Application of the SPARROW Model to Assess 

Surface-Water Nutrient Conditions and

Sources in the United States Pacific Northwest

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2013–5103
Cover: General land-use map of the United States Pacific Northwest (see figure 1 in 

report for detail).
Application of the SPARROW Model to Assess 

Surface-Water Nutrient Conditions and

Sources in the United States Pacific Northwest

By Daniel R. Wise and Henry M. Johnson

Scientific Investigations Report 2013–5103

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director 

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

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Suggested citation:

Wise, D.R., and Johnson, H.M., 2013, Application of the SPARROW model to assess surface-water nutrient 

conditions and sources in the United States Pacific Northwest: U.S. Geological Survey Scientific Investigations 

Report 2013–5103, 32 p., http://pubs.usgs.gov/sir/2013/5103/.
iii

Contents

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

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

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

Description of the Modeling Domain.................................................................................................2

Methods...........................................................................................................................................................4

The SPARROW Model..........................................................................................................................4

Model Input Data Sets .........................................................................................................................4

Surface-Water Drainage Network............................................................................................4

Calibration Data Set ....................................................................................................................4

Catchment Attribute Data...........................................................................................................5

Nutrient Sources.................................................................................................................5

Boundary Loads ..................................................................................................................5

Land-to-Water Delivery ....................................................................................................5

Nutrient Loss in Free-Flowing Streams and Impoundments ................................................6

Model Calibration..................................................................................................................................6

Analysis of Model Predictions............................................................................................................6

Results .............................................................................................................................................................7

Model Calibration..................................................................................................................................7

Model Predictions...............................................................................................................................10

Discussion of Results..................................................................................................................................16

Conclusions...................................................................................................................................................17

Summary........................................................................................................................................................18

Acknowledgments.......................................................................................................................................18

References Cited..........................................................................................................................................18

Appendix A. Compilation of Model Input Data ....................................................................................21

Appendix B. Model Calibration and Prediction Results.....................................................................28
iv

Figures

1. Map showing major drainages, Hydrologic Unit Code (HUC6) watersheds, and

primary land cover types in the United States Pacific Northwest ......................................3

2. Map showing largest local sources of total nitrogen for National Hydrography

Dataset catchments in the United States Pacific Northwest (2002 conditions)..............12

3. Map showing largest local sources of total phosphorus for National Hydrography

Dataset catchments in the United States Pacific Northwest (2002 conditions)..............13

4. Graph showing results for the Willamette River, Oregon, from the total nitrogen

National Hydrography Dataset SPARROW model developed for the United States

Pacific Northwest (2002 conditions)........................................................................................15

5. Graph showing results for the Snake River, Washington, from the total phosphorus

National Hydrography Dataset SPARROW model developed for the United States

Pacific Northwest (2002 conditions)........................................................................................15

Tables

1. Definitions for abbreviations used for six-digit Hydrologic Unit Code (HUC6)

watersheds in the United States Pacific Northwest ..............................................................2

2. Sources of water-quality data used to estimate total nitrogen and total phosphorus

calibration stream loads for the National Hydrography Dataset SPARROW models

developed for the United States Pacific Northwest...............................................................5

3. Model Statistics for the total nitrogen National Hydrography Dataset SPARROW

model developed for the United States Pacific Northwest...................................................8

4. Model statistics for the total phosphorus National Hydography SPARROW model

developed for the United States Pacific Northwest...............................................................9

5. Median incremental yields of total nitrogen and total phosphorus for six-digit

Hydrologic Unit Code (HUC6) watersheds in the United States Pacific Northwest

(2002 conditions).........................................................................................................................10

6. Largest local sources of total nitrogen and total phosphorus and corresponding

median incremental yields for National Hydrography Dataset catchments in the

United States Pacific Northwest (2002 conditions) ..............................................................14

7. Summary statistics for source shares of total nitrogen and total phosphorus load

for National Hydrography Dataset stream reaches in the United States Pacific

Northwest (2002 conditions).....................................................................................................14

8. Comparison of fit statistics for the National Hydrography Dataset and RF1

SPARROW models developed for the United States Pacific Northwest...........................16
v

Conversion Factors, Datum, and Abbreviations and 

Acronyms

Conversion Factors

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 0.0002471 acre 

square kilometer (km2) 247.1 acre

square kilometer (km2) 0.3861 square mile (mi2)

Mass

kilogram per year (kg/yr) 2.205 pound per year (lb/yr)

Application rate

kilograms per hectare per year

[(kg/ha)/yr]

0.8921 pounds per acre per year

[(lb/acre)/yr]

kilograms per square kilometer per 

year [(kg/(km2)/yr]

0.008921 pounds per acre per year 

[(lb/acre)/yr]

Flow

cubic meter per second (m3/s) 35.32 cubic foot per second (ft3

/s)

Datum

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

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

Abbreviations and Acronyms

CLRW Clearwater River basin

CMAQ Community Multiscale Air Quality

HUC6 six-digit Hydrologic Unit Code

MCOL Middle Columbia River basin

NAWQA National water-Quality Assessment Program

NHD National Hydrography Data set

NLCD National Land Cover Database

NLLS nonlinear least squares regression

NOCR northern Oregon coast

NPDES National Pollutant Discharge Elimination System

PNW Pacific Northwest

RMSE root mean squared error

SPARROW Spatially Referenced Regression on Watershed attributes model

SPOK Spokane River basin

TMDL Total Maximum Daily Load

TN total nitrogen

TP total phosphorus

USEPA U.S. Environmental Protection Agency

USGS U.S. Geological Survey

WACR Washington coast 

Conversion Factors, Datum, and Abbreviations and 

Acronyms–Continued
Abstract

The watershed model SPARROW (Spatially Referenced 

Regressions on Watershed attributes) was used to estimate 

mean annual surface-water nutrient conditions (total nitrogen 

and total phosphorus) and to identify important nutrient 

sources in catchments of the Pacific Northwest region of 

the United States for 2002. Model-estimated nutrient yields 

were generally higher in catchments on the wetter, western 

side of the Cascade Range than in catchments on the drier, 

eastern side. The largest source of locally generated total 

nitrogen stream load in most catchments was runoff from 

forestland, whereas the largest source of locally generated 

total phosphorus stream load in most catchments was either 

geologic material or livestock manure (primarily from 

grazing livestock). However, the highest total nitrogen and 

total phosphorus yields were predicted in the relatively small 

number of catchments where urban sources were the largest 

contributor to local stream load. Two examples are presented 

that show how SPARROW results can be applied to large 

rivers—the relative contribution of different nutrient sources 

to the total nitrogen load in the Willamette River and the 

total phosphorus load in the Snake River. The results from 

this study provided an understanding of the regional patterns 

in surface-water nutrient conditions and should be useful to 

researchers and water-quality managers performing local 

nutrient assessments.

Introduction 

The SPARROW (Spatially Related Regressions on 

Watershed attributes) model was used to estimate mean 

annual total nitrogen (TN) and total phosphorus (TP) loads 

in surface-waters across the Pacific Northwest (PNW) 

region of the United States. This modeling effort used 

the National Hydrography Dataset Plus (NHD) (Horizon 

Systems, 2013) as a hydrologic framework and serves as a 

refinement of SPARROW models of the PNW that used the 

RF1 (River Reach File 1) hydrologic framework (Brakebill 

and others,2011; Wise and Johnson, 2011). In addition to 

the hydrologic network, many of the catchment attributes 

that were compiled for the RF1 SPARROW models were 

refined for the new models. Using the NHD as the hydrologic 

framework for SPARROW provided predictions for a 

greater number of stream reaches compared to the RF1 

hydrologic network. The PNW NHD SPARROW models 

included predictions for 232,811 stream reaches compared to 

12,039 stream reaches for the PNW RF1 models.

Surface-water nutrient enrichment has been identified 

as a water-quality problem across the PNW (Wise and 

Johnson, 2011) and modeling plays a central role in waterquality management by providing a means for predicting 

water-quality conditions and assessing the effectiveness of 

proposed improvement strategies (National Research Council, 

2001). The results from the PNW NHD SPARROW models 

Application of the SPARROW Model to Assess

Surface-Water Nutrient Conditions and

Sources in the United States Pacific Northwest

By Daniel R. Wise and Henry M. Johnson
2 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

will allow environmental managers and other stakeholders 

to identify the sources that contribute the largest amounts 

of nutrients to downstream waters and to evaluate nutrient 

reduction scenarios. The inclusion of smaller streams and 

headwater reaches in the model simulation results compared 

to the RF1 models also will allow for refined analyses of 

water-quality issues.

Purpose and Scope

This report describes the methods that were used to 

assess surface-water nutrient conditions in the PNW and to 

present the results from that assessment. The objectives of 

the assessment were (1) to calibrate TN and TP SPARROW 

models for the PNW using the NHD stream network; (2) to 

use the calibrated models to estimate mean annual nutrient 

conditions; and (3) to quantify the relative contribution of 

different nutrient sources to instream nutrient loads.

Description of the Modeling Domain

The domain of the PNW NHD SPARROW models 

covered about 708,000 km2, but did not include the area of 

the Columbia River basin (about 106,000 km2) that drains 

the Canadian part of the river basin. In the United States, the 

domain covered parts of eight states and included five major 

regions (fig. 1; table 1): Pacific Coast, West Side Basins, 

Columbia River Basin, Snake River Basin, and Oregon Closed 

Basins, and 22 level three, six-digit Hydrologic Unit Code 

(HUC6) watersheds (Seaber and others, 1987). The Pacific 

Coast region is characterized by steep, forested watersheds 

that drain directly to the Pacific Ocean. The West Side 

Basins region lies between the Cascade Range and the Coast 

Range (including the Olympic Mountains in Washington). 

Although most of this region is forested, it contains most of 

the population in the PNW model domain (including Seattle 

and Portland) as well as large areas of agricultural land. The 

Columbia River Basin and Snake River Basin regions are 

dominated by sparsely vegetated, rocky areas, high desert 

steppe, semi-arid forests, and areas of intensive agricultural 

production. The Oregon Closed Basins region is characterized 

by alternating regions of narrow, uplifted mountains, flat arid 

plains, playas, and alkali lakes. This is the least populated 

region in the PNW model domain and contains little forestry 

or agricultural activity. In 2001, scrub and grassland covered 

43 percent of the modeling domain, forest covered 40 percent, 

agriculture covered 9 percent, and developed land covered 

7 percent. The remaining 1 percent was various minor land 

cover types (Homer and others, 2004).

Table 1. Definitions for abbreviations used for six-digit 

Hydrologic Unit Code (HUC6) watersheds in the United States 

Pacific Northwest.

Region HUC6 

abbreviation

HUC6

watershed

Pacific Coast WACR Washington Coastal Rivers

NOCR Northern Oregon Coastal Rivers

SOCR Southern Oregon Coastal Rivers

West Side Basins PUGT Puget Sound

LCOL Lower Columbia River

WILL Willamette River

Columbia River 

Basin

KOOT Kootenai River

PDOR Pend Oreille River

SPOK Spokane River

UCOL Upper Columbia River

YAKI Yakima River

JDAY John Day River

DESC Deschutes River

MCOL Middle Columbia River

Snake River 

Basin

SNKH Snake River Headwaters

USNK Upper Snake River

MSBS Middle Snake–Boise Rivers

MSPW Middle Snake–Powder Rivers

SALM Salmon River

CLRW Clearwater River

LSNK Lower Snake River

Oregon Closed 

Basins

ORCB Oregon Closed Basins
Introduction 3

tac13-0821_fig01

ORCB

SOCR

PUGT

WACR

LCOL

MCOL

JDAY

YAKI

UCOL

MSBS

MSPW

USNK

SNKH

CLRW

SALM

PDOR

KOOT

SPOK

DESC

LSNK

NOCR

WILL

Oregon

Closed 

Basins

Columbia

Basin

Snake

River

River

Basin

West

Basins

Side

Pacific

Coast

Seattle Spokane

Portland

Boise

124° 122° 120° 118° 116° 114° 112° 110°

48°

46°

44°

42°

Base map modified from USGS and other U.S. agency and Canadian national digital data sources at 

various scales. Projection: Albers, SP1: 29.5, SP2: 45.5, LO: 23.0, CM: -96.0, Datum: North American 

Datum of 1983.

0 100 200 KILOMETERS

0 100 200 MILES

CALIFORNIA

BRITISH COLUMBIA

OREGON

WASHINGTON

NEVADA UTAH

WYOMING

MONTANA

IDAHO

ALBERTA

Pacific Ocean Pacific Ocean

Pacific Ocean Pacific Ocean

Willamette RWillamette R

Columbia River Columbia River

Snake

River

Snake

River

Clark ForkClark Fork

C

A

S

C

A

D

E

R

A

N

G

E

R

O

C

K

Y

M

O

U

N

T

A

I

N

S

CALIFORNIA

BRITISH COLUMBIA

OREGON

WASHINGTON

NEVADA UTAH

WY

MONTANA

IDAHO

ALBERTA

Portland

Salem

Albany Corvallis

Eugene Springfield

Seattle

Spokane

Boise

King Hill

Twin Falls

Idaho

Falls

UNITED

STATES

CANADA

12301933 12355500

12400520

60A070 49A190

Boundary calibration station

EXPLANATION

Primary land cover type

Scrub and grassland

Forestland

Agriculture

Developed

UCOL HUC Level 6 watershed and

name—full name in table 1.

Basin Major drainage

12301933 Kootenai River below Libby Dam near Libby, MT USGS

12355500 N.F. Flathead River near Columbia Falls, MT USGS

12400520 Columbia River at Northport, WA USGS

60A070 Kettle River near Barstow, WA WADOE

49A190 Okanogan River at Oroville, WA WADOE

Station No. Station name Agency

USGS = U.S. Geological Survey

WADOE = Washington Department of Ecology

Figure 1. Major drainages, Hydrologic Unit Code (HUC6) watersheds, and primary land cover types in the United States Pacific 

Northwest.
4 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

Methods

The SPARROW Model

The SPARROW model is a hybrid statistical and 

mechanistic model for estimating the movement of mass 

through the landscape under long-term, steady state 

conditions (Schwarz and others, 2006). The model uses data 

describing catchment attributes (nutrient sources, landscape 

characteristics, and stream and water body properties) to 

explain the spatial variation in measured, mean annual stream 

load (expressed as kilograms per year). The measured, mean 

annual stream loads are the dependent variable (the calibration 

data set) for the model and the catchment attributes are the 

explanatory variables. In this report, the variables representing 

nutrient sources are called nutrient source terms and the 

variables representing the delivery of nutrients from land 

to water are called delivery terms. A calibrated SPARROW 

model can be used to predict water-quality conditions 

throughout a surface-water network, including areas where no 

water-quality data exists.

Model Input Data Sets

The input data sets used for this study were similar to 

those used to develop the RF1 SPARROW models for the 

PNW (Wise and Johnson, 2011). An important structural 

change from the earlier models was the use of the National 

Hydrography Dataset Plus (NHDPlus) as the surfacewater drainage network for the models rather than the RF1 

hydrologic network. Refinements also were made to many of 

the input data sets, including the calibration data set of mean 

annual nutrient loads, the estimates of nutrient sources, and 

the accounting of irrigation diversions, power returns, and 

discharge from large spring complexes.

Surface-Water Drainage Network

The NHD Plus Version 2 for Hydroregion 17 (Horizon 

Systems, 2013) was used to represent the surface-water 

drainage network in the models developed for this study. 

Hereafter, this data set will be referred to as the NHD. The 

NHD is a comprehensive set of digital spatial data that 

contains information about surface water features such as 

lakes, ponds, streams, and rivers (Simley and Carswell, 

2009). The surface water features represented in the NHD 

largely correspond to the features on 1:100,000 scale USGS 

topographic maps. 

The NHD for the PNW is divided into 232,811 reaches, 

which vary in size from small, ephemeral streams that can 

go years without streamflow to the Columbia River with a 

mean annual streamflow of 9,575 cubic meters per second 

near its confluence with the Pacific Ocean (U.S. Geological 

Survey, 2012). The NHD identifies the incremental catchment 

for each reach. An incremental catchment is defined as the area 

that drains directly to a reach without passing through another 

reach. Most reaches in the NHD represent streams or inland 

water bodies, such as lakes and reservoirs. However, some 

reaches represent coastlines or closed basins, which do not 

have a surface water connection to other reaches in the NHD. 

In building the hydrologic framework for the SPARROW 

models, reaches representing streams, inland water bodies, 

and coastlines were retained, but reaches representing closed 

basins were eliminated from the network. The NHD contains 

minimal information on stream reaches and catchments in 

Canada, but does provide sufficient information to properly 

route surface water into the United States.

Irrigation and power networks in the PNW divert 

large amounts of water from streams and reservoirs, and 

these diversions needed to be accounted for to properly 

estimate nutrient transport through surface waters. The PNW 

SPARROW models included a reach attribute that simulated 

the diversion of streamflow in the drainage network. This was 

done by estimating the fraction of streamflow and, therefore, 

nutrient load that was delivered from one reach to the reach 

immediately downstream (based on long term average 

conditions). In the SPARROW model, nutrient load that is 

removed because of irrigation diversions is not explicitly 

accounted for as return flow through the modeling network, 

although many of the agricultural returns in the PNW are 

represented in the NHD drainage network.

Calibration Data Set

The calibration data set for the models consisted of 

mean annual TN and TP stream loads that were estimated 

from water-quality data obtained from Federal agencies, 

State regulatory agencies, one county government, and one 

water pollution control district and streamflow data collected 

primarily by the U.S. Geological Survey (USGS). Waterquality monitoring stations were selected as TN, TP, or both 

calibration stations if they were close enough to a nearby 

streamflow gaging station and met the minimum criteria for 

the number of TN and TP samples (20), seasonal coverage 

(3 samples per season), and period of record (last sample 

collected no later than 1995 if there were at least 5 years 

of data or last sample collected no later than 1999 if there 

were less than 5 years of data). The mean annual TN and 

TP stream loads were estimated using the USGS Fluxmaster 

model (Schwarz and others, 2006), which relates the loads 

measured at water-quality monitoring stations (the calibration 

stations) to measured streamflow, season, and time. There 

were 179 calibration stations where TN loads were estimated, 

220 where TP loads were estimated, and 177 where both 

TN and TP loads were estimated. This resulted in a total 

of 222 calibration stations. A breakdown of the calibration 

stations by agency and location is provided in table 2.
Methods 5

The mean annual TN and TP stream loads were 

detrended to 2002 to account for differences in record length, 

hydrologic conditions, and sample size among the calibration 

stations (Preston and others, 2009). Differences between the 

calibration stream loads used in the PNW NHD SPARROW 

models and those used in the PNW RF1 SPARROW 

models (see Saad and others, 2011) were a result of closer 

examination of the water-quality and streamflow data used to 

estimate the loads. In one case, however, the difference was 

due to the inclusion of a water-quality monitoring station that 

was located on an NHD stream reach not represented in the 

RF1 network. 

Catchment Attribute Data

The catchment attribute data for computing explanatory 

information used in the PNW NHD SPARROW models 

consisted of nutrient sources, boundary loads, and land-towater delivery factors.

Nutrient Sources

All nutrient sources considered in the PNW RF1 

SPARROW models were considered in the PNW NHD 

SPARROW models. Fixation of nitrogen from the atmosphere 

in forested areas was represented by the area of forestland, and 

the weathering of geologic phosphorus was represented by the 

area of forestland and the area of scrubland plus grassland. 

The approach used to represent geologic phosphorus was 

similar to that used in other SPARROW applications (Smith 

and others, 1997; Alexander and others, 2008). Other nutrient 

sources considered for use in the models were (1) the 

discharge from permitted wastewater treatment facilities 

including fish farms and hatcheries (point sources), (2) the 

area of developed land to represent nonpoint urban sources, 

(3) the number of people living in areas not served by 

municipal sewage districts to represent nitrogen leaching from 

septic tanks, (4) the atmospheric deposition of nitrogen, (5) the 

application of farm and nonfarm fertilizer, (6) the application 

or deposition of livestock manure, and (7) the leaching of 

nitrogen from red alder trees (Alnus rubra). The methods used 

to estimate these nutrient sources are described in appendix A.

The TN and TP NHD models also accounted for nutrient 

loads from the largest spring complexes, which are collections 

of natural springs that discharge into or near a stream and 

contribute a substantial amount of the flow in that stream, and 

the nutrient loads associated with the return of water from 

off-stream power generation facilities. Spring complexes and 

power returns are not sources of nutrients, but are pathways 

for the return of nutrients to a stream. They are represented in 

the models as point sources because there is no mechanism 

within the NHD network or in the SPARROW model to 

accommodate these unique pathways for nutrient movement 

from the landscape to the stream. The methods used to 

estimate these loads also are included in appendix A.

Boundary Loads

Five of the calibration stations were on stream reaches 

with large upstream drainage areas that were primarily in 

Canada (Kettle River, Okanogan River, Columbia River, 

North Fork of the Flathead River, and Kootenai River; fig. 1). 

Because catchment attribute data for computing explanatory 

information in the Canadian part of the modeled area were 

not available, these five calibration stations were used as 

boundary conditions for the models. The TN and TP models 

were configured so that the load entering the stream network 

at these boundary reaches was equal to the measured mean 

annual TN and TP load and that the processes occurring 

upstream of and within their incremental catchments had no 

effect on the calibration of the TN and TP models. There also 

were 184 incremental catchments that included some Canadian 

land but that did not drain to a boundary reach. Incomplete 

attribute data for these catchments was expected to have little 

influence on the model calibrations, however, because they 

represented 0.23 percent of the area of the modeling domain.

Land-to-Water Delivery 

The delivery of nutrients from land to water was modeled 

by considering land cover, climate, soil properties, geology, 

and hydrology. Most of these landscape properties were 

compiled by the National Water-Quality Assessment Program 

(NAWQA) as part of a national effort and were summarized 

for each incremental NHD catchment (Michael Wieczorek, 

U.S. Geological Survey, written commun., June 11, 2011), and 

two landscape properties were compiled specifically for the 

PNW NHD SPARROW models (mean annual solar radiation 

and the extent of arid land irrigation). The methods used to 

compile the two data sets representing mean annual solar 

radiation and the extent of arid land irrigation are described in 

appendix A.

Table 2. Sources of water-quality data used to estimate total 

nitrogen and total phosphorus calibration stream loads for the 

National Hydrography Dataset SPARROW models developed for 

the United States Pacific Northwest.

Level Agency

Number 

of 

stations

Federal U.S. Geological Survey 84

Bureau of Reclamation 12 

State Oregon Department of 

Environmental Quality

58

Washington Department 

of Ecology

55

Local King County, Washington 9

Clean Water Services, 

Oregon

4

Total 222

Location

Number 

of 

stations

Washington 84

Oregon 74

Idaho 51

Montana 9

Wyoming 4

Total 222
6 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

Nutrient Loss in Free-Flowing Streams and 

Impoundments

The SPARROW model can simulate the net effect of 

processes that lead to permanent nutrient loss (particulate 

settling and benthic denitrification) within surface waters. 

When modeling mean annual conditions an assumption is 

made that there is no net gain or loss of nutrients due to the 

growth and decay of aquatic plants (Schwarz and others, 

2006). Nutrient loss in free-flowing streams is modeled in 

SPARROW using a first-order decay formulation that is a 

function of the time of travel for each reach (reach length 

divided by estimated mean annual velocity). As a result, the 

stream loss coefficient in the model is expressed as day- 1. 

Estimates of different instream nutrient-decay rates for 

different stream categories can be specified in the SPARROW 

model. In most SPARROW models developed for other parts 

of the United States, these stream categories were based on 

a gradient of mean annual streamflow, but they also can be 

based on stream type (for example, perennial or intermittent), 

temperature, or other measures that affect nutrient uptake and 

removal. Nutrient loss in impoundments such as lakes and 

reservoirs is modeled in SPARROW as an apparent settling 

velocity that is expressed in units of length per time and is a 

function of the areal hydraulic load (estimated mean annual 

streamflow through an impoundment divided by the surface 

area of the impoundment). As a result, the impoundment loss 

coefficient in the model is expressed as meters per year. All 

of the information needed to compute the parameters used to 

estimate nutrient loss was included with NHDPlus.

Model Calibration

The explanatory variables included in the TN and TP 

models represented statistically significant or otherwise 

important geospatial variables. The significance of the 

coefficients for each of the nutrient source terms (which were 

constrained to be positive) were determined by using a onesided t-test and a significance level of 0.10. The significance 

of the coefficients for each of the delivery terms (which were 

allowed to be positive or negative, reflecting either enhanced 

or attenuated delivery, respectively) was determined by 

using a two-sided t-test and a significance level of 0.05. The 

significance of the coefficients for the variables representing 

nutrient loss in free-flowing streams and impoundments 

(which were constrained to be positive) was determined 

by using a one-sided t-test and a significance level of 0.10. 

Final model selection was based on the overall model fit by 

evaluating the yield R-squared (R2) and the root mean squared 

error (RMSE), and by evaluating the residuals for spatial 

patterns. The yield R-squared is the R-squared value for the 

natural logarithm of yield and is considered a better measure 

of goodness of fit than R-squared because it accounts for the 

effect of contributing area, which can explain much of the 

variation in stream load. The spatial patterns in model fit were 

evaluated by calculating and mapping the studentized residual 

for each calibration station. The studentized residual is equal 

to the model residual (the difference between the natural 

logarithm of measured load and predicted load) divided by an 

estimate of its standard deviation.

The SPARROW model uses a weighted nonlinear least 

squares (NLLS) regression to estimate model coefficients 

and provides a way to assess uncertainty in these estimated 

coefficients. Because of the nonlinear manner in which 

the estimated coefficients enter the model, this uncertainty 

needs to be evaluated using a bootstrap resampling method 

(Schwarz and others, 2006). The method is implemented 

through repeated estimation of the SPARROW model (200 

times for these applications) to obtain a range of values for 

each coefficient, from which a mean value (the nonparametric 

bootstrap estimate) is estimated. The overall stability of 

each of the models was evaluated by comparing the NLLS 

estimates of the model coefficients to the nonparametric 

bootstrap estimates. The 90 percent confidence intervals for 

the NLLS coefficients in each model were generated by using 

the standard errors and a t-distribution with N-k degrees of 

freedom, where N was the number of calibration sites and k 

was the number of coefficients.

Analysis of Model Predictions

The predictions from the PNW NHD SPARROW models 

were analyzed and presented in three ways for this report. 

First, the models were used to estimate the mean annual 

incremental TN and TP yield for each of the 232,811 modeled 

catchments. Incremental yield is equal to the estimated stream 

load per unit area that is attributable to nutrient sources located 

exclusively within each incremental catchment, and is a 

useful tool for comparing the relative intensity of stream load 

between catchments because it normalizes for contributing 

area. The median incremental TN and TP yields were then 

calculated for each of the 22 six-digit hydrologic unit code 

(HUC6) watersheds (table 1) within the study domain. Second, 

the models were used to identify the largest local source of 

TN and TP (that is, the nutrient source contributing the most 

to the incremental TN and TP yield for each catchment). To 

simplify the presentation of the results, the nutrient sources 

were generalized into categories that represented similar 

activities or processes. The incremental catchments were then 

grouped together by their largest local source of TN and TP 

and the median incremental TN and TP yields for each group 

of catchments were calculated and analyzed for statistical 

differences in their median values using the Wilcoxan rank 

sum test with a Simes-Hochberg correction applied to the test 

values (Simes, 1986; Hochberg, 1988). Third, the models were 

used to estimate the contribution from each nutrient source 

to the total TN and TP loads predicted for each reach. Total 

load was the predicted load contributed from all upstream 

landscape nutrient sources. The Willamette and Snake 

Rivers were then used as examples to show how the relative 

contribution to total load from different nutrient sources varied 

along two large rivers.
Results 7

Results

Model Calibration

The TN model included 10 nutrient source terms and 

4 delivery terms and the TP model included 9 nutrient source 

terms and 2 delivery terms (tables 3 and 4). The signs of 

the delivery terms, rather than the magnitudes, provide 

information on how they influenced the models. Three 

delivery terms in the TN model (mean annual precipitation, 

mean annual solar radiation, and arid land irrigation) had 

positive coefficients and acted to enhance the delivery of 

nitrogen from land to water. One delivery term (base flow 

index) had a negative coefficient and acted to attenuate the 

delivery of nitrogen from land to water. One delivery term 

in the TP model (mean annual precipitation) had a positive 

coefficient and one delivery term (base flow index) had a 

negative coefficient. Attenuation was not a significant removal 

mechanism in free-flowing streams or impoundments in either 

the TN or the TP model.

Based on the R2 of yield and the RMSE values, the TN 

model showed better fit with the calibration data set than the 

TP model (tables 3 and 4), and the NLLS coefficient estimates 

for both the TN and TP models were generally close in value 

to the nonparametric bootstrap estimates (meaning that the 

uncertainty of the NLLS coefficients was low). The exceptions 

were the coefficients for scrubland and grassland in the TP 

model and atmospheric deposition and arid land irrigation in 

the TN model. The negative value for scrubland and grassland 

in the TP model indicated that there was large uncertainty 

associated with the coefficient for this source term. Based on 

an evaluation of the model residuals, the best model fit for TN 

was in the lower Columbia River basin (LCOL) and the best 

model fit for TP was in the northern Oregon coastal drainages 

(NOCR) (appendix B; figs. B1 and B2). The poorest model 

fit for TN was in the Yakima River basin (with no substantial 

bias toward over or under prediction) and the poorest model fit 

for TP was in the Snake River headwaters (due exclusively to 

under prediction). There were two clusters of under prediction 

that might have resulted from underestimation of natural 

nutrient sources. The cluster of under prediction in the Middle 

Snake-Boise River basin was in an area with documented 

deposits of nitrate salts (Mansfield and Boardman, 1932), 

which were not accounted for in the TN model, and the Snake 

River headwaters lie in a predominantly forested watershed 

in a region of extensive phosphate deposits called the Western 

Phosphate Field (U.S Geological Survey, 2002).

The nitrogen and phosphorus content of livestock manure 

was represented by two nutrient source terms: confined 

cattle and grazing livestock. The confined cattle source term 

represented the manure from cattle associated with a registered 

dairy or feedlot, and the grazing livestock source term 

primarily represented the manure from cattle that were not 

associated with a registered dairy or feedlot, but also included 

manure from a relatively small number of other non-poultry 

animals. These two types of manure were included as distinct 

nutrient sources in the TP model. In contrast, these two 

nutrient sources were combined and modeled as one source 

in the TN model because of difficulties experienced during 

model calibration. Specifically, there was a conflict between 

the nutrient source terms representing grazing livestock 

manure and atmospheric deposition; neither source term was 

significant when both were included in the model along with 

confined cattle manure. However, when confined cattle and 

grazing livestock manure were combined and represented by 

one nutrient source term, the resulting TN model included 

significant coefficients for both livestock manure (confined 

and grazing) and atmospheric deposition. Combining the two 

types of manure in the TN model was justified because the 

coefficient values of the confined cattle and grazing livestock 

source terms were similar when modeled separately, which 

indicated no substantial difference in availability.

The decision to include source terms representing 

nutrients from nonpoint urban sources and springs and 

power returns was not based solely on the statistical results 

from the calibrations. The nitrogen and phosphorus loading 

from nonpoint urban sources was represented by the area of 

developed land rather than by an alternative surrogate equal 

to non-farm fertilizer use (appendix A). Either one of these 

nutrient source terms was significant in the TN and TP models, 

but developed land was selected because it accounted for most 

nonpoint sources of urban1 nutrients, including fertilizer use, 

leaking sewer lines, animal manure, as well as other sources, 

whereas non-farm fertilizer represented only one source. The 

load from springs and power returns was a significant nutrient 

source term in the TN model but not in the TP model (p-value 

= 0.1408). This nutrient source term was retained in the TP 

model, however, because it was an important local source 

of phosphorus for some stream reaches. This determination 

was based on the large under predictions that were observed 

at calibration stations located downstream of springs and 

power returns when they were not included in the TP 

model calibration.

1

 The exception was nitrogen leaching from septic tanks, which was 

modeled as a separate source in areas that were not served by municipal sewer 

lines in 2002.
8 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

Table 3. Model Statistics for the total nitrogen National Hydrography Dataset SPARROW model developed for the United States Pacific 

Northwest.

[The p-values for the source and aquatic loss variables are based on a one-sided t-test; the p-values for the land-to-water delivery variables are based on a twosided t-test. Abbreviations: NLLS, non-linear least squares; R2

, coefficient of determination; RMSE, root mean squared error; cm, centimeter; kg/yr, kilogram 

per year; kg/km2-yr, kilogram per square kilometer per year; kg, kilogram; km2; square kilometer; m2, square meter; mm, millimeter; m2-day, square meter per 

day; <, less than; –, not applicable]

Parameter

Model 

coefficient 

units

NLLS 

estimate 

of model 

coefficient 

Standard 

error of 

the model 

coefficient

Probability 

level 

(p-value)

90 percent confidence 

interval for the

model coefficient

Nonparametric 

bootstrap 

estimate 

of model 

coefficient Lower Upper 

Sources

Forestland (west)1 (km2) kg/km2-yr 25.5 9.8 0.0104 3.7 41.6 26.8

Forestland (east)2 (km2) kg/km2-yr 71.6 10.7 < 0.0001 49.5 113.5 76.2

Point sources3 (kg/yr) dimensionless 1.03 0.38 0.0075 0.40 1.48 1.04

Developed land4 (km2) kg/km2-yr 337 69 < 0.0001 253 409 334

Nonsewered population5 (number) kg/person-yr 1.17 0.65 0.0735 0.24 1.75 1.08

Atmospheric deposition6 (kg/yr) dimensionless 0.026 0.012 0.0322 -0.082 0.042 0.007

Farm fertilizer7 (kg/yr) dimensionless 0.069 0.012 < 0.0001 0.046 0.097 0.072

Livestock manure8 (kg/yr) dimensionless 0.072 0.030 0.0172 0.025 0.143 0.083

Red alder trees9 (m2) kg/m2-yr 0.28 0.10 0.0054 0.08 0.41 0.26

Springs and power returns10 (kg/yr) dimensionless 0.96 0.35 0.0069 0.43 1.20 0.91

Land-to-water delivery

Precipitation11 (mm) – 1.29 0.14 < 0.0001 1.11 1.78 1.38

Solar radiation12 (watt-hours/m2-day) – 7.85 1.83 < 0.0001 -0.68 11.03 6.59

Arid land irrigation13 (percent) – 0.015 0.005 0.0012 0.006 0.031 0.035

Base flow index14 (percent) – -0.022 0.005 0.0001 -0.031 -0.009 -0.021

Aquatic loss

Free-flowing streams – – – – – – –

Impoundments – – – – – – –

Model diagnostics

R2 of yield – 0.908 – – – – –

RMSE – 0.398 – – – – –

Number of observations – 172 – – – – –

1

 Area of forest land, west side of Cascade Range, 2001.

2

 Area of forest land, east side of Cascade Range, 2001.

3

 Surface-water discharges from permitted wastewater facilities, 2002.

4

 Area of developed land, 2001.

5

 Population without sewer service, 2001.

6

 Wet and dry atmospheric deposition of oxidized and reduce nitrogen, 2002.

7

 Commercial fertilizer applied to agricultural land, 2002.

8

 Manure from cattle in dairies and feedlots and grazing livestock, 2002.

9

 Basal area of red alder trees, circa 2002.

10

Surface-water discharge from springs and power returns, 2002.

11

Natural log of mean annual precipitation (1971–2000).

12

Natural log of mean annual solar radiation (1991–2005); interaction limited to the forest land source terms.

13

Percentage of catchment containing irrigated agriculture; interaction limited to the farm fertilizer source term.

14

Mean percentage of streamflow due to base flow.
Results 9

Table 4. Model statistics for the total phosphorus National Hydography SPARROW model developed for the United States Pacific 

Northwest.

[The p-values for the source and aquatic loss variables are based on a one-sided t-test; the p-values for the land-to-water delivery variables are based on a 

two-sided t-test. Abbreviations: NHD, National Hydrography Dataset; NLLS, non-linear least squares; RMSE, root mean squared error; R2, coefficient of 

determination; cm, centimeter; kg/km2-yr, kilogram per square kilometer per year; kg/yr, kilogram per year; km2; square kilometer; mm, millimeter; <, less than; 

–, not applicable]

Parameter

Model 

coefficient 

units

NLLS 

estimate 

of model 

coefficient 

Standard 

error of 

the model 

coefficient

Probability 

level 

(p-value)

90 percent confidence 

interval for the

model coefficient

Nonparametric 

bootstrap 

estimate 

of model 

coefficient Lower Upper 

Sources

Forestland (west)1 (km2) kg/km2-yr 9.23 2.87 0.0015 5.47 14.08 10.15

Forestland (east)2 (km2) kg/km2-yr 6.81 1.42 < 0.0001 4.29 12.24 8.43

Scrub and grass land3 (km2) kg/km2-yr 0.54 0.32 0.0898 -10.66 0.77 -3.28

Point sources4 (kg/yr) dimensionless 1.20 0.33 0.0003 0.63 1.55 1.17

Developed land5 (km2) kg/km2-yr 24.3 5.5 < 0.0001 19.7 29.9 24.6

Farm fertilizer6 (kg/yr) dimensionless 0.015 0.007 0.0271 0.002 0.025 0.014

Confined cattle7 (kg/yr) dimensionless 0.035 0.014 0.0100 0.019 0.060 0.038

Grazing livestock8 (kg/yr dimensionless 0.124 0.022 < 0.0001 0.094 0.195 0.143

Springs and power returns9 (kg/yr) dimensionless 1.67 1.13 0.1408 1.17 2.53 1.76

Land-to-water delivery

Precipitation10 (mm) – 1.26 0.17 < 0.0001 0.86 1.52 1.20

Base flow index11 (percent) – -0.012 0.006 0.0340 -0.024 -0.001 -0.012

Aquatic loss

Free-flowing streams – – – – – – –

Impoundments – – – – – – –

Model diagnostics

R2 of yield – 0.810 – – – – –

RMSE – 0.549 – – – – –

Number of observations – 220 – – – – –

1

 Area of forest land, west side of Cascade Range, 2001.

2

 Area of forest land, east side of Cascade Range, 2001.

3

 Area of scrub and grass land, 2001. 

4

 Surface-water discharges from permitted wastewater facilities, 2002.

5

 Area of developed land, 2001.

6

 Commercial fertilizer applied to agricultural land, 2002.

7

 Manure from cattle in dairies and feedlots, 2002.

8

 Manure from grazing livestock, 2002.

9

 Surface-water discharge from springs and power returns, 2002.

10

Natural log of mean annual precipitation, 1971–2000.

11

Mean percentage of streamflow due to base flow.
10 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

Model Predictions

The incremental yields of TN and TP were much greater 

on the western side of the Cascade Range compared to the 

eastern side (table 5; figs. B3 and B4). The largest median 

incremental yields of TN and TP were predicted for the 

northern Oregon coast (NOCR) and the Washington coast 

(WACR), respectively. The high yields of TN and TP in 

these watersheds were directly related to the large amount of 

precipitation. The incremental yields of TN and TP in other 

areas west of the Cascade Range (PUGT, LCOL, WILL, 

and SOCR) were less than in WACR and NOCR, but still 

greater than areas east of the Cascade Range. In the east side 

Table 5. Median incremental yields of total nitrogen and total phosphorus for six-digit 

Hydrologic Unit Code (HUC6) watersheds in the United States Pacific Northwest (2002 

conditions).

[HUC6 locations are shown in figures 1–3. Values are based on the results obtained from then National 

Hydrography Dataset (NHD) total nitrogen and total phosphorus SPARROW models developed for the United 

States Pacific Northwest. Incremental yield: is equal to the local load divided by the NHD catchment area, 

where the local load is the load generated exclusively within an NHD catchment. Abbreviation: kg/ha-yr, 

kilogram per hectare per year]

Region HUC6 

abbreviation

Total nitrogen Total phosphorus

Median 

incremental yield

(kg/ha-yr)

Rank

Median 

incremental yield

(kg/ha-yr)

Rank

Pacific Coast WACR 6.25 2 0.745 1

NOCR 10.87 1 0.630 2

SOCR 2.22 6 0.403 6

West Side Basins PUGT 3.88 5 0.540 3

LCOL 4.05 4 0.508 5

WILL 4.55 3 0.517 4

Columbia River Basin KOOT 0.51 16 0.098 11

PDOR 0.63 13 0.128 10

SPOK 0.87 11 0.140 8

UCOL 0.42 17 0.063 19

YAKI 0.93 10 0.070 13

JDAY 0.41 18 0.066 16

DESC 0.28 20 0.068 14

MCOL 1.10 8 0.066 17

Snake River Basin SNKH 0.57 14 0.065 18

USNK 0.33 19 0.050 20

MSBS 0.10 21 0.026 22

MSPW 0.55 15 0.130 9

SALM 0.69 12 0.068 15

CLRW 1.13 7 0.141 7

LSNK 0.94 9 0.097 12

Oregon Closed Basins ORCB 0.10 22 0.040 21

watersheds the greatest median incremental TN yields were 

predicted in the Clearwater (CLRW) and Middle Columbia 

River (MCOL) basins and the greatest median incremental 

TP yields were predicted in the Clearwater and Spokane 

River (SPOK) basins. The large median TN and TP yields 

in the Clearwater River basin were due to high precipitation 

compared to the other east side HUC6 watersheds. The large 

median TN yield in the Middle Columbia River basin was due 

to a combination of high average input from farm fertilizer 

and high average precipitation compared to the other east 

side HUC6 watersheds and the large median TP yield in the 

Spokane River basin was due to high levels of urbanization in 

this watershed compared to other east side HUC6 watersheds.
Results 11

The largest local sources of TN and TP load are shown in 

figures 2 and 3 and summarized in table 6. The figures do not 

show catchments where the largest local source was the load 

at a boundary reach or the load from springs and power returns 

because these represented less than 1 percent of the total 

number of catchments. The largest local source of TN in more 

than 50 percent of the catchments was from forestland or alder 

trees and the largest local source of TP in almost 90 percent of 

the catchments was from livestock manure (primarily grazing 

livestock) and geologic sources. The highest median TN and 

TP yields were predicted for catchments where urban sources 

were the largest local source, the lowest median TN yields 

were predicted for catchments where atmospheric deposition 

was the largest local source, and the lowest median TP yields 

were predicted for catchments where farm fertilizer was 

the largest local source (table 6). There were significant

(α < 0.05) differences between each of the median incremental 

TN yields and each of the median incremental TP yields 

shown in table 6.

On average, the largest contributor to the total TN load 

in PNW streams was forestland, which was responsible for 

at least 63 percent of the TN load in one-half of the reaches 

(table 7). On average, the largest contributors to total TP 

load in PNW streams were geologic phosphorus (which was 

represented by forestland, scrubland, and grassland) and 

livestock manure (primarily grazing livestock). In one-half 

of the reaches these two nutrient sources were responsible 

for at least 58 and 30 percent, respectively, of the TP load. 

Although on average diffuse nutrient sources (both natural 

and anthropogenic) were responsible for most of the total TN 

and TP load in PNW streams, concentrated anthropogenic 

nutrient sources contributed much of the total load in some 

of the large rivers. Two examples were the Willamette River 

in western Oregon (fig. 4) and the Snake River, which flows 

through southern Idaho, northeastern Oregon, and southeastern 

Washington (fig. 5). Both rivers drain watersheds containing a 

mix of agricultural, urban, and undeveloped land. Forestland 

and farm fertilizer were the largest contributors to TN load 

in the Willamette River upstream of Eugene and Springfield 

and farm fertilizer was generally the largest contributor to TN 

load downstream of this point. The exception was the 45 km 

of river immediately downstream of Eugene and Springfield, 

where the inputs of nitrogen from urban sources (primarily 

wastewater treatment plants) resulted in point sources being 

the largest contributor to TN load. Geologic phosphorus was 

the largest contributor to TP load in the Snake River between 

its headwaters and Idaho Falls, whereas point sources and 

agricultural nutrient sources (farm fertilizer and livestock 

manure) were generally the largest contributors downstream 

of this point. The livestock manure generated along this part 

of the Snake River was mostly from cattle in dairies and 

feedlots. Almost all of the contribution from urban nutrient 

sources upstream of the Boise River was from fish farms and 

hatcheries2, whereas downstream, a large percentage was from 

wastewater treatments plants, especially those that discharged 

to the Boise River. The three-fold increase in TP load between 

Twin Falls and King Hill was mostly due to phosphorus input 

from springs and the large number of fish farms and hatcheries 

located along this reach. The contribution from urban nutrient 

sources (almost exclusively fish farms and hatcheries) was as 

high as 50 percent of the total TP load in this segment of the 

Snake River. The three-fold increase in TP load downstream of 

the Boise River was mostly due to phosphorus input from this 

large tributary.

2

 Although fish farms and hatcheries are not typically in urban areas they 

were grouped with urban sources in this study because their effects could not 

be distinguished from urban point sources during model calibration.
12 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

tac13-0821_fig02

ORCB

SOCR

PUGT

WACR

LCOL

MCOL

JDAY

YAKI

UCOL

MSBS

MSPW

USNK

SNKH

CLRW

SALM

PDOR

KOOT

SPOK

DESC

LSNK

NOCR

WILL

Seattle Spokane

Portland

Boise

UNITED STATES

CANADA

124° 122° 120° 118° 116° 114° 112° 110°

48°

46°

44°

42°

0 100 200 KILOMETERS

0 100 200 MILES

EXPLANATION

Base map modified from USGS and other U.S. agency 

and Canadian national digital data sources at various 

scales. Projection: Albers, SP1: 29.5, SP2: 45.5, LO: 23.0, 

CM: -96.0, Datum: North American Datum of 1983.

Largest local source of total nitrogen load

Atmospheric deposition

Urban sources

Farm fertilizer

Livestock manure

Forestland

No predictions

UCOL HUC Level 6 watershed and name—

full name in table 1.

CALIFORNIA

BRITISH COLUMBIA

OREGON

WASHINGTON

NEVADA UTAH

WYOMING

MONTANA

IDAHO

ALBERTA

Pacific Ocean Pacific Ocean

Willamette RWillamette R

Columbia River Columbia River

Snake

River

Snake

River

Clark ForkClark Fork

Figure 2. Largest local sources of total nitrogen for National Hydrography Dataset catchments in the United States Pacific 

Northwest (2002 conditions).
Results 13

tac13-0821_fig03

ORCB

SOCR

PUGT

WACR

LCOL

MCOL

JDAY

YAKI

UCOL

MSBS

MSPW

USNK

SNKH

CLRW

SALM

PDOR

KOOT

SPOK

DESC

LSNK

NOCR

WILL

Seattle Spokane

Portland

Boise

UNITED STATES

CANADA

124° 122° 120° 118° 116° 114° 112° 110°

48°

46°

44°

42°

EXPLANATION

Base map modified from USGS and other U.S. agency 

and Canadian national digital data sources at various 

scales. Projection: Albers, SP1: 29.5, SP2: 45.5, LO: 23.0, 

CM: -96.0, Datum: North American Datum of 1983.

Largest local source of total

phosphorus load

Urban sources

Farm fertilizer

Livestock manure

Geologic sources

No predictions

UCOL HUC Level 6 watershed and name—

full name in table 1.

0 100 200 KILOMETERS

0 100 200 MILES

CALIFORNIA

BRITISH COLUMBIA

OREGON

WASHINGTON

NEVADA UTAH

WYOMING

MONTANA

IDAHO

ALBERTA

Pacific Ocean Pacific Ocean

Willamette RWillamette R

Columbia River Columbia River

Snake

River

Snake

River

Clark ForkClark Fork

C

A

S

C

A

D

E

R

A

N

G

E

R

O

C

K

Y

M

O

U

N

T

A

I

N

S

Figure 3. Largest local sources of total phosphorus for National Hydrography Dataset catchments in the United States Pacific 

Northwest (2002 conditions).
14 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

Table 6. Largest local sources of total nitrogen and total phosphorus and corresponding 

median incremental yields for National Hydrography Dataset catchments in the United States 

Pacific Northwest (2002 conditions).

[Catchments: Percentage of catchments where the source category was the largest contributor to the local load 

within catchments where local load is equal to the load generated exclusively within an National Hydrography 

Dataset (NHD) catchment. Median incremental yield: for catchments where the source was the largest 

contributor to the local load. Incremental yield is equal to the local load divided by the incremental catchment 

area. Abbreviations: kg/ ha-yr, kilogram per hectare per year; –, not applicable because source was not a source 

term used in this model or was the largest source in a small percentage of catchments; <, less than]

Largest 

local source 

category

Total nitrogen Total phosphorus

Catchments

(percent)

Median 

incremental 

yield

(kg/ha-yr)

Catchments

(percent)

Median 

incremental yield

(kg/ha-yr)

Atmospheric deposition 10.5 0.037 – –

Urban sources1 2.92 2.47 1.40 0.42

Farm fertilizer 15.63 1.96 8.43 0.07

Livestock manure2 16.5 0.14 39.1 0.09

Geologic sources3 – – 49.5 0.11

Forestland4 53.7 0.91 See footnote 6

Springs and power returns < 1.0 – < 1.0 –

Unidentified5 < 1.0 – 1.52 –

1

 Contribution from developed land, point sources, and nitrogen leaching from septic tanks.

2

 Contribution from all livestock manure.

3

 Contribution from geologic phosphorus exported from forest, scrub, and grass land.

4

 Contribution from asymbiotic nitrogen fixation in forests and symbiotic nitrogen fixation by red alder trees.

5

 Catchments where no model predictions were made because they were internal sinks or, in the case of total 

phosphorus, there were no modeled nutrient sources.

6

 Phosphorus export from forest land is included in the geologic phosphorus source category.

Table 7. Summary statistics for source shares of total nitrogen and total phosphorus load for National Hydrography Dataset stream 

reaches in the United States Pacific Northwest (2002 conditions).

[Total nitrogen and phosphorus load, source shares: The contribution from each source as a percentage of the total load due to all upstream landscape nutrient 

loading. Abbreviations: NHD, National Hydrography Dataset; –, not applicable because this was not a source used in the model]

Source category

Total nitrogen load (source shares) Total phosphorus load (source shares)

Mean

Percentiles

Mean

Percentiles

10th 25th 50th 75th 90th 10th 25th 50th 75th 90th

Atmospheric deposition 17.3 3.38 6.51 11.5 19.6 38.0 – – – – – –

Urban sources1 2.78 0.00 0.00 0.0 0.5 6.3 1.73 0.00 0.00 0.00 0.02 1.26

Farm fertilizer 12.4 0.00 0.00 0.0 3.11 65.6 7.26 0.00 0.00 0.00 0.62 19.1

Livestock manure2 17.1 0.45 2.31 7.9 21.4 53.0 37.6 1.66 10.4 30.2 62.4 87.4

Geologic sources3 – – – – – – 53.1 3.88 17.8 58.0 85.6 96.8

Forestland4 49.9 0.00 7.89 62.6 80.5 88.4 See footnote5

Springs and power returns 0.18 0.00 0.00 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00 0.00

1

 Contribution from developed land, point sources, and nitrogen leaching from septic tanks.

2

 Contribution from all livestock manure.

3

 Contribution from geologic phosphorus exported from forest, scrub, and grass land.

4

 Contribution from asymbiotic nitrogen fixation in forests and symbiotic nitrogen fixation by red alder trees.

5

 Phosphorus export from forest land is included in the geologic phosphorus source category.
Results 15

tac13-0821_fig04

Distance from mouth, in kilometers

300 250 200 150 100 50 0

Total nitrogen load estimated at calibration stations, in kilograms per year

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

Contribution to total nitrogen load, in percent

0

20

40

60

80

EXPLANATION

Source of total nitrogen load

Forestland

Total nitrogen load at calibration

station

Urban sources

Livestock manure

Farm fertilizer

Atmospheric deposition

Eugene-Springfield

Corvalls-Albany

Salem

Portland

Figure 4. Results for the Willamette River, Oregon, from the total nitrogen National Hydrography Dataset SPARROW model 

developed for the United States Pacific Northwest (2002 conditions). Urban sources include developed land, discharge from 

wastewater-treatment facilities, and leaching from septic tanks; livestock manure is generated by confined and grazing livestock.

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

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

Twin Falls

King Hill

Boise River

Idaho Falls

Distance from mouth, in kilometers

Total phosphorus load estimated at calibration stations, in kilograms per year

Contribution to total phosphorus load, in percent

0

20

40

60

80

EXPLANATION

Source of total phosphorus load

Urban sources

Livestock manure

Springs

Total phosphorus load at calibration

station

Geologic sources

Farm fertilizer

Figure 5. Results for the Snake River, Idaho, Oregon, and Washington, from the total phosphorus National Hydrography Dataset 

SPARROW model developed for the United States Pacific Northwest (2002 conditions). Urban sources include developed land and 

discharge from wastewater-treatment facilities and fish hatcheries; geologic sources include phosphorus runoff from forestland, 

scrubland, and grassland; livestock manure is generated by confined and grazing livestock.
16 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

Discussion of Results

The calibrations of the PNW NHD SPARROW models 

resulted in a better overall fit with the calibration load data 

compared to the RF1 models (table 8). These improvements 

in model fit were likely due to a combination of factors: 

(1) the improved resolution of the NHD network compared 

to the RF1 network, (2) differences in the set of nutrient 

loads used to calibrate the models, and (3) refinements in 

the estimates of anthropogenic nutrient sources. The better 

fit of the NHD models compared to the RF1 models resulted 

in less uncertainty in the predictions of nutrient loads, 

incremental nutrient yields, and nutrient source shares. Model 

uncertainty associated with the PNW RF1 models, which also 

applies to the PNW NHD models, is discussed in Wise and 

Johnson (2011). 

The coefficients for the nutrient source terms (tables 3

and 4) provide some insight into why certain results were 

obtained. The coefficients for the nutrient source terms 

representing land cover types (expressed as kilograms per 

square kilometers per year [(kg/km2)-yr]) were analogous 

(but not directly comparable) to yield values or export 

coefficients, whereas the coefficients for the nutrient source 

terms representing mass loading (expressed as kilograms per 

year [kg/yr]) were an indication of the availability for delivery 

to streams. In both the TN and TP models, developed land 

had the largest coefficient of any land cover source term, 

which reflected the large number of nonpoint nutrient sources 

in urban areas and the relative ease with which they are 

transported to streams. The highest incremental yields were 

predicted for catchments dominated by urban nutrient sources 

because of the large coefficients for developed land and the 

fact that nutrients from point sources were discharged directly 

to streams. In both models the land cover terms representing 

natural nutrient sources had much smaller coefficients than 

developed land, which explained why catchments where the 

largest share of the local load was from these land cover types 

had relatively low incremental yields.

The coefficients for the farm fertilizer source terms 

indicated that a greater proportion of nitrogen was delivered 

to streams compared to phosphorus and this explained why 

catchments where the largest share of the local load was 

from farm fertilizer had relatively high incremental TN 

yields but the lowest incremental TP yields. These results 

are consistent with the physical and chemical processes 

affecting the mobility of nitrogen and phosphorus. Although 

the coefficients for the livestock manure source terms in both 

models were greater than those for the farm fertilizer source 

terms, catchments where the largest share of the local load 

was from livestock manure had relatively low incremental 

yields. This was because most of the manure was from 

grazing livestock, which represents a more diffuse source of 

nutrients compared to farm fertilizer. The coefficients for the 

source term representing point sources and the source term 

representing springs and power returns in the TN model were 

about 1, but the coefficients for these nutrient source terms in 

the TP model were 1.20 and 1.67, respectively. Because these 

coefficients acted as scaling factors for the estimated load, 

the model results indicated that our estimates of TP load from 

point sources and from springs and power returns were likely 

too low.

There were potential advantages to using a geologic 

phosphorus source term that was based on a phosphorus index 

instead of the land cover source terms that were used in the 

NHD TP model. This was the approach that was used in RF1 

SPARROW TP model (Wieczorek and Lamotte, 2013; Wise 

and Johnson, 2011). One potential advantage was that the 

phosphorus index provided a spatially continuous distribution 

of geologic phosphorus. The source terms for land cover did 

not account for geologic phosphorus released from land cover 

types other than forestland, scrubland, and grassland. Geologic 

phosphorus released from agricultural and developed land was 

not a significant nutrient source in the TP model because it was 

negligible compared to the contributions from anthropogenic 

activities (farm fertilizer, confined cattle, and developed 

land). Another potential advantage was that the phosphorus 

index accounted for regional differences in the phosphorus 

content of geologic material whereas the land cover source 

terms did not. There was no evidence, however, that using the 

phosphorus index provided a better model fit in areas with 

relatively high concentrations of geologic phosphorus. The 

under predictions at the calibration stations located in the 

Snake River headwaters (which are in the Western Phosphate 

Field), for example, were greater when the phosphorus index 

was used in place of the land cover source terms.

The better fit for the TP model using the source terms 

for land cover compared to the phosphorus index indicated 

that weathering processes were a more important control on 

the availability of natural phosphorus than the phosphorus 

content of the rocks themselves. Additionally, the coefficient 

for the west-side forest source term being the largest of 

the three geologic source terms indicated that the geologic 

phosphorus in this land cover type was more readily delivered 

to streams compared to the geologic phosphorus in the other 

land cover types (east side forests and scrubland/grassland). 

These results were most likely due to differences between 

the amount of precipitation that falls on the west and east 

sides of the Cascade Range. The mean annual precipitation in 

Table 8. Comparison of fit statistics for the National Hydrography 

Dataset and RF1 SPARROW models developed for the United 

States Pacific Northwest.

[Abbreviations: NHD, National Hydrography Dataset; RF1, River Reach 

File 1; R2, coefficient of determination; RMSE, root mean squared error]

Fit Statistic

Total nitrogen Total phosphorus

NHD 

model

RF1 

model

NHD 

model

RF1 

model

R2 of yield 0.908 0.759 0.810 0.712

RMSE 0.398 0.640 0.549 0.693
Conclusions 17

predominantly (greater than 50 percent) forestland catchments 

on the west side of the Cascade Range (194 cm) was greater 

than the mean annual precipitation in predominantly forestland 

catchments on the east side (84 cm) and predominantly 

scrubland and grassland catchments (43 cm; almost all located 

on the east side). Soil erosion rates in forested catchments 

on the west side of the Cascade Range are greater than soil 

erosion rates in forested catchments on the east side (Elliot, 

2006) and, presumably, also greater than soil erosion rates in 

scrubland and grassland. As a result, the export of geologic 

phosphorus from west-side forestland was expected to be 

greater than the export of geologic phosphorus from eastside forestland, scrubland, and grassland. The relatively 

small coefficient for the scrubland and grassland source 

term (compared to the forestland terms) might have reflected 

slower weathering processes in arid and generally low slope 

environments.

The calibration results for the delivery terms in the TN 

and TP models were consistent with our assumptions about 

the processes that occur within watersheds. The delivery of 

nitrogen and phosphorus to streams in catchments receiving 

high precipitation was larger than in similar catchments 

receiving less precipitation. Similarly, in arid areas agricultural 

catchments having a large percentage of irrigated land 

contributed more nitrogen to nearby streams than similar 

catchments having less irrigated land. Catchments having 

a large baseflow index tended to contribute less nitrogen 

than catchments having a small baseflow index, presumably 

because of denitrification in soil and groundwater. The 

contribution of phosphorus also was less from catchments 

having a large baseflow index compared to catchments having 

a small baseflow index. This most likely resulted from less 

overland flow in catchments having a large baseflow index, 

meaning that less sediment-bound phosphorus was available 

for transport. The use of solar radiation as a delivery term 

is unique to the PNW NHD SPARROW model for TN. 

The positive relation between solar radiation and forest 

productivity could explain the significance of this delivery 

term. Forested catchments that receive more solar radiation 

have greater productivity rates and, presumably greater 

nitrogen uptake from the soil than forested watersheds that 

receive less solar radiation. This could leave less nitrogen 

available for permanent removal through soil denitrification.

The results obtained for nutrient loss in free-flowing 

streams and impoundments in the NHD models were not 

typical of recent SPARROW nutrient applications for large 

hydrologic regions (see Hoos and McMahon, 2009; Brown 

and others, 2011; Garcia and others, 2011; Moore and others, 

2011; Rebich and others, 2011; and Robertson and Saad, 2011) 

but were consistent with the PNW RF1 models. All the other 

regional SPARROW applications identified TN loss in freeflowing streams as a significant removal process, whereas the 

PNW NHD TN model did not. The result could have reflected 

the importance of fixation in the cycling of nitrogen in PNW 

streams, meaning that a substantial amount of the nitrogen 

lost through denitrification was replaced with nitrogen fixed 

from the atmosphere. All of the other regional SPARROW 

applications also identified TP loss in impoundments as a 

significant removal process whereas the PNW NHD TP did 

not. This result indicates that settling within impoundments 

was not an important mechanism for phosphorus removal in 

PNW surface waters. This could have been because a large 

proportion of the phosphorus was in the dissolved form (and 

was not settling out before flowing out of the impoundments) 

or because the settling of phosphorus in impoundments was 

balanced by phosphorus dissolution and resuspension.

Conclusions

Results from the TN and TP models showed that 

anthropogenic nutrient sources were responsible for a 

substantial part of the nutrient load in some reaches. The 

models also showed, however, that the largest average 

contribution to TN and TP load in PNW streams was from 

natural nutrient sources and that most of the contribution 

from anthropogenic nutrient sources was from diffuse rather 

than point sources. This means that regulatory actions taken 

to manage nutrient loads should consider the importance of 

natural sources and diffuse anthropogenic sources such as 

atmospheric deposition, farm fertilizer, and grazing livestock 

as well as point sources. Finally, although urban sources 

were the largest local source of TN and TP in only a small 

percentage of the incremental catchments, the concentration 

of these catchments around Portland and the Puget Sound 

indicated that anthropogenic urban sources in these highly 

developed areas were responsible for a substantial amount of 

the nutrients in surface water.

The findings from this study could help complement 

research and inform water-quality management in the 

PNW. Two examples were presented that showed how the 

SPARROW model can be used to assess nutrient conditions 

in large rivers that drain watersheds with a mix of land cover. 

The Willamette Ecosystem Services Project being developed 

by the U.S. Environmental Protection Agency will examine 

how land use and human activities affect the distribution 

of reactive nitrogen in the Willamette Basin (Compton and 

others, 2009). The results from the SPARROW TN model 

described in this report could complement the Willamette 

Ecosystem Services Project by showing the distribution 

of nitrogen in surface waters within the Willamette Basin. 

The approach used to analyze TN loads and sources in 

the Willamette River also could be used as a template for 

estimating the contribution from different sources to the 

nitrogen load in tributary watersheds. The States of Idaho 

and Oregon have designated most of the Snake River below 

Twin Falls as water-quality impaired and the resulting Total 

Maximum Daily Loads (TMDLs) are focused on reducing 

phosphorus inputs to this stretch of the river. The results of 

this study provided a complete description of the TP load 

throughout the Snake River Basin and the relative contribution 
18 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

from all major phosphorus sources to that load, information 

that should be useful when TMDLs are developed or refined 

in the future. In addition to the model results, researchers and 

water-quality managers might find value in the input data sets 

used in this study. These data sets are the most comprehensive 

collection of surface-water nutrient measurements and 

landscape nutrient loading estimates compiled to date for 

the PNW.

Summary

This regional assessment of surface-water nutrients used 

the National Hydrography Dataset Plus (NHDPlus) stream 

flow network and updated calibration data to develop refined 

SPARROW models for TN and TP for the PNW. These new 

models improve upon and supersede the earlier RF1-based 

models for the PNW. The PNW NHD SPARROW models 

were used to estimate mean annual nutrient conditions for 

2002 and to identify the relative contribution of different 

sources to instream nutrient loads, both at established waterquality monitoring stations and in stream reaches where little 

or no monitoring data were available. The input data sets for 

the NHD models and the model results that resulted from them 

should be useful to researchers and water-quality managers 

when performing local nutrient assessments. The model 

results were used to show how the relative contributions from 

natural and anthropogenic nutrient sources (point sources and 

diffuse sources) change along two large rivers between their 

headwaters and mouths. Future SPARROW nutrient modeling 

for the PNW should be based on a more recent time period and 

could reflect mean seasonal as well as mean annual conditions. 

Additionally, sub-regional SPARROW models could be 

developed to help inform water-quality management decisions 

in specific areas of the PNW.

Acknowledgments

We thank Michael Wieczorek (U.S. Geological Survey) 

for compiling many of the catchment attribute data sets used 

in this study and Esther Duggan-Pischel and Tana Haluska 

(U.S. Geological Survey) for helping to compile the catchment 

attribute data sets that were developed specifically for the 

Pacific Northwest. We thank the agencies that provided the 

water-quality data used in this study and Kenneth Skach (U.S. 

Geological Survey) for helping to compile those data.

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Appendix A 21

This appendix contains brief descriptions of how the data 

sets representing nutrient sources and land-to-water-delivery 

variables were compiled and how these data sets differed 

from those used in the RF1 SPARROW models. Detailed 

descriptions of the methods used to compile these data sets are 

contained in the data archives for each one.

Nutrient Sources

Natural Nutrient Sources

The nutrient source terms representing west-side and 

east-side forestland in the total nitrogen (TN) and total 

phosphorus (TP) models and scrubland and grassland in the TP 

model were used as surrogates for natural sources of nitrogen 

and phosphorus because direct estimates of these natural 

nutrient sources were not available for the Pacific Northwest 

(PNW). These natural nutrient sources result from complex 

biological and abiotic processes. In the TN model the natural 

source of nitrogen was fixation of atmospheric nitrogen in 

forests and in the TP model the natural source of phosphorus 

was the weathering of geologic material. These nutrient source 

terms were derived from the 2001 National Land Cover Data 

Base (NLCD) (Homer and others, 2004) (Michael Wieczorek, 

U.S. Geological Survey, written commun., June 11, 2011). 

The total areas of forestland (NLCD categories 41, 42, and 

43), scrubland (NLCD category 52), and grassland (NLCD 

category 71) were summarized for each incremental NHD 

catchment.

Point Sources

The nutrient source terms representing point sources 

included 785 facilities having a National Pollutant Discharge 

Elimination System (NPDES) permit. The discharge of TN 

and TP for 2002 from sewage treatment and food processing 

facilities were estimated using measured flow at plant outflows 

and either onsite measurements of TN and TP or a regional 

average for a specific industrial classification. The discharge of 

TN and TP were not estimated for facilities without adequate 

flow data. The discharges of TN and TP from fish farms and 

hatcheries for 2002 were estimated by using a mass balance 

of annual fish production and feed usage and an estimate 

of nitrogen and phosphorus content for fish and feed (Hal 

Michael, Washington Department of Fish and Wildlife, oral 

commun., December 2009). The discharge of TN and TP were 

not estimated for hatcheries and other aquaculture facilities 

without adequate production and feed data. The total amount 

of TN and TP discharged during 2002 from all point sources 

was estimated for each incremental NHD catchment (see 

Appendix A. Compilation of Model Input Data

GIS data #14 and #22). The data set of point sources used 

in the NHD SPARROW model included more facilities than 

the one used in the RF1 SPARROW model (785 versus 391) 

(Maupin and Ivahnenko, 2011) and the additional facilities 

were mostly non-major sewage treatment plants, fish farms 

and hatcheries, and food processing operations.

Developed Land

The nutrient source term representing developed land was 

used as surrogate for various nutrient sources originating from 

residential, commercial, and industrial land. The use of this 

source was intended to capture non-point sources of nutrients, 

including fertilizer, animal waste, and failing sewer systems 

from these settings. However, the atmospheric deposition 

of nitrogen or nutrients discharged from permitted point 

sources were not intended to be represented by this source. 

Developed land was equal to the summed areas of NLCD 

categories 21, 22, 23, and 24, minus areas representing roads. 

Areas representing roads were removed for three reasons: 

(1) forested catchments with extensive logging road networks 

would incorrectly exhibit a strong developed land signal, 

(2) an accurate representation of residential land use was 

needed to estimate the distribution of non-sewered population 

and non-farm fertilizer (see section Farm Fertilizer and NonFarm Fertilizer), and (3) the lack of nutrient use along most 

roadways. The total area of developed land was summarized 

for each incremental NHD catchment (GIS data #6 and #19). 

This approach was different from the approach used for the 

RF1 SPARROW models, which included NLCD categories 

22, 23, and 24, but did not explicitly eliminate roads. 

Non-Sewered Population

The nutrient source term representing non-sewered 

population (the number of people not served by a municipal 

wastewater-treatment plant) was used as a surrogate for 

nitrogen leaching from septic tanks. The extent of nonsewered population was computed by (1) overlaying census 

blocks polygons from the 2000 United States census on 

a grid of developed land (see section Developed Land); 

(2) distributing the population of each census block equally 

to each developed land grid cell within that census block; 

and (3) removing any of the grid cells that were within an 

area served by municipal sewers during 2002 (GIS data 

#15). The total non-sewered population was summarized 

for each incremental NHD catchment (GIS data #11 and 

#24). This approach was different from the one used in the 

RF1 SPARROW models, which used 1990 census tract 

data to estimate the population served by septic tanks (U.S. 

Geological Survey, 2011a). 
22 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

Atmospheric Nitrogen Deposition

The nutrient source term representing atmospheric 

nitrogen deposition was based on the results from the U.S. 

Environmental Protection Agency Community Multiscale Air 

Quality (CMAQ) model (R.L. Dennis, U.S. Environmental 

Protection Agency, written commun., June 2011). The CMAQ 

model provided spatially refined atmospheric deposition data, 

which reflected orographic barriers and nitrogen-islands, such 

as cities and farmland, and included estimates of wet and dry 

oxidized nitrogen deposition as well as wet and dry reduced 

nitrogen deposition. The CMAQ atmospheric nitrogen data 

were summarized for each incremental NHD catchment 

(GIS data #1 and #16). This data set was different from the 

atmospheric nitrogen deposition data set used in the RF1 

SPARROW TN model (Wieczorek and Lamotte, 2013), which 

was obtained from the National Atmospheric Deposition 

Program (NADP), because it represented total nitrogen 

deposition for 2002 (wet, dry, oxidized, and reduced), whereas 

the NADP data only represented wet deposition. 

Farm Fertilizer and Non-Farm Fertilizer

The nutrient source terms representing farm and non-farm 

fertilizer were based on county-level estimates of nitrogen 

and phosphorus from fertilizer use on farmland and non-farm 

land. The county-level estimates of 2002 farm and non-farm 

fertilizer use were compiled by the USGS based on statewide 

fertilizer sales and county-level expenditures (Gronberg and 

Spahr, 2012). The county-level USGS data for Idaho, Oregon, 

and Washington, however, were scaled to reflect differences 

between the statewide sales data compiled by the USGS and 

data compiled by those states. The statewide sales data were 

obtained from the Idaho State Department of Agriculture 

(Richard Killebrew, written commun., March 2011), the online 

archives of the Oregon State Library (Oregon Department 

of Agriculture, 2011), and the Washington State Department 

of Agriculture (Lizette Beckman, written commun., March 

2011). The county-level estimates of nitrogen and phosphorus 

from farm fertilizer were disaggregated equally to NLCD 

farmland in each county (NLCD categories 81 and 82) and 

the county-level estimates of nitrogen and phosphorus from 

non-farm fertilizer use were disaggregated equally to the 

developed land (see section Developed Land) in each county. 

The total amount of nitrogen and phosphorus from farm and 

non-farm fertilizer applied during 2002 was summarized for 

each incremental NHD catchment (GIS data #2–5 and #18). 

This approach was different from the approach used for the 

RF1 SPARROW models (Wieczorek and Lamotte, 2013), 

which did not account for differences between USGS and state 

estimates of sales data.

Livestock Manure

The nutrient source terms representing confined cattle 

and grazing livestock manure were based on the animal 

populations at registered dairies and feedlots, county-level 

livestock populations, and animal-specific nutrient

generation factors (nitrogen and phosphorus generated per 

animal per year). 

Confined Cattle Manure

The nutrient source term representing confined cattle 

manure was estimated by multiplying the number of cattle 

at each dairy or feedlot by the nitrogen and phosphorus 

generation factors for dairy cows and feedlot cattle, 

respectively. The location and population of each dairy 

and feedlot for 2002 were determined from permitting and 

inspection records obtained from the Idaho, Oregon, and 

Washington Departments of Agriculture (J.P. Bilderback, 

written commun., December 2009; Melissa Boschee, written 

commun., December 2009; and Virginia Prest, written 

commun., December 2009; respectively). The nitrogen 

and phosphorus generation factors were obtained from the 

USGS (D.K. Mueller, written commun., June 2010). All 

manure generated by a dairy or feedlot was assumed to be 

applied within the incremental NHD catchment containing 

that dairy or feedlot. This assumption was based on the 

facts that (1) most confined operations grow some or most 

of their own feed on nearby farmland and (2) manure is a 

low-cost alternative to commercial fertilizer for those crops 

if it is not trucked long distances (Araji and others, 2001, 

Sanford and others, 2009). The authors recognize that the 

assumption of applying all manure locally may over-represent 

manure applications in some areas because manure at some 

facilities is not applied locally, may be composted, or is used 

for energy production. The data needed to quantify these 

practices, however, does not exist on a regional basis and 

these practices are likely applied to a small fraction of the total 

manure generated in the PNW. The total amount of nitrogen 

and phosphorus generated at dairies and feedlots during 2002 

was summarized for each incremental NHD catchment (GIS 

data #13 and #21). The approach used to develop the nutrient 

source term representing manure from confined cattle for the 

NHD SPARROW models differed from the approach used 

for the RF1 SPARROW models (Wieczorek and Lamotte, 

2013). The data set for the RF1 model was based on livestock 

confinement factors for each county that were obtained from 

survey data from the 1980s (Kellog and others, 2000) rather 

than 2002 estimates of cattle populations at individual dairies 

and feedlots.
Appendix A 23

Grazing Livestock Manure

The nutrient source term representing grazing livestock 

manure included manure generated by rangeland cattle and 

the manure generated by all other non-cattle, non-poultry 

livestock. Each of these nutrient sources were compiled at the 

county level and disaggregated to the land based on assumed 

land use patterns. The manure generated by poultry operations 

was not included in this nutrient source term because poultry 

manure typically was not applied to farmland in the county 

where it was produced. Rather, poultry manure was most often 

sold statewide and regionally as fertilizer for farm and for 

nonfarm use (Julie Walker, Washington State Department of 

Agriculture, oral commun., January 2010), and could not be 

traced to a particular place of application.

The county-level estimates of nitrogen and phosphorus 

from rangeland cattle manure was estimated by subtracting 

the amount of nitrogen and phosphorus generated by dairy 

and feedlot cattle in each county from the total amount 

generated by all cattle. The county-level estimates of total 

of nitrogen and phosphorus generated by all cattle in each 

county were obtained from the USGS (D.K. Mueller, written 

commun., June 2010) and were based on 2002 animal counts 

compiled by the U.S. Department of Agriculture and annual 

nitrogen and phosphorus generation factors. The county-level 

estimates of nitrogen and phosphorus from non-cattle grazing 

livestock manure, which was generally a small percentage of 

the total grazing livestock manure, were also obtained from 

the USGS and based on 2002 animal counts compiled by 

the U.S. Department of Agriculture and annual nitrogen and 

phosphorus generation factors.

The county-level estimates of nitrogen and phosphorus 

from rangeland cattle manure were disaggregated equally to 

the potential grazing land in each county. A raster data set of 

potential rangeland was compiled using NLCD categories 

41 (deciduous forest), 42 (evergreen forest), 43 (mixed 

forest), 52 (shrub/scrub), 71 (grassland/herbaceous), and 

81 (pasture/hay). Potential rangeland was removed if it did 

not meet criteria for maximum slope, maximum canopy cover, 

minimum distance to perennial surface water or wells (David 

Ganskopp, U.S. Department of Agriculture, Agricultural 

Research Service, oral commun., January 2010), or if the land 

was managed by the Bureau of Land Management but was 

not part of a grazing allotment in 2002. The total amount of 

nitrogen and phosphorus from rangeland cattle manure during 

2002 was summarized for each incremental NHD catchment 

(GIS data #9, #10, and #20). The county-level estimates 

(2002) of nitrogen and phosphorus from non-cattle grazing 

livestock manure were disaggregated equally to grassland 

and pasture in each county (NLCD categories 70 and 81, 

respectively). The total amount of nitrogen and phosphorus 

from non-cattle grazing livestock manure during 2002 was 

summarized for each incremental NHD catchment (GIS data 

#7, #8, and #20).

The approach used to develop the nutrient source term 

representing manure from grazing livestock for the NHD 

SPARROW models differed from the method used for the RF1 

SPARROW models (Wieczorek and Lamotte, 2013) primarily 

in the way that the manure was disaggregated to the land. 

In the RF1 models, the land that received grazing livestock 

manure included cultivated crops even though very little 

of that land is used for livestock grazing in the PNW (U.S. 

Department of Agriculture, 2009). Additionally, the landscape 

types that received grazing livestock manure in the RF1 

models omitted much of the rangeland throughout the PNW 

by not including shrub/scrubland and forestland.

Nitrogen Leaching from Red Alder Trees

The nutrient source term representing the leaching 

of nitrogen from read alder trees (Alnus rubra) was based 

on a data set showing the distribution of this plant species 

throughout western Oregon and Washington. The same data 

set was used for the RF1 SPARROW models (U.S. Geological 

Survey, 2011b). The total basal area of red alder trees circa 

2002 was summarized for each incremental NHD catchment 

(GIS data #23).

Springs and Power Returns

A substantial proportion of the flow in some streams in 

the PNW is due to groundwater discharged from large spring 

complexes and a substantial proportion of the flow in some 

streams is due to the return of water that is diverted upstream 

for power generation. Because springs and power returns 

represent a routing of water that cannot be modeled within the 

SPARROW framework they were modeled as nutrient point 

sources. The nutrients in springs and power returns originate 

from some or all of the other natural and anthropogenic 

nutrient sources included in the models, but reach waterways 

in a concentrated manner similar to point sources. There 

were sufficient discharge and water-quality data to estimate 

mean annual TN and TP loads for three of the largest spring 

complexes—Opal Springs on the Crooked River in Oregon, 

Thousand Springs on the Snake River near Hagerman, Idaho, 

and Griffith and Waikiki Springs on the Little Spokane River 

in Washington. The nutrient loads from power returns were 

estimated in one of two ways. One approach was to use the 

fraction of streamflow received from an upstream reach (see 

section Surface-Water Drainage Network) at the point of 

diversion to estimate the percentage of load that was delivered 

to the receiving reach (an assumption was made that no 

nutrients were lost between the two reaches) and apply this 

percentage to the mean annual load estimated for a calibration 

station on or near the diversion reach. The other approach was 

used when no calibration station was on or near the diversion 
24 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

reach. In this case, measurements of discharge and nutrient 

concentrations for the diversion at the point where it entered a 

receiving reach were used to estimate mean annual TN and TP 

loads. The total amount of TN and TP discharged during 2002 

from the three spring complexes and the power returns was 

estimated for each incremental NHD catchment. This nutrient 

source was not included in the RF1 SPARROW models.

Land-to-Water Delivery

Solar Radiation

A data set of mean annual solar radiation was obtained 

for the PNW that represented the period between 1991 and 

2005 (National Renewable Energy Laboratory, 2011). The 

average mean annual solar radiation for each incremental 

NHD catchment was estimated (GIS data #12 and #25).

Arid Land Irrigated Agriculture

Arid land irrigated agriculture was defined as all 

agricultural land east of the Cascade Range to which irrigation 

water was applied. The extent of arid land irrigated agriculture 

was determined by (1) identifying areas of pasture/hay and 

cultivated crops (NLCD categories 81 and 82, respectively) 

that were east of the crest of the Cascade Range and 

(2) removing areas representing non-irrigated wheat from the 

areas identified in (1). Land containing non-irrigated wheat 

was identified based on professional understanding of major 

agricultural cropping patterns in the PNW, research done for 

this study, and evaluating the NLCD and aerial photography 

for obvious signs of irrigation such as center pivot circles or 

irrigation ditches. The percentage of each NHD incremental 

catchment containing arid land irrigation was estimated (GIS 

data #17). This data set was a refinement of the one used in the 

RF1 SPARROW models (Wise and Johnson, 2011).

Metadata Links to GIS Data

GIS Rasters

1. Total atmospheric deposition of oxidized and reduced 

nitrogen in the United States Pacific Northwest for 2002

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of wet and dry 

deposition of oxidized and reduced nitrogen in the Pacific 

Northwest region of the United States (Hydro Region 17; 

MRB7) during 2002.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_AtmosphericDeposition_td_tn

2. Application of fertilizer nitrogen to farm land in the 

United States Pacific Northwest for 2002

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of fertilizer nitrogen 

that was applied to farm land in the Pacific Northwest region 

of the United States (Hydro Region 17; MRB7) during 2002.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_FertilizerDistribution_Farm_fert_n

3. Application of fertilizer phosphorus to farm land in the 

United States Pacific Northwest for 2002

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of fertilizer 

phosphorus that was applied to farm land in the Pacific 

Northwest region of the United States (Hydro Region 17; 

MRB7) during 2002.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_FertilizerDistribution_Farm_fert_p

4. Application of fertilizer nitrogen to nonfarm land in the 

United States Pacific Northwest for 2002

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of fertilizer nitrogen 

that was applied to nonfarm land in the Pacific Northwest 

region of the United States (Hydro Region 17; MRB7) during 

2002.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_FertilizerDistribution_Nonfarm_nfert_n

5. Application of fertilizer phosphorus to nonfarm land in 

the United States Pacific Northwest for 2002

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of fertilizer 

phosphorus that was applied to nonfarm land in the Pacific 

Northwest region of the United States (Hydro Region 17; 

MRB7) during 2002.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_FertilizerDistribution_Nonfarm_nfert_p

6. Nonroad developed land in the United States Pacific 

Northwest for 2001

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the extent of non-road developed 

land in the Pacific Northwest region of the United States 

(Hydro Region 17; MRB7) in 2002.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_LandUseDistribution_dev_nonroad
Appendix A 25

7. Application of nitrogen generated by non-cattle livestock 

to pasture land in the United States Pacific Northwest for 

2002

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of nitrogen generated 

by pastured, non-cattle livestock that was applied to pasture 

land in the Pacific Northwest region of the United States 

(Hydro Region 17; MRB7) during 2002.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_LivestockWasteDistribution_Pasture_past_n

8. Application of phosphorus generated by non-cattle 

livestock to pasture land in the United States Pacific 

Northwest for 2002

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of phosphorus 

generated by pastured, non-cattle livestock that was applied 

to pasture land in the Pacific Northwest region of the United 

States (Hydro Region 17; MRB7) during 2002. 

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_LivestockWasteDistribution_Pasture_past_p

9. Application of nitrogen generated by grazing cattle to 

range land in the United States Pacific Northwest for 2002

Pischel, E.M.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of nitrogen generated 

by grazing cattle that was applied to range land in the Pacific 

Northwest region of the United States (Hydro Region 17; 

MRB7) during 2002.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_LivestockWasteDistribution_Rangeland_range_n

10. Application of phosphorus generated by grazing cattle 

to range land in the United States Pacific Northwest for 

2002

Pischel, E.M.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of phosphorus 

generated by grazing cattle that was applied to range land 

in the Pacific Northwest region of the United States (Hydro 

Region 17; MRB7) during 2002.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_LivestockWasteDistribution_Rangeland_range_p

11. Nonsewered population in the United States Pacific 

Northwest for 2002

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent populations that did not have 

access to municipal sewer service in the Pacific Northwest 

region of the United States (Hydro Region 17; MRB7) in 

2002.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_SewageTreatment_NonSewered_nonsewered

12. Mean annual solar radiation in the United States Pacific 

Northwest (1991–2005)

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent mean annual solar radiation in the 

Pacific Northwest region of the United States (Hydro Region 

17; MRB7) for the period between 1991 and 2005.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_SolarRadiation_ghi_100

GIS Shapefiles

13. Nutrient generation at dairies and feedlots in the United 

States Pacific Northwest for 2002

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of nitrogen and 

phosphorus generated by cattle at dairies and feedlots in the 

Pacific Northwest region of the United States (Hydro Region 

17; MRB7) during 2002.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_PointSources_ConfinedCattle_CAFOs

14. Point source nutrient discharges to surface water in the 

United States Pacific Northwest for 2002

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of total nitrogen and 

total phosphorus discharged to surface waters in the Pacific 

Northwest region of the United States (Hydro Region 17; 

MRB7) during 2002 from individual permitted wastewater 

facilities.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_PointSources_NHD
26 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

15. Areas with access to municipal sewer service in the 

United States Pacific Northwest for 2002

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent areas that had access to municipal 

sewer service in the Pacific Northwest region of the United 

States (Hydro Region 17; MRB7) in 2002.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_SewageTreatment_Sewered_SeweredAreas

Summary Tables

16. Atmospheric deposition of nitrogen in the United States 

Pacific Northwest for 2002 summarized for NHDPlus v2 

catchments

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of atmospheric 

nitrogen deposition in the Pacific Northwest region of the 

United States (Hydro Region 17; MRB7) during 2002 within 

each incremental watershed delineated in the NHDPlus v2 

dataset.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_AtmosphericDeposition_summary

17. Arid land irrigation in the United States Pacific Northwest 

for 2001 summarized for NHDPlus v2 catchments

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the area of arid land irrigation 

in the Pacific Northwest region of the United States (Hydro 

Region 17; MRB7) during 2001 within each incremental 

watershed delineated in the NHDPlus v2 dataset.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_EastsideIrrigation_summary

18. Fertilizer nutrients applied to farm and nonfarm land in 

the United States Pacific Northwest for 2002 summarized 

for NHDPlus v2 catchments

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of fertilizer nitrogen 

and phosphorus that was applied to farm and nonfarm land 

in the Pacific Northwest region of the United States (Hydro 

Region 17; MRB7) during 2002 within each incremental 

watershed delineated in the NHDPlus v2 dataset.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_FertilizerDistribution_summary

19. Nonroad developed land in the United States Pacific 

Northwest for 2001 summarized for NHDPlus v2 

catchments

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the area of non-road developed 

land within each incremental watershed delineated in the 

NHDPlus v2 dataset in the Pacific Northwest region of the 

United States (Hydro Region 17; MRB7) in 2001.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_LandUseDistribution_DevelopedNonRoad_

summary

20. Nutrients generated by livestock applied to farm land, 

pasture land, and range land in the United States 

Pacific Northwest for 2002 summarized for NHDPlus v2 

catchments

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of nitrogen and 

phosphorus that was generated by livestock and applied 

to land in the Pacific Northwest region of the United 

States (Hydro Region 17; MRB7) during 2002 within each 

incremental watershed delineated in the NHDPlus v2 dataset.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_LivestockWasteDistribution_summary

21. Nutrient generation at dairies and feedlots in the United 

States Pacific Northwest for 2002 summarized for 

NHDPlus v2 catchments

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of nitrogen and 

phosphorus generated by cattle at dairies and feedlots in the 

Pacific Northwest region of the United States (Hydro Region 

17; MRB7) during 2002 within each incremental watershed 

delineated in the NHDPlus v2 dataset.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_PointSources_ConfinedCattle_CAFO_summary

22. Point source nutrient discharges to surface water in the 

United States Pacific Northwest for 2002 summarized for 

NHDPlus v2 catchments

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the amount of total nitrogen and 

total phosphorus discharged to surface waters in the Pacific 

Northwest region of the United States (Hydro Region 17; 

MRB7) during 2002 from all permitted wastewater facilities 

located within each incremental watershed delineated in the 

NHDPlus v2 dataset.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_PointSources_NHD_summary
Appendix A 27

23. Red alder trees distribution in the United States Pacific 

Northwest for 2002 summarized for NHDPlus v2 

catchments

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the basal area of red alder trees 

within each incremental watershed delineated in the NHDPlus 

v2 dataset in the Pacific Northwest region of the United States 

(Hydro Region 17; MRB7) in 2001.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_RedAlder_summary

24. Nonsewered population in the United States Pacific 

Northwest for 2002 summarized for NHDPlus v2 

catchments

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent the population within each 

incremental watershed delineated in the NHDPlus v2 dataset 

in the Pacific Northwest region of the United States (Hydro 

Region 17; MRB7) that did not have access to municipal 

sewer service in 2002.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_SewageTreatment_NonSewered_summary

25. Mean annual solar radiation in the United States Pacific 

Northwest (1991–2005) summarized for NHDPlus v2 

catchments

Wise, D.R.

This spatial data set was created by the U.S. Geological 

Survey (USGS) to represent mean annual solar radiation in the 

Pacific Northwest region of the United States (Hydro Region 

17; MRB7) for the period between 1991 and 2005 within 

each incremental watershed delineated in the NNHDPlus v2 

dataset.

http://water.usgs.gov/lookup/getspatial?sir2013-5103_PNW_

SPARROW_SolarRadiation_summary

References Cited

Araji, A.A., Abdo, Z.O., and Joyce, P., 2001, Efficient 

use of animal manure on cropland–Economic analysis: 

Bioresource Technology, v. 79, no. 2, p. 179–191.

Gronberg, J.M., and Spahr, N.E., 2012, County-level 

estimates of nitrogen and phosphorus from commercial 

fertilizer for the conterminous United States, 1987–2006: 

U.S. Geological Survey Scientific Investigations Report 

2012-5207, 30 p.

Homer, Collin, Huang, Chengquan, Yang, Limin, Wylie, 

Bruce, and Coan, Michael, 2004, Development of a 

2001 national land-cover database for the United States: 

Photogrammetric Engineering and Remote Sensing, v. 70, 

no. 7, p. 829–840.

Kellogg, R.L., Lander, C.H., Moffitt, D.H., and Gollehon, 

Noel, 2000, Manure nutrients relative to the capacity 

of cropland and pastureland to assimilate nutrients—

Spatial and temporal trends for the United States: U.S. 

Department of Agriculture, Natural Resources Conservation 

Service, accessed March 17, 2013, at http://www.nrcs.

usda.gov/wps/portal/nrcs/detail/national/technical/nra/

rca/?&cid=nrcs143_014126

Maupin, M.A., and Ivahnenko, Tamara, 2011, Nutrient 

loadings to streams of the continental United States from 

municipal and industrial effluent: Journal of the American 

Water Resources Association, v, 47, no. 5, p. 950–964.

National Renewable Energy Laboratory, 2011, PV Solar 

Radiation (10 km)—Static Maps: National Renewable 

Energy Laboratory Web site, accessed March 5, 2013, 

at http://www.nrel.gov/gis/solar.html.

Oregon Department of Agriculture, 2011, 2005 Oregon 

tonnage summary: Salem, Oreg., Department of Agriculture 

Fertilizer Program, accessed March 5, 2013, at http://library.

state.or.us/repository/2006/200610061405575/tonnage05_2.

pdf.

Sanford, G.R., Posner, J.L., and Hadley, G.L., 2009, 

Economics of hauling dairy slurry and its value in 

Wisconsin corn grain systems: Journal of Agricultural, 

Food, and Environmental Sciences, v. 3, no. 1, p. 1–10.

U.S. Department of Agriculture, 2009, Total grazing land, by 

region and States, United States, 2007: U.S. Department 

of Agriculture, National Agricultural Statistics Service, 

accessed March 18, 2013, at http://www.ers.usda.gov/

datafiles/Major_Land_Uses/Summary_tables/Summary_

Table_4_total_grazing_land_by_region_and_state_2007.xls.

U.S Geological Survey, 2011a, Location of septic sewer 

systems in the Pacific Northwest: U.S. Geological Survey, 

Water Resources NSDI Node Web site, accessed March 5, 

2013, at http://water.usgs.gov/GIS/metadata/usgswrd/XML/

septicSystems_MRB7.xml.

U.S Geological Survey, 2011b, Red alder basal area, by stream 

reach, for the Pacific Northwest: U.S. Geological Survey, 

Water Resources NSDI Node Web site, accessed March 5, 

2013, at http://water.usgs.gov/GIS/metadata/usgswrd/XML/

redAlderSources_MRB7.xml. 

Wieczorek, M.E., and Lamotte, A.E., 2013, Attributes for 

MRB_E2RF1 Catchments by major river basins in the 

conterminous United States. U.S. Geological Survey Digital 

Data Series DS-491, accessed March 18, 2013 at http://

water.usgs.gov/nawqa/modeling/rf1attributes.html.

Wise, D.R., and Johnson, H.M., 2011, Surface-water nutrient 

conditions and sources in the United States Pacific 

Northwest: Journal of the American Water Resources 

Association, v. 47, no. 5, p. 1110–1135.
28 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

Appendix B. Model Calibration and Prediction Results

The studentized residuals for the PNW NHD SPARROW 

models for TN and TP are shown in figures B1 and B2, 

respectively. The studentized residual is equal to the model 

residual (the difference between the natural logarithm of 

measured load and predicted load) divided by an estimate 

of its standard deviation. The negative values indicate over 

prediction and the positive values indicate under prediction. 

The incremental yields for total nitrogen and total 

phosphorus in kilograms per hectare per year are shown 

in figures B3 and B4, respectively. The mean annual total 

nitrogen and phosphorus loads and yields predicted by the 

PNW NHD SPARROW models are available online in a tabdelimited ASCII file at http://pubs.usgs.gov/sir/2013/5103/. 

The file includes predictions for individual stream reaches in 

the Pacific Northwest as defined by the National Hydrography 

Dataset Plus (NHDPlus, Pacific Northwest region [17]) 

medium resolution [1:100,000-scale] geospatial data set 

(Horizon Systems, 2013). SPARROW prediction variables in 

the ASCII file are described in the header (denoted by lines 

starting with “#”), and include COMID (common identifier 

of an NHD reach), AreaSqKM (area of the incremental NHD 

catchment, in square kilometers), TotDASqKM (total area 

draining to a reach, in square kilometers), predictions of the 

local mean annual load for each reach (in kilograms per year), 

predictions of the total mean annual load for each reach that 

is attributable to all upstream nutrient sources (in kilograms 

per year), and predictions of the total mean annual load for 

each reach that is attributable to individual upstream nutrient 

sources (in kilograms per year).

Reference Cited

Horizon Systems, 2013, NHDPlusV2Data: Horizon Systems 

database, accessed March 18, 2013, at http://www.horizonsystems.com/nhdplus/.
Appendix B 29

tac13-0821_figB1

CALIFORNIA

BRITISH COLUMBIA

OREGON

WASHINGTON

NEVADA UTAH

WYOMING

MONTANA

IDAHO

ALBERTA

Pacific Ocean Pacific Ocean

Willamette RWillamette R

Columbia River Columbia River

Snake

River

Snake

River

Clark ForkClark Fork

C

A

S

C

A

D

E

R

A

N

G

E

R

O

C

K

Y

M

O

U

N

T

A

I

N

S

ORCB

SOCR

WACR

MCOL

JDAY

YAKI

UCOL

MSBS

MSPW

USNK

SNKH

CLRW

SALM

PDOR

KOOT

SPOK

DESC

LSNK

NOCR

Oregon

Closed 

Basins

Columbia

Basin

Snake

River

River

Basin

PUGT

LCOL

WILL

West

Basins

Side

Pacific

Coast

Seattle Spokane

Portland

Boise

124° 122° 120° 118° 116° 114° 112° 110°

48°

46°

44°

42°

Base map modified from USGS and other U.S. agency 

and Canadian national digital data sources at various 

scales. Projection: Albers, SP1: 29.5, SP2: 45.5, LO: 23.0, 

CM: -96.0, Datum: North American Datum of 1983.

0 100 200 KILOMETERS

0 100 200 MILES

EXPLANATION

UCOL HUC Level 6 watershed and name—

full name in table 1.

Basin Major drainage

-3.27 to -3.00 (1)

-2.99 to -2.00 (5)

-1.99 to -1.00 (17)

-0.99 to 0.00 (61)

0.001 to 1.00 (62)

1.01 to 2.00 (19)

2.01 to 2.37 (7)

Studentized residual: Measured minus

predicted total nitrogen—value in 

parentheses is number of calibration 

stations.

Figure B1. Spatial distribution of residual stream load for the total nitrogen National Hydrography Dataset SPARROW model 

developed for the United States Pacific Northwest.
30 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

tac13-0821_figB2

ORCB

SOCR

PUGT

WACR

LCOL

MCOL

JDAY

YAKI

UCOL

MSBS

MSPW

USNK

SNKH

CLRW

SALM

PDOR

KOOT

SPOK

DESC

LSNK

NOCR

WILL

Oregon

Closed 

Basins

Snake

River

River

Basin

West

Basins

Side

Columbia

Basin

Pacific

Coast

Seattle Spokane

Portland

Boise

UNITED STATES

CANADA

124° 122° 120° 118° 116° 114° 112° 110°

48°

46°

44°

42°

EXPLANATION

Base map modified from USGS and other U.S. agency 

and Canadian national digital data sources at various 

scales. Projection: Albers, SP1: 29.5, SP2: 45.5, LO: 23.0, 

CM: -96.0, Datum: North American Datum of 1983.

UCOL HUC Level 6 watershed and name—

full name in table 1.

Basin Major drainage

-4.12 to -3.00 (2)

-2.99 to -2.00 (6)

-1.99 to -1.00 (16)

-0.99 to 0.00 (93)

0.001 to 1.00 (72)

1.01 to 2.00 (24)

2.01 to 3.00 (6)

3.01 to 3.47 (1)

0 100 200 KILOMETERS

0 100 200 MILES

Studentized residual: Measured minus 

predicted total phosphorus—value 

in parentheses is number of 

calibration stations. 

CALIFORNIA

BRITISH COLUMBIA

OREGON

WASHINGTON

NEVADA UTAH

WYOMING

MONTANA

IDAHO

ALBERTA

Pacific Ocean Pacific Ocean

Willamette RWillamette R

Columbia River Columbia River

Snake

River

Snake

River

Clark ForkClark Fork

C

A

S

C

A

D

E

R

A

N

G

E

R

O

C

K

Y

M

O

U

N

T

A

I

N

S

Figure B2. Spatial distribution of residual stream load for the total phosphorus National Hydrography Dataset SPARROW model 

developed for the United States Pacific Northwest.
Appendix B 31

tac13-0821_figB3

ORCB

SOCR

PUGT

WACR

LCOL

MCOL

JDAY

YAKI

UCOL

MSBS

MSPW

USNK

SNKH

CLRW

SALM

PDOR

KOOT

SPOK

DESC

LSNK

NOCR

WILL

Oregon

Closed 

Basins

Snake

River

River

Basin

West

Basins

Side

Columbia

Basin

Pacific

Coast

Seattle Spokane

Portland

Boise

UNITED STATES

CANADA

124° 122° 120° 118° 116° 114° 112° 110°

48°

46°

44°

42°

EXPLANATION

Base map modified from USGS and other U.S. agency 

and Canadian national digital data sources at various 

scales. Projection: Albers, SP1: 29.5, SP2: 45.5, LO: 23.0, 

CM: -96.0, Datum: North American Datum of 1983.

UCOL HUC Level 6 watershed and name—

full name in table 1.

Basin Major drainage

Incremental total nitrogen yield, in 

kilograms per hectare-year

0 100 200 KILOMETERS

0 100 200 MILES

CALIFORNIA

BRITISH COLUMBIA

OREGON

WASHINGTON

NEVADA UTAH

WYOMING

MONTANA

IDAHO

ALBERTA

Pacific Ocean Pacific Ocean

Willamette RWillamette R

Columbia River Columbia River

Snake

River

Snake

River

Clark ForkClark Fork

C

A

S

C

A

D

E

R

A

N

G

E

R

O

C

K

Y

M

O

U

N

T

A

I

N

S

< -0.100

0.101 to 0.500

0.501 to 1.000

1.001 to 1.500

1.501 to 2.000

2.001 to 2.500

2.501 to 3.000

> 3.00

No prediction

Figure B3. Incremental total nitrogen yields for National Hydrography Dataset catchments in the United States Pacific Northwest 

(2002 conditions).
32 Application of the SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest

tac13-0821_figB4

ORCB

SOCR

PUGT

WACR

LCOL

MCOL

JDAY

YAKI

UCOL

MSBS

MSPW

USNK

SNKH

CLRW

SALM

PDOR

KOOT

SPOK

DESC

LSNK

NOCR

WILL

Oregon

Closed 

Basins

Snake

River

River

Basin

West

Basins

Side

Columbia

Basin

Pacific

Coast

Seattle Spokane

Portland

Boise

UNITED STATES

CANADA Incremental total phosphorus yield, 

in kilograms per hectare-year

124° 122° 120° 118° 116° 114° 112° 110°

48°

46°

44°

42°

EXPLANATION

Base map modified from USGS and other U.S. agency 

and Canadian national digital data sources at various 

scales. Projection: Albers, SP1: 29.5, SP2: 45.5, LO: 23.0, 

CM: -96.0, Datum: North American Datum of 1983.

UCOL HUC Level 6 watershed and name—

full name in table 1.

Basin Major drainage

0 100 200 KILOMETERS

0 100 200 MILES

CALIFORNIA

BRITISH COLUMBIA

OREGON

WASHINGTON

NEVADA UTAH

WYOMING

MONTANA

IDAHO

ALBERTA

Pacific Ocean Pacific Ocean

Willamette RWillamette R

Columbia River Columbia River

Snake

River

Snake

River

Clark ForkClark Fork

C

A

S

C

A

D

E

R

A

N

G

E

R

O

C

K

Y

M

O

U

N

T

A

I

N

S

< 0.0400

0.0401 - 0.0800

0.0801 - 0.1200

0.1201 - 0.1600

0.1601 - 0.2000

0.2001 - 0.2400

0.2401 - 0.2800

> 0.2800

No prediction

Figure B4. Incremental total phosphorus yields for National Hydrography Dataset catchments in the United States Pacific 

Northwest (2002 conditions).
For more information concerning the research in this report, contact the

Director, Oregon Water Science Center

U.S. Geological Survey

2130 SW 5th Avenue

Portland, Oregon 97201

http://or.water.usgs.gov

Publishing support provided by the U.S. Geological Survey

Publishing Network, Tacoma Publishing Service Center
Wise and Johnson— Application of SPARROW Model to Assess Surface-Water Nutrient Conditions and Sources, Pacific Northwest—Scientific Investigations Report 2013–5103

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