U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2008–1163

National Water-Quality Assessment Program

Data to Support Statistical Modeling of Instream 

Nutrient Load Based on Watershed Attributes, 

Southeastern United States, 2002

Data to Support Statistical Modeling of Instream 

Nutrient Load Based on Watershed Attributes, 

Southeastern United States, 2002

By Anne B. Hoos, Silvia Terziotti, Gerard McMahon, Katerina Savvas, Kirsten C. Tighe, 

and Ruth Alkons-Wolinsky

National Water-Quality Assessment Program

Open-File Report 2008–1163

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2008

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Suggested citation:

Hoos, A.B., Terziotti, Silvia, McMahon, Gerard, Savvas, Katerina, Tighe, K.C., and Alkons-Wolinsky, Ruth, 2008, Data 

to support statistical modeling of instream nutrient load based on watershed attributes, southeastern United States, 

2002: U.S. Geological Survey Open-File Report 2008–1163, 50 p.
iii

Foreword

The U.S. Geological Survey (USGS) is committed to providing the Nation with credible scientific 

information that helps to enhance and protect the overall quality of life and that facilitates 

effective management of water, biological, energy, and mineral resources (http://www.usgs.gov/). 

Information on the Nation’s water resources is critical to ensuring long-term availability of water 

that is safe for drinking and recreation and is suitable for industry, irrigation, and fish and wildlife. Population growth and increasing demands for water make the availability of that water, 

now measured in terms of quantity and quality, even more essential to the long-term sustainability of our communities and ecosystems.

The USGS implemented the National Water-Quality Assessment (NAWQA) Program in 1991 

to support national, regional, State, and local information needs and decisions related to 

water-quality management and policy (http://water.usgs.gov/nawqa). The NAWQA Program is 

designed to answer: What is the condition of our Nation’s streams and ground water? How are 

conditions changing over time? How do natural features and human activities affect the quality 

of streams and ground water, and where are those effects most pronounced? By combining 

information on water chemistry, physical characteristics, stream habitat, and aquatic life, the 

NAWQA Program aims to provide science-based insights for current and emerging water issues 

and priorities. From 1991–2001, the NAWQA Program completed interdisciplinary assessments 

and established a baseline understanding of water-quality conditions in 51 of the Nation’s river 

basins and aquifers, referred to as Study Units (http://water.usgs.gov/nawqa/studyu.html).

In the second decade of the Program (2001–2012), a major focus is on regional assessments 

of water-quality conditions and trends. These regional assessments are based on major river 

basins and principal aquifers, which encompass larger regions of the country than the Study 

Units. Regional assessments extend the findings in the Study Units by filling critical gaps in 

characterizing the quality of surface water and ground water, and by determining status and 

trends at sites that have been consistently monitored for more than a decade. In addition, the 

regional assessments continue to build an understanding of how natural features and human 

activities affect water quality. Many of the regional assessments employ modeling and other 

scientific tools, developed on the basis of data collected at individual sites, to help extend 

knowledge of water quality to unmonitored, yet comparable areas within the regions. The 

models thereby enhance the value of our existing data and our understanding of the hydrologic 

system. In addition, the models are useful in evaluating various resource-management scenarios 

and in predicting how our actions, such as reducing or managing nonpoint and point sources of 

contamination, land conversion, and altering flow and (or) pumping regimes, are likely to affect 

water conditions within a region.

Other activities planned during the second decade include continuing national syntheses of 

information on pesticides, volatile organic compounds (VOCs), nutrients, selected trace elements, and aquatic ecology; and continuing national topical studies on the fate of agricultural 

chemicals, effects of urbanization on stream ecosystems, bioaccumulation of mercury in stream 

ecosystems, effects of nutrient enrichment on stream ecosystems, and transport of contaminants to public-supply wells.
iv

The USGS aims to disseminate credible, timely, and relevant science information to address 

practical and effective water-resource management and strategies that protect and restore 

water quality. We hope this NAWQA publication will provide you with insights and information 

to meet your needs, and will foster increased citizen awareness and involvement in the protection and restoration of our Nation’s waters. 

The USGS recognizes that a national assessment by a single program cannot address all waterresource issues of interest. External coordination at all levels is critical for cost-effective management, regulation, and conservation of our Nation’s water resources. The NAWQA Program, 

therefore, depends on advice and information from other agencies—Federal, State, regional, 

interstate, Tribal, and local—as well as nongovernmental organizations, industry, academia, and 

other stakeholder groups. Your assistance and suggestions are greatly appreciated.

 

Matthew C. Larsen

Acting Associate Director for Water
v

Contents

Foreword ........................................................................................................................................................iii

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

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

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

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

Hydrologic Network of Reaches and Associated Catchments .............................................................4

Watershed Attributes....................................................................................................................................5

Nutrient-Source Attributes .................................................................................................................5

Nutrient Mass in Atmospheric Deposition ............................................................................18

Land Cover and Impervious Surface ......................................................................................18

Nutrient Mass in Fertilizer and Manure.................................................................................19

Nitrogen Mass Point-Source Wastewater Discharge ........................................................19

Nutrient-Transport Attributes ...........................................................................................................19

Soil Characteristics ...................................................................................................................19

Landform Characteristics.........................................................................................................27

Climate Characteristics ............................................................................................................27

Accumulation of Catchment-Level Estimates of Watershed Attributes 

to Estimates for the Total Upstream Watershed ..............................................................27

Reach Attributes ..........................................................................................................................................28

Mean Annual Nitrogen and Phosphorus Load at Stream Monitoring Sites ......................................28

Selection of Monitoring Sites ...........................................................................................................28

Review and Revision of Nutrient Concentration Results .............................................................28

Estimation of Nutrient Load Representing Long-Term Mean for 1975–2004, 

Normalized to 2002................................................................................................................30

Error Associated with Estimating Mean Nitrogen and Phosphorus Load 

from Monitoring Data ...........................................................................................................30

Characteristics of Monitored Mean Annual Nitrogen and 

Phosphorus Load and Streamflow .....................................................................................31

Nitrogen.......................................................................................................................................31

Phosphorus.................................................................................................................................40

Summary........................................................................................................................................................45

References Cited..........................................................................................................................................46

Data Files.......................................................................................................................................................49
vi

Figures

1–6. Maps showing—

 1. Location of the South Atlantic-Gulf Region and the Tennessee River basin 

in the southeastern United States, and hydrologic subregion boundaries ..............3

 2. Illustration of the network of stream segments and catchments used 

as model framework for the SAGT SPARROW model ..................................................4

 3. Estimates for individual catchments in the SAGT SPARROW 

model area, 2002, of:

 A. Wet deposition of inorganic nitrogen.......................................................................7

 B. Area in urban land.......................................................................................................8

 C. Area in agricultural land.............................................................................................9

 D. Area in forested land.................................................................................................10

 E. Area of impervious surface......................................................................................11

 F. Nitrogen mass in fertilizer applied to farmland ...................................................12

 G. Nitrogen mass in manure from livestock production ..........................................13

 H. Nitrogen mass in permitted wastewater discharge............................................14

 4. Estimates for individual catchments in the SAGT SPARROW 

model area, 2002, of:

 A. Phosphorus mass in fertilizer applied to farmland ..............................................15

 B. Phosphorus mass in manure from livestock production ....................................16

 C. Phosphorus mass in permitted wastewater discharge ......................................17

 5. Estimates for individual catchments in the SAGT SPARROW 

model area, 2002, of:

 A. Soil permeability ........................................................................................................20

 B. Available water-holding capacity of soil ...............................................................21

 C. Depth to bedrock .......................................................................................................22

 D. Soil erodibility.............................................................................................................23

 E. Land-surface slope....................................................................................................24

 F. Precipitation ...............................................................................................................25

 G. Air temperature..........................................................................................................26

 6A. Mean annual nitrogen yield estimated from stream monitoring 

data from 637 sites in the SAGT river basins, normalized to 2002 ............................35

 6B. Mean annual nitrogen flow-weighted mean concentration estimated 

from stream monitoring data from 637 sites in the SAGT river basins, 

normalized to 2002 ............................................................................................................36
vii

7. Graph showing relation of nitrogen flow-weighted mean concentration 

to nitrogen yield and runoff rate for the SAGT river basins.................................................37

8–11. Maps showing—

 8A. Mean annual streamflow yield in the SAGT river basins estimated from 

stream monitoring data from 759 sites, normalized to 2002.......................................38

 8B. Mean annual streamflow in the SAGT river basins shown as contour 

lines and compared with estimates of excess precipitation.....................................39

 9. Water-quality monitoring sites for which nitrogen load is estimated 

for 2002, with the subset of sites associated with a 

SAGT ERF1_2 reach segment .........................................................................................41

 10A. Mean annual phosphorus yield estimated from stream monitoring data 

from 747 sites in the SAGT river basins, normalized to 2002 .....................................42

 10B. Mean annual phosphorus flow-weighted mean concentration estimated 

from stream monitoring data from 747 sites in the SAGT river basins, 

normalized to 2002 ............................................................................................................43

 11. Water-quality monitoring sites for which phosphorus load is estimated for 

2002, with the subset of sites associated with a SAGT ERF1_2 reach segment....44

Tables

1. Variation across the SAGT SPARROW model area in attributes 

representing nutrient source inputs and factors controlling 

land-to-water nutrient transport rates......................................................................................6

2. Sources of water-quality monitoring data used to estimate 

mean annual nutrient load ........................................................................................................29

3. Variation across the SAGT SPARROW model area in monitored mean 

annual load and concentration of nitrogen and phosphorus, and 

in monitored mean annual streamflow, normalized to 2002 ................................................32
viii

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

hectare (ha) 2.471 acre

square kilometer (km2

) 247.1 acre

hectare (ha) 0.003861 square mile (mi2

) 

Flow rate

millimeter per year (mm/yr) 0.03937 inch per year (in/yr) 

Mass

kilogram (kg) 2.205 pound avoirdupois (lb)

Application rate

kilograms per hectare per year 

[(kg/ha)/yr]

0.8921 pounds per acre per year 

[(lb/acre)/yr]

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

°F=(1.8×°C)+32

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

(NAVD 88).

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

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

or micrograms per liter (μg/L).
Abstract

This report presents and describes the digital datasets that 

characterize nutrient source inputs, environmental characteristics, and instream nutrient loads for the purpose of calibrating 

and applying a nutrient water-quality model for the southeastern United States for 2002. The model area includes all of 

the river basins draining to the south Atlantic and the eastern 

Gulf of Mexico, as well as the Tennessee River basin (referred 

to collectively as the SAGT area). The water-quality model 

SPARROW (SPAtially-Referenced Regression On Watershed 

attributes), developed by the U.S. Geological Survey, uses a 

regression equation to describe the relation between watershed 

attributes (predictors) and measured instream loads (response). 

Watershed attributes that are considered to describe nutrient input conditions and are tested in the SPARROW model 

for the SAGT area as source variables include atmospheric 

deposition, fertilizer application to farmland, manure from 

livestock production, permitted wastewater discharge, and land 

cover. Watershed and channel attributes that are considered 

to affect rates of nutrient transport from land to water and are 

tested in the SAGT SPARROW model as nutrient-transport 

variables include characteristics of soil, landform, climate, 

reach time of travel, and reservoir hydraulic loading. Datasets 

with estimates of each of these attributes for each individual 

reach or catchment in the reach-catchment network are presented in this report, along with descriptions of methods used 

to produce them.

Measurements of nutrient water quality at stream monitoring sites from a combination of monitoring programs were 

used to develop observations of the response variable—mean 

annual nitrogen or phosphorus load—in the SPARROW 

regression equation. Instream load of nitrogen and phosphorus 

was estimated using bias-corrected log-linear regression models using the program Fluxmaster, which provides temporally 

detrended estimates of long-term mean load well-suited for 

spatial comparisons. The detrended, or normalized, estimates 

of load are useful for regional-scale assessments but should 

be used with caution for local-scale interpretations, for which 

use of loads estimated for actual time periods and employing 

more detailed regression analysis is suggested. The mean 

value of the nitrogen yield estimates, normalized to 2002, for 

637 stations in the SAGT area is 4.7 kilograms per hectare; 

the mean value of nitrogen flow-weighted mean concentration 

is 1.2 milligrams per liter. The mean value of the phosphorus 

yield estimates, normalized to 2002, for the 747 stations in 

the SAGT area is 0.66 kilogram per hectare; the mean value 

of phosphorus flow-weighted mean concentration is 0.17 milligram per liter.

Nutrient conditions measured in streams affected by 

substantial influx or outflux of water and nutrient mass across 

surface-water basin divides do not reflect nutrient source and 

transport conditions in the topographic watershed; therefore, inclusion of such streams in the SPARROW modeling 

approach is considered inappropriate. River basins identified 

with this concern include south Florida (where surface-water 

flow paths have been extensively altered) and the Oklawaha, 

Crystal, Lower Sante Fe, Lower Suwanee, St. Marks, and 

Chipola River basins in central and northern Florida (where 

flow exchange with the underlying regional aquifer may 

represent substantial nitrogen influx to and outflux from the 

surface-water basins).

Introduction

Riverine and coastal eutrophication from nutrient loading is a serious water-quality problem throughout the United 

States. Excessive nitrogen and phosphorus loading has been 

cited as causing impairment in more than 50,000 miles of the 

Nation’s rivers and streams, which represents about 20 percent 

of the approximately 270,000 impaired river and stream 

miles (U.S. Environmental Protection Agency, 2002). Eutrophic conditions have been documented in 44 of the Nation’s 

estuaries, or about 35 percent of the estuarine surface area 

of the conterminous United States, with freshwater inflows 

of nitrogen identified as an influencing factor in over half of 

these (Bricker and others, 1999). Improved understanding of 

the sources, transport, and fate of nutrients in the watersheds 

contributing to impaired water bodies is needed in order to 

design effective load-reduction programs.

Data to Support Statistical Modeling of Instream 

Nutrient Load Based on Watershed Attributes, 

Southeastern United States, 2002

By Anne B. Hoos, Silvia Terziotti, Gerard McMahon, Katerina Savvas, Kirsten C. Tighe, 

and Ruth Alkons-Wolinsky
2 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Nutrient loading to rivers and coastal areas is determined 

by source inputs, such as wastewater discharge and runoff 

from agricultural and urban land areas, and by environmental factors, such as geology, topography, climate, and stream 

channel hydraulics, which influence transport rates along 

the pathway from source to target water body. The waterquality model SPARROW (SPAtially-Referenced Regression 

On Watershed attributes), developed by the U.S. Geological 

Survey, statistically relates source inputs and environmental 

factors to instream loads. Specifically, SPARROW quantifies 

the relation between each measured source input and the nutrient mass delivered to water bodies, and quantifies the effect of 

various environmental factors on the transport of mass along 

the pathway from source to target water body. The model can 

be used to evaluate alternative hypotheses about the important sources and environmental factors that control transport 

(Smith and others, 1997; Schwarz and others, 2006).

The SPARROW model uses a nonlinear regression 

equation to describe the relation between spatially referenced 

watershed and channel attributes (predictors) and instream 

load (response). A spatially distributed model structure allows 

separate estimation of mass transport from sources to streams 

and transport within the stream network (Schwarz and others, 

2006, p. 2). SPARROW’s hybrid process-based and statistical 

approach to watershed modeling incorporates the modeling 

strategies recommended by the National Research Council 

(2001) for water-quality assessments, including assessments 

needed for the Total Maximum Daily Load (TMDL) program 

for impaired water bodies. The recommended strategy and the 

SPARROW model approach relate water body nutrient conditions to watershed characteristics using a physically based 

description of processes, while also providing for estimates of 

the errors associated with predictions of stream nutrient load.

The SPARROW model has been applied to assess stream 

nutrient loading and to evaluate nutrient reduction strategies 

at the national scale (Smith and others, 1997; Alexander and 

others, 2000; Smith and Alexander, 2000) and for individual 

regions and river basins, such as the Chesapeake Bay watershed, New England river basins (Moore and others, 2004), 

eastern North Carolina river basins (McMahon and others, 

2003), and Tennessee, Kentucky, and Alabama river basins 

(Hoos, 2005). Beginning in 2005, the National Water-Quality 

Assessment (NAWQA) Program of the U.S. Geological 

Survey identified eight large geographical regions across the 

Nation (referred to as “major river basins”) as the basis for 

assessments of status and trends; in 2007 the NAWQA Program began to integrate the SPARROW modeling approach in 

the interpretation of nutrient water quality in six of these major 

river basins.

The area included in the major river basin model assessment for the southeastern United States includes the South 

Atlantic–Gulf Region, comprising all of the river basins 

draining to the south Atlantic and the eastern Gulf of Mexico, 

as well as the Tennessee River basin. This collection of river 

basins is referred to in this report as the SAGT river basins 

(fig. 1). The river basins in south Florida are excluded from 

the model area because surface-water flow paths in this area 

have been altered, instream nutrient conditions therefore 

may not reflect conditions in the topographic watershed, and 

consequently application of the regional SPARROW modeling 

approach is inappropriate.

SPARROW model development begins with compilation 

of the extensive datasets used for model input. A broad array 

of spatial datasets describing watershed and channel features 

are gathered from U.S. Geological Survey (USGS) programs 

and other Federal and State organizations; for example, data 

describing wastewater discharges of nutrients to streams are 

gathered from the U.S. Environmental Protection Agency 

(USEPA) as well as individual State databases. Comparable 

data on nutrient concentrations in streams are compiled from 

monitoring programs operated by the USGS and other Federal, 

regional, State, and local organizations. The procedures used to 

compile the datasets that support the SPARROW model assessment for the SAGT river basins are documented in this report. 

Purpose and Scope

This report presents and documents the digital datasets that characterize nutrient source inputs, environmental 

characteristics, and instream nutrient loads for the purpose 

of calibrating and applying SPARROW nutrient models for 

the southeastern United States for 2002. The spatial datasets 

defining the reach and catchment network, the digital datasets 

of attributes, and the corresponding metadata are presented in 

downloadable files. The metadata include detailed descriptions 

of the sources and methods used to create the datasets and 

descriptions of each data attribute. The area described by these 

datasets includes all of the SAGT river basins, equivalent to 

hydrologic regions 03 and 06 (Seaber and others, 1987), with 

the exception of the southern Florida drainage basins (hydrologic subregion 0309). 

Acknowledgments

This work was supported by the National Water-Quality 

Assessment Program of the USGS. The authors thank Richard 

Alexander, Richard Smith, Greg Schwarz, John Brakebill, and 

Stephen Preston, USGS, for guidance in planning and data 

compilation. The authors also acknowledge the time and extra 

effort of individuals who provided stream monitoring data—

Lynn Sisk, Alabama Department of Environmental Management; Jeff Thomas, Mississippi Department of Environmental 

Quality; and Tyler Baker, Tennessee Valley Authority. This 

work further benefitted from review comments and suggestions from Mike Wieczorek and Jonathan Musser, USGS. 
Introduction 3

Figure 1. Location of the South Atlantic-Gulf Region and the Tennessee River Basin in the southeastern United States, and 

 hydrologic subregion boundaries.

0301

0302

0303

0304

0305 0306

0307

0308

0311

0310

0312

0604

0313

0314

0315

0316

0317

0318

0601

0602

0603

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of

Mexico

Atlantic

Ocean

Hydrologic subregion and number

 Chowan-Roanoke

 Neuse-Pamlico

 Cape Fear

 Pee Dee

 Edisto-Santee

 Ogeechee-Savannah

 Altamaha-St. Marys

 St. Johns

 Peace-Tampa Bay

 Suwanee

 Ochlockonee

 Apalachicola

 Choctawhatchee-Escambia

 Alabama (Coosa-Tallapoosa)

 Mobile-Tombigbee

 Pascagoula

 Pearl

 Upper Tennessee, headwaters to

 confluence with Sequatchie

 Lower Tennessee, below confluence

 with Sequatchie

 Subregion 0309 in Florida is not 

 included in this study

Study area boundary

 

0308

0301

0302

0303

0304

0305

0306

0307

0308

0310

0311

0312

0313

0314

0315

0316

0317

0318

0601

0602

0603

0604

EXPLANATION

90 85 80

30

35

}

}

Figure 1. Location of the South Atlantic-Gulf Region and the Tennessee River basin in the southeastern United States, and hydrologic 

subregion boundaries.
4 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Hydrologic Network of Reaches and 

Associated Catchments 

The SPARROW modeling framework is a hydrologic 

network of stream- or reservoir-reach segments and associated 

catchments. The network is used to determine flow pathways 

between the sources of the modeled constituents and the locations of water-quality monitoring sites; the downstream end of 

each reach corresponds to a model computation node.

The hydrologic network used for the SPARROW model 

of the SAGT river basins (fig. 2) is based on USEPA’s 

1:500,000-scale Reach File 1 (RF1), a national dataset of more 

than 60,000 stream segments (about 8,000 within the SAGT 

area) that describes surface-water flow paths using from-node 

and to-node topology (Dewald and others, 1985; U.S. Environmental Protection Agency, 1996). USEPA’s RF1 has been 

enhanced (Alexander and others, 1999; Nolan and others, 

2002) to support national- and regional-scale water-quality 

modeling. Each stream segment (also referred to as a reach) 

in the Enhanced River Reach File 2.0 (ERF1_2) includes 

additional attributes such as estimates of mean time of travel 

in river reaches and reservoirs and catchment drainage area 

derived from 1-kilometer elevation data.

The ERF1_2 reach set was further enhanced for the 

SAGT nutrient SPARROW model by inserting 433 segment 

boundaries, which was accomplished by splitting 433 reaches 

into two segments each. The locations of the added boundaries, and thus of the added model computation nodes, correspond with the locations of sites where mean annual nutrient load could be estimated from monitoring data. Methods 

similar to those used by Brakebill and others (2001) for the 

Chesapeake Bay SPARROW model were used to create the 

additional reach segments: (1) load estimation sites were 

0 25 50 75 MILES

0 25 50 75 KILOMETERS

78 76 80

38

36

Local area (catchment) draining

 directly to a reach segment

Figure 2. Illustration of the network of stream segments and catchments used as model framework for the SAGT SPARROW model.

EXPLANATION

Digital segmented network based on

1:500,000-scale Reach File 1

Reach segment

Figure 2. Illustration of the network of stream segments and catchments used as model framework for the SAGT SPARROW model.
Watershed Attributes 5

identified on the ERF1_2 stream network; (2) for sites located 

in the middle of a stream reach, the reach was split at that 

location; and (3) a unique node and reach were added to the 

upstream portion of the split reach. The values assigned for the 

unique reach identifier (variable name wshed) for the segments 

added to the ERF1_2 through this procedure were selected 

from the unassigned series (65,747 through 79,000) of values 

in ERF1_2, to maintain the unique identifier in the data model. 

The geospatial dataset defining the SAGT ERF1_2 digital 

segmented network is available as a compressed ArcInfo 

shapefile (erf1_spar.zip, 5.1 megabytes, MB); with metadata 

descriptions (erf1_spar.html, 213 kilobytes, KB). Reach identification and connectivity information also are available in the 

data file SAGT_ERF1_input.xls.zip (2.1 MB). 

The drainage boundary for the catchment associated 

with each of the 8,421 reach segments in the SAGT ERF1_2 

set was delineated to create an area or zone for summarizing 

attribute data that could be associated to individual reaches. In 

this report, the terms catchment and incremental area are used 

interchangeably to refer to the local area that drains directly 

to a reach. The source for the drainage area delineation was 

a 100-meter resolution elevation dataset resampled from the 

30-meter National Elevation Dataset (NED) (Falcone, 2003). 

The elevation data were forced to conform to the ERF1_2 

reach segments with the insertion of a raster representation of 

the streams into the elevation data. The process, also referred 

to as “stream burning” (Saunders, 2000) uses a tool developed 

by Hellweger and Maidment (1997) to create an artificially 

low stream channel to ensure that the elevation surface would 

flow towards the stream segments. Depressions and sinks 

were removed from the elevation dataset and the streams were 

incorporated, then individual watersheds (catchments) were 

created around every uniquely identified stream reach. The 

geospatial dataset defining the SAGT ERF1_2 segmented 

catchments is available as a compressed Arc Info shapefile 

(shed_cov.zip, 13 MB) with metadata descriptions (shed_cov.

html, 122 KB). The drainage area for the catchment associated 

with each SAGT ERF1_2 reach is included, as variable name 

sqkm, in the file SAGT_ERF1_input.xls.zip (2.1 MB). 

Watershed Attributes

The SPARROW model uses a regression equation to 

describe the relation between watershed attributes (predictors) and measured instream load (response). The regression 

equation is structured to model two different types of effects 

of watershed predictors on instream load: source and transport. 

Watershed attributes that are considered to describe input conditions, such as atmospheric deposition of nitrogen, fertilizer 

application rates, and land cover, are included as source variables in the regression equation. Watershed attributes that are 

considered to affect rates of transport from land to water, such 

as characteristics of soil, landform, and climate, are included 

as land-to-water transport variables in the equation. In this 

report, the terms land-to-water transport variable and delivery 

variable are used interchangeably to refer to the watershed 

attributes that quantify the rate at which nutrient inputs to the 

land surface are delivered, by both overland and subsurface 

transport, to the adjacent stream reach. 

Each watershed-attribute dataset has been georeferenced 

and allocated to the SAGT ERF1_2 catchment dataset. Unless 

otherwise noted, the ZONALMEAN function from the Arc/Info

GRID module (Environmental Systems Research Institute, 

2008) was used to allocate average values of the attributes 

to every catchment in the network. The catchment areas are 

the zones within which values are averaged. For every zone 

(catchment), the cells of the attribute variable that overlap the 

zone are summed then divided by the number of cells within 

the zone; this provides a zonal mean of the attribute for every 

catchment, which can be interpreted as the average value for 

the catchment. 

Nutrient-Source Attributes

Most sources of nutrients are related to human activities: 

therefore, inputs from these sources are expected to change 

over time. Because temporal variation introduces noise to 

spatial comparisons of watershed attributes and instream load, 

nutrient-source data prepared for the SAGT nutrient SPARROW models describe conditions for years corresponding as 

closely as possible to a single time period. The year 2002 was 

selected because of the availability of datasets describing land 

cover and agricultural activities.

The watershed attributes considered as nutrient-source 

predictors for the SAGT SPARROW models, and the spatial 

datasets that were used to represent their distribution, are 

described in the following paragraphs. Nutrient sources are 

characterized by both mass-based attributes, such as total 

annual nutrient mass associated with atmospheric deposition, 

and area-based attributes, such as areas of urban or agricultural 

land. The catchment-level estimates of nutrient-source attributes are included in the file SAGT_ERF1_input.xls (4.5 MB). 

Variability across the SAGT area in catchment-level 

estimates for each attribute is described in table 1 and figures 3 

and 4. The estimates of mass-based attributes (except pointsource discharge) and area-based attributes are normalized by 

the total area of the catchment so that the percentiles of distribution (table 1) and the mapped distribution (figs. 3 and 4) 

illustrate variation in intensity only and are not affected by 

variation in catchment size. Most of the attributes considered 

as nutrient-source predictors vary greatly across the individual 

catchments in the SAGT area; that is, ratio of 90 percentile of 

the distribution to 10 percentile of the distribution is greater 

than 10 (last column, table 1). Wet deposition of inorganic 

nitrogen and area in forested land are the exceptions, varying 

only by factors of about 2 and 5, respectively.
6 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States Table 1. Variation across the SAGT SPARROW model area in attributes representing nutrient source inputs and factors controlling land-to-water nutrient transport rates. [For certain attributes, the measurement unit used for summarizing distribution differs from the unit of the estimates presented in the dataset “SAGT_modelinput.csv” (for example, expressed as mass per unit area of catchment rather than mass per catchment) to provide for comparisons of intensity of the input or factor; kg/yr, kilogram per year; kg/ha/yr, kilogram per hectare per year; —, not calculated; km2, square kilometer; %, percent; CA, catchment area; in/h, inch per hour; in/yr, inch per year; in., inch; mm/yr, millimeter per year; HSG, hydrologic soil group; C, Celsius] Attribute Units for attribute (as presented in the dataset “SAGT_model input.csv”) Description Units used for summary statistics Distribution of attribute across all SAGT ERF1_2 catchments Number of catch- ments with observa- tions Mean value for SAGT area Standard deviation Minimum 10- percentile 25- percentile Median 75- percentile 90- percentile Maximum Range, calculated as ratio of 90- to 10- percentile Nutrient source attributes nadp_kg kg/yr Wet deposition of inorganic nitrogen (ammonia and nitrate) kg/ha/yr 8,311 4.2 0.65 2.8 3.2 3.6 4.3 4.8 4.9 7.3 1.5 lc2_sqkm km2 Area in urban land % of CA 8,311 8.9 12 0.00 1.9 3.4 5.2 8.6 19 100 9.8 lc8_sqkm km2 Area in agricultural land % of CA 8,311 18 15 0.00 1.5 6.1 15 27 40 95 27 lc4_sqkm km2 Area in forested land % of CA 8,311 47 22 0.00 16 31 48 63 75 111 4.8 impsurf_sqkm km2 Area in impervious surface % of CA 8,311 2 4 0.00 0.1 0.3 0.6 1.3 3.9 51 36 wffert_n_2002 kg/yr Nitrogen mass in fertilizer applied to farmland kg/ha/yr 8,309 7.8 9.0 0.00 0.39 1.7 5.1 11 20 109 51 wffert_p_2002 kg/yr Phosphorus mass in fertilizer applied to farmland kg/ha/yr 8,309 1.9 2.3 0.00 0.08 0.35 1.1 2.5 4.7 33 57 wlvtotal_n_2002 kg/yr Nitrogen mass in manure from livestock production kg/ha/yr 8,309 10 15 0.00 0.38 1.8 5.2 12 25 196 64 wlvtotal_p_2002 kg/yr Phosphorus mass in manure from livestock production kg/ha/yr 8,309 3.1 5.1 0.00 0.11 0.52 1.5 3.6 7.5 83 69 kgn_02 kg/yr Nitrogen mass in permitted wastewater discharge kg/yr 8,321 6,118 45,000 0.00 0.00 0.00 0.00 0.00 3,009 2,052,772

—

kgp_02 kg/yr Phosphorus mass in permitted wastewater discharge kg/yr 8,321 1,674 45,076 0.00 0.00 0.00 0.00 0.00 516 3,903,606

—

Nutrient transport attributes

hsg1 km2 Area underlain by soils of HSG Aa % of CA 8,309 0.06 0.18 0.00 0.00 0.00 0.00 0.00 0.24 1.00

—

hsg2 km2 Area underlain by soils of HSG Ba % of CA 8,309 51 40 0.00 0.00 5.2 55 95 100 100

—

hsg3 km2 Area underlain by soils of HSG Ca % of CA 8,309 19 29 0.00 0.00 0.00 0.04 30 69 100

—

hsg4 km2 Area underlain by soils of HSG D (and others)a % of CA 8,309 21 32 0.00 0.00 0.00 0.00 32 82 100

—

hsg5 km2 Area underlain by soils of HSG W (Water)a % of CA 8,309 2.5 10.0 0.00 0.00 0.00 0.00 0.00 3.5 100

—

perml in/h Soil permeability, low value for the range in permeability rate across soil layer or horizon in/h 8,307 1.5 1.2 0.00 0.6 0.7 1.0 1.8 3 11 5.1

awch in./in. Available water-holding capacity of soil, high value 

for the range across soil layer or horizon

in./in. 8,307 0.15 0.03 0.00 0.12 0.14 0.15 0.16 0.17 0.41 1.5

clayl

% Clay content, low value for the range across soil layer 

and horizon

% 8,307 17 7 0 8 12 17 23 26 47 3.3

rockdepl in. Depth to bedrock, low value for the range across the 

map unit

in. 8,307 53 10.2 0 37 48 58 60 60 60 1.6

kfact dimensionless Soil erodibility factor (not adjusted for rock fragments) dimensionless 8,307 0.24 0.06 0.00 0.16 0.21 0.25 0.28 0.29 0.43 1.9

slope_mean

% Land-surface slope

% 8,311 4.9 5.5 0.0 0.56 1.7 3.5 5.5 10 44 19

p_flat fraction 

of CA

Fraction of catchment with slope less than 1% fraction 

of CA

8,311 0.26 0.31 0.00 0.02 0.04 0.11 0.40 0.82 1.0 55

precip_mm mm/yr Annual precipitation, 1971–2000 mean mm/yr 8,311 1,358 157 966 1,188 1,220 1,334 1,463 1,572 2,163 1.3

pmpe_inches in/yr Precipitation minus evapotranspiration, 

1961–90 mean

in/yr 8,296 17 6.3 3.1 10 12 17 22 24 44 2.4

meantemp_c degrees C Air temperature, 1971–2000 mean of daily mean degrees C 8,311 17 2.3 8.7 14 15 17 18 19 23 1.4

a Definitions of hydrologic soil group are from U.S. Department of Agriculture, 2002: HSG A, high infiltration rate when thoroughly wetted; HSG B, moderate infiltration rate when thoroughly wetted; 

HSG C, slow infiltration rate when thoroughly wetted; HSG D, very slow infiltration rate when thoroughly wetted, includes certain wet soils that can be adequately drained; HSG W, water body.
Watershed Attributes 7

Figure 3A. Estimates of wet deposition of inorganic nitrogen for individual catchments in the SAGT SPARROW model area, 2002. 

West Virginia Virginia

North Carolina

South Carolina

Georgia

Florida

Alabama

Louisiana

Arkansas

Missouri

Mississippi

Tennessee

Kentucky

Indiana Illinois

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Wet deposition of inorganic nitrogen, 2002,

 in kilograms per hectare per year

EXPLANATION

90 85 80

30

35

Less than or equal to 3.19 

Greater than 4.94 

Study area boundary

3.20 to 4.29 

4.30 to 4.94 

Interval bounds are the 10, 50, and

 90 percentiles of the distribution
8 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Figure 3B. Estimates of area in urban land for individual catchments in the SAGT SPARROW model area, 2002. 

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Area in urban land, 2002, in percent of

 catchment area

EXPLANATION

90 85 80

30

35

Less than or equal to 1.9 

Greater than 19 

Study area boundary

2.0 to 5.2

5.3 to 19 

Interval bounds are the 10, 50, and

 90 percentiles of the distribution
Watershed Attributes 9

Figure 3C. Estimates of area in agricultural land for individual catchments in the SAGT SPARROW model area, 2002. 

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Area in agricultural land, 2002, in percent of

 catchment area

EXPLANATION

90 85 80

30

35

Less than or equal to 1.5 

Greater than 40

Study area boundary

1.6 to 15

15.1 to 40

Interval bounds are the 10, 50, and

 90 percentiles of the distribution
10 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Figure 3D. Estimates of area in forested land for individual catchments in the SAGT SPARROW model area, 2002. 

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Area in forested land, 2002, in percent of

 catchment area

EXPLANATION

90 85 80

30

35

Less than or equal to 16

Greater than 75

Interval bounds are the 10, 50, and

 90 percentiles of the distribution

Study area boundary

16.1 to 48

48.1 to 75
Watershed Attributes 11

Figure 3E. Estimates of area of impervious surface for individual catchments in the SAGT SPARROW model area, 2002. 

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Area of impervious surface, 2002, in percent of

 catchment area

EXPLANATION

90 85 80

30

35

Less than or equal to 0.11

Greater than 3.9

Interval bounds are the 10, 50, and

 90 percentiles of the distribution

Study area boundary

0.12 to 0.56

0.57 to 3.9
12 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Figure 3F. Estimates of nitrogen mass in fertilizer applied to farmland for individual catchments in the SAGT SPARROW 

model area, 2002. 

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Nitrogen mass in fertilizer applied to farmland,

 2002, in kilograms per hectare per year

EXPLANATION

90 85 80

30

35

Less than or equal to 0.39

Greater than 20

Interval bounds are the 10, 50, and

 90 percentiles of the distribution

Study area boundary

0.40 to 5.1

5.2 to 20
Watershed Attributes 13

Figure 3G. Estimates of nitrogen mass in manure from livestock production for individual catchments in the SAGT SPARROW 

model area, 2002. 

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Nitrogen mass in manure from livestock production,

 2002, in kilograms per hectare per year

EXPLANATION

90 85 80

30

35

Less than or equal to 0.38

Greater than 25

Interval bounds are the 10, 50, and

 90 percentiles of the distribution

Study area boundary

0.39 to 5.2

5.21 to 25
14 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Figure 3H. Estimates of nitrogen mass in permitted wastewater discharge for individual catchments in the SAGT SPARROW 

model area, 2002. 

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Nitrogen mass in permitted wastewater discharge,

 2002, in kilograms per hectare per year

EXPLANATION

90 85 80

30

35

Less than or equal to 0.01

Greater than 3,009

Interval bounds are the 50 and 90

 percentiles of the distribution

Study area boundary

0.01 to 3,009
Watershed Attributes 15

Figure 4A. Estimates of phosphorus mass in fertilizer applied to farmland for individual catchments in the SAGT SPARROW 

model area, 2002.

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

90 85 80

30

35

Phosphorus mass in fertilizer applied to farmland,

 2002, in kilograms per hectare per year

EXPLANATION

Less than or equal to 0.08

Greater than 4.7

Interval bounds are the 10, 50, and

 90 percentiles of the distribution

Study area boundary

0.09 to 1.1

1.2 to 4.7
16 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Figure 4B. Estimates of phosphorus mass in manure from livestock production for individual catchments in the SAGT SPARROW 

model area, 2002.

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

90 85 80

30

35

Phosphorus mass in manure from livestock production,

 2002, in kilograms per hectare per year

EXPLANATION

Less than or equal to 0.11

Greater than 7.5

Interval bounds are the 10, 50, and

 90 percentiles of the distribution

Study area boundary

0.12 to 1.5

1.6 to 7.5
Watershed Attributes 17

Figure 4C. Estimates of phosphorus mass in permitted wastewater discharge for individual catchments in the SAGT SPARROW 

model area, 2002.

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

90 85 80

30

35

Phosphorus mass in permitted wastewater discharge,

 2002, in kilograms per hectare per year

EXPLANATION

Less than or equal to 0.01

Greater than 516

Interval bounds are the 50 and

 90 percentiles of the distribution

Study area boundary

0.02 to 516
18 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Nutrient Mass in Atmospheric Deposition

Atmospheric deposition of nitrogen has been shown to 

contribute substantially to instream nitrogen loads in streams 

(for example, Moore and others, 2004; Potter and others, 

2006). Inorganic forms of nitrogen are released into the 

atmosphere as byproducts of many human activities, such as 

combustion of fossil fuels, livestock production, and fertilizer 

application, and from natural processes, such as volatilization of decomposing soil organic matter; these compounds are 

transported by wind before re-deposition on the land surface 

with precipitation (wet deposition) or as dry deposition. 

Observations of wet deposition of inorganic nitrogen during 1990–2005 (National Atmospheric Deposition Program, 

2006) were used to estimate mean annual wet deposition for 

each of 186 measurement stations in the United States. A 

detrending procedure was applied to the wet deposition record 

(1990–2005) at each station to produce a detrended estimate of 

annual load to the base year 2002 (K. Savvas, U.S. Geological 

Survey, written commun., July 2006). Two models, a precipitation and deposition model, were developed to detrend the 

record for each station. The precipitation model (precipM) and 

the detrended estimate of precipitation (precipDT) take the form: 

precipM(i,t) = a0(i) + a1(i) * year (t), and

precipDT(i,t) = precip(i,t) + a1(i) * (base_year – year(t));

where 

i is the station index, 

t is time, and 

a0 and a1 are coefficients to be estimated in the analysis.

The deposition model (depM) and the detrended estimate of 

deposition (depDT) take the form: 

depM(i,t) = b0(i) + b1(i) * precipM(i,t) + b2(i) * year(t), and 

depDT(i,t) = dep(i,t) + b1(i) * a1(i) * (base_year – year(t)) 

+ b2(i) * (base_year - year(t));

where 

b0, b1, and b2 are coefficients to be estimated in the analysis 

(G. Schwarz, U.S. Geological Survey, 

written commun., June 2006).

 The detrended estimates of wet deposition of inorganic nitrogen (in kilograms per year) were interpolated to a 

5-kilometer grid using an inverse-distance weighting method, 

and an estimate of wet deposition for each catchment was 

computed from the gridded values using the ZONALMEAN 

function. The catchment level estimates (variable name 

nadp_kg) are presented in the file SAGT_ERF1_input.xls.zip

(2.1 MB). Spatial distribution of wet deposition, normalized 

by catchment area and expressed as kilograms per hectare, is 

illustrated in figure 3A. 

Atmospheric deposition of phosphorus may contribute 

substantially to instream phosphorus loads (Kuntz, 1980; 

Redfield and Efron, 2007). In contrast with nitrogen, however, 

releases of phosphorus to the atmosphere from combustion or 

other industrial sources are minor (Murphy, 1974); the major 

source (comprising about 90 percent) of particulate phosphorus in the atmosphere are soil particles containing both naturally occurring and fertilizer-derived phosphorus (Graham and 

Duce, 1979). The spatial distribution of atmospheric releases 

and deposition of fertilizer-derived particulate phosphorus 

may be adequately represented by the catchment estimates of 

phosphorus mass in applied fertilizer, hence inclusion of both 

atmospheric deposition and fertilizer application as source 

predictors could amount to double accounting of agricultural 

sources of phosphorus. For this reason, and because phosphorus deposition data are not widely available for the SAGT area, 

atmospheric deposition of phosphorus is not considered as a 

source predictor for the SAGT phosphorus SPARROW model.

Land Cover and Impervious Surface

Land cover classes of urban, agriculture and forested 

land were summarized by catchment. The 2001 National Land 

Cover Dataset (NLCD) is classified using an Anderson scale 

with 8 major classes (level 1) and 21 total classes (level 2) of 

land cover types (Homer and others, 2007). Level 1 classes 

are defined as water, developed, barren land/unconsolidated 

shore, forest, scrub/shrub, grassland/herbaceous, agricultural 

land (pasture/hay/crops), and wetlands (U.S. Geological Survey, 2001). The sources used for classification are primarily 

Landsat 5 and 7 imagery, as well as ancillary datasets appropriate for the mapping zones used to develop the final product 

(Homer and others, 2004). NLCD is distributed as a 30-meter 

raster dataset, with each pixel assigned a value for the corresponding land cover type (U.S. Geological Survey, 2001). 

The NLCD zones were merged to create a seamless dataset for 

the SAGT area, and each land cover class was summarized by 

catchment zone. Estimates of land cover area (in square kilometers) for each SAGT ERF1_2 catchment for level 1 classes 

of developed (urban), agriculture, and forested lands (variable names lc2_sqkm, lc8_sqkm, and lc4_sqkm, respectively) 

are listed in the file SAGT_ERF1_input.xls.zip (2.1 MB). 

Spatial distributions of land cover classes are illustrated in 

figures 3B–D; each catchment-level estimate is normalized by 

the total area of the catchment and expressed as percent.

Increases in the percent impervious surface within a 

watershed have been linked with increases in stream nutrient 

loads in numerous studies (Driver and Tasker, 1990; Brabec 

and others, 2002; Yong and Chen, 2002). The estimates of 

percent impervious surface area included in the NLCD raster 

dataset for each 30-meter cell (U.S. Geological Survey, 2001) 

were summarized by catchment zone to derive estimates of 

impervious surface area (in square kilometers) for the SAGT 

ERF1_2 catchment dataset. The catchment-level estimates 

(variable name impsurf_sqkm) are listed in the file SAGT_

ERF1_input.xls.zip (2.1 MB). The spatial distribution of 

impervious surface is illustrated in figure 3E; each catchmentlevel estimate is normalized by the total area of the catchment 

and expressed as percent.
Watershed Attributes 19

Nutrient Mass in Fertilizer and Manure

Nitrogen and phosphorus mass in fertilizer and manure 

were calculated for nonfarm and farmland for each catchment 

in the SAGT area. Estimates of nitrogen and phosphorus were 

based on county-level estimates compiled from fertilizer sales, 

census of agriculture, and population estimates, updated for 

2002 (Ruddy and others, 2006). To more accurately represent 

the spatial distribution of the county-level data, nitrogen and 

phosphorus estimates for fertilizer and manure for farmland 

were applied only to agricultural land, and nonfarm estimates 

were distributed to landcover classes of developed, forest, 

shrub/scrub, and grasslands. Water, barren, and wetland classes 

were not used in this analysis. The total amount of nitrogen 

or phosphorus for farmland for each county was divided by 

the number of 30-meter cells within the county that contained 

agricultural land. Each cell of agricultural land in the county 

was assigned the proportional value. Areas that were not farmland were assigned values of 0. Nonfarm estimates of nitrogen 

and phosphorus from fertilizer were apportioned within each 

county to areas that were defined as nonfarm land from the 

NLCD, with values of 0 assigned to other cells. The apportioned amounts then were combined and summed for each 

ERF1_2 catchment area. The catchment level estimates of 

nitrogen and phosphorus mass in fertilizer applied to farmland 

in 2002 (variable names wffert_n_2002 and wffert_p_2002) 

and nitrogen and phosphorus mass in manure (variable names 

wlvtotal_n_2002 and wlvtotal_p_2002) are included in the file 

SAGT_ERF1_input.xls.zip (2.1 MB). Spatial distributions of 

nutrient mass in fertilizer application and manure production 

are illustrated in figures 3F–3G and 4A–4B; each catchmentlevel estimate is normalized by the total area of the catchment 

and expressed as kilograms per hectare.

Nitrogen Mass Point-Source 

Wastewater Discharge

McMahon and others (2007) estimated total nitrogen 

and total phosphorus loads for 2002 for approximately 3,000 

point-source dischargers of municipal and industrial wastewater in the southeastern United States, for use in calibration and 

application of the SAGT nutrient SPARROW models. Locations of point-source discharges permitted under the National 

Pollutant Discharge Elimination System were obtained from 

the USEPA Permit Compliance System database and from 

individual site databases. For dischargers with a complete 

effluent monitoring record, effluent-flow and nutrientconcentration data were used to develop estimates of nitrogen 

and phosphorus loads for 2002. When effluent-flow data were 

available but nutrient-concentration data were missing or 

incomplete for 2002, typical pollutant-concentration values 

of total nitrogen and total phosphorus were used to estimate 

load. Detailed descriptions of the approach for developing 

typical pollutant-concentration values and of the complete 

procedure for estimating effluent load are given in McMahon 

and others (2006). Each point-source discharge location was 

assigned to a SAGT ERF1_2 catchment, and nutrient load 

estimates (in kilograms per year) were summed by catchment. 

The catchment-level estimates (variable names kgn_02 and 

kgp_02 for total nitrogen and total phosphorus, respectively) 

are included in the file SAGT_ERF1_input.xls.zip (2.1 MB). 

Spatial distribution of point-source wastewater discharge is 

illustrated in figures 3H and 4C.

Nutrient-Transport Attributes

The watershed attributes considered as nutrient-transport 

predictors for the SAGT SPARROW models, and the spatial datasets that were used to represent their distribution, 

are described in the following paragraphs. In contrast to the 

datasets used to describe distribution of nutrient sources, these 

datasets are not restricted to representing conditions in a single 

time period because these attributes are, for the most part, 

physical properties that do not change over the period of time 

(30 years) for which these datasets have been compiled. 

The catchment-level estimates of nutrient-transport 

attributes are included in the file SAGT_ERF1_input.xls.zip

(2.1 MB). Variability across the SAGT area in catchment-level 

estimates for each attribute is described in table 1 and illustrated, for selected attributes, in figure 5. In general, watershed 

attributes considered as candidate nutrient-transport variables 

do not range in value as greatly as attributes considered as 

nutrient-source predictors; that is, ratio of the 90 percentile of 

the distribution to the 10 percentile is less than 5 for most attributes (last column, table 1).

Soil Characteristics

Soil properties considered as candidate nutrient-transport 

predictors include hydrologic soil group classification (hsg), 

soil permeability (perm), available water-holding capacity 

(awc), clay content (clay), depth to bedrock (rockdep), and 

soil erodibility (kfact). Estimates of all these properties were 

derived from the 1:250,000-scale Natural Resources Conservation Service State Soil Geographic (STATSGO) data (U.S. 

Department of Agriculture, 1994). Information on variability 

of these properties at the soil-component scale is generalized 

to the broader scale of soil mapping unit (MU) to allow for 

georeferencing; such generalization is considered acceptable 

for modeling variability of soil properties for regional- or 

national-scale assessments. The composition of each MU 

with respect to hydrologic soils group is described as the areal 

percentage of soil components classed in five groups according to infiltration rate (U.S. Department of Agriculture, 1994). 

For the other soil properties (perm, awc, clay, rockdep, and 

kfact), the information on variability of the property within a 

MU was processed by Wolock and others (1997) into a set of 

weighted average values for each MU. The MU values for all 

soil properties were aggregated to the SAGT ERF1_2 catchment grid to derive catchment-level estimates; these estimates 

are presented, along with more detailed definitions of each 

variable, in the file SAGT_ERF1_input.xls.zip (2.1 MB). Spatial distributions of catchment-level estimates of soil permeability, available water-holding capacity, depth to bedrock, and 

erodibility are illustrated in figures 5A–D.
20 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Figure 5A. Estimates of soil permeability for individual catchments in the SAGT SPARROW model area, 2002.

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

90 85 80

30

35

Soil permeability, in inches per hour

EXPLANATION

Less than or equal to 0.59

Greater than 3.0

Interval bounds are the 10, 50, and

 90 percentiles of the distribution

Study area boundary

0.60 to 1.0

1.1 to 3.0
Watershed Attributes 21

Figure 5B. Estimates of available water-holding capacity of soil for individual catchments in the SAGT SPARROW model area, 2002.

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

90 85 80

30

35

Available water-holding capacity of soil,

 in inches per inch

EXPLANATION

Less than or equal to 0.12

Greater than 17

Interval bounds are the 10, 50, and

 90 percentiles of the distribution

Study area boundary

0.13 to 0.15

0.16 to 0.17
22 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Figure 5C. Estimates of depth to bedrock for individual catchments in the SAGT SPARROW model area, 2002.

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

90 85 80

30

35

Depth to bedrock, in inches

EXPLANATION

Less than or equal to 44

Greater than 59

Interval bounds are the 10 and

 50 percentiles of the distribution

Study area boundary

45 to 59
Watershed Attributes 23

Figure 5D. Estimates of soil erodibility for individual catchments in the SAGT SPARROW model area, 2002.

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

90 85 80

30

35

Soil erodibility factor

EXPLANATION

Less than or equal to 0.16

Greater than 0.29

Interval bounds are the 10, 50, and

 90 percentiles of the distribution

Study area boundary

0.17 to 0.25

0.26 to 0.29
24 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Figure 5E. Estimates of land-surface slope for individual catchments in the SAGT SPARROW model area, 2002.

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

90 85 80

30

35

Land-surface slope, in percent

EXPLANATION

Less than or equal to 0.56

Greater than 10

Interval bounds are the 10, 50, and

 90 percentiles of the distribution

Study area boundary

0.57 to 3.5

3.6 to 10
Watershed Attributes 25

Figure 5F. Estimates of precipitation for individual catchments in the SAGT SPARROW model area, 2002.

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

90 85 80

30

35

Mean annual precipitation, in millimeters

EXPLANATION

Less than or equal to 1,180

Greater than 1,570

Interval bounds are the 10, 50, and

 90 percentiles of the distribution

Study area boundary

1,190 to 1,330

1,340 to 1,570
26 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Figure 5G. Estimates of air temperature for individual catchments in the SAGT SPARROW model area, 2002.

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

90 85 80

30

35

Air temperature, in degrees Celsius

EXPLANATION

Less than or equal to 14

Greater than 19

Interval bounds are the 10, 50, and

 90 percentiles of the distribution

Study area boundary

14.1 to 17

17.1 to 19
Watershed Attributes 27

Landform Characteristics

Landform characteristics considered as candidate 

nutrient-transport predictors include land-surface slope and 

proportion of flatland. The average percent-slope of the land 

surface was determined for each SAGT ERF1_2 catchment 

using a seamless digital elevation model (DEM) created from 

the 100-meter surface-elevation dataset for the SAGT area 

(Falcone, 2003). The SLOPE function in ArcInfo’s GRID 

module (Environmental Systems Research Institute, Inc., 

2008) was used to create a dataset that contains a percentslope value for each 100-meter cell. A mean percent-slope 

value was calculated for each catchment using the ZONALMEAN function in the GRID module. Proportion of flatland was determined as the number of cells within a catchment 

with a slope of less than or equal to 1 percent, divided by the 

total number of cells within a catchment. Estimates for each 

catchment of mean percent slope (variable name slope_mean) 

and proportion of flatland (variable name p_flat) are listed 

in the file SAGT_ERF1_input.xls.zip (2.1 MB). The spatial distribution of catchment-level estimates of mean percent slope is 

illustrated in figure 5E.

Climate Characteristics

Climate characteristics considered as candidate nutrienttransport predictors include mean annual precipitation, air 

temperature, and excess precipitation. Estimates of mean 

annual precipitation were obtained from PRISM (Parameterelevation Regressions on Independent Slopes Model), developed by Oregon State University, PRISM Group (Daly and 

others, 2002), specifically from the dataset United States 

Average Annual Precipitation data, 1971–2000. The PRISM 

dataset uses precipitation data from many climatological 

networks, and refines interpolation of a continuous surface by 

incorporating digital elevation model (DEM) parameters such 

as elevation and topographic facet. The final surfaces are distributed as 800-meter resolution raster datasets (Oregon State 

University, PRISM Group, 2007). The PRISM precipitation 

data were averaged within each catchment to arrive at an average annual precipitation value, in millimeters (variable name 

precip_mm in the file SAGT_ERF1_input.xls.zip, 2.1 MB). 

Spatial distribution of the catchment-level estimates of annual 

precipitation is illustrated in figure 5F.

The PRISM Group also distributes average annual air 

temperature data for the climatological period 1971–2000. 

These data, like the precipitation data, incorporate a variety of 

climatological network data and refine the interpolation of a 

continuous surface with ancillary data such as elevation. The 

800-meter gridded surface of the 30-year mean value of daily 

mean temperature (in degrees Celsius) was used to calculate 

temperature estimates for each catchment (variable name 

meantemp_c in the file SAGT_ERF1_input.xls.zip, 2.1 MB). 

Spatial distribution of the catchment-level estimates of air 

temperature is illustrated in figure 5G.

Excess precipitation is represented by the variable pmpe, 

the mean annual precipitation minus potential evapotranspiration, which indicates the volume of precipitation that is 

available for direct runoff. This variable, developed by Wolock 

and McCabe (1999), is based on estimates of mean annual precipitation and potential evaporation at meterological stations, 

computed from mean monthly data from 1961–1990 and interpolated to a 1-kilometer grid using an inverse-distance weighting method. Gridded values were then averaged (Wolock, 

2003) for watersheds of approximately 500 square kilometers 

in area. These watershed-average values were used to calculate 

(using the ZONALMEAN function) estimates for each catchment in the SAGT model area (variable name pmpe_inches in 

the file SAGT_ERF1_input.xls.zip, 2.1 MB).

Accumulation of Catchment-Level Estimates of 

Watershed Attributes to Estimates for the 

Total Upstream Watershed

The catchment-level estimates of nutrient source and 

transport attributes presented in the file SAGT_ERF1_input.

xls.zip (2.1 MB) represent conditions in the incremental or 

local area that drains directly to each reach segment. Information discretized in this way preserves detail on spatial distribution of source attributes relative to transport attributes and 

allows for incorporating spatial referencing in the regression 

analysis, a key feature of the SPARROW model approach. The 

watershed-attribute estimates compiled for this report may be 

useful for purposes other than SPARROW modeling, however—for example, comparing watershed conditions among 

a set of stream sites, or examining relations between stream 

attributes (not necessarily nutrient flux) and watershed attributes. These types of applications require watershed attributes 

estimated for the total upstream drainage area for the stream 

site rather than the incremental or catchment area associated 

with the stream reach segment. 

Estimates for each of the nutrient source and transport 

attributes for the total upstream watershed contributing to each 

reach segment are included in the file SAGT_accumulatedfortotalwatershed.xls.zip (1.9 MB). For watershed attributes 

expressed as mass or area (for example, nitrogen mass in 

fertilizer or area in forested land), the accumulated value is 

the sum of the catchment-level estimates for all catchments 

upstream from (and including) the reach segment. For all other 

watershed attributes (for example soil permeability or mean 

annual precipitation), the accumulated value is the mean of 

all the catchment-level estimates for all catchments upstream 

from (and including) the reach segment; catchment-level 

estimates are weighted by catchment area in the calculation of 

mean value.
28 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Reach Attributes

Modeling transport and fate of nutrients in water streams 

and reservoirs requires information about physical characteristics of the channel. Previous SPARROW models have modeled 

transport in streams and reservoirs as first-order contaminant 

loss, relating loss rates in streams to reach residence time 

(computed as quotient of reach length and reach mean annual 

stream velocity); and relating loss rates in reservoirs to areal 

hydraulic load (computed as quotient of mean annual reservoir 

outflow and surface area). The estimates used in the SAGT 

nutrient SPARROW model for reach length (length_m), mean 

annual velocity (meanv), mean annual streamflow (meanq), 

and water body surface area (surfarea_km) are taken from 

ERF1_2 (Enhanced River Reach File 2.0, Nolan and others, 2002). Assignment of estimates to the reaches unique to 

SAGT ERF1 that were added to accommodate monitoring 

site locations (described in the section “Hydrologic Network 

of Reaches and Associated Catchments”) required additional 

steps. For these reaches, the attributes length and time of travel 

were recalculated using the length of the split reach, and other 

channel attributes were assigned values from the next downstream segment. The reach-level estimates of channel characteristics for all reaches in SAGT ERF1 are included in the file 

SAGT_ERF1_input.xls.zip (2.1 MB).

Mean Annual Nitrogen and Phosphorus 

Load at Stream Monitoring Sites

Measurements of nutrient water quality at stream monitoring sites from a combination of monitoring programs were 

used to develop observations of the response variable—mean 

annual nitrogen or phosphorus load—in the SPARROW 

regression equation. Mean annual load is estimated as the 

product of daily streamflow and estimated daily concentration, which is modeled from nutrient water-quality data and 

streamflow data.

Selection of Monitoring Sites

The nutrient water-quality data used for instream-load 

estimation were collected by Federal, State, and local agencies during 1975–2004. Data from the ambient monitoring 

programs of agencies other than the USGS (table 2) were 

obtained from either the STOrage and RETrieval (STORET) 

database of the USEPA or from individual State agency databases. Data from USGS monitoring programs were obtained 

from the National Water Information System (NWIS) database 

of the USGS. 

Nutrient load was estimated for monitoring sites on 

streams and rivers—reservoir sites were excluded—from 

which samples were collected at least quarterly, with a minimum of 10 samples collected since 1995, for at least a 2-year 

period during which daily streamflow data also were collected 

(or could be estimated from a nearby gage). Although more 

than 3,000 sites in the region met the criteria for sampling frequency, only 782 sites had sufficient data for load estimation. 

Of the 782 sites, 202 were collocated with a USGS streamflow 

gaging station; the additional 580 sites were located close to 

and on the same stream as a USGS streamflow gaging station 

with at least 2 years of concurrent streamflow data. The criterion for close proximity between the paired water-quality and 

streamflow monitoring sites is based on the ratio between the 

drainage areas: the streamflow monitoring site was considered sufficiently close if the ratio was between 0.75 and 1.33. 

Station information for each of the 782 water-quality monitoring sites selected for load estimation, and for each corresponding streamflow gaging station, are included in the file SAGT_

monitoredload.xls (700 KB). 

Review and Revision of Nutrient 

Concentration Results

Data retrieved from all sources were reviewed and revised 

to a standard format. Revisions were of two types. First, cases 

of obviously erroneous concentration results (for example, 

an ammonia concentration value of 800 milligrams per liter 

[mg/L]) were identified and revised to missing values. Second, 

differences among data sources in the format or convention 

for recording results were resolved to a standard format. For 

example, an analytical result “less than 0.02” recorded as 

–0.02, or as 0.02 with remark code “K,” would be converted 

to the qualifying-code convention used in NWIS, which is 

0.02 with remark code “<.” Two computer programs, Reformat_ModSTORET_WQdata.sas and Convert_remarkcoding_and_otherproblematic.sas, were used to revise the results 

to a standard format. The programs are coded in Statistical 

Analysis Systems (SAS) programming language (Statistical 

Analysis Systems Institute, 2000); text-file versions of these 

programs also are provided (Reformat_ModSTORET_WQdata.

txt and Convert_remarkcoding_and_otherproblematic.txt). 

Concentrations of total nitrogen (TN) were computed 

using analytical results for dissolved or total nitrite plus nitrate 

and total Kjeldahl nitrogen (TKN). Where TKN is missing, 

values were computed from analytical results for the separate 

constituents organic nitrogen and ammonia, or results for 

dissolved Kjeldahl nitrogen and suspended Kjeldahl nitrogen. 

Whenever two analytical results were combined to produce a 

value for a calculated parameter and either or both result was 

censored, rules were applied to produce the value and qualifying code for the calculated parameter. The procedures and 

rules for combining analytical results to produce a result for 

TN are described in the SAS computer program Combine_

nutrient_constituents.sas.
Mean Annual Nitrogen and Phosphorus Load at Stream Monitoring Sites 29

Table 2. Sources of water-quality monitoring data used to estimate mean annual nutrient load.

[Program identifier corresponds to value of attribute ‘Program’ for the station in the SAGT_monitoredload dataset; agency abbreviation corresponds to the first 

8 digits in the station identification (attribute ‘station_id’) in the SAGT_monitoredload dataset; NA, not applicable (agency abbreviation is not included in the station 

identification); NWIS, U.S. Geological Survey’s National Water Information System; STORET, U.S. Environmental Protection Agency’s STOrage and RETrieval 

system; Leg, Legacy; Mod, Modernized]

Monitoring agency Program identifer Agency 

abbreviation Database

U.S. Geological Survey USGS NA NWIS

U.S. Geological Survey, National Water-Quality 

Assessment Program

USGS-NAWQA NA NWIS

Tennessee Valley Authority Tennessee Valley Authority 131TVAC STORET - Leg for data through 1999, and 

file provided by Tyler Baker, TVA1

Alabama Department of Environmental Management ALA DEPT ENVIRON MGMT 21AWIC STORET - Leg

Alabama Department of Environmental Management 

(collaborating with Auburn University)

AU-ADEMResLd and 

AUM-ADEMResL

21AWIC File provided by Lynn Sisk, ADEM2

 

Alabama Department of Environmental Management 

(collaborating with University of Alabama)

UA-ADEMResLd 21AWIC File provided by Lynn Sisk, ADEM2

 

Florida Department of Environmental Protection FLORIDA DEPT ENV PROTECTN 21FLA STORET - Leg and Mod

Florida Department of Environmental Protection FL DEPT OF ENVIRON REG 21FLBFA STORET - Leg and Mod

Florida Department of Environmental Protection FL Dept. of Environmental Protection 21FLGW STORET - Leg and Mod

Hillsborough County Environmental Protection Commission Hillsborough County Environmental 

(Florida)

21FLHILL STORET - Leg and Mod

Hillsborough County Environmental Protection Commission HILLS COUNTY ENV 21FLHILL STORET - Leg and Mod

IMC Agrico Company IMC Agrico (Florida) 21FLIMCA STORET - Leg and Mod

Lake County Water Resource Management Lake County Water Resource 

Management (Florida)

21FLLCPC STORET - Leg and Mod

Manatee County Department of Environmental Management Manatee County Environmental 

Management Dept (Florida)

21FLMANA STORET - Leg and Mod

Manatee County Department of Environmental Management ENVIRONMENTAL 21FLMANA STORET - Leg and Mod

Orange County Environmental Protection Division Orange County Environmental 

Protection (Florida)

21FLORAN STORET - Leg and Mod

Orange County Environmental Protection Division ORANGE COUNCY ENV 21FLORAN STORET - Leg and Mod

Peace River Manasota Regional Water Supply Authority Peace River Manasota Regional 

Water Supply Authority

FLPRMRWS STORET - Leg and Mod

St. Johns Water Management District St. Johns Water 

Management District

21FLSJWM STORET - Leg and Mod

St. Johns Water Management District ST. JOHN’S RIVER WATER 21FLSJWM STORET - Leg and Mod

Suwanee River Water Management District SUWANNEE R WTR MGNT DIST 21FLSUW STORET - Leg and Mod

Southwest Florida Water Management District Southwest Florida Water 

Management District

21FLSWFD STORET - Leg and Mod

Volusia County Environmental Health Laboratory VOLUSIA ENV HEALTH LAB 21FLVEMD STORET - Leg and Mod

Volusia County Environmental Health Laboratory Volusia County Environmental 

Health Lab (Florida)

21FLVEMD STORET - Leg and Mod

Georgia Department of Natural Resources, 

Environmental Protection Division

GA DEPT OF NAT RESOURCES 21GAEPD STORET - Leg and Mod

Kentucky Natural Resources and Environmental Protection Cabinet KY DEPT NAT RES & ENV PRO 21KY STORET - Leg and Mod

Mississippi Department of Environmental Quality MISSISSIPPI DEPT NAT RES 21MSWQ STORET - Leg for data through 1999, and 

file provided by Jeff Thomas, MSDEQ3

Mississippi Department of Environmental Quality AMBN 21MSWQ STORET - Leg for data through 1999, and 

file provided by Jeff Thomas, MSDEQ3

 

North Carolina Department of Environment and Natural Resources NCDENR-DWQ (2nd) 21NC02WQ STORET - Mod

South Carolina Department of Health and Environmental Control SC DEPT HEALTH & ENV CON 21SC60WQ STORET - Leg and Mod

South Carolina Department of Health and Environmental Control SC PUBLIC SERVICE AUTHRTY 21SCSANT STORET - Leg and Mod

Tennessee Department of Environment and Conservation Tennessee Department of 

Environment and Conservation

TDECWPC STORET - Leg and Mod (changes in station identification provided by Linda 

Cartwright, TDEC4

)

Virginia Department of Environmental Quality VA DEPT OF ENVIRONMENTAL 21VASWCB STORET - Leg for data through 1999, and 

database retrieval provided by Roger 

Stewart, VADEQ5

The authors gratefully acknowledge the following individuals for providing monitoring data to supplement data available in STORET: 1

 Tyler Baker, Tennessee Valley Authority. 

2

 Lynn Sisk, Alabama Department of Environment and Management. 

3

 Jeff Thomas, Mississippi Department of Environmental Quality.

4

 Linda Cartwright, Tennessee Department of Environment and Conservation.

5

 Roger Stewart, Virginia Department of Environmental Quality, Water Quality Monitoring Data Retrieval Application, 

http://gisweb.deq.virginia.gov/monapp/mon_data_retrieval_app.html, accessed December 2005.
30 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Estimation of Nutrient Load Representing 

Long-Term Mean for 1975–2004, 

Normalized to 2002

Instream loads of nitrogen and phosphorus were estimated using bias-corrected, log-linear regression models, 

within the program Fluxmaster (Schwarz and others, 2006, 

based on methods described by Cohn and others, 1989 and 

1992, and Gilroy and others, 1990). A special feature is available in Fluxmaster to compute temporally-detrended estimates 

of long-term mean load. Without detrending, the estimate 

of mean annual load for each station would represent mean 

conditions centered on the year at the midpoint of the station’s 

concurrent concentration and streamflow record. The variability in the midpoint year (ranging from 1990 to 2003) for the 

set of stations in this analysis would introduce temporal bias in 

the estimates of mean load that could hamper spatial comparison of load. To compensate for this, the estimates of mean load 

are detrended, or normalized, to a common or base year; 2002 

was selected in this analysis because it matches the period of 

nutrient-input estimates. 

Daily mean concentration was modeled by regressing the 

available instantaneous measurements of nutrient concentration against the variables streamflow, season, and time:

ln[C] = b0 + b1(ln[Q]) + b2(ln[T]) + b3(sine[2pT]) 

+ b4(cosine[2pT]) + e

where

ln[] is natural logarithm function;

C is instantaneous daily concentration;

Q is daily streamflow;

T is decimal time;

	 p	 is 3.14169;

	 b0–b4 are coefficients to be estimated in the 

regression analysis; and

e is model error.

Daily mean load is estimated as the product of estimated 

daily mean concentration and measured daily streamflow. The 

series of estimated daily values of mean load is then summed 

to produce a series of annual values of mean load. Computation of the detrended estimate of load normalized to 2002, 

however, requires an estimate of temporal trend in streamflow; 

temporal trend was modeled by regressing the streamflow 

record (daily mean values) against the variables season and 

time, incorporating an autoregressive process to specify the 

serial correlation structure and thus correct for serial correlation in errors inherent to the time-series data (Schwarz and 

others, 2006):

ln[Q] = b0 + b1(ln[T]) + b2(sine[2pT]) 

+ b3(cosine[2pT]) + AR + e

where

ln[] is natural logarithm function;

Q is daily streamflow;

T is decimal time;

p is 3.14169;

AR is an autoregressive model estimated with 

a specified number of lags, L (for this 

application, L = 30); 

	 b0–b4 are coefficients to be estimated in the 

regression analysis; and

e is model error.

Long-term mean nitrogen and phosphorus loads, normalized to 2002, were estimated for 637 (for nitrogen) and 747 

(for phosphorus) of the 782 sites. The fewer number of sites 

with nitrogen load estimates reflects sparser concentration data 

for a chemical constituent, organic nitrogen, required to estimate total nitrogen concentration and load. The load estimates 

are included in the file SAGT_monitoredload.xls (700 KB). 

Careful consideration should be given to the fact that these 

estimates represent a hypothetical condition—the load that 

would have occurred at each station in 2002 if streamflow, and 

the relation between water quality and streamflow and season, 

corresponded to conditions detrended to 2002 from the available record during the period 1974–2005. This hypothetical 

load is useful for regional-scale assessments of water-quality 

conditions but should be used with caution for local-scale 

interpretations. For local-scale interpretations use of loads estimated for actual time periods and employing a more detailed 

regression analysis, such as stepwise linear regression and 

consideration of additional explanatory variables, is suggested.

Error Associated with Estimating Mean Nitrogen 

and Phosphorus Load from Monitoring Data

The standard error of the mean annual nitrogen load 

estimates, expressed as a percentage of the estimated value, for 

the 637 sites where nitrogen load was estimated was typically 

(for 80 percent of the sites) less than 15 percent. The standard 

error of the mean annual phosphorus load estimates for the 

747 sites with load estimates was typically (for 80 percent of 

the sites) less than 25 percent. Large values of standard error 

(up to 90 percent of the estimate for nitrogen and more than 

150 percent for phosphorus) at some sites reflect uncertainty 

in the calibration of the daily concentration model, or in the 

detrended estimate of mean streamflow and load (Schwarz and 

others, 2006, p. 27). 

The 4-parameter log-linear regression approach used to 

model daily concentration for this analysis may be inadequate 

for estimating annual load accurately at some sites, for example where the concentration-streamflow relation is influenced 

by hysteresis or antecedent conditions. The decision to employ 

the 4-parameter model uniformly for all stations was considered appropriate for this regional-scale assessment of water 

quality and in view of the available resources.
Mean Annual Nitrogen and Phosphorus Load at Stream Monitoring Sites 31

Load estimates for water-quality monitoring sites that 

are not collocated with the associated streamflow monitoring sites are less certain due to uncertainty in the estimates of 

daily streamflow because the streamflow record for these sites 

had to be estimated based on an area-weighted adjustment of 

streamflow record from the nearby gage. Load estimates for 

water-quality sites for which the corresponding streamflow 

gage record is relatively short also are less certain due to 

uncertainty in estimating long-term mean streamflow. Loads 

estimated based on streamflow records shorter than 5 years 

may be biased due to short-term variation in streamflow, for 

example below-normal streamflow for 3 consecutive years. 

Estimates based on streamflow record of 5 years or less were 

screened for this bias by comparing the value of mean annual 

streamflow computed from the streamflow record with the 

value of runoff computed from the Unit Runoff Method 

(Bondelid and others, 1999); the sites for which these values 

differed by more than 40 percent were excluded from further 

analysis. These sites (17) are listed in the file SAGT_monitoredload.xls (700 KB) along with information about station location 

and record; load estimates are not shown for these sites.

Characteristics of Monitored Mean Annual 

Nitrogen and Phosphorus Load and Streamflow

Estimates of observed mean annual nitrogen and phosphorus load and mean annual streamflow, normalized to the 

base year 2002, are summarized in table 3. To facilitate spatial 

comparisons of instream loads at sites draining watersheds of 

differing size and streamflow characteristics, the load estimates at each site were scaled in two ways. First, the load estimate was divided by the total upstream area for the monitoring 

site, producing an estimate of yield in kilograms per hectare 

per year. Estimates of yield are useful for comparisons among 

sites of mass output, and comparison with inputs in a mass 

balance analysis. Second, the load estimate was divided by the 

mean annual streamflow at the monitoring site, producing the 

equivalent of the flow-weighted mean of the model-estimated 

daily concentrations in milligrams per liter. Estimates of flowweighted mean concentration are useful for evaluating average 

water-quality conditions at the site and for comparisons with 

national datasets. For the purpose of spatial comparisons of 

mean annual streamflow among sites, the streamflow estimate 

at each site was scaled by dividing by the total upstream area, 

producing an estimate of runoff rate over the upstream area in 

inches per year. 

Nitrogen

The mean value of the nitrogen yield estimates, normalized to 2002, for the 637 stations in the SAGT area for which 

nitrogen load could be estimated is 4.7 kilograms per hectare 

(kg/ha), median value is 3.8 kg/ha, and 10- and 90-percentile 

values are 1.9 and 7.7 kg/ha, respectively (table 3). This distribution is placed in context with the national distribution of 

stream nitrogen yield, by comparing with estimates of mean 

annual nitrogen yield for 477 sites monitored by the NAWQA 

Program during 1992–2001 (Mueller and Spahr, 2005). The 

median value of nitrogen yield estimates for the SAGT area 

(3.8 kg/ha) is similar to the median value for the national set 

(4.1 kg/ha); however the 90-percentile value for the national 

distribution is much larger (22 kg/ha compared to 7.7 kg/ha), 

as is the mean value for the national distribution (8.1 kg/ha 

compared to 4.7 kg/ha) (table 3).

The spatial pattern of mean annual nitrogen yield for 

2002 is shown in figure 6, along with the boundaries of the 

hydrologic subregions. The highest 10 percent of observations 

of nitrogen yield (>7.9 kg/ha) occur at sites throughout the 

SAGT area; however, clusters of high-yield observations occur 

in the Peace-Tampa Bay subregion (0310), near metropolitan 

areas in central Georgia, Alabama, and North Carolina, and 

in the northeastern part of the Mobile-Tombigbee subregion 

(0316). The lowest 10 percent of observations of nitrogen 

yield (<1.9 kg/ha) occur throughout the eastern half of the 

SAGT area, and especially in the Chowan-Roanoke (0301) and 

Peace-Tampa Bay (0310) subregions.

The spatial pattern in monitored nitrogen yield was evaluated with respect to the hydrologic subregion boundaries using 

Tukey’s multiple comparison test. Although nitrogen yield 

distribution overlapped among many subregions, distributions between some subregions were sufficiently different 

(at alpha = 0.05) to enable the division of the subregions into 

three groups: (1) the Lower Tennessee subregion (0603 and 

0604) with consistently high observations of yield (mean value 

12 kg/ha); (2) a grouping of 8 subregions with consistently 

low observations of yield (mean value <3.9 kg/ha), including 

the Chowan-Roanoke subregion (0301), the drainages to the 

Atlantic in South Carolina and Georgia, and three drainages 

to the Gulf (Suwanee, Ochlockonee, and ChoctawhatcheeEscambia subregions; 0311, 0312, and 0314, respectively); 

and (3) a grouping of 11 subregions with yield observations 

ranging too widely within each subregion to permit characterization as consistently high or low. The hydrologic subregion 

framework is clearly not appropriate for delineating regions 

of relatively homogeneous nitrogen yield in the SAGT area; 

however, the subregion boundaries are useful for describing 

certain local-scale patterns. 

The mean value of nitrogen flow-weighted mean concentration for the SAGT station set is 1.2 mg/L, median value 

is 0.95 mg/L, and 10- and 90-percentile values are 0.47 and 

2.0 mg/L, respectively (table 3). As with estimates of yield, 

values of flow-weighted mean concentration for the SAGT 

area for mean and 90-percentile values are substantially lower 

than the national set. The spatial pattern of mean annual 

nitrogen flow-weighted concentration for 2002 is shown in 

figure 6B. Nitrogen yield and nitrogen flow-weighted mean 

concentration for monitoring sites in the SAGT are strongly 

correlated (r2 = 0.68), suggesting that, in general, concentration 

variability explains variability of yield (fig. 7). In many cases, 

however, sites with the highest observations of nitrogen flowweighted concentration do not have the highest observations 
32 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States Table 3. Variation across the SAGT SPARROW model area in monitored mean annual load and concentration of nitrogen and phosphorus, and in monitored mean annual streamflow, normalized to 2002. [km2, square kilometer; kg/ha/yr, kilogram per hectare per year; mg/L, milligram per liter; Tukey class, interpreted to indicate difference in means such that means with the same Tukey class letter are not sig- nificantly different at 0.05; color indicator of Tukey rank: white/black (L/H) - subregions with values that are significantly different from and lower (L) / higher (H) than (respectively) each other; light gray/dark gray (O) - subregions for which distribution of values overlap distribution in other subregions] Area Sub- region number Sub- region area, km2 Median of basin area for sites, km2 Number of sites Nitrogen load, in kg/ha/yr Nitrogen flow-weighted mean concentration, in mg/L Mean value for subregion Standard deviation 10- percentile Median 90- percentile Tukey class Color indicator of Tukey class Mean value for subregion Standard deviation 10- percentile Median 90- percentile Tukey class Color indicator of Tukey class SAGT (all subregions combined) 637 4.7 3.9 1.9 3.8 7.7 1.2 0.8 0.47 0.95 2.0 (Compared to nationwide set of NAWQA Cycle 1 sites) 481 8.06 11 0.54 4.1 22 2.7 4.7 0.33 1.3 6.8 Subregion name Chowan–Roanoke 0301 47,300 1,722 59 2.7 0.7 1.8 2.7 3.6

D

L 0.73 0.19 0.49 0.70 1.0 EF

O

Neuse–Pamlico 0302 33,900 437 39 5.3 4.2 2.3 4.0 9.3 BCD

O 1.4 1.1 0.80 1.1 2.3 ABCDE

O

Cape Fear 0303 25,100 838 26 8.5 7.8 3.0 5.1 21 AB

O 1.9 1.2 0.84 1.4 3.6 AB

H

Pee Dee 0304 47,900 3,357 34 3.4 1.5 2.1 3.2 4.6

D

L 0.91 0.37 0.52 0.90 1.2 DEF

O

Edisto–Santee 0305 61,100 884 74 3.9 4.5 1.5 2.5 5.6

D

L 0.85 0.81 0.36 0.62 1.2 DEF

O

Ogeechee–Savannah 0306 42,200 530 22 2.3 1.4 1.3 2.1 4.3

D

L 0.50 0.33 0.19 0.47 0.70 F L

Altamaha–St. Marys 0307 53,000 2,846 22 2.8 1.4 1.7 2.0 4.8

D

L 0.80 0.32 0.53 0.66 1.3 DEF

O

St. Johns 0308 30,000 5,798 74 4.8 1.5 2.9 5.1 6.4 BCD

O 1.5 0.44 1.0 1.5 2.0 ABCD

O

Peace–Tampa Bay 0310 26,000 365 88 5.3 3.3 1.4 5.1 9.4 BCD

O 1.7 0.83 0.91 1.5 2.8 ABC

O

Suwanee 0311 35,700 4,838 28 3.5 3.5 2.2 2.8 4.6

D

L 1.1 0.81 0.82 1.0 1.3 BCDEF

O

Ochlockonee 0312 9,500 2,042 6 3.1 1.2 1.7 2.7 5.0

D

L 0.76 0.26 0.36 0.78 1.1 DEF

O

Apalachicola 0313 53,100 6,866 33 5.7 3.7 1.9 4.1 10 BCD

O 1.0 0.45 0.48 0.89 1.7 CDEF

O

Choctawhatchee–

Escambia

0314 38,800 1,358 17 3.4 0.9 2.6 3.2 4.2

D

L 0.5 0.14 0.31 0.51 0.65 EF

O

Alabama (Coosa–

Tallapoosa)

0315 58,800 1,051 27 5.3 1.7 3.9 4.8 6.9 BCD

O 0.9 0.38 0.62 0.79 1.2 DEF

O

Mobile–Tombigbee 0316 56,700 675 16 7.9 6.0 2.7 6.0 15 ABC

O 1.3 1.1 0.51 1.0 2.3 ABCDE

O

Pascagoula 0317 31,300 886 17 4.5 1.7 2.7 4.1 6.0 CD

O 0.7 0.37 0.41 0.64 1.1 DEF

O

Pearl 0318 22,600 4,715 8 5.3 1.2 3.6 5.3 7.9 BCD

O 1.1 0.26 0.73 1.0 1.6 CDEF

O

Upper Tennesseea 0601 and 

0602

57,900 989 35 4.7 2.3 2.2 4.2 7.3 BCD

O 0.8 0.63 0.28 0.71 1.5 DEF

O

Lower Tennesseeb 0603 and 

0604

47,400 1,128 12 12 10 5.2 9.4 17

A

H 2.1 1.8 0.84 1.7 3.5

A

H

Sites with nitrogen load estimates placed on the 

SAGT ERF1_2 network

333 4.8 4.4 2.0 3.6 7.9 1.1 0.84 0.45 0.87 1.9
Mean Annual Nitrogen and Phosphorus Load at Stream Monitoring Sites 33

Area

Subregion 

number

Subregion 

area, 

km2

Median 

of basin 

area for 

sites, 

km2

Phosphorus load, in kg/ha/yr Phosphorus flow-weighted mean concentration, in mg/L

Number 

of sites

Mean 

value for 

subregion

Standard 

deviation

10-

percentile Median 90- percentile Tukey class

Color 

indicator 

of Tukey 

class

Mean 

value for 

subregion

Standard 

deviation

10-

percentile Median 90- percentile Tukey class

Color 

indicator 

of Tukey 

class

SAGT (all subregions combined) 747 0.66 1.1 0.13 0.33 1.3 0.17 0.32 0.03 0.08 0.32

(Compared to nationwide set of NAWQA 

Cycle 1 sites)

454 0.71 1.3 0.05 0.32 1.6 0.28 1.0 0.02 0.12 0.54

Subregion nameChowan–Roanoke 0301 47,300 1,722 59 0.26 0.10 0.16 0.24 0.41

C

L 0.07 0.03 0.04 0.06 0.11

B

L

Neuse–Pamlico 0302 33,900 437 39 0.54 0.43 0.21 0.44 0.95

C

L 0.14 0.10 0.07 0.12 0.23

B

L

Cape Fear 0303 25,100 838 26 0.96 0.88 0.35 0.61 3.0 ABC

O 0.21 0.13 0.09 0.17 0.49

B

L

Pee Dee 0304 47,900 3,214 34 0.29 0.24 0.11 0.27 0.49

C

L 0.08 0.07 0.03 0.07 0.13

B

L

Edisto–Santee 0305 61,100 1,043 84 0.58 1.04 0.14 0.28 0.95

C

L 0.13 0.20 0.03 0.07 0.21

B

L

Ogeechee–Savannah 0306 42,200 1,656 37 0.28 0.16 0.10 0.27 0.45

C

L 0.07 0.04 0.02 0.06 0.11

B

L

Altamaha–St. Marys 0307 53,000 2,031 41 0.31 0.34 0.09 0.23 0.64

C

L 0.08 0.06 0.03 0.06 0.14

B

L

St. Johns 0308 30,000 4,776 80 0.32 0.17 0.11 0.32 0.50

C

L 0.09 0.04 0.05 0.09 0.14

B

L

Peace–Tampa Bay 0310 26,000 361 88 1.9 2.31 0.09 1.3 4.1

A

H 0.62 0.73 0.05 0.37 1.5

A

H

Suwanee 0311 35,700 4,307 31 0.50 1.00 0.17 0.32 0.51

C

L 0.15 0.24 0.06 0.11 0.18

B

L

Ochlockonee 0312 9,500 2,403 7 0.30 0.11 0.13 0.33 0.44

C

L 0.09 0.06 0.02 0.08 0.16

B

L

Apalachicola 0313 53,100 3,281 70 0.48 0.80 0.12 0.27 0.70

C

L 0.09 0.14 0.03 0.05 0.13

B

L

Choctawhatchee–

Escambia

0314 38,800 1,507 14 0.21 0.06 0.11 0.22 0.28

C

L 0.03 0.01 0.01 0.03 0.05

B

L

Alabama (Coosa–

Tallapoosa)

0315 58,800 1,148 44 0.65 0.38 0.23 0.59 1.2

C

L 0.11 0.07 0.04 0.09 0.18

B

L

Mobile–Tombigbee 0316 56,700 461 14 1.2 1.4 0.14 0.55 3.8 ABC

O 0.20 0.24 0.02 0.10 0.58

B

L

Pascagoula 0317 31,300 886 17 0.47 0.31 0.07 0.45 0.95

C

L 0.08 0.06 0.01 0.08 0.18

B

L

Pearl 0318 22,600 4,715 8 0.84 0.40 0.41 0.74 1.7 BC

O 0.17 0.08 0.08 0.14 0.34

B

L

Upper Tennesseea 0601 and 

0602

57,900 1,109 42 0.42 0.35 0.15 0.29 1.0

C

L 0.07 0.07 0.02 0.04 0.17

B

L

Lower Tennesseeb 0603 and 

0604

47,400 1,128 12 1.8 1.67 0.42 1.1 4.3 AB

O 0.31 0.27 0.06 0.22 0.73

B

L

Sites with phopshorus load estimates placed on 

the SAGT ERF1_2 network 378 0.67 1.2 0.14 0.35 1.4 0.16 0.33 0.03 0.08 0.32

Table 3. Variation across the SAGT SPARROW model area in monitored mean annual load and concentration of nitrogen and phosphorus, and in monitored mean annual streamflow, normalized to 2002.—Continued[km2, square kilometer; kg/ha/yr, kilogram per hectare per year; mg/L, milligram per liter; Tukey class, interpreted to indicate difference in means such that means with the same Tukey class letter are not significantly different at 0.05; color indicator of Tukey rank: white/black (L/H) - subregions with values that are significantly different from and lower (L) / higher (H) than (respectively) each other; light gray/dark gray (O) - subregions for which distribution of values overlap distribution in other subregions]
34 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States Area Sub- region number Sub- region area, km2 Median of basin area for sites, km2 Streamflow, in inches per year Number of sitesc Mean value for subregion Standard deviation 10- percentile Median 90- percentile Tukey class Color indicator of Tukey class SAGT (all subregions combined) 759 17 7.2 11 16 26 (Compared to nationwide set of NAWQA Cycle 1 sites) 490 17 12 2.4 14 25 Subregion name Chowan–Roanoke 0301 47,300 1,722 59 15 1.9 13 14 18 EFGH

O

Neuse–Pamlico 0302 33,900 437 39 14 1.7 12 14 17 FGH

O

Cape Fear 0303 25,100 838 26 16 3.4 13 14 21 EFGH

O

Pee Dee 0304 47,900 3,245 35 15 2.8 12 14 16 EFGH

O

Edisto–Santee 0305 61,100 1,043 86 17 4.2 13 16 23 DEFGH

O

Ogeechee–Savannah 0306 42,200 1,572 38 19 11 11 16 43 CDEFG

O

Altamaha–St. Marys 0307 53,000 2,031 41 14 3.5 11 13 18 FGH

O

St. Johns 0308 30,000 4,776 80 14 4.9 9.0 15 17 GH

O

Peace–Tampa Bay 0310 26,000 365 89 13 8.6 5.7 12 21

H

L

Suwanee 0311 35,700 4,307 31 12 2.3 9.1 12 13

H

L

Ochlockonee 0312 9,500 2,403 7 16 6.8 9.4 13 26 EFGH

O

Apalachicola 0313 53,100 3,281 70 21 8.3 14 20 27 BCDE

O

Choctawhatchee–

Escambia

0314 38,800 1,358 17 28 9.8 22 23 36

A

H

Alabama (Coosa–

Tallapoosa)

0315 58,800 1,148 44 23 3.2 20 23 27 ABCD

O

Mobile–Tombigbee 0316 56,700 523 18 23 3.1 19 23 28 ABC

O

Pascagoula 0317 31,300 886 17 25 4.6 20 24 30 ABC

O

Pearl 0318 22,600 4,715 8 20 1.4 18 20 23 BCDE

O

Upper Tennesseea 0601 and 

0602

57,900 1,109 42 26 7.7 17 24 34 AB

O

Lower Tennesseeb 0603 and 

0604

47,400 1,128 12 22 3.1 19 24 25 ABCD

O

a The Upper Tennessee subregion is defined for this summary as the combined subregions 0601 and 0602.b The Lower Tennessee subregion is defined for this summary as the combined subregions 0603 and 0604. Monitored basins that include areas upstream from this subregion (that is, in the Upper Tennessee 

subregion) are excluded from the summary by subregion.

c Mean annual streamflow, and nitrogen or phosphorus or both nitrogen and phosphorus load, was estimated for a total of 782 sites in the SAGT model area. Streamflow and load are not reported or included 

Table 3.

in this summary for 21 of these sites, however, due to uncertainty about the boundary of the watershed contributing to the site or due to concern that the streamflow and load estimates may be biased by short streamflow record.

Variation across the SAGT SPARROW model area in monitored mean annual load and concentration of nitrogen and phosphorus, and in monitored mean annual 

streamflow, normalized to 2002.—Continued[km2, square kilometer; kg/ha/yr, kilogram per hectare per year; mg/L, milligram per liter; Tukey class, interpreted to indicate difference in means such that means with the same Tukey class letter are not significantly different at 0.05; color indicator of Tukey rank: white/black (L/H) - subregions with values that are significantly different from and lower (L) / higher (H) than (respectively) each other; light gray/dark gray (O) - subregions for which distribution of values overlap distribution in other subregions]
Mean Annual Nitrogen and Phosphorus Load at Stream Monitoring Sites 35

Figure 6A. Mean annual nitrogen yield estimated from stream monitoring data from 637 sites in the SAGT river basins, normalized 

to 2002.

Figure 6A. Mean annual nitrogen yield estimated from stream monitoring data from 637 sites in the SAGT River basins, normalized to 2002.

0301

0302

0303 0304

0305

0306

0307

0308

0311

0310

0312

0604

0313

0314

0315

0316

0317

0318

0602 0601

0603

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Hydrologic subregion and number

Mean annual nitrogen yield, in

 kilograms per hectare

Study area boundary

Less than 1.9

2.0 to 3.8

3.9 to 7.7

Greater than 7.7

Interval bounds are the 10, 50, and

 90 percentiles of distribution. 

0308

EXPLANATION

90 85 80

30

35

DENSITY

MEAN ANNUAL NITROGEN YIELD, IN KILOGRAMS PER HECTARE

0.10

0.20

0.25

0.05

0.15

0 0 4 8 12 16 20 24 28 32 36 40 44
36 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Figure 6B. Mean annual nitrogen flow-weighted mean concentration estimated from stream monitoring data from 637 sites in the 

SAGT river basins, normalized to 2002.

0301

0302

0303 0304

0305

0306

0307

0308

0311

0310

0312

0604

0313

0314

0315

0316

0317

0318

0602 0601

0603

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Hydrologic subregion and number

Mean annual nitrogen flow-

 weighted mean concentration,

 in milligrams per liter

Study area boundary

Less than 0.47

0.48 to 0.95

0.96 to 2.0

Greater than 2.0

Interval bounds are the 10, 50,

 and 90 percentiles of

 distribution. 

0308

EXPLANATION

90 85 80

30

35

DENSITY

MEAN ANNUAL NITROGEN FLOW-WEIGHTED MEAN

CONCENTRATION, IN MILLIGRAMS PER LITER

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5

0.2

0

0.4

0.6

0.8

0.10
Mean Annual Nitrogen and Phosphorus Load at Stream Monitoring Sites 37

of nitrogen yield. For many of the sites in the St. Johns and 

Peace-Tampa Bay subregions with flow-weighted mean concentration among the highest (>2.0 mg/L) in the SAGT area, 

observations of nitrogen yield are among the lowest in the 

SAGT area (<1.0 kg/ha) (figs. 6 and 7). Conversely, many sites 

with relatively high yield values have relatively low values 

of flow-weighted concentration, such as sites in the CoosaTallapoosa subregion (0315) (fig. 6).

The noted divergence from a directly proportional relation is due to the fact that flow-weighted mean concentration 

varies as a function not only of mass yield, but also of streamflow yield, or runoff. The discrepancies between the spatial 

distribution of high and low values for nitrogen yield compared with the spatial distribution of nitrogen flow-weighted 

mean concentration (figures 6A and 6B) are, therefore, a 

function of differences in streamflow yield. Streamflow yield 

is relatively low for many sites in the St. Johns (0308) and 

Peace-Tampa Bay (0310) subregions, and relatively high 

for many sites in the Coosa-Tallapoosa (0315) and Upper 

Tennessee (0601 and 0602) subregions (figure 8A). The 

general pattern of variation in streamflow yield for the SAGT 

area is evident from the contoured surface prepared by Gebert 

and others (1987) and shown in figure 8B: streamflow yield is 

generally higher (>20 inches) in drainages to the Gulf extending eastward to the Ochlockonee subregion (0312) and in the 

Tennessee River basin, and generally lower (<20 inches) in 

drainages to the Atlantic and in the Peace-Tampa Bay (0310) 

and Suwanee (0311) subregions. 

The influence of streamflow yield on the relation between 

yield and concentration is illustrated in figure 7 by the three 

lines showing the expected value of yield for a specified value 

of flow-weighted mean concentration assuming a specific 

streamflow yield of 11, 16, or 26 inches, which corresponds 

with the 10, 50, and 90 percentile of the distribution of 

streamflow yield for the load estimation sites in the SAGT 

area. For sites with low values of streamflow yield (plotting 

position to the left of the 11-inch line), nitrogen yield may be 

relatively low (<3 kg/ha) and flow-weighted concentration 

relatively high (>2 mg/L). 

Conversely, for some sites 

with high values of streamflow yield (plotting position to 

the right of the 26-inch line), 

nitrogen yield is relatively high 

(>20 kg/ha) and flow-weighted 

mean concentration relatively 

low (1.4 mg/L). Many of the 

sites with streamflow yield 

<11 inches or greater than 

26 inches are located in drainage basins influenced by large 

springs (Miller, 1990) or losing reaches (Rumenik, 1988).

Variation of streamflow 

yield in the SAGT area is 

caused by variation in the 

volume of water from precipitation that is available for 

direct runoff, termed excess 

precipitation (fig. 8B). Calculated as the difference between 

precipitation and potential 

evaporation, estimates of 

excess precipitation (Wolock 

and others, 2003) correspond 

closely to contoured values 

of streamflow yield for most 

of the SAGT area. In many 

areas in Florida, however, 

contoured streamflow yield 

does not compare closely with 

estimates of excess precipitation because streamflow yield 

is affected by factors other 

than direct runoff from the 

10

8

6

4

2

0

0 10 20 30 40 50

Observed concentration - all sites except 

 selected sites in Florida

EXPLANATION

Observed concentration - sites in the Oklawaha, 

 Crystal, Lower Santa Fe, Lower Suwanee, 

 St. Marks, and Chipola River Basins, Florida 

Flow-weighted mean concentration computed for

 given yield and runoff equal to:

11 inches

16 inches

26 inches

FLOW-WEIGHTED MEAN ANNUAL CONCENTRATION,

IN MILLIGRAMS PER LITER

MEAN ANNUAL NITROGEN YIELD, IN KILOGRAMS PER HECTARE

Figure 7. Relation of nitrogen flow-weighted mean concentration to nitrogen yield and 

runoff rate for the SAGT river basins. 
38 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Figure 8A. Mean annual streamflow yield in the SAGT river basins estimated from stream monitoring data from 759 sites, 

normalized to 2002.

0301

0302

0303 0304

0305

0306

0307

0308

0311

0310

0312

0604

0313

0314

0315

0316

0317

0318

0602 0601

0603

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Hydrologic subregion and number

Mean annual streamflow per unit area of

 drainage basin, termed runoff (inches)

Study area boundary

Less than 11

12 to 16

17 to 26

Greater than 26

Interval bounds are the 10, 50, and 90 percentiles

 of distribution. Runoff in inches is calculated as

 the product of mean annual streamflow (cubic

 feet per second), inverse of drainage basin area

 (square miles), and a conversion factor (13.6)

0308

EXPLANATION

90 85 80

30

35
Mean Annual Nitrogen and Phosphorus Load at Stream Monitoring Sites 39

Figure 8B. Mean annual streamflow in the SAGT river basins shown as contour lines and compared with estimates of 

excess precipitation.

15

16

18

14

24

25

12

22

26

10

28

8

30

11

35

40

6

13

50

18

20

22

11

14

28

11

20

24

15

18

18

12

22

12

12

28

20

12

22

18

25

28

35 30

26

20

26

12

15

14

30

22

30

12

16

30

12

25

15

24

20

15

16

20

12

40

15

20

20

16

30

30

22

16

14

20

24

28

16

12

16

11

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of

Mexico

Atlantic

Ocean

Volume of water from precipitation available as direct

 runoff, termed excess precipitation (inches), from

 Wolock, 2003

Line of equal value of mean annual streamflow per

 unit area, termed runoff (inches),

 from Gebert and others, 1987

Study area boundary

 

EXPLANATION

90 85 80

30

35

Less than 10.0

10.1 to 20.0

20.1 to 30.0

Greater than 30.0
40 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

surface-water basin (Rumenik, 1988). Instream nutrient load 

at stream sites in these areas may not reflect conditions in the 

associated topographic watershed, and thus the SPARROW 

approach of explaining instream loads based on watershed 

attributes may be inappropriate. River basins identified with 

this concern include south Florida (where surface-water flow 

paths have been extensively altered) and the Oklawaha, Crystal, Lower Sante Fe, Lower Suwanee, St. Marks, and Chipola 

River basins in central and northern Florida (where flow 

exchange with the underlying regional aquifer may represent 

substantial nitrogen influx to and outflux from the surfacewater basins; Rumenik, 1988; Miller, 1990).

Of the 637 stations with estimates of nitrogen load, only 

333 can be placed on the SAGT ERF1_2 digital segmented 

network and used to calibrate a nitrogen SPARROW model 

based on SAGT ERF1_2 (shown in figure 9 as black triangles). The other 304 sites (shown in figure 9 as white triangles) 

were excluded for a variety of reasons: sites are located on 

tributaries too small to be represented in the relatively coarse 

1:500,000 ERF1_2 network; sites lack independent information for calibration due to proximity (for example, within 1 

kilometer) to another site with a nitrogen load estimate; or 

the SAGT ERF1_2 network failed to reliably model the flow 

path upstream from the site (judged to be the case if the ratio 

of the site drainage area to the upstream drainage area for the 

ERF1_2 reach associated with the site is outside the range of 

0.75–1.33). Summary statistics of estimates of nitrogen yield 

and flow-weighted mean concentration for this subset of 333 

sites are listed in table 3 for comparison with the more complete set of sites. The distribution of yield and concentration 

estimates for this subset of 333 sites is almost identical to the 

distribution for the complete set. Concern about flow exchange 

with the underlying regional aquifer representing substantial 

nitrogen influx to and outflux from the surface-water basins 

further reduces the set of stations used to calibrate the nitrogen 

model from 333 to 321.

Phosphorus

The mean value of the phosphorus yield estimates, 

normalized to 2002, for the 747 stations in the SAGT area is 

0.66 kg/ha, median is 0.33 kg/ha, and 10- and 90-percentile 

values are 0.13 and 1.3 kg/ha, respectively (table 3). This 

distribution is almost identical to the national distribution of 

stream phosphorus yield (Mueller and Spahr, 2005); the mean, 

median, and 90-percentile values for the SAGT distribution are 

within 5 percent of the values for the national distribution. The 

similar values for the 90-percentile indicate that the estimates 

for some streams in the SAGT area are among the highest in 

the Nation. This contrasts with results from comparing stream 

nitrogen yield distribution among the two sets, in which values 

for the SAGT area are substantially lower than the national set.

The mean value of phosphorus flow-weighted mean 

concentration for the SAGT station set is 0.17 mg/L, median 

value is 0.08 mg/L, and 10- and 90-percentile values are 0.03 

and 0.32 mg/L, respectively (table 3). These values are lower 

than the corresponding values for the national set. This result 

is expected, despite the comparable values for stream phosphorus yield, because the lower values of flow-weighted mean 

concentration are due to the higher mean annual streamflow 

yields in the SAGT area. 

Estimates of mean annual phosphorus load for the 747 

monitored sites are shown in figure 10. The highest 10 percent 

of observations of phosphorus yield (>1.3 kg/ha) occur at sites 

throughout the SAGT area; however, high-yield observations, 

as well as low-yield observations, appear to be clustered in the 

Peace-Tampa Bay subregion (0310). Clustering of high-yield 

observations in metropolitan areas is not as pronounced as it is 

with high-yield observations of nitrogen.

The spatial pattern in monitored phosphorus yield was 

evaluated with respect to the hydrologic subregion boundaries 

using Tukey’s multiple comparison test. The analysis divides 

the observed values into two statistically distinct groupings 

of subregions: a grouping of 14 subregions with consistently 

low observations of yield (mean value <0.66 kg/ha), and a 

grouping with consistently high observations of yield (mean 

value >1.8 kg/ha) that includes the Peace-Tampa Bay (0310) 

and Lower Tennessee (0603 and 0604) subregions. These 

high-yield subregions include areas of phosphate-rich soil and 

regolith. Complete characterization of watershed inputs of 

phosphorus in these subregions requires data on the phosphorus content of natural surficial materials. Regionalization, based on lithologic boundaries, of chemical analyses 

of soils and streambed sediment could provide estimates of 

soil phosphorus content for each catchment in the SAGT 

area (S.E. Terziotti, U.S. Geological Survey, written commun., 2007). 

Of the 747 stations with estimates of phosphorus load, 

only 378 can be placed on the SAGT ERF1_2 digital segmented network and used to calibrate a phosphorus SPARROW model based on SAGT ERF1_2 (shown in figure 11 

as black triangles). The other 369 sites (shown in figure 11 

as white triangles) were excluded for the same set of reasons 

described for the nitrogen load station set. Summary statistics 

of estimates of phosphorus yield and flow-weighted mean 

concentration for this subset of 378 sites are listed in table 3, 

for comparison with the more complete set of sites. The distribution of yield and concentration estimates for this subset of 

378 sites is almost identical to the distribution for the complete 

set. Concern about flow exchange with the underlying regional 

aquifer representing substantial phosphorus influx to and 

outflux from the surface-water basins further reduces the set of 

stations used to calibrate the nitrogen model from 378 to 368.
Mean Annual Nitrogen and Phosphorus Load at Stream Monitoring Sites 41

Figure 9. Water-quality monitoring sites for which nitrogen load is estimated for 2002, with the subset of sites associated with a 

SAGT ERF1_2 reach segment.

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Monitoring site

Study area boundary

with nitrogen load estimate, 2002,

 but not associated with SAGT ERF1_2

 reach segment

with nitrogen load estimate, 2002,

 and associated with SAGT ERF1_2

 reach network

EXPLANATION

90 85 80

30

35
42 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Figure 10A. Mean annual phosphorus yield estimated from stream monitoring data from 747 sites in the SAGT river basins, 

normalized to 2002.

0301

0302

0303 0304

0305

0306

0307

0308

0311

0310

0312

0604

0313

0314

0315

0316

0317

0318

0602 0601

0603

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Hydrologic subregion and number

Study area boundary

Mean annual phosphorus yield, in

 kilograms per hectare

Less than 0.13

0.14 to 0.33

0.34 to 1.3

Greater than 1.3

Interval bounds are the 10, 50, and

 90 percentiles of distribution.

0308

EXPLANATION

90 85 80

30

35

DENSITY

MEAN ANNUAL PHOSPHORUS YIELD, IN KILOGRAMS PER HECTARE

1.5

1.0

0.5

0 0 2 4 6 8 10 12 14
Mean Annual Nitrogen and Phosphorus Load at Stream Monitoring Sites 43

Figure 10B. Mean annual phosphorus flow-weighted mean concentration estimated from stream monitoring data from 747 sites in 

the SAGT river basins, normalized to 2002.

Figure 10B. Mean annual phosphorus flow-weighted mean concentration estimated from stream monitoring data from 747 sites in

 the SAGT River basins, normalized to 2002.

0.00 0.40 0.80 1.20 1.60 2.00 2.40 2.80 3.20 3.60 4.00

2.0

1.0

5.0

7.0

3.0

0

4.0

6.0

0301

0302

0303 0304

0305

0306

0307

0308

0311

0310

0312

0604

0313

0314

0315

0316

0317

0318

0602 0601

0603

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Hydrologic subregion and number

Mean annual phosphorus flow-

 weighted mean concentration,

 in milligrams per liter

Less than 0.03

0.04 to 0.08

0.09 to 0.32

Greater than 0.32

Interval bounds are the 10, 50, and

 90 percentiles of distribution.

Study area boundary 

0308

EXPLANATION

90 85 80

30

35

DENSITY

MEAN ANNUAL PHOSPHORUS FLOW-WEIGHTED MEAN CONCENTRATION,

IN MILLIGRAMS PER LITER
44 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

Figure 11. Water-quality monitoring sites for which phosphorus load is estimated for 2002, with subset of sites associated with a

 SAGT ERF1_2 reach segment.

0 130 260 MILES

0 190 380 KILOMETERS

Gulf

of Mexico

Atlantic

Ocean

Monitoring site

Study area boundary

with phosphorus load estimate, 2002,

 but not associated with SAGT ERF1_2

 reach segment

with phosphorus load estimate, 2002,

 and associated with SAGT ERF1_2

 reach network

EXPLANATION

90 85 80

30

35

Figure 11. Water-quality monitoring sites for which phosphorus load is estimated for 2002, with the subset of sites associated with 

a SAGT ERF1_2 reach segment.
Summary 45

Summary

This report describes the digital datasets that characterize nutrient source inputs, environmental characteristics, and 

instream nutrient loads for the purpose of calibrating and 

applying a nutrient water-quality model for the southeastern 

United States for 2002. The water-quality model SPARROW 

(SPAtially-Referenced Regression On Watershed attributes) 

uses a regression equation to describe the relation between 

watershed attributes (predictors) and measured instream load 

(response). This application of the SPARROW model is based 

on a 1:500,000-scale description of the stream network and a 

1:100,000-scale delineation of the catchments associated with 

the stream reaches. 

Watershed attributes that are considered to describe nutrient input conditions are included as source variables in the 

regression equation; the nutrient-source variables to be tested 

in the SAGT SPARROW model include atmospheric deposition, fertilizer application to farmland, manure from livestock 

production, permitted wastewater discharge, and land cover. 

Watershed attributes that are considered to affect rates of nutrient transport from land to water are included in the regression 

equation as land-to-water transport variables; the nutrienttransport variables to be tested in the SAGT SPARROW model 

include characteristics of soil, landform, and climate. Channel 

attributes considered as nutrient transport predictors for the 

SAGT SPARROW model include reach time of travel and 

reservoir hydraulic loading.

Measurements of nutrient water quality at stream monitoring sites from a combination of monitoring programs were 

used to develop observations of the response variable—mean 

annual nitrogen or phosphorus load—in the SPARROW 

regression equation. Nutrient load was estimated for monitoring sites on streams and rivers (reservoir sites were excluded) 

for which samples were collected at least quarterly, with a 

minimum of 10 samples collected since 1995, for at least a 

2-year period during which daily streamflow data also were 

collected (or could be estimated from a nearby gage). Instream 

loads of nitrogen and phosphorus were estimated from biascorrected, log-linear regression models using the program 

Fluxmaster. A special feature available in Fluxmaster to 

compute detrended estimates of long-term mean load corrects 

for bias introduced by uneven record length among the stations 

and thus produces load estimates more suitable for spatial 

comparisons. The 4-parameter log-linear regression approach 

used to model daily concentration for this analysis may be 

inadequate for estimating annual load accurately at some sites, 

such as where the concentration-streamflow relation is influenced by hysteresis or antecedent conditions. The decision to 

employ the 4-parameter model uniformly for all stations was 

considered appropriate for this regional-scale assessment of 

water quality and in view of the available resources.

The mean value of the nitrogen yield estimates, normalized to 2002, for the 637 stations in the SAGT area is 4.7 kilograms per hectare (kg/ha), median value is 3.8 kg/ha, and 10- 

and 90-percentile values are 1.9 and 7.7 kg/ha, respectively. 

The mean value of nitrogen flow-weighted mean concentration for the SAGT station set is 1.2 mg/L, median value is 

0.95 mg/L, and 10- and 90-percentile values are 0.47 and 

2.0 mg/L, respectively. The highest 10 percent of observations 

of nitrogen yield (>7.7 kg/ha) occur at sites throughout the 

SAGT area; however, clusters of high-yield observations are in 

the Peace River-Tampa Bay basin in Florida, near metropolitan 

areas in central Georgia, Alabama, and North Carolina, and in 

the northeastern part of the Tombigbee River basin. The lowest 

10 percent of observations of nitrogen yield (<1.9 kg/ha) occur 

throughout the eastern half of the SAGT area, and especially 

in the Chowan–Roanoke River basins and the Peace River–

Tampa Bay basins.

The mean value of the phosphorus yield estimates, 

normalized to 2002, for the 747 stations in the SAGT area is 

0.66 kg/ha, median is 0.33 kg/ha, and 10- and 90-percentile 

values are 0.13 and 1.3 kg/ha, respectively. The mean value of 

phosphorus flow-weighted mean concentration for the SAGT 

station set is 0.17 mg/L, median value is 0.08 mg/L, and 10- 

and 90-percentile values are 0.03 and 0.32 mg/L, respectively. 

The highest 10 percent of observations of phosphorus yield 

(>1.3 kg/ha) occur at sites throughout the SAGT area; however, high-yield observations, as well as low-yield observations, appear to be clustered in the Peace River–Tampa Bay 

basins. The areas with high instream yield of phosphorus 

correspond to areas known to contain phosphate-rich soil and 

regolith. Complete characterization of watershed inputs of 

phosphorus in the SAGT area would require data on the phosphorus content of natural surficial materials.

Sites with the highest observations of flow-weighted concentration do not, in many cases, have the highest observations 

of yield. The noted divergence from a directly proportional 

relation is due to the fact that flow-weighted mean concentration varies as a function not only of mass yield, but also of 

streamflow yield. The discrepancies between the spatial distribution of high and low values for mass yield compared with 

the spatial distribution of flow-weighted mean concentration 

are, therefore, a function of differences in streamflow yield. 

Nutrient conditions measured in streams affected by 

substantial influx or outflux of water and nutrient mass across 

surface-water basin divides do not reflect nutrient source and 

transport conditions in the topographic watershed; inclusion of 

such streams in the SPARROW modeling approach is considered inappropriate. River basins identified with this concern 

include south Florida (where surface-water flow paths have 

been extensively altered) and the Oklawaha, Crystal, Lower 

Sante Fe, Lower Suwanee, St. Marks, and Chipola River 

basins in central and northern Florida (where flow exchange 

with the underlying regional aquifer may represent substantial 

nitrogen influx to and outflux from the surface-water basins).
46 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

References Cited

Alexander, R.B., Brakebill, J.W., Brew, R.E., and Smith, R.A., 

1999, ERF1—Enhanced River Reach File 1.2, accessed 

September 19, 2007, at http://water.usgs.gov/GIS/metadata/

usgswrd/XML/erf1.xml

Alexander, R.B., Smith, R.A., and Schwarz, G.E., 2000, Effect 

of stream channel size on the delivery of nitrogen to the 

Gulf of Mexico: Nature, v. 403, p. 758–761.

Bondelid, T., Griffiths, C., and Van Houten, G., 1999, A 

national water pollution control assessment model—draft 

technical report prepared for U.S. Environmental Protection 

Agency, Office of Science and Technology: Durham, North 

Carolina, Research Triangle Park.

Brabec, Elizabeth, Schulte, Stacey, and Richards, P.L., 2002, 

Impervious surfaces and water quality: a review of current 

literature and its implications for watershed planning: Journal of Planning Literature, v. 16, no. 4, p. 499–514.

Brakebill, J.W., Preston, S.D., and Martucci, S.K., 2001, Digital data used to relate nutrient inputs to water quality in the 

Chesapeake Bay Watershed, version 2.0: Open-File Report 

01–251, 17 p. 

Bricker, S.B., Clement, C.G., Pirhalla, D.E., Orlando, S.P., and 

Farrow, D.R.G., 1999, National Estuarine Eutrophication 

Assessment—Effects of nutrient enrichment in the Nation’s 

estuaries: Silver Spring, Maryland, National Oceanic and 

Atmospheric Administration, National Ocean Service, 

Special Projects Office and the National Centers for Coastal 

Ocean Science, 71 p.

Cohn, T.A., Delong, L.L., Gilroy, E.J., Hirsch, R.M., and 

Wells, D.K., 1989, Estimating constituent loads: Water 

Resources Research, v. 25, no. 5, p. 937–942.

Cohn, T.A., Gilroy, E.J., and Baier, W.G., 1992, Estimating 

fluvial transport of trace constituents using a regression 

model with data subject to censoring, in Proceedings of 

the Section on Statistics and the Environment: American 

Statistical Association, Boston, Mass., August 9–13, 1992, 

p. 142–151.

Daly, Christopher, Gibson, W.P., Taylor, G.H., Johnson, G.L., 

and Pasteris, P.A., 2002, A knowledge-based approach to 

the statistical mapping of climate: Climate Research, v. 22, 

p. 99–113.

Dewald, T.G., Horn, Robert, Greenspun, Robert, Taylor, Phillip, Manning, Lee, and Montalbano, Ann, 1985, STORET 

Reach Retrieval Documentation: Washington, D.C., 

U.S. Environmental Protection Agency. 

Driver, N.E., and Tasker, G.D., 1990, Techniques for estimation of storm-runoff loads, volumes and selected constituent 

concentrations in urban watersheds in the United States: 

U.S. Geological Survey Water-Supply Paper 2363, 44 p.

Environmental Systems Research Institute, Inc., 2008, ArcGIS Desktop Help 9.2—Spatial Analyst: Environmental 

Systems Research Institute, Inc., Redlands, California, 

accessed March 26, 2008, at http://webhelp.esri.com/

arcgisdesktop/9.2/index.cfm?TopicName=An_overview_of_

the_Surface_tools

Falcone, James, 2003, National elevation data, resampled 

to 100m: U.S. Geological Survey, Reston, VA, accessed 

October 22, 2007, at ftp://disftp.er.usgs.gov/pub/NAWQA/

ecology/GIS_DATA/elevation

Gebert, W.A., Graczyk, D.J., and Krug, W.R., 1987, Average 

annual runoff in the United States, 1951–80: U.S. Geological Survey Hydrologic Atlas 170.

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.

Graham, W.F., and Duce, R.A., 1979, Atmospheric pathways 

of the phosphorus cycle: Geochimica et Cosmochimica 

Acta, v. 43, no. 8, p. 1195–1208.

Hellweger, F.L., and Maidment, D.R., 1997, AGREE-DEM 

surface rconditioning system: Austin, Texas, University 

of Texas, accessed October 26, 2007, at http://www.crwr.

utexas.edu/gis/gishyd98/quality/agree/agree.htm

Homer, Collin, Dewitz, J., Fry, J., Coan, M., Hossain, N., 

Larson, C., Herold, N., McKerrow, A., VanDriel, J., and 

Wickham, J., 2007, Completion of the 2001 National Land 

Cover Database for the Conterminous United States: Photogrammetric Engineering and Remote Sensing, v. 73, no. 4, 

p. 337–341.

Homer, Collin, Huang, C., Yang, L., Wylie, B., and Coan, M., 

2004, Development of a 2001 National Landcover Database 

for the United States: Photogrammetric Engineering and 

Remote Sensing, v. 70, no. 7, p. 829–840.

Hoos, A.B., 2005, Evaluation of the SPARROW model for 

estimating transport of nitrogen and phosphorus in streams 

of the Interior Low Plateau Ecoregion, Tennessee, Kentucky, and Alabama, during 1992–2002 [abs.], in Tennessee 

Water Resources Symposium, 15th, Burns, Tenn., 2005, 

Proceedings: Tennessee Section of the American Water 

Resources Association, p. 2A-47.

Kuntz, K.W., 1980, Atmospheric bulk precipitation in the Great 

Lakes basin: Scientific Series No. 115, Inland Waters Directorate, Water Quality Branch, Burlington, Ontario, Canada.
References Cited 47

McMahon, Gerard, Alexander, R.B., and Qian, S., 2003, Support of TMDL programs using spatially referenced regression models: ASCE Journal of Water Resources Planning 

and Management, v. 129, p. 315–329.

McMahon, Gerard, Tervelt, Larinda, and Donehoo, William, 

2007, Methods for estimating annual wastewater nutrient 

loads in the southeastern United States: U.S. Geological Survey Open-File Report 2007–1040, 81 p., accessed 

August 6, 2007, at http://pubs.usgs.gov/of/2007/1040/

Miller, J.A., 1990, Ground water atlas of the United States; 

Alabama, Florida, Georgia, and South Carolina: U.S. Geological Survey Hydrologic Atlas 730-G.

Moore, R.B., Johnston, C.M., Robinson, K.W., and Deacon, 

J.R., 2004, Estimation of total nitrogen and phosphorus in 

New England streams using spatially referenced regression 

models: U.S. Geological Survey Scientific Investigations 

Report 2004–5012, 42 p.

Mueller, D.K., and Spahr, N.E., 2005, Water-quality, streamflow, and ancillary data for nutrients in streams and rivers 

across the Nation, 1992–2001: U.S. Geological Survey Data 

Series 152, accessed August 2007 at http://pubs.usgs.gov/

ds/2005/152/index.htm

Murphy, J.J., 1974, Sources of phosphorus inputs from the 

atmosphere and their significance to oligotrophic lakes: 

University of Illinois, Research Report No. 92 UJLCWRC-74-0092.

National Atmospheric Deposition Program, 2006, The 

National Atmospheric Deposition Program Data Access, 

accessed June 2006 at http://nadp.sws.uiuc.edu/sites/

ntnmap.asp

National Resources Council, 2001, Assessing the TMDL 

approach to water quality management: Washington, D.C., 

National Academy Press, 405 p.

Nolan, J.V., Brakebill, J.W., Alexander, R.B., and Schwarz, 

G.E., 2002, ERF1_2—Enhanced River Reach File 2.0, 

accessed August 2007 at http://water.usgs.gov/GIS/

metadata/usgswrd/XML/erf1_2.xml

Oregon State University, PRISM Group, 2007, 800m normals 

(1971–2000): Corvallis, Oregon, Oregon State University, 

accessed August 2007 at http://prism.oregonstate.edu/

Potter, S.R., Andrews, Susan, Atwood, J.D., Kellogg, R.L., 

Lemunyon, Jerry, Norfleet, Lee, and Oman, Dean, 2006, 

Model simulation of soil loss, nutrient loss and change in 

soil organic carbon associated with crop production: U.S. 

Department of Agriculture, Natural Resources Conservation 

Service, Conservation Effects Assessment Project, accessed 

September 19, 2007, at http://www.nrcs.usda.gov/technical/

nri/ceap/croplandreport/

Preston, S.D., and Brakebill, J.W., Application of spatially 

referenced regression modeling for the evaluation of total 

nitrogen loading in the Chesapeake Bay Watershed: U.S. 

Geological Survey Water-Resources Investigations Report 

99–4054, 12 p.

Redfield, G., Efron, S., Goforth, G., James, R.T., Van Horn, S., 

Adorisio, C., Jones, B., Piccone, T., Petro, K., and Zhang, J., 

2007, An integrative perspective on regional water quality 

and phosphorus: 2007 South Florida Environmental Report, 

Chapter 1B, South Florida Water Management District, West 

Palm Beach, Florida, variously paginated.

Ruddy, B.C., Lorenz, D.L., and Mueller, D.K., 2006, Countylevel estimates of nutrient inputs to the land surface of the 

conterminous United States, 1982–2001: U.S. Geological 

Survey Scientific Investigations Report 2006–5012, 17 p.

Rumenik, R.P., 1988, Runoff to streams in Florida: Florida 

Geological Survey Map series 122, Tallahassee, Florida, 

Florida Geological Survey, 1 sheet.

Saunders, W., 2000, Preparation of DEMs for use in environmental modeling analysis, in Maidment, D.R., and 

Djokic, D., eds., 2000, Hydrologic and Hydraulic Modeling 

Support: Redlands, CA, ESRI Press, p. 29–51.

Schwarz, G.E., Hoos, A.B., Alexander, R.B., and Smith, R.A., 

2006, The SPARROW surface water-quality model—theory, 

application, and user documentation: U.S. Geological 

Survey Techniques and Methods, book 6, section B, chap. 3, 

accessed August 6, 2007, at http://pubs.usgs.gov/tm/2006/

tm6b3/

Seaber, P.R., Kapinos, F.P., and Knapp, G.L., 1987, Hydrologic unit maps: U.S. Geological Survey Water-Supply 

Paper 2294, 63 p.

Smith, R.A., and Alexander, R.B., 2000, Sources of nutrients 

in the Nation’s watersheds, in Managing nutrients and 

pathogens from animal aagriculture, proceedings from the 

Natural Resource, Agriculture, and Engineering Service 

Conference for Nutrient Management Consultants, Extension Educators, and Producer Advisors, March 28–30, 2000, 

Camp Hill, Pennsylvania. 

Smith, R.A., Schwarz, G.E., and Alexander, R.B., 1997, 

Regional interpretation of water-quality monitoring data: 

Water Resources Research, v. 33, no. 12, p. 2781–2798.

Statistical Analysis Systems Institute, 2000, SAS language 

reference—dictionary, version 8: Cary, NC, SAS Institute, 

Inc., 500 p.

U.S. Department of Agriculture, 1994, State soil geographic 

(STATSGO) database data use information: U.S. Department of Agriculture-Natural Resources Conservation Service Miscellaneous Publication 1492, 113 p.
48 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

U.S. Environmental Protection Agency, 1996, USEPA Reach 

File Version 1.0 (RF1) for the Conterminous United States 

(CONUS): Washington, D.C., U.S. Environmental Protection Agency, accessed August 2007 at http://www.epa.gov/

waters/doc/rf1_meta.html

U.S. Environmental Protection Agency, 2002, National Water 

Quality Inventory, 2000 Report: U.S. Environmental Protection Agency Office of Water, EPA-841-R-02-001, 

variously paginated.

U.S. Geological Survey, 2001, National Land Cover Database 

2001 (NLCD 2001): U.S. Geological Survey, accessed 

August 2007 at http://www.mrlc.gov/mrlc2k_nlcd_map.asp/

Wolock, D.M., 1997, STATSGO soil characteristics for the 

conterminous United States: U.S. Geological Survey OpenFile Report 97–656, digital data release, accessed October 2007 at http://water.usgs.gov/GIS/metadata/usgswrd/

muid.xml

Wolock, D.M., 2003, Hydrologic landscape regions of the 

United States: U.S. Geological Survey Open-File Report 

03–145, digital data release, accessed October 2007 at 

http://water.usgs.gov/GIS/metadata/usgswrd/XML/hlrus.xml

Wolock, D.M., and McCabe, G.J., 1999, Explaining variability 

in mean annual runoff in the conterminous United States: 

Climate Research, v. 11, p. 140–159.

Wolock, D.M., Winter, T.C., and McMahon, Gerard, 2004, 

Delineation and evaluation of hydrologic-landscape regions 

in the United States using geographic information system 

tools and multivariate statistical analyses: Environmental 

Management, v. 34, p. S71–S88.

Yong, S.T.Y., and Chen, Wenli, 2002, Modeling the relationship between land use and surface water quality: Journal of 

Environmental Management, v. 66, p. 377–393.
Data Files 49

Data Files

Geospatial datasets are available to download as Arc Info 

shapefiles (zipped using Winzip). Data files of attributes are 

available to download in Excel (version 2003) format and in 

tab-delimited text format. Each Excel workbook contains a 

data sheet and a sheet (named README) with variable name 

definitions and notes. 

Description Downloadable datafile and 

metadata

Section of 

report 

describing data

SAGT ERF1_2 

digital segmented 

network (geospatial dataset)

erf1_spar.zip (5.1 MB), 

erf1_spar.html

Hydrologic 

network of 

reaches and 

associated 

catchments

SAGT ERF1_2 

segmented catchments (geospatial 

dataset)

shed_cov.zip (13 MB), 

shed_cov.html

Hydrologic 

network of 

reaches and 

associated 

catchments

Catchment-level 

estimates of watershed and reach attributes evaluated 

for incremental 

catchments and 

reaches

Excel version: SAGT_

ERF1_input.xls.

zip (2.1 MB) (metadata included in the 

README sheet)

Textfile version:

SAGT_ERF1_input.txt

(2.1 MB), README_

SAGT_ERF1_input.txt

Watershed attributes, reach 

attributes

Estimates of watershed attributes 

accumulated for 

the total upstream 

watershed contributing to the reach 

segment

Excel version: SAGT_accumulatedfortotalwatershed.xls.zip (2.1 MB) 

(metadata included in the 

README sheet)

Textfile version:

SAGT_accumulatedfortotalwatershed.txt

(2.3 MB), README_

SAGT_ERF1_input.txt

Accumulation 

of catchmentlevel estimates 

of watershed 

attributes to estimates for the 

total upstream 

watershed

Monitoring sites, 

station characteristics, and nutrient 

load estimates

Excel version:

SAGT_monitoredload.xls

(700 KB) (metadata included in the README 

sheet)

Textfile version:

SAGT_monitoredload.txt

(28 KB), README_

SAGT_monitoredload.txt

Mean annual 

nitrogen and 

phosphorus 

load at stream 

monitoring 

sites

Routines used to modify nutrient-constituent concentration data for load estimation are available to download in SAS 

(version 9) format and in text format. 

Description Downloadable 

program file

Section of 

report 

describing data

Reformats the waterquality data file from 

modernized STORET 

(tilde-delimited) to 

a SAS datafile in 

the format used by 

Fluxmaster (more 

details provided in 

paragraphs following 

this table)

Reformat_ModSTORET_

WQdata.sas (42 KB)

Text version: Reformat_

ModSTORET_WQdata.txt (42 KB)

Review and 

revision of 

nutrient concentration 

results

Resolves the differences among data 

sources in the format 

or convention for recording results (more 

details provided in 

paragraphs following 

this table)

Convert_remarkcoding_

and_otherproblematic.

sas (16 KB) 

Text version: Convert_remarkcoding_and_

otherproblematic.txt

(16 KB)

Review and 

revision of 

nutrient concentration 

results

Assigns or calculates a 

value for a total nitrogen (TN) parameter 

code, P60000, and 

for a total phosphorus (TP) parameter 

code, P66500 (more 

details provided in 

paragraphs following 

this table)

Combine_nutrient_

constituents.sas

(8 KB)

Text version : Combine_

nutrient_constituents.

txt (8 KB)

Review and 

revision of 

nutrient concentration 

results

The file “Reformat_ModSTORET_WQdata.sas” reformats the water-quality data file from modernized STORET 

(tilde-delimited) to a tab-delimited file, interpreting information from several variables (characteristic name, sample fraction, and media) into an assignment of parameter code (pcode) 

following the convention used in Legacy STORET and in 

NWIS, and populating the associated remark code variable for 

results below detection. The tab-delimited file is then converted 

to a SAS datafile in the format (one line per sample) used by 

the load estimation program Fluxmaster (Schwarz and others, 

2006). Multiple stations may be included in the analysis.

The program “Convert_remarkcoding_and_otherproblematic.sas” resolves the differences among data sources in 

the format or convention for recording results, by revising the 
50 Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States

data records retrieved from Legacy and Modernized STORET 

to match the NWIS format or convention. (The load estimation program, Fluxmaster, is programmed to work with data 

coded using the NWIS convention.) This routine also corrects 

cases of obviously erroneous concentration results, such as 

extremely large values. 

Summary of changes for legacy STORET data records:

1. Replace the nonsense numbers (positive and negative) 

with missing values.

2. Replace the zero and negative values that indicate below 

detection with appropriate detection limit values, and set 

remark code to ‘<’.

3. Replace all remark codes that mean ‘<’ (K and U) with ‘<’.

4. Replace remark codes that mean ‘>’ (L) to ‘>’.

Summary of changes for modernized STORET data records:

1. For less than result for which detection-limit was not 

available in the retrieved data in order to populate the 

value field (P field) during reformatting: set value field 

equal to a reasonable estimate of detection limit (75 percentile of all detection limits reported in the SAGT project 

dataset from STORET, which can be obtained from distribution of values in the detection-limit field, or D field). 

2. Replace all remark codes that mean ‘<’ (U) with ‘<’.

The program “Combine_nutrient_constituents.sas” 

assigns or calculates a value for a total nitrogen (TN) parameter code, P60000, and for a total phosphorus (TP) parameter 

code, P66500. The code P60000 is assigned a value equal to 

P00600; or if P00600 is missing, it is calculated by combining 

total Kjeldahl nitrogen results and nitrate results (if available), 

or by combining ammonia nitrogen results, organic nitrogen 

results, and nitrate results. The code P66500 is assigned a 

value equal to P00665, or if P00665 is missing, it is calculated 

by combining dissolved and suspended phosphorus (if available, although this is rarely the case). The rules for combining 

results include how to handle the case of one or more of the 

constituents having censored values, and how to populate the 

remark code for the calculated parameter.
Prepared by:

USGS Publishing Network

Raleigh Publishing Service Center

3916 Sunset Ridge Road

Raleigh, NC 27607

For additional information regarding this publication, contact:

Anne B. Hoos, Hydrologist

USGS Tennessee Water Science Center

640 Grassmere Park, Suite 100

Nashville, TN 37211

email: abhoos@usgs.gov

Or visit the USGS National Water-Quality Assessment Program website at:

http://water.usgs.gov/nawqa/
Hoos and others—Data to Support Statistical Modeling of Instream Nutrient Load Based on Watershed Attributes, Southeastern United States—Open-File Report 2008–1163