U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2008–5218

Revised February 2009

Prepared in cooperation with the City of Tulsa, Oklahoma

Estimated Nutrient Concentrations and Continuous WaterQuality Monitoring in the Eucha-Spavinaw Basin, 

Northwestern Arkansas and Northeastern Oklahoma, 

2004–2007

Beaty Creek near Jay, Oklahoma
Cover: Photograph is Beaty Creek near Jay, Delaware County, Oklahoma, upstream from the streamflow gaging station (07191222), taken 

March 2008. Photographer: Stephanie Buck, U.S. Geological Survey
Estimated Nutrient Concentrations and 

Continuous Water-Quality Monitoring in 

the Eucha-Spavinaw Basin, Northwestern 

Arkansas and Northeastern Oklahoma, 

2004–2007

By Victoria G. Christensen, Rachel A. Esralew, and Monica L. Allen

Prepared in cooperation with the City of Tulsa, Oklahoma

Scientific Investigations Report 2008–5218

Revised February 2009

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

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

Revised: February 2009

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

reproduce any copyrighted materials contained within this report.

Suggested citation:

Christensen, V.G., Esralew, R.A., and Allen, M.L., 2008, Estimated nutrient concentrations and continuous waterquality monitoring in the Eucha-Spavinaw basin, northwestern Arkansas and northeastern Oklahoma, 2004–2007: 

U.S. Geological Survey Scientific Investigations Report 2008–5218, 32 p. (Revised February 2009)
iii

Contents

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

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

Purpose and Scope ..............................................................................................................................3

Description of Study Area ...................................................................................................................4

Streamflow in the Eucha-Spavinaw Basin..............................................................................5

Sources of Nutrients ...................................................................................................................5

Acknowledgments.........................................................................................................................................8

Methods...........................................................................................................................................................8

Data Collection and Analysis..............................................................................................................8

Quality Assurance and Quality Control .............................................................................................9

Development of Regression Equations .............................................................................................9

Uncertainty Associated with Regression-Based Estimates........................................................10

Nutrient Concentrations in the Eucha-Spavinaw Basin .......................................................................11

Measured Concentrations ................................................................................................................11

Regression-Estimated Concentrations and Continuous Water-Quality Monitoring................11

Nitrogen Equations....................................................................................................................11

Phosphorus Equations ..............................................................................................................15

Continuous Estimates of Total Nitrogen and Total Phosphorus.........................................16

Benefits of Continuous, Real-Time Water-Quality Monitoring and Future Work .....................19

Summary and Conclusions ........................................................................................................................22

References Cited..........................................................................................................................................23

Appendixes ...................................................................................................................................................27

1. Water-quality data for Spavinaw Creek near Colcord, Oklahoma, water years 

2005-2007, that were used to develop regression equations to estimate total 

nitrogen and total phosphorus .................................................................................................29

2. Water-quality data for Beaty Creek near Jay, Oklahoma, water years 2005-2007, 

that were used to develop regression equations to estimate total nitrogen and 

total phosphorus .........................................................................................................................31

Figures

 1. Map showing the Eucha-Spavinaw basin, Arkansas and Oklahoma, with

locations of selected streamflow and water-quality stations........................................2

 2. Photograph showing algal bloom in Lake Eucha, July 11, 2001.....................................4

 3. Map showing comparison of land use in the basins draining to Spavinaw Creek

near Colcord, Oklahoma, and Beaty Creek near Jay, Oklahoma, in the EuchaSpavinaw basin, Arkansas and Oklahoma........................................................................6

 4. Photograph showing a water-quality monitor..................................................................8

 5. Graph showing streamflow divided into total flow and base flow, mean of daily

mean streamflow for the period of discharge record, and base-flow and runoff
iv

water-quality samples during Water Years 2005–2007 at (A) Spavinaw Creek

near Colcord, Oklahoma, and (B) Beaty Creek near Jay, Oklahoma. .........................12

6–7. Plots showing:

 6. Comparison of measured and regression-estimated total nitrogen concentrations

in water from (A) Spavinaw Creek near Colcord, Oklahoma, and (B) Beaty Creek

near Jay, Oklahoma.............................................................................................................15

 7. Comparison of measured and regression-estimated total phosphorus concentrations in water from (A) Spavinaw Creek near Colcord, Oklahoma, and (B) Beaty

Creek near Jay, Oklahoma. ................................................................................................16

8–10. Graphs showing:

 8. Comparison of measured and continuous regression-estimated total nitrogen concentrations in water from (A) Spavinaw Creek near Colcord, Oklahoma, and (B)

Beaty Creek near Jay, Oklahoma, Water Years 2005–2007. .........................................17

 9. Comparison of measured and continuous regression-estimated total phosphorus

concentrations in water from (A) Spavinaw Creek near Colcord, Oklahoma, and 

(B) Beaty Creek near Jay, Oklahoma, Water Year 2005–2007......................................18

 10. Daily mean specific conductance and daily mean streamflow at (A) Spavinaw

Creek near Colcord, Oklahoma, and (B) Beaty Creek near Jay, Oklahoma, Water

Years 2005–2007...................................................................................................................20

 11. Daily mean turbidity and daily mean streamflow at (A) Spavinaw Creek near

Colcord, Oklahoma, and (B) Beaty Creek near Jay, Oklahoma, Water Years

2005–2007..............................................................................................................................21

Tables

1. Estimates of fertilizer application, number of cattle and calves, and number of

broilers and other chickens sold for counties in the Eucha-Spavinaw basin,

Arkansas and Oklahoma, 2002....................................................................................................7

2. Streamflow statistics for continuous water-quality monitoring stations in the

Eucha-Spavinaw basin, Arkansas and Oklahoma. .................................................................7

3. Drainage areas, periods of record, and total nitrogen and total phosphorus loads

from water-quality stations in the Eucha-Spavinaw basin, Arkansas and Oklahoma, 

2002 .................................................................................................................................................7

4. Summary statistics for water-quality properties and constituents of manually

collected water samples for Spavinaw Creek near Colcord, Oklahoma, and Beaty 

Creek near Jay, Oklahoma, October 2004- September 2007................................................13

5. Regression equations for estimated total nitrogen and total phosphorus 

concentrations for Spavinaw Creek near Colcord, Oklahoma, and Beaty Creek

near Jay, Oklahoma, October 2004-September 2007............................................................14
v

Conversion Factors and Datums

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

mile (mi) 1.609 kilometer (km)

Area

acre 4,047 square meter (m2)

acre 0.004047 square kilometer (km2)

square mile (mi2) 2.590 square kilometer (km2) 

Flow rate

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

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

Mass

pound per year (lb/yr) 0.4536 kilogram per year (kg/yr)

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

°F=(1.8×°C)+32

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

°C=(°F-32)/1.8

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

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

25 °C).

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

Method detection limit (MDL)—Minimum concentration of a substance that can be measured 

and reported with 99-percent confidence that the analyte concentration is greater than zero. 

It is determined from the analysis of a sample in a given matrix containing the analyte (U.S. 

Environmental Protection Agency, 2002). At the MDL concentration, the risk of a false positive is 

predicted to be less than or equal to 1 percent.
vi
Abstract 

The Eucha-Spavinaw basin is the source of water for 

Lake Eucha and Spavinaw Lake, which are part of the water 

supply for the City of Tulsa. The City of Tulsa has received 

complaints of taste and odor in the finished drinking water 

because of deteriorating water quality. The deterioration is 

largely because of algal growth from the input of nutrients 

from the Eucha-Spavinaw basin. The U.S. Geological Survey, 

in cooperation with the City of Tulsa, implemented a continuous, real-time water-quality monitoring program in the 

Eucha-Spavinaw basin to better understand the source of the 

nutrient loading. This program included the manual collection of samples analyzed for nutrients and the collection of 

continuous, in-stream data from water-quality monitors. 

Continuous water-quality monitors were installed at two 

existing continuous streamflow-gaging stations—Spavinaw 

Creek near Colcord, Oklahoma, and Beaty Creek near Jay, 

Oklahoma, from October 2004 through September 2007. 

Total nitrogen concentrations for manually collected water 

samples ranged from 2.08 to 9.66 milligrams per liter for the 

water samples collected from Spavinaw Creek near Colcord, 

Oklahoma, and from 0.67 to 5.12 milligrams per liter for 

manually collected water samples from Beaty Creek near Jay, 

Oklahoma. Total phosphorus concentrations ranged from 0.04 

to 1.5 milligrams per liter for the water samples collected from 

Spavinaw Creek near Colcord and from 0.028 to 1.0 milligram 

per liter for the water samples collected from Beaty Creek 

near Jay. Data from water samples and in-stream monitors at 

Spavinaw and Beaty Creeks (specific conductance and turbidity) were used to develop linear regression equations relating 

in-stream water properties to total nitrogen and total phosphorus concentrations. The equations developed for the Spavinaw 

and Beaty sites are site specific and only valid for the concentration ranges of the explanatory variables used in the analysis. The range in estimated and measured phosphorus is not 

representative for the range of historic streamflow at the Beaty 

site and that regression equation would benefit from more high 

flow and high turbidity samples. In addition, all three study 

years had below average annual precipitation for the area, and 

streamflow was especially low in Water Year 2006. Average 

nutrient concentrations from October 2004 through September 

2007, which were drier than others, may not be a good indication of conditions in future wetter years.

The equations for the Spavinaw and Beaty sites may be 

used to estimate instantaneous nutrient concentrations, which 

can be used to compute loads and yields in real time in order 

to better characterize the effect of land-management practices 

in these watersheds on the transport of nutrients to Lake Eucha 

and Spavinaw Lake. The methods used in this study show 

promise for monitoring future effectiveness of implemented 

best management practices, development and monitoring of 

total maximum daily loads, early detection of taste-and-odor 

occurrences, and to anticipate treatment needs for water suppliers.

Introduction

The Eucha-Spavinaw basin is primarily in Benton County 

in northwestern Arkansas and in Delaware County in northeastern Oklahoma (fig. 1). Construction of Lake Eucha and 

Spavinaw Lake was completed in 1954 and 1924, respectively 

(Tortorelli, 2006) to provide wildlife habitat, recreational 

opportunities, and a reliable municipal water supply for the 

City of Tulsa. Lake Eucha receives water from Spavinaw 

Creek, Beaty Creek, Brush Creek, Rattlesnake Creek, Dry 

Creek, and a few minor tributaries upstream from Spavinaw 

Lake. Lake Eucha serves as an environmental and hydrologic 

buffer to Spavinaw Lake (City of Tulsa, 2008). The water 

flows from Spavinaw Lake to the City of Tulsa through a 

series of 60-mile long pipelines (Tortorelli, 2006). The water 

serves about 600,000 residents in the Tulsa metropolitan area 

and northeastern Oklahoma (Bob Brownwood, Water Supply 

Manager, City of Tulsa, written commun., July 2008).

Estimated Nutrient Concentrations and Continuous 

Water-Quality Monitoring in the Eucha-Spavinaw Basin, 

Northwestern Arkansas and Northeastern Oklahoma, 

2004–2007

By Victoria G. Christensen, Rachel A. Esralew, and Monica L. Allen
2 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007

Figure 1. The Eucha-Spavinaw basin, Arkansas and Oklahoma, with locations of selected streamflow and water-quality stations.

!

!

!

!

#*#*

#*

#*#*

#*

#*#*

#*

#*

#*

#*

#*

#*

!

!

!

!

!

Creek

Creek

Maysville

Decatur

Hiwasse

Gravette

Cherokee

City

Oklahoma

Arkansas

Dry

Beaty Creek

near Jay

07191222

Spavinaw Creek

near Colcord

071912213

Spavinaw Creek near

Sycamore 07191220

Spavinaw Creek

near Maysville

07191160

Lake Eucha

near Eucha

07191285

Spavinaw Lake

at Spavinaw

07191300

Spavinaw Creek

near Eucha

07191288

Lake Eucha

Spavinaw

Lake

Beaty Creek

Creek

Brush

Spavinaw

Rattlesnake Creek

Spavinaw Creek

near Cherokee

07191179

Jay

Eucha

Colcord

Sycamore

!

Tulsa Benton County

Delaware County

Oklahoma

Arkansas

Based from U.S. Geological Survey digital data, 1:100,000, 1983and U.S. Geological Survey National Hydrography DatasetAlbers Equal-Area Conic projection, North American Datum of 1983

0 5 10 Miles

0 5 10 Kilometers

94°15'W 94°30'W 94°45'W 95°0'W

36°30'N36°15'N

#* 07191222

071912200719128807191285

EXPLANATION

Boundary of sampling watershedBoundary of sampling watershed used in analysisTown with water-treatment plant that discharges into streams in theEucha-Spavinaw basin

#*

!

U.S. Geological Survey lake-level station and number

#* U.S. Geological Survey streamflow-gaging station and number#*

U.S. Geological Survey streamflow-gaging station and continuouswater-quality monitoring station and numberU.S. Geological Survey streamflow-gaging station and water-qualitymonitoring station and number
Introduction 3

The City of Tulsa has received complaints of taste and 

odor in finished drinking water linked to the eutrophication 

of Lake Eucha and Spavinaw Lake (Oklahoma Conservation 

Commission, 2007). Eutrophication results when excess nutrients cause the growth of algae and other aquatic plants. Taste 

and odor compounds in drinking water result in increased 

treatment costs in the treatment plant and removal seldom 

succeeds completely (Walker and others, 1989); therefore, 

identifying the factors that drive the algal growth is imperative for mitigating taste-and-odor problems (Christensen and 

others, 2006). Monitoring has identified some key factors that 

may affect taste and odor. Nutrient loading to the reservoir is 

one of these key factors. 

Water quality has been deteriorating in Lake Eucha and 

Spavinaw Lake because of increased phosphorus levels (City 

of Tulsa, 2007). Excess phosphorus in water can fertilize 

aquatic plants and cause excessive algal growth (fig. 2). Monitoring showed Lake Eucha and Spavinaw Lake to be impaired 

for fish most years because of low dissolved oxygen (City of 

Tulsa, 2007). Currently (2008), Lake Eucha and Spavinaw 

Lake are not supporting the Fish and Wildlife Propagation 

and Aesthetics designated uses (Oklahoma Department of 

Environmental Quality, 2002). The Spavinaw Creek and 

Beaty Creek basins are valuable for investigating the source 

of nutrients to the Lakes because of the basins size and the 

amount of water and nutrients supplied to Lake Eucha, relative 

to other tributaries. Storm and others (2002) estimated that 

Beaty Creek and Spavinaw Creek supply about 85 percent of 

the phosphorus entering Lake Eucha. 

Although previous monitoring has indicated increasing 

phosphorus loads to Lake Eucha and Spavinaw Lake (Wagner 

and Woodruff, 1997), nutrient monitoring historically has been 

biased toward sampling during low flow (Tortorelli, 2006), 

possibly leading to underestimated historical nutrient loads 

in the basin. The collection of runoff samples and continuous water-quality monitoring could more accurately identify seasonal trends in selected water-quality properties and 

estimated nutrient mass transported in the Eucha-Spavinaw 

basin. Runoff water-quality samples were collected at Beaty 

Creek near Jay and Spavinaw Creek near Colcord, and three 

other water-quality collection sites in the Spavinaw basin, as 

part of a U.S. Geological Survey (USGS) study in 2002–2004 

(Tortorelli, 2006). These data, in addition to fixed-period 

monthly sampling, were used to estimate average annual and 

seasonal nitrogen and phosphorus loads by using S-LOADEST (Insightful Corporation, 2005).

The collection of continuous water-quality data could 

help identify water-quality responses in the basin in real time 

and identify monthly and seasonal trends, which are valuable because high streamflow can have a substantial effect on 

chemical loads. Continuous water-quality data are valuable 

because concentration data from manually collected samples 

commonly are not sufficient for accurate load estimates 

(Christensen and others, 2000). In addition, continuous estimates of water-quality constituents in real time eliminate the 

waiting time inherent in laboratory analyses of chemical data, 

and may assist in early detection of water-quality issues in the 

watershed.

Collection of chemical constituents such as nitrogen and 

phosphorus can be labor-intensive and cost-prohibitive. As an 

alternative, water-quality properties measured by continuous, 

real-time water-quality monitors can be related to concentrations of these constituents in order to develop regression 

equations that can estimate constituent concentrations in real 

time. Instantaneous loads and yields may be computed from 

these estimated concentrations to gain a better perspective 

about the supply of nutrients to Lake Eucha and Spavinaw 

Lake in real time. Thus, a study was conducted by the USGS, 

in cooperation with the City of Tulsa to monitor nutrients and 

water properties at Spavinaw and Beaty Creeks, which flow 

into Lake Eucha and Spavinaw Lake. 

Specific objectives of the study were to:

1. Enhance the current water-quality monitoring efforts 

in the Eucha-Spavinaw basin by implementing continuous, real-time water-quality monitoring at two 

existing continuous streamflow-gaging stations—

Spavinaw Creek near Colcord (USGS station number 071912213) and Beaty Creek near Jay (USGS 

station number 07191222) (fig. 1)—to measure 

specific conductance, pH, water temperature, turbidity, and dissolved oxygen; and 

2. Develop regression models or equations by using 

water-quality properties (specific conductance and 

turbidity) and streamflow as surrogates for estimated 

total nitrogen and total phosphorus.

Purpose and Scope

This report describes the results of manually collected 

water samples from Spavinaw Creek and Beaty Creek and 

analyzed for total nitrogen and total phosphorus, and describes 

the continuous real-time water-quality data from monitors at 

Spavinaw Creek near Colcord and Beaty Creek near Jay and 

the benefits of continuous and real-time data. The report also 

describes the process for using the water-quality properties to 

estimate total nitrogen and total phosphorus concentrations. 

Separate sets of equations were created for the Spavinaw and 

Beaty streamflow-gaging stations to assist the City of Tulsa in 

determining the source of nutrients entering Lake Eucha and 

Spavinaw Lake.

Increasing interest in water quality makes this study of 

regional and national importance because this study shows 

how nutrient concentrations may be estimated continuously 

and in real time by using continuous monitoring of other 

surrogate properties. Once an equation is created between a 

surrogate and a nutrient, that equation can be used to estimate 

concentrations, loads, and yields for that site. The methods 

used in this study may be used for other sites in Oklahoma 

and the Nation to monitor future effectiveness of implemented 

land-resource best-management practices. Additionally, real-
4 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007

time monitoring can be used by water suppliers to anticipate 

treatment needs.

This report summarizes estimated nutrient concentrations and continuous water-quality monitoring in the EuchaSpavinaw basin during 2004–07. The information can be used 

to (1) assist in determining the effects of land-use practices 

on surface-water quality, (2) aid in supporting Federal, State, 

Tribal, and local efforts to improve water quality in the EuchaSpavinaw basin, (3) provide water-resources information that 

is useful to multiple parties for planning and operation through 

the dissemination of real-time hydrologic data, and (4) provide 

information that environmental officials, land managers, 

and land owners need to assess human health and ecosystem 

effects. 

Description of Study Area

The meandering streams in the Eucha-Spavinaw basin 

have low gradients and wide valleys (Oklahoma Conservation 

Commission, 2007) and drain about 388 square miles (mi2 ) in 

northeastern Oklahoma and northwestern Arkansas (Tortorelli, 

2008). The water quality of the Eucha-Spavinaw basin is 

affected by various environmental factors. Physiography, geology, soil type, climate, land use, population, and water use can 

directly or indirectly affect water quality (Adamski and others, 

1995). Therefore, a brief description of these environmental 

factors follows.

The Eucha-Spavinaw basin is in the Ozark Plateaus 

physiographic province (Fenneman, 1938) and the Springfield 

Plateau subdivision. The basin is underlain by limestone and 

cherty limestone of Mississippian age (Fenneman, 1938) and 

the Springfield Plateau aquifer (Adamski and others, 1995; 

Renken, 1998). Most of the soils in the Ozark Plateau have a 

high potential for nutrients and other dissolved constituents to 

be leached to the ground water and have a high potential for 

runoff to surface-water systems (Adamski and others, 1995). 

The karstic landscape of the Ozark Plateau was shaped by the 

dissolution of layers of soluble rock. Nitrate is easily transported to ground water in a karstic system through sinkholes 

and open fractures. 

The mean annual temperature in the Eucha-Spavinaw 

basin is about 61o

F, January being the coldest month (average 

minimum temperature 27oF) and July and August being the 

warmest months (average daytime temperature 92oF) (National 

Oceanic and Atmospheric Administration, 2002). Mean annual 

precipitation is about 44 inches (1971–2000), and September is the month having the highest average precipitation 

Figure 2. Algal bloom in Lake Eucha, July 11, 2001.
Introduction 5

(for the Spavinaw Temperature and Precipitation Station No. 

168, which is located at the USGS streamflow gaging station 

number 07191300, Spavinaw Lake at Spavinaw, Oklahoma, 

National Oceanic and Atmospheric Administration, 2002).

Mean annual precipitation was 33.41 inches in 2005, 

35.75 inches in 2006, and 42.61 inches in 2007 (data from 

the Jay Weather Station, Oklahoma Climatological Survey, 

2008a). Precipitation for all 3 years of the study was less than 

the mean annual precipitation reported for the nearby Spavinaw Weather Station (44 inches, 1971–2000, National Oceanic 

and Atmospheric Administration, 2002) and substantially less 

than the mean annual precipitation from 1971–2000 reported 

for the Jay Weather Station (46.5 inches, Oklahoma Climatological Survey, 2008b). Precipitation is essential because precipitation contributes to runoff, which may result in increased 

concentrations of sediment and nutrients reaching the stream. 

All three study years had less than average annual precipitation for the area.

The basin supports a variety of land uses (fig. 3), including agriculture (row crops, and pasture and hay) and forest and 

small urban areas throughout the watershed (Storm and others, 

2002; DeLaune and others, 2006). The significance of agriculture in the basin is evident from the estimates of commercial 

fertilizer and manure applications for counties in the EuchaSpavinaw basin (table 1). Application of nutrients to crops 

and pasture as commercial fertilizer and manure can result 

in increased nutrients in surface and ground water. The total 

nutrient input from fertilizers is computed by adding commercial fertilizer to manure. Commercial fertilizer and manure 

applied is greater in Benton County, Arkansas, than in Delaware County, Oklahoma, (U.S. Department of Agriculture, 

2002). Benton County also has a larger cattle and calf inventory, and more broilers and chickens sold when compared to 

Delaware County (table 1). 

U.S. Census figures for 2006 show a population of 

126,000 residents for Benton County, Arkansas, (U.S. Census 

Bureau, 2008). Delaware County, Oklahoma, had 37,000 residents in 2000 (2006 data not available; U.S. Census Bureau, 

2008). The largest towns in the basin include Colcord, Decatur, Gravette, Jay, and Maysville (fig. 1). Ratios of surfacewater use to ground-water use are 41.1/8.8 in Benton County 

and 2.6/1.9 in Delaware County (in millions of gallons per 

day; Adamski and others, 1995). Most of this water is used for 

public supply and very little agricultural water use is reported 

for these two counties. 

Streamflow in the Eucha-Spavinaw Basin

The combined drainage areas of Spavinaw and Beaty 

Creeks account for about 73 percent of the drainage basin of 

Lake Eucha. Several small tributaries contribute surface water 

and are possible additional sources of nutrients and other 

chemicals to the Lakes. 

Streamflow and other water data in this report are compared and analyzed in terms of water year. The water year is 

a 12-month period that begins on October 1 and ends on September 30. For example, the 2005 water year begins October 

1, 2004, and ends on September 30, 2005.

Annual mean daily streamflow in the Eucha-Spavinaw 

basin was computed for Water Years 2005-2007. Streamflow 

was greater in Water Years 2005 and 2007 than in 2006 (table 

2). During the period of real-time water-quality data collection 

(2005-2007), Beaty Creek near Jay was dry for 86 days.

Water Year 2006 was especially dry when compared 

to the period of record at Beaty Creek near Jay, water years 

1999–2007 (Blazs and others, 1998-2007). The maximum 

daily mean streamflow during the period of record for the 

two sites are not comparable because the period of record for 

Spavinaw did not include 1999. When observing the period 

from October 2002 through September 2007, the maximum 

peak flow and highest daily mean flow in both streams was in 

2004 and the minimum flow in Spavinaw Creek was in 2006. 

During the period of record, zero flow was recorded at Beaty 

Creek near Jay several times during summer (July through 

September). However, Beaty had extreme flows in 1999, 

which is reflected in the maximum daily mean streamflow 

for the period of record (table 2). Mean annual runoff often 

is used for comparing runoff characteristics between basins. 

Runoff is defined as the water that drains from the land into 

the stream or river channels after precipitation and is a function of precipitation amounts, topography, geology, soil moisture, and other factors (Adamski and others, 1995). Runoff can 

be computed by using the Base-Flow Index program (Institute 

of Hydrology, 1980a, 1980b; Wahl and Wahl, 1995). The 

mean fraction of annual total flow that is runoff, computed by 

using streamflow data for the period of record, is 44 percent at 

Spavinaw Creek near Colcord, and 68 percent at Beaty Creek 

near Jay. The lower runoff percentage at Spavinaw Creek may 

indicate the importance of ground-water inputs at this site.

Sources of Nutrients

Nitrogen and phosphorus loads are greater at Spavinaw Creek than Beaty Creek because of greater streamflow 

at Spavinaw Creek. However, the runoff component of the 

annual nitrogen and phosphorus loads from Beaty Creek was 

higher (85 percent and 98 percent, respectively; Tortorelli, 

2006) than the runoff component of annual nitrogen and phosphorus loads from Spavinaw Creek (60 percent and 66 percent, respectively; Tortorelli, 2006). Total nitrogen delivered 

during runoff was substantially less for all Spavinaw Creek 

sites compared to the Beaty Creek near Jay site (table 3). 

Nitrate and phosphorus tend to have different transport 

mechanisms in watersheds. Nitrate in the dissolved form often 

is transported in ground water. Total nitrogen and phosphorus 

tend to bind with organics or metals in soils and, therefore, are 

more conspicuous during runoff. 

Sources of nutrients, including nitrogen and phosphorus, 

to the streams in the Eucha-Spavinaw basin include point and 

nonpoint sources. Point sources include wastewater-treatment 

plant discharges from urban areas (fig. 1) and septic systems. 

Spavinaw Creek (fig. 1) receives wastewater-treatment plant 
6 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007

Figure 3. Comparison of land use in the basins draining to Spavinaw Creek near Colcord, Oklahoma (071912213), and Beaty Creek near Jay, Oklahoma (07191222), in the EuchaSpavinaw basin, Arkansas and Oklahoma.
Introduction 7

Table 1. Estimates of fertilizer application, number of cattle and calves, and number of broilers and other chickens sold for counties 

in the Eucha-Spavinaw basin, Arkansas and Oklahoma, 2002. (Data modified from U.S. Department of Agriculture, 2002).

County

Percentage 

of Beaty 

Creek 

basin in

county

Percentage 

of 

Spavinaw 

Creek 

basin in 

county

Land in 

farms 

(acres)

Acres 

treated 

with 

commercial 

fertilizer

Acres 

treated 

with 

manure1

Total 

acres 

treated

Percentage 

of county 

area treated 

with 

fertilizer1

Number 

of cattle 

and

calves

Number of 

broilers and 

other

chickens

sold

Delaware, 

Okla.

67.4 23.5 282,106 53,185 21,346 74,531 26 74,719 37,154,935

Benton, 

Ark.

32.6 76.5 312,646 84,703 62,829 147,532 47 113,588 128,066,609

1

 Acres treated with manure includes poultry manure.

2

 Fertilizer application to land other than farms (for example, golf courses or residences) was not included.

Table 2. Streamflow statistics for continuous water-quality monitoring stations in the Eucha-Spavinaw basin, Arkansas and 

Oklahoma.

[all years are water years; ft3/s, cubic feet per second]

USGS site 

number Site name

Annual mean daily streamflow (ft3/s) Mean daily streamflow 

for period of record (ft3/s)

2005 2006 2007 Period of record (dates)

Minimum 

(date)

Maximum 

(date)

071912213 Spavinaw Creek near Colcord, 

Okla.

152.8 30.9 85.4 106

(2002-2007)

8.7

(7/29/2006)

7,720

(7/3/2004)

07191222 Beaty Creek near Jay, Okla. 52.8 5.79 33.8 39.6

(1999-2007)

01 17,400

(6/30/1999)

1

 Zero flow reported on several occasions throughout the period of record.

Table 3. Drainage areas, periods of record, and total nitrogen and total phosphorus loads from water-quality stations in the EuchaSpavinaw basin, Arkansas and Oklahoma, 2002. (Modified from Tortorelli, 2006).

[USGS, U.S. Geological Survey; mi2, square miles; lb/yr, pounds per year]

USGS site

number Site name

Drainage 

area

 (mi2)

Period 

of

record

Mean annual nitrogen load Mean annual phosphorus load

Total load 

(lb/yr)

Percent 

delivered 

during runoff

Total load 

(lb/yr)

Percent 

delivered 

during runoff

07191160 Spavinaw Creek near Maysville, Ark. 88.2 2002-2007 521,000 64 20,000 93

07191179 Spavinaw Creek near Cherokee, Ark. 104 2002-2007 656,000 60 34,500 66

07191220 Spavinaw Creek near Sycamore, Okla. 133 1962-2007 914,000 66 47,800 78

071912213 Spavinaw Creek near Colcord, Okla.1 163 2002-2007 1,090,000 61 50,700 80

07191222 Beaty Creek near Jay, Okla.1 59.2 1999-2007 257,000 85 27,000 98

1

Sites that have continuous water-quality monitors
8 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007

discharges; whereas, Beaty Creek does not (Tortorelli, 2006). 

The Arkansas towns of Decatur and Gravette are permitted 

wastewater-treatment facilities in the Eucha-Spavinaw basin 

(fig. 1). 

 Nonpoint sources include agricultural runoff, urban 

runoff (Oklahoma Water Resources Board, 2002), and runoff 

from soils. Agricultural activities contribute nutrients from 

the pasturing and confined feeding of livestock, and the use of 

synthetic fertilizers. Agricultural runoff may contain nutrients 

from inorganic phosphates added to soils as fertilizer and 

manure from animal production. A possible major source of 

nutrients in the Eucha-Spavinaw basin is the phosphorus-rich 

waste of commercial poultry growing operations (DeLaune 

and others, 2006). Poultry litter is used as a fertilizer for pastures in the basin (table 1). Geologic formations contain small 

amounts of phosphorus (Hem, 1992) and may contribute to 

phosphorus load. 

Acknowledgments

The authors thank many people for their contributions to 

the data collection and station analysis presented in this report. 

There were numerous City of Tulsa personnel that participated 

in monthly water-quality sampling. There were numerous 

USGS personnel that participated in the runoff-event waterquality sampling and water-quality monitor maintenance. The 

entire Oklahoma Water Science Center Data Section and all 

three field offices added to the effort, but special thanks goes 

to the Tulsa Field Office for their contribution to the data collection and data processing. 

Methods

Data Collection and Analysis

Continuously recording water-quality monitors (fig. 4) 

were installed at Spavinaw Creek near Colcord in November 

2004 and Beaty Creek near Jay in March 2005. The waterquality properties measured by the monitors were specific 

conductance, pH, water temperature, turbidity, and dissolved 

oxygen. The monitors were programmed to collect data readings every hour at Spavinaw Creek near Colcord, and every 

half-hour at Beaty Creek near Jay. These data were transmitted 

every 4 hours to the USGS offices in Tulsa. 

Each sensor on the water-quality monitor has a certain 

range of operation. Specific conductance, pH, water temperature, and dissolved oxygen sensors have wide ranges of 

operation that were not exceeded during the study and are not 

likely to be exceeded in the streams in the Eucha-Spavinaw 

basin. The turbidity sensors at these sites have maximum 

readings of about 1,200 formazin nephelometric units (FNU). 

This value was not exceeded on the in-stream monitors from 

October 2004 through September 2007. However, a value of 

1,140 FNU was recorded on the in-stream monitor and a value 

of 1,480 FNU was recorded on the field monitor at the Spavinaw site during the study period and the range of the turbidity 

meters possibly will be exceeded in the future. The regression equations described in this report are only valid for the 

range of sensor values measured from October 2004 through 

September 2007. 

The USGS manually collected samples during high flow 

by using depth and width integrating techniques in addition 

to the data collected by the in-stream water-quality monitors 

(U.S. Geological Survey, 2006). The City of Tulsa collected 

low-flow samples about once per month. The samples collected by the City of Tulsa generally were not depth and width 

integrated; these samples were collected from a single point 

near the midpoint of the stream. 

Samples collected by using depth and width integrating 

techniques generally are more representative of stream-water 

quality than those samples collected from a single point (Ward 

and Harr, 1990); however, the water-quality monitor collected 

data from a single point in the stream. Therefore, differences 

between measurements in depth- and width-integrated water 

samples and measurements made by using the water-quality 

monitor existed, because of different sampling techniques. 

The City of Tulsa provided the analytical services for 

all samples collected by USGS and City of Tulsa personnel. 

Samples were analyzed for water properties and nutrients 

including nitrite plus nitrate (U.S Environmental Protection 

Agency Method 353.2; O’Dell, 1993a), total Kjeldahl nitrogen 

(U.S Environmental Protection Agency Method 351.2; O’Dell, 

1993b), and total phosphorus (U.S Environmental Protection Agency Method 365.2; U.S. Environmental Protection 

Figure 4. A water-quality monitor.
Methods 9

Agency, 1983), by using colorimetry methods. Total nitrogen 

was computed by adding nitrite plus nitrate and total Kjeldahl 

nitrogen values. Total nitrogen was computed by assuming that any concentration of nitrite plus nitrate or Kjeldahl 

nitrogen that was measured less than method detection limits 

were equal to 0.0 milligram per liter (mg/L). Total nitrogen 

and total phosphorus concentrations are listed in appendix 1 

and appendix 2. 

Quality Assurance and Quality Control

Quality assurance was achieved mainly through following a prescribed method of protocols and procedures as 

described in the National Field Manual (U.S. Geological 

Survey, 2006) for environmental samples and in Guidelines 

and Standard Procedures for Continuous Water-Quality Monitors (Wagner and others, 2006) for the monitors. Additionally, 

the collection and analysis of quality-control (QC) samples 

are mandated components of USGS water-quality field studies 

(U.S. Geological Survey, 2006) and compose a valuable part 

of the overall quality assurance of this project. The goal of 

QC sampling is to identify, quantify, and document bias and 

variability in data that result from the collection, processing, 

shipping, and handling of samples. Field blanks and field replicates were collected by the USGS at a rate of 10-20 percent 

of the number of environmental samples. The City of Tulsa 

collected QC samples at about the same rate.

Total nitrogen components (total Kjeldahl nitrogen and 

nitrite plus nitrate) were nondetectable in blank samples, and 

the median relative percent difference from sequential replicate analyses for the components of total nitrogen resulted in 

a difference of calculated total nitrogen that was acceptable at 

less than one percent. However, two blank samples analyzed 

for total phosphorus from the Beaty Creek site had values 

of 0.011 and 0.006 mg/L. Those results indicate that some 

contamination resulted in the collection or processing of these 

samples and that potential contamination should be considered 

when evaluating the phosphorus data contained in this report. 

Concentrations of total phosphorus for this report ranged from 

0.028 to 1.50 mg/L. The duplicate total phosphorus analyses 

were acceptable with a median relative percent difference of 

2.6.

The monitors were cleaned and each sensor calibration 

was checked during site visits. The water-quality monitors 

were inspected onsite by USGS personnel at least monthly to 

maintain calibration. Transmitted data were reviewed daily, 

and in the event of instrument malfunction or rapidly changing conditions, the monitors were cleaned and calibrated more 

often. Each monitor location was evaluated to determine if 

it needed to be moved because of changing streamflow or 

streambed conditions. Calibration procedures followed the 

suggested manufacturer’s guidelines (Yellow Springs Instruments, Inc., 2002). Data corrections were occasionally applied 

based on calibration differences and changes in instrumentation response because of cleaning and general maintenance, 

in accordance with procedures detailed by Wagner and others 

(2006).

Development of Regression Equations

A system of estimating water-quality constituents was 

developed by the USGS Kansas Water Science Center. More 

information on this method can be found in Christensen and 

others (2000), Christensen (2001), and Rasmussen and Ziegler 

(2003). The following section is a brief explanation of those 

methods.

Total nitrogen or total phosphorus can be expressed in 

terms of a surrogate chemical property by using simple linear 

regressions (Helsel and Hirsch, 1992). Data from water-quality samples and the water-quality monitors at Spavinaw Creek 

and Beaty Creek were used to develop regression equations. 

These regression equations can be used to estimate instantaneous concentrations, daily loads, annual loads, and yields of 

total nitrogen and total phosphorus. The simplest regression 

equation is expressed as:

yi = mxi + b + ei (1)

where 

yi is the ith observation of the response 

(dependent) variable;

m is the slope;

xi is the ith observation of the explanatory 

(independent) variable;

b is the intercept;

ei is the random error for the ith observation; 

and 

i = 1, 2, ...number of samples.

The parameters m and b were estimated from the dataset 

by using least-squares estimation (Helsel and Hirsch, 1992). A 

step-wise procedure (Ott, 1993, p. 656) and an overall method 

(Helsel and Hirsch, 1992, p. 312-314) were used. The possible 

explanatory variables included each of the sensor measurements of the water-quality monitor (specific conductance, pH, 

water temperature, turbidity, and dissolved oxygen), streamflow, and time. If several acceptable equations were available, 

the one with the lowest PRESS statistic (Helsel and Hirsch, 

1992, p. 248) and a low Mallow’s Cp value, close to the 

number of parameters in the model, was selected (Draper and 

Smith, 1998, p. 332). 

Common diagnostic statistics, in addition to the PRESS 

and Mallow’s Cp, which were used to select the equations, 

were used to evaluate the selected regression equations. These 

diagnostic statistics are the root mean square error (RMSE) 

and coefficient of determination (R2

). RMSE is the square root 

of the mean square error (MSE).
10 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007

RMSE is calculated as follows:

2

∑

n

  y −   i E (yi) , RMSE = i=1 (2)

n − 2

 

where 

yi is the value of y at the ith data point;

E(yi) is the estimated value of the y at the ith data 

point;

n is the number of samples.

The RMSE is determined for each equation to assess the 

variance between estimated and measured values. 

The coefficient of determination, R2 is the fraction of the 

variation explained by regression:

R2 = 1.0 – (SSE / SSy) (3)

The sum of squares of errors (SSE) is a dimensional 

measure of fitting y on x and is a measure of the unexplained 

variability. The sum of squares about the mean (SSy) represents the total variability (explained and unexplained) about 

the mean in y values. SSE and SSy are dimensional measures.

Graphical plots were constructed to examine the linearity 

of the relation between explanatory and response variables. 

Certain explanatory variables and response variables were 

transformed to convert all equations in this report to linear 

equations. Transformations (for example, base-10 logarithmic 

or power) of variables can eliminate curvature and simplify 

analysis of the data (Ott, 1993, p. 454). Retransformation of 

regression estimated concentrations can cause an underestimated chemical load when adding individual load estimates 

over a long period of time. The smearing bias-correction factor

(Duan, 1983) can be used to correct for underestimation. Cohn 

and others (1989), Gilroy and others (1990), and Hirsch and 

others (1993) provide additional information on interpreting 

the results of regression-based load estimates. 

In-stream monitor data were used in the regression analysis if USGS field instrument values were not available. USGS 

field instrument values were not available for samples collected by the City of Tulsa. For these samples, if an in-stream 

monitor value time did not correspond to the sample collection

time, the in-stream values were interpolated. For example, 

at the Spavinaw Creek near Colcord site, which has hourly 

data, the nearest hourly values collected before and after the 

sample were used for interpolation. At times when turbidity 

values were changing rapidly, these turbidity values may have 

been interpolated as much as 3 hours before or 3 hours after 

the sample time depending on the variability of the conditions and to prevent interpolation among anomalous turbidity 

spikes. These spikes are more likely to happen during rapidly 

changing stream conditions such as streamflow events where 

turbidity is highly varied or localized stream disturbances that 

may result when bathing cattle kick up sediment in the stream 

upstream from the in-stream monitor. For Beaty Creek near 

Jay, a similar procedure was used, except that the in-stream 

monitor transmits every half hour instead of every hour. The 

field values and in-stream monitor values used in the regression analyses are provided in appendix 1 and appendix 2.

Uncertainty Associated with Regression-Based 

Estimates

All the estimates in this report have some uncertainty. 

The uncertainty of these equations should be considered if the 

equations are to be used to estimate future concentrations or 

nutrient loads. The R2, RMSE, and bias-correction factor all 

give an indication of uncertainty. In addition, the in-stream 

water-quality monitors have an upper limit of measurement 

regarding turbidity (about 1,200). This upper limit can cause 

underestimated concentrations and may have a substantial 

effect on load and yield estimates that use turbidity as a surrogate. However, except for one value of 1,480 FNU, turbidity 

values did not reach this upper limit during the study period. 

Another limitation is the lack of intermediate flow and 

turbidity data available for the development of the regression 

equations. The USGS collected data during most streamflow 

peaks and, therefore, many samples were collected during the 

highest flows. City of Tulsa collected routine samples and, 

therefore, many samples were collected during low flows. 

However, samples were not consistently collected during 

intermediate ranges of streamflow. The calibration dataset may 

not be representative of the various future conditions for each 

stream because of the lack of samples during high and intermediate flow and turbidity. For example, the small number of 

high flow or high turbidity data available during 2004–2007 

may not be representative of the various historic or future 

conditions in the stream. Average nutrient concentrations 

estimated by using 2004–2007 data may not be an accurate 

indication of conditions in future years because all three study 

years were below the average annual precipitation for the area. 

Differences in water-quality monitor instrumentation, 

even of the same model type, can result in inconsistent readings among instruments. These differences may be caused 

by several factors, including monitor placement, differences 

in calibration, and age or wear of the instrument. Although 

quality assurance practices were performed to ensure that 

in-stream and field monitor values were accurate, differences 

often were observed. The use of water-property values from 

the same instrument may be preferable to develop the regression equation because the regression equations are being 

developed to estimate nutrient concentrations from water 

properties of the in-stream water-quality monitor. Either an 

interpolated value or the field instrument value was used in the 

regression because the in-stream values were not always available at the same time that a sample was collected. 
Nutrient Concentrations in the Eucha-Spavinaw Basin 11

Nutrient Concentrations in the EuchaSpavinaw Basin

Measured Concentrations

Streamflow, base flow, and runoff during the years of 

sample collection affected water-quality. For Spavinaw Creek 

and Beaty Creek daily mean streamflow from October 2004 

through September 2007 generally was less than the mean 

daily streamflow during the period of record (water years 

2001–2007 for Spavinaw Creek and 1999–2007 for Beaty 

Creek) (fig. 5). Streamflow measurements for water years 

2005 to 2007 at Spavinaw Creek ranged from 11 to 5,700 

cubic feet per second (ft3/s) and from 0 to 2,050 ft3/s at Beaty 

Creek (table 4). 

All three study years had below average annual precipitation for the area, and streamflow was especially low in Water 

Year 2006 (table 2). Average nutrient concentrations from 

October 2004 through September 2007, which were drier than 

others, may not be a good indication of conditions in future 

wetter years.

Water-quality samples were collected and analyzed for 

water properties and selected chemicals from October 2004 

through September 2007 (table 4). These samples were collected throughout various streamflow conditions (fig. 5). 

Streamflow was low in 2006 at both stations, as evident from 

the graph, and no runoff samples were collected for nearly a 

year during 2005. 

Water properties also were collected at the same time as 

manually collected water-quality samples in addition to the 

continuous in-stream water-quality monitor measurements. 

The turbidity was notably different between sites from October 2004 through September 2007 (table 4). The maximum 

turbidity at Spavinaw Creek was 1,480 FNU; whereas, the 

maximum turbidity at Beaty Creek was 450 FNU. Part of this 

difference is likely because of the lower streamflow at Beaty 

Creek. The maximum turbidity at Spavinaw Creek 

(1,480 FNU, collected on June 12, 2007, at 2:45 PM) measured in manually collected samples was higher than the 

maximum turbidity measured by the in-stream water-quality 

monitor (1,140 FNU, collected on July 1, 2005, at 2:00 PM) 

possibly because of the different instruments used to measure 

the turbidity. On June 12, 2007, when the concentration for 

the manually collected sample was 1,480 FNU, the in-stream 

monitor recorded 1,090 FNU (measured on the same date at 

3:00 PM). This difference may be because of rapidly changing 

conditions between the time the sample was collected and the 

time the monitor recorded the turbidity value.

Seasonal variation in water quality was difficult to evaluate for some periods because of the number of samples collected and annual median nitrogen and phosphorus concentrations were similar for all 3 years. Total nitrogen concentrations 

from water samples collected at Spavinaw Creek ranged from 

2.08 to 9.66 mg/L; nitrogen concentrations were lower for 

water samples collected at Beaty Creek and ranged from 0.67 

to 5.12 mg/L. Total phosphorus concentrations ranged from 

0.04 to 1.5 mg/L at Spavinaw Creek and from 0.028 to

1 mg/L at Beaty Creek. Land use practices likely have an 

effect on the nutrient concentrations at each site. Total nitrogen 

and phosphorus concentrations may be higher in the Spavinaw 

Creek basin, when compared with Beaty Creek basin, because 

of the number of chicken farms and the spreading of manure 

on pastures, which is greater in the Spavinaw basin (Storm and 

Others, 2002), as well as wastewater effluent which affects 

Spavinaw Creek (Tortorelli, 2006).

Water samples also were periodically analyzed for suspended sediment. The results are included in table 4 because 

of the relation of suspended sediment to turbidity. Suspended 

sediment represents suspended solid-phase material in a water 

sample (Gray and others, 2000); turbidity is the amount of 

opaqueness in a water sample, caused by suspended material. 

Regression-Estimated Concentrations and 

Continuous Water-Quality Monitoring

Water properties can be measured directly and continuously by the in-stream water-quality monitor. However, in 

order to get continuous measures of total nitrogen and total 

phosphorus, concentrations were estimated on the basis of 

in-stream water-quality monitor measurements and regression 

analysis. Therefore, the relation between concentrations of 

nitrogen and phosphorus and water properties was determined, 

and regression equations were developed (table 5). R2, RMSE, 

and bias-correction factor were calculated for each equation.

Through the least-squares regression process, certain 

explanatory variables were selected that have a significant 

relation to the response variable. However, an explanatory 

variable was included in the regression equation only if a 

physical basis or explanation necessitated inclusion. Each 

equation in table 5, is site specific (applies only to that site) 

and are valid only for the concentration ranges given in 

table 5. For example, the maximum turbidity value that can be 

used to predict total phosphorus at Beaty is 450 FNU compared to 1,480 FNU at Spavinaw Creek. More sampling is 

necessary to verify the Beaty total phosphorus model at higher 

turbidity values.

Nitrogen Equations

Nitrogen in water includes elemental nitrogen, ammonia, 

nitrite, or nitrate. Having an estimate for total nitrogen from 

the Spavinaw Creek and Beaty Creek sites is necessary for 

understanding the sources of nitrogen to Lake Eucha, where 

nitrogen can lead to excessive algal growth. A comparison of 

measured and regression-estimated nitrogen at Spavinaw and 

Beaty Creeks are substantially different (fig. 6). 

The explanatory variables for estimated nitrogen in water 

from Spavinaw Creek were streamflow, turbidity, and specific 

conductance. Streamflow is positively related to nitrogen for 

Spavinaw Creek and Beaty Creek. A positive relation may 
12 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007

Figure 5. Streamflow divided into total flow and base flow, mean of daily mean streamflow for the period of discharge record, and 

base-flow and runoff water-quality samples during Water Years 2005–2007 at (A) Spavinaw Creek near Colcord, Oklahoma, (period 

of record, 2002–2007) and (B) Beaty Creek near Jay, Oklahoma, (period of record 1999–2007). STREAMFLOW; IN CUBIC FEET PER SECOND

2004 2005 2006 2007

YEAR

Total flow (daily mean streamflow)

Base flow (daily mean streamflow)

Mean of daily mean streamflow

for the period of record

Base flow water-quality sample

(Instantaneous streamflow)

Runoff water-quality sample

(Instantaneous streamflow)

EXPLANATION

A. Spavinaw Creek near Colcord, Oklahoma (station 071912213)

B. Beaty Creek near Jay, Oklahoma (station 07191222)

101

102

103

104

100

10-1

10-2

101

102

103

104

100
Nutrient Concentrations in the Eucha-Spavinaw Basin 13

Table 4. Summary statistics for water-quality properties and constituents of manually 

collected water samples for Spavinaw Creek near Colcord, Oklahoma, and Beaty Creek near 

Jay, Oklahoma, October 2004–September 2007.

[ft3/s, cubic feet per second; μS/cm, microsiemens per centimeter at 25 degrees Celsius; oC, degrees Celsius; 

FNU, formazin nephelometric units; mg/L, milligrams per liter]

Water

property or

constituent

Sample size Minimum Maximum Mean Median

Spavinaw Creek near Colcord, Okla. (071912213)

Streamflow, ft3/s 57 11 5,700 279 68

Specific conductance,

μS/cm1

21 88 377 304 323

pH, standard units 21 6.4 7.8 7.4 7.4

Water temperature,

o

C

21 10.2 23.4 18.1 17.9

Turbidity,

FNU1

44 0.3 1,480 47.6 1.1

Dissolved oxygen,

mg/L

21 6.2 11.1 8.8 8.2

Nitrogen, total, mg/L 56 2.08 9.66 3.8 3.39

Phosphorus, total, 

mg/L

52 0.04 1.5 0.15 0.082

Suspended sediment,

mg/L

18 1 1,810 120 9

Beaty Creek near Jay, Okla. (07191222)

Streamflow, ft3/s 54 0 2,050 157 18.5

Specific conductance,

μS/cm1

20 124 322 253 272

pH, standard units 20 6.8 7.7 7.4 7.4

Water temperature,

o

C

20 8 24.1 18.6 19.6

Turbidity,

FNU1

43 0 450 19.1 0.7

Dissolved oxygen,

mg/L

20 5.3 11.9 8.2 8.1

Nitrogen, total,

mg/L

52 0.67 5.12 2.35 2.06

Phosphorus, total,

mg/L

50 0.028 1 0.118 0.053

Suspended sediment, 

mg/L

18 2 664 93.4 8.5

1

Field values were measured at the time of sample collection and do not include interpolated values for 

specific conductance and turbidity that were used in regression equations (table 5).
14 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007 Table 5. Regression equations for estimated total nitrogen and total phosphorus concentrations for Spavinaw Creek near Colcord, Oklahoma, and Beaty Creek near Jay, Oklahoma, October 2004–September 2007. [R2, coefficient of determination; RMSE, root mean square error; n, number of samples used to develop regression equation; TN, total nitrogen in milligrams per liter; Q, streamflow in cubic feet per second; FNU, turbidity in formazin nephelometric units; SC, specific conductance in microsiemens per centimeter at 25 degrees Celsius; TP, total phosphorus in milligrams per liter] Constituent Equation R2 RMSE Bias- correction factor 1

2 n

Sample

concentration 

range

Standard 

deviation p-value

Spavinaw Creek near Colcord, Okla. (071912213)

Total nitrogen log10TN = 0.150log10Q-0.124log10FNU-1.11log10SC+3.06 0.708 0.0794 1.02 46 TN 2.08-9.66 1.47 --

Q 11-5,700 881 <0.0001

FNU 0-1,480 224 0.0002

SC 88-377 54 <0.0001

Total phosphorus TP = 0.0009FNU+0.0001Q+0.0806 0.958 0.0557 1.01 43 TP 0.04-1.5 0.27 --

FNU 0-1,480 232 <0.0001

Q 12-5,700 910 <0.0001

Beaty Creek near Jay, Okla. (07191222)

Total nitrogen log10TN = 0.170log10Q+0.0924 0. 602 0.1431 1.06 50 TN 0.67-5.12 1.13 --

Q 0-2,050 340 <0.0001

Total phosphorus TP = 0.0013FNU+0.0003Q+0.0425 0.978 0.0021 0.97 41 TP 0.028-0.89 0.19 --

FNU 0-450 57 <0.0001

Q 0-2,050 208 <0.0001

1After Duan (1983)2Appendixes 1 and 2 list all samples used in the regression analyses
Nutrient Concentrations in the Eucha-Spavinaw Basin 15

indicate a nonpoint source of nitrogen. Turbidity is significantly related to total nitrogen at Spavinaw Creek, but not at 

Beaty Creek. Turbidity is, however, negatively related to total 

nitrogen. This negative relation was not expected and could be 

the result of a high concentration of dissolved forms of nitrogen in Spavinaw Creek. Nitrate is transported as a dissolved 

water-quality constituent (Hem, 1992). The negative relation 

could be explained if the total nitrogen in samples from Spavinaw Creek contained high concentrations of nitrate relative 

to the total nitrogen concentration. Unfortunately, none of 

the water samples analyzed for nitrogen species were filtered 

and, therefore, the concentrations of dissolved nitrite or nitrate 

are unavailable. Another possible explanation for the negative relation between total nitrogen and turbidity is the high 

leverage of the base-flow samples. When only the base-flow 

samples are plotted, the relation is obviously negative, perhaps 

because of the influence of nitrate from the wastewater-treatment plants at Decatur and Gravette and septic systems. When 

the four highest flow samples are plotted, the relation between 

turbidity and total nitrogen is positive. 

Specific conductance is an explanatory variable for total 

nitrogen at the Spavinaw Creek site, but not at the Beaty Creek 

site. The negative relation between total nitrogen and specific 

conductance for Spavinaw Creek may indicate the importance 

of the influence of ground water at this site. Concentrations of 

dissolved constituents, as indicated by specific conductance, in 

surface water generally are highest during low flows because 

of the larger relative contributions of ground water (Adamski and others, 1995). Specific conductance in surface water 

generally is lowest during high flows because of dilution. 

The specific conductance at the Spavinaw Creek site may be 

necessary for defining the relation during low flow, when the 

Creek is gaining streamflow through ground-water contribution, as many streams do in northern Arkansas (Freiwald, 

1987). Specific conductance varied seasonally—generally 

higher in summer and lower in winter. This variation can be 

used to describe the seasonal variation in concentrations of 

nitrogen in Spavinaw Creek, which in turn may reflect the 

seasonality of agricultural activities such as fertilizer application, or the seasonal influence of ground water discharges to 

the stream. 

Phosphorus Equations

Phosphorus includes elemental phosphorus or dissolved 

orthophosphorus. Although nutrients are vital to growth and 

reproduction in an ecosystem, in excess amounts nutrients 

can interfere with these functions (Mueller and Helsel, 1996; 

Sharpley and Rekolainen, 1997). For example, elemental 

phosphorus may be toxic to aquatic organisms and may bioaccumulate in the same manner as mercury (U.S. Environmental 

Protection Agency, 1986). 

Total phosphorus includes the dissolved (for example, 

organic phosphate) and particulate forms. The explanatory 

Figure 6. Comparison of measured and regression-estimated total nitrogen concentrations in water from (A) Spavinaw Creek near 

Colcord, Oklahoma, and (B) Beaty Creek near Jay, Oklahoma.

B. Beaty Creek near Jay, Oklahoma

(station 07191222)

1

2

3

4

5

6

0 2 4 6 8 10 0 1 2 3 45 6

A. Spavinaw Creek near Colcord,

Oklahoma (station 071912213)

2

4

6

8

10

MEASURED TOTAL NITROGEN CONCENTRATION, IN MILLIGRAMS PER LITER

ESTIMATED TOTAL NITROGEN CONCENTRATION,

IN MILLIGRAMS PER LITER
16 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007

variables used for estimated total phosphorus at the Spavinaw 

and Beaty Creek sites were streamflow and turbidity. The 

two variables are related because turbidity is a measure of the 

amount of opaqueness caused by particulate matter in water 

and because phosphorus in water is mostly in the particulate 

form. The positive relation between total phosphorus and 

streamflow may indicate mostly nonpoint sources. When 

streamflow is high, runoff from the soils in the watershed is 

high and carries fertilizer, manure, and other farm chemicals to 

the stream. This phosphorus, in turn, continues to Lake Eucha 

providing nutrients for algae growth. Accelerated growth of 

algae can lead to eutrophication of a water body. Eutrophication is undesirable for many reasons including taste and odor, 

fish health, aesthetics, and infilling of reservoirs. 

The total phosphorus equation for Beaty Creek was similar to, and had the same significant variables, as the equation 

for Spavinaw Creek. The range in estimated and measured 

phosphorus is not well distributed for Spavinaw Creek (fig. 

7a) or Beaty Creek (fig. 7b). Total phosphorus includes the 

dissolved (for example, organic phosphate) and particulate 

forms. The Beaty equation is strongly influenced by one measurement at the high end. More high-flow and high-turbidity 

samples would help define the relation at the high end of the 

scale. However, the model coefficients did not change substantially when the high sample was omitted.

Continuous Estimates of Total Nitrogen and Total 

Phosphorus

The equations for nitrogen and phosphorus concentrations in table 5 were applied to the continuous data available 

during the 2005-2007 water years (figs. 8 and 9). The measured total nitrogen and total phosphorus concentrations also 

are shown in the figures. 

Several of the measured total nitrogen concentrations for 

Spavinaw Creek plot above the estimated value during the 

winter (from about December through March) during 2006 

and 2007. These differences may indicate that two equations 

are necessary to define total nitrogen for this site. Nitrogen 

has a negative relation to turbidity at low flows and a positive 

relation to turbidity at higher flows. Incorporating sine and 

cosine functions (as described in Helsel and Hirsch, 1992) 

in the equations might be possible with more runoff samples 

to accommodate the seasonality of total nitrogen. Seasonality functions have been applied successfully to estimate total 

nitrogen at other sites (Christensen, 2001; Ryberg, 2006). The 

sinusoidal curve in the total nitrogen data can be seen in 2005 

and 2007 (fig. 8), but is not evident during 2006, when streamflow was considerably below normal. The use of specific conFigure 7. Comparison of measured and regression-estimated total phosphorus concentrations in water from (A) Spavinaw Creek near 

Colcord, Oklahoma, and (B) Beaty Creek near Jay, Oklahoma. ESTIMATED TOTAL PHOSPHORUS CONCENTRATION, IN MILLIGRAMS PER LITER

MEASURED TOTAL PHOSPHORUS CONCENTRATION, IN MILLIGRAMS PER LITER

0 0.2 0.4 0.6 0.8 1.0

0.2

0.4

0.6

0.8

1.0

B. Beaty Creek near Jay, Oklahoma

(station 07191222)

0 0.5 1.0 1.5 2.0

0.5

1.0

1.5

2.0

A. Spavinaw Creek near Colcord, Oklahoma

(station 071912213)
Nutrient Concentrations in the Eucha-Spavinaw Basin 17

Figure 8. Comparison of measured and continuous regression-estimated total nitrogen concentrations in water from (A) Spavinaw 

Creek near Colcord, Oklahoma, and (B) Beaty Creek near Jay, Oklahoma, Water Years 2005–2007. TOTAL NITROGEN CONCENTRATION, IN MILLIGRAMS PER LITER

A. Spavinaw Creek near Colcord, Oklahoma

(station 071912213)

B. Beaty Creek near Jay, Oklahoma

(station 07191222)

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

No continuous water-quality

data available during period

Zero streamflow

during period

Zero

streamflow

during

period

Zero streamflow

during period

Measured

concentration

Regression-estimated

concentration

No continuous water-quality

data available during period

Water Year 2005

Incomplete record of continuous

water-quality data during period

Water Year 2006

Incomplete record of continuous

water-quality data during period

Water Year 2007

Water Year 2005

Water Year 2006

Water Year 2007

2

4

6

8

10

MONTH MONTH

Zero streamflow

during period

2

1

3

4

0

2

1

3

4

0

2

1

3

4

0

0

2

4

6

8

10

0

2

4

6

8

10

0
18 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007

Figure 9. Comparison of measured and continuous regression-estimated total phosphorus concentrations in water from (A) Spavinaw 

Creek near Colcord, Oklahoma, and (B) Beaty Creek near Jay, Oklahoma, Water Year 2005–2007.

TIME

ROHPSOHP LATOT ARTNECNOC SU RETIL REP SMARGILLIM NI ,NOIT

A. Spavinaw Creek near Colcord, Oklahoma

(station 071912213)

B. Beaty Creek near Jay, Oklahoma

(station 07191222)

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

No continuous water-quality

data available during period

Zero streamflow

during period

Zero

streamflow

during

period

Zero streamflow

during period

Measured

concentration

Regression-estimated

concentration

Water Year 2005

Incomplete record of continuous

water-quality data during period

Water Year 2006

Incomplete record of continuous

water-quality data during period

Water Year 2007

Water Year 2005

Water Year 2006

Water Year 2007

TIME

Zero streamflow

during period

0.5

1.0

1.5

0

No continuous

water-quality

data available

during period

Incomplete record of

continuous waterquality data during

period

Incomplete record of

continuous waterquality data during

period

0.5

1.0

1.5

0

0.5

1.0

1.5

0

1.0

0.8

0.6

0.4

0.2

0

1.0

0.8

0.6

0.4

0.2

0

1.0

0.8

0.6

0.4

0.2

0
Nutrient Concentrations in the Eucha-Spavinaw Basin 19

ductance in the equation may act as a surrogate for seasonality 

for the total nitrogen estimates at Spavinaw Creek.

A few measured values stand out from the estimated total 

nitrogen values (fig. 8). One measured total nitrogen concentration during water year 2007 (8.11 mg/L) appears to plot 

substantially higher than the estimated concentration; however, concentrations were not estimated for total nitrogen that 

day because of missing in-stream monitor data.

Differences between measured and estimated total phosphorus concentrations are small, with a few exceptions 

(fig. 9). Concentrations were very low in 2006 at Spavinaw 

Creek, where water samples were collected at low flow and 

low turbidity relative to other years. Figure 9 shows that 

few water samples were collected at high total phosphorus 

concentrations at either site during 2005-2007. Two high total 

phosphorus samples at Spavinaw Creek (in 2005 and 2007) 

and one high total phosphorus sample at Beaty Creek have a 

strong effect on the regression. The regression equations at 

both sites would benefit from more data collected during high 

flow, high turbidity, and high total phosphorus concentrations. 

Benefits of Continuous, Real-Time Water-Quality 

Monitoring and Future Work

Data from water-quality monitors presented in this 

report are available continuously and in real time. Continuous 

monitoring enables identification of seasonal trends in selected 

water properties and chemical constituents, can be used to 

estimate the chemical mass transported to Lake Eucha and 

Spavinaw Lake, and can reveal diurnal patterns. Identification 

of concentrations during runoff events is especially valuable 

because high flows have such a substantial effect on chemical loads, and concentration data from manual samples often 

are not available. Water properties monitored on a continuous 

basis provide resource managers with real-time information on 

streamflow, specific conductance, and turbidity, as shown in 

figures 10 and 11. Specific conductance (fig. 10) and turbidity (fig. 11) have different responses than streamflow and can 

give a better indication of water quality at any given time than 

streamflow alone.

Tortorelli (2008) has estimated annual and seasonal loads 

for water-quality stations in the Eucha-Spavinaw basin by 

using linear regression relating calculated constituent loads, 

which is a product of streamflow and constituent concentration of manually collected samples, to daily mean streamflow, 

a time parameter in decimal years, and a seasonality parameter 

by using a S-LOADEST model. By using this model, the daily 

constituent load can be estimated, but the instantaneous loads 

cannot be calculated. 

Loads are higher in Spavinaw Creek than in Beaty Creek 

presumably because of the higher rate of litter application 

in Spavinaw Creek basin than in Beaty Creek basin (Storm 

and others, 2002), in addition to wastewater effluent which 

affects Spavinaw Creek (Tortorelli, 2006). However, the runoff component of nutrient loads is higher from Beaty Creek, 

indicating nutrients may be primarily from nonpoint sources. 

Certain best management practices designed to address runoff 

may help mitigate the effect of nutrients from Beaty Creek 

basin on Lake Eucha and Spavinaw Lake. Loading changes 

from these implemented best management practices could be 

monitored by using continuous data to estimate instantaneous 

total nitrogen and phosphorus (as described in Christensen, 

2001). Estimated constituent loads during streamflow events 

provide a resolution of load data that is not available by using 

other load calculation methods (such as the S-LOADEST 

model; Tortorelli, 2008) and, therefore, continuous water-quality monitoring of surrogates for the estimate of water-quality 

constituents in streamflow may increase the accuracy of load 

estimation. 

Historically, nutrient loads may have been underestimated 

(Tortorelli, 2006). Continuous estimates of water-quality constituents may increase the accuracy of load estimates during 

runoff. More data need to be collected at both sites, however, 

in order to reduce the uncertainty of the surrogate regressions 

at higher nutrient concentrations. Improving the regressions 

in this range is necessary in order to reduce the uncertainty of 

nutrient loading because a large amount of nutrients are carried to Lake Eucha during runoff (Tortorelli, 2006). Reducing 

the uncertainty will increase the accuracy of the surrogate 

regressions for calculation of instantaneous loads, which could 

be used to calculate loads during longer-term periods, such as 

months, seasons, or years.

The methods described in this report also provide the 

data in real time. Previously, annual and seasonal loads were 

estimated for water-quality stations in the Eucha-Spavinaw 

basin by using linear regression to estimate daily constituent 

load (Tortorelli, 2006) but the instantaneous loads could not 

be calculated. The benefit of using water-quality surrogates as 

described in this report is that hourly loads can be calculated 

instantaneously in real time. 

Constituent loads calculated by using real-time surrogate 

measurements may be necessary in the process of establishing and monitoring total maximum daily loads (TMDLs). A 

TMDL calculation is a calculation of the maximum mass of 

a constituent, such as total phosphorus, that a water body can 

receive and still meet water-quality standards. The increasing 

interest in TMDLs makes this method potentially valuable 

for immediate monitoring of implemented best management 

practices.

Real-time data are valuable when water resources have 

human health and ecosystem effects. Water suppliers can use 

real-time data to adjust treatment processes. Real-time data 

may be disseminated to allow recreationists to make water-use 

decisions. 

Real-time monitoring is a potential tool for estimating 

taste-and-odor related compounds, such as an earthy smell 

(geosmin), chlorophyll-a, and Anabaena, in drinking water 

supplies (Christensen and others, 2006). Methods for designing taste-and-odor studies, with the possibility of using surrogate measures, have been developed (Graham and others, 

2008), providing templates for possible future studies in Lake 
20 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007

Figure 10. Daily mean specific conductance and daily mean streamflow at (A) Spavinaw Creek near Colcord, Oklahoma, and (B) 

Beaty Creek near Jay, Oklahoma, Water Years 2005–2007.

200

250

300

350

400

150

A. Spavinaw Creek near Colcord, Oklahoma (station 071912213)

0

1000

2000

3000

4000

5000

Zero streamflow

during these

periods

Incomplete specific

conductance record

during this period

No continuous

water-quality

data available

during period

Zero streamflow

during these

periods

No continuous water-quality

data available during period

Incomplete specific

conductance record

during these periods Incomplete specific

conductance record

during this period

200

250

300

350

400

150 0

500

1000

1500

2000

Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul

2004 2005 2006 2007

Daily mean specific conductance, in microsiemens per centimeter at 25 degrees celcius

Daily mean streamflow, in cubic feet per second

EXPLANATION

B. Beaty Creek near Jay, Oklahoma (station 07191222)

DAILY MEAN SPECIFIC CONDUCTANCE, IN MICROSIEMENS PER CENTIMETER AT 25 DEGREES CELCIUS

DAILY MEAN STREAMFLOW, IN CUBIC FEET PER SECOND

DATE

SAMPLE COLLECTION DATE

2500 Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul

SAMPLE COLLECTION DATE

Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul

Date of sample collection
Nutrient Concentrations in the Eucha-Spavinaw Basin 21

Figure 11. Daily mean turbidity and daily mean streamflow at (A) Spavinaw Creek near Colcord, Oklahoma, and (B) Beaty Creek 

near Jay, Oklahoma, Water Years 2005–2007.

A. Spavinaw Creek near Colcord, Oklahoma (station 071912213)

0

1000

2000

3000

4000

5000

Incomplete

turbidity record

during these periods

Incomplete turbidity

record during these

periods

2004 2005 2006 2007

Daily mean turbidity, in formazin nephelometric units

EXPLANATION

B. Beaty Creek near Jay, Oklahoma (station 07191222)

DAILY MEAN TURBIDITY, IN FORMAZIN NEPHELOMETRIC UNITS

DAILY MEAN STREAMFLOW, IN CUBIC FEET PER SECOND

500

1000

1500

2000

100 2500

0.1

100

10

1

0.1

300

No continuous

water-quality

data available

during period

DATE

SAMPLE COLLECTION DATE

Daily mean streamflow, in cubic feet per second

Date of sample collection

Zero streamflow

during these

periods

Incomplete turbidity

record during this period

Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul

0

0

10

1

Zero streamflow during these

periods

No continuous

water-quality

data available

during period

Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul

SAMPLE COLLECTION DATE

Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul
22 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007

Eucha and Spavinaw Lake. Reliable tools such as the use of 

real-time surrogates for early detection and predictive models 

would allow resource managers time to respond more effectively to taste-and-odor events and minimize effects of these 

occurrences.

Summary and Conclusions 

Lake Eucha and Spavinaw Lake serve as a water supply for the City of Tulsa, Oklahoma. Water quality has been 

deteriorating in Lake Eucha and Spavinaw Lake because of 

increased phosphorus levels. The drainage areas of Spavinaw 

and Beaty Creeks account for 73 percent of the drainage basin 

of Lake Eucha, the upstream reservoir in this two reservoir 

system. A previous study reported that Beaty and Spavinaw 

Creek supply an estimated 85 percent of the phosphorus entering Lake Eucha. 

The basin supports a variety of land uses; however, the 

main land use is agriculture and forest. The agricultural land 

use consists of applying nutrients to crops and pasture as 

commercial fertilizer and manure. A possible major source of 

nutrients in the Eucha-Spavinaw basin is the phosphorus-rich 

waste of commercial poultry growing operations, which is 

used as a fertilizer for pastures.

 The U.S. Geological Survey, in cooperation with the City 

of Tulsa, began a study to assist in determining the effects of 

land use practices on surface-water quality in the Spavinaw 

and Beaty Creek basins. Continuous in-stream water-quality 

monitors were installed at two existing continuous streamflow-gaging stations—Spavinaw Creek near Colcord, Oklahoma, and Beaty Creek near Jay, Oklahoma, in order to better 

characterize the nutrients entering Lake Eucha and Spavinaw 

Lake. The water-quality properties measured by the monitors 

were specific conductance, pH, water temperature, turbidity, 

and dissolved oxygen. Water samples were analyzed for total 

nitrogen, total phosphorus, and suspended sediment concentrations.

Hydrologic conditions for Spavinaw Creek and Beaty 

Creek varied during the years of in-stream monitoring and 

for the period of record. Precipitation in the basin during the 

study period was less than for the period of record. Precipitation is essential because precipitation contributes to runoff, 

which may result in increased concentrations of sediment and 

nutrients reaching the stream. Streamflow measurements for 

water years 2005 to 2007 at Spavinaw Creek ranged from 11 

to 5,700 cubic feet per second and from 0 to 2,050 cubic feet 

per second at Beaty Creek. Daily mean streamflow measurements during 2004 to 2007 at Spavinaw and Beaty Creeks 

were frequently lower than the mean daily streamflow for the 

period of record. During a period of nearly a year in 2005 no 

runoff samples were collected. All three study years had below 

average annual precipitation for the area, and streamflow was 

especially low in Water Year 2006. Average nutrient concentrations from October 2004 through September 2007, which 

were drier than others, may not be a good indication of conditions in future wetter years.

Total nitrogen concentrations from water samples collected at Spavinaw Creek ranged from 2.08 to 9.66 milligrams 

per liter; concentrations in water samples from Beaty Creek 

were less and ranged from 0.67 to 5.12 milligrams per liter. 

Total phosphorus concentrations ranged from 0.04 to 1.5 milligrams per liter at Spavinaw Creek and 0.028 to 1 milligram 

per liter at Beaty Creek.

Data from water-quality samples and the water-quality 

monitors at Spavinaw and Beaty Creeks were used to develop 

regression equations for total nitrogen and total phosphorus. 

Total nitrogen was related to streamflow, specific conductance, 

and turbidity; and total phosphorus was related to streamflow and turbidity for Spavinaw Creek. The total phosphorus equation for Beaty Creek was similar and had the same 

significant variables. However, the total nitrogen equation 

for Beaty Creek includes streamflow only as an explanatory 

variable. Spavinaw Creek possibly has more of an influence 

from ground water, because of the negative relation between 

nitrogen and specific conductance. 

This method has limitations in estimating concentrations. 

First, the calibration dataset may not be representative of the 

various future conditions for each stream. For example, the 

small number of high flow or high turbidity data available 

during 2004–2007 may not be representative of the various 

historic or future conditions in the stream. Average nutrient 

concentrations estimated by using 2004–2007 data may not 

be an accurate indication of conditions in future years because 

all three study years were below the average annual precipitation for the area. The uncertainty of these equations should be 

considered if the equations are to be used to estimate future 

concentrations or nutrient loads. 

The use of continuous, real-time monitoring and continued manual sample collection at Spavinaw Creek and 

Beaty Creek may be a useful tool for future monitoring of 

implemented best management practices. Spavinaw Creek has 

greater measured and estimated nutrient concentrations than 

Beaty Creek. However, the runoff component of nutrient loads 

is higher from Beaty Creek, indicating that much of the source 

for nutrients may be nonpoint. Therefore, certain best management practices may help mitigate the effect of nutrients from 

Beaty Creek basin on Lake Eucha and Spavinaw Lake. 

Continuous, real-time monitoring is a potential tool 

for resource managers in determining the concentrations of 

nutrients and other chemicals. Instantaneous nutrient concentrations from the equations for the Beaty and Spavinaw sites 

may be used to estimate loads in order to better characterize 

the effect from these watersheds on Lake Eucha and Spavinaw 

Lake. The methods used in this study show promise for monitoring future effectiveness of implemented best management 

practices, development and monitoring of total maximum 

daily loads, early detection of taste-and-odor occurrences, and 

to anticipate treatment needs for water suppliers. 
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yields in the Eucha-Spavinaw basin, Arkansas and Oklahoma, 2002−2006: U. S. Geological Survey Scientific 

Investigations Report 2008−5174, 56 p.
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J.A., 1989, Design and evaluation of eutrophication control 

measures for the St. Paul water supply: Lake and Reservoir 

Management, v. 5, no. 1, p. 71−83.

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processing of surface-water and bed-material samples for 

physical and chemical analyses: U.S. Geological Survey 

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26 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007
Appendixes 27

Appendixes
28 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007
Appendixes 29

Appendix 1. Water-quality data for Spavinaw Creek near Colcord, Oklahoma, water years 2005-2007, that were used to develop 

regression equations to estimate total nitrogen and total phosphorus.—Continued

[hhmm, hours and minutes; COT, City of Tulsa; USGS, U.S. Geological Survey; --, no data; ft3/s, cubic feet per second; FNU, formazin nephelometric units; 

μS/cm, microsiemens per centimeter at 25 degrees Celsius; mg/L, milligrams per liter; N, nitrogen; P, phosphorus]

Date

Sample 

time 

(hhmm)

Agency 

collecting 

sample

Streamflow 

(ft3/s)

Specific 

conductance 

(μS/cm) 1

Turbidity 

(FNU) 1

Total 

nitrogen, 

calculated 

(mg/L as N)2

Total

phosphorus, 

unfiltered

(mg/L as P)

Sample used

for total 

nitrogen 

regression 

equation3

Sample used 

for total 

phosphorus 

regression 

equation4

12/09/2004 0927 COT 374 290 1.84 5.03 0.075 Yes Yes

01/05/2005 1430 USGS 5,700 172 400 9.66 1.1 Yes Yes

01/06/2005 0945 COT 2,190 224 67.4 5.54 0.23 Yes Yes

02/10/2005 0917 COT 155 282 0.5 5.18 0.081 Yes Yes

03/10/2005 0920 COT 129 288 0.71 4.59 0.082 Yes Yes

04/07/2005 0928 COT 296 284 9.02 3.9 0.093 Yes Yes

05/12/2005 0907 COT 84 309 0.56 4.07 0.11 Yes Yes

06/09/2005 0950 COT 47 323 0.94 3.64 0.075 Yes Yes

06/15/2005 1350 USGS 90 323 1.9 3.63 0.15 Yes Yes

07/14/2005 0915 COT 34 334 0.16 2.9 0.087 Yes Yes

08/11/2005 0850 COT 30 351 7.22 3.02 0.081 Yes Yes

09/15/2005 0925 COT 21 351 0.39 2.9 0.066 Yes Yes

11/16/2005 0845 COT 27 366 0.72 3.46 0.071 Yes Yes

12/15/2005 0857 COT 21 350 0.6 3.55 0.065 Yes Yes

01/12/2006 0855 COT 21 347 0.6 3.64 0.26 Yes Yes

02/09/2006 0913 COT 22 349 0.5 3.61 0.083 Yes Yes

03/09/2006 0850 COT 30 337 0.7 3.32 0.069 Yes Yes

04/12/2006 0850 COT 34 344 0.78 2.94 -- Yes No

04/29/2006 1355 USGS 68 326 1.8 2.67 0.097 Yes Yes

05/02/2006 1410 USGS 153 338 1.5 3.41 0.092 Yes Yes

05/10/2006 0917 COT 142 317 3.04 3.18 0.098 Yes Yes

05/10/2006 1150 USGS 191 306 6.3 3.41 0.13 Yes Yes

06/08/2006 0925 COT 39 346 0.2 -- 0.1 No Yes

07/13/2006 0947 COT 13 360 0.5 -- 0.074 No Yes

07/13/2006 1300 USGS 12 367 0.6 2.47 0.065 Yes Yes

08/10/2006 0842 COT 11 376 0.46 2.28 -- Yes No

08/28/2006 1345 USGS 30 364 1.0 2.08 0.074 Yes Yes

09/14/2006 0927 COT 29 368 0.97 3.01 -- Yes No

10/12/2006 0926 COT 15 366 1.4 3.07 -- Yes No

11/16/2006 0815 COT 24 366 0.93 2.94 -- Yes No

12/06/2006 0940 COT 132 336 0.73 5.03 -- Yes No

01/11/2007 1002 COT 82 324 0 5.44 0.067 No Yes

01/23/2007 1600 USGS 306 283 1.3 5.69 0.087 Yes Yes

02/08/2007 0945 COT 99 305 0.6 5.84 0.079 Yes Yes

03/07/2007 0938 COT 88 308 0.5 5.01 0.065 Yes Yes

04/19/2007 0942 COT 90 328 1 3.79 0.08 Yes Yes

05/07/2007 1410 USGS 329 257 42 3.14 0.35 Yes Yes

05/08/2007 0918 COT 325 291 5 3.12 0.12 Yes Yes
30 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007

Appendix 1. Water-quality data for Spavinaw Creek near Colcord, Oklahoma, water years 2005-2007, that were used to develop 

regression equations to estimate total nitrogen and total phosphorus.—Continued

[hhmm, hours and minutes; COT, City of Tulsa; USGS, U.S. Geological Survey; --, no data; ft3/s, cubic feet per second; FNU, formazin nephelometric units; 

μS/cm, microsiemens per centimeter at 25 degrees Celsius; mg/L, milligrams per liter; N, nitrogen; P, phosphorus]

Date

Sample 

time 

(hhmm)

Agency 

collecting 

sample

Streamflow 

(ft3/s)

Specific 

conductance 

(μS/cm) 1

Turbidity 

(FNU) 1

Total 

nitrogen, 

calculated 

(mg/L as N)2

Total

phosphorus, 

unfiltered

(mg/L as P)

Sample used

for total 

nitrogen 

regression 

equation3

Sample used 

for total 

phosphorus 

regression 

equation4

05/08/2007 1320 USGS 311 290 2.6 3.17 0.24 Yes Yes

05/29/2007 1245 USGS 57 319 2 2.68 0.04 Yes Yes

06/12/2007 1445 USGS 700 88 1,480 8.11 1.5 Yes Yes

06/13/2007 1315 USGS 199 286 6.0 2.73 0.096 Yes Yes

06/18/2007 1030 USGS 82 332 1.7 3.02 0.082 Yes Yes

07/12/2007 0918 COT 53 355 3.32 2.6 0.064 Yes Yes

07/24/2007 1000 USGS 34 360 1.1 2.38 0.06 Yes Yes

08/02/2007 1330 USGS 28 358 1.4 2.2 0.07 Yes Yes

08/09/2007 0900 COT 21 363 1.8 2.25 0.067 Yes Yes

09/10/2007 1315 USGS 187 377 2.4 3.36 0.097 Yes Yes

09/13/2007 0930 COT 129 377 1.4 3.37 0.071 Yes Yes

1

 If field measurement was unavailable, interpolation between hourly measurements from continuous water-quality monitor was used.

2

 Total nitrogen is calculated by adding unfiltered total Kjeldahl nitrogen and unfiltered nitrite plus nitrate analyses.

3

 Values were not used in total nitrogen regression equation if total nitrogen could not be calculated or if streamflow or turbidity data were not available or 

were equal to zero.

4

 Values were not used in total phosphorus regression equation if streamflow, turbidity, or total phosphorus data were not available.
Appendixes 31

Appendix 2. Water-quality data for Beaty Creek near Jay, Oklahoma, water years 2005-2007, that were used to develop regression 

equations to estimate total nitrogen and total phosphorus.—Continued

[hhmm, hours and minutes; COT, City of Tulsa; --, no data; USGS, U.S. Geological Survey; ft3/s, cubic feet per second; FNU, formazin nephelometric units; 

μS/cm, microsiemens per centimeterat 25 degrees Celsius; mg/L, milligrams per liter; N, nitrogen; P, phosphorus]

Date

Sample 

time 

(hhmm)

Agency 

collecting 

sample

Streamflow

(ft3/s)

Specific

conductance

(μS/cm) 1

Turbidity 

(FNU) 1

Total 

nitrogen, 

calculated 

(mg/L as N)2

Total

phosphorus,

unfiltered 

(mg/L as P)

Sample used

for total 

nitrogen 

regression 

equation3

Sample used 

for total 

phosphorus 

regression 

equation4

10/07/2004 0720 COT 4.0 -- -- 1.84 0.047 Yes No

11/01/2004 1120 USGS 2,050 124 -- 4.24 1 Yes No

11/03/2004 0738 COT 181 -- -- 3.05 0.102 Yes No

12/09/2004 0740 COT 144 -- -- 3.67 0.077 Yes No

01/06/2005 0720 COT 603 -- -- 4.11 0.27 Yes No

01/13/2005 1325 USGS 486 170 35 3.5 0.23 Yes Yes

02/10/2005 0730 COT 48 -- -- 3.78 0.042 Yes No

03/10/2005 0730 COT 25 263 0.5 3.13 0.044 Yes Yes

04/07/2005 0735 COT 142 256 3.8 3.28 0.068 Yes Yes

05/12/2005 0625 COT 15 276 0.2 2.58 0.056 Yes Yes

06/09/2005 0755 COT 9.6 292 0.8 2.07 0.047 Yes Yes

06/14/2005 1030 USGS 85 280 1.2 1.89 0.07 Yes Yes

07/14/2005 0725 COT 4.0 298 0.5 1.47 0.055 Yes Yes

08/11/2005 0735 COT 0.61 -- -- 0.89 0.043 Yes No

09/15/2005 0755 COT 0.09 297 29.7 0.88 0.068 Yes Yes

10/06/2005 0745 COT 0.04 298 0.6 0.99 0.054 Yes Yes

11/16/2005 0734 COT 2.8 283 0.3 1.16 0.04 Yes Yes

12/15/2005 0750 COT 1.7 271 0 1.18 0.029 Yes Yes

01/12/2006 0740 COT 2.1 269 0.4 1.37 0.1 Yes Yes

02/09/2006 0905 COT 1.7 266 0.3 1.21 0.031 Yes Yes

03/09/2006 0806 COT 2.6 267 0.3 1.45 0.028 Yes Yes

04/12/2006 0738 COT 2.1 273 0.2 1.04 -- Yes No

04/29/2006 1210 USGS 26 273 0.3 1.16 0.036 Yes Yes

05/02/2006 1125 USGS 35 278 0.7 1.57 0.034 Yes Yes

05/10/2006 0810 COT 535 209 46.5 2.7 0.23 Yes Yes

05/10/2006 1030 USGS 519 207 33 3.11 0.21 Yes Yes

06/08/2006 0745 COT 7.5 293 0.8 -- 0.039 No Yes

07/13/2006 0735 COT 0.00 306 1 -- 0.063 No Yes

08/29/2006 1125 USGS 19 322 0.0 1.77 0.051 Yes Yes

09/14/2006 0745 COT 2.8 311 0.3 0.67 -- Yes No

11/16/2006 0835 COT 0.67 290 0.5 0.91 -- Yes No

12/06/2006 0745 COT 32 282 0.8 3.47 -- Yes No

01/11/2007 0800 COT 16 281 0.8 4.07 0.045 Yes Yes

01/23/2007 1130 USGS 159 237 2.0 4.02 0.1 Yes Yes

02/08/2007 0750 COT 10 268 0.3 4.29 0.04 Yes Yes

03/07/2007 0755 COT 8.3 270 0.3 3.28 0.035 Yes Yes

04/19/2007 0754 COT 10 283 0.3 2.06 0.038 Yes Yes

05/07/2007 1217 USGS 22 275 0.8 1.57 0.037 Yes Yes
32 Estimated Nutrient Concentrations in the Eucha-Spavinaw Basin, Arkansas and Oklahoma, 2004-2007

Publishing support provided by:

Lafayette Publishing Service Center

For more information concerning this publication, contact:

Director, USGS Oklahoma Water Science Center

202 NW 66th St., Bldg. 7

Oklahoma City, OK 73116

(405) 810-4400

Or visit the Oklahoma Water Science Center Web site at:

http://ok.water.usgs.gov

Appendix 2. Water-quality data for Beaty Creek near Jay, Oklahoma, water years 2005-2007, that were used to develop regression 

equations to estimate total nitrogen and total phosphorus.—Continued

[hhmm, hours and minutes; COT, City of Tulsa; --, no data; USGS, U.S. Geological Survey; ft3/s, cubic feet per second; FNU, formazin nephelometric units; 

μS/cm, microsiemens per centimeterat 25 degrees Celsius; mg/L, milligrams per liter; N, nitrogen; P, phosphorus]

Date

Sample 

time 

(hhmm)

Agency 

collecting 

sample

Streamflow

(ft3/s)

Specific

conductance

(μS/cm) 1

Turbidity 

(FNU) 1

Total 

nitrogen, 

calculated 

(mg/L as N)2

Total

phosphorus,

unfiltered 

(mg/L as P)

Sample used

for total 

nitrogen 

regression 

equation3

Sample used 

for total 

phosphorus 

regression 

equation4

05/07/2007 1635 USGS 258 261 13 2.66 0.21 Yes Yes

05/08/2007 0745 COT 406 254 7.6 3.58 0.12 Yes Yes

05/08/2007 1500 USGS 355 255 3.4 2.88 0.18 Yes Yes

05/29/2007 1330 USGS 21 279 0.6 2.7 0.04 Yes Yes

06/07/2007 0800 COT 6.5 294 0.6 2.41 0.044 Yes Yes

06/12/2007 1645 USGS 950 171 450 5.12 0.89 Yes Yes

06/13/2007 1120 USGS 390 239 8.7 2.54 0.13 Yes Yes

06/18/2007 1215 USGS 76 270 0.6 2.53 0.061 Yes Yes

07/12/2007 0750 COT 12 305 0.73 1.81 0.046 Yes Yes

07/24/2007 1130 USGS 9.1 302 0.3 1.54 0.039 Yes Yes

08/02/2007 1530 USGS 27 301 0.7 1.7 0.046 Yes Yes

08/09/2007 0800 COT 3.5 301 0.8 1.62 0.046 Yes Yes

09/12/2007 1200 USGS 18 308 0.1 1.26 0.052 Yes Yes

09/13/2007 0735 COT 12 305 1.3 1.3 0.055 Yes Yes

1

 If field measurement was unavailable, interpolation between half-hour measurements from continuous water-quality monitor was used.

2

 Total nitrogen is calculated by adding unfiltered total Kjeldahl nitrogen and unfiltered nitrite plus nitrate analyses.

3

 Values were not used in total nitrogen regression equation if total nitrogen could not be calculated or streamflow data was not available or was equal to 

zero.

4

 Values were not used in total phosphorus regression equation if streamflow, turbidity, or total phosphorus data were not available.