the amount of precipitation that occurs throughout each 

watershed basin. Annual precipitation data for the POR of 

each study site were compiled for use in examining the stream 

nutrient yields. The precipitation data were generated by 

compiling up to 12 years of average annual precipitation data 

(1997 to 2008) from the PRISM Climate Group at Oregon 

State University (PRISM, 2010). The precipitation values 

for each year were summarized by watershed basin, and an 

annual average precipitation amount, in inches, was computed 

for each basin by using GIS spatial analysis. The compiled 

precipitation data are presented, by site and year, in appendix 

table A2-6. 

Anthropogenic Factors

Data for NPDES wastewater discharge facilities and 

CAFOs (cattle and swine) permitted by the State of North 

Carolina were used to assess potential anthropogenic influences on nutrient yields at the study sites. GIS analyses were 

performed to identify NPDES permitted discharge facilities 

and permitted CAFOs located within each of the watershed 

study sites.

NPDES Wastewater Discharge Facilities

Two types of NPDES permits are issued to wastewater 

dischargers—general permits and individual permits. 

General permits are issued for a given statewide activity, 

such as noncontact cooling water discharges and domestic 

discharges from single family residences. Individual permits 

are specifically tailored to individual facilities for wastewater 

discharge activities not covered by general permits (see 

http://portal.ncdenr.org/web/wq/swp/ps/npdes/permitprocess). 

Individual permits are further designated as major or minor 

permits based on regulatory classifications. For example, 

municipal wastewater treatment facilities with greater than 

1 million gallons (Mgal) per day permitted flow or with a 

pretreatment program are classified as major dischargers. The 

major or minor designations applied to industrial/commercial 

facilities are based on several factors, including flow amounts, 

wastewater characteristics, and water-quality and health 

impacts. The examination of wastewater discharge facilities 

in this study was based on facilities having individual NPDES 

permits.

Information on the NPDES wastewater discharge 

facilities used in this study was provided by DWQ (Michael 

Tutwiler, North Carolina Division of Water Quality, written 

commun., December 2011). Evaluation of the NPDES 

permitted dischargers for the study sites focused on the major 

municipal, minor municipal, major industrial/commercial, 

and 100 percent domestic discharge facilities. These 

facility types represent the most influential point-source 

contributors of nutrients within the watersheds. Analysis 

of the permitted major industrial/commercial facilities for 

this study excluded examination of permitted electric power 

plants because these facilities typically take in water for 

cooling purposes and return the water without contributing additional nutrients. Information on the individual 

NPDES permitted major municipal, minor municipal, 

major industrial/commercial, and 100 percent domestic 

dischargers, including their allowable permitted daily flows, 

compiled within the watershed drainages of each site are 

summarized in appendix table A3-1. In some cases, NPDES 

permitted facilities were identified for multiple sites where 

the watersheds of upstream study sites also were included 

within downstream watershed sites. Some study sites 

did not include any of the examined NPDES wastewater 

dischargers. Although determination of actual nutrient loads 

discharged by the permitted facilities within the watershed 

study sites was beyond the scope of this study, the pointsource flows and flow yields were used as surrogates to 

examine potential relations between NPDES wastewater 

dischargers and stream-nutrient yields in the watersheds.

The annual point-source flows (that is, wastewater 

discharged) to the stream sites were estimated for each 

NPDES permitted facility. The actual daily flows reported 

by the permitted facilities to DWQ (Michael Tutwiler, 

North Carolina Division of Water Quality, written commun., December 2011) were used for estimating the annual 
24 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

flows for the municipal and industrial/commercial dischargers. In most cases, daily flow values at the facilities were 

not available for every day of the year; thus, the available 

daily flow data were used to compute an average daily flow 

for each year, which was then multiplied by the number 

of actual days in that year to estimate the annual flow. For 

some facilities, there were cases where some years had 

no reported daily flow data; consequently, an interpolated 

average daily flow was assigned based on available average 

daily flows in years preceding and following the data gaps. 

The estimated annual flow data compiled for the major 

municipal, minor municipal, and industrial/commercial 

facilities are presented in appendix table A3-1.

For the 100 percent domestic facilities, insufficent data 

were available on actual daily flows for use in computing 

annual flows. Consequently, the NPDES permitted daily 

flows associated with each domestic facility identified at 

a site were summed to determine the daily total permitted 

flow (appendix table A3-1). Although these computed 

total permitted flows may overestimate the actual flows, 

domestic flows typically represent a small fraction of the 

point-source flows associated with municipal and industrial/

commercial sources. When present, the major municipal 

dischargers tend to dominate the total point-source flows 

within a watershed.

The annual flows (in million gallons) estimated for the 

individual permitted facilities were then summed to provide 

the annual total major municipal flows, minor municipal 

flows, and major industrial/commercial flows for each study 

site (appendix table A3-2). The daily total permitted flow 

value determined for all domestic facilities within each 

study site was multiplied by the number of days in each 

year to calculate the annual total domestic flows. For each 

watershed study site, the annual total point-source flows 

were computed by summing the annual major municipal, 

minor municipal, major industrial/commercial, and domestic flows (appendix table A3-2). The annual point-source 

flow yields (in million gallons per square mile) also were 

computed for each site.

The annual point-source flow data were combined with 

the annual streamflow data to determine the percentage of 

streamflow that is wastewater from the NPDES permitted 

discharge facilities at each study site (appendix table A3-2). 

The annual streamflows (in million gallons) and streamflow 

yields (in million gallons per square mile) for each study 

site (appendix table A3-2) were determined on the daily 

streamflow data compiled for each site (appendix 1) for 

estimating nutrient loads with the S-LOADEST program. 

Median annual point-source flows and streamflows were 

determined for the POR for each site during the 1997 to 

2008 study period. The median annual point-source flow 

and streamflow data are summarized in appendix table A3-2 

as well as the mean annual BFI values determined from 

streamflow hydrograph separation analyses.

CAFOs

Examination of CAFOs focused on active cattle and swine 

production facilities having DWQ issued permits within each of 

the watershed study basins (Keith Larick, North Carolina Division 

of Water Quality, written commun., December 2011). Data were 

not available to determine the number of actively permitted cattle 

and swine CAFOs located within each study site on an annual 

basis during the 1997 to 2008 study period; thus, CAFO data were 

based on 2011 DWQ permit data for cattle and swine CAFOs. The 

2011 CAFO data are assumed to appropriately characterize CAFO 

conditions at the study sites for the purpose of examining potential 

relations to annual median nutrient yields during the 1997 to 2008 

study period.

On the basis of the 2011 CAFO data, all DWQ permitted 

CAFOs and steady state live weight (SSLW) of cattle and swine 

allowed under each permit were compiled for each study site 

(appendix table A3-3). Information on the individual permits was 

used to compute the total CAFO animal SSLW (tons) for all cattle 

and swine CAFOs within each watershed study site (appendix 

table A3-4). Total CAFO animal densities (in tons per square mile) 

were determined for each site and compared to stream nutrient 

yields.

Statistical Analysis of Nutrient Yield Data

 Kruskal-Wallis one-way analysis of variance (ANOVA) 

(Helsel and Hirsch, 1992) was used to test for significant differences in median annual nutrient yields (nitrate, total N, and total P) 

among categories of land use (UN, LAG, HAG, LUR, HUR, MIX, 

and HPS; table 5). Kruskal-Wallis is a non-parametric ANOVA that 

assesses differences among categories (treatments) based on ranked 

data. This procedure is more appropriate than parametric ANOVA 

when analyzing data with non-normal distributions and relatively 

small sample sizes. When ANOVA indicated a significant difference among categories, a Tukey pair-wise multiple comparison 

test (Helsel and Hirsch, 1992) based on the ranked data was used 

to identify the pairs of categories that were significantly different. 

ANOVA and pair-wise multiple comparison analyses were 

conducted within the S-Plus software suite (by TIBCO Software 

Inc.). Statistical differences were tested at the 95 percent (α = 0.05) 

confidence level.

Relations between environmental variables and median 

annual nutrient (nitrate, total N, and total P) yields were modeled 

using regression tree analysis (R package “rpart;” Therneau and 

Atkinson, 2010). Regression tree-based modeling is an exploratory 

technique for uncovering structure in the data. The technique 

is particularly useful for identifying predictor (environmental) 

variables and devising prediction rules. Regression tree analysis 

uses binary recursive partitioning to successively split data into 

increasingly homogeneous subsets. Each split of the data considers 

all possible splits for each predictor variable and determines which 

split maximizes the reduction in deviance. For example, the maximum reduction in deviance might be achieved by splitting sites into 

two groups on the basis of drainage area: greater than or equal to 

(≥) 100 mi2

 and <100 mi2. After each split the process is repeated 
Selection of Nutrient Yield Data for Statistical Evaluation 25

for each subset of the data to produce new splits that produce a 

tree consisting of all possible splits. The resulting regression tree 

is simplified (pruned) by removing splits that do not contribute to 

a reduction in model error. Trees were pruned by examining the 

cross-validation error and identifying the split at which crossvalidation errors begin to rise. All splits derived after this point were 

removed. Yield data were transformed either by log10 or 4th root, 

depending on whichever was best for normalizing the data (log10 

for nitrate and 4th root for total N and total P), prior to regression 

tree analysis to reduce the influence of extreme values. 

Relation of Streamflow and Nutrient 

Loads

Variability in the estimated nutrient loads of nitrate, total 

N, and total P for the watershed study sites reflects variability 

in both the nutrient concentrations and streamflows used to 

compute the loads. Nutrient concentrations in streams vary in 

response to changes in many integrated environmental factors, 

such as wastewater dischargers, CAFOs, land cover, streamflow, 

and geochemical processes. Streamflow is one of the dominant 

factors that influences stream nutrient concentrations and loads. 

The concentrations of some constituents (such as total P) may 

increase at higher streamflows because of associated increases 

in particulate matter, whereas other constituents (such as nitrate) 

may decrease at higher streamflows because of dilution. Although 

nutrient concentrations may vary with streamflow, the overall mass 

of nutrients transported tends to be higher during periods of higher 

flows because significantly larger volumes of water are being 

flushed through the watershed. 

Streamflow variability among the study sites reflects the size 

of the watershed drainage areas and the amount of precipitation 

that occurs within the watersheds. Examination of the median 

annual streamflows and drainage areas for the 48 study sites (fig. 7) 

indicates that there is a strong relation between streamflow and 

drainage area. Similarly, median annual nutrient loads (nitrate, total 

N, and total P) are strongly related to drainage areas and to median 

annual streamflows (figs. 8 and 9, respectively). Annual loads of 

nitrate, total N, and total P increase as both drainage areas and 

streamflows increase. This makes it difficult to examine relations 

between other watershed attributes, such as land-cover type and 

nutrient sources, and stream nutrient loads because variations in the 

loads are largely controlled by variations in streamflow. Therefore, 

nutrient yield data, which normalize the effects of drainage area 

and streamflow differences among the sites, were used to examine 

relations between watershed attributes and nutrient export.

Selection of Nutrient Yield Data for 

Statistical Evaluation

Median annual nutrient yields were evaluated using two 

datasets. The first included yields determined for all 48 study sites 

based on 5 to 12 years of available data over the period 1997 to 

2008 (table 2). The second was a subset of 45 sites that constituted 

a consistent 7-year POR from 2002 to 2008 (table 3). The median 

annual yields for nitrate, total N, and total P for the 1997 to 2008 

data were compared to the 2002 to 2008 data (fig. 10) to determine 

if the two nutrient-yield datasets were similar or whether there were 

substantial differences based on the number of years of available 

data examined.

Comparison between the two datasets of the median annual 

nitrate yields (fig. 10A) indicates nearly a 1:1 correspondence 

with the exception of one outlier (site 36). Site 36 had a higher 

median yield for 1997 to 2008 (7.24 tons per square mile [ton/

mi2

]) than for 2002 to 2008 (3.59 ton/mi2) because of decreases in 

nitrate concentrations in wastewater discharged from the Triangle 

wastewater treatment plant (WWTP) (NPDES permit NC0026051) 

as a result of upgrades to waste treatment in July 2005. These 

upgrades reduced the concentrations of nitrate, and hence total 

N, discharged from the facility (Joseph Pearce, Durham County 

Engineering and Environmental Services Department, oral commun., April 2011). The annual total N, streamflow, and point-source 

flow yield data compiled for site 36 (fig. 11) highlight the beneficial 

effects that enhancements to a WWTP can have as part of nutrientreduction strategies. During 1997 to 2008, annual streamflow 

yields at site 36 ranged from about 227 to 467 Mgal/mi2

, while 

point-source flow yields were fairly uniform and ranged from about 

59 to 81 Mgal/mi2

. Annual point-source flows constituted 15 to 

33 percent of the annual streamflows. Annual total N yields showed 

an increasing trend (4.32 to 8.17 ton/mi2

) from 1997 to 2004 (average yield 6.28 ton/mi2

) before dramatically falling in 2005 when 

upgrades to the Triangle WWTP were completed (fig. 11). During 

2006 to 2008, the annual total N yield averaged 2.28 ton/mi2

, 

which represents a 64 percent reduction in total N yield following 

technology improvements at the WWTP in this small (21.5 mi2

) 

urban watershed.

Median annual total N yields for the two datasets (1997–2008 

and 2002–2008) were comparable (nearly 1:1 correspondence) 

with no obvious outliers (fig. 10B). Comparison of the median 

annual total P yields (fig. 10C) showed a similar nearly 1:1 

correspondence between the two datasets with the exception of 

outliers for sites 27 and 30. These two sites are classified as HAG 

(site 27) and HPS (site 30) basins and both contain an upstream 

municipal WWTP, which may influence the total P yields. The 

higher median annual total P yield for 2002 to 2008 (0.70 ton/mi2

) 

relative to 1997 to 2008 (0.44 ton/mi2

) at site 27 reflects generally 

higher annual total P yields in the latter part of the POR. In 

contrast, the higher median annual total P yield for 1997 to 2008 

(0.89 ton/mi2

) relative to 2002 to 2008 (0.59 ton/mi2) at site 30 

reflects generally higher annual total P yields in the earlier part of 

the POR. With a few exceptions, there is good agreement between 

the annual median nutrient yields summarized for all sites and 

PORs (1997 to 2008) and for sites with similar PORs (2002 to 

2008). Therefore, it was not considered necessary to use both 

datasets in the statistical analyses. The statistical evaluations 

in this report are based on data from 1997 to 2008 for the 48 

study sites.
26 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

R2 = 0.99

1,200,000

1,000,000

800,000

600,000

400,000

200,000

0

0 1,000 2,000 3,000

Drainage area, in square miles

4,000 5,000 6,000

Median annual streamflow, in million gallons 

Figure 7. Relation between drainage area and median annual streamflow for the study sites, 1997–2008.

R2 = 0.96

R2 = 0.89

R2 = 0.94

6,000

5,000

4,000

3,000

2,000

1,000

0

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

Median annual nutrient loads, in tons

Drainage area, in square miles

Nitrate

Total Nitrogen

Total Phosphorus

EXPLANATION

Figure 8. Relation between drainage area and median annual nutrient loads for the study sites, 1997–2008.
Selection of Nutrient Yield Data for Statistical Evaluation 27

R2 = 0.96

R2 = 0.88

R2 = 0.94

6,000

5,000

4,000

3,000

2,000

1,000

0

0 200,000 400,000 600,000 800,000 1,000,000 1,200,000

Median annual nutrient loads, in tons

Median annual streamflow, in million gallons

Nitrate

Total Nitrogen

Total Phosphorus

EXPLANATION

Figure 9. Relation between median annual streamflow and median annual nutrient loads for the study sites, 1997–2008.
28 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

8

7

6

5

4

3

2

1

0

Median annual yield (2002–2008), in tons per square mile 

Median annual yield (1997–2008), in tons per square mile

A

Dashed line indicates

1:1 correspondence

Site 36

Dashed line indicates

1:1 correspondence

Dashed line indicates

1:1 correspondence

Site 27

Site 30

10

9

8

7

6

5

4

3

2

1

0

B

C

0 1 2 3 4 5 6 7 8

0 1 2 3 4 5 6 7 8 9 10

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Figure 10. Comparison of median annual yields for (A) nitrate, (B) total nitrogen, and (C) total phosphorus for 1997–2008 and 2002–2008.
Selection of Nutrient Yield Data for Statistical Evaluation 29

0

1

2

3

4

5

6

7

8

9

1996 1998 2000 2002 2004 2006 2008

A

500

450

400

350

300

250

200

150

100

50

0

1996 1998 2000

Point-source

flow yield

2002

Years

2004 2006 2008

B

Annual total nitrogen yield, in tons per square mile

Annual flow yield, in million gallons per square mile

Streamflow yield

Figure 11. Annual data for (A) total nitrogen yields and (B) streamflow and point-source 

flow yields for study site 36, 1997–2008.
30 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

Relation of Watershed Setting and 

Stream Nutrient Yields

The results of data evaluations used to characterize 

relations between watershed setting and stream nutrient yields 

(nitrate, total N, and total P) are discussed in this section. Data 

were evaluated first with ANOVA and multiple comparison 

statistical tests to examine relations between stream nutrient 

yields and designated land-use categories. Regression tree 

analyses then were performed to identify whether particular 

watershed attributes may be useful indicators of a watershed’s 

potential for exporting nutrients.

Comparison of Stream Nutrient Yields by LandUse Category

The relation between land-use categories and nutrient 

yields was evaluated to determine whether differences in 

yields of nitrate, total N, and total P were discernible among 

the land-use categories assigned to the study sites (table 5) 

based on the classification criteria (table 4) used to differentiate the predominant land use(s) among the sites. The study 

sites were grouped on the basis of their designated land-use 

category (UN, LAG, HAG, LUR, HUR, MIX, and HPS) and 

tested for statistical differences in median annual nutrient 

yields. The number of sites within each land-use category 

and the median annual nutrient yields and land-cover class 

percentages for all sites in each category are summarized in 

table 6. The median annual nutrient yield for each land-use 

category (table 6) represents the median of the median annual 

yields for the sites (table 2) within each category (table 5).

Results of the ANOVA tests and multiple comparison 

tests performed on the median annual yields of nitrate, total N, 

and total P (table 2) grouped by land-use category are summarized in table 7. The ANOVA test results showed that median 

annual nitrate, total N, and total P yields were significantly 

different (α = 0.05) for some land-use categories. Box plots 

summarizing the distribution of median annual nitrate, total 

N, and total P yields based on the seven land-use categories 

are presented in figure 12. The box plots provide a visual 

reference for examining potential differences in nutrient yields 

among the land-use categories.

An initial examination of the box plots (fig. 12) indicated 

that the distribution of each of the nutrient yields (nitrate, total 

N, and total P) follows a similar pattern among the land-use 

categories. In general, there tends to be an overall trend where 

the median annual nutrient yields increase from the UN sites 

to the agricultural (LAG and HAG) and urban (LUR and 

HUR) sites. The nutrient yields for the HAG sites tend to be 

higher than the LAG sites; likewise, the nutrient yields for the 

HUR sites tend to be higher than the LUR sites. This pattern 

in nutrient yields likely reflects the transition in land-cover 

composition among the designated land-use categories where 

the amount of forested land in the watershed decreases and 

the amount of agricultural or developed lands increases 

between the UN, LAG, HAG, LUR, and HUR sites (table 6). 

For example, the UN, LAG, and HAG sites have similar and 

low median percentages of developed land (5.2, 5.6, and 

7.7 percent, respectively; table 6). The median percentage 

of agricultural land increases between the UN (3.8 percent), 

LAG (24.6 percent, and HAG (36.6 percent) sites. The 

median percentage of forested land is similar between the UN 

(60.0 percent) and LAG (60.2 percent) sites but lower for the 

HAG (36.8 percent) sites. The nutrient yields for the MIX 

sites tend to be similar to those of the agricultural and urban 

sites (fig. 12). The higher nutrient yields are associated with 

the HPS sites where the annual contributions of point-source 

flows to the streams are >10 percent.

The results of the ANOVA tests and examination of 

the box plots indicated some differences in nutrient yields 

among the land-use categories. The data were analyzed 

further using the multiple comparison tests to identify those 

land-use comparison pairs that had statistically different 

median annual yields of nitrate, total N, and total P at the 

0.05 significance level (table 7). Statistically significant 

differences in median annual nitrate yields were noted for 

two land-use comparison pairs. The median annual nitrate 

yield for HPS sites (3.93 ton/mi2

) was significantly higher 

than the median annual nitrate yields for UN (0.12 ton/mi2

) 

and LAG (0.24 ton/mi2

) sites (tables 6 and 7). For median 

annual total N yields, significant differences were identified 

for five land-use comparison pairs. The median annual total 

N yields for HUR (1.18 ton/mi2

) and HPS (3.60 ton/mi2) sites 

were significantly higher than both the UN (0.40 ton/mi2

) and 

LAG (0.67 ton/mi2

) sites. The median annual total N yield 

for the HPS sites also was significantly higher than the MIX 

(0.81 ton/mi2

) sites.

Of all the nutrients, total P had the most diverse 

combination of land-use comparison pairs (10) identified as 

having statistically significant differences in median annual 

yields (table 7). The median annual total P yields for the HAG 

(0.10 ton/mi2

), LUR (0.10 ton/mi2), HUR (0.20 ton/mi2), MIX 

(0.10 ton/mi2

), and HPS (0.33 ton/mi2) sites were all significantly higher than the UN (0.02 ton/mi2

) sites. The median 

annual total P yield for the HAG, HUR, and HPS sites also 

were significantly higher than the LAG (0.07 ton/mi2

) sites. 

The median annual total P yield for the HUR and HPS sites 

also were significantly higher than the MIX (0.10 ton/mi2

) 

sites (table 6).

Results of the multiple comparison tests identified some 

statistically significant differences in median annual nutrient 

yields when grouped by the watershed land-use classification. 

When statistical differences in median annual yields were 

noted, the results for nitrate, total N, and total P were similar 

in that the HUR and (or) HPS sites had higher yields relative 

to the UN and LAG sites (table 7). The primary difference in 

the comparison test results is that the median annual yields 

were statistically higher for total P, but not for nitrate or total 

N, for the HAG sites as compared to the UN and LAG sites, 

and for the LUR sites as compared to the UN sites. This 
Relation of Watershed Setting and Stream Nutrient Yields 31

Table 6. Summary of number of sites and median drainage area, annual nutrient yields, and land-cover class percentages by 

land-use category. (Land-use categories are described in tables 4 and 5.)

[mi2, square miles; N, nitrogen; P, phosphorus]

Land-use category

(code)

Number 

of sites

Median 

drainage 

area (mi2

)

Median annual nutrient yield Median land-cover class percentage

Nitrate yield 

(ton/mi2

)

Total N yield 

(ton/mi2)

Total P yield 

(ton/mi2) Developed Agriculture Forested Wetlands

Undeveloped (UN) 4 59.6 0.12 0.40 0.02 5.2 3.8 60.0 10.7

Low agricultural (LAG) 14 166.5 0.24 0.67 0.07 5.6 24.6 60.2 0.9

High agricultural 

(HAG)

8 122.2 0.54 1.08 0.10 7.7 36.6 36.8 16.2

Low urban (LUR) 2 186.7 0.31 0.87 0.10 19.1 12.1 48.9 10.5

High urban (HUR) 4 27.4 0.40 1.18 0.20 68.0 4.1 21.0 0.6

Mixed (MIX) 10 1,835 0.37 0.81 0.10 15.5 23.7 49.4 1.9

High point-source flow 

(HPS)

6 58.7 3.93 3.60 0.33 47.4 6.5 38.2 1.0

Table 7. Summary results of the ANOVA and Tukey tests of median annual nutrient yields for sites compiled by land-use 

category.

[The null hypothesis was that the medians of each distribution were the same. ANOVA, analysis of variance; N, nitrogen; P, phosphorus; 

*, indicates significance at α = 0.05]

Constituent

ANOVA test Multiple comparison test

p-value Land-use comparison pairs significant at α = 0.05

Nitrate yield 0.0174* (UN) Undeveloped - High point-source flow (HPS)

(LAG) Low agricultural - High point-source flow (HPS)

Total N yield 0.0014* (UN) Undeveloped - High urban (HUR)

(UN) Undeveloped - High point-source flow (HPS)

(LAG) Low agricultural - High urban (HUR)

(LAG) Low agricultural - High point-source flow (HPS)

(MIX) Mixed - High point-source flow (HPS)

Total P yield 0* (UN) Undeveloped - High agricultural (HAG)

(UN) Undeveloped - Low urban (LUR)

(UN) Undeveloped - High urban (HUR)

(UN) Undeveloped - Mixed (MIX)

(UN) Undeveloped - High point-source flow (HPS)

(LAG) Low agricultural - High agricultural (HAG)

(LAG) Low agricultural - High urban (HUR)

(LAG) Low agricultural - High point-source flow (HPS)

(MIX) Mixed - High urban (HUR)

(MIX) Mixed - High point-source flow (HPS)
32 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

10 A

5

1

0.50

0.10

0.05

0.01

UN LAG HAG LUR HUR MIX HPS

4 14

Number of observations

8 2 4 10 6

C

10

B

5

1

0.5

0.1

1

0.5

0.1

0.05

0.01

Outside value–more than 1.5 times

the interquartile range from the

upper or lower end of the box

EXPLANATION

Upper whisker–the last observation

within 1.5 times the interquartile range

of the upper end of the box

Lower whisker–the last observation

within 1.5 times the interquartile range

of the lower end of the box

Land-use category

Undeveloped

Low agricultural

High agricultural

Low urban

High urban

Mixed

High point-source flow

UN

LAG

HAG

LUR

HUR

MIX

HPS

75th percentile

25th percentile

Median

Median annual yield, in tons per square mile

Figure 12. Distributions of median annual yields for (A) nitrate, (B) total nitrogen, and (C) total phosphorus for study sites 

based on land-use category.
Relation of Watershed Setting and Stream Nutrient Yields 33

finding does not necessarily indicate that the nitrate and total N 

yields do not differ among these land-use categories; it simply 

means that a significant difference in median yields between the 

categories, if any, was not large enough to be detected given the 

sample size and data variability for each category. Although the 

results of this examination are based on a dataset with a limited 

number of sites for many of the land-use categories and some a 

priori divisions used to divide the categories, the results suggest 

that evaluating stream nutrient yields using the watershed landuse classification scheme devised in this report may be a useful 

approach for characterizing differences in watershed setting and 

stream nutrient yields. Further statistical evaluation of a more 

comprehensive dataset, including a larger number of sites for 

individual land-use categories, would be needed to more fully 

characterize similarities and differences in stream nutrient yields 

based on watershed land-use conditions.

The annual nutrient, streamflow, and point-source flow 

yield data during the period 2001 to 2008 for sites 29 and 30 

(appendix 1, appendix table A3-2, and fig. 13) further illustrate 

the effects that point-source dischargers can have on streamnutrient yields. Sites 29 and 30 in the Upper Cape Fear River 

basin (fig. 5) are designated as HUR and HPS watersheds, 

respectively, that have similar drainage areas (33.9 and 37.2 mi2

, 

respectively), high percentages of developed land (86.8 and 

83.7 percent, respectively), and no permitted CAFOs. The 

primary difference between these watersheds is that site 30 

receives point-source discharges from a single major municipal 

WWTP (NPDES permit NC0024325) and site 29 contains no 

NPDES permitted wastewater dischargers. The overall trend 

and variability in annual streamflow yields was comparable 

for both sites (median annual streamflow yields of 321.3 and 

339.5 Mgal/mi2

 for sites 29 and 30, respectively) during 2001 

to 2008 (fig. 13). Although the trends in annual nutrient yields 

follow the trends in streamflow yields at each site, the nutrient 

yields are substantially elevated at site 30, where the median 

annual point-source flow yield of 111.6 Mgal/mi2

 for the WWTP 

constitutes about 33 percent of the median annual streamflow. 

Comparison of the median annual yields of nitrate (1.13 ton/mi2

), 

total N (1.96 ton/mi2

), and total P (0.18 ton/mi2) for site 29 to the 

median annual yields of nitrate (6.50 ton/mi2

), total N (8.32 ton/

mi2

), and total P (0.69 ton/mi2) for site 30 during 2001 to 2008 

suggests that wastewater discharges from the WWTP increased 

the individual constituent yields at site 30 by 83 percent for 

nitrate, 76 percent for total N, and 74 percent for total P.

10

8

6

4

2

0

800

600

400

200

0

2000 2001 2002

Annual nutrient yield, in tons per square mile Annual flow yield, in million gallons per square mile

2003 2004

Years Years

2005 2006 2007 2008 2009 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Nitrate

Total Nitrogen

Total Phosphorus

A C

B D

EXPLANATION

Nitrate

Total Nitrogen

Total Phosphorus

EXPLANATION

Site 29

Site 29 Site 30

Site 30

Point-source

flow yield

Streamflow yield Streamflow yield

Figure 13. Annual data for (A) nutrient yields and (B) streamflow yields for study site 29, and annual data for (C) nutrient yields 

and (D) streamflow and point-source flow yields for study site 30, 2001–2008.
34 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

Identification of Watershed Environmental 

Variables Influencing Stream Nutrient Yields

Regression tree models were developed to examine 

relations between the watershed environmental (predictor) 

variables and median annual yields of nitrate, total N, and 

total P (response variables) based on data from 1997 to 2008. 

Models were developed for each nutrient individually as a 

means of determining which characteristics or combination 

of characteristics are associated with basins that are likely 

to have high or low nutrient yields. Data for the nutrient 

yields and associated environmental variables examined as 

part of the regression tree analyses are presented in appendix 

table A4-1.

The regression tree models evaluate the median annual 

nutrient yield data (response variable) and all associated 

watershed environmental (predictor) variables in order to 

identify the predictor variables that best partition, or split, the 

response variable into increasingly homogeneous subsets. 

The hierarchical structure of the tree model provides a set of 

rules that define the relative importance of the environmental 

variables that best predict the observed nutrient yields. The 

first split defined in the regression tree identifies the predictor 

variable that explains the highest percentage of the total 

deviance in the constituent yield data and subsequent splits in 

the regression tree identify variables that explain successively 

lower percentages of the total deviance in the yield dataset.

Four regression tree models were developed to analyze 

the relation between watershed environmental variables and 

observed nutrient yields for the study sites. Regression tree 

Model 1 examined the nutrient yields for all 48 study sites 

regardless of basin size and median annual percentages of 

point-source flow contributions to streamflow. Regression tree 

Model 2 examined study sites where the point-source flow 

contributions to streamflow are less than or equal to (<) 10 

percent in an effort to minimize the influence of point-source 

discharges and facilitate examination of nonpoint-source 

activities. Regression tree Models 3 and 4 also examined sites 

having low (<10 percent) point-source flow contributions 

as well as further subsetting sites on the basis of drainage 

areas (Model 3 <1,000 mi2

 and Model 4 <100 mi2) to better 

understand environmental influences on stream nutrient yields 

at different watershed scales. Results of the regression tree 

analyses include the number of splits in the tree model, the 

selected predictor variable and value defining each split in 

constituent yields, and the percentage of the total deviance in 

the constituent yield data explained by the selected predictor 

variables (tables 8–11; figs. 14–17). A terminal node in 

the regression trees represents the average median annual 

constituent yields for the number of observations, or sites, in 

the node.

Regression Tree Model 1

Model 1 analyzed median annual yields for nitrate, total N, 

and total P for the 48 study sites regardless of basin size (range: 1.1 

to 5,261 mi2

) and median annual percentages of point-source flow 

(range: 0 to 38.6 percent) (table 8; fig. 14). The Model 1 regression 

tree for nitrate yield identified one split where the selected predictor variable of median annual point-source flow yield (split value 

<70.08 Mgal/mi2

) explained 40.4 percent of the total deviance in 

the nitrate yields. When the median annual point-source flow yield 

in the watershed was <70.08 Mgal/mi2

, the average median annual 

nitrate yield was 0.28 ton/mi2

; otherwise, the average median 

annual nitrate yield was 6.83 ton/mi2

 (fig. 14A).

Model 1 results for the total N yields identified three splits 

in the data where the most influential predictor variable of median 

annual point-source flow yield explained 63.2 percent of the total 

deviance in the total N yields (table 8; fig. 14B). Subsequent splits 

2 and 3 were based on the percentage of forested land in the watershed and the percentage of agricultural land in the 50-ft stream 

buffer within the watershed, respectively. The highest average 

median total N yield (7.27 ton/mi2

) occurred for those watersheds 

having median annual point-source flow yields >70.08 Mgal/mi2

(fig. 14B). When median annual point-source flow yield in the 

watershed was <70.08 Mgal/mi2

, the average median total N yield 

was determined by the percentage of forested land in the watershed. When forested land in the watershed was ≥52.1 percent, the 

average median total N yield was 0.55 ton/mi2

. When forested 

land in the watershed was <52.1 percent, the average median total 

N yield was determined by the percentage of agricultural land in 

the stream buffer. Of these watersheds, average median total N 

yields were lower when agricultural land in the stream buffer was 

<19.3 percent (1.00 ton/mi2

) than when agricultural land in the 

stream buffer was ≥19.3 percent (2.67 ton/mi2

).

Model 1 for total P yields identified five splits and six 

terminal nodes in the regression tree defining the relations 

between the predictor variables and median annual total P 

yields (table 8; fig. 14C). Median annual streamflow yield was 

selected as the most influential predictor variable, explaining 48.9 

percent of the total deviance in the total P yields (table 8). The 

highest total P yields were noted for watersheds having median 

annual streamflow yields ≥307.42 Mgal/mi2

 (fig. 14C). Of these 

watersheds, average median total P yields were higher for those 

receiving ≥27.2 percent (0.70 ton/mi2

) median annual point-source 

flow contributions to the stream than those receiving <27.2 percent 

(0.28 ton/mi2

) of flow from point sources. The lowest average 

median total P yield (0.02 ton/mi2

) occurred for those watersheds 

having median annual streamflow yields <307.42 Mgal/mi2

 and 

agricultural land in the stream buffer that was <1.1 percent. When 

median annual streamflow yields were <307.42 Mgal/mi2

 and 

agricultural land in the stream buffer was ≥1.1 percent, the more 

intermediate total P yields were further split by the predictor 

variable of median annual streamflow yield and the predictor 

variable of percentage of forested land plus wetlands in the 

watershed (fig. 14C). 
Relation of Watershed Setting and Stream Nutrient Yields 35

Table 8. Regression tree Model 1 results for all 48 study sites.

[<, less than; ≥, greater than or equal to; Mgal/mi2, million gallons per 

square mile; %, percent]

Split Predictor variable and

split value

Percent of total deviance 

in response variable data 

explained by predictor 

variable

Response variable: Nitrate yields

1 Median annual point-source 

flow yield <70.08 Mgal/mi2

40.4

Response variable: Total nitrogen yields

1 Median annual point-source 

flow yield <70.08 Mgal/mi2

63.2

2 Forested in watershed ≥52.1 % 13.1

3 Agriculture in buffer <19.3 % 6.8

Response variable: Total phosphorus yields

1 Median annual streamflow 

yield <307.42 Mgal/mi2

48.9

2 Agriculture in buffer <1.1 % 13.4

3 Median annual percent pointsource flow <27.28.9

4 Median annual streamflow 

yield <132.91 Mgal/mi2

6.1

5 Forested plus wetlands in 

watershed ≥53.5 %

4.4
36 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

< 70.08 PSFlwYld ≥ 70.08

0.28

N=45

6.83

N=3

< 70.08 PSFlwYld ≥ 70.08

≥ 52.1 < 52.1

< 19.3 ≥ 19.3

< 132.91 ≥ 132.91

0.55

N=19

1.0

N=24

2.67

N=2

7.27

N=3

For-W

A. Nitrate yields

B. Total nitrogen yields

< 307.42 StrFlwYld ≥ 307.42

< 1.1 ≥ 1.1

≥ 53.5 < 53.5

< 27.2 ≥ 27.2

0.02

N=4

0.04

N=5

0.08

N=20

0.14

N=12

0.28

N=5

0.70

N=2

ForWt-W

%PSFlw

Agr-B

StrFlwYld

Agr-B

C. Total phosphorus yields

Median annual point-source flow yield, in million 

gallons per square mile

Percentage of forested land in watershed

Percentage of agricultural land in stream buffer

Median annual streamflow yield, in million 

gallons per square mile

Percentage of forested land plus wetlands

in watershed

Median annual percentage point-source flow

contribution to stream

Split variable and split value selected by model

to allocate observations (or sites) from parent

node into one of two resulting child nodes.

Terminal node with average median annual

 constituent yield in tons per square mile.

 N indicates the number of observations

 at the node.

PSFlwYld

For-W

Agr-B

StrFlwYld

ForWt-W

%PSFlw

PSFlwYld

< 70.08

6.83

N=3

EXPLANATION

Figure 14. Regression tree for Model 1 identifying those predictor (environmental) variables that best explain observed variations in 

median annual yields of (A) nitrate, (B) total nitrogen, and (C) total phosphorus for all 48 study sites.
Relation of Watershed Setting and Stream Nutrient Yields 37

Regression Tree Model 2

Model 2 analyzed median annual yields for total N 

and total P for the 42 study sites that had point-source flow 

contributions ≤10 percent (table 9; fig. 15). Regression tree 

analysis for Model 2 did not uncover any structure in the 

nitrate yield dataset that allowed splitting of the data into more 

homogeneous subsets. This finding indicates that the observed 

variability in nitrate yields for sites with point-source flow 

contributions ≤10 percent could not be explained by any of the 

environmental predictors included in the analysis. This same 

result was found in the analysis of nitrate yields by Model 3 

and Model 4; consequently, the nitrate yields are dropped from 

further discussion of the regression tree analyses.

Model 2 results for the total N yields identified three 

splits in the data where the most influential predictor variable, 

percentage of forested land in the watershed, explained 

40.5 percent of the total deviance in the median annual 

total N yields (table 9; fig. 15A). When forested land in the 

watershed was ≥41.1 percent, the average median total N 

yield was 0.43 ton/mi2

 when the median annual streamflow yield 

was <155.68 Mgal/mi2

 and 0.72 ton/mi2 when the median annual 

streamflow yield was ≥155.68 Mgal/mi2

. When forested land 

in the watershed was <41.1 percent, the average median total N 

yield was 1.10 ton/mi2

 when agricultural land in the stream buffer 

was <19.3 percent and 2.67 ton/mi2

 when agricultural land in the 

stream buffer was ≥19.3 percent.

Model 2 also identified three splits for the total P yields that 

were similar to those observed for the model of total N yields. The 

percentage of forested land in the watershed was selected as the 

most influential predictor variable, explaining 37.4 percent of the 

total deviance in the total P yields (table 9; fig. 15B). When forested land in the watershed was ≥41.1 percent, the average median 

total P yield was 0.03 ton/mi2

 when agricultural land in the stream 

buffer was ≤5.4 percent and 0.08 ton/mi2

 when agricultural land 

in the stream buffer was ≥5.4 percent. When forested land in the 

watershed was <41.1 percent, the average median total P yield was 

0.10 ton/mi2

 when the watershed drainage area was ≥381.8 mi2

and 0.23 ton/mi2

 when the drainage area was <381.8 mi2.

Table 9. Regression tree Model 2 results for the 42 study sites with 

point-source flow contributions less than or equal to 10 percent.

[<, less than; ≥, greater than or equal to; Mgal/mi2, million gallons per square 

mile; %, percent; mi2, square miles]

Split Predictor variable and

split value

Percent of total deviance in response variable data explained 

by predictor variable

Response variable: Total nitrogen yields

1 Forested in watershed ≥41.1 % 40.5

2 Median annual streamflow 

yield <155.68 Mgal/mi2

16.5

3 Agriculture in buffer <19.3 % 10.8

Response variable: Total phosphorus yields

1 Forested in watershed ≥41.1 % 37.4

2 Agriculture in buffer <5.4 % 19.2

3 Drainage area ≥381.8 mi2 14.7
38 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

≥ 41.1 For-W < 41.1

A. Total nitrogen yields

B. Total phosphorus yields

< 155.68 ≥ 155.68 < 19.3 ≥ 19.3

0.43

N=8

0.72

N=21

1.10

N=11

2.67

N=2

StrFlwYld Agr-B

≥ 41.1 For-W < 41.1

< 5.4 ≥ 5.4 ≥ 381.8 < 381.8

0.03

N=9

0.08

N=20

0.10

N=6

0.23

N=7

Agr-B DA

Percentage of forested land in watershed

Percentage of agricultural land in stream buffer

Median annual streamflow yield, in million 

gallons per square mile

Watershed drainage area, in square miles

Split variable and split value selected by model

to allocate observations (or sites) from parent

node into one of two resulting child nodes.

Terminal node with average median annual

 constituent yield in tons per square mile.

 N indicates the number of observations

 at the node.

For-W

Agr-B

StrFlwYld

DA

For-W

< 41.1

2.67

N=2

EXPLANATION

Figure 15. Regression tree for Model 2 identifying those predictor (environmental) variables that best explain observed variations in 

median annual yields of (A) total nitrogen and (B) total phosphorus for the 42 study sites with point-source flow contributions less than 

or equal to 10 percent.
Relation of Watershed Setting and Stream Nutrient Yields 39

Regression Tree Model 3

Model 3 analyzed median annual yields for total 

N and total P for the 33 study sites with drainage 

areas ≤1,000 mi2 and point-source flow contributions 

≤10 percent (table 10; fig. 16). For total N yields, the 

regression tree identified two splits in the data where 

the most influential predictor variable, percentage of 

forested land in the watershed, explained 52.2 percent 

of the total deviance in the median annual total N yields 

(table 10; fig. 16A). When forested land in the watershed 

was ≥41.1 percent, the average median total N yield was 

0.53 ton/mi2 when forested land in the stream buffer was 

≥20.7 percent and 1.06 ton/mi2 when forested land in the 

stream buffer was <20.7 percent. When forested land in the 

watershed was <41.1 percent, the average median total N 

yield was 1.49 ton/mi2.

For total P yields, regression tree Model 3 identified 

one split where the selected predictor variable of forested 

land in the watershed explained 57.7 percent of the total 

deviance in the total P yields (table 10). When forested 

land in the watershed was ≥41.1 percent, the average 

median total P yield was 0.05 ton/mi2

; otherwise, the 

average median total P yield was 0.20 ton/mi2 (fig. 16B).

Table 10. Regression tree Model 3 results for the 33 study sites 

with drainage areas less than or equal to 1,000 square miles and 

point-source flow contributions less than or equal to 10 percent.

[≥, greater than or equal to; %, percent]

Split Predictor variable and

split value

Percent of total deviance 

in response variable data 

explained by predictor 

variable

Response variable: Total nitrogen yields

1 Forested in watershed ≥41.1 % 52.2

2 Forested in buffer ≥20.7 % 13.7

Response variable: Total phosphorus yields

1 Forested in watershed ≥41.1 % 57.7

≥ 41.1 For-W < 41.1

A. Total nitrogen yields

B. Total phosphorus yields

≥ 20.7 < 20.7

0.53

N=20

1.06

N=4

For-B

≥ 41.1 For-W < 41.1

0.05

N=24

0.20

N=9

Percentage of forested land in watershed

Percentage of forested land in stream buffer

Split variable and split value selected by model

to allocate observations (or sites) from parent

node into one of two resulting child nodes.

Terminal node with average median annual

 constituent yield in tons per square mile.

 N indicates the number of observations

 at the node.

For-W

For-B

For-W

< 41.1

0.20

N=9

EXPLANATION

1.49

N=9

Figure 16. Regression tree for Model 3 identifying those 

predictor (environmental) variables that best explain observed 

variations in median annual yields of (A) total nitrogen and 

(B) total phosphorus for the 33 study sites with drainage areas 

less than or equal to 1,000 square miles and point-source flow 

contributions less than or equal to 10 percent.
40 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

Regression Tree Model 4

Model 4 analyzed median annual yields for total 

N and total P for the 17 study sites with drainage areas 

≤100 mi2

 and point-source flow contributions ≤10 percent 

(table 11; fig. 17). Model 4 results for the total N yields 

identified three splits in the data where the most influential 

predictor variable, percentage of forested land in the 

watershed, explained 67.6 percent of the total deviance 

in the total N yields (table 11; fig. 17A). When forested 

land in the watershed was ≥46.0 percent, the average 

median total N yield was 0.28 ton/mi2

 when agricultural 

land in the watershed was <7.1 percent and 0.63 ton/mi2

when agricultural land in the watershed was ≥7.1 percent. 

When forested land in the watershed was <46.0 percent, 

the average median total N yield was 1.31 ton/mi2

 when 

agricultural land in the stream buffer was <19.3 percent 

and 2.67 ton/mi2

 when agricultural land in the stream 

buffer was ≥19.3 percent.

Model 4 results also identified three splits for the total 

P yields that were similar to the model for total N yields. 

The percentage of forested land in the watershed was the 

most influential predictor variable, explaining 70.5 percent 

of the total deviance in the median annual total P yields 

(table 11; fig. 17B). When forested land in the watershed 

was ≥42.0 percent,the average median total P yield was 

0.02 ton/mi2

 when agricultural land in the watershed was 

<8.8 percent and 0.07 ton/mi2

 when agricultural land in 

the watershed was ≥8.8 percent. When forested land in the 

watershed was <42.0 percent, the average median total P 

yield was 0.21 ton/mi2

 when the watershed drainage area 

was <80.8 mi2

 and 0.44 ton/mi2 when the drainage area 

was ≥80.8 mi2

.

Table 11. Regression tree Model 4 results for the 17 study sites 

with drainage areas less than or equal to 100 square miles and 

point-source flow contributions less than or equal to 10 percent.

[<, less than; ≥, greater than or equal to; %, percent; mi2, square miles]

Split Predictor variable and

split value

Percent of total 

deviance in response 

variable data explained by predictor 

variable

Response variable: Total nitrogen yields

1 Forested in watershed ≥46.0 % 67.6

2 Agriculture in watershed <7.1 % 14.6

3 Agriculture in buffer <19.3 % 9.7

Response variable: Total phosphorus yields

1 Forested in watershed ≥42.0 % 70.5

2 Agriculture in buffer <8.8 % 15.2

3 Drainage area <80.8 mi2 6.4
Relation of Watershed Setting and Stream Nutrient Yields 41

≥ 46.0 For-W < 46.0

A. Total nitrogen yields

B. Total phosphorus yields

< 7.1 ≥ 7.1 < 19.3 ≥ 19.3

0.28

N=2

0.63

N=7

1.31

N=6

2.67

N=2

Agr-W Agr-B

≥ 42.0 For-W < 42.0

< 8.8 ≥ 8.8 < 80.8 ≥ 80.8

0.02

N=3

0.07

N=7

0.21

N=6

0.44

N=1

Agr-W DA

Percentage of forested land in watershed

Percentage of agricultural land in stream buffer

Percentage of agricultural land in watershed

Watershed drainage area, in square miles

Split variable and split value selected by model

to allocate observations (or sites) from parent

node into one of two resulting child nodes.

Terminal node with average median annual

 constituent yield in tons per square mile.

 N indicates the number of observations

 at the node.

For-W

Agr-B

Agr-W

DA

For-W

< 42.0

0.44

N=1

EXPLANATION

Figure 17. Regression tree for Model 4 identifying those predictor (environmental) variables that best explain observed variations 

in median annual yields of (A) total nitrogen and (B) total phosphorus for the 17 study sites with drainage areas less than or equal to 

100 square miles and point-source flow contributions less than or equal to 10 percent.
42 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

Summary and Application of the Regression Tree 

Model Results

The regression tree analyses indicate that the environmental variables examined in this study were useful for 

predicting observed yields of nitrate, total N, and total P. For 

nitrate, the regression tree analysis based on Model 1 identified annual point-source flow yields as the primary watershed 

environmental variable influencing the observed stream yields 

for nitrate. Models 2, 3, and 4 could not identify any watershed environmental variables that could adequately explain the 

observed variability in the nitrate yields among the set of sites 

examined by each of these models.

All four models were successful in identifying particular 

watershed environmental variables that influenced the total N 

and total P yields among the study sites. The regression tree 

analysis that was based on all 48 study sites (Model 1), which 

included those watersheds having high annual percentages of 

point-source flow contributions to the streams (>10 percent), 

identified annual point-source flow yields as the primary 

variable influencing the observed stream yields for total 

N and annual streamflow yields as the primary variable 

influencing the observed stream yields for total P (table 8; 

fig. 14). When the effects of sites having high point-source 

flows (>10 percent) were excluded from the regression tree 

analyses (Models 2–4), the percentage of forested land in 

the watersheds was identified by each model as the primary 

environmental variable influencing stream yields for both 

total N and total P. Additional environmental variables found 

to further influence the stream nutrient yields, as identified 

by subsequent splits in the regression tree models, included 

median annual percentage of point-source flows, variables of 

land cover (percentage of forested land, agricultural land, and 

(or) forested land plus wetlands) in the watershed and (or) 

stream buffer, and drainage area (tables 8–11).

Many of the environmental variables compiled for the 

watersheds (such as BFI, HSGs, CAFO animal density, and 

basinwide annual precipitation; appendix table A4-1) were 

not identified by the models as one of the influential predictor 

variables for explaining variations in the observed nutrient 

yields; however, this does not imply that these environmental 

variables do not influence nutrient yields among the study 

sites. It is likely that the influential predictor variables (such as 

streamflow yield or percentage forested land in the watershed) 

selected by the models also serve as surrogates that reflect the 

integrated effects of additional environmental influences on 

the stream nutrient yields.

The regression tree models based on the study data were 

not developed with the intent to precisely predict stream 

nutrient yields, but rather, to explore differences in select 

watershed environmental variables that would help identify 

watersheds where the potential for total N or total P export 

is relatively high or low. The regression tree models can also 

serve as a tool for better understanding the environmental 

factors that influence stream yields of total N and total P 

under different watershed settings. This information can 

help water-resource managers in developing strategies for 

improving water-quality conditions in nutrient impaired 

streams. For example, the regression trees developed for 

Models 2, 3, and 4 all indicate that watersheds with higher 

percentages of forested land (exceeding 41 to 46 percent) have 

lower median annual total N and total P yields compared to 

watersheds with lower percentages of forested land (below 41 

to 46 percent), which have higher median annual total N and 

total P yields (figs. 15–17). Watersheds with lower proportions 

of forested lands also have proportionately higher amounts of 

agricultural and (or) developed urban lands in the watersheds, 

which contributes to higher total N and total P yields. Results 

from Models 2 and 4 further indicate that the average median 

annual yields of total N and total P were higher for sites with 

relatively larger percentages of agricultural land in the stream 

buffer or watershed. Although the split values for the percentage of agricultural land within the stream buffer or watershed 

were relatively low (5.4 to 19.3 percent), the results suggest 

that agricultural land does not necessarily need to dominate 

the stream buffer or watershed to influence total N and total P 

yields. Median annual yields of total N were lower for those 

sites having higher amounts of forested land in the stream 

buffer based on Model 3. These results suggest that increasing 

the relative amounts of forested land within the watersheds 

and the 50-ft buffers along the streams for watersheds with 

point-source flow contributions ≤10 percent would lower total 

N and total P yields in the streams. Conversely, high pointsource flow contributions from wastewater treatment facilities 

would negate the effects of increased forested land on stream 

nutrient yields.

A particularly useful application of the regression tree 

models is for characterizing the relative export potential of 

total N and total P in nonmonitored watersheds where streamflow and (or) water-quality data are not actively collected, but 

other readily available information can be compiled for those 

influential watershed environmental variables (such as land 

cover, drainage areas, and point-source flows) identified by 

the models. For example, consider a scenario where there is 

interest in knowing which of two nutrient impaired watersheds 

may be more prone to exporting nutrients and which might 

warrant additional investigation and potential development of 

remedial actions for reducing nutrient loads in the stream. For 

both watersheds, GIS analyses were used to compile information on the drainage areas, land cover percentages within 

the watersheds, and land cover percentages within a 50-ft 

buffer bounding the stream in each watershed. The compiled 

information indicates that the watersheds have the following 

characteristics:

 

Watershed 1 Watershed 2

Drainage area 70 mi2 75 mi2

Forested land in watershed 55% 35%

Agricultural land in watershed 15% 35%

Agricultural land in stream buffer 5% 15%
Summary and Conclusions 43

With both watersheds having drainage areas <100 mi2 and 

no point-source flow contributions to the receiving streams, 

the Model 4 regression trees (fig. 17) can be used for examining potential differences in the export potential of total N and 

total P between these watersheds. Comparing the results of the 

environmental variables compiled for the two nonmonitored 

watersheds to the Model 4 regression tree results indicates a 

predicted annual total N yield of 0.63 ton/mi2

 and total P yield 

of 0.07 ton/mi2

 for watershed 1 and a predicted annual total 

N yield of 1.31 ton/mi2

 and total P yield of 0.21 ton/mi2 for 

watershed 2. The important result for this comparison is not in 

the actual values of the predicted total N and total P yields but 

rather the implication that the nutrient export for watershed 

2 may be more than twice that for watershed 1, indicating 

that watershed 2 might be the most appropriate candidate for 

targeting management actions to reduce stream nutrient loads. 

Similar exercises can be conducted on other watersheds of 

interest using any of the developed regression tree models but 

will be contingent on the availability of necessary data for 

characterizing those watershed environmental variables that 

influence the stream nutrient yields.

The regression tree models developed in this study 

provide a simple analytical approach for relating differences 

in select watershed attributes to differences in stream nutrient 

yields, particularly total N and total P. The models were based 

on data compiled during 1997 to 2008 for 48 watershed study 

sites located throughout central and eastern North Carolina. 

The models can be refined as more recent information on 

streamflows, point-source flows, and nutrient loads become 

available for existing monitoring sites. In addition, inclusion 

of streamflow and nutrient load data for additional watershed 

sites that reflect varying degrees of land use and anthropogenic 

inputs (such as AG watersheds with high CAFO animal 

densities) can allow further evaluation and identification of 

watershed variables that influence nutrient yields in streams 

throughout North Carolina.

Summary and Conclusions

As part of efforts to improve water-quality conditions 

in impaired streams, approaches are needed to help identify 

watersheds where the export potential of nutrients in streams 

is relatively high or low. Such approaches can provide 

water-resource managers and policy makers with beneficial 

information for targeting those watersheds where restoration 

efforts can be implemented to achieve the most beneficial 

improvements in stream water quality. In this report, environmental and analytical data compiled for 48 stream study sites, 

distributed throughout the Roanoke, Chowan, Tar-Pamlico, 

Neuse, Cape Fear, and Lumber River basins in central and 

eastern North Carolina, were used to explore relations between 

watershed settings and stream nutrient yields.

For the 1997 to 2008 study period, available nutrient 

concentration data and streamflow data were used to compute 

stream nutrient loads (nitrate, total N, and total P) for the 

study sites. All LOADEST model estimates of the annual, 

seasonal, monthly, and daily nutrient loads for each site are 

compiled as part of this report; however, the annual nutrient 

loads and yields were used as the basis for data analyses in 

this investigation. For each site, the annual nutrient loads 

were normalized by drainage area to compute annual yields of 

nitrate, total N, and total P that were used to explore relations 

between watershed setting and stream export of nutrients.

Data were compiled for selected environmental variables 

to characterize the watershed conditions for the study sites. 

The environmental dataset includes variables for land cover, 

HSGs, precipitation, BFI, streamflows (median annual 

streamflows and yields), wastewater discharge facilities 

(median annual point-source flows, yields, and percentage 

contributions to the streams), and CAFOs (number of permits, 

total animal SSLW, and animal density). The land-cover 

datasets were compiled for the entire watershed drainage area 

of each site and for a 50-foot buffer zone bounding the streams 

within each watershed. Twelve of the 15 land-cover classes 

compiled for the study sites were aggregated into four primary 

classes (developed, forested, agricultural, and wetlands) for 

evaluating relations between land cover and nutrient yields. 

The aggregated land-cover data were used to assign each 

watershed study site to one of six land-use categories on the 

basis of the relative percentages of developed and agricultural 

lands within the watershed. An additional land-use category 

was used to designate any site having high annual pointsource flow contributions to the stream. The undeveloped 

(UN) category includes sites where developed land is ≤10 

percent and agricultural land is ≤15 percent of the total land 

cover in the watershed. The low agricultural (LAG) and high 

agricultural (HAG) land-use categories reflect watersheds with 

minimal developed land (≤10 percent) relative to agricultural 

land (>15 and ≤30 percent for LAG sites and >30 percent for 

HAG sites). The low urban (LUR) and high urban (HUR) 

land-use categories reflect watersheds with lower agricultural 

land (≤15 percent) relative to developed land (>10 and ≤30 

percent for LUR sites and >30 percent for HUR sites). The 

mixed (MIX) land-use category includes sites where developed land is >10 percent and agricultural land is >15 percent 

of the watershed land cover. The high point-source flow (HPS) 

category includes any site having high (>10 percent) annual 

point-source flow contributions to the stream, regardless of the 

amount of developed or agricultural lands within the watershed. Half of the study sites were classified as either LAG 

(14 sites) or MIX (10 sites) land uses. There were 8 HAG sites 

and 6 HPS sites. Fewer sites were classified as UN (4), HUR 

(4), or LUR (2).

Relations between land-use categories and nutrient 

yields were evaluated to determine whether differences in 

yields of nitrate, total N, and total P were discernible among 

the land-use categories assigned to the study sites. An initial 

examination of the data indicated that the highest median 

annual nutrient yields occurred at both agricultural and urban 

sites, especially for urban sites having large percentages of 
44 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

point-source flow contributions to the streams. The results 

of ANOVA and multiple comparison tests identified some 

statistical differences, at the 0.05 significance level, in nutrient 

yields among the land-use categories. Median annual nitrate 

yields for HPS sites were from 16 to 32 times higher than 

for LAG and UN sites, respectively. Significant differences 

in median annual total N yields were identified for five 

land-use comparison pairs. Median annual total N yields were 

significantly higher for HUR sites (about 2 to 3 times) and 

HPS sites (about 5 to 9 times) relative to both the LAG and 

UN sites. The median annual total N yield for the HPS sites 

also was about 4 times higher than the MIX sites. Total P had 

the most diverse combination of land-use comparison pairs 

(10) identified as having statistically significant differences in 

median annual yields. The median annual total P yields for the 

HAG, LUR, HUR, MIX, and HPS sites were all significantly 

higher (from 5 to 16 times) than the UN sites. The median 

annual total P yield for the HAG, HUR, and HPS sites also 

were significantly higher (up to about 5 times) than the LAG 

sites. The median annual total P yield for the HUR and HPS 

sites also were significantly higher (about 2 to 3 times) than 

the MIX sites.

Although the dataset was based on a limited number 

of sites for many of the land-use categories, the results of 

this evaluation suggest that grouping and examining stream 

nutrient yields on the basis of the watershed land-use classification scheme devised in this report may be a useful approach 

for characterizing relations between watershed settings and 

nutrient yields in streams located throughout central and 

eastern North Carolina. Further statistical evaluation of a more 

comprehensive dataset, including a larger number of sites with 

nutrient yield data for individual land-use categories, would be 

needed to more fully characterize similarities and differences 

in stream nutrient yields on the basis of watershed land-use 

conditions.

As indicated by the statistical analyses, watersheds with 

high point-source flows from wastewater treatment facilities 

had a significant effect on stream nutrient yields. The study 

data for several sites were further used to exemplify the influences of municipal WWTPs on stream nutrient yields. The 

annual total N yield data examined for site 36 highlight the 

beneficial effects that enhancements to a WWTP can have as 

part of nutrient-reduction strategies. During 1997 to 2008, the 

annual point-source flows from the Triangle WWTP constituted 15 to 33 percent of the annual streamflows in this small 

HPS watershed. The annual stream yields of total N increased 

during 1997 to 2004 (average yield 6.28 ton/mi2

) before 

dramatically falling in 2005 when upgrades in waste-treatment 

technologies at the Triangle WWTP were completed. During 

2006 to 2008, the annual total N yield averaged 2.28 ton/mi2

, 

representing a 64 percent reduction in the stream yield of 

total N following technology improvements at the WWTP. 

Comparison of study data during 2001 to 2008 for sites 29 

and 30 further illustrated the effects of wastewater dischargers 

on stream-nutrient yields. The primary difference between 

these watersheds was that HPS site 30 received point-source 

discharges from a single major municipal WWTP constituting 

33 percent of the median annual streamflow and HUR site 29 

contained no NPDES permitted wastewater facilities (municipal, major industrial/commercial, or domestic). Although the 

annual streamflow yields were comparable for both sites, the 

median annual nutrient yields at site 30 relative to site 29 were 

higher by 83 percent for nitrate, 76 percent for total N, and 

74 percent for total P.

Regression tree analyses also were performed to examine 

relations between the watershed environmental variables and 

median annual yields of nitrate, total N, and total P based 

on data from 1997 to 2008. Four regression tree models 

were developed for each nutrient as a means of determining 

which characteristics or combination of characteristics result 

in basins that are likely to have high or low nutrient yields. 

Regression tree Model 1 examined the nutrient yields for 

all 48 study sites regardless of basin size or the amount of 

point-source flow contributions to the streams. Regression 

tree Model 2 examined study sites where point-source flow 

contributions to the streams were ≤10 percent. Regression tree 

Models 3 and 4 also examined sites having low (≤10 percent) 

point-source flow contributions as well as further subsetting 

sites based on drainage areas (Model 3 ≤1,000 mi2

 and 

Model 4 ≤100 mi2

).

The regression tree analyses indicated that the watershed 

environmental variables examined in this study were useful 

for predicting annual yields of nitrate, total N, and total P. The 

regression tree analysis based on Model 1 identified annual 

point-source flow yields as the primary environmental variable 

influencing the observed stream yields for nitrate and total N 

and annual streamflow yields as the primary environmental 

variable influencing the observed stream yields for total P. The 

Model 1 results indicated that watersheds with median annual 

point-source flow yields greater than 70 Mgal/mi2

 had median 

annual yields of total N and nitrate that were up to 13 to 24 

times higher, respectively, than watersheds with point-source 

flow yields less than 70 Mgal/mi2

. Watersheds with median 

annual streamflow yields greater than about 307 Mgal/mi2

had median annual yields of total P that were from 2 to 35 

times higher than watersheds with streamflow yields less than 

307 Mgal/mi2

.

When sites having high point-source flows (>10 percent 

of total streamflow) were excluded from the analyses 

(Models 2–4), the percentage of forested land in the watersheds was identified by each model as the primary environmental variable influencing stream yields for both total N and 

total P. Regression tree analyses of nitrate for Models 2, 3, 

and 4 did not identify any watershed environmental variables 

that could adequately explain the observed variability in the 

nitrate yields among the set of sites examined by each of 

these models. The results for Models 2, 3, and 4 indicated that 

watersheds with higher percentages of forested land (ranging 

from 41 to 46 percent) had median annual total N and total P 

yields that typically were 2 to 3 times lower than watersheds 

with lower percentages of forested land. Watersheds with 

lower proportions of forested lands also have proportionately 
References Cited 45

higher amounts of agricultural and (or) developed urban lands 

in the watersheds, which contributes to higher total N and 

total P yields. The amount of agricultural land or forested land 

within the 50-ft stream buffers within the watersheds also 

was found to influence the total N and total P yields. Median 

annual yields of total N and (or) total P were higher for sites 

having higher amounts of agricultural land in the stream buffer 

based on Models 2 and 4. Median annual yields of total N 

were lower for those sites having higher amounts of forested 

land in the stream buffer based on Model 3. 

Additional environmental variables found to further 

influence the stream nutrient yields included median annual 

percentage of point-source flows, variables of land cover 

(percentage of forested land, agricultural land, and (or) 

forested land plus wetlands) in the watershed and (or) stream 

buffer, and drainage area. The models can be refined as 

additional environmental and nutrient yield data become 

available for sites included as part of this evaluation, as well 

as additional watershed sites with varying degrees of land 

use and anthropogenic inputs, to allow further evaluation and 

identification of watershed variables that influence nutrient 

yields in streams throughout North Carolina.

The regression tree models developed in this study 

provide a simple analytical approach for relating differences 

in select watershed attributes to differences in stream nutrient 

yields, particularly total N and total P. The regression tree 

models can serve as a tool for exploring differences in select 

watershed environmental variables to help identify watersheds 

where the potential for nitrate, total N, and (or) total P export 

is relatively high or low. This may be particularly useful for 

examining non-monitored watersheds where streamflow and 

(or) water-quality data are not actively collected but other 

readily available information can be compiled for those 

watershed environmental variables (such as land cover, 

drainage areas, and point-source flows) found to influence 

the stream nutrient yields. This type of information can assist 

water-resource managers in efforts to develop NSW management strategies for nutrient impaired streams and identifying 

watersheds where increased nutrient reduction efforts may be 

needed.

References Cited

Akaike, H., 1974, A new look at the statistical model identification: IEEE Transactions on Automatic Control, v. 19, 

no. 6, p. 716–723.

Bales, J.D., Oblinger, C.J., and Sallenger, A.H., Jr., 2000, 

Two months of flooding in eastern North Carolina, 

September–October 1999: Hydrologic, water-quality, and 

geologic effects of Hurricanes Dennis, Floyd, and Irene: 

U.S. Geological Survey Water-Resources Investigations 

Report 00–4093, 47 p.

Bales, J.D., Weaver, J.C., and Robinson, J.B., 1999, Relation of land use to streamflow and water quality at selected 

sites in the City of Charlotte and Mecklenburg County, 

North Carolina, 1993–98: U.S. Geological Survey WaterResources Investigations Report 99–4180, 95 p.

Burkholder, J.M., Dickey, D.A., Kinder, C.A., Reed, R.A., 

Mallin, M.A., McIver, M.R., Cahoon, L.B., Melia, G., 

Brownie, C., Smith, J., Deamer, N., Springer, J., Glasgow, 

H.B., and Toms, D., 2006, Comprehensive trend analysis of 

nutrients and related variables in a large eutrophic estuary—

A decadal study of anthropogenic and climatic influences: 

Limnology and Oceanography, v. 51, p. 463–487.

Burkholder, J.M., and Glasgow, H.B., Jr., 1997, Pfiesteria 

piscicida and other Pfiesteria-like dinoflagellates—

Behavior, impacts and environmental controls: Limnology 

and Oceanography, v. 42, p. 1052–1075.

Burkholder, J.M., Glasgow, H.B., Jr., and Hobbs, C.W., 1995, 

Distribution and environmental conditions for fish kills 

linked to a toxic ambush predator dinoflagellates: Marine 

Ecology Progress Series, v. 124, p. 43–61.

Cohn, T.A., 2005, Estimating contaminant loads in rivers—An 

application of adjusted maximum likelihood to type 1 censored data: Water Resources Research, v. 41, no. 7, 13 p., 

W07003, doi:10.1029/2004WR003833.

Cohn, T.A., Caulder, D.L., Gilroy, E.J., Zynjul, 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.

Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., 

Barnes, C., Herold, N., and Wickman, J., 2011: Completion 

of the 2006 national land cover database for the conterminous United States, Photogrammetric Engineering & 

Remote Sensing, v. 77, no. 9, p. 858–864.

Gilliam, J.W., Osmond, D.L., and Evans, R.O., 1997, Selected 

best management practices to control nitrogen in the Neuse 

River Basin: Raleigh, North Carolina State University, 

North Carolina Agricultural Research Service Technical 

Bulletin 311.

Gilroy, E.J., Hirsch, R.M., and Cohn, T.A., 1990, Mean square 

error of regression-based constituent transport estimates: 

Water Resources Research, v. 26, no. 9, p. 2069–2077.
46 Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008 

Glasgow, H.B., and Burkholder, J.M., 2000, Water quality 

trends and management implications from a five-year study 

of a eutrophic estuary: Ecological Applications, v. 10, no. 4, 

p. 1024–1046.

Harden, S.L., and Spruill, T.B., 2004, Ionic composition and 

nitrate in drainage water from fields fertilized with different nitrogen sources, Middle Swamp Watershed, North 

Carolina, August 2000–August 2001: U.S. Geological 

Survey Scientific Investigations Report 2004–5123, 14 p.

Harden, S.L., and Spruill, T.B., 2008, Factors affecting nitrate 

delivery to streams from shallow ground water in the North 

Carolina Coastal Plain: U.S. Geological Survey Scientific 

Investigations Report 2008–5021, 39 p.

Helsel, D.R., and Hirsch, R.M., 1992, Statistical methods in 

water resources: Elsevier, 522 p. (Also available online as 

U.S. Geological Survey Techniques of Water-Resources 

Investigations, book 4, chap. A3 (2002), accessed July 2012 

at http://pubs.usgs.gov/twri/twri4a3/html/pdf.html.)

Homer, C., Huang, C., Yang, L., Wylie, B., and Coan, M., 

2004, Development of a 2001 national land-cover database 

for the United States: Photogrammetric Engineering & 

Remote Sensing, v. 70, no. 7, p. 829–840.

Lim, K.J, Engel, B.A., Tang, Z., Choi, J., Kim, K-S, 

Muthukrishnan, S., and Tripathy, D., 2005, Automated web 

GIS based hydrograph analysis tool, WHAT: Journal of 

the American Water Resources Association, v. 41, no. 6, 

p. 1407–1416.

Luettich, R.A., McNinch, J.E., Paerl, H., Peterson, C.H., 

Wells, J.T., Alperin, M., Martens, C.S., and Pinckney, J.L., 

2000, Neuse River estuary modeling and monitoring project 

stage 1—Hydrography and circulation, water column nutrients and productivity, sedimentary processes and benthicpelagic coupling, and benthic ecology: The University of 

North Carolina Water Resources Research Institute, Report 

325-B.

Mallin, M.A., McIver, M.R., Wells, H.A, Parsons, D.C., and 

Johnson, V.L., 2005, Reversal of eutrophication following 

sewage treatment upgrades in the New River estuary, 

North Carolina: Estuaries, v. 28, no. 5, p. 750–760.

 McMahon, G., and Harned, D.A., 1998, Effect of environmental setting on sediment, nitrogen, and phosphorus 

concentrations in Albemarle-Pamlico Drainage Basin, 

North Carolina and Virginia, U.S.A.: Environmental 

Management, v. 22, no. 6, p. 887–903.

North Carolina Division of Water Quality, 2005, 2005 

Cape Fear River basinwide water quality plan: North 

Carolina Department of Environment and Natural 

Resources Division of Water Quality, Planning Section, 

accessed July 2012 at http://portal.ncdenr.org/web/wq/ps/

bpu/basin/capefear/2005.

North Carolina Division of Water Quality, 2006, 2006 

Roanoke River basinwide water quality plan: North 

Carolina Department of Environment and Natural 

Resources Division of Water Quality, Planning Section, 

accessed July 2012 at http://portal.ncdenr.org/web/wq/ps/

bpu/basin/roanoke/2006.

North Carolina Division of Water Quality, 2007, 2007 Chowan 

River basinwide water quality plan: North Carolina Department of Environment and Natural Resources Division of 

Water Quality, Planning Section, accessed July 2012 at 

http://portal.ncdenr.org/web/wq/ps/bpu/basin/chowan/2007.

North Carolina Division of Water Quality, 2009, 2009 Neuse 

River basinwide water quality plan: North Carolina Department of Environment and Natural Resources Division of 

Water Quality, Planning Section, accessed July 2012 at 

http://portal.ncdenr.org/web/wq/ps/bpu/basin/neuse/2009.

North Carolina Division of Water Quality, 2010, 2010 Lumber 

River basinwide water quality plan: North Carolina Department of Environment and Natural Resources Division of 

Water Quality, Planning Section, accessed July 2012 at 

http://portal.ncdenr.org/web/wq/ps/bpu/basin/lumber/2010.

North Carolina Division of Water Quality, 2011, 2010 TarPamlico River basinwide water quality plan: North Carolina Department of Environment and Natural Resources 

Division of Water Quality, Planning Section, accessed 

July 2012 at http://portal.ncdenr.org/web/wq/ps/bpu/basin/

tarpamlico/2010.

North Carolina Floodplain Mapping Program, 2012, Floodplain Mapping Information System, accessed January 2012 

at http://floodmaps.nc.gov/fmis/Download.aspx.

Paerl, H.W., Valdes, L.M., Joyner, A.R., and Piehler, M.F., 

2004, Solving problems resulting from solutions: Evolution 

of a dual management strategy for the eutrophying Neuse 

River estuary, North Carolina: Environmental Science and 

Technology, v. 38, p. 3068–3073.

PRISM Climate Group, 2010, Gridded climate data for the 

contiguous United States, accessed March 4, 2013, at 

http://prism.oregonstate.edu/.

Rothenberger, M.B., Burkholder, J.M., and Brownie, C., 2009, 

Long-term effects of changing land use practices on surface 

water quality in a coastal river and lagoonal estuary: Environmental Management, v. 44, p. 505–523.

Runkel, R.L., Crawford, C.G., and Cohn, T.A., 2004, Load 

Estimator (LOADEST): A FORTRAN program of estimating constituent loads in streams and rivers: U.S. Geological 

Survey Techniques and Methods book 4, chap. A5, 69 p.
References Cited 47

Soil Survey Staff, Natural Resources Conservation Service, 

United States Department of Agriculture [n.d.]: Soil survey 

geographic (SSURGO) database for counties of North 

Carolina, accessed March 4, 2013, at http://soildatamart.

nrcs.usda.gov/.

Spruill, T.B., Harned, D.A., Ruhl, P.M., Eimers, J.L., McMahon, G., Smith, K.E., Galeone, D.R., and Woodside, M.D., 

1998, Water quality in the Albemarle-Pamlico drainage 

basin, North Carolina and Virginia, 1992–95: U.S. Geological Survey Circular 1157, 36 p.

Spruill, T.B., Tesoriero, A.J., Mew, H.E., Jr., Farrell, K.M., 

Harden, S.L., Colosimo, A.G., and Kramer, S.R., 2005, 

Geochemistry and characteristics of nitrogen transport 

at a confined animal feeding operation in a Coastal Plain 

agricultural watershed, and implications for nutrient loading in the Neuse River basin, North Carolina, 1999–2002: 

U.S. Geological Survey Scientific Investigations Report 

2004–5283, 57 p.

Stow, C.A., Borsuk, M.E., and Stanley, D.W., 2001, Longterm changes in watershed nutrient inputs and riverine 

exports in the Neuse River, North Carolina: Water Research, 

v. 35, p. 1489–1499.

Therneau, T.M., and Atkinson, B., 2010, Rpart: Recursive partitioning, R package version 3.1-46: accessed 

March 4, 2013, at http://crantastic.org/packages/rpart/

versions/6190.

Trench, E.C.T., Moore, R.B., Ahearn, E.A., Mullaney, J.R., 

Hickman, R.E., and Schwarz, G.E., 2012, Nutrient concentrations and loads in the northeastern United States—Status 

and trends, 1975–2003: U.S. Geological Survey Scientific 

Investigations Report 2011–5114, 169 p., accessed 

July 2012 at http://pubs.usgs.gov/sir/2011/5114/.

For further information about this publication contact:

Director

U.S. Geological Survey

North Carolina Water Science Center

3916 Sunset Ridge Road

Raleigh, NC 27607

Or visit the North Carolina Water Science Center Web site at

http://nc.water.usgs.gov/

Prepared by the Raleigh Publishing Service Center

A PDF version of this publication is available online at 

http://pubs.usgs.gov/sir/2013/5007/
Harden and others—Relation of Watershed Setting and Stream Nutrient Yields at Sites in Central and Eastern North Carolina, 1997–2008– Scientific Investigations Report 2013–5007