U.S. Department of the Interior

U.S. Geological Survey

Characterization of Surface-Water Quality Based

on Real-Time Monitoring and Regression Analysis

Quivira National Wildlife Refuge, South-Central

Kansas, December 1998 Through June 2001

By VICTORIA G. CHRISTENSEN

Water-Resources Investigations Report 01–4248

Prepared in cooperation with the

U.S. FISH AND WILDLIFE SERVICE

Lawrence, Kansas

2001
U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

Charles G. Groat, Director

For additional information write to: Copies of this report can be purchased from

U.S. Geological Survey

Information Services

Building 810, Federal Center

Box 25286

Denver, CO 80225–0286

District Chief

U.S. Geological Survey

4821 Quail Crest Place

Lawrence, KS 66049–3839
Content

CONTENTS

Abstract.................................................................................................................................................................................. 

Introduction ........................................................................................................................................................................... 

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

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

Methods ................................................................................................................................................................................. 

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

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

Calculation of Measured and Estimated Chemical Loads and Yields ........................................................................ 

Chemical Concentrations, Loads, Yields, and Variability ..................................................................................................... 

Measured Concentrations ............................................................................................................................................ 

Regression-Estimated Concentrations Using Real-Time Water-Quality Monitoring.................................................. 

Physical Properties, Solids, and Sediment......................................................................................................... 

Major Ions.......................................................................................................................................................... 

Nutrients ............................................................................................................................................................ 

Fecal Coliform Bacteria..................................................................................................................................... 

Estimated Loads, Yields, and Variability..................................................................................................................... 

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

Summary................................................................................................................................................................................ 

References Cited.................................................................................................................................................................... 

FIGURES

1. Maps showing location of Rattlesnake Creek Basin and marshes, ponds, and canals at Quivira National

Wildlife Refuge, south-central Kansas ................................................................................................................... 

2. Photograph of in-stream water-quality monitor used to continuously measure and record specific conductance,

pH, water temperature, turbidity, and dissolved oxygen......................................................................................... 

3–5. Graphs showing:

3. Comparison of mean daily streamflow and long-term mean monthly streamflow for Rattlesnake Creek

near Zenith......................................................................................................................................................... 

4. Comparison of 1999 and 2000 monthly precipitation at Rattlesnake Creek near Zenith gaging station .......... 

5. Percentage change in median concentrations of selected water-quality chemicals between 1999 and 2000

for Rattlesnake Creek near Zenith ..................................................................................................................... 

6–11. Graphs showing comparison of measured and regression-estimated:

6. Alkalinity, dissolved solids, total suspended solids, and suspended sediment concentrations in water from

Rattlesnake Creek near Zenith, December 1998 through June 2001................................................................. 

7. Major ion concentrations in water from Rattlesnake Creek near Zenith, December 1998 through

June 2001........................................................................................................................................................... 

8. Nutrient concentrations in water from Rattlesnake Creek near Zenith, December 1998 through

June 2001........................................................................................................................................................... 

9. Fecal coliform bacteria densities in water from Rattlesnake Creek near Zenith, December 1998 through

June 2001........................................................................................................................................................... 

10. Fecal coliform bacteria densities in water from Rattlesnake Creek near Zenith, April 1 through

October 31, 2000 ............................................................................................................................................... 

11. Total organic nitrogen and total phosphorus loads in water from Rattlesnake Creek near Zenith, 2000.......... 
IV Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001

TABLES

1. Statistical summary of water-quality data collected from Rattlesnake Creek near Zenith, Kansas, Decem

through June 2001 ......................................................................................................................................

2. Linear regression equations for estimation of physical properties, solids, sediment, major ions, nutrients

bacteria in Rattlesnake Creek near Zenith, Kansas, December 1998 through June 2001 ...........................

3. Estimated loads and yields of selected chemicals in water from Rattlesnake Creek near Zenith, Kansas, 

and 2000 ......................................................................................................................................................

Temperature can be converted to degrees Celsius (oC) or degrees Fahrenheit (oF) by the equations: oC=5/9 (oF - 32) o

F=9/5 (oC) + 32.

CONVERSION FACTORS, ABBREVIATIONS, AND DEFINITIONS

Multiply By To obtain

acre 4,047 square meter

cubic foot per second (ft3

/s) 0.02832 cubic meter per second

inch (in.) 2.54 centimeter

microgram per liter (μg/L) 0.00539 pound per day

mile (mi) 1.609 kilometer

milligram per liter (mg/L) 5.39 pound per day

milliliter (mL) 0.0338 ounce, fluid

pound (lb) 453.6 gram

pound per acre (lb/acre) 1.121 kilogram per hectare

pound per acre per year [(lb/acre)/yr] 1.121 kilogram per hectare per year

pound per day (lb/d) 453.6 gram per day

pound per year (lb/yr) 453.6 gram per year

square mile (mi2

) 2.590 square kilometer

ton (short) 0.9072 megagram
Abstrac

Characterization of Surface-Water Quality Based on RealTime Monitoring and Regression Analysis, Quivira National

Wildlife Refuge, South-Central Kansas, December 1998

Through June 2001

By Victoria G. Christensen

Abstract

Because of the considerable wildlife benefits

offered by the Quivira National Wildlife Refuge

in south-central Kansas, there is a desire to ensure

suitable water quality. To assess the quality of

water flowing from Rattlesnake Creek into the

refuge, the U.S. Geological Survey collected periodic water samples from December 1998 through

June 2001 and analyzed the samples for physical

properties, dissolved solids, total suspended solids, suspended sediment, major ions, nutrients,

metals, pesticides, and indicator bacteria. Concentrations of 10 of the 125 chemicals analyzed did

not meet water-quality criteria to protect aquatic

life and drinking water in a least one sample.

These were pH, turbidity, dissolved oxygen, dissolved solids, sodium, chloride, phosphorus, total

coliform bacteria, E. coli bacteria, and fecal

coliform bacteria. No metal or pesticide concentrations exceeded water-quality criteria. Twentytwo of the 43 metals analyzed were not

detected, and 36 of the 46 pesticides analyzed

were not detected.

Because dissolved solids, sodium, chloride,

fecal coliform bacteria, and other chemicals that

are a concern for the health and habitat of fish and

wildlife at the refuge cannot be measured continuously, regression equations were developed from

a comparison of the analytical results of periodic

samples and in-stream monitor measurements of

specific conductance, pH, water temperature,

turbidity, and dissolved oxygen. A continuous

record of estimated chemical concentrations w

developed from continuously recorded in-stre

measurements.

Annual variation in water quality was eva

ated by comparing 1999 and 2000 sample dat

the 2 years for which complete data sets were

available. Median concentrations of alkalinity

fluoride, nitrate, and fecal coliform bacteria w

smaller or did not change from 1999 to 2000. 

solved solids, total suspended solids, sodium,

chloride, sulfate, total organic nitrogen, and to

phosphorus had increases in median concentr

tions from 1999 to 2000. Increases in the med

concentrations of the major ions were expecte

due to decreased rainfall in 2000 and very low

streamflow late in the year. Increases for soli

and nutrients may have been due to the unusu

high streamflow in the early spring of 2000. T

was the time of year when fields were tilled,

exposing solids and nutrients that were transported with runoff to Rattlesnake Creek.

Load estimates indicate the chemical mas

transported into the refuge and can be used in 

development of total maximum daily loads (a

specified by the U.S. Environmental Protectio

Agency) for water-quality contaminants in Ra

snake Creek. Load estimates also were used t

evaluate seasonal variation in water quality. S

sonal variation was most pronounced in the es

mates of nutrient loads, and most of the nutrie

load transported to the refuge occurred during 
2 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001

a few periods of surface runoff in the spring and

summer. This information may be used by

resource managers to determine when waterdiversion strategies would be most beneficial.

Load estimates also were used to calculate yields,

which are useful for site comparisons.

The continuous and real-time nature of the

record of estimated concentrations, loads, and

yields may be important for resource managers,

recreationalists, or others for evaluating waterdiversion strategies, making water-use decisions,

or assessing the environmental effects of chemicals in time to prevent adverse effects on fish or

other aquatic life at the refuge.

INTRODUCTION

The Quivira National Wildlife Refuge is a natural

wetlands area located in the Rattlesnake Creek Basin

of south-central Kansas (fig. 1) and is managed by the

U.S. Fish and Wildlife Service (USFWS). The funding

for the initial acquisition of the refuge was obtained in

1955; the refuge applied for a water permit in 1957

and development began. The area provides food,

water, cover, and protection for many species of birds,

fish, and other wildlife. Several types of waterfowl

take advantage of the refuge in their annual migration

including the endangered whooping crane. Since the

initial acquisition, the refuge has been enhanced and

includes more than 30 marshes and ponds covering

more than 22,000 acres in Stafford, Reno, and

Rice Counties.

The importance of maintaining an adequate water

supply to preserve valuable wildlife habitat was recognized early on. A channel was constructed by local

duck clubs in the late 1920s or early 1930s to permit

Rattlesnake Creek to flow directly into the Little Salt

Marsh (fig. 1), one of two large saline marshes on the

refuge. Additional canals and ponds have been constructed since, providing the entire area with a more

dependable surface-water supply. However, in the late

summer of dry years, an adequate water supply is still

a problem as upstream water demands reduce the flow

from Rattlesnake Creek to the Quivira National Wildlife Refuge. The U.S. Geological Survey (USGS)

developed a computer-based, water-budget model

(Jian, 1998) to assist the USFWS in determining the

outcome of possible management options with respect

to water supply.

 Not only is there a desire to ensure a dep

water supply to Quivira National Wildlife Re

there also is a strong desire to ensure suitable 

quality. In response to the Federal Clean Wate

1972, the Kansas Department of Health and E

ment (1998) listed segments of Rattlesnake C

Little and Big Salt Marshes as water-quality i

A water body is designated “water-quality im

if it does not meet water-quality criteria estab

protect the water body’s designated uses.

 Rattlesnake Creek is listed as being impa

chloride and sulfate. The Little and Big Salt 

are identified as being eutrophic (abundance o

ents and seasonal deficiency of oxygen) and i

for specific uses by pH. The Little Salt Marsh 

impaired by fecal coliform bacteria. The Fed

Clean Water Act requires that the State of Ka

establish total maximum daily loads (TMDLs

established water-quality criteria and to ensur

tion of the creek’s and marshes’ designated b

uses. A TMDL is a calculation and allocation 

sources of the maximum amount of a contami

a water body can receive and still meet water

criteria (U.S. Environmental Protection Agen

1999).

 A proposal for a large hog-processing pl

Great Bend, Kansas (Suber, 1998), was antici

lead to significant changes in land use in the R

snake Creek Basin. Great Bend is located nor

Rattlesnake Creek Basin (fig. 1), and the cons

of a hog-processing plant would lead to subst

increases in the production of feed grains for 

in hog farming in the Rattlesnake Creek Basi

changes in land use could have substantial eff

water quality such as an increase in the use of 

ers and potential transport of hog wastes to st

that, in turn, may affect the aquatic ecosystem 

the refuge.

The presence of chemicals that do not me

quality criteria and the lack of a substantial w

quality database for Quivira National Wildlife 

have led to the need for further evaluation. Fu

more, the lack of chemical data makes it diffi

calculate loads for the evaluation of TMDLs. 

increased regulatory emphasis on TMDLs rel

nonpoint-source pollution, refuge managers i

future may need to quantify loads of nutrients

ria, and other chemicals entering the refuge. T

study was conducted by the USGS in coopera

the USFWS to (1) quantify concentrations an
Introductio

Reno County

Rice County

Stafford County

County Road 484

Dead Horse Slough

Rattlesnake C r

Salt Creek

Big

Salt

Marsh RC Canal

Darrynane Canal

Rattlesnake

Creek

near Zenith

Refuge

headquarters

Rattlesnake Cr

Main

can

al

Little

Salt

Marsh 07142575

07142575

kee Cr ke na les Ratt

ek Cre ke a sn

Rattle

98°32'30" 98°25'

38°12'30"

38°05'

0 1 2 3 KILOMETERS

0 1 2 3 MILES

Marsh or pond

Boundary of Quivira National

 Wildlife Refuge

Canal

Direction of flow

U.S. Geological Survey 

 streamflow-gaging station 

 and number

EXPLANATION

OKLAHOMA

MISSOURI

NEBRASKA

COLORADO

KANSAS

Index map

Arkansas

River

Great

Bend

Saint

John

Quivira National

Wildlife Refuge

Rattlesnake

Creek Basin

Base from U.S. Geological Survey digital data, 1:100,000, 1983

Lambert Conformal Conic projection

Standard parallels 33° and 45°, central meridian -98°15'

Figure 1. Location of Rattlesnake Creek Basin and marshes, ponds, and canals at Quivira National

Wildlife Refuge, south-central Kansas.
4 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001

temporal variation in selected water-quality chemicals

and properties and to (2) estimate chemical concentrations, loads, and yields transported into the Quivira

National Wildlife Refuge during a wide range of

flow conditions.

Purpose and Scope

This report characterizes the surface-water quality

of Rattlesnake Creek immediately upstream from the

Quivira National Wildlife Refuge. Data were collected at the USGS streamflow-gaging station on Rattlesnake Creek near Zenith, Kansas (station 07142575,

fig. 1). Concentrations of selected water-quality constituents were identified through the manual collection

and subsequent analysis of water samples from

December 1998 through June 2001. These water samples were collected throughout a range of streamflow

conditions so as to reflect conditions that may affect

the variability of chemical concentrations and loads.

Annual variation was evaluated by comparing 1999

and 2000 data—the 2 years for which complete data

sets were available. However, because of the limited

number of samples that were collected, seasonal variability was better evaluated using continuous waterquality data and regression-based estimates, as

described in the following paragraphs.

Chemical concentrations and loads transported

into the Quivira National Wildlife Refuge were estimated for alkalinity, dissolved solids, total suspended

solids, sediment, sodium, chloride, fluoride, sulfate,

nitrate, total organic nitrogen, total phosphorus, and

fecal coliform bacteria. Because these chemicals cannot be measured continuously, regression equations

were developed between in-stream (in situ)

measurements of physical properties (specific conductance, pH, water temperature, turbidity, and dissolved

oxygen) and the chemicals of concern to the refuge

(alkalinity, dissolved solids, total suspended solids,

sediment, sodium, chloride, fluoride, sulfate, nitrate,

total organic nitrogen, total phosphorus, and fecal

coliform bacteria). Regression equations were developed using all available in-stream and collected data

sets (December 1998 through June 2001). Concentration and load estimates are available in real time to refuge managers and others on the World Wide Web at

URL http://ks.water.usgs.gov/Kansas/rtqw/.

This study shows how chemical loads may be estimated using continuous data and that real-time application of regression equations can be used by resource

managers to protect wildlife. The methods use

study could be used at other sites in Kansas a

Nation to estimate chemical loads, providing 

data for the development of TMDLs. Results 

contribute toward improved understanding of 

such as land use that could affect water qualit

refuge.

Description of Study Area

The Quivira National Wildlife Refuge is 

downstream end of the Rattlesnake Creek Ba

tlesnake Creek drains about 1,047 mi2 before 

the refuge. Nearly one-half of the drainage ar

sidered noncontributing (Putnam and others, 

Water supply to the refuge includes diversion

Rattlesnake Creek into the Little Salt Marsh l

the southern part of the refuge (fig. 1). Additi

substantial quantities of water are supplied by 

ground-water seepage in the northern part of 

uge near Big Salt Marsh (Sophocleous and Pe

1992). The Rattlesnake Creek Basin occurs in 

of high salinity ground water (Buchanan, 198

naturally occurring intrusion of this highly m

ized ground water affects Rattlesnake Creek, 

Marsh, and Little Salt Marsh. The outflow fro

refuge re-enters Rattlesnake Creek to the nort

confluence with the Arkansas River.

Oil and gas are produced in the basin, and 

ous oil-production facilities are located at or n

refuge (Allen, 1991); however, land use is do

by agriculture, which includes production of 

and crops. Agricultural chemicals applied to c

the basin include fertilizers and pesticides. Th

chemicals can be transported through surface 

ground water and suspended sediment and m

detrimental effects on the aquatic ecosystem a

uge. Nutrient enrichment, intensified by fertil

can lead to eutrophication of the water bodies 

refuge, which can lead to loss of habitat and f

sources for migratory waterfowl.

The dominance of agriculture in the basin 

semiarid climate have led to widespread use o

tion in the area. Not only is irrigation a conce

relation to water supply at the refuge, but also 

water-quality concern. Irrigation return flows 

tlesnake Creek may contain increased concen

of certain chemicals (selenium, for example) 

cause adverse health effects and bioaccumula

aquatic organisms and birds at the refuge.
Method

Turbidity

pH

Specific

conductan

and water

temperatu

Dissolved

oxygen

METHODS

Data Collection and Analysis

A continuously recording (15-minute to

60-minute intervals) water-quality monitor

(fig. 2) was installed at the USGS streamflow-gaging station on Rattlesnake Creek

near Zenith, Kansas, on October 28, 1998

(station 07142575, fig. 1). This gaging station is located approximately 2.5 stream mi

upstream from the inlet to the wildlife refuge. Water-quality properties measured with

the monitor from Decem-ber 1998 through

June 2001 included specific conductance,

pH, water temperature, turbidity, and dissolved oxygen. Measurements were transmitted every 4 hours via satellite to the

USGS office in Lawrence, Kansas, and were

made available in real time on the World

Wide Web at URL

http://ks.water.usgs.gov/Kansas/rtqw/. The

water-quality monitor was inspected onsite

by USGS personnel approximately every

2 weeks to maintain calibration. Guidelines

and standard procedures for maintaining the

sites and reporting the data are described in

Wagner and others (2000).

Each sensor on the water-quality monitor has a

certain range of operation. Specific conductance, pH,

water temperature, and dissolved oxygen sensors have

very wide ranges of operation that were not exceeded

during the study and are not likely to be exceeded

in any Kansas stream. The turbidity sensor at this

site has a maximum reading of approximately

1,750 nephelometric turbidity units (NTU). Although

a limit of 1,750 NTU could be problematic for Kansas

streams, the Rattlesnake Creek turbidity did not reach

this limit in 1999 and 2000.

In addition to the data collected with the in-stream

water-quality monitor, the USGS manually collected

water-quality samples using depth- and widthintegrating techniques (Edwards and Glysson, 1988).

Four quarterly (base-flow) samples, five event (baseflow plus runoff) samples, and one quality-assurance

sample were collected each year and analyzed for

physical properties, solids, sediment, major ions,

nutrients, and bacteria. The event-related samples also

were analyzed for metals and pesticides.

The USGS National Water-Quality Laborator

Denver, Colorado, provided the analytical service

most chemicals, with the exception of triazine her

cides and bacteria analyses. The water samples w

analyzed for triazine herbicides at the USGS Org

Geochemistry Research Laboratory in Lawrence, 

sas, according to methods presented in Thurman 

others (1990) and Zimmerman and Thurman (199

and water samples were analyzed for total colifor

E. coli, enterococci, fecal coliform, and fecal stre

cocci bacteria at the USGS office in Wichita, Kan

according to methods presented in Myers and

Sylvester (1997). Individual data values for all sa

ples collected are on file at the USGS office in

Lawrence, Kansas.

Water samples may be collected from a single

point in the stream section, along a vertical line

between the surface and the streambed (depthintegrated), or at several vertical lines across the e

stream section (depth- and width-integrated). A s

ple collected using depth- and width-integrating t

niques is considered more representative of the en

cross-sectional streamflow than a sample collected 

single point in the stream (Wilde and others, 1999

Figure 2. In-stream water-quality monitor used to continuously measure an

record specific conductance, pH, water temperature, turbidity, and dissolv

oxygen.
6 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001

Samples were collected using depth- and widthintegrating techniques whereas the continuous waterquality monitor probes the water at a single point in

the stream. Therefore, part of the difference between

measurements in collected water samples and measurements using the water-quality monitor was due to

different sampling techniques. No adjustments were

applied to the regression equations to correct for these

small differences.

Development of Regression Equations

It is possible to express one chemical in terms of

another chemical or property (often called a surrogate)

using simple linear regression equations (Helsel and

Hirsch, 1992). Therefore, data from the water-quality

samples and the water-quality monitor were used to

develop regression equations used in estimating daily

and annual mass loads of chemicals of concern at the

refuge. The simplest regression equation can be

expressed as:

yi

 = mxi + b +ei i = 1, 2,..., n, (1)

where

yi is the i th observation of the response

(dependent) variable;

m is the slope;

xi is the i th observation of the explanatory

(independent) variable;

b is the intercept;

ei is the random error for the i th

observation; and

n is the number of samples.

The parameters m and b must be estimated from

the data set. The most common estimation technique is

called least squares estimation (Helsel and Hirsch,

1992). The error term, ei

, is assumed to be normally

distributed with a mean equal to zero and a constant

variance, s

2

.

The first step in choosing a regression equation

was to plot each possible explanatory variable against

the response variable and to look for patterns in the

data. Next, to determine which explanatory variable

or variables to include in the regression equation for

each chemical of concern, both a step-wise procedure

(Ott, 1993, p. 656) and an overall method (Helsel and

Hirsch, 1992, p. 312–314) were used. In the step-wise

procedure, each explanatory variable was added to the

regression equation one at a time to determine if there

was a statistically significant correlation. The possible

explanatory variables included each of the sen

surements of the water-quality monitor (speci

ductance, pH, water temperature, turbidity, an

dissolved oxygen), streamflow, and time. Exp

variables were considered significant if the p-

(probability value) was less than 0.05. The ov

method evaluated all possible regression equa

there were several acceptable equations, the o

the lowest PRESS statistic was chosen. PRES

nym for “PRediction Error Sum of Squares”) 

the best measures of the goodness of fit of a re

equation (Helsel and Hirsch, 1992, p. 248). A

ally, explanatory variables were included in a

tion only if there was a physical basis or expl

for their inclusion. When more than one expl

variable is used to express the response variab

called multiple regression.

In addition to the PRESS, four common d

statistics were used to evaluate regression equ

These are the mean square error (MSE), the co

of determination (R2), the relative mean absol

(RMAE), and relative percentage difference (R

MSE is calculated as follows:

, 

where yi represents the value of y at the i th da

E(yi) is the estimated value of y at the i th dat

(where E(yi

) = mxi + b), and n is the number 

ples. The MSE is determined for each equati

assess the variance between estimated and

measured values.

MSE is a dimensional measure. Dimensio

measures often are required in practice for the 

of comparing chemicals or properties with di

dimensions (units of measure). A dimensionl

sure of fitting y on x is the R2, or the fraction 

variance explained by the regression:

R2 = 1.0 - (SSE/SSy). 

SSE and SSy are calculated as follows:

, and 

, 

MSE

yi E yi [ ] – ( ) 2

i = 1

n

∑

n – 2 = ----------------------------------------

SSE yi E yi [ ] – ( ) 2

i = 1

n

= ∑

SSy yi ( ) – y

2

i = 1

n

= ∑
Chemical Concentrations, Loads, Yields, and Variabilit

in which y is the mean of y. The R2 ranges from 0 to 1

and often is called the multiple coefficient of determination in multiple linear regression.

The RMAE, expressed as a percentage, is calculated as follows:

, (6)

where A is the estimated concentration, B is the measured concentration, and MB is the mean (average) of

all the measured concentrations.

Relative percentage differences (RPDs) between

measured and estimated chemical concentrations were

calculated for each simultaneous data set using the following equation:

RPD =[ |B -A|/(A) ] x 100, (7)

where

RPD is the relative percentage difference;

A is the measured chemical

concentration; and

B is the estimated chemical concentration.

The RPD also was used to evaluate the differences in

replicate data sets (quality-assurance samples).

 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 presented herein 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).

Outliers were identified graphically and investigated to determine their validity. No outliers were

eliminated from the data used to develop the equations

contained in this report. One sample (collected on

December 7, 1999) had total organic nitrogen and total

phosphorus concentrations that were less than the analytical detection limit (0.10 mg/L for total organic

nitrogen and 0.05 mg/L for total phosphorus). Values

of one-half the detection limits were used for this sample in developing the total organic nitrogen and total

phosphorus regression equations. Nitrate concentrations were calculated by subtracting nitrite concentration from the nitrite plus nitrate as nitrogen

concentrations reported by the USGS National WaterQuality Laboratory.

Some of the response variables in the regression

equations for estimating water quality at the Rattlesnake Creek gaging station (07142575, fig. 1) were

transformed to eliminate curvature and achieve a 

pler linear equation; therefore, retransformation o

regression-estimated concentrations was necessar

However, retransformation can cause an underest

tion of chemical loads when adding individual loa

estimates over a long period of time. Applying Du

bias correction factor (Duan, 1983) to the annual 

calculation presented in this report allowed correc

for this underestimation. The bias correction facto

for estimates in this report were between 1.01 and

1.33. Cohn and others (1989), Gilroy and others

(1990), and Hirsch and others (1993) provide

additional information on interpreting the results 

regression-based load estimates.

Calculation of Measured and Estimated Chem

Loads and Yields

Measured and estimated instantaneous chemi

loads, expressed in units of pounds per day, were 

culated by multiplying instantaneous chemical co

centrations (in milligrams per liter) by instantane

streamflow (in cubic feet per second) and an appr

ate conversion factor (5.39). Instantaneous concen

tion and instantaneous streamflow correspond to a

single moment in time as opposed to an average o

sum. Annual loads were calculated by multiplying 

instantaneous loads (expressed as daily load) by 3

Measured and estimated instantaneous yields (in

pounds per acre) were calculated by dividing the

instantaneous chemical load by the contributing d

age area (1,047 mi2) in acres (670,080 acres).

CHEMICAL CONCENTRATIONS, LOADS, YIELD

AND VARIABILITY

Measured Concentrations

From December 1998 through March 2001, w

quality samples were collected and analyzed for

125 physical properties and chemicals (table 1). T

samples were collected throughout a range of stre

flow conditions, and analytical results showed sea

sonal and hydrologic variability for many chemic

Two quality-assurance (replicate) samples (one in

1999 and one in 2000) also were collected and an

lyzed. These two samples indicated a mean

RMAE

1

n

-- A B –

i = 1

n

∑

M B

= ----------------------------- × 100
8 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001

Table 1. Statistical summary of water-quality data collected from Rattlesnake Creek near Zenith, Kansas, December 1998 throug

June 2001—Continued

Water-quality chemical or property

(unit of measurement)

Number of

samples

Concentration

 Range Mean Median Water-qua

Physical properties, solids, and sediment

Instantaneous streamflow (ft3/s) 25 3.6–840 130 53 

Specific conductance (μS/cm) 29 453–9,930 3,560 3,040 

pH (standard units) 27 7.3–8.8 8.1 8.2 6.5–8

Water temperature (degrees Celsius) 30 3.4–34.0 19.4 21.2 

Turbidity (NTU) 24 5.0–480 124 83 9.9 

Oxygen, dissolved (mg/L) 24 3.1–12.8 9.5 10 5 (

Alkalinity (mg/L as CaCO3) 18 91–224 180 190 

Total organic carbon (mg/L) 10 2.4–17 11 11 

Total suspended solids (mg/L) 18 14–270 106 80 

Dissolved solids (mg/L) 18 264–5,460 1,870 1,640 500 (S

Suspended sediment (mg/L) 10 14–1,820 418 325 

Major ions

Calcium, dissolved (mg/L) 18 21–100 73 79 

Calcium, total (mg/L) 17 22–100 78 83 

Magnesium, dissolved (mg/L) 18 3.1–35 15 14 

Magnesium, total (mg/L) 17 3.6–37 16 16 

Potassium, dissolved (mg/L) 18 3.5–9.3 5.7 5.4 

Potassium, total (mg/L) 17 4.4–11 6.4 6.1 

Sodium, dissolved (mg/L) 18 50–1,880 585 466 20 (D

Sodium, total (mg/L) 17 49–1,940 576 453 

Chloride, dissolved (mg/L) 18 67–3,000 927 757 860 

Fluoride, dissolved (mg/L) 18 0.2–0.6 .4 .5 4.0 (

Silica, dissolved (mg/L) 18 5.1–21 15 15 

Sulfate, dissolved (mg/L) 18 11–269 104 95 352 

Bromide, dissolved (mg/L) 7 <0.01–0.36 .12* .04* 

Nutrients

Ammonia plus organic nitrogen (mg/L) 20 0.19–1.1 .50 .50 

Nitrogen, total organic (mg/L) 20 0.10–2.5 1.2 1.1 2.18 

Ammonia nitrogen (mg/L) 20 <0.02–0.19 .03* .01 * 

Nitrite plus nitrate, dissolved (mg/L) 20 <0.05–2.1 .77* .74* 

Nitrite nitrogen (mg/L) 20 <0.01–0.05 .02* .01* 

Phosphorus, dissolved (mg/L) 20 <0.05–0.23 .09* .06* 

Ortho phosphorus (mg/L) 20 <0.01–0.21 .07* .03* 

Phosphorus, total (mg/L) 20 <0.05–0.76 .27* .22* .076 

Table 1. Statistical summary of water-quality data collected from Rattlesnake Creek near Zenith, Kansas, December 1998 throug

[ft3/s, cubic feet per second; μS/cm, microsiemens per centimeter at 25 degrees Celsius; NTU, nephelometric turbidity units; mg/L, milligram

CaCO3, calcium carbonate; *, mean and median values were estimated using a log-probability regression to predict values of data less than the de

col/100 mL, colonies per 100 milliliters of water; --, not calculated or not applicable; μg/L, micrograms per liter; AL, State of Kansas aquatic li

teria; VI, USEPA recommended criteria for Ecoregion VI; SDWR, Secondary Drinking Water Regulation; MCL, Maximum Contaminant Leve

Drinking Water Equivalent Level; <, less than; HAL, Health Advisory Level; MCLG, Maximum Contaminant Level Goal; EPA CRC, U.S. En

Protection Agency Contact Recreational Criteria; KS CRC, State of Kansas Contact Recreational Criteria]
Chemical Concentrations, Loads, Yields, and Variability 

Metals

Aluminum, dissolved (μg/L) 10 not detected -- -- 50 (SDWR

Aluminum, total (μg/L) 10 188–5,890 2,060 1,730 --

Antimony, dissolved (μg/L) 10 not detected -- -- 6.0 (MCL

Antimony, total (μg/L) 10 not detected -- -- --

Arsenic, dissolved (μg/L) 10 1.2–3.7 2.4 2.3 10 (MCL)

Arsenic, total (μg/L) 10 1–5 3 3 --

Barium, dissolved (μg/L) 10 51–205 135 144 2,000 (MCL

Barium, total (μg/L) 10 <100–253 173* 173* --

Beryllium, dissolved (μg/L) 10 not detected -- -- 4.0 (MCL

Beryllium, total (μg/L) 10 <20–68 -- -- --

Cadmium, dissolved (μg/L) 10 not detected -- -- --

Cadmium, total (μg/L) 10 not detected -- -- --

Chromium, dissolved (μg/L) 10 not detected -- -- --

Chromium, total (μg/L) 10 <4–4 2* 2* --

Cobalt, dissolved (μg/L) 10 not detected -- -- --

Cobalt, total (μg/L) 10 <2–3 2* 2* --

Copper, dissolved (μg/L) 10 not detected -- -- --

Copper, total (μg/L) 10 <1–7 3* 3* --

Cyanide, dissolved (mg/L) 10 <0.01–0.15 -- -- --

Cyanide, total (mg/L) 10 not detected -- -- --

Iron, dissolved (μg/L) 18 <50–80 16* 9.8* 300 (SDWR

Iron, total (μg/L) 18 <155–4,670 1,920* 1,630* --

Lead, dissolved (μg/L) 10 not detected -- -- 15 (MCL)

Lead, total (μg/L) 10 <4–6 3* 3* --

Lithium, dissolved (μg/L) 10 5.8–75 27 22 --

Lithium, total (μg/L) 10 7.2–63 30 29 --

Manganese, dissolved (μg/L) 18 <20–120 16* 9* 50 (SDWR

Manganese, total (μg/L) 18 30–238 114 94 --

Mercury, dissolved (μg/L) 10 not detected -- -- 2.0 (MCL

Mercury, total (μg/L) 10 not detected -- -- --

Molybdenum, dissolved (μg/L) 10 not detected -- -- --

Molybdenum, total (μg/L) 10 1–5 3 3 --

Nickel, dissolved (μg/L) 10 not detected -- -- --

Nickel, total (μg/L) 10 <1–7 3* 3* --

Selenium, dissolved (μg/L) 10 not detected -- -- 50 (MCL)

Selenium, total (μg/L) 10 not detected -- -- --

Silver, dissolved (μg/L) 10 not detected -- -- 100 (MCL

Silver, total (μg/L) 10 not detected -- -- --

Strontium, dissolved (μg/L) 10 133–1,140 521 465 --

Table 1. Statistical summary of water-quality data collected from Rattlesnake Creek near Zenith, Kansas, December 1998 through

June 2001—Continued

Water-quality chemical or property

(unit of measurement)

Number of

samples

Concentration

 Range Mean Median Water-quality cr
10 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001

Metals—Continued

Strontium, total (μg/L) 10 140–1,150 580 589 

Vanadium, dissolved (μg/L) 10 not detected -- -- 

Zinc, dissolved (μg/L) 10 not detected -- -- 

Zinc, total (μg/L) 10 not detected -- -- 

Pesticides (dissolved)

2,6-Diethylaniline (μg/L) 9 not detected -- -- 

Acetochlor (μg/L) 9 <0.008–0.005 -- -- 

Alachlor (μg/L) 9 <0.01–0.12 -- -- 2.0 (

Atrazine (μg/L) 9 0.01–1.5 .37 .15 3.0 (

Benfluralin (μg/L) 9 not detected -- -- 

Butylate (μg/L) 9 not detected -- -- 350 (

Carbaryl (μg/L) 9 not detected -- -- 700 (

Carbofuran (μg/L) 9 not detected -- -- 40 (M

Chlorpyrifos (μg/L) 9 not detected -- -- 

Cyanazine (μg/L) 9 not detected -- -- 1.0 (

Deethylatrazine (μg/L) 9 0.01–0.13 .06 .03 

Diazinon (μg/L) 9 not detected -- -- .600 

Dieldrin (μg/L) 9 not detected -- -- 

Disulfoton (μg/L) 9 not detected -- -- 

EPTC (μg/L) 9 not detected -- -- 

Ethalfluralin (μg/L) 9 not detected -- -- 

Ethoprop (μg/L) 9 not detected -- -- 

Fonofox (μg/L) 9 not detected -- -- 

Lindane (μg/L) 9 not detected -- -- 

Linuron (μg/L) 9 not detected -- -- 

Malathion (μg/L) 9 <0.005–0.009 -- -- 

Methyl azinphos (μg/L) 9 not detected -- -- 

Methyl parathion (μg/L) 9 not detected -- -- 

Metolachlor (μg/L) 9 <0.01–0.87 .12* .02* 70 (H

Metribuzin (μg/L) 9 <0.004–0.150 -- -- 100 (

Molinate (μg/L) 9 not detected -- -- 

Napropamide (μg/L) 9 not detected -- -- 

p,p’DDE (μg/L) 9 not detected -- -- 

Parathion (μg/L) 9 not detected -- -- 

Pebulate (μg/L) 9 not detected -- -- 

Pendimethalin (μg/L) 9 not detected -- -- 

Permethrin (μg/L) 9 not detected -- -- 

Phorate (μg/L) 9 not detected -- -- 

Prometon (μg/L) 9 <0.02–1.3 -- -- 100 (

Table 1. Statistical summary of water-quality data collected from Rattlesnake Creek near Zenith, Kansas, December 1998 throug

June 2001—Continued

Water-quality chemical or property

(unit of measurement)

Number of

samples

Concentration

 Range Mean Median Water-qua
Chemical Concentrations, Loads, Yields, and Variability 

RPD of about 2 percent for all properties and

chemicals analyzed.

Ten of the 125 properties and chemicals (39 of

which have established or proposed criteria) did not

meet water-quality criteria at some time during the

study. These were pH, turbidity, dissolved oxygen, dissolved solids, sodium, chloride, phosphorus, total

coliform bacteria, E. coli bacteria, and fecal coliform

bacteria. Although water-quality criteria represent

goals to support designated uses of a water body, for

some chemicals enforceable standards for the designated use do not exist. Therefore, water-quality conditions may be compared to proposed criteria or criteria

for an alternate use (drinking water, for example).

pH levels below 6.5 or above 8.5 have caused both

the Little and Big Salt Marshes to be listed as pH

impaired (Kansas Department of Health and Environment, 2001). The pH range for water from

Rattlesnake Creek near Zenith during the period of

manual data collection was 7.3 to 8.8 standard units

(table 1). Turbidity ranged from 5.0 to 480 NTU. The

recommended USEPA guideline for turbidity is

9.9 NTU (U.S. Environmental Protection Agency

2000a) for rivers and streams. Dissolved oxygen i

necessary for the survival of fish and other aquatic 

and therefore, the Kansas Department of Health a

Environment (KDHE) has established an aquatic 

(minimum) criteria of 5.0 mg/L. One sample, wi

dissolved oxygen concentration of 3.1 mg/L, did 

meet this minimum criteria. Dissolved solids ran

from 264 to 5,460 mg/L. The Secondary Drinking

Water Regulation (SDWR) for dissolved solids is

500 mg/L (U.S. Environmental Protection Agenc

2000b). The USEPA has established a Drinking-W

Equivalent Level (DWEL) for sodium of 20 mg/L

which was exceeded in all 18 samples from Rattle

snake Creek near Zenith with a range of 50 to

1,880 mg/L. Chloride concentrations ranged from

67 to 3,000 mg/L; 15 of the 18 samples exceeded 

USEPA’s SDWR of 250 mg/L (U.S. Environment

Protection Agency, 2000b), and 6 of the 18 sampl

exceeded KDHE’s acute aquatic life criteria of

860 mg/L (Kansas Department of Health and

Environment, 2000).

Pesticides—Continued

Pronamide (μg/L) 9 not detected -- -- --

Propachlor (μg/L) 9 not detected -- -- 90 (HAL)

Propanil (μg/L) 9 not detected -- -- --

Propargite (μg/L) 9 not detected -- -- --

Simazine (μg/L) 9 <0.005–0.009 -- -- 4.0 (MCL

Tebuthiuron (μg/L) 9 not detected -- -- 500 (HAL

Terbacil (μg/L) 9 not detected -- -- 90 (HAL)

Terbufos (μg/L) 9 not detected -- -- --

Thiobencarb (μg/L) 9 not detected -- -- --

Triallate (μg/L) 9 not detected -- -- --

Triazine (μg/L) 18 <0.1–2.0 0.6* 0.3* --

Trifluralin (μg/L) 9 not detected -- -- 5.0 (HAL)

Bacteria

Total coliform bacteria (col/100 mL) 4 105–43,900 -- -- 0 (MCLG

E. coli bacteria (col/100 mL) 17 13–1,830 360 162 256 (EPA CR

Enterococci bacteria (col/100 mL) 3 62–1,550 -- -- --

Fecal coliform bacteria (col/100 mL) 23 7–20,000 1,400 500 200 (KS CR

Fecal streptococci bacteria (col/100 mL) 20 <2–4,600 410 170 --

Table 1. Statistical summary of water-quality data collected from Rattlesnake Creek near Zenith, Kansas, December 1998 through

June 2001—Continued

Water-quality chemical or property

(unit of measurement)

Number of

samples

Concentration

 Range Mean Median Water-quality cr
12 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001

Because States and Tribes consistently identify

excessive levels of nutrients as a major reason why

surface water does not meet water-quality criteria, the

USEPA has recommended new water-quality criteria

for nutrients (U.S. Environmental Protection Agency,

2000a). In the new recommended criteria, the United

States is divided into 14 nutrient ecoregions, according

to procedures described in the “Nutrient Criteria Technical Guidance Manual—Rivers and Streams” (U.S.

Environmental Protection Agency, 2000c). Kansas is

divided among four of the 14 ecoregions. The northeast part of the State is in Ecoregion VI (Corn Belt and

Northern Great Plains). The Quivira National Wildlife

Refuge is in Ecoregion V (South-Central Cultivated

Great Plains); however, recommended criteria for

Ecoregion VI are used as a reference in table 1 as

nutrient recommendations have not been established

yet (June 2001) for Ecoregion V. Total phosphorus

concentrations ranged from less than 0.05 to

0.76 mg/L in Rattlesnake Creek, exceeding the Ecoregion VI proposed criteria of 0.076 mg/L.

No metal or pesticide concentrations exceeded

water-quality criteria. In fact, 22 of the 43 metals analyzed were not detected, and 36 of the 46 pesticides

analyzed were not detected.

Total coliform bacteria densities ranged from

105 to 43,900 col/100 mL (colonies per 100 milliliters

of water). The USEPA has established a Maximum

Contaminant Level Goal (MCLG) of 0 col/100 mL for

drinking water. E. coli bacteria ranged from 13 to

1,830 col/100 mL. Five of 17 samples exceeded the

USEPA Contact Recreational Criteria of

256 col/100 mL. Fecal coliform bacteria densities

ranged from 7 to 20,000 col/100 mL. The segment of

Rattlesnake Creek upstream from the refuge is designated for contact recreation by KDHE. Primary contact recreation means recreational activity whereby the

body is immersed in the water to the extent that some

inadvertent ingestion of water is probable (swimming,

for example) (Kansas Department of Health and Environment, 1997). Because of public-health concerns

associated with fecal contamination, KDHE established water-quality criteria of 2,000 col/100 mL for

secondary contact recreation (fishing, for example)

and 200 col/100 mL (from April 1 through

October 31) for primary contact recreation on the

basis of a geometric mean (Kansas Department of

Health and Environment, 2001). Nine percent of the

water samples had fecal coliform bacteria densities

greater than the secondary contact criteria, and 70

percent of the water samples had densities gre

the primary contact criteria.

Hydrologic conditions within the Rattlesn

Creek Basin differed slightly between 1999 to 

(fig. 3A) especially during September and Oc

The 1999 and 2000 mean monthly streamflow

fered in comparison to long-term (October 19

December 2000) mean monthly streamflow (fi

Mean monthly precipitation was 2.2 in. in 19

pared to 1.5 in. in 2000; differences between 

2 years for individual months were generally 

(fig. 4). Precipitation contributes to runoff, wh

result in increased concentrations of sedimen

ria, and other chemicals reaching the stream. 

variation in water quality was difficult to eval

many chemicals because of the limited numb

samples collected (one to two per season). Ho

differences between 1999 and 2000 were eval

comparing annual median concentrations for 

chemicals (fig. 5). Percentage change was ca

by dividing the difference between 1999 and 

median concentrations (1999 concentration m

2000 concentration) by the 1999 median conc

and multiplying by 100.

Chemicals that had a decrease or essentia

change in median concentration between 199

2000 included alkalinity, fluoride, nitrate, and 

coliform bacteria. Chemicals that had an incr

median concentration between 1999 and 2000

included dissolved solids, total suspended sol

sodium, chloride, sulfate, total organic nitrog

total phosphorus.

Increases in the median concentrations of 

major ions (sodium, chloride, and sulfate) in 

were reasonable considering the decreased ra

(fig. 4) and the very low streamflow in Septem

October (fig. 3). Low streamflow corresponds 

increase in that part of streamflow coming fro

ground water (base flow), which has naturally 

concentrations of sodium, chloride, and sulfa

study area (Kansas Department of Health and

Environment, 2001).

Increases in dissolved solids, total suspen

ids, total organic nitrogen, and total phosphor

not as readily explained. However, unusually 

streamflow occurred in early spring of 2000 (

which corresponded to the time of year when 

were tilled, exposing the solids and sediment 

eventually transported to Rattlesnake Creek. F

applied to the agricultural fields also may be 
Chemical Concentrations, Loads, Yields, and Variability 

1.0

10

100

1,000

Mean daily streamflow, in cubic feet per second

1999

2000

Sample collected

A

0

20

40

60

80

100

120

140

160

180

Mean monthly streamflow, in cubic feet per second

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

2000

1999

Long-term

(1973–2000)

B

source of the nitrogen and phosphorus in Rattlesnake

Creek, and concentrations of these nutrients in runoff

can be several times greater during high flows in the

spring than during low flow. In 1998, it was estimated

that 42,104 tons of fertilizer were bought and potentially used in the basin (Kansas Department of Health

and Environment, 2001).

The changes in median concentrations between

1999 and 2000 may be due in part to the times of the

year when samples were collected. Because only nine

samples were collected per year for most chemicals, a

level of uncertainty e

due to the large gaps 

time between collec

of samples. The avai

ability of continuous 

(and regression-base

estimates) would red

this uncertainty, as w

the collection of add

tional samples.

Regression-Estima

Concentrations Us

Real-Time WaterQuality Monitoring

Whereas pH and 

bidity can be measur

directly by the in-str

water-quality monito

concentrations of mo

chemicals of concer

needed to be estimat

on the basis of in-str

water-quality monito

measurements and

regression analysis. 

addition to those me

sured chemicals that

sometimes that do n

meet water-quality c

ria (dissolved solids

sodium, chloride, an

fecal coliform bacte

other chemicals also

were a concern for th

health and habitat of 

and wildlife at the

Quivira National Wildlife Refuge. A discussion o

each chemical of concern and the associated regre

sion equation (table 2) follows.

Through the least-squares process, certain exp

atory variables were selected that have a significa

relation (p-value less than 0.05) to the response v

able. However, an explanatory variable was inclu

in the regression equation only if there was a phy

basis or explanation for its inclusion. Of the equa

presented in table 2, each is site specific and appl

only to Rattlesnake Creek near Zenith, Kansas. A

Figure 3. Comparison of (A) mean daily streamflow and (B) long-term mean monthly streamflow

(1973–2000) for Rattlesnake Creek near Zenith.
14 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001 January February Precipitation, in inches March April

May

June

July

August

September

October

November

December

0

1

2

3

4

5

6

7

1999

2000 Alkalinity

Dissolved solids

Total suspended solids

Percentage change in median concentration

Sodium

Chloride

Fluoride

Sulfate

Nitrate

Total organic nitrogen

Total phosphorous

Fecal coliform bacteria

-100

-50

0

50

100

150

200

Figure 5. Percentage change in median concentrations of selected water-quality chemicals between 1999 and 2000 (19

concentration minus 2000 concentration, divided by 1999 concentration) for Rattlesnake Creek near Zenith.

Figure 4. Comparison of 1999 and 2000 monthly precipitation at Rattlesnake Creek near Zenith gaging station (07142575, fig
Chemical Concentrations, Loads, Yields, and Variability 15

Table 2. Linear regression equations for estimation of physical properties, solids, sediment, major ions, nutrients, and bacteria in Rattlesnake Creek nthrough June 2001—Continued

Water-quality

chemical or property Regression equation n Concentration range1 p-value MSE 

Physical properties, solids, and sediment

Alkalinity Log10ALK=-0.000368

Q– 0.000148WT2 + 2.36 18 ALK 91–224 -- 0.00454 0.7

Q 3.6–840 <0.001 -- -- 

WT 3.4–31.5 .015 -- -- 

Dissolved solids DS = 0.549SC + 14.3 18 DS 264–5,460 -- 707 .9

SC 453–9,930 <.001

Total suspended solids Log10TSS = 0.818log10NTU+0.348 18 TSS 14–270

NTU 5–270

--

 <.001

.0241

 --

.8

 --

Suspended sediment Log10SSC = 0.926log10NTU + 0.438 9 SSC 14.3–1,820

NTU 5–480

--

 <.001

.0991

 --

.9

 --

Major ions

Sodium Na = 0.203SC + 0.0938

Q - 117 18 Na 50–1,880 -- 597 .9

SC 453–9,930 <.001 -- -- 

Q 3.6–840 .006 -- -- 

Chloride Cl = 0.319SC +0.113

Q-172 18 Cl 67–3,000 -- 654 .9

SC 453–9,930 <.001 -- -- 

Q 3.6–840 .002 -- -- 

Fluoride log10

F =-0.000255

Q + 0.162log10SC - 0.892 18

F 0.2–0.6 -- .00317 .8

Q 3.6–840 .018 -- -- 

SC 453–9,930 .022 -- -- 

Sulfate SO4 = 0.0268SC + 13.17 18 SO4 12–269 -- 75.3 .9

SC 453–9,930 <.001 -- -- 

Nutrients

Nitrate Log10NO3 = –0.000442SC+2.60log10 SC-0.000998WT2-

7.37

20 NO3 0.014–2.13

SC 453–9,930

--

 <.001

.0942

 --

.8

 --

WT 3.4–31.5 .002 -- -- 

Total organic nitrogen TN = 0.00317NTU + 0.0234WT – 0.0000655SC +0.469 20 TN 0.050–2.5

NTU 5–480

WT 3.4–31.5

SC 453–9,930

--

 <.001 .008 .050

 .0798

 -- -- --

 .8

 -- -- --

Table 2. Linear regression equations for estimation of physical properties, solids, sediment, major ions, nutrients, and bacteria in Rattlesnake Creek n

through June 2001

[n, number of samples; MSE, mean square error; R2, coefficient of determination; RMAE, relative mean absolute error; RPD, relative percentage difference; log 10, ba

ligrams per liter; WT, water temperature, in degrees Celsius (oC);

Q, streamflow, in cubic feet per second; DS, dissolved solids, in milligrams per liter; SC, specific cond

at 25 oC; TSS, total suspended solids, in milligrams per liter; NTU, turbidity, in nephelometric turbidity units; SSC, suspended sediment concentration, in milligrams p

liter; Cl, chloride, in milligrams per liter;

F, fluoride, in milligrams per liter; SO4, sulfate, in milligrams per liter; NO3, nitrate, in milligrams per liter; TN, total organic n

phosphorus, in milligrams per liter; FCB, fecal coliform bacteria density, in colonies per 100 milliliters of water; --, not applicable]
16 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, South-Central

Kansas, December 1998 Through June 2001 Nutrients—Continued Total phosphorus TP = 0.00103NTU -0.227log10SC + 0.00570WT +0.776 20 TP 0.025–0.755 NTU 5–480 SC 453–9,930 WT 3.4–31.5 -- <0.001 <.001 <.001 0.00179 -- -- -- 0.

 -- -- -- 

Bacteria Fecal coliform bacteria2 Log10FCB = -3.40log10WT +0.432log10NTU+6.53 18 FCB 90–20,000 WT 9.3–32.2 NTU 5–480 -- <.001 .036 .124 -- -- .

 -- -- 

1Concentration ranges and sample sizes are not always the same as those presented in table 1 because these data represent a subset of table 1. 2Equation developed only for samples during the recreation period from April 1 through October 31.

Table 2. Linear regression equations for estimation of physical properties, solids, sediment, major ions, nutrients, and bacteria in Rattlesnake Creek through June 2001—Continued

Water-quality

chemical or property Regression equation n Concentration range1 p-value MSE 
Chemical Concentrations, Loads, Yields, and Variability 

0

0

50

100

150

200

250

1

10

100

1,000

10,000

0

1,000

2,000

3,000

4,000

5,000

6,000

300

0

50

100

150

200

250

50 100 150 200 250 300

Measured dissolved-solids concentration,

in milligrams per liter

Estimated dissolved-solids concentration,

in milligrams per liter

Measured total suspended solids

concentration, in milligrams per liter

Estimated total suspended solids

concentration, in milligrams per liter

Measured suspended sediment

concentration, in milligrams per liter

Estimated suspended sediment

concentration, in milligrams per liter

Measured alkalinity concentration,

in milligrams per liter

Estimated alkalinity concentration,

in milligrams per liter

1 10 100 1,000 10,0

0 50 100 150 200 250 0 1,000 2,000 3,000 4,000 5,000 6,0

Alkalinity Dissolved solids

Total suspended 

solids (TSS)

Suspended sediment 

concentration (SSC)

tionally, the equations presented in table 2 are valid

only for the concentration ranges given in table 2.

Physical Properties, Solids, and Sediment

The physical properties, solids, and sediment discussed in this report are alkalinity, dissolved solids,

total suspended solids (TSS), and suspended sediment

concentration (SSC). A comparison of the measured

and regression-estimated concentrations is shown in

figure 6.

 It is important to consider these properties and

chemicals in water because they can be indicators of

stream health. Streams with a low alkalinity have a

low buffering capacity, which may pose a risk to

aquatic life. Excessively large concentrations of

dissolved solids are objectionable in drinking

water because of possible physiological effects

(U.S. Environmental Protection Agency, 1986). T

and SSC can cause problems for fish by clogging 

and for aquatic plants by reducing light penetratio

and thus limiting growth. In addition, solids and s

ment provide a medium for accumulation and tran

port of other chemicals such as phosphorus and

bacteria. For these reasons, it is important to mon

alkalinity, dissolved solids, TSS, and SSC not only

because of the effect on fish, but also on waterfow

depend on fish for their survival.

The alkalinity of water may be defined as the

capacity for solutes in the water to react with and 

Figure 6. Comparison of measured and regression-estimated alkalinity, dissolved solids, total suspended solids, and suspended

sediment concentrations in water from Rattlesnake Creek near Zenith, December 1998 through June 2001.
18 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001

tralize acid. Alkalinity is important in determining a

stream’s ability to neutralize acidic pollution from

rainfall or wastewater. Streams with low alkalinity are

not well buffered and often are affected adversely by

acid inputs. The pH levels in low-alkalinity streams

can decrease to a point that eliminates acid-intolerant

forms of aquatic life. Fish are particularly affected by

low pH water. Explanatory variables for the estimation

of alkalinity in water from Rattlesnake Creek near

Zenith were streamflow and water temperature. Shallow ground water beneath irrigated cropland and

petroleum-production land in south-central Kansas

was shown to have significantly larger concentrations

of alkalinity (Helgesen and others, 1994) than water

beneath undeveloped rangeland. This ground water

may then become base flow to streams. Values of

streamflow and alkalinity are correlated because of the

dilution effect of runoff. In addition, Hem (1992,

p. 107) notes that temperature has an effect on the percentage of dissolved carbon dioxide species, and in

most natural water, alkalinity is produced by the

dissolved carbon dioxide species, bicarbonate

and carbonate.

Dissolved solids include both organic and inorganic material dissolved in a sample of water (Bates

and Jackson, 1984). Dissolved solids may be the result

of natural dissolution of rocks and minerals or of discharges from municipal sources (Christensen and

Pope, 1997). Large dissolved-solids concentrations,

like those in Rattlesnake Creek, may be expected in

areas underlain by ancient marine sediment containing

large salt deposits (Bevans, 1989; Gillespie and Hargadine, 1994). Ground water may dissolve these salts

and eventually may discharge into Rattlesnake Creek.

This process may be augmented by the use of ground

water for irrigation, some of which may become

“return flows.” Helgesen and others (1994) also

showed that dissolved-solids concentrations were significantly larger in ground water beneath petroleumproduction land (much of the land immediately

upstream from the Rattlesnake Creek streamflowgaging station is in this category) than in water

beneath undeveloped rangeland. This ground water

could become base flow to streams or be used for irrigation. Because of widespread use of ground water for

irrigation in Rattlesnake Creek Basin, it may be

important to monitor the dissolved solids entering the

refuge. Dissolved solids in water at the Rattlesnake

Creek streamflow-gaging station were highly correlated with specific conductance.

TSS and SSC represent suspended solid-p

material in a water sample. SSC differs from 

that SSC data are produced by measuring the 

weight of all the sediment of a known volume 

water-sediment mixture (unlike TSS in which 

are produced from a subsample of the origina

and others, 2000). Certain solute-sediment int

(between sediment and organic compounds, f

ple) make TSS and SSC important water-qual

tors. Both TSS and SSC were highly correlate

turbidity. The turbidity ranges for TSS and SS

ferent because analysis of SSC did not begin u

March 2000. Turbidity is an indicator of sedim

other solid material transported in a stream an

fore, has a relation to TSS and SSC.

Major Ions

Major ions discussed in this report are so

chloride, fluoride, and sulfate. A comparison 

sured and regression-estimated concentration

shown in figure 7. The sources of sodium, chl

fluoride, and sulfate in south-central Kansas a

part, geologic (dissolution of natural deposits 

ground water) (Buchanan, 1984; Bevans, 198

Gillespie and Hargadine, 1994). Sodium and 

concentrations varied seasonally in Rattlesnak

and were larger in the winter when most of th

flow was from ground-water discharge. Altho

some organisms can adapt to saline condition

excessively large concentrations of ions can b

toxic to other organisms and plants (Mitsch a

Gosselink, 1993).

Sodium and chloride concentrations in wa

the Rattlesnake Creek Basin are naturally hig

dissolution of natural salt deposits. In additio

increased chloride concentrations in the basin 

due in part to oil-and-gas production. The pro

of oil and gas results in a brine (water with a 

tent of dissolved salt) that often is disposed of 

injection wells or disposal ponds. Helgesen an

(1994) showed that there were significantly la

centrations of sodium and chloride in shallow 

water beneath petroleum-production land tha

water beneath undeveloped rangeland. Nume

production facilities are located at and near th

(Allen, 1991).

Sodium and chloride are charged ionic spe

make water conductive. As sodium and chlor

centrations increase, the conductivity of a sol

increases (Hem, 1992), and because sodium a
Chemical Concentrations, Loads, Yields, and Variability 

0

200400600800

1,0001,2001,4001,6001,8002,000

200

0

0

0 0

50

100

150

200

250

300

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 50 100 150 200 250 30

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0

500

1,0001,5002,0002,5003,0003

500

0

500

1,000

1,500

2,000

2,500

3,000

4,000

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

Measured sodium concentration,

in milligrams per liter

Estimated sodium concentration,

in milligrams per liter

Measured fluoride concentration,

in milligrams per liter

Estimated fluoride concentration,

in milligrams per liter

Measured sulfate concentration,

in milligrams per liter

Estimated sulfate concentration,

in milligrams per liter

Measured chloride concentration,

in milligrams per liter

Estimated chloride concentration,

in milligrams per liter

Sodium Chloride

Fluoride Sulfate

ride are the most abundant charged ions in Rattlesnake

Creek, specific conductance is directly related to both

sodium and chloride. The multiple linear regression

equations for both sodium and chloride included specific conductance and streamflow.

Concentrations of fluoride present in water from

Rattlesnake Creek were small (a median concentration

of 0.5 mg/L during the study period) as it is in most

natural water (Hem, 1992). The explanatory variables

used in the regression equation for fluoride were specific conductance and streamflow. Because the source

of fluoride is geologic, higher concentrations of fluoride were present during base flow when specific conductance also was high. During higher streamflow

base flow was diluted, and fluoride concentration

decreased.

Specific conductance was used for estimating 

fate concentrations in water from Rattlesnake Cre

Sulfate, like sodium, chloride, and fluoride, is a

charged ionic species, and specific conductance is 

related with sulfate concentration. Sulfate in wate

with saline base flow (such as in Rattlesnake Cree

can have well-defined relations with specific cond

tance (Hem, 1992), except during high flows whe

mineralized ground water is diluted.

Figure 7. Comparison of measured and regression-estimated major ion concentrations in water from Rattlesnake Creek near

Zenith, December 1998 through June 2001.
20 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001

0.01

0.1

1

10

Measured nitrate concentration

in milligrams per liter

Estimated nitrate concentration,

in milligrams per liter

0.01 0.1 1 

Nitrate

Measured total organic nitrogen

concentration, in milligrams per l

Estimated total organic nitrogen

concentration, in milligrams per liter

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 

Total organic nitrogen

0

0.1

0.2

0.3

0.4

0.5

0.6

Measured total phosphorus concentr

in milligrams per liter

Estimated total phosphorus concentration,

in milligrams per liter

0 0.1 0.2 0.3 0.4 0.5 

Total phosphorus

Nutrients

Nutrients included in this report are nitrate, total

organic nitrogen, and total phosphorus. A comparison

of measured and regression-estimated nutrient concentrations is shown in figure 8. Nitrogen occurs in water

in several forms including elemental nitrogen, ammonia, nitrite, and nitrate. Phosphorus also occurs in several forms including elemental phosphorus and

dissolved orthophosphorus. Although nutrients are

vital to growth and reproduction in an ecosystem, in

excess amounts they can interfere with these functions

(Mueller and Helsel, 1996; Sharpley and Rekolainen,

1997). Accelerated growth of algae can lead to

eutrophication of a water body. Phosphorus in its elemental form may be toxic to aquatic organisms and

may bioaccumulate in much the same manner as mercury (U.S. Environmental Protection Agency, 1986).

Major sources of nutrients include agricultural

activities, such as the pasturing and confined feeding

of livestock, and the use of synthetic fertilizers.

Another source for nutrients is wastewater discharges

from communities within a basin. In the Rattlesnake

Creek Basin, the town of Saint John is the only permitted wastewater treatment facility and seems to have

minimal effect on the basin (Kansas Department of

Health and Environment, 2001). Geologic formations

contain small amounts of phosphorus (as much as

0.5 percent of total weight) and may contribute to

phosphorus load (Kansas Department of Health and

Environment, 2001).

The explanatory variables for the estimation of

nitrate in water from Rattlesnake Creek near Zenith

were specific conductance and water temperature. The

explanatory variables for the estimation of total

organic nitrogen were turbidity, water temperature,

and specific conductance. Figure 8 shows that larger

measured and estimated nitrate concentrations are

more widely scattered than those at the lower left of

the graph. In fact, the nitrate regression is not as good

(on the basis of RPD and R2

) as the regressions for the

other nutrients, indicating that the best surrogate for

nitrate concentrations has not been found.

Specific conductance and water temperature varied seasonally and can be used to describe the seasonal

variation in concentrations of nitrate and total organic

nitrogen in Rattlesnake Creek, which in turn may

reflect the seasonality of agricultural activities such as

fertilizer application. Turbidity varied directly with

total organic nitrogen concentration.

Figure 8. Comparison of measured and regressionestimated nutrient concentrations in water from

Rattlesnake Creek near Zenith, December 1998 thro

June 2001.
Chemical Concentrations, Loads, Yields, and Variability 

1

100

1,000

10,000

100,000

10

Measured fecal coliform bacteria,

in colonies per 100 milliliters of water

Estimated fecal coliform bacteria,

in colonies per 100 milliliters of water

1 10 100 1,000 10,000 10

Fecal coliform bacteria

The explanatory variables used for the estimation

of total phosphorus were turbidity, specific conductance, and water temperature. Total phosphorus

includes the dissolved (for example, organic phosphate) and particulate forms. Because turbidity is a

measure of the amount of particulate matter in water

and because phosphorus in water is mostly present in

the particulate form, the two variables are related.

Specific conductance, which was high during low

flow (because of the larger contribution from ground

water), may have had a secondary indirect relation

with total phosphorus, which tended to be higher during high flow. Alternatively, water with high specific

conductance values may affect the precipitation and

adsorption of phosphorus in the sample after collection, which in turn may affect the total phosphorus

concentration. Organic phosphate species, which are

synthesized by plants and animals, constitute a fraction of total phosphorus (Hem, 1992). The distribution of dissolved phosphate species is pH and

temperature dependent (Hem, 1992), and therefore,

total phosphorus concentration shows a relation to

water temperature. Alternatively, specific conductance and water temperature may be surrogates for

time (season); however, neither day of year nor the

sine or cosine of time (to represent seasonal cycles)

were correlated with total phosphorus for this data set.

Fecal Coliform Bacteria

 A comparison of measured and regressionestimated bacteria densities is shown in figure 9. The

presence of fecal coliform bacteria in surface water

indicates fecal contamination and possibly the presence of other organisms that could cause disease.

Fecal coliform bacteria analyses were chosen for

regression analysis because current (2001) State of

Kansas water-quality criteria (2,000 col/100 mL for

secondary contact recreation and 200 col/100 mL for

primary contact recreation, on the basis of a geometric

mean) are based on fecal coliform bacteria densities

(Kansas Department of Health and Environment,

2000). The primary contact recreational criterion is

valid from April 1 through October 31.

Because runoff from a basin may transport fecal

coliform bacteria to streams, it is expected that bacteria densities would vary with streamflow and, additionally, with time of year (as runoff characteristics

vary seasonally). Previously developed fecal coliform

bacteria regression equations for south-central Kansas

sites used time (either day of year or month of year) as

the explanatory variable (Christensen and others,

2000, 2001). However, no acceptable equation wa

found for the 23 samples collected between Decem

1998 and June 2001. This may be due in part to th

number of samples (5 of the 23) for which plate co

were outside the ideal count range (Myers and W

1999), primarily those samples with either very lo

very high reported densities.

Because concern over bacteria densities is gre

during the summer months when recreational use

may be exposed to the bacteria in surface water, t

regression equation for fecal coliform was develo

using only the 18 samples collected between Apr

and October 31. This equation used water tempera

and turbidity as explanatory variables and is not v

for estimating bacteria densities from November

through March.

The relatively low R2 (0.661) and relatively h

median RPD (50.0 percent) for the fecal coliform 

teria equation (table 2) indicate that the equation h

higher degree of uncertainty compared with the e

tions for other chemicals. It is noted, however, tha

large part of the uncertainty may be due to lack o

cision in the analytical method rather than to lack 

of the regression. Analytical error for the determ

tion of fecal coliform bacteria can be as high as 50 

cent (American Public Health Association and oth

1992); in addition 3 of 18 water samples from the

April 1 to October 31 data set were not in the idea

ony count range for fecal coliform bacteria.

Figure 9. Comparison of measured and regression-estimated fec

coliform bacteria densities in water from Rattlesnake Creek nea

Zenith, December 1998 through June 2001.
22 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001

10

100

April May June July August September October

1,000

10,000

100,000

1,000,000

Fecal coliform bacteria density, in colonies per

100 milliliters of water (col/100 mL) 

Measured

density

Regression-estimated density

Secondary contact recreational criterion

(2,000 col/100 mL)

Primary contact recreational criterion

(200 col/100 mL)

2000

Despite the uncertainty, the usefulness of the

regression-estimated concentrations to recreationalists and resource managers is evident (fig. 10). The

regression equation was applied to continuously

(hourly) collected water temperature and turbidity data

in 2000 to show when regression-estimated concentrations of fecal coliform bacteria in Rattlesnake Creek

exceeded primary contact and secondary contact recreation criteria. During 2000, primary contact criterion

for fecal coliform bacteria was exceeded many times.

In July, a popular month for recreational activities, the

30-day mean estimated density was 480 col/100 mL

(on the basis of about 700 hourly measurements),

exceeding the geometric mean criterion of

200 col/100 mL (requiring at least five individual measurements during a 30-day period). However, with a

contaminant such as fecal coliform bacteria, it is the

density at the time of contact with the water that is

important, not a monthly mean. When regression-estimated densities are available in real time (currently

available at http://ks.water.usgs.gov/Kansas/rtqw/),

immediate action may be taken to avoid contact with

the water until water-quality conditions improve or to

make other water-use decisions.

Estimated Loads, Yields, and Variability

Although concentrations are useful for evaluating

Rattlesnake Creek with respect to current waterquality criteria, load estimates are useful for regulatory authorities and resource managers. A chemical

load is the chemical concentration multiplied 

streamflow and an appropriate conversion fac

gives an indication of the mass of that chemic

ported past a given site during a given time. R

tory authorities may use load estimates in the

development of TMDLs. Estimates of chemic

for Rattlesnake Creek are particularly importa

resource managers because those loads are tra

into Quivira National Wildlife Refuge and co

a harmful effect on aquatic life or threatened 

endangered species. Chemical loads also may 

to evaluate trends, which can be compared to 

and other factors affecting water quality.

Regression-estimated loads and yields of 

chemical were calculated for 1999 and 2000 

on the basis of approximately 8,760 measurem

(hourly measurements for 1 year) made by th

uously recording in-stream water-quality mon

data from the water-quality monitor were not 

able, annual load estimates would have to be 

the nine samples collected in 1999 and the ni

ples collected in 2000. There are several com

programs available to help estimate loads by 

lating chemical concentrations between samp

(or) by using only discharge as a surrogate (co

used because it is the only surrogate available 

ously at most USGS streamflow-gaging statio

Loads for most chemicals, especially those th

significantly related to discharge, could not b

mated as accurately using these programs.

Figure 10. Comparison of measured and regression-estimated fecal coliform bacteria densities in water from Rattlesna

Creek near Zenith, April 1 through October 31, 2000. Recreational water-quality criteria established by Kansas Departme

of Health and Environment (2000).
Chemical Concentrations, Loads, Yields, and Variability 

Evaluating the differences in loads can be valuable

for evaluating methods. For example, TSS load is

about one-half the SSC load (table 3). TSS and SSC are

both used to quantify concentrations of suspended

solid-phase material in surface water (Gray and others,

2000) and theoretically should be the same. However,

the uncertainty (RPD, table 2) of the TSS equation was

substantially larger than the uncertainty for the SSC

equation, possible due to a less reliable analytical

method.

Loads of major ions can be compared from year to

year to evaluate the effects of ground-water discharge

and irrigation return flows on water quality. Annual

nutrient loads can be evaluated with respect to annual

variability in algal booms, which may be a sign of

eutrophication problems in ponds at the refuge. Seasonal variation in nutrients are substantial, and most of

the total organic nitrogen and total phosphorus loads

(fig. 11) are transported to the refuge during just a few

periods of surface runoff. This information may be

used by the resource managers to determine when

water-diversion strategies would be most beneficial.

Fecal coliform bacteria loads may be compared from

year to year to evaluate effective wastewater-treatment

strategies or the effects that changes in land use (such

as cattle production) or wildlife may have on a water

body. Estimated annual fecal coliform bacteria loads

were not included in table 3 because the equation for

fecal coliform (table 2) was not developed with data

from, nor is valid for, the entire year.

Yield estimates also are useful to resource ma

ers. A yield is an area normalized load, or in othe

words, the chemical load divided by the drainage 

Yields thus enable comparison of sites with differ

drainage areas. The differences in estimated nutri

yields also may help assess and prioritize landmanagement practices within or among basins. R

tlesnake Creek 1999 and 2000 estimated nitrate y

(0.248 and 0.194 lb/acre/yr, respectively) were co

rable to a median annual basin yield (0.232 lb/acr

or 26 kg/km2

/yr for 1990–95) for 85 relatively un

veloped basins across the United States (Clark an

others, 2000). On the other hand, 1999 and 2000 

phosphorus yields for Rattlesnake Creek (0.0267 

0.0289 lb/acre/yr, respectively) were less than a

median annual basin yield (0.0758 lb/acre/yr or

8.5 kg/km2

/yr for 1990–95) for those same 85 ba

Yields were calculated using total drainage area f

Rattlesnake Creek Basin and the 85 undeveloped

basins (G.M. Clark, USGS, written commun., No

ber 6, 2001). However, nearly one-half of the Ra

snake Creek Basin is considered noncontributing, 

if yield calculations only used contributing draina

area, phosphorus yields in Rattlesnake Creek Bas

would approach the median annual basin yield fo

undeveloped basins.

Estimated alkalinity and dissolved-solids load

and yields were smaller in 2000 compared to 199

whereas estimated total suspended solids and sus

pended sediment loads and yields were larger in 2

compared to 1999. Estimated loads and yields for 

Table 3. Estimated loads and yields of selected chemicals in water from Rattlesnake Creek near Zenith, Kansas, 1999 and 2000

[All loads are expressed in pounds per year, and all yields are expressed in pounds per acre per year]

Chemical

Estimated load Estimated yield

1999 2000 1999 2000

Physical properties, solids, and sediment

Alkalinity 20,200,000 19,000,000 30.1 28.3

Dissolved solids 170,000,000 151,000,000 254 225

Total suspended solids (TSS) 8,660,000 10,000,000 12.9 15.0

Suspended sediment (SSC) 17,500,000 21,300,000 26.1 31.8

Major ions

Sodium 51,200,000 45,000,000 76.4 67.1

Chloride 82,000,000 71,500,000 122 107

Fluoride 47,400 42,900 .071 .064

Sulfate 9,650,000 8,580,000 14.4 12.8

Nutrients

Nitrate 166,000 130,000 .248 .194

Total organic nitrogen 93,100 92,000 .139 .137

Total phosphorus 17,900 19,300 .0267 .028
24 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001

2000

Estimated total organic nitrogen load—Calculated

 as an instantaneous load (from regression-estimated

 concentration and instantaneous streamflow).

 Expressed in units of pounds per day

Estimated total phosphorus load—Calculated as

 an instantaneous load (from regression-estimated

 concentration and instantaneous streamflow).

 Expressed in units of pounds per day

Measured total organic nitrogen load—Calculat

 instantaneous load (from sample concentration

 and instantaneous streamflow). Expressed in

 units of pounds per day

Measured total phosphorus load—Calculated as

 instantaneous load (from sample concentration

 and instantaneous streamflow). Expressed in

 units of pounds per day

EXPLANATION

0

2,000

3,000

1,000

4,000

5,000

6,000

7,000

8,000

Total load, in pounds per day

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. De

the major ions were smaller in 2000 compared to

1999. Estimated nitrate and total organic nitrogen

loads and yields were slightly smaller, whereas estimated total phosphorus loads and yields were slightly

larger in 2000 compared to 1999 (table 3).

Uncertainty Associated with Regression-Based

Estimates

It would not be prudent to consider the estimates

presented herein without understanding the uncertainty involved. The R2

, MSE, RMAE, and RPD

(table 2) all give an indication of uncertainty. The

nitrate and bacteria equations have a higher degree of

uncertainty than do those of the other chemicals. The

high RPD for bacteria not only includes the uncertainty due to regression but also includes analytical

uncertainty, which can be as high as 50 percent for

bacteria analysis. In addition, the water-quality monitors have an upper limit of measurement with respect

to turbidity (1,750 NTU). This upper limit can cause

an underestimation of concentrations and may have a

substantial effect on load and yield estimates 

the 12 chemicals of concern that use turbidity 

rogate. However, turbidity values did not appr

upper limit during the study period. Another 

tion was the small number of samples (about 

ples for most chemicals) available for the deve

of the regression equations.

SUMMARY

The Quivira National Wildlife Refuge is 

wetlands area located in the Rattlesnake Cree

of south-central Kansas. To improve the wate

and preserve valuable wildlife habitat, canals 

ponds have been constructed throughout the r

since the early part of the 20th century. The r

now contains more than 30 ponds and marshe

provides food, water, cover, and protection fo

species of birds, fish, and other wildlife.

Not only is there a desire to ensure a depe

water supply to Quivira National Wildlife Re

there also is a strong desire to ensure suitable 

Figure 11. Comparison of measured and regression-estimated total organic nitrogen and total phosphorus loads in water 

Rattlesnake Creek near Zenith, 2000.
Summary 

quality. In response to the Federal Clean Water Act of

1972, the Kansas Department of Health and Environment listed segments of Rattlesnake Creek as waterquality impaired. Little and Big Salt Marshes are

identified as being eutrophic and impaired for specific

uses by pH, and the Little Salt Marsh is impaired by

fecal coliform bacteria. Rattlesnake Creek is listed as

being impaired by chloride and sulfate. The Federal

Clean Water Act requires that the State of Kansas

establish TMDLs to meet established water-quality

criteria and to ensure protection of the creek’s and

marshes’ designated beneficial uses.

There are several factors that affect the water quality at the refuge. Substantial quantities of water are

supplied by highly mineralized ground-water seepage

near Big Salt Marsh. The water quality in the refuge

also may be affected by oil-and-gas production and

agriculture. The dominance of agriculture in the basin

and a semiarid climate have lead to the widespread use

of irrigation in the area. Irrigation return flows may

contain increased concentrations of certain chemicals

that may cause adverse health effects in aquatic organisms and birds at the refuge.

In response to the need for additional chemical

data, concentrations of selected water-quality

chemicals were identified through the collection and

analysis of water samples from December 1998

through June 2001 at the USGS streamflow-gaging

station on Rattlesnake Creek near the inlet to the refuge. Samples were collected throughout a range of

streamflow conditions and analyzed for physical properties, solids, sediment, major ions, nutrients, and indicator bacteria. Event-related (base-flow plus runoff)

samples also were analyzed for metals and pesticides.

Ten of the 125 chemicals analyzed did not meet established or proposed water-quality criteria to protect

aquatic life and drinking water in at least one sample.

These were pH, turbidity, dissolved oxygen, dissolved

solids, sodium, chloride, phosphorus, total coliform

bacteria, E. coli bacteria, and fecal coliform bacteria.

No metal or pesticide concentrations exceeded waterquality criteria. Twenty-two of the 43 metals analyzed

were not detected, and 36 of the 46 pesticides analyzed were not detected.

Annual variation in water quality was evaluated by

comparing median concentrations for selected chemicals between 1999 and 2000. Median concentrations

of alkalinity, fluoride, nitrate, and fecal coliform bacteria were either smaller in 2000 compared with 1999 or

did not change. Chemicals with larger median

concentrations in 2000 compared to 1999 include

dissolved solids, total suspended solids, sodium, c

ride, sulfate, total organic nitrogen, and total phos

rus. Increases in the median concentrations of the

major ions (sodium, chloride, and sulfate) in 2000

were somewhat expected due to the decreased rai

in 2000 and very low streamflow late in the year. 

streamflow corresponds to an increase in that part 

streamflow coming from ground water, which has 

urally high concentrations of sodium, chloride, an

sulfate. Increases in dissolved solids, total suspen

solids, total organic nitrogen, and total phosphoru

concentrations were not as readily explained. How

ever, unusually high streamflow occurred in early

spring of 2000, which corresponded to the time of 

when fields were tilled, exposing the solids, sedim

and associated nutrients that were eventually tran

ported to Rattlesnake Creek. Fertilizer applied to t

agricultural fields also may be a major source of t

nitrogen and phosphorus (components of fertilize

Rattlesnake Creek. Periodic samples collected fr

December 1998 through June 2001 may not repre

a typical range in hydrologic and environmental c

tions. Below-average streamflow and precipitation

occurred in 1999, and 2000 was unusually dry for 

tlesnake Creek Basin. In addition, the changes in

median concentrations between 1999 and 2000 ma

due in part to the times of the year when samples 

collected. Because only nine samples were collec

per year for most chemicals, uncertainty exists du

the large gaps in time between collection of perio

samples. The availability of continuous data woul

reduce this uncertainty as would the collection of 

tional samples.

To meet the need for continuous data, a conti

ously recording water-quality monitor was install

the USGS streamflow-gaging station on Rattlesna

Creek near Zenith on October 28, 1998. Water-qu

chemicals measured with the monitor from Decem

1998 through June 2001 were specific conductanc

pH, water temperature, turbidity, and dissolved

oxygen.

 Regression equations were developed betwee

real-time measurements (specific conductance, pH

water temperature, turbidity, and dissolved oxyge

and the chemicals of concern to the refuge (alkali

dissolved solids, total suspended solids, sediment

sodium, chloride, fluoride, sulfate, nitrate, total

organic nitrogen, total phosphorus, and fecal colif

bacteria). Although pesticides are a concern to
26 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001

wildlife, there were very few detections for any of

the pesticides analyzed as part of this study, and

regression equations for these chemicals were

not developed.

 From the regression equations, loads and yields

also were calculated. Estimated alkalinity and dissolved-solids loads and yields were smaller in 2000

compared to 1999, whereas estimated total suspended

solids and suspended sediment loads and yields were

larger in 2000 compared to 1999. Estimated loads and

yields for all the major ions were smaller in 2000 compared to 1999. Estimated nitrate and total organic

nitrogen loads and yields were slightly smaller,

whereas estimated total phosphorus loads and yields

were slightly larger in 2000 compared to 1999. When

Rattlesnake Creek nutrient yields were compared to

85 relatively undeveloped basins across the United

States, nitrate yields were similar, whereas total

phosphorus yields were smaller in the Rattlesnake

Creek Basin.

Continuous data collection with the water-quality

monitor allowed for an evaluation of variation in seasonal loading. The seasonal cycle in total organic

nitrogen and total phosphorus loads was pronounced,

and most of the nutrient load transported into the refuge occurred during just a few periods of surface runoff. This information may be used by the resource

managers to determine when water-diversion strategies would be most beneficial.

 The development of regression equations using

in-stream water-quality measurements and chemical

concentrations from samples collected periodically

enables a more accurate representation of daily loads.

The regression-estimated loads may be more reflective

of actual loads because they are based on continuous

(hourly) data for the explanatory variables. On the

other hand, loads calculated from collected samples

are based on a limited number of discrete samples collected throughout the year, and fluctuations in concentration are often missed.

The innovative approach used in this study to estimate chemical concentrations, loads, yields, and variability of water flowing into the Quivira National

Wildlife Refuge will allow resource managers to protect wildlife by reacting quickly to changes in water

quality and allow recreationalists to make water-use

decisions. The increasing public interest in TMDLs

and the concern for threatened and endangered species

make this study of regional, as well as national, importance because it shows how chemical loads may be

estimated using continuous data and that real

application of regression equations can be use

resource managers to protect wildlife. The me

used in this study may be used for other sites 

sas and the Nation to estimate chemical loads 

providing input data for the development of T

REFERENCES CITED

Allen, G.T., 1991, Petroleum hydrocarbons, chlor

hydrocarbons, and metals in soils and sedime

Quivira National Wildlife Refuge—1989: U.

and Wildlife Service, 10 p.

American Public Health Association, American W

Works Association, and Water Environment 

tion, 1992, Standard methods for examination 

and wastewater (18th ed.): Washington, D.C

can Public Health Association, p. 2–8 to 2–1

Bates, R.L., and Jackson, J.A., eds., 1984, Diction

of geologic terms: New York, Doubleday,

various pagination.

Bevans, H.E., 1989, Water resources of Sedgwick 

Kansas: U.S. Geological Survey Water-Reso

Investigations Report 88–4225, 119 p.

Buchanan, Rex, 1984, Kansas geology: Lawrence

sity Press of Kansas, 208 p.

Christensen, V.G., Jian, Xiaodong, and Ziegler, A

Regression analysis and real-time water-qual

toring to estimate constituent concentrations, 

and yields in the Little Arkansas River, south

Kansas, 1995–99: U.S. Geological Survey W

Resources Investigations Report 00–4126, 36 

Christensen, V.G., and Pope, L.M., 1997, Occurre

solved solids, nutrients, atrazine, and fecal co

bacteria during low flow in the Cheney Reser

watershed, south-central Kansas, 1996: U.S.

Geological Survey Water-Resources Investig

Report 97–4153, 13 p.

Christensen, V.G., Rasmussen, P.P., and Ziegler, A

Real-time water-quality monitoring and regre

analysis to estimate nutrient and bacteria con

tions in Kansas streams, in Melching, C.S., a

Emre, eds., Proceedings of the 5th Internatio

ference on Diffuse/Nonpoint Pollution and W

Management, June 10–15, 2001, Milwaukee

sin: International Water Association, CD-RO

session 9, p. 1–9.

Clark, G.M., Mueller, D.K., and Mast, M.A., 2000

concentrations and yields in undeveloped stre

basins of the United States: Journal of the A

Water Resources Association, v. 36, no. 4, p. 
References Cited 

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

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

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

Duan, N., 1983, Smearing estimate—a nonparametric

retransformation method: Journal of the American Statistical Association, v. 78, p. 605–610.

Gillespie, J.B., and Hargadine, G.D., 1994, Geohydrology

and saline ground-water discharge to the South Fork

Ninnescah River in Pratt and Kingman Counties,

south-central Kansas: U.S. Geological Survey WaterResources Investigations Report 93–4177, 51 p.

Gilroy, E.J., Hirsch, R.M., and Cohn, T.A., 1990, Mean

square error of regression-based constituent transport

estimates: Water Resources Research, v. 26, no. 9,

p. 2069–2077.

Gray, J.R., Glysson, G.D., Turcios, L.M., and Schwartz,

G.E., 2000, Comparability of suspended-sediment concentration and total suspended solids data: U.S. Geological Survey Water-Resources Investigations

Report 00–4191, 14 p.

Helgesen, J.O., Stullken, L.E., and Rutledge, A.T., 1994,

Assessment of nonpoint-source contamination of the

High Plains aquifer in south-central Kansas, 1987:

U.S. Geological Survey Water-Supply Paper 2381–C,

51 p.

Helsel, D.R., and Hirsch, R.M., 1992, Statistical methods in

water resources: New York, Elsevier, 4th Impression,

522 p.

Hem, J.D., 1992, Study and interpretation of chemical characteristics of natural water (3d ed.): U.S. Geological

Survey Water-Supply Paper 2254, 263 p.

Hirsch, R.M., Helsel, D.R., Cohn, T.A., and Gilroy, E.J.,

1993, Statistical analysis of hydrologic data, in Maidment, D.R., ed., Handbook of hydrology: New York,

McGraw-Hill, Inc., p. 17.1–17.55.

Jian, Xiaodong, 1998, Simulation of canal and control-pond

operation at the Quivira National Wildlife Refuge,

south-central Kansas: U.S. Geological Survey WaterResources Investigations Report 97–4289, 183 p.

Kansas Department of Health and Environment, 1997, Kansas register: Topeka, Kansas, Secretary of State, v. 16,

no. 28, p. 183–200.

———1998, 1998 Kansas water-quality limited segments

(303(d) list): accessed November 2, 2001, at URL

http://www.kdhe.state.ks.us/befs/303d/

index.html#tables

———2000, Proposed amended regulation to article 16—

surface water quality standards: accessed June 28,

2001, at URL http://www.kdhe.state.ks.us/water/

index.html#regs

———2001, TMDLs for the Lower Arkansas River Basin:

accessed August 14, 2001, at URL

http://www.kdhe.state.ks.us/tmdl/latmdl.htm

Mitsch, W.J., and Gosselink, J.G., 1993, Wetlands: New

York, Van Nostrand Reinhold, p. 166–187.

Mueller, D.K., and Helsel, D.R., 1996, Nutrients in the

Nation’s waters—too much of a good thing?: U.S

Geological Survey Circular 1136, 24 p.

Myers, D.N., and Sylvester, M.A., 1997, Fecal indicat

bacteria, in Myers, D.N., and Wilde, F.D, eds., 19

Biological indicators, in National field manual fo

collection of water-quality data: U.S. Geological 

vey Techniques of Water-Resources Investigation

book 9, chap. A7, p. 3–38.

Ott, R.L., 1993, An introduction to statistical methods 

data analysis: Belmont, California, Duxbury Pres

1051 p.

Putnam, J.E., Lacock, D.L., Schneider, D.R., and Carl

M.D., 2001, Water resources data, Kansas, water 

2000: U.S. Geological Survey Water-Data Report

KS–00–1, 505 p.

Sharpley, A.N., and Rekolainen, S., 1997, Phosphorus 

agriculture and its environmental conditions, in T

ney, H., Carton, O.T., Brookes, P.C., and Johnson

A.E., eds., Phosphorus loss from soil to water: N

York, Cab International, p. 1–53.

Sophocleous, Marios, and Perkins, S.P., 1992, Streamaquifer and mineral intrusion modeling of the low

Rattlesnake Creek basin with emphasis on the Qu

National Wildlife Refuge: Kansas Geological Su

Open-File Report 92–6, 205 p.

Suber, Jim, 1998, Seaboard Farms continues to expand

Topeka Capitol Journal, July 31, 1998: accessed

November 5, 2001, at URL http://www.cjonline.c

stories/073198/bus_seaboard.shtml

Thurman, E.M., Meyer, Michael, Pomes, Michael, Per

C.A., and Schwab, A.P., 1990, Enzyme-linked im

osorbent assay compared with gas chromatography/mass spectrometry for the determination of

triazine herbicides in water: Analytical Chemistry

v. 62, p. 2043–2048.

U.S. Environmental Protection Agency, 1986, Quality 

ria for water, 1986: U.S. Environmental Protectio

Agency Report 440/5–86–001, unnumbered page

———1999, Total Maximum Daily Load (TMDL) Pr

gram: accessed August 27, 1999, at URL

http://www.epa.gov/OWOW/tmdl/intro.html

———2000a, Ambient water quality criteria recomme

tions, rivers and streams in nutrient ecoregion VI

U.S. Environmental Protection Agency

Report 822/B–00–017, 28 p.

———2000b, Drinking water standards and health ad

ries: U.S. Environmental Protection Agency

Report 822/B–00–001, 12 p.

———2000c, Nutrient criteria technical guidance

manual—rivers and streams: U.S. Environmental 

tection Agency Report 822/B–00–002, various pa

Wagner, R.J., Mattraw, H.C., Ritz, G.F., and Smith, B.

2000, Guidelines and standard procedures for con

ous water-quality monitors—site selection, field o
28 Characterization of Surface-Water Quality Based on Real-Time Monitoring and Regression Analysis, Quivira National Wildlife Refuge, S

Kansas, December 1998 Through June 2001

tion, calibration, record computation, and reporting:

U.S. Geological Survey Water-Resources Investigations Report 00–4252, 53 p.

Wilde, F.D., Radke, D.B., Gibs, Jacob, and Iwatsubo, R.T.,

eds., 1999, Collection of water samples, in National

field manual for the collection of water-quality data:

U.S. Geological Survey Techniques of WaterResources Investigations, book 9, chap A4, p. 7–48.

Zimmerman, L.R., and Thurman, E.M., 1999, Me

analysis by the U.S. Geological Survey Orga

Geochemistry Research Group—determinati

triazine and chloroacetanilide herbicides in w

solid-phase extraction and capillary-column 

matography/mass spectrometry with selected

monitoring: U.S. Geological Survey Open-Fi

Report 98–634, 21 p.