Monitoring

station

Land to 

water transport

Urban

runoff

 Cultivated 

land runoff

Wastewater

discharges

Pasture

land runoff

Instream transport

and removal

Land to 

water transport

Monitoring

station

Urban

runoff

 Cultivated 

land runoff

 Cultivated 

land runoff

Wastewater

discharges

Pasture

land runoff

Instream transport

and removal

Monitoring

station

Atmospheric

deposition

Adapted from The State of the Nation’s Ecosystems 2008:

 Focus on Nitrogen 

The H. John Heinz III Center for Science,

Economics and the Environment 

Graphic by Grabhorn Studios

Benefits of Integrated Monitoring 

and Modeling

Successful management of our Nation’s 

water resources requires an integrated 

approach to environmental assessment that 

includes both monitoring and modeling. 

Monitoring provides direct observations, 

often over time, of water-quality properties 

and characteristics, whereas models are 

tools for interpreting these observations. 

Modeling results can advance understanding of the relation of water quality 

to human activities and natural processes 

that affect spatial variations in quality.

Specifically, models can be used to 

(1) establish links between water quality 

and constituent sources; (2) track the 

transport of constituents to streams and 

downstream receiving waters, such as 

estuaries; (3) assess the natural processes 

that attenuate constituents as they are 

transported from land and downstream; and 

(4) predict changes in water quality that 

may result from management actions or 

changes in land use.

Continued integration of monitoring 

and modeling is key to our future understanding and management of the Nation’s 

water quality. Modeling results can help in 

a variety of management decisions, including 

those related to contaminant-reduction and 

protection strategies across broad regions 

and decisions about future monitoring and 

assessments of streams that are highly 

vulnerable to environmental degradation. 

Modeling can help in meeting regulatory 

requirements, such as those related to 

nutrient-management strategies and the 

development of total maximum daily loads 

(TMDLs). Finally, modeling can help in 

identifying gaps and priorities in monitoring; 

including identifying monitoring that 

might be redundant or unnecessary. 

SPARROW Modeling

To support the need for water-quality 

modeling, USGS scientists developed a 

model that integrates monitoring data with 

landscape information. This model, known 

as SPARROW (SPAtially-Referenced 

Regression On Watershed attributes), is 

watershed based and designed for use in 

predicting long-term average values of 

water characteristics, such as concentrations and amounts of selected constituents 

that are delivered to downstream receiving 

waters. Statistical methods are used in 

SPARROW modeling to explain in-stream 

measurements of water quality (constituent

mass or load) in relation to upstream sources 

and watershed properties (soil characteristics, precipitation amounts, and land cover) 

that influence the transport of constituents 

to streams and their delivery to receiving 

water bodies, including estuaries (fig. 1).

Figure 1. Generalized major land-use features included in the 

SPARROW watershed model. Statistical methods are used to relate 

water-quality monitoring data to upstream sources and watershed 

characteristics that affect the fate and transport of constituents to 

streams, estuaries, and other receiving water bodies.

The information provided here is intended to assist water-resources managers with interpretation of the U.S. Geological 

Survey (USGS) SPARROW model and its products. SPARROW models can be used to explain spatial patterns in monitored 

stream-water quality in relation to human activities and natural processes as defined by detailed geospatial information. 

Previous SPARROW applications have identified the sources and transport of nutrients in the Mississippi River basin, 

Chesapeake Bay watershed, and other major drainages of the United States. New SPARROW models with improved accuracy 

and interpretability are now being developed by the USGS National Water Quality Assessment (NAWQA) Program for six 

major regions of the conterminous United States. These new SPARROW models are based on updated geospatial data and 

stream-monitoring records from local, State, and other federal agencies.

U.S. Department of the Interior

U.S. Geological Survey

Fact Sheet 2009–3019

March 2009

SPARROW MODELING—Enhancing Understanding of the 

Nation’s Water Quality

Printed on recycled paper
2

SPARROW models can be used to 

predict water-quality levels in streams 

across spatial scales ranging from smallsized watersheds (tens to hundreds of 

square kilometers [km2]) to large river 

drainages (several million km2). Simulation 

results can be used to identify atmospheric 

and land-based sources of constituents that 

affect water quality over large spatial scales 

or to estimate the origin and fate of constituents in streams and receiving bodies. In 

all cases, model estimates can be illustrated 

through detailed maps that provide information about constituent loadings at multiple 

scales for specific watersheds or geographic 

areas, especially in unmonitored areas.

SPARROW models integrate two data 

components—water-quality and streamflow 

data collected at numerous monitoring sites. 

These data are used to estimate the mass 

(annual load) of a constituent that is transported by the stream at each site. Geospatial 

data sets then are used to relate land-use 

features with load estimates, which provide 

information on constituent sources and 

natural factors that can affect constituent 

fate and transport (fig. 2). 

Constituent sources for the purpose of 

SPARROW modeling include estimates of 

nitrogen mass in atmospheric deposition, 

nutrients (nitrogen and phosphorus) in 

commercial fertilizer applied to agricultural land, and nutrients in runoff from 

urban and other land uses. Watershed 

characteristics that affect land-to-water 

delivery of nutrients include soil permeability, wetland area, land-surface slope, 

and mean annual climatic factors, such as 

precipitation and temperature.

Geospatial data sets continually 

advance and change as new geographic 

information becomes available or as spatial 

resolution improves. Consequently, published SPARROW models have improved 

over time as updated and refined data 

have been used. In some cases, special 

geospatial data sets reflect unique local 

and regional characteristics in SPARROW 

models. One such example is a geospatial 

data set that characterizes the phosphate 

content of surficial geologic materials; this 

information is useful in quantifying natural 

sources of phosphorus to streams, particularly in the southeastern United States.

The spatial framework for SPARROW 

modeling is provided by a digital network of 

stream and reservoir-reach segments that are 

linked to contributing watersheds that were 

delineated using a digital-elevation model. 

For each stream segment, average watershed 

characteristics, constituent-source estimates, 

and monitoring-site locations are referenced 

to this digital network. Digital referencing 

links load estimates at monitoring sites to 

upstream watershed characteristics. It also 

links individual constituent sources to the 

river network, so that the source contribution to stream loads can be tracked and 

evaluated as the constituent attenuates 

during transport downstream. Finally, using 

the spatial framework, SPARROW model 

estimates can be illustrated through detailed 

maps that provide information about constituent loads at multiple scales, from single 

stream basins to larger geographic areas, 

including estimates for areas where little or 

no monitoring information is available.

Calibration and Uncertainty

SPARROW models are calibrated by 

using a statistical procedure that estimates 

model coefficient values to minimize the 

error between predicted and observed 

values of annual constituent loads at fixed 

monitoring sites. The calibrated model 

includes only the explanatory variables that 

show statistically significant relations to 

spatial patterns in annual constituent loads. 

This feature of the model provides an 

objective means of evaluating the relative 

importance of various watershed characteristics and sources of constituents (soil 

type, geology, animal manure) as predictors 

of water-quality and constituent loads in 

streams, which helps to show, for instance, 

whether discharges from point sources, 

such as wastewater-treatment plants, are a 

stronger predictor of water quality and load 

than nonpoint-source loads, such as runoff 

from farm lands.

Statistical model calibration also 

provides a means for quantifying a level 

of certainty (or margin of error) in load 

estimates for streams. The level of certainty 

depends on the number of monitoring sites 

and the resolution of the spatial data used 

to calibrate the model, but the level of 

certainty generally increases as monitoring 

networks and the accuracy and resolution 

of geospatial data sets improve.

All models, including SPARROW, are 

imperfect representations of reality; thus, 

simulation results reflect uncertainties in the 

available spatial and monitoring data sets. 

Knowledge of model uncertainty is important in making environmental-management 

decisions, and the statistical basis of SPARROW provides a means of quantifying 

uncertainty (Robertson and others, 2009).

Figure 2. SPARROW models define spatial patterns in water quality, based on 

data collected at numerous monitoring sites (A). Estimates of constituent loads 

at these sites are related to constituent sources, land use, and other factors 

that affect water quality (soils, precipitation), as well as stream characteristics 

(channel, velocity) (B). Based on these relations, the model is used to predict 

water quality in unmonitored stream reaches for an entire region (C).

A. Monitoring data, 375 sites B. Geographic data layers

Land use

Soils

Precipitation

Stream and

 reservoir water velocity

C. Model predictions, 62,000 stream reaches
3

Knowledge of model uncertainties can be 

used to prioritize future monitoring and 

assessment activities. For example, under 

or over predictions of nutrient loads in a 

watershed may indicate that additional 

monitoring and assessment are needed 

to improve models and clarify local 

management strategies. 

Understanding SPARROW 

Predictions 

Several characteristics of the 

SPARROW model must be taken into 

account when applying the model results 

to management decisions and water-quality 

assessments. Important among these are 

that the SPARROW model (1) focuses on 

spatial rather than temporal detail; 

(2) integrates long-term discharge and 

water-quality records to calculate annual 

stream-load values used for calibration; 

(3) includes only the water-quality factors 

that are represented in available geospatial 

data and correlated statistically with stream 

load; and (4) favors water-quality comparisons across broad regions as opposed to 

within single catchments.

How are stream-load measurements that 

vary over time used to calculate a mean 

annual load for SPARROW models? 

The mean annual stream-load 

calculation integrates long-term discharge 

and water-quality data to estimate a value 

that is representative of the water-quality 

conditions during a base year and normalized 

to average hydrologic conditions (Schwarz 

and others, 2006). The adjustment of the load 

to a base year accounts for differences in 

monitoring record lengths and sample sizes, 

and ensures that a contemporaneous period 

is used among all sites being considered. 

Normalizing to average hydrologic conditions ensures that spatial patterns in rainfall 

in any single year do not interfere with 

identifying the environmental factors that 

affect water quality over longer periods. 

Based on this approach, the stream-load 

values used to calibrate SPARROW models 

can be interpreted as the mean annual load 

that would have occurred in a specified base 

year if mean annual-flow conditions, based 

on long-term flow data, had prevailed during 

that year. Emphasis on long-term mean 

annual hydrology enhances the capability 

of the model to identify major constituent 

sources and watershed processes that affect 

the long-term supply, transport, and fate of 

water-quality constituents in watersheds.

magnitude. Thus, the SPARROW mean 

load is disproportionately influenced 

by high-flow data. SPARROW load 

predictions, therefore, generally are more 

indicative of the spatial variability in 

stream load that occurs during high-flow 

periods than during other periods of the 

year. This is supported by the generally 

close agreement observed between 

SPARROW mean annual loads and mean 

spring (March to May) loads. 

Why don’t some SPARROW models 

account for factors I consider to be 

important in my watershed?

Some watershed characteristics that 

may be critical in understanding waterquality conditions are not well documented 

for large watersheds that cover multistate 

regions, such as those typically modeled 

with SPARROW. Examples include landmanagement or conservation practices, 

manure applications during recent years, 

and the effects of urban-contaminant 

sources, such as combined sewer overflows. Because data on many of these 

characteristics are not currently available 

for use in SPARROW modeling, the role 

of these data in determining constituent 

loads cannot be explicitly evaluated by the 

model. The effects of such characteristics, 

however, can be evaluated by extension 

through other variables with which they are 

correlated spatially (for example, effects 

of combined sewer overflows as reflected 

by urban land cover), or explored indirectly by evaluating model uncertainty. In 

general, if these variables are not correlated 

spatially with other variables already in a 

SPARROW model, then their influence will 

be reflected in the margin of error associated with the SPARROW predictions. 

How is a base year selected for 

SPARROW models?

Two factors are considered in selecting 

the base year. First, the base year typically 

falls within the period of greatest overlap in 

the water-quality and discharge monitoring 

records. This overlap helps to ensure temporal consistency in the mean annual load 

calculations at the calibration monitoring 

sites. Second, the base year is selected for 

consistency with the available national and 

regional geospatial data sets. Major effort 

is required to produce the geospatial data at 

high spatial resolutions; thus, the data are 

limited to specific years and reported only 

periodically. For example, national land-use 

and land-cover data suitable for large-scale 

SPARROW modeling were produced by the 

USGS only for 1992 and 2002. Agricultural 

data on animal nutrients and crop land area 

and production are available only every 

5 years (for example, 1992, 1997, 2002) 

from the U.S. Department of Agriculture 

census. Thus, the selected base years for 

past and current SPARROW models typically have reflected the availability of the 

most critical geospatial data sets. 

Do the mean annual load values used to 

calibrate SPARROW models adequately 

account for brief high-flow periods that 

can carry most of the annual load of a 

chemical constituent?

Measurements of concentration and 

discharge collected at each site throughout 

the year and across many years are used 

to calculate mean load in the SPARROW 

model. In these calculations, measurements 

from annual high-flow periods, such as 

spring runoff months, typically receive 

greater weight because of their large 

“The Kansas Department of Health and Environment (KDHE) 

utilized the results of the SPARROW model for Mississippi watershed 

nitrogen transport as the unifying theme behind the state’s Nutrient 

Reduction Plan. The visual output of the model is a powerful tool 

for explaining nitrogen impacts on a watershed-by-watershed basis 

in Kansas as well as all Mississippi River watershed states. We look 

forward to continued refinement of the SPARROW modeling efforts to 

identify high priority watersheds for mitigation of nitrogen and other 

pollutants. We are unaware of any similar projects that have been as 

valuable in helping to identify nitrogen contributions to the Gulf of 

Mexico, and upstream states on a watershed-by-watershed basis.”

Michael B. Tate, P.E., Chief Bureau of Water, Technical Services Section, 

Kansas Department of Health and Environment, 2005
4

Why are the SPARROW model 

predictions for the stream on my 

property different than expected?

Inconsistency in levels of detail and 

other scaling issues commonly associated

with geospatial data sets can affect model 

construction and performance. Geospatial

data often are available only at the county 

level or even coarser scales. In these 

instances, conditions in relatively small 

streams that are affected by local environmental conditions or human activities

may not be represented well by the 

model. The performance of the model 

also can be limited by the scale or relative

density of the monitoring network 

used for calibration. Small watersheds, 

especially first-order headwater streams, 

are monitored relatively infrequently and 

may be underrepresented in calibration 

data sets. Thus, model estimates for stream 

reaches draining small watersheds likely 

have higher levels of uncertainty than the 

statistics reported by the model. Because 

of this, model estimates frequently are 

summarized and reported only for larger 

watersheds that consist of multiple stream 

reaches (Alexander and others, 2008; 

Robertson and others, 2009). In general, 

SPARROW models illustrate the broad 

spatial patterns of water quality well, but 

may be less accurate for a single stream 

because of limitations in the underlying 

data sets.

SPARROW Models—Current 

and Future

The SPARROW modeling approach 

originally was developed and applied to 

assess nutrient-source contributions, transport, and water-quality conditions at the 

national scale for the base year 1987 (Smith 

and others, 1997). Subsequently, refined 

national models were developed to simulate 

nitrogen and phosphorus loading for the 

year 1992, and these models have been used 

to estimate nutrient sources and transport in 

the Mississippi River basin (Alexander and 

others, 2008; Robertson and others, 2009).

SPARROW models also have been 

developed for other regions across the 

country, including nutrient models for 

the Chesapeake Bay watershed (Preston 

and Brakebill, 1999), selected North 

Carolina coastal drainages (McMahon 

and others, 2003), and the New England 

region (Moore and others, 2004). In 

addition, a salinity model of the Southwest 

was developed to estimate the spatial 

distribution of total dissolved solids and 

natural and human factors controlling 

salinity throughout the region (Anning 

and others, 2007). 

These regional models provide a 

more detailed focus on the factors that 

influence water quality locally but may 

not be important factors everywhere in 

the country. Regional models also 

provide more intensive water-quality 

data compilation, which often includes 

more monitoring sites, thus leading to 

enhanced calibration and greater precision 

in model predictions than can be achieved 

at larger scales. 

Water-quality models 

are considered either 

deterministic or statistical 

in basic form and approach 

and vary in temporal and 

spatial scale, and process 

detail and complexity 

(Singh, 1995). Deterministic 

models generally estimate 

water-quality conditions by balancing 

mass or energy for explicit physical 

environmental processes. Because of 

underlying computational and structural complexity, these models usually 

are limited in application to small sites 

or single stream reaches and do not 

account directly for uncertainty or error. 

The Hydrologic Simulation ProgramFortran (HSPF) and Soil and Water 

Assessment Tool (SWAT) represent 

some of the more clearly deterministic 

approaches, although these models 

can include statistical components. 

These models commonly are used by 

managers to simulate temporal variations in water quality by simulating 

rainfall-runoff processes and estimating 

contaminant transport from watersheds 

to individual monitoring sites.

Statistical models tend to be less 

complex in structure and estimate 

water-quality conditions by relating 

field observations to causative environmental factors. As such, statistical 

models quantify and attempt to 

minimize uncertainty over a specified 

region. These models may be based on 

a variety of underlying forms, including 

artificial neural networks (Conrads 

and Roehl, 2007) and simple–multiple 

or linear–nonlinear regression models 

(Helsel and Hirsch, 2002). 

Models at either end of this 

spectrum are valuable in assessing 

water quality. Deterministic models 

can offer insight into the role of key 

environmental processes, such as 

denitrification, and can help to evaluate 

the effectiveness of proposed conservation practices or policies. Statistical 

models can improve understanding of 

the relation of water quality to various 

environmental factors and are amenable 

to broad regional comparisons, uncertainty analysis, and risk assessment.

How does SPARROW compare with other water-quality models?

SPARROW modeling 

is a relatively new approach 

to water-quality modeling 

and has some attributes 

of both deterministic 

and statistical models 

(Schwarz and others, 2006). 

SPARROW models are 

deterministic in nature in that 

they incorporate nonlinear physicallybased functions, mass-balance requirements, and simulations of certain 

physical processes (attenuation). 

SPARROW models are statistical in 

nature in the way they are calibrated—

using established statistical procedures 

designed to optimize model fit by 

minimizing error between model predictions and measured water-quality 

data. Although purely deterministic 

models can be calibrated by using 

these same statistical procedures, the 

task generally is more challenging 

than with SPARROW because of the 

differences in complexity (number of 

coefficients and processes). Because 

calibration of deterministic models 

becomes increasingly difficult 

for large watersheds, such as the 

Mississippi River basin and its major 

tributaries, these models rarely are 

applied at such scales.

Statistical Deterministic

ANN

(Artificial Neural

Networks)

Kriging

Regression

(steady rate)

GWLF (Generalized

Watershed Loading Function

SPARROW

(spatially referenced

nonlinear regression)

HSPF

(Hydrologic 

Simulation

Program-Fortran)

SWAT (Soil Water

Assessment Tool)
5

The NAWQA Program has adopted 

SPARROW modeling to assess nutrient 

conditions in six large regions across 

the Nation for the base year 2002 (fig. 3 

on p. 6). The findings from these regional 

models are used to compare nutrient sources 

and watersheds that contribute elevated 

nutrient loads to estuaries and other receiving waters, such as the South Atlantic and 

Gulf of Mexico, inland and coastal waters 

of the Northeast, the Upper Mississippi 

and Great Lakes, and Puget Sound. These 

models will be useful in evaluating streamwater quality for management or regulatory 

objectives. The 2002 regional models 

include nutrient-monitoring data from 

local, State, and other federal agencies, 

which substantially increases the number 

of model-calibration sites. For instance, 

the South Atlantic–Gulf model includes 

data from 321 monitoring sites, which 

is 233 sites more than were used in the 

national model (based on USGS monitoring 

only) for the same region. By including 

more sites, preliminary regional models generally display a lower degree of uncertainty. 

For the regional SPARROW models, 

2002 base-year data are used to describe 

atmospheric deposition, commercial 

fertilizer applied to agricultural land, 

animal-manure production, point-source 

discharges, population density, and land 

cover (urban, agricultural, and forested). 

The data describing many of these nutrient sources and watershed characteristics 

have been refined since earlier SPARROW 

models. The combination of more calibration sites and refined geospatial data 

provides significant improvement over 

previous models in prediction accuracy 

and the identification of regional nutrient 

sources and transport factors.

SPARROW models are developed and applied to assess a variety 

of water-quality constituents over a range of spatial scales, including 

the national scale. For example, SPARROW nutrient models developed 

at the national scale provide estimates of the delivery of loads from 

62,000 stream reaches contributing to the Nation’s major rivers and estuaries (fig. 2; Smith and others, 1997; Alexander and others, 2008). These 

models improve understanding of sources, transport, and delivery of 

nutrients to downstream reservoirs and estuaries and provide additional 

insight in many areas, including the role of headwater streams in downstream water quality, natural background concentrations of nutrients 

in streams, atmospheric sources of nitrogen to major estuaries, and the 

effect of stream-channel size on nitrogen delivery to the Gulf of Mexico.

A recently developed national-scale SPARROW model (Alexander 

and others, 2008) provides estimates of nutrient sources and transport 

in the 3-million-km2 Mississippi River basin and estimates of nutrient 

transport and delivery from the Nation’s largest watershed to the Gulf 

of Mexico. Findings from this model indicate that agriculture is the 

predominant nutrient source to the Gulf. About 52 percent of the nitrogen 

entering the Gulf from the Mississippi River basin is from lands cultivated in corn and soybeans. Animal manure on pasture and rangelands 

combined with commercial fertilizer applied to crops are the largest 

contributors of phosphorus. Nonagricultural sources are also important 

contributors of nutrients to the Gulf of Mexico. About 30 percent of the 

nitrogen delivered to the Gulf is from regional atmospheric deposition 

and various urban sources that may include nitrogen from wastewater discharges, septic systems, and emissions from power plants and vehicles.

SPARROW models can be used to track nutrient delivery 

(A) locally to the outlets of inland watersheds (incremental yield) and 

(B) regionally over longer distances to downstream water bodies. This 

example illustrates the use of a national SPARROW model to map 

delivery of phosphorus yields (A) locally and (B) regionally to the Gulf 

of Mexico. Differences in the spatial patterns of phosphorus yield are 

attributable to differences in phosphorus removal during transport to 

downstream receiving waters. In certain regions, the maps highlight 

watersheds in which sources are likely to have a greater effect on local 

water-quality conditions than on hypoxia in the Gulf. For example, although phosphorus inputs from watersheds above reservoirs in the Tennessee Valley are known 

to create water-quality concerns in some of the reservoirs, only small quantities of 

phosphorus are delivered from these watersheds to the Gulf of Mexico. The delivered 

yields of watersheds in these areas in map B are smaller than the yields for the same 

watersheds in map A. Therefore, phosphorus sources in these watersheds are unlikely 

to influence Gulf hypoxia.

SPARROW: Estimating Water-Quality Conditions at National and Regional Scales

GULF OF MEXICO

B. Yield delivered to Gulf of Mexico

A. Incremental yield

Total Phosphorus

Less than 1

Yield, in kilograms per square 

kilometer per year

EXPLANATION

1 to 25

25 to 50

50 to 100

100 to 200

200 to 800
6

 Although development of SPARROW 

models has advanced the understanding 

of water-quality conditions, considerable 

room for improvement still exists. Ongoing refinements of geospatial data sets 

will improve the precision of future models. 

Also, improvements in the modeling infrastructure will allow insights to be gained by 

the development of multiyear and seasonal 

models. Finally, the distributions of many 

important constituents remain to be considered in a SPARROW analysis. 

References

Alexander, R.B., Smith, R.A., 

Schwarz, G.E., Boyer, E.W., Nolan, J.V., 

and Brakebill, J.W., 2008, Differences 

in phosphorus and nitrogen delivery to 

the Gulf of Mexico from the Mississippi 

River basin: American Chemical Society, 

Environmental Science & Technology, 

v. 42, no. 3, p. 822–830, available online 

at http://pubs.acs.org/doi/abs/10.1021/

es0716103.

Anning, D.W., Bauch, N.J., Gerner, S.J., 

Flynn, M.E., Hamlin, S.N., Moore, S.J., 

Schaefer, D.H., Anderholm, S.K., 

and Spangler, L.E., 2007, Dissolved 

solids in basin-fill aquifers and streams 

in the southwestern United States: 

U.S. Geological Survey Scientific 

Investigations Report 2006–5315, 

187 p., available online at http://pubs.

er.usgs.gov/usgspubs/sir/sir20065315.

Preston, S.D., and Brakebill, J.W., 1999, 

Application of spatially reference 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., available online at 

http://pubs.er.usgs.gov/usgspubs/wri/

wri994054.

Robertson, D.M., Schwarz, G.E., Saad, D.A.,

and Alexander, R.B., 2009, Incorporating uncertainty into the ranking of 

SPARROW model nutrient yields from 

Mississippi/Atchafalaya River basin 

watersheds: Journal of the American 

Water Resources Association, v. 45, 

no. 2, p. 534–549, available online at 

http://www3.interscience.wiley.com/cgibin/fulltext/122278551/PDFSTART.

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 Report, book 6, chap. B3, 

248 p., available online at http://pubs.

er.usgs.gov/usgspubs/tm/tm6B3.

Singh, V.P., ed., 1995, Computer models 

of watershed hydrology: Highlands 

Ranch, CO, Water Resources Publications, LLC, 1,130 p. 

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

1997, Regional interpretation of waterquality monitoring data: Water Resources 

Research, v. 33, p. 2781–2798, available online at http://www.agu.org/pubs/

crossref/1997/97WR02171.shtml.

Conrads, P.A., and Roehl, E.A., Jr., 2007, 

Analysis of salinity intrusion in the 

Waccamaw River and Atlantic Intracoastal Waterway near Myrtle Beach, 

South Carolina, 1995–2002: U.S. 

Geological Survey Scientific Investigations Report 2007–5110, 41 p., 2 apps., 

available online at http://pubs.er.usgs.

gov/usgspubs/sir/sir20075110.

Helsel, D.R., and Hirsch, R.M., 2002, 

Statistical methods in water resources: 

U.S. Geological Survey Techniques of 

Water-Resources Investigations, book 4, 

chap. A3, 523 p., 4 apps., available 

online at http://pubs.usgs.gov/twri/

twri4a3.

McMahon, Gerard, Alexander, R.B., and 

Qian, S., 2003, Support of total maximum daily load programs using spatially 

referenced regression models: American 

Society of Civil Engineers, Journal of 

Water Resources Planning and Management, v. 129, p. 315–329., available 

online at http://ascelibrary.aip.org/

getabs/servlet/GetabsServlet?prog=nor

mal&id=JWRMD500012900000400031

5000001&idtype=cvips&gifs=Yes.

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, 50 p., available online at 

http://pubs.usgs.gov/sir/2004/5012/.

Richard Alexander, ralex@usgs.gov

Stephen Preston, spreston@usgs.gov

Information on SPARROW 

modeling applications, data,

and documentation can be 

accessed at http://water.usgs.gov/

nawqa/sparrow/

For more information contact:

By Stephen D. Preston, Richard B. Alexander, 

Michael D. Woodside, and Pixie A. Hamilton

Figure 3. The SPARROW modeling approach is being applied to assess 

nutrient conditions in 2002 in six large regions across the Nation. 

New England 

 and Mid-Atlantic

South Atlantic–Gulf 

 and Tennessee

Great Lakes, Ohio, Upper Mississippi, 

Missouri and Souris–Red–Rainy

Lower Mississippi,

Arkansas–White–Red,

and Texas–Gulf

Pacific Northwest