U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2013–1123

Prepared in cooperation with the City of Wichita, Kansas

Model Documentation for Relations Between Continuous 

Real-Time and Discrete Water-Quality Constituents in 

Cheney Reservoir near Cheney, Kansas, 2001–2009

Model Documentation for Relations 

Between Continuous Real-Time and 

Discrete Water-Quality Constituents in 

Cheney Reservoir near Cheney, Kansas, 

2001–2009

By Mandy L. Stone, Jennifer L. Graham, and Jackline W. Gatotho

Prepared in cooperation with the City of Wichita, Kansas

Open-File Report 2013–1123

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

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

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Suggested citation:

Stone, M.L., Graham, J.L., and Gatotho, J.W., 2013, Model documentation for relations between continuous real-time 

and discrete water-quality constituents in Cheney Reservoir near Cheney, Kansas, 2001–2009: U.S. Geological Survey 

Open-File Report 2013–1123, 100 p., http://pubs.usgs.gov/of/2013/1123/.
iii

Contents

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

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

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

Description of Study Area ...................................................................................................................2

Methods...........................................................................................................................................................2

Continuous Water-Quality Monitoring...............................................................................................2

Discrete Water-Quality Samples........................................................................................................3

Development of Linear Regression Models to Compute Constituent Concentrations .............4

Development of Logistic Regression Models for Geosmin and Microcystin .............................5

Calculation of Conversion Factors for Turbidity Sensors...............................................................6

Quality Assurance and Quality Control .............................................................................................6

Results of Linear Regression Analysis for Selected Constituents ........................................................7

Results of Multiple Logistic Regression Analysis for Geosmin and Microcystin .............................86

Turbidity, YSI Model 6026 and 6136 Sensors ...........................................................................................86

Summary........................................................................................................................................................98

References Cited..........................................................................................................................................99

Figures

1. Map showing location of continuous real-time water-quality monitoring station in

Cheney Reservoir and land use in the Cheney Reservoir watershed..................................3

2. S+® output of regression model development using specific conductance (SC) as

an explanatory variable for dissolved solids (DS) concentrations for Cheney

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, during 2001

through 2009 ................................................................................................................................54

3. S+® output graphs from simple linear regression analysis showing specific

conductance (SC) compared to dissolved solids (DS) concentrations, computed

compared to actual DS concentrations, computed DS concentrations compared to

regression residuals, and standard normal quantiles compared to regression

residuals for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central

Kansas, 2001 through 2009 ........................................................................................................55

4. S+® output of regression model development using specific conductance as an

explanatory variable for sodium concentrations for Cheney Reservoir near Cheney,

Kansas (site 07144790), south-central Kansas, during 2001 through 2009 ........................56

5. S+® output graphs from simple linear regression analysis showing specific

conductance (SC) compared to sodium (NA) concentrations, computed compared

to actual NA concentrations, computed NA concentrations compared to

regression residuals, and standard normal quantiles compared to regression

residuals for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central

Kansas, 2001 through 2009 ........................................................................................................57

6. S+® output of regression model development using reservoir elevation (ELV) and

specific conductance (SC) as explanatory variables for chloride (CL)

concentrations for Cheney Reservoir near Cheney, Kansas (site 07144790),

south-central Kansas, during 2001 through 2009 ..................................................................58
iv

7. S+

® output graphs from simple linear regression analysis using reservoir elevation

(ELV) and specific conductance (SC) as explanatory variables for chloride (CL) 

showing computed compared to actual CL concentrations, computed CL

concentrations compared to regression residuals, and standard normal quantiles

compared to regression residuals for Cheney Reservoir near Cheney, Kansas 

(site 07144790), south-central Kansas, 2001 through 2009...................................................59

8. S+

® output of regression model development using specific conductance (SC) as

an explanatory variable for magnesium (MG) concentrations for Cheney Reservoir

near Cheney, Kansas (site 07144790), south-central Kansas, during 2001 through

2009 ...............................................................................................................................................60

9. S+

® output graphs from simple linear regression analysis showing

log-transformed specific conductance (SC) compared to magnesium (MG)

concentrations, computed compared to actual MG concentrations, computed

MG concentrations compared to regression residuals, and standard normal 

quantiles compared to regression residuals for Cheney Reservoir near 

Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009 .....................61

10. S+

® output of regression model development using turbidity (TBY, YSI model 6026

sensor) and fluorescence (CHL) as explanatory variables for total suspended solids

(TSS) concentrations for Cheney Reservoir near Cheney, Kansas (site 07144790),

south-central Kansas, during 2001 through 2009 ..................................................................62

11. S+

® output graphs from simple linear regression analysis using turbidity (TBY,

YSI model 6026 sensor) and fluorescence (CHL) as explanatory variables for

total suspended solids (TSS) showing computed compared to actual TSS

concentrations, computed log-transformed TSS concentrations compared to

regression residuals, and standard normal quantiles compared to regression

residuals for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central

Kansas, 2001 through 2009 ........................................................................................................63

12. S+

® output of regression model development using turbidity (TBY, YSI model 6026

sensor) as an explanatory variable for suspended sediment concentrations (SSC)

for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas,

during 2001 through 2009...........................................................................................................64

13. S+

® output graphs from simple linear regression analysis showing turbidity (TBY,

YSI model 6026 sensor) compared to suspended-solids concentration (SSC) data, 

computed compared to actual SSC, comparison of estimated SSC and regression 

residuals, and standard normal quantiles compared to regression residuals for 

Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 

through 2009 ................................................................................................................................65

14. S+

® output of regression model development using turbidity (TBY, YSI model 6026

sensor), dissolved oxygen (DO), and water temperature (T) as explanatory

variables for total Kjeldahl nitrogen (TKN) concentrations for Cheney Reservoir

near Cheney, Kansas (site 07144790), south-central Kansas, during 2001 through

2009 ...............................................................................................................................................66

15. S+

® output graphs from simple linear regression analysis using turbidity (TBY, 

YSI model 6026 sensor), dissolved oxygen (DO), and water temperature (T) as 

explanatory variables for total Kjeldahl nitrogen (TKN) showing computed 

compared to actual TKN concentrations, computed TKN concentrations compared 

to regression residuals, and standard normal quantiles compared to regression 

residuals for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central 

Kansas, 2001 through 2009 ........................................................................................................67
v

16. S+

® output of linear regression model development of pH (PH) and specific

conductance (SC) as explanatory variables for nitrate plus nitrite (NO3NO2)

concentrations for Cheney Reservoir near Cheney, Kansas (site 07144790),

south-central Kansas, during 2001 through 2009 ..................................................................68

17. S+

® output graphs from simple linear regression analysis using pH (PH) and specific 

conductance (SC) as explanatory variables for nitrate plus nitrite (NO3NO2) showing 

computed compared to actual NO3NO2 concentrations, computed log-transformed 

NO3NO2 concentrations compared to regression residuals, and standard normal 

quantiles compared to regression residuals for Cheney Reservoir near Cheney, 

Kansas (site 07144790), south-central Kansas, 2001 through 2009.....................................69

18. S+

® output of regression model development using season (SIN and COS) and turbidity 

(TBY, YSI model 6026 sensor) as explanatory variables for total phosphorus (TP) 

concentrations for Cheney Reservoir near Cheney, Kansas (site 07144790), southcentral Kansas, during 2001 through 2009..............................................................................70

19. S+

® output graphs from simple linear regression analysis using season (SIN and 

COS) and turbidity (TBY, YSI model 6026 sensor) as explanatory variables for total 

phosphorus (TP) showing computed compared to actual TP concentrations, 

computed log-transformed TP concentrations compared to regression residuals, and 

standard normal quantiles compared to regression residuals for Cheney Reservoir 

near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009............71

20. S+

® output of regression model development using turbidity (TBY, YSI model 6026 

sensor), water temperature (T), and fluorescence (CHL) as explanatory variables for 

orthophosphate (OP) concentrations for Cheney Reservoir near Cheney, Kansas (site 

07144790), south-central Kansas, during 2001 through 2009 ...............................................72

21. S+

® output graphs from simple linear regression analysis using turbidity (TBY, YSI 

model 6026 sensor), water temperature (T), and fluorescence (CHL) as explanatory 

variables for orthophosphate (OP) showing computed compared to actual OP 

concentrations, computed log-transformed OP concentrations and regression 

residuals, and standard normal quantiles compared to regression residuals for Cheney 

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 

2009 ...............................................................................................................................................73

22. S+

® output of regression model development using dissolved oxygen (DO), specific 

conductance (SC), and fluorescence (CHL) as explanatory variables for dissolved 

phosphorus (DP) concentrations for Cheney Reservoir near Cheney, Kansas (site 

07144790), south-central Kansas, during 2001 through 2009 ...............................................74

23. S+

® output graphs from simple linear regression analysis using dissolved oxygen 

(DO), specific conductance (SC), and fluorescence (CHL) as explanatory variables for 

dissolved phosphorus (DP) showing computed compared to actual DP concentrations, 

computed log-transformed DP concentrations compared to regression residuals, and 

standard normal quantiles compared to regression residuals for Cheney Reservoir 

near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009............75

24. S+

® output of regression model development using reservoir elevation (ELV), water 

temperature (T), and fluorescence (CHL) as explanatory variables for chlorophylla

(CHLA) concentrations for Cheney Reservoir near Cheney, Kansas (site 07144790), 

south-central Kansas, during 2001 through 2009 ..................................................................76

25. S+

® output graphs from simple linear regression analysis using reservoir elevation 

(ELV), water temperature (T), and fluorescence (CHL) as explanatory variables for 

chlorophylla (CHLA) showing computed compared to actual CHLA concentrations, 

computed CHLA concentrations compared to regression residuals, and standard 

normal quantiles compared to regression residuals for Cheney Reservoir near 

Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009 .....................77
vi

26. S+

® output of regression model development using reservoir elevation (ELV),

turbidity (TBY, YSI model 6026 sensor), and water temperature (T) as explanatory

variables for actinomycetes bacteria (ACT) densities for Cheney Reservoir near

Cheney, Kansas (site 07144790), south-central Kansas, during 2001 through 2009.........78

27. S+

® output graphs from simple linear regression analysis using reservoir elevation

(ELV), turbidity (TBY, YSI model 6026 sensor), and water temperature (T) as

explanatory variables for actinomycetes bacteria (ACT) showing computed

compared to actual ACT concentrations, computed log-transformed ACT

concentrations compared to regression residuals, and standard normal quantiles

compared to regression residuals for Cheney Reservoir near Cheney, Kansas (site

07144790), south-central Kansas, 2001 through 2009............................................................79

28. S+

® output of regression model development using reservoir elevation (ELV) and

water temperature (T) as explanatory variables for Anabaena spp. (ANA)

concentrations for Cheney Reservoir near Cheney, Kansas (site 07144790),

south-central Kansas, during 2001 through 2009 ..................................................................80

29. S+

® output graphs from simple linear regression analysis using reservoir elevation

(ELV) and water temperature (T) as explanatory variables for Anabaena spp. (ANA)

showing computed compared to actual ANA concentrations, computed

log-transformed ANA concentrations compared to regression residuals, and

standard normal quantiles compared to regression residuals for Cheney Reservoir

near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009............81

30. S+

® output of regression model development using turbidity (TBY, YSI model 6026

sensor) and pH (PH) as explanatory variables for geosmin (GEO) concentrations for

Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas,

during 2001 through 2009...........................................................................................................82

31. S+

® output graphs from simple linear regression analysis using turbidity (TBY, YSI

model 6026 sensor) and pH (PH) as explanatory variables for geosmin (GEO)

showing computed compared to actual GEO concentrations, computed

log-transformed GEO concentrations compared to regression residuals, and

standard normal quantiles compared to regression residuals for Cheney Reservoir

near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009............83

32. S+

® output of regression model development using season (SIN and COS) and

fluorescence (CHL) as explanatory variables for microcystin (MC) concentrations

for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas,

during 2001 through 2009...........................................................................................................84

33. S+

® output graphs from simple linear regression analysis using season (SIN and

COS) and fluorescence (CHL) as explanatory variables for microcystin (MC)

showing computed compared to actual MC concentrations, computed

log-transformed MC concentrations compared to regression residuals, and

standard normal quantiles compared to regression residuals for Cheney Reservoir

near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009............85

34. SigmaPlot

® output results of multiple logistic regression analysis using season

(SIN and COS), turbidity (TBY), and geosmin (GEO) data.....................................................94

35. SigmaPlot

® output results of multiple logistic regression analysis using season

(SIN and COS), fluorescence (CHL), and microcystin (MC) data........................................95

36. Relation between Yellow Springs Instruments (YSI) model 6026 (YSI6026) and YSI

model 6136 (YSI6136) turbidity sensor values at Cheney Reservoir near Cheney,

Kansas (site 07144790), September 2006 through September 2010....................................96
vii

Tables

1. Christensen and others (2006), updated, best updated, and new linear regression

models and summary statistics for continuous concentration computations for

Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001

through 2009 ..................................................................................................................................8

2. Dissolved solids linear regression dataset using specific conductance as the

explanatory variable for Cheney Reservoir near Cheney, Kansas (site 07144790),

south-central Kansas, 2001 through 2009...............................................................................12

3. Sodium linear regression dataset using specific conductance as the explanatory

variable for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central

Kansas, 2001 through 2009 ........................................................................................................13

4. Chloride linear regression dataset using reservoir elevation and specific

conductance as explanatory variables for Cheney Reservoir near Cheney, Kansas

(site 07144790), south-central Kansas, 2001 through 2009...................................................14

5. Magnesium linear regression dataset using specific conductance as the

explanatory variable for Cheney Reservoir near Cheney, Kansas (site 07144790),

south-central Kansas, 2001 through 2009...............................................................................17

6. Total suspended solids linear regression dataset using turbidity and fluorescence

as explanatory variables for Cheney Reservoir near Cheney, Kansas (site

07144790), south-central Kansas, 2001 through 2009............................................................18

7. Suspended-sediment concentration linear regression dataset using turbidity as the

explanatory variable for Cheney Reservoir near Cheney, Kansas (site 07144790),

south-central Kansas, 2001 through 2009...............................................................................21

8. Total Kjeldahl nitrogen linear regression dataset using turbidity, dissolved oxygen,

and water temperature as explanatory variables for Cheney Reservoir near

Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009 .....................24

9. Nitrate plus nitrite linear regression dataset using pH and specific conductance as

explanatory variables for Cheney Reservoir near Cheney, Kansas (site 07144790),

south-central Kansas, 2001 through 2009...............................................................................27

10. Total phosphorus linear regression dataset using seasonal data and turbidity as

explanatory variables for Cheney Reservoir near Cheney, Kansas (site 07144790),

south-central Kansas, 2001 through 2009...............................................................................30

11. Orthophosphate linear regression dataset using turbidity, water temperature, and

fluorescence as explanatory variables for Cheney Reservoir near Cheney, Kansas

(site 07144790), south-central Kansas, 2001 through 2009...................................................33

12. Dissolved phosphorus linear regression dataset using dissolved oxygen, specific

conductance, and fluorescence as explanatory variables for Cheney Reservoir

near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009............36

13. Chlorophyll-a linear regression dataset using reservoir elevation, water

temperature, and fluorescence as explanatory variables for Cheney Reservoir near

Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009 .....................39

14. Actinomycetes bacteria linear regression dataset using reservoir elevation,

turbidity, and water temperature as explanatory variables for Cheney Reservoir

near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009............42

15. Anabaena spp. linear regression dataset using reservoir elevation and water

temperature as explanatory variables for Cheney Reservoir near Cheney, Kansas

(site 07144790), south-central Kansas, 2001 through 2009...................................................45
viii

16. Geosmin linear regression dataset using turbidity and pH as explanatory variables

for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas,

2001 through 2009 .......................................................................................................................48

17. Microcystin linear regression dataset using seasonal data and fluorescence as

explanatory variables for Cheney Reservoir near Cheney, Kansas (site 07144790),

south-central Kansas, 2001 through 2009...............................................................................51

18. Best fit multiple logistic regression models and summary statistics for Cheney

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through

2009 ...............................................................................................................................................87

19. Geosmin logistic regression dataset using seasonal data and turbidity as

explanatory variables for Cheney Reservoir near Cheney, Kansas (site 07144790),

south-central Kansas, 2001 through 2009...............................................................................88

20. Microcystin logistic regression dataset using seasonal data and fluorescence as

explanatory variables for Cheney Reservoir near Cheney, Kansas (site 07144790),

south-central Kansas, 2001 through 2009...............................................................................91

22. Updated Christensen and others (2006) linear regression models for Cheney

Reservoir near Cheney, Kansas (site 07144790) ....................................................................97

21. Summary statistics for data used in turbidity sensor linear regression analyses for

Cheney Reservoir near Cheney, Kansas (site 07144790), September 2006 through

September 2010...........................................................................................................................97

23. Updated new linear regression models for Cheney Reservoir near Cheney, Kansas

(site 07144790) .............................................................................................................................98

24. Updated new logistic regression model for Cheney Reservoir near Cheney, Kansas

(site 07144790) .............................................................................................................................98
ix

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.03281 foot (ft)

meter (m) 3.281 foot (ft)

mile (mi) 1.609 kilometer (km)

micrometer (μm) 0.001 millimeter (mm)

nanometer (nm) 0.000001 millimeter (mm)

Area

square mile (mi2) 2.590 square kilometer (km2)

square meter (m2) 10.76 square feet (ft2)

Volume

milliliter (mL) 0.0338 ounce, fluid (oz)

Flow rate

milligram per liter (mg/L) 1 parts per million (ppm)

microgram per liter (μg/L) 1 parts per billion (ppb)

Mass

milligram (mg) 0.001 gram (g) 

microgram (μg) 0.0.000001 gram (g) 

ton per year (ton/yr) 0.9072 metric ton per year

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

°F=(1.8×°C)+32

Vertical coordinate information is referenced to the North American Vertical Datum of 1929 

(NAVD 29).

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

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

25°C).

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

or micrograms per liter (μg/L).
x

Abbreviations

1Q first quartile

3Q third quartile

adjr2 adjusted R2

cooksD Cook’s distance

Cp Mallow’s Cp

Df degrees of freedom

dfits change in fitted values

F-statistic ratio of the mean square of the regression to the estimated variance

F Value explained variance divided by unexplained variance

Max maximum

Mean Sq mean square

Min minimum

Nvars number of explanatory variables

press prediction error sum of squares

Pr(F) probability value of an F-test

Pr(>|t|) probability of observing a test statistic that follows a Student’s t distribution if the 

null hypothesis is supported

p-value probability of observing a test statistic that is as extreme or more extreme than 

currently observed assuming that the null hypothesis is true

resids residuals 

Std. Error standard error

Stderr standard error

stnd.res standardized residuals

stud.res studentized residuals 

Sum of Sq sum of squares 

t value coefficient divided by its standard error

yhat predicted values
Model Documentation for Relations Between Continuous 

Real-Time and Discrete Water-Quality Constituents in 

Cheney Reservoir near Cheney, Kansas, 2001–2009

By Mandy L. Stone, Jennifer L. Graham, and Jackline W. Gatotho

Abstract

Cheney Reservoir, located in south-central Kansas, is one 

of the primary water supplies for the city of Wichita, Kansas. 

The U.S. Geological Survey has operated a continuous realtime water-quality monitoring station in Cheney Reservoir 

since 2001; continuously measured physicochemical properties include specific conductance, pH, water temperature, dissolved oxygen, turbidity, fluorescence (wavelength range 650 

to 700 nanometers; estimate of total chlorophyll), and reservoir elevation. Discrete water-quality samples were collected 

during 2001 through 2009 and analyzed for sediment, nutrients, taste-and-odor compounds, cyanotoxins, phytoplankton 

community composition, actinomycetes bacteria, and other 

water-quality measures. Regression models were developed 

to establish relations between discretely sampled constituent 

concentrations and continuously measured physicochemical 

properties to compute concentrations of constituents that are 

not easily measured in real time. The water-quality information in this report is important to the city of Wichita because it 

allows quantification and characterization of potential constituents of concern in Cheney Reservoir.

This report updates linear regression models published in 

2006 that were based on data collected during 2001 through 

2003. The update uses discrete and continuous data collected 

during May 2001 through December 2009. Updated models 

to compute dissolved solids, sodium, chloride, and suspended 

solids were similar to previously published models. However, 

several other updated models changed substantially from 

previously published models. In addition to updating relations 

that were previously developed, models also were developed 

for four new constituents, including magnesium, dissolved 

phosphorus, actinomycetes bacteria, and the cyanotoxin 

microcystin. In addition, a conversion factor of 0.74 was 

established to convert the Yellow Springs Instruments (YSI) 

model 6026 turbidity sensor measurements to the newer YSI 

model 6136 sensor at the Cheney Reservoir site.

Because a high percentage of geosmin and microcystin 

data were below analytical detection thresholds (censored 

data), multiple logistic regression was used to develop models 

that best explained the probability of geosmin and microcystin 

concentrations exceeding relevant thresholds. The geosmin 

and microcystin models are particularly important because 

geosmin is a taste-and-odor compound and microcystin is a 

cyanotoxin.

Introduction

The Bureau of Reclamation, U.S. Department of the 

Interior, constructed Cheney Reservoir (fig. 1) between 1962 

and 1965. Cheney Reservoir, located in south-central Kansas, 

was constructed to provide a municipal water supply for the 

city of Wichita, Kansas, downstream flood control, wildlife 

habitat, and recreational areas. Water from Cheney Reservoir 

contributed between 51 and 69 percent of Wichita’s water 

supply during 1995 through 2010 (Ziegler and others, 2010). 

Because of population growth and urban development, watersupply needs and reliance on Cheney Reservoir will continue 

to increase. 

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

city of Wichita, has continuously monitored water-quality in 

Cheney Reservoir (USGS site 07144790; fig. 1) since April 

2001. Continuously monitored water-quality physicochemical 

properties that have been measured in Cheney Reservoir since 

2001 include specific conductance, pH, water temperature, 

dissolved oxygen, turbidity, and fluorescence at a wavelength 

range from 650 to 700 nanometers. When measured at this 

wavelength, fluorescence may provide an estimate of total 

chlorophyll concentration. Discrete water-quality samples 

collected during 2001 through 2003 were used to develop 

regression models and establish relations between continuously monitored water-quality physicochemical properties and 

discretely monitored water-quality constituents (Christensen 

and others, 2006). Numerous discrete water-quality samples 

have been collected since the 2006 models were published and 

the relations between continuously monitored water-quality 

physicochemical properties and water-quality constituents 

need to be updated. 
2 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Purpose and Scope

The purpose of this report is to update and document 

regression models that establish relations between continuous 

and discrete water-quality data collected from Cheney Reservoir near Cheney, Kansas (USGS station 07144790; fig. 1). 

Linear regression models for 12 water-quality constituents, 

including suspended solids and sediment, dissolved solids 

and major ions, nutrient species, chlorophyll-a, taste-andodor compounds, and taste-and-odor cyanotoxin producers, 

originally were published by Christensen and others (2006) 

using data collected during 2001 through 2003. In this report, 

the linear regression models published in 2006 are updated 

using data collected through 2009, and additional models are 

developed for magnesium, dissolved phosphorus, actinomycetes bacteria, and the cyanotoxin microcystin. Because the 

necessary assumptions for using linear approaches were not 

met with geosmin, a taste-and-odor coupound, and microcystin data, new logistic regression models are developed for 

these constituents to estimate the probability of concentrations to exceed relevant thresholds. These models are useful 

for evaluating how computed concentrations of water-quality 

constituents compare with water-quality criteria, characterizing changes in water-quality conditions through time, characterizing potentially harmful cyanobacterial bloom events, 

and indicating changes in water-quality conditions that may 

affect drinking-water treatment processes. The water-quality 

information in this report is important to the city of Wichita 

because it allows quantification and characterization of potential constituents of concern in Cheney Reservoir.

Description of Study Area

 The Cheney Reservoir watershed is located in southcentral Kansas (fig. 1) and has a contributing drainage area 

of 933 square miles (mi2). Land use in the Cheney Reservoir 

watershed predominately is rural (fig. 1); less than 1 percent 

of the land use in the watershed is classified as urban. All 

agricultural crops, including wheat, comprise about 51 percent 

of land use. About 26 percent of the Cheney Reservoir watershed is grassland and about 18 percent is conservation reserve 

program (CRP) land (Peterson and others, 2005).

Cheney Reservoir (fig. 1) is a eutrophic impoundment 

constructed by the Bureau of Reclamation, U.S. Department 

of the Interior, between 1962 and 1965 to provide downstream 

flood control, wildlife habitat, recreation, and a municipal 

water supply for the city of Wichita, Kansas. The North Fork 

Ninnescah River is the largest tributary to Cheney Reservoir 

and contributes about 70 percent of the inflow (Christensen 

and others, 2006). Cheney Reservoir has a mean depth of 

16 feet (ft), a maximum depth of 43 ft, and a surface area of 

about 12 mi2. Thermal and chemical stratification rarely occur 

in Cheney Reservoir primarily because of the relatively shallow depths and persistent winds (Smith and others, 2002).

The city of Wichita’s population in 2011 was 

384,445 people (State of Kansas, 2012). The city of Wichita 

currently (2013) takes about 60 percent of its water supply 

from Cheney Reservoir (Shelly Bloesser, city of Wichita, 

written commun., June 6, 2012). Objectionable tastes and 

odors have been a problem in the finished drinking water from 

Cheney Reservoir since 1990. Cheney Reservoir is listed as 

an impaired waterway under section 303(d) of the 1972 Clean 

Water Act. Siltation is listed as an impairment to water supply 

and eutrophication and pH are listed as impairments to aquatic 

life in Cheney Reservoir (Kansas Department of Health and 

Environment, 2012).

Methods

Continuous and discrete water-quality data were collected at one site in Cheney Reservoir (fig. 1; USGS station 

07144790, Cheney Re nr Cheney, Kansas) located near the 

city of Wichita’s drinking-water intake by the dam. Water 

quality has been measured continuously at this site since April 

2001; discrete water-quality samples have been routinely collected since May 2001. Continuous and discrete water-quality 

data collected by the USGS at the Cheney Reservoir site from 

May 2001 through December 2009 were used to develop sitespecific regression models. 

Continuous Water-Quality Monitoring

The reservoir site was equipped with a YSI 6600 

Extended Deployment System water-quality monitor that measured specific conductance, pH, water temperature, dissolved 

oxygen (YSI Clark cell or optical dissolved oxygen sensors), turbidity [YSI model 6026 (YSI6026) and 6136 (YSI6136) 

turbidity sensors], and fluorescence (YSI model 6025 sensor; 

wavelength range 650 to 700 nanometers; estimate of total 

chlorophyll). The YSI Clark cell dissolved-oxygen sensor was 

used from April 2001 through January 2007 and was replaced 

by the YSI optical dissolved-oxygen sensor in February 2007. 

YSI6026 turbidity sensors were used from April 2001 through 

December 2010 and YSI6136 turbidity sensors were added in 

September 2006. Reservoir elevation was measured using a 

Design Analysis H-350 nonsubmersible pressure transducer 

and H-355 gas system. The monitor and pressure transducer 

were maintained in accordance with standard USGS procedures (Wilde and Radke, 1998; Wagner and others, 2006; 

Sauer and Turnipseed, 2010). Continuous water-quality data 

were recorded hourly and are available on the USGS website 

at http://waterdata.usgs.gov/ks/nwis.

Sensor maxima were not exceeded for any of the physicochemical properties measured, with the exception of one 

fluorescence measurement [409 micrograms per liter (μg/L); 

sensor maxima = 400 μg/L] on August 11, 2003. Continuous 
Methods 3

!

!

SEDGWICK

COUNTY

KINGMAN

COUNTY

KIOWA

COUNTY

PRATT

COUNTY

PAWNEE

COUNTY

STAFFORD

COUNTY

EDWARDS

COUNTY

RENO

COUNTY

Stafford

Castleton

Nester Ck

Gar Ck

Sand Ck

WildHors eCk

South Fork Ninnescah River

Smoots Ck

Wild Run Ck

Pat Ck

Kisi waCk

Pai n terCk

Pet y t Ck

B ear Ck

SpringCk

SugarCk

Rock Ck

Li ttl e Wild H orse Ck

Ra ttles nakeCk

NorthForkNinnescah River

Red Rock Ck

Wolf Ck

IrishCk

S ilverCk

Goos e Ck

Cheney

Reservoir

07144790

99°W 98°45'W 98°30'W 98°15'W 98°W 97°45'W

38°N

37°50'N

37°40'N

Base modified from U.S. Geological Survey

1:100,000-scale digital data, 2005

Land use data modified from Kansas Applied 

Remote Sensing Program (2009)

Wichita

EXPLANATION

Land use, 2005

Urban

Cropland

Grassland

Conservation Reserve Program

Woodland

Water

Wheat

Watershed boundary

U.S. Geological Survey station and identifier

Cheney Reservoir

drainage area

0 5 10 15 20 Miles

0 5 10 20 15 Kilometers

Figure 1. Location of continuous real-time water-quality monitoring station in Cheney Reservoir and land use in the Cheney Reservoir 

watershed.

data during the study period generally required corrections of 

less than 10 percent, which classifies the data quality rating as 

good according to established guidelines (Wagner and others, 

2006). Time-series measurements occasionally were missing 

or deleted from the dataset because of equipment malfunction, excessive fouling caused by environmental conditions, or 

temporary removal of the sensors because of ice on the reservoir. During 2001 through 2009, approximately 6 percent of 

the water temperature, specific conductance, and pH records, 

9 percent of the turbidity record, and 8 percent of the dissolved 

oxygen and fluorescence records were missing or deleted, 

largely because of sensor removal during ice cover.

Discrete Water-Quality Samples

Discrete water-quality samples collected during May 

2001 through July 2004 were collected near the surface 

using a Teflon Kemmerer bottle or a weighted bottle sampler with a 1-liter Teflon bottle following USGS methods 

(U.S. Geological Survey, variously dated); these samples 

were not depth integrated. Starting in August 2004 discrete 

water-quality samples were collected as integrated photic-zone 

(depth at which light is approximately 1 percent of that at the 

surface) samples using a double check-valve bailer (Lane and 

others, 2003); these samples were depth integrated. Vertical 

profiles collected in Cheney Reservoir indicate that thermal 

stratification rarely occurs and water-quality conditions typically are uniform throughout the water column. Water-quality 

results collected before and after the sampling procedure 

change in summer 2004 were similar. All water samples were 

analyzed for dissolved solids, major ions (sodium, chloride, 

and magnesium), suspended solids and sediment, nutrients 

(total Kjeldahl nitrogen, nitrate plus nitrite, total phosphorus, 

orthophosphate, and dissolved phosphorus), chlorophyll-a, 

phytoplankton community composition, and the taste-and-odor 

causing compounds geosmin and 2-methylisoborneol (MIB). 

Starting in 2005, all samples also were analyzed for actinomycetes bacteria (added April) and the cyanotoxin microcystin 

(added June).
4 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Dissolved and suspended solids, major ions, and nutrients (except for total Kjeldahl nitrogen) were analyzed by 

the Wichita Municipal Water and Wastewater Laboratory in 

Wichita, Kansas, according to standard methods (American 

Public Health Association and others, 1995); selected replicate 

samples were sent to the USGS National Water Quality Laboratory (NWQL) in Lakewood, Colorado and analyzed according to methods presented in Fishman and Friedman (1989). 

Total Kjeldhal nitrogen (ammonia plus organic nitrogen) was 

analyzed by the NWQL using methods presented in Patton and 

Truitt (2000). Chlorophyll-a also was analyzed by the NWQL. 

During April 2001 through September 2004, chlorophyll-a 

was analyzed by high-performance liquid-chromatography; 

during October 2005 through December 2009, chlorophyll-a

was analyzed using fluorometric methods (Arar and Collins, 

1997). Suspended sediment was analyzed at the USGS Iowa 

Sediment Laboratory in Iowa City, Iowa according to methods 

described in Guy (1969). Actinomycetes bacteria were analyzed by the USGS Ohio Water Science Center Microbiology 

Laboratory in Columbus, Ohio, using standard plate counts 

(American Public Health Association, American Water Works 

Association, and Water Environment Federation, 2012) and 

reported in colonies per milliliter.

Phytoplankton samples (preserved with a 9:1 Lugol’s 

iodine:acetic acid solution) were analyzed for taxonomic identification and enumeration by BSA Environmental Services, 

Inc., Beachwood, Ohio. Phytoplankton were enumerated to 

the lowest possible taxonomic level using membrane-filtered 

slides (McNabb, 1960). A minimum of 400 natural units (colonies, filaments, and unicells) were counted from each sample; 

in accordance with Lund and others (1958), counting 400 natural units provides accuracy within 90-percent confidence limits. In addition, an entire strip of the filter was counted at high 

magnification (usually 630 X) along with one-half of the filter 

at a lower magnification (usually 400 X) to ensure complete 

species reporting.

Throughout the course of the study, Montgomery Watson 

Laboratories, Pasadena, California (2001–2003), the USGS 

Organic Geochemistry Research Laboratory (OGRL), Lawrence, Kansas (2003–2007), and Engineering Performance 

Solutions (EPS), LLC, Gainesville, Florida (2007–2009) provided analyses for taste-and-odor compounds. All laboratories 

used solid phase microextraction gas chromatography/mass 

spectrometry for analysis of taste-and-odor compounds and 

generally followed USGS methods (Zimmerman and others, 

2002). Each time laboratories were changed, an among-laboratory comparison was conducted before the change was made. 

Total microcystin was analyzed by the USGS OGRL. 

Total microcystin concentration includes particulate and 

dissolved phase microcystin. All samples were lysed by 

three sequential freeze-thaw cycles and filtered using 

0.7-micrometer (μm) glass-fiber filters before analysis (Loftin 

and others, 2008). Abraxis® enzyme-linked immunosorbent 

assays (ELISA) were used to measure microcystin (congener 

independent).

Development of Linear Regression Models to 

Compute Constituent Concentrations 

Models were developed using simple linear (ordinary 

least squares) regression analyses (Helsel and Hirsch, 2002) to 

relate discrete sample concentrations or densities of waterquality constituents to continuously measured water-quality 

physicochemical properties (Rasmussen and others, 2008). 

The methods used for the development of these models and 

quantifying uncertainty are described in detail in Rasmussen 

and others (2009). All data for this report were analyzed using 

TIBCO Spotfire S+® 8.1 for Windows® statistical software 

(TIBCO Software, Inc., 2008).

Original models were developed from datasets containing 16 to 33 discrete samples. Models were developed in this 

report from datasets containing 93 to 125 discrete samples. 

All continuously measured physicochemical properties and 

seasonal components (sine and cosine variables) were tested 

for significance for each response variable. Concomitant insitu continuous measurements were used to correspond with 

discrete measurements as described in Rasmussen and others 

(2009). 

Outliers were identified and removed as described in 

Rasmussen and others (2009). Data points in discrete samples 

were removed from the model dataset when they were extreme 

outliers on bivariate plots of actual compared to computed 

concentrations and when there were identifiable issues with 

laboratory analyses. For example, data points where an orthophosphate concentration was higher than the concomitant dissolved phosphorus concentration were considered outliers. On 

average, approximately 2 percent of the discrete-sample data 

were extreme outliers on bivariate plots and 9 percent of the 

discrete-sample data had orthophosphate concentrations that 

were larger than the concomitant dissolved phosphorus concentrations and were removed from linear regression models. 

Linear regression models were evaluated based on 

diagnostic statistics (R2, coefficient of determination; Mallow’s Cp

; RMSE, root mean square error; PRESS, prediction error sum of squares), patterns in residual plots, and the 

range and distribution of discrete and continuous data (Helsel 

and Hirsch, 2002). The best model for each constituent was 

selected to maximize the amount of variance in the response 

variable explained by the model (multiple R2 for models with 

one explanatory variable and adjusted R2 for models with 

more than one explanatory variable), maximize fit to the data 

(Mallow’s Cp ), and minimize heteroscedasticity (irregular 

scatter) in the residual plots and uncertainty associated with 

computed values (RMSE and PRESS). Model simplicity also 

was considered for model selection because as more variables 

are included the likelihood that the variability of the system 

is not described by the sampling dataset increases. Significant 

(p-value less than 0.05) additional explanatory variables were 

included in final models when retaining them increased the 

amount of variance explained by the model by 10 percent or 
Methods 5

more, decreased Mallow’s Cp, and minimized heteroscedasticity in residual plots. Models for all constituents with previously published (Christensen and others, 2006) models were 

updated. Models were considered suitable to use for constituent concentration computations if the amount of variance 

explained by the models (R2) was 0.40 or greater. 

Mean square error (MSE) and RMSE were calculated 

for each model to assess the variance between predicted and 

observed values (Helsel and Hirsch, 2002). The model standard percentage error (MSPE) was calculated as a percentage 

of the RMSE (Hardison, 1969). Because transformation of 

estimates back into original units results in a low biased estimate (Helsel and Hirsch, 2002), a bias correction factor (BCF) 

was calculated for models with logarithmically transformed 

response variables (Duan, 1983). 

Development of Logistic Regression Models for 

Geosmin and Microcystin

Geosmin and microcystin were commonly detected 

compounds in Cheney Reservoir during May 2001 through 

December 2009. However, 60 percent of geosmin samples 

and 53 percent of microcystin samples were below analytical detection thresholds. Because of the high percentage of 

censored values, ordinary least squares regression is not an 

appropriate modeling technique for these constituents (Helsel and Hirsch, 2002). Multiple logistic regression was used 

to develop models to identify factors that best explained the 

probability of geosmin and microcystin concentrations exceeding relevant thresholds. 

Logistic regression models the probability of the response 

variable being in one of two categorical response groups (for 

example, 0 equals a negative response and 1 equals a positive 

response). Logistic regression transforms estimated probabilities into a continuous response variable, with possible values 

ranging from negative to positive infinity. The transformed 

response variable can then be modeled as a linear function 

of one or more explanatory variables in a logistic regression 

(Helsel and Hirsch, 2002). The logistic regression equation 

can be expressed as:

 0

1

p ln b bX

p

    = +

  − (1)

where:

ln is the natural logarithm,

( )

1

p

− p is the odds ratio, with p equal to the 

probability of a 1 (positive) response,

b0 is the intercept,

X is a vector of k explanatory variables, and

bX includes the slope coefficients for 

each explanatory variable so that 

bX=b1X1+b2X2...bkXk.

In this form, the logistic regression models the probability of 

obtaining a 1 (positive) response (Helsel and Hirsch, 2002; 

Systat Software, Inc., 2009). Model output is the natural 

logarithm of the odds ratio. The natural logarithm of the odds 

ratio can be converted into a probability using the following 

equation:

0 0 / [1 ] b bX b bX pe e + + = + (2)

where:

p is the probability of a response of 1,

e is the base of the natural logarithm 

(approximately equal to 2.71828),

b0 is the intercept,

X is a vector of k explanatory variables, and

bX includes the slope coefficients for 

each explanatory variable so that 

bX=b1X1+b2X2...bkXk.

For Cheney Reservoir model development, the response 

variable was based on a category assigned to constituent 

concentrations in which a value of 1 was assigned to concentrations greater than or equal to the human detection threshold 

for geosmin (0.005 μg/L; Taylor and others, 2005) and greater 

than or equal to the analytical detection threshold for microcystin (0.01 μg/L) and 0 was assigned to concentrations below 

these thresholds. Explanatory variables selected as inputs to 

the multiple logistic regression analyses were those physicochemical properties that were used in the linear models for 

other constituents: specific conductance, pH, water temperature, dissolved oxygen, turbidity, fluorescence (wavelength 

range 650 to 700 nanometers; estimate of total chlorophyll), 

and reservoir elevation. Seasonal components (sine and cosine 

variables) were used as explanatory variables in the models to determine if seasonal changes affected the model. All 

combinations of physicochemical properties and a seasonal 

component were evaluated to determine which combinations 

produced the best models.

Logistic model equations were developed using the 

multiple logistic regression routine in SigmaPlot® version 11.0 

(Systat Software, Inc., 2008). Continuous water-quality physicochemical properties initially were evaluated individually 

using the variance inflation factor (VIF, Systat Software, Inc., 

2008) and then the Wald Statistic p-value (Menard, 2002). All 

combinations of selected initial parameters were evaluated and 

the best final model was selected based on statistical tests in 

the following order: variance inflation factor, Wald Statistic 

p-value, Pearson Chi-Square Statistic, Likelihood Ratio Test 

statistic, -2 log likelihood statistic, and Hosmer-Lemeshow 

p-value (Hosmer and others, 2013). 

The VIF measures multicollinearity by quantifying the 

inflation of the standard error of each regression coefficient for 

an independent variable because of redundant information in 

other independent variables. Physicochemical properties were 
6 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

selected for model development when their VIF was closest 

to 1 because a VIF value of 1 indicates no redundant information in the other independent variable. The Wald Statistic is 

the regression coefficient divided by the standard error and its 

p-value is the probability of being wrong in concluding that 

there is a true association between the variables. Physicochemical properties were selected for model development when 

their Wald Statistic p-value was less than 0.05. The Pearson 

Chi-Square statistic is the sum of the squared Pearson residuals and is a measure of the agreement between the observed 

and predicted values of the dependent variable. Goodness 

of fit is greater at lower Pearson Chi-Square statistic values. 

Larger Pearson Chi-Square statistic values indicate a poor 

agreement between the logistic regression equation and the 

data. Models with smaller Pearson Chi-Square statistic values 

were retained for final model selection. The Likelihood Ratio 

Test statistic is derived from the sum of the squared deviance residuals and indicates how well the logistic regression 

equation fits the data by comparing the likelihood of obtaining observations if the independent variables had no effect on 

the dependent variable with the likelihood of obtaining the 

observations if the independent variables had an effect on the 

dependent variables. A larger Likelihood Ratio Test statistic 

value indicates a good fit between the model equation and the 

data. Models with larger Likelihood Ratio Test statistic values 

were retained for final model selection. The -2 log likelihood 

statistic is a measure of the goodness of fit between the actual 

observations and the predicted probabilities. Models with a 

-2 log likelihood value that were closer to 0 were retained for 

final model selection because values closer to 0 reflect a better fit of the model to the data. The Hosmer-Lemsow p-value 

indicates how well the logistic regression equation fits the data 

by comparing the number of observations with each outcome 

to the number expected based on the logistic equation, and 

tests the null hypothesis that the logistic equation describes the 

data. Smaller Hosmer-Lemsow p-values indicate a poor fit of 

the model equation to the data. Models with Hosmer-Lemsow 

p-values that were greater than 0.2 were retained for final 

model selection (Systat Software, Inc., 2008).

After the best model was selected, the threshold probability for positive classification (TPPC) for the model was 

adjusted based on the fraction of positive observed geosmin 

or microcystin values to make the model more conservative 

(more likely to overestimate geosmin or microcystin presence) 

by guarding more strongly against false negatives. The regression then used the newly adjusted thresholds, which changed 

the number of predictions for positive and reference responses, 

but the model constants and other statistical outputs remained 

the same. 

Calculation of Conversion Factors for Turbidity 

Sensors

All models in this report were developed using YSI6026

turbidity sensors; however, turbidity sensors were updated 

from YSI6026 to YSI6136 in January 2011. YSI6136 turbidity sensors were operated alongside the YSI6026 turbidity sensors during September 2006 through December 2010. Because of the 

change in turbidity sensor instrumentation, 4 years of concurrent YSI6026 and YSI6136 hourly turbidity measurements (September 2006 through September 2010) were used to develop a 

site-specific conversion factor. Ordinary least squares regression analyses were performed on the concurrent turbidity 

measurements to aid in conversion factor selection (Helsel and 

Hirsch, 2002). A median conversion factor was calculated as 

the ratio of the YSI6136 sensor value to the YSI6026 sensor value 

because the median was less likely to be affected by outliers 

(Rasmussen and others, 2009). The conversion factor was then 

applied to YSI6026-based models without additional modification so the models developed in this report can be used with 

turbidity data collected by either sensor. 

Quality Assurance and Quality Control

Quality-assurance and quality-control (QA/QC) samples 

were collected to evaluate variability in sample collection 

and processing techniques and among-laboratory variability 

in analytical techniques. About 8 percent of discrete waterquality samples were QA/QC samples. Approximately 180 

sequential replicate constituent pairs were collected during 

2001 through 2009. Relative percentage difference (RPD) was 

used to evaluate differences in analyte concentrations detected 

in replicate water samples. The RPD was calculated using the 

following equation:

 / x1 00

2

A B RPD A B  +   = −         (3)

where A and B are concentrations in each replicate pair. Replicate pairs with an RPD within 10 percent were considered 

acceptable for inorganic constituents (Ziegler and Combs, 

1997) and replicate pairs with an RPD within 20 percent were 

considered acceptable for nutrient and organic constituents. 

The median RPD between all constituent replicate pairs was 

less than their respective acceptability limits. All inorganic 

constituent replicate pairs had median RPDs that were less 

than 5, except for suspended sediment (9 percent). All nutrient 

and organic constituent replicate pairs had median RPDs that 

were less than 10 except for total phosphorus (16 percent), 

dissolved phosphorus (17 percent), and geosmin (11 percent). 

Larger RPDs generally resulted when the values were near the 

laboratory reporting level.

A comparison of taste-and-odor analysis by Montgomery 

Watson (analytical detection threshold=0.003 μg/L) and the 
Results of Linear Regression Analysis for Selected Constituents 7

OGRL (analytical detection threshold=0.005 μg/L) was done 

on 8 laboratory split-replicate samples and detection frequencies of geosmin were identical between the two laboratories. 

The RPDs in samples with detections by both laboratories 

ranged from 4 to 34 percent for geosmin (median: 12 percent; number of pairs=8). The absolute difference in geosmin 

concentrations between replicate pairs was 0.01 μg/L or less 

(median: less than 0.002 μg/L; number of pairs=13). 

A comparison of taste-and-odor analysis by EPS (analytical detection threshold=0.002 μg/L) and the OGRL was 

done on 21 laboratory split-replicate samples. Geosmin was 

detected in 33 percent of the samples analyzed by EPS and 

14 percent of the samples analyzed by the OGRL. Detections by EPS and not the OGRL only occurred when geosmin 

concentrations were less than the OGRL analytical method 

threshold but above the EPS analytical method detection 

threshold (between 0.002 and 0.005 μg/L). Three samples 

had geosmin detections by both laboratories; RPDs for these 

samples were 4, 18, and 54 percent. The absolute difference in 

geosmin concentrations between replicate pairs ranged from 

0.004 to 0.006 μg/L.

Results of Linear Regression Analysis 

for Selected Constituents

Relations between in-situ continuous measurements and 

discrete constituents were developed and evaluated using 

ordinary least squares regression. Linear regression models for 

seven constituents developed from data collected during 2001 

through 2003 (Christensen and others, 2006) were successfully 

updated to use for constituent concentration computations. 

The successfully updated models were for dissolved solids, 

sodium, chloride, suspended-sediment concentration, total 

phosphorus, orthophosphate, and chlorophyll-a. The linear 

regression models for five previously published (Christensen 

and others, 2006) models could not be successfully updated 

for computing constituent concentrations because they did not 

meet model selection criteria (R2 values greater than 0.40). 

However, the best possible models were developed for these 

constituents, which included total suspended solids, total Kjeldahl nitrogen, nitrate plus nitrite, Anabaena spp. (a cyanobacterial genus known to have toxin and taste-and-odor producing 

strains), and geosmin. New linear regression models were 

developed for four additional constituents not described in 

Christensen and others (2006) and included magnesium, dissolved phosphorus, actinomycetes bacteria, and microcystin. 

Updated, best updated, and newly developed models were 

developed from data collected during 2001 through 2009. 

Models are shown in table 1. Best updated models with R2 values less than 0.40 are shown for comparative purposes, even 

though the R2 values did not meet the requisite criteria. Model 

datasets are presented in tables 2–17 and model S+® statistical 

output is presented in figures 2–33. 

Specific conductance was the single explanatory variable for dissolved solids and most other major ions. Dissolved solids, sodium, chloride, and magnesium were strongly 

positively correlated with specific conductance. Specific 

conductance also was an explanatory variable for dissolved 

phosphorus. Turbidity was an explanatory variable for total 

phosphorus, orthophosphate, and actinomycetes bacteria. 

Dissolved oxygen was an explanatory variable for total Kjeldahl nitrogen and dissolved phosphorus. Reservoir elevation 

was an explanatory variable for chloride, chlorophyll-a, and 

actinomycetes bacteria. Fluorescence was an explanatory 

variable for total suspended solids, orthophosphate, dissolved 

phosphorus, chlorophyll-a, and microcystin. Water temperature was an explanatory variable for total Kjeldahl nitrogen, 

orthophosphate, chlorophyll-a, and actinomycetes bacteria. 

Total phosphorus and microcystin models included a seasonal 

component. 

Magnesium, dissolved phosphorus, actinomycetes bacteria, and microcystin (table 1) models were newly developed 

for this report. Additional data collection collected throughout a wider range of hydrological conditions facilitated the 

development of these models. The actinomycetes bacteria and 

microcystin models are particularly important because actinomycetes bacteria may be related to taste-and-odor occurrences 

and microcystin is a cyanotoxin.

Updated dissolved solids, sodium, chloride, and suspended solids model forms were similar to original (Christensen and others, 2006) models (table 1). The amount of 

variance explained in the original and updated dissolved solids 

models was the same. Fourteen percent less variance was 

explained by the updated sodium model and 9 percent more 

variance was explained by the updated chloride model.

The model form for five constituents is different in the 

updated models than in the original (Christensen and others, 

2006) models (table 1). The updated models that changed 

substantially were for constituents that are affected by biological processes. The updated total phosphorus model includes 

a seasonal component instead of temperature and 8 percent 

less variance is explained by the updated model (table 1). The 

updated orthophosphate model form includes turbidity, water 

temperature, and fluorescence likely because orthophosphate 

concentrations are largely controlled by algal uptake (Wetzel, 2001). Thirteen percent more variance is explained by 

the updated orthophosphate model than the original model 

(table 1). 

The updated chlorophyll-a model changed from the original form and includes reservoir elevation, water temperature, 

and fluorescence; increased reservoir elevation is related to 

decreases in fluorescence because there is less time for development of substantial biomass when water residence times are 

shorter (reservoir elevation is one indicator of water residence 

time), higher water temperature allows for more growth in the 

summer (Wetzel, 2001), and fluorescence is an estimate of 

chlorophyll. Two percent more variance is explained by the 

updated chlorophyll-a model than the original model (table 1). 
Table 1. Christensen and others (2006), updated, best updated, and new linear regression models and summary statistics for continuous concentratCheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009. [R2, coefficient of determination; MSE, mean square error; RMSE, root mean square error; MSPE, model standard percentage error; %, percent; ±, plus or minus; n, number of diper liter; SC, specific conductance in microsiemens per centimeter at 25 degrees Celsius; ELV, reservoir elevation in feet above from National Geodetic Vertical Datum of 1929; from YSI sensor model 6026 in formazin nephelometric units; <, less than; CHL, fluorescence at a wavelength of 650 to 700 nanometers; DO, dissolved oxygen in milligrams pT, water temperature in degrees Celsius; sin, sine; D, day of year; cos, cosine; μ/L, micrograms per liter; col/mL, colonies per milliliter; μm3/mL, micrometers cubed per millilite

Model Multiple R2 R2 adjusted MSE RMSE MSPE (upper) MSPE (lower) Bias correction factor (Duan, 1983) 90% prediction interval, in ± % Discre

n Range of values in variable measure- ments 

Dissolved solids (DS), in mg/L Christensen and others (2006) DS = 0.5500(SC) - 8.59 0.81 0.80 0.5700 0.7550 0.2 -0.2 1.00 41 19 DS: 414–501 

SC: 761–911 

Updated DS = 0.5093(SC) + 28.38 0.80 0.80 144.5 12.02 2.7 -2.7 1.00 39 120 DS: 371–509 

SC: 728–940 

Sodium (Na), in mg/L Christensen and others (2006) Na = 0.1815(SC) - 48.97 0.88 0.87 8.341 2.888 2.9 -2.9 1.00 41 19 Na: 87.5–116 

SC: 761–911 

Updated Na = 0.1636(SC) - 32.80 0.74 0.74 20.26 4.501 4.5 -4.5 1.00 39 119 Na: 80.1–124 

SC: 728–940 

Chloride (Cl), mg/L Christensen and others (2006) Cl = 0.2600(SC) - 72.41 0.61 0.59 78.80 8.877 6.3 -6.3 1.00 41 19 Cl: 116–162 

SC: 761–911 

Updated Cl = -3.0417(ELV-1,400) + 0.2009(SC) + 37.19 0.70 0.69 55.37 7.441 5.5 -5.5 1.00 39 120 Cl: 105–166 

ELV: 1,418–1,425 

SC: 728–940 

Magnesium (Mg), in mg/L New Mg = 28.68log(SC) - 69.80 0.59 0.59 0.3541 0.5951 4.4 -4.4 1.00 39 122 Mg: 11.4–15.5 

SC: 728–940 

Total suspended solids (TSS), mg/L Christensen and others (2006) TSS = 0.2125(TBY6026) + 2.8474 0.42 0.39 8.509 2.9170 43.7 -43.7 1.00 41 19 TSS: <4–12 

TBY6026: 1.6–40 
Model Multiple R2 R2 adjusted MSE RMSE MSPE (upper) MSPE (lower)

Bias 

correction 

factor 

(Duan, 1983)

90% 

prediction 

interval, 

in ± %

Discre

n

Range of values in 

variable measurements 

Best updated1

log(TSS) = 0.4807log(TBY6026) + 

0.3030log(CHL) + 0.02151

0.22 0.21 0.0661 0.2571 80.8 44.7 1.15 45 121 TSS: <4.00–38.0 

TBY6026: 1.60–42.0 

CHL: 2.00–49.4 

Suspended-sediment concentration (SSC), in mg/L

Christensen and others (2006)

log(SSC) = 0.5966log(TBY6026) + 0.3610 0.69 0.67 0.0246 0.1570 43.5 30.3 1.06 44 17 SSC: 3–25 

TBY6026: 1.6–40 

Updated

SSC = 0.6304(TBY6026) + 2.9310 0.66 0.65 17.08 4.133 28.1 -28.1 1.00 39 119 SSC: 2–36 

TBY6026: 1.6–48 

Total Kjeldahl Nitrogen (TKN), in mg/L

Christensen and others (2006)

TKN = 0.0085(TBY6026) + 0.7039log(DO) + 

0.6577(pH) -5.5840

0.66 0.59 0.0331 0.1818 23.6 -23.6 1.00 41 20 TKN: 0.46–1.40 

TBY6026: 1.6–40 

DO: 5.9–17.4 

pH: 8.0–9.0 

Best updated1

TKN = 0.0045(TBY6026) + 0.0608(DO) + 

0.0197(T) - 0.24371

0.33 0.31 0.0162 0.1271 17.4 -17.4 1.00 39 122 TKN: 0.34–1.40 

TBY6026: 1.6–48 

DO: 4.3–16.4 

T: 0.3–28.6 

Nitrate plus nitrite (NO3NO2), in mg/L

Christensen and others (2006)

log(NO3NO2) = -2.500log(DO) + 

0.7094log(TBY6026) - 0.0014(T)2 + 0.9793

0.56 0.55 0.1037 0.3220 110 52.4 1.31 56 19 NO3NO2: <0.02–0.36 

DO: 5.9–17.4 

TBY6026: 1.6–40 

T: 0.6–27.9 

Best updated1

log(NO3NO2) = -0.5585(pH) - 0.0026(SC) 

+ 5.89341

0.15 0.14 0.1999 0.4471 180 64.3 1.55 61 122 NO3NO2: <0.02–0.97 

pH: 7.7–8.9 

SC: 728–940 

Table 1. Christensen and others (2006), updated, best updated, and new linear regression models and summary statistics for continuous concentratCheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued[R2, coefficient of determination; MSE, mean square error; RMSE, root mean square error; MSPE, model standard percentage error; %, percent; ±, plus or minus; n, number of diper liter; SC, specific conductance in microsiemens per centimeter at 25 degrees Celsius; ELV, reservoir elevation in feet above from National Geodetic Vertical Datum of 1929; from YSI sensor model 6026 in formazin nephelometric units; <, less than; CHL, fluorescence at a wavelength of 650 to 700 nanometers; DO, dissolved oxygen in milligrams pT, water temperature in degrees Celsius; sin, sine; D, day of year; cos, cosine; μ/L, micrograms per liter; col/mL, colonies per milliliter; μm3/mL, micrometers cubed per millilite
Model Multiple R2 R2 adjusted MSE RMSE MSPE (upper) MSPE (lower) Bias correction factor (Duan, 1983) 90% prediction interval, in ± % Discre

n Range of values in variable measure- ments 

Total phosphorus (TP), mg/L Christensen and others (2006) TP = 0.0022(T) + 0.0010(TBY6026) + 0.0218 0.77 0.76 0.0004 0.0200 22.2 -22.2 1.00 41 33 TP: <0.03–0.15 

TBY6026: 1.6–40 

T: 0.6–27.9 

Updated log(TP) = -0.0633sin(2πD/365) - 0.1357cos(2πD/365) + 0.4390log(TBY6026) - 1.6626 0.69 0.68 0.0186 0.1364 36.9 27.0 1.05 41 109 TP: <0.03–0.23 

TBY6026: 1.6–48 

Orthophosphate (OP), in mg/L Christensen and others (2006) log(OP) = 1.0100log(TBY6026) - 2.7600 0.53 0.52 0.1218 0.3490 123 55.2 1.44 48 33 OP: <0.01–0.09 

TBY6026: 1.6–40 

Updated log(OP) = 0.4942log(TBY6026) + 0.0189(T) - 0.5408log(CHL) - 2.0430 0.66 0.65 0.0709 0.2662 84.6 45.8 1.18 46 113 OP: <0.01–0.09 

TBY6026: 1.6–48 

T: 0.3–28.6 

CHL: 2.0–49.4 

Dissolved phosphorus (DP), in mg/L New log(DP) = -0.8764log(DO) - 0.0019(SC) - 0.0137(CHL) + 1.0851 0.59 0.57 0.0423 0.2056 60.5 37.7 1.10 43 109 DP: <0.03–0.16 

DO: 4.3–16.4 

SC: 728–913 

CHL: 2.0–31.2 

Chlorophyll-a (CHLA), μg/L Christensen and others (2006) log(CHLA) = 0.0145(TBY6026) + 0.9872log(DO) - 0.2902 0.49 0.44 0.0256 0.1599 44.5 30.8 1.06 43 26 CHLA: 1.9–25.6 

TBY6026: 1.6–36 

DO: 5.9–18.2 

Updated CHLA = -1.9656(ELV-1,400) + 0.3204(T) + 

0.8463(CHL) + 40.25

0.51 0.49 38.73 6.223 48.6 -48.6 1.00 39 117 CHLA: 0.8–47.2 

ELV: 1,418–1,425 

T: 0.3–28.6 

CHL: 0.2–49.4 

Table 1. Christensen and others (2006), updated, best updated, and new linear regression models and summary statistics for continuous concentratCheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued[R2, coefficient of determination; MSE, mean square error; RMSE, root mean square error; MSPE, model standard percentage error; %, percent; ±, plus or minus; n, number of diper liter; SC, specific conductance in microsiemens per centimeter at 25 degrees Celsius; ELV, reservoir elevation in feet above from National Geodetic Vertical Datum of 1929; from YSI sensor model 6026 in formazin nephelometric units; <, less than; CHL, fluorescence at a wavelength of 650 to 700 nanometers; DO, dissolved oxygen in milligrams pT, water temperature in degrees Celsius; sin, sine; D, day of year; cos, cosine; μ/L, micrograms per liter; col/mL, colonies per milliliter; μm3/mL, micrometers cubed per millilite
Model Multiple R2 R2 adjusted MSE RMSE MSPE (upper) MSPE (lower)

Bias 

correction 

factor 

(Duan, 1983)

90% 

prediction 

interval, 

in ± %

Discre

n

Range of values in 

variable measurements 

Actinomycetes bacteria (ACT), in col/mL

New

log(ACT) = 11.06log(ELV - 1,400) + 

0.9924log(TBY6026) - 0.0163(T) - 14.93

0.61 0.59 0.0913 0.3021 100 50.1 1.23 48 93 ACT: <1.0–63.0 

TBY6026: 2.7–48 

ELV: 1,418–1,425 

T: 0.7–27.9 

Anabaena spp. (ANA), μm3/mL

Christensen and others (2006)

(ANA)E5 = 4.36(TBY6026) - 7.01 0.47 0.44 2,200 46.90 0.00 -0.00 1.00 42 16 ANA: 48,300–

22,700,000

4

TBY6026: 1.6–35 

Best updated1

log(ANA) = -25.81log(ELV - 1,400) + 

0.1203(T) + 34.491

0.37 0.36 2.6212 1.6190 4,059 97.6 98.3 3,845 114 ANA: 0–708,775 2

ELV: 1,418–1,425 

T: 0.3–28.6 

Geosmin (GEO), in μg/L

Christensen and others (2006)

log(GEO) = -1.0664log(TBY6026) - 

0.0097(SC) + 7.2310

0.71 0.67 0.0471 0.2170 64.8 39.3 1.12 47 18 GEO: <0.003–0.06 

TBY6026: 1.6–36 

SC: 792–911 

Best updated1

log(GEO) = -0.6356log(TBY6026) + 

0.6115(pH) - 7.00151

0.22 0.21 0.2263 0.4757 199 66.6 2.37 93 127 GEO: <0.002–0.11 

TBY6026: 1.6–48 

pH: 7.7–8.9 

Microcystin (MC), in μg/L

New

log(MC) = -0.5916sin(2πD/365) - 

0.3244cos(2πD/365) + 0.8574log(CHL) - 1.8153

0.48 0.46 0.1877 0.4333 171 63.1 1.63 64 94 MC: <0.10–3.80 

CHL: 3.1–49.4 

1 Best updated model did not meet selection criteria and should not be used to compute constituent concentrations.

Table 1. Christensen and others (2006), updated, best updated, and new linear regression models and summary statistics for continuous concentratCheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued[R2, coefficient of determination; MSE, mean square error; RMSE, root mean square error; MSPE, model standard percentage error; %, percent; ±, plus or minus; n, number of diper liter; SC, specific conductance in microsiemens per centimeter at 25 degrees Celsius; ELV, reservoir elevation in feet above from National Geodetic Vertical Datum of 1929; from YSI sensor model 6026 in formazin nephelometric units; <, less than; CHL, fluorescence at a wavelength of 650 to 700 nanometers; DO, dissolved oxygen in milligrams pT, water temperature in degrees Celsius; sin, sine; D, day of year; cos, cosine; μ/L, micrograms per liter; col/mL, colonies per milliliter; μm3/mL, micrometers cubed per millilite
12 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 2. Dissolved solids linear regression dataset using specific conductance as the explanatory variable for Cheney Reservoir near 

Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; μS/cm, microsiemens per centimeter; °C, degrees Celsius; mg/L, milligrams per liter]

Date Time, in 

hhmm

Specific conductance, 

in μS/cm at 25°C

Dissolved solids, 

in mg/L

May 3, 2001 1055 789 421

June 4, 2001 1030 809 454

June 22, 2001 1155 756 417

July 24, 2001 1055 776 414

August 29, 2001 1125 799 421

May 15, 2002 1115 913 492

June 19, 2002 0955 814 438

August 7, 2002 1000 863 436

September 4, 2002 1015 863 435

September 25, 2002 1010 842 444

January 23, 2003 1055 879 474

February 10, 2003 1250 891 501

March 3, 2003 1025 885 483

June 17, 2003 1045 792 438

June 20, 2003 0905 802 444

July 7, 2003 0940 806 444

July 17, 2003 1155 797 429

July 28, 2003 1150 799 431

March 10, 2004 1115 806 436

April 8, 2004 1015 801 437

May 5, 2004 1115 825 471

June 3, 2004 1000 838 464

July 15, 2004 0950 760 407

August 12, 2004 0915 738 424

August 27, 2004 1115 743 407

September 9, 2004 0935 751 404

February 2, 2005 1035 827 452

March 16, 2005 1015 841 471

April 13, 2005 1000 868 478

May 4, 2005 1120 884 487

May 16, 2005 0955 885 488

June 1, 2005 1020 890 481

June 15, 2005 0945 850 470

June 29, 2005 1000 804 432

July 13, 2005 0920 739 387

July 27, 2005 0850 745 417

August 10, 2005 0855 753 420

August 30, 2005 0945 733 371

September 7, 2005 1030 730 388

October 13, 2005 1030 745 408

October 27, 2005 1005 751 457

January 11, 2006 1210 792 458

March 1, 2006 1105 820 439

March 29, 2006 1005 833 462

April 25, 2006 1245 857 469

May 17, 2006 1055 845 473

May 31, 2006 1140 849 438

June 14, 2006 1045 852 461

June 28, 2006 0955 845 456

July 13, 2006 0940 859 456

July 26, 2006 1115 847 467

August 10, 2006 1005 844 457

August 22, 2006 1000 837 483

September 6, 2006 1020 830 461

September 20, 2006 1015 840 454

October 11, 2006 0950 854 470

October 25, 2006 0945 866 478

December 12, 2006 1115 907 487

February 7, 20071 1115 822 517

March 7, 2007 1135 940 509

April 9, 2007 1045 884 477

May 8, 2007 0940 845 450

Date Time, in 

hhmm

Specific conductance, 

in μS/cm at 25°C

Dissolved solids, 

in mg/L

May 31, 2007 1020 754 450

June 13, 2007 1140 728 398

June 25, 2007 1000 739 406

July 9, 2007 0950 732 402

July 23, 2007 1020 732 410

August 7, 2007 0930 748 422

August 15, 2007 0955 746 394

August 28, 2007 1055 765 406

September 12, 2007 1025 776 434

September 24, 2007 1025 779 422

October 15, 2007 1005 792 423

October 29, 2007 1045 792 438

November 13, 2007 1040 806 439

December 19, 2007 1100 837 443

February 11, 2008 1100 866 463

February 14, 2008 1105 869 461

February 15, 2008 1025 867 461

March 10, 2008 1125 864 471

April 1, 2008 0955 861 475

March 16, 20081 1140 864 326

April 29, 2008 1035 861 463

May 13, 2008 1020 853 460

June 3, 2008 0945 812 446

June 18, 2008 0920 802 427

June 25, 2008 1155 806 436

July 7, 2008 1035 796 427

July 21, 2008 1050 804 428

July 28, 2008 0955 797 427

August 4, 2008 1020 802 423

August 18, 2008 0945 808 444

September 2, 2008 1000 808 440

September 17, 2008 1000 778 422

October 1, 2008 1055 781 440

October 15, 2008 1150 752 388

November 4, 2008 0950 784 432

December 2, 2008 0950 805 443

January 6, 2009 1010 842 469

January 20, 2009 1010 836 461

February 2, 2009 1000 837 442

February 18, 2009 1010 819 446

February 25, 2009 1030 825 458

March 3, 2009 1050 843 457

March 9, 2009 1050 841 462

March 16, 2009 1100 841 457

March 25, 2009 1020 847 460

April 8, 2009 1010 848 461

April 29, 2009 1030 842 464

May 27, 2009 1140 765 421

June 9, 2009 1100 768 421

June 23, 2009 1045 772 427

July 7, 2009 1100 759 412

July 21, 2009 1020 763 412

August 5, 2009 1030 773 420

August 24, 2009 1000 778 418

September 2, 2009 1030 785 426

September 16, 2009 1030 773 428

October 5, 2009 1100 773 411

October 19, 2009 1100 781 422

November 23, 2009 1100 803 421

December 16, 2009 1140 838 434

1

Data point removed from final analysis because it was an extreme outlier 

on a bivariate plot of actual as compared to computed concentrations.
Results of Linear Regression Analysis for Selected Constituents 13

Table 3. Sodium linear regression dataset using specific conductance as the explanatory variable for Cheney Reservoir near Cheney, 

Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; μS/cm, microsiemens per centimeter; °C, degrees Celsius; mg/L, milligrams per liter]

Date Time, in 

hhmm

Specific conductance, 

in μS/cm at 25°C

Sodium, 

in mg/L

May 3, 2001 1055 789 90.1

June 4, 2001 1030 809 95.2

June 22, 2001 1155 756 87.5

July 24, 2001 1055 776 90.2

August 29, 2001 1125 799 97.4

May 15, 2002 1115 913 116

June 19, 2002 0955 814 98.7

August 7, 2002 1000 863 106

September 4, 2002 1015 863 102

September 25, 2002 1010 842 107

January 23, 2003 1055 879 112

February 10, 2003 1250 891 112

March 3, 2003 1025 885 109

June 17, 2003 1045 792 93.8

June 20, 2003 0905 802 94.4

July 7, 2003 0940 806 99.9

July 17, 2003 1155 797 104

July 28, 2003 1150 799 101

March 10, 2004 1115 806 104

April 8, 2004 1015 801 99.0

May 5, 2004 1115 825 107

June 3, 2004 1000 838 96.5

July 15, 2004 0950 760 89.3

August 12, 2004 0915 738 87.9

August 27, 20041 1115 743 99.3

September 9, 2004 0935 751 96.2

February 2, 2005 1035 827 102

March 16, 2005 1015 841 104

April 13, 2005 1000 868 109

May 4, 2005 1120 884 110

May 16, 2005 0955 885 103

June 1, 2005 1020 890 105

June 15, 2005 0945 850 112

June 29, 2005 1000 804 102

July 13, 2005 0920 739 91.8

July 27, 2005 0850 745 90.1

August 10, 2005 0855 753 88.9

August 30, 2005 0945 733 88.7

September 7, 2005 1030 730 89.1

October 13, 2005 1030 745 93.2

October 27, 2005 1005 751 88.1

January 11, 2006 1210 792 98.5

March 1, 2006 1105 820 109

March 29, 2006 1005 833 105

April 25, 2006 1245 857 107

May 17, 2006 1055 845 107

May 31, 2006 1140 849 110

June 14, 2006 1045 852 108

June 28, 2006 0955 845 108

July 13, 2006 0940 859 109

July 26, 2006 1115 847 117

August 10, 2006 1005 844 109

August 22, 2006 1000 837 111

September 6, 2006 1020 830 108

September 20, 2006 1015 840 112

October 11, 2006 0950 854 117

October 25, 2006 0945 866 118

December 12, 2006 1115 907 124

February 7, 20071 1115 822 129

March 7, 2007 1135 940 123

April 9, 2007 1045 884 116

May 8, 2007 0940 845 106

Date Time, in 

hhmm

Specific conductance, 

in μS/cm at 25°C

Sodium, 

in mg/L

May 31, 2007 1020 754 89.2

June 13, 2007 1140 728 87.3

June 25, 2007 1000 739 85.7

July 9, 2007 0950 732 86.2

July 23, 2007 1020 732 85.6

August 7, 2007 0930 748 87.3

August 15, 2007 0955 746 88.2

August 28, 2007 1055 765 89.8

September 12, 2007 1025 776 95.5

September 24, 2007 1025 779 97.8

October 15, 2007 1005 792 98.7

October 29, 2007 1045 792 98.3

November 13, 2007 1040 806 100

December 19, 2007 1100 837 97.5

February 11, 2008 1100 866 105

February 14, 2008 1105 869 104

February 15, 2008 1025 867 102

March 10, 2008 1125 864 104

April 1, 2008 0955 861 104

April 16, 2008 1140 864 107

April 29, 2008 1035 861 104

May 13, 2008 1020 853 105

June 3, 2008 0945 812 94.5

June 18, 2008 0920 802 92.9

June 25, 2008 1155 806 92.9

July 7, 2008 1035 796 95.2

July 21, 2008 1050 804 105

July 28, 2008 0955 797 99.0

August 4, 2008 1020 802 99.2

August 18, 2008 0945 808 101

September 2, 2008 1000 808 101

September 17, 2008 1000 778 96.2

October 1, 2008 1055 781 97.5

October 15, 2008 1150 752 94.3

November 4, 2008 0950 784 94.8

December 2, 2008 0950 805 95.2

January 6, 2009 1010 842 94.7

January 20, 2009 1010 836 101

February 2, 2009 1000 837 105

February 18, 2009 1010 819 109

February 25, 2009 1030 825 111

March 3, 2009 1050 843 110

March 9, 2009 1050 841 104

March 16, 2009 1100 841 104

March 25, 2009 1020 847 106

April 8, 2009 1010 848 114

April 29, 2009 1030 842 103

May 27, 2009 1140 765 90.1

June 9, 2009 1100 768 92.1

June 23, 2009 1045 772 90.8

July 7, 2009 1100 759 92.0

July 21, 2009 1020 763 80.1

August 5, 20091 1030 773 82.7

August 24, 2009 1000 778 95.3

September 2, 2009 1030 785 95.9

September 16, 2009 1030 773 93.2

October 5, 2009 1100 773 93.5

October 19, 2009 1100 781 94.3

November 23, 2009 1100 803 94.8

December 16, 2009 1140 838 91.3

1

Data point removed from final analysis because it was an extreme outlier 

on a bivariate plot of actual as compared to computed concentrations.
14 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 4. Chloride linear regression dataset using reservoir elevation and specific conductance as explanatory variables 

for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; μS/cm, microsiemens per centimeter; °C, degrees Celsius; mg/L, milligrams per liter]

Date Time, in 

hhmm

Reservoir elevation1, 

in feet 

Specific conductance, 

in μS/cm at 25°C Chloride, in mg/L

May 3, 2001 1055 1,422 789 120

June 4, 2001 1030 1,422 809 126

June 22, 2001 1155 1,422 756 116

July 24, 2001 1055 1,421 776 122

August 29, 2001 1125 1,419 799 129

May 15, 2002 1115 1,419 913 158

June 19, 2002 0955 1,421 814 141

August 7, 2002 1000 1,420 863 151

September 4, 2002 1015 1,420 863 144

September 25, 2002 1010 1,420 842 145

January 23, 2003 1055 1,422 879 162

February 10, 2003 1250 1,422 891 155

March 3, 2003 1025 1,422 885 159

June 17, 2003 1045 1,422 792 144

June 20, 2003 0905 1,422 802 140

July 7, 2003 0940 1,421 806 144

July 17, 2003 1155 1,421 797 146

July 28, 2003 1150 1,421 799 157

March 10, 2004 1115 1,423 806 146

April 8, 2004 1015 1,422 801 137

May 5, 2004 1115 1,422 825 140

June 3, 2004 1000 1,421 838 147

July 15, 2004 0950 1,422 760 136

August 12, 2004 0915 1,422 738 138

August 27, 2004 1115 1,422 743 128

September 9, 2004 0935 1,422 751 128

February 2, 2005 1035 1,422 827 139

March 16, 2005 1015 1,422 841 139

April 13, 2005 1000 1,422 868 136

May 4, 2005 1120 1,422 884 142

May 16, 2005 0955 1,422 885 147

June 1, 2005 1020 1,422 890 146

June 15, 2005 0945 1,424 850 137

June 29, 2005 1000 1,423 804 127

July 13, 2005 0920 1,423 739 105

July 27, 2005 0850 1,422 745 116

August 10, 2005 0855 1,421 753 119

August 30, 2005 0945 1,422 733 111

September 7, 2005 1030 1,422 730 114

October 13, 2005 1030 1,421 745 123

October 27, 2005 1005 1,421 751 120
Results of Linear Regression Analysis for Selected Constituents 15

Date Time, in 

hhmm

Reservoir elevation1, 

in feet 

Specific conductance, 

in μS/cm at 25°C Chloride, in mg/L

January 11, 2006 1210 1,421 792 132

March 1, 2006 1105 1,421 820 134

March 29, 2006 1005 1,422 833 146

April 25, 2006 1245 1,421 857 140

May 17, 2006 1055 1,422 845 133

May 31, 2006 1140 1,422 849 139

June 14, 2006 1045 1,421 852 137

June 28, 2006 0955 1,421 845 131

July 13, 2006 0940 1,421 859 152

July 26, 2006 1115 1,420 847 156

August 10, 2006 1005 1,420 844 152

August 22, 2006 1000 1,420 837 150

September 6, 2006 1020 1,420 830 151

September 20, 2006 1015 1,419 840 156

October 11, 2006 0950 1,419 854 160

October 25, 2006 0945 1,418 866 161

December 12, 2006 1115 1,418 907 166

February 7, 2007 1115 1,419 822 160

March 7, 2007 1135 1,419 940 166

April 9, 2007 1045 1,421 884 160

May 8, 2007 0940 1,424 845 130

May 31, 2007 1020 1,425 754 120

June 13, 2007 1140 1,424 728 120

June 25, 2007 1000 1,422 739 120

July 9, 2007 0950 1,423 732 120

July 23, 2007 1020 1,423 732 110

August 7, 2007 0930 1,423 748 120

August 15, 2007 0955 1,422 746 120

August 28, 2007 1055 1,421 765 120

September 12, 2007 1025 1,421 776 130

September 24, 2007 1025 1,421 779 130

October 15, 2007 1005 1,420 792 130

October 29, 2007 1045 1,420 792 130

November 13, 2007 1040 1,420 806 130

December 19, 2007 1100 1,420 837 140

February 11, 2008 1100 1,421 866 140

February 14, 2008 1105 1,421 869 140

February 15, 2008 1025 1,421 867 140

March 10, 2008 1125 1,422 864 140

April 1, 2008 0955 1,422 861 140

April 16, 2008 1140 1,422 864 140

Table 4. Chloride linear regression dataset using reservoir elevation and specific conductance as explanatory variables 

for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; μS/cm, microsiemens per centimeter; °C, degrees Celsius; mg/L, milligrams per liter]
16 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Date Time, in 

hhmm

Reservoir elevation1, 

in feet 

Specific conductance, 

in μS/cm at 25°C Chloride, in mg/L

April 29, 2008 1035 1,423 861 140

May 13, 2008 1020 1,424 853 140

June 3, 2008 0945 1,423 812 130

June 18, 2008 0920 1,423 802 120

June 25, 2008 1155 1,423 806 130

July 7, 2008 1035 1,422 796 130

July 21, 2008 1050 1,422 804 130

July 28, 2008 0955 1,422 797 130

August 4, 2008 1020 1,421 802 130

August 18, 2008 0945 1,421 808 130

September 2, 20082 1000 1,421 808 79

September 17, 2008 1000 1,422 778 130

October 1, 2008 1055 1,422 781 130

October 15, 2008 1150 1,422 752 130

November 4, 2008 0950 1,422 784 130

December 2, 2008 0950 1,422 805 130

January 6, 2009 1010 1,422 842 130

January 20, 2009 1010 1,422 836 140

February 2, 2009 1000 1,422 837 140

February 18, 2009 1010 1,422 819 140

February 25, 2009 1030 1,422 825 140

March 3, 2009 1050 1,422 843 150

March 9, 2009 1050 1,422 841 140

March 16, 2009 1100 1,422 841 140

March 25, 2009 1020 1,422 847 140

April 8, 2009 1010 1,422 848 140

April 29, 2009 1030 1,424 842 140

May 27, 2009 1140 1,424 765 110

June 9, 2009 1100 1,422 768 110

June 23, 2009 1045 1,422 772 120

July 7, 2009 1100 1,422 759 120

July 21, 2009 1020 1,421 763 120

August 5, 2009 1030 1,421 773 120

August 24, 2009 1000 1,421 778 120

September 2, 2009 1030 1,421 785 130

September 16, 2009 1030 1,422 773 120

October 5, 2009 1100 1,422 773 120

October 19, 2009 1100 1,422 781 120

November 23, 20092 1100 1,422 803 170

December 16, 2009 1140 1,422 838 120

1

Reservoir elevation above National Geodetic Vertical Datum of 1929.

2

Data point removed from final analysis because it was an extreme outlier on a bivariate plot of actual as compared to computed concentrations.

Table 4. Chloride linear regression dataset using reservoir elevation and specific conductance as explanatory variables 

for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; μS/cm, microsiemens per centimeter; °C, degrees Celsius; mg/L, milligrams per liter]
Results of Linear Regression Analysis for Selected Constituents 17

Table 5. Magnesium linear regression dataset using specific conductance as the explanatory variable for Cheney Reservoir near 

Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; μS/cm, microsiemens per centimeter; °C, degrees Celsius; mg/L, milligrams per liter]

Date Time, in 

hhmm

Specific conductance, 

in μS/cm at 25°C

Magnesium, 

in mg/L

May 3, 2001 1055 789 13.4

June 4, 2001 1030 809 14.2

June 22, 2001 1155 756 12.5

July 24, 2001 1055 776 13.4

August 29, 2001 1125 799 14.1

May 15, 2002 1115 913 15.2

June 19, 2002 0955 814 13.4

August 7, 2002 1000 863 14.2

September 4, 2002 1015 863 13.8

September 25, 2002 1010 842 14.4

January 23, 2003 1055 879 14.7

February 10, 2003 1250 891 14.5

March 3, 2003 1025 885 14.8

June 17, 2003 1045 792 13.4

June 20, 2003 0905 802 13.5

July 7, 2003 0940 806 13.9

July 17, 2003 1155 797 14.1

July 28, 2003 1150 799 13.3

March 10, 2004 1115 806 14.2

April 8, 2004 1015 801 13.9

May 5, 2004 1115 825 14.6

June 3, 2004 1000 838 13.5

July 15, 2004 0950 760 12.6

August 12, 2004 0915 738 12.1

August 27, 2004 1115 743 12.3

September 9, 2004 0935 751 12.4

February 2, 2005 1035 827 14.4

March 16, 2005 1015 841 15.1

April 13, 2005 1000 868 15.3

May 4, 2005 1120 884 15.5

May 16, 2005 0955 885 15.0

June 1, 2005 1020 890 15.2

June 15, 2005 0945 850 14.9

June 29, 2005 1000 804 14.1

July 13, 2005 0920 739 12.6

July 27, 2005 0850 745 13.5

August 10, 2005 0855 753 12.7

August 30, 2005 0945 733 12.4

September 7, 2005 1030 730 13.4

October 13, 2005 1030 745 13.3

October 27, 2005 1005 751 12.7

January 11, 2006 1210 792 13.8

March 1, 2006 1105 820 14.7

March 29, 2006 1005 833 14.3

April 25, 2006 1245 857 14.3

May 17, 2006 1055 845 14.2

May 31, 2006 1140 849 14.4

June 14, 2006 1045 852 14.3

June 28, 2006 0955 845 14.1

July 13, 2006 0940 859 14.2

July 26, 2006 1115 847 14.8

August 10, 2006 1005 844 14.0

August 22, 2006 1000 837 14.1

September 6, 2006 1020 830 13.5

September 20, 2006 1015 840 13.7

October 11, 2006 0950 854 14.3

October 25, 2006 0945 866 14.2

December 12, 2006 1115 907 15.3

February 7, 2007 1115 822 15.2

March 7, 2007 1135 940 14.9

April 9, 2007 1045 884 13.9

Date Time, in 

hhmm

Specific conductance, 

in μS/cm at 25°C

Magnesium, 

in mg/L

May 8, 2007 0940 845 13.2

May 31, 2007 1020 754 11.8

June 13, 2007 1140 728 12.0

June 25, 2007 1000 739 11.8

July 9, 2007 0950 732 11.9

July 23, 2007 1020 732 12.0

August 7, 2007 0930 748 11.8

August 15, 2007 0955 746 11.9

August 28, 2007 1055 765 12.0

September 12, 2007 1025 776 12.5

September 24, 2007 1025 779 12.7

October 15, 2007 1005 792 13.4

October 29, 2007 1045 792 13.5

November 13, 2007 1040 806 13.3

December 19, 2007 1100 837 13.4

February 11, 2008 1100 866 13.6

February 14, 2008 1105 869 13.3

February 15, 2008 1025 867 13.1

March 10, 2008 1125 864 13.3

April 1, 2008 0955 861 14.1

April 16, 2008 1140 864 14.0

April 29, 2008 1035 861 13.8

May 13, 2008 1020 853 14.3

June 3, 2008 0945 812 13.1

June 18, 2008 0920 802 13.0

June 25, 2008 1155 806 13.0

July 7, 2008 1035 796 13.5

July 21, 2008 1050 804 14.3

July 28, 2008 0955 797 13.5

August 4, 2008 1020 802 13.5

August 18, 2008 0945 808 13.7

September 2, 2008 1000 808 13.4

September 17, 2008 1000 778 12.9

October 1, 2008 1055 781 13.1

October 15, 2008 1150 752 12.6

November 4, 2008 0950 784 12.8

December 2, 2008 0950 805 13.2

January 6, 2009 1010 842 13.2

January 20, 2009 1010 836 13.9

February 2, 2009 1000 837 14.3

February 18, 2009 1010 819 14.9

February 25, 2009 1030 825 15.1

March 3, 2009 1050 843 14.9

March 9, 2009 1050 841 14.2

March 16, 2009 1100 841 14.2

March 25, 2009 1020 847 14.4

April 8, 2009 1010 848 15.4

April 29, 2009 1030 842 14.6

May 27, 2009 1140 765 13.6

June 9, 2009 1100 768 13.5

June 23, 2009 1045 772 13.3

July 7, 2009 1100 759 13.3

July 21, 2009 1020 763 11.4

August 5, 2009 1030 773 11.7

August 24, 2009 1000 778 13.7

September 2, 2009 1030 785 14.0

September 16, 2009 1030 773 13.6

October 5, 2009 1100 773 13.1

October 19, 2009 1100 781 13.1

November 23, 2009 1100 803 13.3

December 16, 2009 1140 838 12.9
18 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 6. Total suspended solids linear regression dataset using turbidity and fluorescence as explanatory variables for Cheney 

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; YSI, Yellow Springs Instruments; <, less than]

Date Time, in 

hhmm

Turbidity, in formazin nephelometric 

units (YSI model 6026 sensor)

Fluorescence at a wavelength range 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Total suspended solids, 

in milligrams per liter

May 3, 2001 1055 38 8.1 9

June 4, 2001 1030 20 6.5 6

June 22, 2001 1155 22 13 <4

July 24, 2001 1055 18 6.8 4

August 29, 2001 1125 13 4.8 4

May 15, 2002 1115 27 6.0 12

June 19, 2002 0955 36 18 7

August 7, 2002 1000 13 5.2 5

September 4, 2002 1015 12 7.3 9

September 25, 2002 1010 20 8.0 8

January 23, 2003 1055 3.9 24 <4

February 10, 2003 1250 1.6 4.7 4

March 3, 2003 1025 3.3 25 5

June 17, 2003 1045 14 8.4 12

June 20, 2003 0905 3.5 5.3 <4

July 7, 2003 0940 24 23 11

July 17, 2003 1155 27 10 11

July 28, 2003 1150 29 8.3 12

March 10, 2004 1115 22 22 12

April 8, 2004 1015 31 14 14

May 5, 2004 1115 21 2.0 <4

June 3, 2004 1000 21 6.5 11

July 15, 2004 0950 7.0 2.9 <4

August 12, 2004 0915 23 9.4 <4

August 27, 2004 1115 32 3.3 13

September 9, 2004 0935 26 3.1 12

February 2, 2005 1035 2.7 22 5

March 16, 2005 1015 10 25 17

April 13, 2005 1000 18 9.3 8

May 4, 2005 1120 10 4.1 9

May 16, 2005 0955 8.1 6.4 5

June 1, 2005 1020 8.1 6.4 5

June 15, 2005 0945 14 12 7

June 29, 2005 1000 25 7.1 10

July 13, 2005 0920 15 12 12

July 27, 2005 0850 20 8.0 12

August 10, 2005 0855 12 5.4 8

August 30, 2005 0945 22 12 12

September 7, 2005 1030 12 7.4 12

October 13, 2005 1030 18 10 <4

October 27, 2005 1005 20 9.2 18

January 11, 2006 1210 8.9 20 <4

March 1, 2006 1105 3.2 19 7

March 29, 2006 1005 10 23 7
Results of Linear Regression Analysis for Selected Constituents 19

Date Time, in 

hhmm

Turbidity, in formazin nephelometric 

units (YSI model 6026 sensor)

Fluorescence at a wavelength range 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Total suspended solids, 

in milligrams per liter

April 25, 2006 1245 34 8.5 23

May 17, 2006 1055 20 12 12

May 31, 2006 1140 10 8.9 10

June 14, 2006 1045 25 6.6 8

June 28, 2006 0955 13 6.9 13

July 13, 2006 0940 15 5.5 8

July 26, 2006 1115 14 11 8

August 10, 2006 1005 17 10 12

August 22, 2006 1000 21 6.6 9

September 6, 2006 1020 10 15 8

September 20, 2006 1015 22 10 23

October 11, 2006 0950 29 7.3 12

October 25, 2006 0945 35 6.8 8

December 12, 2006 1115 9.2 10 11

February 7, 2007 1115 14 6.9 <4

March 7, 2007 1135 14 34 <4

April 9, 2007 1045 23 17 11

May 8, 2007 0940 11 3.1 14

May 31, 2007 1020 27 4.8 14

June 13, 2007 1140 35 5.0 <4

June 25, 2007 1000 25 5.1 <4

July 9, 2007 0950 21 7.7 17

July 23, 2007 1020 21 11 10

August 7, 2007 0930 27 10 7

August 15, 2007 0955 23 8.5 8

August 28, 2007 1055 28 7.0 12

September 12, 2007 1025 13 8.4 8

September 24, 2007 1025 17 9.0 8

October 15, 2007 1005 29 7.1 17

October 29, 2007 1045 26 7.1 10

November 13, 2007 1040 25 11 14

December 19, 2007 1100 8.1 5.8 <4

February 11, 2008 1100 2.7 12 <4

February 14, 2008 1105 2.3 13 4

February 15, 2008 1025 6.4 14 7

March 10, 2008 1125 9.0 26 6

April 1, 2008 0955 19 30 18

April 16, 2008 1140 28 11 38

April 29, 2008 1035 42 8.1 16

May 13, 2008 1020 30 5.7 10

June 3, 2008 0945 22 5.9 7

June 18, 2008 0920 31 6.8 12

June 25, 2008 1155 17 11 18

July 7, 2008 1035 18 5.3 10

Table 6. Total suspended solids linear regression dataset using turbidity and fluorescence as explanatory variables for Cheney 

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments; <, less than]
20 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Date Time, in 

hhmm

Turbidity, in formazin nephelometric 

units (YSI model 6026 sensor)

Fluorescence at a wavelength range 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Total suspended solids, 

in milligrams per liter

July 21, 2008 1050 26 12 11

July 28, 2008 0955 18 24 9

August 4, 2008 1020 18 6.2 7

August 18, 2008 0945 15 3.7 4

September 2, 2008 1000 16 5.1 8

September 17, 2008 1000 20 4.9 10

October 1, 2008 1055 16 5.2 8

November 4, 2008 0950 27 8.0 10

December 2, 2008 0950 18 7.1 11

January 6, 2009 1010 7.3 23 6

January 20, 2009 1010 8.5 31 10

February 2, 2009 1000 6.9 49 12

February 18, 2009 1010 15 23 17

February 25, 2009 1030 10 24 10

March 3, 2009 1050 10 24 8

March 9, 2009 1050 10 22 9

March 16, 2009 1100 9.3 23 10

March 25, 2009 1020 25 24 19

April 8, 2009 1010 24 16 16

April 29, 2009 1030 34 4.9 <4

May 27, 2009 1140 36 7.6 16

June 9, 2009 1100 25 15 6

June 23, 2009 1045 17 16 8

July 7, 2009 1100 12 9.1 5

July 21, 2009 1020 18 3.6 6

August 5, 2009 1030 14 7.7 6

August 24, 2009 1000 16 6.9 8

September 2, 2009 1030 20 6.2 7

September 16, 2009 1030 19 4.3 6

October 5, 2009 1100 24 6.2 23

October 19, 2009 1100 22 6.2 8

November 23, 2009 1100 17 6.5 6

December 16, 2009 1140 16 4.5 <4

Table 6. Total suspended solids linear regression dataset using turbidity and fluorescence as explanatory variables for Cheney 

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments; <, less than]
Results of Linear Regression Analysis for Selected Constituents 21

Table 7. Suspended-sediment concentration linear regression dataset using turbidity as the explanatory variable for 

Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; YSI, Yellow Springs Instruments]

Date Time, in hhmm Turbidity, in formazin nephelometric 

units (YSI model 6026 sensor)

Suspended-sediment concentration, 

in milligrams per liter

May 3, 2001 1055 38 25

June 4, 2001 1030 20 16

June 22, 2001 1155 22 21

July 24, 2001 1055 18 16

August 29, 2001 1125 13 9

May 15, 2002 1115 27 21

June 19, 2002 0955 36 22

July 11, 2002 1005 12 7

August 7, 2002 1000 13 13

September 4, 2002 1015 12 11

January 23, 2003 1055 3.9 5

February 10, 2003 1250 1.6 4

June 17, 2003 1045 14 8

June 20, 2003 0905 3.5 3

July 7, 2003 0940 24 18

July 17, 2003 1155 27 12

July 28, 2003 1150 29 14

March 10, 2004 1115 22 15

April 8, 2004 1015 31 33

May 5, 2004 1115 21 17

June 3, 2004 1000 21 15

July 15, 2004 0950 7.0 9

August 12, 2004 0915 23 15

August 27, 2004 1115 32 20

September 9, 2004 0935 26 20

February 2, 2005 1035 2.7 5

March 16, 2005 1015 10 11

April 13, 2005 1000 18 15

May 4, 2005 1120 10 15

May 16, 2005 0955 8.1 10

June 1, 2005 1020 8.1 7

June 15, 2005 0945 14 13

June 29, 2005 1000 25 20

July 13, 2005 0920 15 5

July 27, 2005 0850 20 2

August 10, 2005 0855 12 14

August 30, 2005 0945 22 11

September 7, 2005 1030 12 12

October 13, 2005 1030 18 12

October 27, 2005 1005 20 21

January 11, 2006 1210 8.9 7
22 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Date Time, in hhmm Turbidity, in formazin nephelometric 

units (YSI model 6026 sensor)

Suspended-sediment concentration, 

in milligrams per liter

March 1, 2006 1105 3.2 4

March 29, 2006 1005 10 7

April 25, 2006 1245 34 34

May 17, 2006 1055 20 15

May 31, 2006 1140 10 7

June 14, 2006 1045 25 12

June 28, 2006 0955 13 9

July 13, 2006 0940 15 12

July 26, 2006 1115 14 9

August 10, 2006 1005 17 14

August 22, 2006 1000 21 13

September 6, 2006 1020 10 10

September 20, 2006 1015 22 26

October 11, 2006 0950 29 21

October 25, 2006 0945 35 22

December 12, 2006 1115 9.2 9

February 7, 2007 1115 14 2

March 7, 2007 1135 14 12

April 9, 2007 1045 23 16

May 8, 2007 0940 11 8

May 31, 2007 1020 27 11

June 13, 2007 1140 35 25

June 25, 2007 1000 25 8

July 9, 2007 0950 21 20

July 23, 2007 1020 21 14

August 7, 2007 0930 27 16

August 15, 2007 0955 23 14

August 28, 2007 1055 28 24

September 12, 2007 1025 13 12

September 24, 2007 1025 17 15

October 15, 2007 1005 29 34

October 29, 2007 1045 26 14

November 13, 2007 1040 25 20

December 19, 2007 1100 8.1 6

February 11, 2008 1100 2.7 4

February 14, 2008 1105 2.3 3

March 10, 2008 1125 9.0 7

April 1, 2008 0955 19 15

April 16, 2008 1140 28 23

April 29, 2008 1035 42 36

May 13, 2008 1020 30 27

Table 7. Suspended-sediment concentration linear regression dataset using turbidity as the explanatory variable for 

Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments]
Results of Linear Regression Analysis for Selected Constituents 23

Date Time, in hhmm Turbidity, in formazin nephelometric 

units (YSI model 6026 sensor)

Suspended-sediment concentration, 

in milligrams per liter

June 3, 2008 0945 22 19

June 18, 2008 0920 31 26

July 7, 2008 1035 18 18

July 21, 2008 1050 26 20

July 28, 2008 0955 18 16

August 4, 2008 1020 18 14

August 18, 2008 0945 15 14

September 2, 2008 1000 16 12

September 17, 2008 1000 20 16

October 1, 2008 1055 16 15

October 15, 2008 1150 48 26

November 4, 2008 0950 27 18

December 2, 2008 0950 18 16

January 6, 2009 1010 7.3 8

January 20, 2009 1010 8.5 10

February 2, 2009 1000 6.9 12

February 18, 2009 1010 15 18

February 25, 2009 1030 10 11

March 3, 2009 1050 10 10

March 9, 2009 1050 10 9

March 16, 2009 1100 9.3 11

March 25, 2009 1020 25 22

April 8, 2009 1010 24 21

April 29, 2009 1030 34 17

May 27, 2009 1140 36 35

June 9, 2009 1100 25 18

June 23, 2009 1045 17 18

July 7, 2009 1100 12 10

July 21, 2009 1020 18 17

August 5, 2009 1030 14 16

August 24, 2009 1000 16 16

September 2, 2009 1030 20 13

September 16, 2009 1030 19 16

October 5, 2009 1100 24 20

October 19, 2009 1100 22 14

November 23, 2009 1100 17 13

December 16, 2009 1140 16 11

Table 7. Suspended-sediment concentration linear regression dataset using turbidity as the explanatory variable for 

Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments]
24 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 8. Total Kjeldahl nitrogen linear regression dataset using turbidity, dissolved oxygen, and water temperature as explanatory 

variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; YSI, Yellow Springs Instruments]

Date Time, in 

hhmm

Turbidity, in formazin 

nephelometric units 

(YSI model 6026 sensor)

Dissolved oxygen, in 

milligrams per liter

Water temperature, 

in degrees Celsius

Total Kjeldahl nitrogen, in 

milligrams per liter

May 3, 2001 1055 38 7.8 18.4 0.63

June 4, 2001 1030 20 7.9 19.2 0.58

June 22, 2001 1155 22 10.7 24.1 0.61

July 24, 2001 1055 18 7.0 27.7 0.63

August 29, 2001 1125 13 7.3 26.2 0.46

May 15, 2002 1115 27 9.6 17.9 0.81

June 19, 2002 0955 36 7.5 22.6 0.67

July 11, 2002 1005 12 9.7 26.9 1.20

August 7, 2002 1000 13 6.1 27.2 0.59

September 4, 2002 1015 12 7.0 25.9 0.56

September 25, 2002 1010 20 6.3 20.8 0.65

January 23, 2003 1055 3.9 15.2 0.3 0.58

February 10, 2003 1250 1.6 14.4 1.2 0.56

March 3, 2003 1025 3.3 16.1 1.8 0.77

June 17, 2003 1045 14 12.2 25.0 1.40

June 20, 2003 0905 3.5 6.2 22.5 0.62

July 7, 2003 0940 24 8.0 25.7 0.79

July 17, 2003 1155 27 8.4 27.5 1.30

July 28, 2003 1150 29 11.2 28.6 1.20

March 10, 2004 1115 22 12.4 6.4 0.65

April 8, 2004 1015 31 9.3 14.7 0.71

May 5, 2004 1115 21 9.4 16.1 0.65

June 3, 2004 1000 21 7.7 21.7 0.63

July 15, 2004 0950 7.0 6.2 26.0 0.72

August 12, 2004 0915 23 7.3 24.9 0.69

August 27, 2004 1115 32 6.8 24.4 0.80

September 9, 2004 0935 26 4.3 23.3 0.76

February 2, 2005 1035 2.7 16.4 1.8 0.58

March 16, 2005 1015 10 10.8 8.5 0.77

April 13, 2005 1000 18 10.1 13.4 0.69

May 4, 2005 1120 10 10.3 14.7 0.73

May 16, 2005 0955 8.1 9.5 18.4 0.71

June 1, 2005 1020 8.1 7.2 22.1 0.74

June 15, 2005 0945 14 7.2 23.5 0.79

June 29, 2005 1000 25 6.1 25.2 0.64

July 13, 2005 0920 15 9.5 26.8 0.92

July 27, 2005 0850 20 6.4 25.7 0.81

August 30, 2005 0945 22 6.9 26.6 0.81

September 7, 2005 1030 12 4.6 25.1 0.70

October 13, 2005 1030 18 9.4 18.1 0.75

October 27, 2005 1005 20 8.5 14.4 0.74
Results of Linear Regression Analysis for Selected Constituents 25

Date Time, in 

hhmm

Turbidity, in formazin 

nephelometric units 

(YSI model 6026 sensor)

Dissolved oxygen, in 

milligrams per liter

Water temperature, 

in degrees Celsius

Total Kjeldahl nitrogen, in 

milligrams per liter

January 11, 2006 1210 8.9 13.8 4.2 0.73

March 1, 2006 1105 3.2 14.9 3.7 0.73

March 29, 2006 1005 10 12.7 7.6 0.75

April 25, 2006 1245 34 8.5 17.0 0.89

May 17, 2006 1055 20 10.2 18.1 0.74

May 31, 2006 1140 10 9.0 23.6 0.84

June 14, 2006 1045 25 6.5 24.3 0.77

June 28, 2006 0955 13 8.0 25.0 0.78

July 13, 2006 0940 15 6.8 25.3 0.86

July 26, 2006 1115 14 7.2 26.7 0.76

August 10, 2006 1005 17 8.6 27.6 1.20

August 22, 2006 1000 21 4.3 26.8 0.90

September 6, 2006 1020 10 9.2 24.2 0.94

September 20, 2006 1015 22 7.5 20.8 0.91

October 11, 2006 0950 29 8.0 17.1 0.82

October 25, 2006 0945 35 9.0 12.7 0.76

December 12, 2006 1115 9.2 13.2 3.8 0.94

February 7, 2007 1115 14 13.6 5.6 0.71

March 7, 2007 1135 14 12.3 6.7 0.68

April 9, 2007 1045 23 10.0 11.4 0.81

May 8, 2007 0940 11 7.2 18.2 0.75

May 31, 2007 1020 27 6.8 21.3 0.91

June 13, 2007 1140 35 6.5 22.6 0.63

June 25, 2007 1000 25 6.3 24.4 0.60

July 9, 2007 950 21 6.6 25.2 0.74

July 23, 2007 1020 21 6.2 26.1 0.73

August 7, 2007 0930 27 5.7 27.0 0.72

August 15, 2007 0955 23 6.9 27.9 0.70

August 28, 2007 1055 28 6.5 26.6 0.70

September 12, 2007 1025 13 7.7 23.7 0.67

September 24, 2007 1025 17 7.5 22.1 0.72

October 15, 2007 1005 29 8.0 18.9 0.68

October 29, 2007 1045 26 8.9 13.9 0.67

November 13, 2007 1040 25 9.7 11.8 0.68

December 19, 2007 1100 8.1 12.6 1.8 0.60

February 11, 2008 1100 2.7 13.6 1.8 0.67

February 14, 2008 1105 2.3 13.6 2.0 0.71

February 15, 2008 1025 6.4 13.2 1.8 0.67

March 10, 2008 1125 9.0 13.3 4.3 0.77

April 1, 2008 0955 19 11.2 9.1 0.77

April 16, 2008 1140 28 10.5 10.1 0.85

Table 8. Total Kjeldahl nitrogen linear regression dataset using turbidity, dissolved oxygen, and water temperature as explanatory 

variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments]
26 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Date Time, in 

hhmm

Turbidity, in formazin 

nephelometric units 

(YSI model 6026 sensor)

Dissolved oxygen, in 

milligrams per liter

Water temperature, 

in degrees Celsius

Total Kjeldahl nitrogen, in 

milligrams per liter

April 29, 2008 1035 42 8.8 13.8 0.97

May 13, 2008 1020 30 8.8 16.0 1.00

June 3, 2008 0945 22 6.8 22.7 0.68

June 18, 2008 0920 31 6.0 23.5 0.84

June 25, 2008 1155 17 6.4 24.7 0.66

July 7, 2008 1035 18 6.8 25.3 0.75

July 21, 2008 1050 26 7.5 26.2 0.83

July 28, 2008 0955 18 6.7 26.5 0.72

August 4, 2008 1020 18 6.3 26.8 0.66

August 18, 2008 0945 15 5.4 24.6 0.60

September 2, 2008 1000 16 7.5 25.1 0.72

September 17, 2008 1000 20 8.0 20.2 0.64

October 1, 2008 1055 16 8.4 21.6 0.65

October 15, 2008 1150 48 8.4 16.8 0.65

November 4, 2008 0950 27 9.4 12.7 0.61

December 2, 2008 0950 18 11.6 5.3 0.54

January 6, 2009 1010 7.3 13.2 0.8 0.34

January 20, 2009 1010 8.5 15.2 0.9 0.80

February 2, 2009 1000 6.9 15.2 0.7 0.74

February 18, 2009 1010 15 11.9 5.2 0.68

February 25, 2009 1030 10 12.3 5.4 0.68

March 3, 2009 1050 10 12.3 4.4 0.63

March 9, 2009 1050 10 11.9 6.4 0.62

March 16, 2009 1100 9.3 11.6 6.3 0.82

March 25, 2009 1020 25 10.2 11.0 0.68

April 8, 2009 1010 24 10.9 7.6 0.75

April 29, 2009 1030 34 8.1 14.9 0.84

May 27, 2009 1140 36 8.1 22.1 0.79

June 9, 2009 1100 25 6.5 22.0 0.67

June 23, 2009 1045 17 7.1 25.0 0.70

July 7, 2009 1100 12 5.6 25.8 0.74

July 21, 2009 1020 18 5.3 25.4 0.73

August 5, 2009 1030 14 5.9 25.1 0.59

August 24, 2009 1000 16 6.1 24.3 0.63

September 2, 2009 1030 20 6.6 23.2 0.64

September 16, 2009 1030 19 6.5 22.0 0.49

October 5, 2009 1100 24 8.0 17.3 0.55

October 19, 2009 1100 22 9.9 12.2 0.58

November 23, 2009 1100 17 9.9 9.8 0.55

December 16, 2009 1140 16 12.6 0.9 0.56

Table 8. Total Kjeldahl nitrogen linear regression dataset using turbidity, dissolved oxygen, and water temperature as explanatory 

variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments]
Results of Linear Regression Analysis for Selected Constituents 27

Table 9. Nitrate plus nitrite linear regression dataset using pH and specific conductance as explanatory variables for Cheney 

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; <, less than]

Date Time, in 

hhmm pH, in standard units Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

Nitrate plus nitrite, 

in milligrams per liter

May 3, 2001 1055 8.5 789 0.29

June 4, 2001 1030 8.4 809 0.36

June 22, 2001 1155 8.7 756 0.17

July 24, 2001 1055 8.3 776 0.06

August 29, 2001 1125 8.2 799 0.05

May 15, 2002 1115 8.4 913 0.10

June 19, 2002 0955 8.3 814 0.11

August 7, 2002 1000 8.4 863 0.03

September 4, 2002 1015 8.4 863 0.07

January 21, 2003 1210 8.9 871 0.02

January 23, 2003 1055 8.8 879 <0.02

February 10, 2003 1250 8.0 891 <0.02

March 3, 2003 1025 8.6 885 <0.02

June 17, 2003 1045 8.9 792 <0.02

June 20, 2003 0905 8.2 802 0.17

July 7, 2003 0940 8.6 806 0.03

July 17, 2003 1155 8.7 797 0.02

July 28, 2003 1150 8.8 799 <0.02

March 10, 2004 1115 8.3 806 0.32

April 8, 2004 1015 8.2 801 0.75

May 5, 2004 1115 8.0 825 0.39

June 3, 2004 1000 8.2 838 0.38

July 15, 2004 0950 8.3 760 0.04

August 12, 2004 0915 8.2 738 0.97

August 27, 2004 1115 8.4 743 0.02

September 9, 2004 0935 7.7 751 0.06

February 2, 2005 1035 8.9 827 0.04

March 16, 2005 1015 8.7 841 <0.02

April 13, 2005 1000 8.4 868 0.03

May 4, 2005 1120 8.5 884 0.06

May 16, 2005 0955 8.5 885 0.05

June 1, 2005 1020 8.3 890 0.03

June 15, 2005 0945 8.1 850 0.05

June 29, 2005 1000 8.2 804 0.18

July 13, 2005 0920 8.8 739 0.57

July 27, 2005 0850 8.2 745 0.07

August 10, 2005 0855 8.2 753 0.16

August 30, 2005 0945 8.3 733 0.12

September 7, 2005 1030 8.1 730 0.13

October 13, 2005 1030 8.7 745 0.04

October 27, 2005 1005 8.5 751 0.04
28 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 9. Nitrate plus nitrite linear regression dataset using pH and specific conductance as explanatory variables for Cheney 

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; <, less than]

Date Time, in 

hhmm pH, in standard units Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

Nitrate plus nitrite, 

in milligrams per liter

January 11, 2006 1210 8.6 792 0.17

March 1, 2006 1105 8.6 820 0.04

March 29, 2006 1005 8.5 833 0.10

April 25, 2006 1245 8.3 857 0.15

May 17, 2006 1055 8.4 845 0.22

May 31, 2006 1140 8.3 849 0.08

June 14, 2006 1045 8.0 852 0.20

June 28, 2006 0955 8.7 845 0.39

July 13, 2006 0940 8.3 859 0.04

July 26, 2006 1115 8.3 847 0.03

August 10, 2006 1005 8.5 844 0.17

August 22, 2006 1000 8.2 837 0.08

September 6, 2006 1020 8.4 830 0.20

September 20, 2006 1015 8.3 840 0.06

October 11, 2006 0950 8.4 854 0.18

October 25, 2006 0945 8.3 866 0.03

December 12, 2006 1115 8.5 907 0.15

February 7, 2007 1115 8.5 822 0.20

March 7, 2007 1135 8.6 940 0.18

April 9, 2007 1045 8.6 884 0.18

May 8, 2007 0940 8.4 845 0.11

May 31, 2007 1020 8.0 754 0.25

June 13, 2007 1140 8.0 728 0.54

June 25, 2007 1000 8.2 739 0.47

July 9, 2007 0950 8.3 732 0.37

July 23, 2007 1020 8.4 732 0.24

August 7, 2007 0930 8.4 748 0.07

August 15, 2007 0955 8.5 746 0.07

August 28, 2007 1055 8.2 765 0.17

September 12, 2007 1025 8.5 776 0.14

September 24, 2007 1025 8.4 779 0.12

October 15, 2007 1005 8.4 792 0.12

October 29, 2007 1045 8.4 792 0.21

November 13, 2007 1040 8.6 806 0.15

December 19, 2007 1100 8.5 837 0.17

February 11, 2008 1100 8.6 866 0.19

February 14, 2008 1105 8.6 869 0.20

February 15, 2008 1025 8.6 867 0.19

March 10, 2008 1125 8.6 864 0.17

April 1, 2008 0955 8.6 861 0.06

April 16, 2008 1140 8.3 864 <0.04
Results of Linear Regression Analysis for Selected Constituents 29

Table 9. Nitrate plus nitrite linear regression dataset using pH and specific conductance as explanatory variables for Cheney 

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; <, less than]

Date Time, in 

hhmm pH, in standard units Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

Nitrate plus nitrite, 

in milligrams per liter

April 29, 2008 1035 8.1 861 0.10

May 13, 2008 1020 8.2 853 0.18

June 3, 2008 0945 8.2 812 0.54

June 18, 2008 0920 8.2 802 0.54

June 25, 2008 1155 8.3 806 0.53

July 7, 2008 1035 8.4 796 0.24

July 21, 2008 1050 8.4 804 0.17

July 28, 2008 0955 8.3 797 0.13

August 4, 2008 1020 8.4 802 0.11

August 18, 2008 0945 8.2 808 0.24

September 2, 2008 1000 8.5 808 0.06

September 17, 2008 1000 8.4 778 0.21

October 1, 2008 1055 8.4 781 0.20

October 15, 2008 1150 8.3 752 0.25

November 4, 2008 0950 8.3 784 0.42

December 2, 2008 0950 8.7 805 0.48

January 6, 2009 1010 8.2 842 0.47

January 20, 2009 1010 8.6 836 0.22

February 2, 2009 1000 8.6 837 0.09

February 18, 2009 1010 8.7 819 0.07

February 25, 2009 1030 8.8 825 0.07

March 3, 2009 1050 8.8 843 0.06

March 9, 2009 1050 8.7 841 0.12

March 16, 2009 1100 8.6 841 <0.02

March 25, 2009 1020 8.6 847 <0.02

April 8, 2009 1010 8.6 848 0.02

April 29, 2009 1030 8.1 842 0.07

May 27, 2009 1140 8.3 765 0.36

June 9, 2009 1100 8.1 768 0.49

June 23, 2009 1045 8.4 772 0.40

July 7, 2009 1100 8.6 759 0.04

July 21, 2009 1020 8.5 763 0.07

August 5, 2009 1030 8.4 773 0.18

August 24, 2009 1000 8.6 778 0.22

September 2, 2009 1030 8.3 785 0.15

September 16, 2009 1030 8.3 773 0.28

October 5, 2009 1100 8.5 773 0.37

October 19, 2009 1100 8.4 781 0.42

November 23, 2009 1100 8.5 803 0.51

December 16, 2009 1140 8.6 838 0.58
30 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 10. Total phosphorus linear regression dataset using seasonal data and turbidity as explanatory variables for Cheney Reservoir 

near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; sin, sine; D, day of year; cos, cosine; YSI, Yellow Springs Instruments; <, less than]

Date Time, in 

hhmm sin(2πD/365) cos(2πD/365) Turbidity, in formazin nephelometric units (YSI model 6026 sensor)

Total phosphorus, 

in milligrams per liter

May 3, 2001 1055 0.8543 -0.5197 38 0.06

June 4, 2001 1030 0.4559 -0.8900 20 0.08

June 22, 2001 1155 0.1628 -0.9867 22 0.08

July 24, 2001 1055 -0.3777 -0.9259 18 0.09

August 29, 2001 1125 -0.8453 -0.5344 13 0.09

May 15, 2002 1115 0.7296 -0.6839 27 0.11

June 19, 20021 0955 0.2135 -0.9769 36 0.15

August 7, 2002 1000 -0.5878 -0.8090 13 0.11

September 4, 2002 1015 -0.8958 -0.4444 12 0.11

September 25, 2002 1010 -0.9951 -0.0988 20 0.07

January 21, 2003 1210 0.3537 0.9354 6.5 0.04

January 23, 2003 1055 0.3857 0.9226 3.9 0.04

February 10, 2003 1250 0.6486 0.7611 1.6 <0.03

March 3, 2003 1025 0.8759 0.4825 3.3 0.05

June 17, 20031 1045 0.2470 -0.9690 14 0.08

June 20, 2003 0905 0.1967 -0.9805 3.5 0.05

July 7, 2003 0940 -0.0945 -0.9955 24 0.13

July 17, 2003 1155 -0.2637 -0.9646 27 0.13

July 28, 2003 1150 -0.4405 -0.8977 29 0.13

March 10, 2004 1115 0.9338 0.3577 22 0.08

April 8, 2004 1015 0.9911 -0.1330 31 0.08

May 5, 2004 1115 0.8264 -0.5632 21 0.11

June 3, 2004 1000 0.4559 -0.8900 21 0.12

July 15, 2004 0950 -0.2470 -0.9690 7.0 0.05

August 12, 2004 0915 -0.6681 -0.7441 23 0.08

August 27, 2004 1115 -0.8359 -0.5488 32 0.14

September 9, 2004 0935 -0.9369 -0.3496 26 0.11

February 2, 2005 1035 0.5380 0.8429 2.7 0.03

March 16, 2005 1015 0.9611 0.2761 10 0.05

April 13, 2005 1000 0.9796 -0.2009 18 0.09

May 4, 2005 1120 0.8452 -0.5344 10 0.11

May 16, 2005 0955 0.7177 -0.6964 8.1 0.06

June 1, 2005 1020 0.5012 -0.8653 8.1 0.08

June 15, 20051 0945 0.2802 -0.9599 14 0.04

June 29, 2005 1000 0.0430 -0.9991 25 0.17

July 13, 20051 0920 -0.1967 -0.9805 15 0.10

July 27, 2005 0850 -0.4250 -0.9052 20 0.10

August 10, 20051 0855 -0.6288 -0.7776 12 0.03

August 30, 20051 0945 -0.8543 -0.5197 22 0.11

September 7, 20051 1030 -0.9176 -0.3975 12 0.04

October 13, 2005 1030 -0.9778 0.2093 18 0.05

October 27, 2005 1005 -0.8996 0.4367 20 0.07
Results of Linear Regression Analysis for Selected Constituents 31

Date Time, in 

hhmm sin(2πD/365) cos(2πD/365) Turbidity, in formazin nephelometric units (YSI model 6026 sensor)

Total phosphorus, 

in milligrams per liter

January 11, 2006 1210 0.1882 0.9821 8.9 <0.03

March 1, 2006 1105 0.8588 0.5124 3.2 <0.03

March 29, 2006 1005 0.9984 0.0559 10 0.07

April 25, 2006 1245 0.9176 -0.3975 34 0.09

May 17, 2006 1055 0.7056 -0.7086 20 0.08

May 31, 2006 1140 0.5161 -0.8566 10 0.07

June 14, 20061 1045 0.2967 -0.9550 25 0.17

June 28, 2006 0955 0.0602 -0.9982 13 0.07

July 13, 20061 0940 -0.1967 -0.9805 15 0.09

July 26, 2006 1115 -0.4094 -0.9124 14 0.08

August 10, 2006 1005 -0.6288 -0.7776 17 0.08

August 22, 2006 1000 -0.7749 -0.6321 21 0.12

September 6, 2006 1020 -0.9106 -0.4133 10 0.11

September 20, 20061 1015 -0.9829 -0.1840 22 0.09

October 11, 2006 0950 -0.9845 0.1755 29 0.13

October 25, 2006 0945 -0.9141 0.4054 35 0.09

December 12, 2006 1115 -0.3213 0.9470 9.2 0.09

February 7, 2007 1115 0.6085 0.7936 14 0.06

March 7, 20071 1135 0.9070 0.4211 14 0.11

April 9, 2007 1045 0.9911 -0.1330 23 0.12

May 8, 20071 0940 0.8065 -0.5913 11 0.09

May 31, 2007 1020 0.5161 -0.8566 27 0.17

June 13, 2007 1140 0.3131 -0.9497 35 0.15

June 25, 2007 1000 0.1117 -0.9937 25 0.23

July 9, 2007 0950 -0.1288 -0.9917 21 0.22

July 23, 2007 1020 -0.3617 -0.9323 21 0.17

August 7, 2007 0930 -0.5878 -0.8090 27 0.19

August 15, 2007 0955 -0.6933 -0.7207 23 0.12

August 28, 2007 1055 -0.8359 -0.5488 28 0.12

September 12, 2007 1025 -0.9484 -0.3172 13 0.11

September 24, 2007 1025 -0.9933 -0.1159 17 0.11

October 15, 2007 1005 -0.9701 0.2429 29 0.10

October 29, 2007 1045 -0.8841 0.4674 26 0.10

November 13, 2007 1040 -0.7354 0.6776 25 0.07

December 19, 2007 1100 -0.2051 0.9787 8.1 0.07

February 11, 2008 1100 0.6616 0.7498 2.7 <0.03

February 14, 2008 1105 0.6995 0.7147 2.3 <0.03

February 15, 2008 1025 0.7117 0.7025 6.4 <0.03

March 10, 2008 1125 0.9338 0.3577 9.0 0.06

April 1, 2008 0955 0.9999 -0.0129 19 0.06

April 16, 2008 1140 0.9635 -0.2678 28 0.08

April 29, 2008 1035 0.8800 -0.4750 42 0.08

Table 10. Total phosphorus linear regression dataset using seasonal data and turbidity as explanatory variables for Cheney Reservoir 

near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; sin, sine; D, day of year; cos, cosine; YSI, Yellow Springs Instruments; <, less than]
32 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 10. Total phosphorus linear regression dataset using seasonal data and turbidity as explanatory variables for Cheney Reservoir 

near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; sin, sine; D, day of year; cos, cosine; YSI, Yellow Springs Instruments; <, less than]

Date Time, in 

hhmm sin(2πD/365) cos(2πD/365) Turbidity, in formazin nephelometric units (YSI model 6026 sensor)

Total phosphorus, 

in milligrams per liter

May 13, 2008 1020 0.7412 -0.6713 30 0.09

June 3, 2008 0945 0.4559 -0.8900 22 0.09

June 18, 2008 0920 0.2135 -0.9769 31 0.08

June 25, 20081 1155 0.0945 -0.9955 17 0.07

July 7, 2008 1035 -0.1117 -0.9937 18 0.12

July 21, 2008 1050 -0.3456 -0.9384 26 0.10

July 28, 2008 955 -0.4559 -0.8900 18 0.09

August 4, 2008 1020 -0.5596 -0.8288 18 0.09

August 18, 2008 0945 -0.7412 -0.6713 15 0.11

September 2, 2008 1000 -0.8881 -0.4597 16 0.09

September 17, 2008 1000 -0.9760 -0.2177 20 0.11

October 1, 2008 1055 -0.9998 0.0215 16 0.06

October 15, 2008 1150 -0.9657 0.2595 48 0.10

November 4, 2008 0950 -0.8215 0.5703 27 0.09

December 2, 2008 0950 -0.4635 0.8861 18 0.09

January 6, 2009 1010 0.1031 0.9947 7.3 0.06

January 20, 2009 1010 0.3375 0.9413 8.5 0.04

February 2, 20091 1000 0.5380 0.8429 6.9 <0.03

February 18, 2009 1010 0.7470 0.6649 15 0.04

February 25, 2009 1030 0.8215 0.5702 10 0.04

March 3, 2009 1050 0.8759 0.4825 10 0.05

March 9, 2009 1050 0.9210 0.3896 10 0.04

March 16, 2009 1100 0.9611 0.2761 9.3 0.03

March 25, 2009 1020 0.9922 0.1245 25 0.07

April 8, 2009 1010 0.9933 -0.1159 24 0.09

April 29, 2009 1030 0.8881 -0.4597 34 0.08

May 27, 2009 1140 0.5738 -0.8190 36 0.13

June 9, 2009 1100 0.3777 -0.9259 25 0.13

June 23, 2009 1045 0.1458 -0.9893 17 0.16

July 7, 2009 1100 -0.0945 -0.9955 12 0.09

July 21, 2009 1020 -0.3294 -0.9442 18 0.11

August 5, 2009 1030 -0.5596 -0.8288 14 0.10

August 24, 2009 1000 -0.7962 -0.6050 16 0.10

September 2, 2009 1030 -0.8800 -0.4749 20 0.09

September 16, 2009 1030 -0.9679 -0.2512 19 0.09

October 5, 2009 1100 -0.9973 0.0731 24 0.07

October 19, 2009 1100 -0.9511 0.3090 22 0.09

November 23, 2009 1100 -0.6085 0.7936 17 0.09

December 16, 2009 1140 -0.2553 0.9669 16 0.10

1

Data point removed from final analysis because of laboratory issues with phosphorus analysis. The filtered orthophosphate value was larger than the 

dissolved phosphorus value.
Results of Linear Regression Analysis for Selected Constituents 33

Table 11. Orthophosphate linear regression dataset using turbidity, water temperature, and fluorescence as explanatory variables for 

Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; YSI, Yellow Springs Instruments; <, less than]

Date Time, in 

hhmm

Turbidity, in formazin 

nephelometric units

Water temperature, in 

degrees Celsius

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Orthophosphate, in 

milligrams per liter

May 3, 2001 1055 38 18.4 8.1 0.04

June 4, 2001 1030 20 19.2 6.5 0.05

June 22, 2001 1155 22 24.1 12.5 0.06

July 24, 2001 1055 18 27.7 6.8 0.05

August 29, 2001 1125 13 26.2 4.8 0.03

May 15, 2002 1115 27 17.9 6.0 0.06

June 19, 20021 0955 36 22.6 18.1 0.09

August 7, 2002 1000 13 27.2 5.2 <0.01

September 4, 2002 1015 12 25.9 7.3 0.05

September 25, 2002 1010 20 20.8 8.0 0.03

January 21, 2003 1210 6.5 1.4 13.7 <0.01

January 23, 2003 1055 3.9 0.3 24.2 <0.01

February 10, 2003 1250 1.6 1.2 4.7 <0.01

March 3, 2003 1025 3.3 1.8 24.5 <0.01

June 17, 20031 1045 14 25.0 8.4 0.04

June 20, 2003 0905 3.5 22.5 5.3 <0.01

July 7, 2003 0940 24 25.7 22.8 0.06

July 17, 2003 1155 27 27.5 9.6 0.04

July 28, 2003 1150 29 28.6 8.3 0.03

March 10, 2004 1115 22 6.4 21.7 <0.01

April 8, 2004 1015 31 14.7 14.1 0.01

May 5, 2004 1115 21 16.1 2.0 0.03

June 3, 2004 1000 21 21.7 6.5 0.04

July 15, 2004 0950 7.0 26.0 2.9 0.03

August 12, 2004 0915 23 24.9 9.4 0.02

August 27, 2004 1115 32 24.4 3.3 0.04

September 9, 2004 0935 26 23.3 3.1 0.04

February 2, 2005 1035 2.7 1.8 21.5 <0.01

March 16, 2005 1015 10 8.5 25.3 <0.01

April 13, 2005 1000 18 13.4 9.3 <0.01

May 4, 2005 1120 10 14.7 4.1 0.02

May 16, 2005 0955 8.1 18.4 6.4 0.02

June 1, 2005 1020 8.1 22.1 6.4 0.02

June 15, 2005 0945 14 23.5 12.2 0.03

June 29, 2005 1000 25 25.2 7.1 0.06

July 13, 20051 0920 15 26.8 11.6 0.06

July 27, 2005 0850 20 25.7 8.0 0.06

August 10, 2005 0855 12 26.2 5.4 0.05

August 30, 20051 0945 22 26.6 11.8 0.09

September 7, 20051 1030 12 25.1 7.4 0.05

October 13, 2005 1030 18 18.1 9.5 0.02
34 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Date Time, in 

hhmm

Turbidity, in formazin 

nephelometric units

Water temperature, in 

degrees Celsius

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Orthophosphate, in 

milligrams per liter

October 27, 2005 1005 20 14.4 9.2 0.01

January 11, 2006 1210 8.9 4.2 19.7 <0.01

March 1, 2006 1105 3.2 3.7 19.1 <0.01

March 29, 2006 1005 10 7.6 23.0 <0.01

April 25, 2006 1245 34 17.0 8.5 0.03

May 17, 2006 1055 20 18.1 11.7 0.02

May 31, 2006 1140 10 23.6 8.9 0.03

June 14, 20061 1045 25 24.3 6.6 0.04

June 28, 2006 0955 13 25.0 6.9 0.02

July 13, 2006 0940 15 25.3 5.5 0.02

July 26, 2006 1115 14 26.7 10.7 0.02

August 10, 2006 1005 17 27.6 10.2 <0.01

August 22, 2006 1000 21 26.8 6.6 0.03

September 6, 2006 1020 10 24.2 14.7 0.02

September 20, 20061 1015 22 20.8 9.9 0.04

October 11, 2006 0950 29 17.1 7.3 0.03

October 25, 2006 0945 35 12.7 6.8 0.03

December 12, 2006 1115 9.2 3.8 10.2 <0.01

February 7, 2007 1115 14 5.6 6.9 <0.01

March 7, 20071 1135 14 6.7 33.6 0.05

April 9, 2007 1045 23 11.4 17.2 0.02

May 8, 20071 0940 11 18.2 3.1 0.05

May 31, 2007 1020 27 21.3 4.8 0.08

June 13, 2007 1140 35 22.6 5.0 0.09

June 25, 2007 1000 25 24.4 5.1 0.09

July 9, 2007 0950 21 25.2 7.7 0.08

July 23, 2007 1020 21 26.1 10.6 0.06

August 7, 2007 0930 27 27.0 9.9 0.05

August 15, 2007 0955 23 27.9 8.5 0.04

August 28, 2007 1055 28 26.6 7.0 0.06

September 12, 2007 1025 13 23.7 8.4 0.04

September 24, 2007 1025 17 22.1 9.0 0.03

October 15, 2007 1005 29 18.9 7.1 0.04

October 29, 2007 1045 26 13.9 7.1 0.04

November 13, 2007 1040 25 11.8 10.6 0.02

December 19, 2007 1100 8.1 1.8 5.8 0.02

February 11, 2008 1100 2.7 1.8 12.5 <0.01

February 14, 2008 1105 2.3 2.0 12.8 <0.01

February 15, 2008 1025 6.4 1.8 14.2 <0.01

March 10, 2008 1125 9.0 4.3 25.6 <0.01

April 1, 2008 0955 19 9.1 29.6 <0.01

Table 11. Orthophosphate linear regression dataset using turbidity, water temperature, and fluorescence as explanatory variables for 

Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments; <, less than]
Results of Linear Regression Analysis for Selected Constituents 35

Date Time, in 

hhmm

Turbidity, in formazin 

nephelometric units

Water temperature, in 

degrees Celsius

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Orthophosphate, in 

milligrams per liter

April 16, 2008 1140 28 10.1 10.7 <0.01

April 29, 2008 1035 42 13.8 8.1 0.03

May 13, 2008 1020 30 16.0 5.7 0.05

June 3, 2008 0945 22 22.7 5.9 0.05

June 18, 2008 0920 31 23.5 6.8 0.05

June 25, 20081 1155 17 24.7 11.1 0.07

July 7, 2008 1035 18 25.3 5.3 0.05

July 21, 2008 1050 26 26.2 11.7 0.05

July 28, 2008 0955 18 26.5 24.4 0.05

August 4, 2008 1020 18 26.8 6.2 0.05

August 18, 2008 0945 15 24.6 3.7 0.07

September 2, 2008 1000 16 25.1 5.1 0.03

September 17, 2008 1000 20 20.2 4.9 0.05

October 1, 2008 1055 16 21.6 5.2 0.04

October 15, 2008 1150 48 16.8 5.7 0.04

November 4, 2008 0950 27 12.7 8.0 0.06

December 2, 2008 0950 18 5.3 7.1 0.04

January 6, 2009 1010 7.3 0.8 23.1 <0.01

January 20, 2009 1010 8.5 0.9 31.2 <0.01

February 2, 2009 1000 6.9 0.7 49.4 <0.01

February 18, 2009 1010 15 5.2 23.0 <0.01

February 25, 2009 1030 10 5.4 23.7 <0.01

March 3, 2009 1050 10 4.4 24.4 <0.01

March 9, 2009 1050 10 6.4 22.0 <0.01

March 16, 2009 1100 9.3 6.3 23.1 <0.01

March 25, 2009 1020 25 11.0 23.5 <0.01

April 8, 2009 1010 24 7.6 16.2 <0.01

April 29, 2009 1030 34 14.9 4.9 0.04

May 27, 2009 1140 36 22.1 7.6 0.06

June 9, 2009 1100 25 22.0 14.8 0.08

June 23, 2009 1045 17 25.0 15.5 0.07

July 7, 2009 1100 12 25.8 9.1 0.05

July 21, 2009 1020 18 25.4 3.6 0.08

August 5, 2009 1030 14 25.1 7.7 0.06

August 24, 2009 1000 16 24.3 6.9 0.06

September 2, 2009 1030 20 23.2 6.2 0.05

September 16, 2009 1030 19 22.0 4.3 0.07

October 5, 2009 1100 24 17.3 6.2 0.07

October 19, 2009 1100 22 12.2 6.2 0.07

November 23, 2009 1100 17 9.8 6.5 0.07

December 16, 2009 1140 16 0.9 4.5 0.07

1

Data point removed from final analysis because of laboratory issues with phosphorus analysis. Orthophosphorus was larger than dissolved phosphorus.

Table 11. Orthophosphate linear regression dataset using turbidity, water temperature, and fluorescence as explanatory variables for 

Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments; <, less than]
36 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 12. Dissolved phosphorus linear regression dataset using dissolved oxygen, specific conductance, and fluorescence as 

explanatory variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; mg/L, milligrams per liter; μS/cm, microsiemens per centimeter; °C, degrees Celsius; YSI, Yellow Springs Instruments; <, less than]

Date Time, in 

hhmm

Dissolved oxygen, 

in mg/L

Specific conductance, 

in μS/cm at 25°C

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Dissolved 

phosphorus, 

in mg/L

May 3, 2001 1055 7.8 789 8.1 0.04

June 4, 2001 1030 7.9 809 6.5 0.06

June 22, 2001 1155 10.7 756 12.5 0.06

July 24, 2001 1055 7.0 776 6.8 0.06

August 29, 2001 1125 7.3 799 4.8 0.07

May 15, 2002 1115 9.6 913 6.0 0.06

June 19, 20021 0955 7.5 814 18.1 0.08

August 7, 2002 1000 6.1 863 5.2 0.09

September 4, 2002 1015 7.0 863 7.3 0.07

September 25, 2002 1010 6.3 842 8.0 0.05

January 21, 2003 1210 15.8 871 13.7 <0.03

January 23, 2003 1055 15.2 879 24.2 <0.03

February 10, 2003 1250 14.4 891 4.7 <0.03

March 3, 2003 1025 16.1 885 24.5 <0.03

June 17, 2003 1045 12.2 792 8.4 <0.03

June 20, 2003 0905 6.2 802 5.3 <0.03

July 7, 2003 0940 8.0 806 22.8 0.07

July 17, 2003 1155 8.4 797 9.6 0.05

July 28, 2003 1150 11.2 799 8.3 0.05

March 10, 2004 1115 12.4 806 21.7 <0.03

April 8, 2004 1015 9.3 801 14.1 0.03

May 5, 2004 1115 9.4 825 2.0 0.03

June 3, 2004 1000 7.7 838 6.5 0.06

July 15, 2004 0950 6.2 760 2.9 0.03

August 12, 2004 0915 7.3 738 9.4 0.03

August 27, 2004 1115 6.8 743 3.3 0.05

September 9, 2004 0935 4.3 751 3.1 0.06

February 2, 2005 1035 16.4 827 21.5 <0.03

March 16, 2005 1015 10.8 841 25.3 <0.03

April 13, 2005 1000 10.1 868 9.3 <0.03

May 4, 2005 1120 10.3 884 4.1 0.03

May 16, 2005 0955 9.5 885 6.4 0.03

June 1, 2005 1020 7.2 890 6.4 0.04

June 15, 20051 0945 7.2 850 12.2 0.06

June 29, 2005 1000 6.1 804 7.1 0.07

July 13, 20051 0920 9.5 739 11.6 0.06

July 27, 2005 0850 6.4 745 8.0 0.09

August 10, 20051 0855 4.7 753 5.4 0.07

August 30, 20051 0945 6.9 733 11.8 0.06

September 7, 2005 1030 4.6 730 7.4 <0.03

October 13, 2005 1030 9.4 745 9.5 <0.03
Results of Linear Regression Analysis for Selected Constituents 37

Date Time, in 

hhmm

Dissolved oxygen, 

in mg/L

Specific conductance, 

in μS/cm at 25°C

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Dissolved 

phosphorus, 

in mg/L

October 27, 2005 1005 8.5 751 9.2 <0.03

January 11, 2006 1210 13.8 792 19.7 <0.03

March 1, 2006 1105 14.9 820 19.1 <0.03

March 29, 2006 1005 12.7 833 23.0 <0.03

April 25, 2006 1245 8.5 857 8.5 0.04

May 17, 2006 1055 10.2 845 11.7 0.04

May 31, 2006 1140 9.0 849 8.9 <0.03

June 14, 2006 1045 6.5 852 6.6 <0.03

June 28, 2006 0955 8.0 845 6.9 <0.03

July 13, 20061 0940 6.8 859 5.5 0.10

July 26, 2006 1115 7.2 847 10.7 0.03

August 10, 2006 1005 8.6 844 10.2 0.03

August 22, 2006 1000 4.3 837 6.6 0.07

September 6, 2006 1020 9.2 830 14.7 <0.03

September 20, 20061 1015 7.5 840 9.9 0.04

October 11, 2006 0950 8.0 854 7.3 <0.03

October 25, 2006 0945 9.0 866 6.8 0.04

December 12, 2006 1115 13.2 907 10.2 <0.03

February 7, 2007 1115 13.6 822 6.9 0.04

March 7, 2007 1135 12.3 940 33.6 <0.03

April 9, 2007 1045 10.0 884 17.2 <0.03

May 8, 20071 0940 7.2 845 3.1 0.04

May 31, 2007 1020 6.8 754 4.8 0.11

June 13, 2007 1140 6.5 728 5.0 0.11

June 25, 2007 1000 6.3 739 5.1 0.11

July 9, 2007 0950 6.6 732 7.7 0.16

July 23, 2007 1020 6.2 732 10.6 0.13

August 7, 2007 0930 5.7 748 9.9 0.13

August 15, 2007 0955 6.9 746 8.5 0.06

August 28, 2007 1055 6.5 765 7.0 0.09

September 12, 2007 1025 7.7 776 8.4 0.06

September 24, 2007 1025 7.5 779 9.0 0.06

October 15, 2007 1005 8.0 792 7.1 0.04

October 29, 2007 1045 8.9 792 7.1 0.06

November 13, 2007 1040 9.7 806 10.6 <0.03

December 19, 2007 1100 12.6 837 5.8 0.06

February 11, 2008 1100 13.6 866 12.5 <0.03

February 14, 2008 1105 13.6 869 12.8 <0.03

February 15, 2008 1025 13.2 867 14.2 <0.03

March 10, 2008 1125 13.3 864 25.6 <0.03

April 1, 2008 0955 11.2 861 29.6 <0.03

Table 12. Dissolved phosphorus linear regression dataset using dissolved oxygen, specific conductance, and fluorescence as 

explanatory variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; mg/L, milligrams per liter; μS/cm, microsiemens per centimeter; °C, degrees Celsius; YSI, Yellow Springs Instruments; <, less than]
38 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 12. Dissolved phosphorus linear regression dataset using dissolved oxygen, specific conductance, and fluorescence as 

explanatory variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; mg/L, milligrams per liter; μS/cm, microsiemens per centimeter; °C, degrees Celsius; YSI, Yellow Springs Instruments; <, less than]

Date Time, in 

hhmm

Dissolved oxygen, 

in mg/L

Specific conductance, 

in μS/cm at 25°C

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Dissolved 

phosphorus, 

in mg/L

April 16, 2008 1140 10.5 864 10.7 0.03

April 29, 2008 1035 8.8 861 8.1 0.04

May 13, 2008 1020 8.8 853 5.7 0.07

June 3, 2008 0945 6.8 812 5.9 0.08

June 18, 2008 0920 6.0 802 6.8 0.05

June 25, 20081 1155 6.4 806 11.1 0.04

July 7, 2008 1035 6.8 796 5.3 0.07

July 21, 2008 1050 7.5 804 11.7 0.08

July 28, 2008 0955 6.7 797 24.4 0.06

August 4, 2008 1020 6.3 802 6.2 0.07

August 18, 2008 0945 5.4 808 3.7 0.09

September 2, 2008 1000 7.5 808 5.1 0.06

September 17, 2008 1000 8.0 778 4.9 0.07

October 1, 2008 1055 8.4 781 5.2 0.06

October 15, 2008 1150 8.4 752 5.7 0.05

November 4, 2008 0950 9.4 784 8.0 0.06

December 2, 2008 0950 11.6 805 7.1 0.06

January 6, 2009 1010 13.2 842 23.1 <0.03

January 20, 2009 1010 15.2 836 31.2 <0.03

February 2, 20091 1000 15.2 837 49.4 0.04

February 18, 2009 1010 11.9 819 23.0 <0.03

February 25, 2009 1030 12.3 825 23.7 <0.03

March 3, 2009 1050 12.3 843 24.4 <0.03

March 9, 2009 1050 11.9 841 22.0 <0.03

March 16, 2009 1100 11.6 841 23.1 <0.03

March 25, 2009 1020 10.2 847 23.5 <0.03

April 8, 2009 1010 10.9 848 16.2 0.03

April 29, 2009 1030 8.1 842 4.9 0.05

May 27, 2009 1140 8.1 765 7.6 0.08

June 9, 2009 1100 6.5 768 14.8 0.09

June 23, 2009 1045 7.1 772 15.5 0.11

July 7, 2009 1100 5.6 759 9.1 0.06

July 21, 2009 1020 5.3 763 3.6 0.08

August 5, 2009 1030 5.9 773 7.7 0.08

August 24, 2009 1000 6.1 778 6.9 0.08

September 2, 2009 1030 6.6 785 6.2 0.06

September 16, 2009 1030 6.5 773 4.3 0.08

October 5, 2009 1100 8.0 773 6.2 0.07

October 19, 2009 1100 9.9 781 6.2 0.09

November 23, 2009 1100 9.9 803 6.5 0.08

December 16, 2009 1140 12.6 838 4.5 0.09

1

Data point removed from final analysis because of issues with phosphorus analysis. The filtered orthophosphate value was larger than the dissolved 

phosphorus value.
Results of Linear Regression Analysis for Selected Constituents 39

Table 13. Chlorophyll-a linear regression dataset using reservoir elevation, water temperature, and fluorescence as explanatory 

variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; YSI, Yellow Springs Instruments]

Date Time, in 

hhmm

Reservoir elevation1, 

in feet 

Water temperature, 

in degrees Celsius

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Chlorophyll-a, in 

micrograms per liter

December 3, 2001 1210 1,418 6.9 6.8 3.0

March 19, 2002 1400 1,418 5.8 0.2 10.4

June 19, 2002 0955 1,421 22.6 18.1 6.1

July 11, 2002 1005 1,421 26.9 12.8 6.2

August 7, 2002 1000 1,420 27.2 5.2 3.1

September 4, 2002 1015 1,420 25.9 7.3 5.2

September 25, 2002 1010 1,420 20.8 8.0 6.7

January 21, 2003 1210 1,422 1.4 13.7 6.8

January 23, 2003 1055 1,422 0.3 24.2 7.3

February 10, 2003 1250 1,422 1.2 4.7 8.7

March 3, 2003 1025 1,422 1.8 24.5 7.6

March 12, 2003 1200 1,422 4.2 11.8 9.0

March 13, 2003 1200 1,422 4.4 6.1 7.0

June 20, 2003 0905 1,422 22.5 5.3 1.9

July 7, 2003 0940 1,421 25.7 22.8 12.2

July 17, 2003 1155 1,421 27.5 9.6 25.6

July 28, 2003 1150 1,421 28.6 8.3 14.7

March 10, 2004 1115 1,423 6.4 21.7 12.2

April 8, 2004 1015 1,422 14.7 14.1 7.6

May 5, 2004 1115 1,422 16.1 2.0 2.2

June 3, 2004 1000 1,421 21.7 6.5 17.0

July 15, 2004 0950 1,422 26.0 2.9 3.7

August 12, 2004 0915 1,422 24.9 9.4 12.5

August 27, 2004 1115 1,422 24.4 3.3 11.6

September 9, 2004 935 1,422 23.3 3.1 11.8

March 16, 2005 1015 1,422 8.5 25.3 18.7

April 13, 2005 1000 1,422 13.4 9.3 7.5

May 4, 2005 1120 1,422 14.7 4.1 3.2

May 16, 2005 955 1,422 18.4 6.4 14.9

June 1, 2005 1020 1,422 22.1 6.4 0.8

June 15, 2005 0945 1,424 23.5 12.2 6.6

June 29, 2005 1000 1,423 25.2 7.1 3.7

July 13, 2005 0920 1,423 26.8 11.6 19.7

July 27, 2005 0850 1,422 25.7 8.0 24.4

August 10, 2005 0855 1,421 26.2 5.4 4.0

August 30, 2005 0945 1,422 26.6 11.8 12.1

September 7, 2005 1030 1,422 25.1 7.4 12.4

October 13, 2005 1030 1,421 18.1 9.5 18.8

October 27, 2005 1005 1,421 14.4 9.2 8.1

January 11, 2006 1210 1,421 4.2 19.7 21.8

March 1, 2006 1105 1,421 3.7 19.1 19.8
40 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 13. Chlorophyll-a linear regression dataset using reservoir elevation, water temperature, and fluorescence as explanatory 

variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments]

Date Time, in 

hhmm

Reservoir elevation1, 

in feet 

Water temperature, 

in degrees Celsius

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Chlorophyll-a, in 

micrograms per liter

March 29, 2006 1005 1,422 7.6 23.0 19.2

April 25, 2006 1245 1,421 17.0 8.5 11.1

May 17, 2006 1055 1,422 18.1 11.7 16.3

May 31, 2006 1140 1,422 23.6 8.9 16.1

June 14, 2006 1045 1,421 24.3 6.6 10.8

June 28, 2006 0955 1,421 25.0 6.9 12.2

July 13, 2006 0940 1,421 25.3 5.5 14.0

July 26, 2006 1115 1,420 26.7 10.7 20.5

August 10, 2006 1005 1,420 27.6 10.2 27.5

August 22, 2006 1000 1,420 26.8 6.6 23.6

September 6, 2006 1020 1,420 24.2 14.7 41.2

September 20, 2006 1015 1,419 20.8 9.9 31.3

October 11, 2006 0950 1,419 17.1 7.3 21.9

October 25, 2006 0945 1,418 12.7 6.8 16.2

December 12, 2006 1115 1,418 3.8 10.2 15.0

February 7, 2007 1115 1,419 5.6 6.9 10.4

March 7, 2007 1135 1,419 6.7 33.6 20.2

April 9, 2007 1045 1,421 11.4 17.2 18.4

May 8, 2007 0940 1,424 18.2 3.1 1.2

May 31, 2007 1020 1,425 21.3 4.8 2.7

June 13, 2007 1140 1,424 22.6 5.0 2.2

June 25, 2007 1000 1,422 24.4 5.1 4.2

July 9, 2007 0950 1,423 25.2 7.7 12.4

July 23, 2007 1020 1,423 26.1 10.6 20.6

August 7, 2007 0930 1,423 27.0 9.9 18.8

August 15, 2007 0955 1,422 27.9 8.5 14.8

August 28, 2007 1055 1,421 26.6 7.0 15.6

September 12, 2007 1025 1,421 23.7 8.4 18.6

September 24, 2007 1025 1,421 22.1 9.0 16.6

October 15, 2007 1005 1,420 18.9 7.1 11.7

October 29, 2007 1045 1,420 13.9 7.1 9.7

November 13, 2007 1040 1,420 11.8 10.6 12.7

December 19, 2007 1100 1,420 1.8 5.8 4.5

February 11, 2008 1100 1,421 1.8 12.5 10.8

February 14, 2008 1105 1,421 2.0 12.8 7.9

April 1, 2008 0955 1,422 9.1 29.6 24.9

April 16, 2008 1140 1,422 10.1 10.7 9.6

April 29, 2008 1035 1,423 13.8 8.1 3.5

May 13, 2008 1020 1,424 16.0 5.7 2.5

June 3, 2008 0945 1,423 22.7 5.9 7.4

June 18, 2008 0920 1,423 23.5 6.8 6.5
Results of Linear Regression Analysis for Selected Constituents 41

Table 13. Chlorophyll-a linear regression dataset using reservoir elevation, water temperature, and fluorescence as explanatory 

variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments]

Date Time, in 

hhmm

Reservoir elevation1, 

in feet 

Water temperature, 

in degrees Celsius

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Chlorophyll-a, in 

micrograms per liter

July 7, 2008 1035 1,422 25.3 5.3 11.0

July 21, 2008 1050 1,422 26.2 11.7 26.9

July 28, 2008 0955 1,422 26.5 24.4 22.3

August 18, 2008 0945 1,421 24.6 3.7 7.2

September 2, 2008 1000 1,421 25.1 5.1 10.9

September 17, 2008 1000 1,422 20.2 4.9 11.2

October 1, 2008 1055 1,422 21.6 5.2 10.5

October 15, 2008 1150 1,422 16.8 5.7 8.4

November 4, 2008 0950 1,422 12.7 8.0 3.7

December 2, 2008 0950 1,422 5.3 7.1 6.7

January 6, 2009 1010 1,422 0.8 23.1 21.1

January 20, 2009 1010 1,422 0.9 31.2 22.4

February 2, 2009 1000 1,422 0.7 49.4 47.2

February 18, 2009 1010 1,422 5.2 23.0 19.8

February 25, 2009 1030 1,422 5.4 23.7 21.9

March 3, 2009 1050 1,422 4.4 24.4 24.9

March 9, 2009 1050 1,422 6.4 22.0 22.6

March 12, 2009 1120 1,422 5.9 24.2 23.5

March 16, 2009 1100 1,422 6.3 23.1 27.5

March 25, 2009 1020 1,422 11.0 23.5 25.9

April 8, 2009 1010 1,422 7.6 16.2 20.8

April 29, 2009 1030 1,424 14.9 4.9 2.2

May 27, 2009 1140 1,424 22.1 7.6 10.6

June 9, 2009 1100 1,422 22.0 14.8 2.6

June 23, 2009 1045 1,422 25.0 15.5 29.1

July 7, 2009 1100 1,422 25.8 9.1 20.2

July 21, 2009 1020 1,421 25.4 3.6 3.4

August 5, 2009 1030 1,421 25.1 7.7 3.8

August 24, 2009 1000 1,421 24.3 6.9 5.8

September 2, 2009 1030 1,421 23.2 6.2 7.4

September 16, 2009 1030 1,422 22.0 4.3 2.4

October 5, 2009 1100 1,422 17.3 6.2 4.0

October 19, 2009 1100 1,422 12.2 6.2 2.5

November 23, 2009 1100 1,422 9.8 6.5 2.8

December 16, 2009 1140 1,422 0.9 4.5 2.8

1

Reservoir elevation above National Geodetic Vertical Datum of 1929.
42 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 14. Actinomycetes bacteria linear regression dataset using reservoir elevation, turbidity, and water temperature as explanatory 

variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; YSI, Yellow Springs Instruments; <, less than]

Date Time, in 

hhmm

Reservoir elevation1, 

in feet 

Turbidity, in formazin 

nephelometric units 

(YSI model 6026 sensor)

Water temperature, 

in degrees Celsius

Actinomycetes bacteria, 

in colonies per milliliter

April 13, 2005 1000 1,422 18 13.4 6.0

May 4, 2005 1120 1,422 10 14.7 3.0

May 16, 2005 0955 1,422 8.1 18.4 1.0

June 1, 2005 1020 1,422 8.1 22.1 2.0

June 15, 2005 0945 1,424 14 23.5 12

June 29, 2005 1000 1,423 25 25.2 9.0

July 13, 2005 0920 1,423 15 26.8 5.0

July 27, 2005 0850 1,422 20 25.7 2.0

August 10, 2005 0855 1,421 12 26.2 2.0

August 30, 2005 0945 1,422 22 26.6 8.0

September 7, 2005 1030 1,422 12 25.1 2.0

October 13, 2005 1030 1,421 18 18.1 2.0

October 27, 2005 1005 1,421 20 14.4 5.0

January 11, 2006 1210 1,421 8.9 4.2 4.0

March 1, 2006 1105 1,421 3.2 3.7 1.0

March 29, 2006 1005 1,422 10 7.6 7.0

April 25, 2006 1245 1,421 34 17.0 8.0

May 17, 2006 1055 1,422 20 18.1 21

May 31, 2006 1140 1,422 10 23.6 4.0

June 14, 2006 1045 1,421 25 24.3 4.0

June 28, 2006 0955 1,421 13 25.0 1.0

July 13, 2006 0940 1,421 15 25.3 2.0

July 26, 2006 1115 1,420 14 26.7 1.0

August 10, 2006 1005 1,420 17 27.6 <1.0

August 22, 2006 1000 1,420 21 26.8 <1.0

September 6, 2006 1020 1,420 10 24.2 2.0

September 20, 2006 1015 1,419 22 20.8 6.0

September 26, 2006 1130 1,419 10 19.7 1.0

October 11, 2006 0950 1,419 29 17.1 4.0

October 25, 2006 0945 1,418 35 12.7 <1.0

December 12, 2006 1115 1,418 9.2 3.8 1.0

February 7, 2007 1115 1,419 14 5.6 1.0

March 7, 2007 1135 1,419 14 6.7 2.0

April 9, 2007 1045 1,421 23 11.4 30

May 8, 2007 0940 1,424 11 18.2 24

May 31, 2007 1020 1,425 27 21.3 60

June 13, 2007 1140 1,424 35 22.6 63

June 25, 2007 1000 1,422 25 24.4 24

July 9, 2007 0950 1,423 21 25.2 16

July 23, 2007 1020 1,423 21 26.1 3.0

August 7, 2007 0930 1,423 27 27.0 8.0
Results of Linear Regression Analysis for Selected Constituents 43

Date Time, in 

hhmm

Reservoir elevation1, 

in feet 

Turbidity, in formazin 

nephelometric units 

(YSI model 6026 sensor)

Water temperature, 

in degrees Celsius

Actinomycetes bacteria, 

in colonies per milliliter

August 15, 2007 0955 1,422 23 27.9 8.0

August 28, 2007 1055 1,421 28 26.6 12

September 12, 2007 1025 1,421 13 23.7 4.0

September 24, 2007 1025 1,421 17 22.1 8.0

October 15, 2007 1005 1,420 29 18.9 14

October 29, 2007 1045 1,420 26 13.9 11

November 13, 2007 1040 1,420 25 11.8 4.0

December 19, 2007 1100 1,420 8.1 1.8 6.0

February 11, 2008 1100 1,421 2.7 1.8 4.0

March 10, 2008 1125 1,422 9.0 4.3 8.0

April 1, 2008 0955 1,422 19 9.1 12

April 16, 2008 1140 1,422 28 10.1 17

April 29, 2008 1035 1,423 42 13.8 28

May 13, 2008 1020 1,424 30 16.0 17

June 3, 2008 0945 1,423 22 22.7 17

June 18, 2008 0920 1,423 31 23.5 15

June 25, 2008 1155 1,423 17 24.7 9.0

July 7, 2008 1035 1,422 18 25.3 12

July 21, 2008 1050 1,422 26 26.2 <1.0

July 28, 2008 0955 1,422 18 26.5 5.0

August 4, 2008 1020 1,421 18 26.8 3.0

August 18, 2008 0945 1,421 15 24.6 3.0

September 2, 2008 1000 1,421 16 25.1 2.0

September 17, 2008 1000 1,422 20 20.2 29

October 1, 2008 1055 1,422 16 21.6 8.0

October 15, 2008 1150 1,422 48 16.8 14

November 4, 2008 0950 1,422 27 12.7 15

December 2, 2008 0950 1,422 18 5.3 9.0

January 6, 2009 1010 1,422 7.3 0.8 2.0

January 20, 2009 1010 1,422 8.5 0.9 10

February 2, 2009 1000 1,422 6.9 0.7 3.0

February 18, 2009 1010 1,422 15 5.2 8.0

February 25, 2009 1030 1,422 10 5.4 7.0

March 3, 2009 1050 1,422 10 4.4 4.0

March 9, 2009 1050 1,422 10 6.4 1.0

March 16, 2009 1100 1,422 9.3 6.3 5.0

March 25, 2009 1020 1,422 25 11.0 7.0

April 8, 2009 1010 1,422 24 7.6 13

April 29, 2009 1030 1,424 34 14.9 34

May 27, 2009 1140 1,424 36 22.1 37

June 9, 2009 1100 1,422 25 22.0 12

Table 14. Actinomycetes bacteria linear regression dataset using reservoir elevation, turbidity, and water temperature as explanatory 

variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments; <, less than]
44 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Date Time, in 

hhmm

Reservoir elevation1, 

in feet 

Turbidity, in formazin 

nephelometric units 

(YSI model 6026 sensor)

Water temperature, 

in degrees Celsius

Actinomycetes bacteria, 

in colonies per milliliter

June 23, 2009 1045 1,422 17 25.0 7.0

July 7, 2009 1100 1,422 12 25.8 3.0

July 21, 2009 1020 1,421 18 25.4 5.0

August 5, 2009 1030 1,421 14 25.1 7.0

August 24, 2009 1000 1,421 16 24.3 7.0

September 2, 2009 1030 1,421 20 23.2 5.0

September 16, 2009 1030 1,422 19 22.0 11

October 5, 2009 1100 1,422 24 17.3 13

October 19, 2009 1100 1,422 22 12.2 9.0

November 23, 2009 1100 1,422 17 9.8 7.0

December 16, 2009 1140 1,422 16 0.9 7.0

1

Reservoir elevation above National Geodetic Vertical Datum of 1929.

Table 14. Actinomycetes bacteria linear regression dataset using reservoir elevation, turbidity, and water temperature as explanatory 

variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments; <, less than]
Results of Linear Regression Analysis for Selected Constituents 45

Table 15. Anabaena spp. linear regression dataset using reservoir elevation and water temperature as explanatory 

variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes]

Date Time, in hhmm Reservoir elevation1

, 

in feet 

Water temperature, 

in degrees Celsius

Anabaena spp., in 

micrometers cubed per milliliter

June 22, 2001 1155 1,422 24.1 34,721

August 29, 2001 1125 1,419 26.2 966

May 15, 2002 1115 1,419 17.9 531

July 11, 2002 1005 1,421 26.9 1,455

August 7, 2002 1000 1,420 27.2 0

September 4, 2002 1015 1,420 25.9 5,194

January 21, 2003 1210 1,422 1.4 1,190

January 23, 2003 1055 1,422 0.3 1,169

February 10, 2003 1250 1,422 1.2 83

March 3, 2003 1025 1,422 1.8 48

March 12, 2003 1200 1,422 4.2 11,485

March 13, 2003 1200 1,422 4.4 4,768

June 17, 2003 1045 1,422 25.0 54,010

June 20, 2003 0905 1,422 22.5 3,748

July 7, 2003 0940 1,421 25.7 9,622

July 17, 2003 1155 1,421 27.5 28,872

July 28, 2003 1150 1,421 28.6 72,403

March 10, 2004 1115 1,423 6.4 0

April 8, 2004 1015 1,422 14.7 0

May 5, 2004 1115 1,422 16.1 0

July 15, 2004 0950 1,422 26.0 12,126

August 12, 2004 0915 1,422 24.9 571

August 27, 2004 1115 1,422 24.4 2,440

September 9, 2004 0935 1,422 23.3 94

February 2, 2005 1035 1,422 1.8 0

March 16, 2005 1015 1,422 8.5 0

April 13, 2005 1000 1,422 13.4 0

May 4, 2005 1120 1,422 14.7 0

May 16, 2005 0955 1,422 18.4 0

June 1, 2005 1020 1,422 22.1 108

June 15, 2005 0945 1,424 23.5 14,916

June 29, 2005 1000 1,423 25.2 771

July 13, 2005 0920 1,423 26.8 33,502

July 27, 2005 0850 1,422 25.7 25,548

August 10, 2005 0855 1,421 26.2 64

October 27, 2005 1005 1,421 14.4 0

January 11, 2006 1210 1,421 4.2 0

March 1, 2006 1105 1,421 3.7 0

March 29, 2006 1005 1,422 7.6 0

April 25, 2006 1245 1,421 17.0 0

May 17, 2006 1055 1,422 18.1 1,481
46 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Date Time, in hhmm Reservoir elevation1

, 

in feet 

Water temperature, 

in degrees Celsius

Anabaena spp., in 

micrometers cubed per milliliter

May 31, 2006 1140 1,422 23.6 708,775

June 14, 2006 1045 1,421 24.3 54

June 28, 2006 0955 1,421 25.0 33,348

July 13, 2006 0940 1,421 25.3 75,065

July 26, 2006 1115 1,420 26.7 102,349

August 10, 2006 1005 1,420 27.6 83,432

August 22, 2006 1000 1,420 26.8 53,810

September 6, 2006 1020 1,420 24.2 202,296

September 20, 2006 1015 1,419 20.8 2,363

October 11, 2006 0950 1,419 17.1 274,097

October 25, 2006 0945 1,418 12.7 201,177

December 12, 2006 1115 1,418 3.8 18,196

February 7, 2007 1115 1,419 5.6 70

March 7, 2007 1135 1,419 6.7 0

April 9, 2007 1045 1,421 11.4 0

May 8, 2007 0940 1,424 18.2 0

May 31, 2007 1020 1,425 21.3 233

June 13, 2007 1140 1,424 22.6 0

June 25, 2007 1000 1,422 24.4 542

July 9, 2007 0950 1,423 25.2 6,179

July 23, 2007 1020 1,423 26.1 11,521

August 7, 2007 0930 1,423 27.0 5,047

August 15, 2007 0955 1,422 27.9 17,177

August 28, 2007 1055 1,421 26.6 55,286

September 12, 2007 1025 1,421 23.7 96,876

September 24, 2007 1025 1,421 22.1 81,340

October 15, 2007 1005 1,420 18.9 29,020

October 29, 2007 1045 1,420 13.9 5,719

November 13, 2007 1040 1,420 11.8 0

December 19, 2007 1100 1,420 1.8 2,243

February 11, 2008 1100 1,421 1.8 0

February 14, 2008 1105 1,421 2.0 0

February 15, 2008 1025 1,421 1.8 0

March 10, 2008 1125 1,422 4.3 0

April 1, 2008 0955 1,422 9.1 0

April 16, 2008 1140 1,422 10.1 0

April 29, 2008 1035 1,423 13.8 0

May 13, 2008 1020 1,424 16.0 0

June 3, 2008 0945 1,423 22.7 0

June 18, 2008 0920 1,423 23.5 5,719

July 7, 2008 1035 1,422 25.3 705

Table 15. Anabaena spp. linear regression dataset using reservoir elevation and water temperature as explanatory 

variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes]
Results of Linear Regression Analysis for Selected Constituents 47

Date Time, in hhmm Reservoir elevation1

, 

in feet 

Water temperature, 

in degrees Celsius

Anabaena spp., in 

micrometers cubed per milliliter

July 21, 2008 1050 1,422 26.2 63,356

August 4, 2008 1020 1,421 26.8 0

August 18, 2008 0945 1,421 24.6 21,936

September 17, 2008 1000 1,422 20.2 779

October 1, 2008 1055 1,422 21.6 3,173

October 15, 2008 1150 1,422 16.8 1,723

November 4, 2008 0950 1,422 12.7 0

December 2, 2008 0950 1,422 5.3 0

January 6, 2009 1010 1,422 0.8 0

January 20, 2009 1010 1,422 0.9 2,308

February 2, 2009 1000 1,422 0.7 0

February 18, 2009 1010 1,422 5.2 0

February 25, 2009 1030 1,422 5.4 0

March 3, 2009 1050 1,422 4.4 0

March 9, 2009 1050 1,422 6.4 0

March 16, 2009 1100 1,422 6.3 0

March 25, 2009 1020 1,422 11.0 0

April 8, 2009 1010 1,422 7.6 0

April 29, 2009 1030 1,424 14.9 0

May 27, 2009 1140 1,424 22.1 0

June 9, 2009 1100 1,422 22.0 2,341

June 23, 2009 1045 1,422 25.0 0

July 7, 2009 1100 1,422 25.8 30,108

July 21, 2009 1020 1,421 25.4 25,456

August 5, 2009 1030 1,421 25.1 12,847

August 24, 2009 1000 1,421 24.3 0

September 2, 2009 1030 1,421 23.2 0

September 16, 2009 1030 1,422 22.0 0

October 5, 2009 1100 1,422 17.3 0

October 19, 2009 1100 1,422 12.2 0

November 23, 2009 1100 1,422 9.8 0

December 16, 2009 1140 1,422 0.9 0

1

Reservoir elevation above National Geodetic Vertical Datum of 1929.

Table 15. Anabaena spp. linear regression dataset using reservoir elevation and water temperature as explanatory 

variables for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes]
48 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 16. Geosmin linear regression dataset using turbidity and pH as explanatory variables for Cheney Reservoir near Cheney, 

Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; YSI, Yellow Springs Instruments; <, less than]

Date Time, in 

hhmm

Turbidity, in formazin nephelometric 

units (YSI model 6026 sensor) pH, in standard units Geosmin, in micrograms per liter

May 3, 2001 1055 38 8.5 <0.003

June 4, 2001 1030 20 8.4 0.005

June 22, 2001 1155 22 8.7 0.011

July 24, 2001 1055 18 8.3 0.003

August 29, 2001 1125 13 8.2 0.004

May 15, 2002 1115 27 8.4 <0.003

June 19, 2002 0955 36 8.3 <0.002

July 11, 2002 1005 12 8.7 0.007

August 7, 2002 1000 13 8.4 0.004

September 4, 2002 1015 12 8.4 0.005

September 25, 2002 1010 20 8.4 0.006

January 21, 2003 1210 6.5 8.9 0.029

January 23, 2003 1055 3.9 8.8 0.024

February 10, 2003 1250 1.6 8.0 0.022

March 3, 2003 1025 3.3 8.6 0.012

March 12, 2003 1200 4.5 8.8 0.006

March 13, 2003 1200 5.6 8.7 0.005

June 17, 2003 1045 14 8.9 0.015

June 20, 2003 0905 3.5 8.2 0.063

July 7, 2003 0940 24 8.6 0.007

July 17, 2003 1155 27 8.7 0.113

March 10, 2004 1115 22 8.3 <0.005

April 8, 2004 1015 31 8.2 <0.005

May 5, 2004 1115 21 8.0 <0.005

June 3, 2004 1000 21 8.2 <0.005

July 15, 2004 0950 7.0 8.3 0.005

August 12, 2004 0915 23 8.2 <0.005

August 27, 2004 1115 32 8.4 <0.005

September 9, 2004 0935 26 7.7 <0.005

February 2, 2005 1035 2.7 8.9 0.024

March 16, 2005 1015 10 8.7 <0.005

April 13, 2005 1000 18 8.4 <0.005

May 4, 2005 1120 10 8.5 <0.005

May 16, 2005 0955 8.1 8.5 <0.005

June 1, 2005 1020 8.1 8.3 <0.005

June 15, 2005 0945 14 8.1 0.043

June 29, 2005 1000 25 8.2 <0.005

July 13, 2005 0920 15 8.8 0.043

July 27, 2005 0850 20 8.2 0.064

August 10, 2005 0855 12 8.2 <0.005

August 30, 2005 0945 22 8.3 <0.005

September 7, 2005 1030 12 8.1 <0.005

October 13, 2005 1030 18 8.7 <0.005
Results of Linear Regression Analysis for Selected Constituents 49

Date Time, in 

hhmm

Turbidity, in formazin nephelometric 

units (YSI model 6026 sensor) pH, in standard units Geosmin, in micrograms per liter

October 27, 2005 1005 20 8.5 <0.005

January 11, 2006 1210 8.9 8.6 <0.005

March 1, 2006 1105 3.2 8.6 0.005

March 29, 2006 1005 10 8.5 <0.005

April 25, 2006 1245 34 8.3 <0.005

May 17, 2006 1055 20 8.4 <0.005

May 31, 2006 1140 10 8.3 <0.005

June 14, 2006 1045 25 8.0 <0.005

June 28, 2006 0955 13 8.7 <0.005

July 13, 2006 0940 15 8.3 <0.005

July 26, 2006 1115 14 8.3 <0.005

August 10, 2006 1005 17 8.5 <0.005

August 22, 2006 1000 21 8.2 <0.005

September 6, 2006 1020 10 8.4 0.005

September 20, 2006 1015 22 8.3 <0.005

September 26, 2006 1130 10 8.6 <0.005

September 28, 2006 1120 28 8.7 <0.005

September 29, 2006 1130 28 8.6 <0.005

October 11, 2006 0950 29 8.4 <0.005

October 25, 2006 0945 35 8.3 0.005

December 12, 2006 1115 9.2 8.5 <0.002

February 7, 2007 1115 14 8.5 0.032

March 7, 2007 1135 14 8.6 0.020

April 9, 2007 1045 23 8.6 <0.005

May 8, 2007 0940 11 8.4 <0.005

May 31, 2007 1020 27 8.0 <0.005

June 13, 2007 1140 35 8.0 <0.005

June 25, 2007 1000 25 8.2 <0.002

July 9, 2007 0950 21 8.3 <0.002

July 23, 2007 1020 21 8.4 <0.002

August 7, 2007 0930 27 8.4 0.0024

August 15, 2007 0955 23 8.5 <0.002

August 28, 2007 1055 28 8.2 <0.002

September 12, 2007 1025 13 8.5 <0.005

September 24, 2007 1025 17 8.4 0.0022

October 15, 2007 1005 29 8.4 <0.002

October 29, 2007 1045 26 8.4 <0.002

November 13, 2007 1040 25 8.6 <0.002

December 19, 2007 1100 8.1 8.5 <0.002

February 11, 2008 1100 2.7 8.6 0.0058

March 10, 2008 1125 9.0 8.6 0.0083

April 1, 2008 0955 19 8.6 0.0106

April 16, 2008 1140 28 8.3 0.0139

Table 16. Geosmin linear regression dataset using turbidity and pH as explanatory variables for Cheney Reservoir near Cheney, 

Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments; <, less than]
50 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Date Time, in 

hhmm

Turbidity, in formazin nephelometric 

units (YSI model 6026 sensor) pH, in standard units Geosmin, in micrograms per liter

April 29, 2008 1035 42 8.1 <0.002

May 13, 2008 1020 30 8.2 <0.002

June 3, 2008 0945 22 8.2 <0.002

June 18, 2008 0920 31 8.2 <0.002

June 25, 2008 1200 17 8.3 0.0061

July 7, 2008 1035 18 8.4 0.0043

July 21, 2008 1050 26 8.4 <0.002

July 28, 2008 0955 18 8.3 <0.002

August 4, 2008 1200 18 8.4 <0.002

August 18, 2008 0945 15 8.2 <0.002

September 2, 2008 1000 16 8.5 0.005

September 17, 2008 1000 20 8.4 <0.002

October 1, 2008 1055 16 8.4 0.003

October 15, 2008 1150 48 8.3 <0.002

November 4, 2008 0950 27 8.3 <0.002

December 2, 2008 0950 18 8.7 <0.002

January 6, 2009 1010 7.3 8.2 0.003

January 20, 2009 1010 8.5 8.6 <0.002

February 2, 2009 1000 6.9 8.6 <0.002

February 18, 2009 1010 15 8.7 0.010

February 25, 2009 1030 10 8.8 0.008

March 3, 2009 1050 10 8.8 0.017

March 9, 2009 1050 10 8.7 0.012

March 12, 2009 1120 16 8.7 0.011

March 16, 2009 1100 9.3 8.6 0.009

March 25, 2009 1020 25 8.6 0.009

April 8, 2009 1010 24 8.6 0.004

April 29, 2009 1030 34 8.1 <0.002

May 27, 2009 1140 36 8.3 <0.002

June 9, 2009 1100 25 8.1 <0.002

June 23, 2009 1045 17 8.4 0.005

July 7, 2009 1100 12 8.6 0.014

July 21, 2009 1020 18 8.5 0.005

August 5, 2009 1030 14 8.4 <0.002

August 24, 2009 1000 16 8.6 <0.002

September 2, 2009 1030 20 8.3 <0.002

September 16, 2009 1030 19 8.3 <0.002

October 5, 2009 1100 24 8.5 <0.002

October 19, 2009 1100 22 8.4 <0.002

November 23, 2009 1100 17 8.5 <0.002

December 16, 2009 1140 16 8.6 <0.002

Table 16. Geosmin linear regression dataset using turbidity and pH as explanatory variables for Cheney Reservoir near Cheney, 

Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; YSI, Yellow Springs Instruments; <, less than]
Results of Linear Regression Analysis for Selected Constituents 51

Table 17. Microcystin linear regression dataset using seasonal data and fluorescence as explanatory variables for Cheney Reservoir 

near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; sin, sine; D, day of year; cos, cosine; YSI, Yellow Springs Instruments; <, less than]

Date Time, in 

hhmm sin(2πD/365) cos(2πD/365)

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Microcystin, in 

micrograms per liter

June 1, 2005 1020 0.5012 -0.8653 6.4 <0.1

June 15, 2005 0945 0.2802 -0.9599 12.2 0.1

June 29, 2005 1000 0.0430 -0.9991 7.1 <0.1

July 13, 2005 0920 -0.1967 -0.9805 11.6 1.7

July 27, 2005 0850 -0.4250 -0.9052 8.0 0.6

August 10, 2005 0855 -0.6288 -0.7776 5.4 0.2

August 30, 2005 0945 -0.8543 -0.5197 11.8 2.3

September 7, 2005 1030 -0.9176 -0.3975 7.4 2.1

October 13, 2005 1030 -0.9778 0.2093 9.5 0.4

October 27, 2005 1005 -0.8996 0.4367 9.2 0.2

January 11, 2006 1210 0.1882 0.9821 19.7 <0.1

March 1, 2006 1105 0.8588 0.5124 19.1 <0.1

March 29, 2006 1005 0.9984 0.0559 23.0 <0.1

April 25, 2006 1245 0.9176 -0.3975 8.5 <0.1

May 17, 2006 1055 0.7056 -0.7086 11.7 <0.1

May 31, 2006 1140 0.5161 -0.8566 8.9 <0.1

June 14, 2006 1045 0.2967 -0.9550 6.6 <0.1

June 28, 2006 0955 0.0602 -0.9982 6.9 0.2

July 13, 2006 0940 -0.1967 -0.9805 5.5 0.2

July 26, 2006 1115 -0.4094 -0.9124 10.7 0.2

August 10, 2006 1005 -0.6288 -0.7776 10.2 1.6

August 22, 2006 1000 -0.7749 -0.6321 6.6 0.6

September 6, 2006 1020 -0.9106 -0.4133 14.7 2.6

September 20, 2006 1015 -0.9829 -0.1840 9.9 1.2

September 26, 2006 1130 -0.9967 -0.0817 11.8 3.6

September 28, 2006 1120 -0.9989 -0.0473 10.9 3.8

September 29, 2006 1130 -0.9995 -0.0301 9.7 3.4

October 11, 2006 0950 -0.9845 0.1755 7.3 0.7

October 25, 2006 0945 -0.9141 0.4054 6.8 0.3

December 12, 2006 1115 -0.3213 0.9470 10.2 <0.1

February 7, 2007 1115 0.6085 0.7936 6.9 <0.1

March 7, 2007 1135 0.9070 0.4211 33.6 <0.1

April 9, 2007 1045 0.9911 -0.1330 17.2 <0.1

May 8, 2007 0940 0.8065 -0.5913 3.1 0.1

May 31, 2007 1020 0.5161 -0.8566 4.8 0.2

June 13, 2007 1140 0.3131 -0.9497 5.0 <0.1

June 25, 2007 1000 0.1117 -0.9937 5.1 <0.1

July 9, 2007 0950 -0.1288 -0.9917 7.7 0.2

July 23, 2007 1020 -0.3617 -0.9323 10.6 0.6

August 7, 2007 0930 -0.5878 -0.8090 9.9 0.7

August 15, 2007 0955 -0.6933 -0.7207 8.5 1.4
52 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Date Time, in 

hhmm sin(2πD/365) cos(2πD/365)

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Microcystin, in 

micrograms per liter

August 28, 2007 1055 -0.8359 -0.5488 7.0 1.1

September 12, 2007 1025 -0.9484 -0.3172 8.4 0.6

September 24, 2007 1025 -0.9933 -0.1159 9.0 0.7

October 15, 2007 1005 -0.9701 0.2429 7.1 0.3

October 29, 2007 1045 -0.8841 0.4674 7.1 0.1

November 13, 2007 1040 -0.7354 0.6776 10.6 <0.1

December 19, 2007 1100 -0.2051 0.9787 5.8 <0.1

February 11, 2008 1100 0.6616 0.7498 12.5 0.1

February 14, 2008 1105 0.6995 0.7147 12.8 <0.1

February 15, 2008 1025 0.7117 0.7025 14.2 0.1

March 10, 2008 1125 0.9338 0.3577 25.6 <0.1

April 1, 2008 0955 0.9999 -0.0129 29.6 0.1

April 16, 2008 1140 0.9635 -0.2678 10.7 <0.1

April 29, 2008 1035 0.8800 -0.4750 8.1 <0.1

May 13, 2008 1020 0.7412 -0.6713 5.7 <0.1

June 3, 2008 0945 0.4559 -0.8900 5.9 <0.1

June 18, 2008 0920 0.2135 -0.9769 6.8 0.1

July 7, 2008 1035 -0.1117 -0.9937 5.3 0.8

July 21, 2008 1050 -0.3456 -0.9384 11.7 1.1

July 28, 2008 0955 -0.4559 -0.8900 24.4 0.4

August 4, 2008 1020 -0.5596 -0.8288 6.2 0.2

August 18, 2008 0945 -0.7412 -0.6713 3.7 0.2

September 2, 2008 1000 -0.8881 -0.4597 5.1 <0.1

September 17, 2008 1000 -0.9760 -0.2177 4.9 0.1

October 1, 2008 1055 -0.9998 0.0215 5.2 <0.1

October 15, 2008 1150 -0.9657 0.2595 5.7 <0.1

November 4, 2008 0950 -0.8215 0.5703 8.0 0.1

December 2, 2008 0950 -0.4635 0.8861 7.1 <0.1

January 6, 2009 1010 0.1031 0.9947 23.1 <0.1

January 20, 2009 1010 0.3375 0.9413 31.2 <0.1

February 2, 2009 1000 0.5380 0.8429 49.4 <0.1

February 18, 2009 1010 0.7470 0.6649 23.0 <0.1

February 25, 2009 1030 0.8215 0.5702 23.7 <0.1

March 3, 2009 1050 0.8759 0.4825 24.4 <0.1

March 9, 2009 1050 0.9210 0.3896 22.0 <0.1

March 12, 2009 1120 0.9399 0.3416 24.2 <0.1

March 16, 2009 1100 0.9611 0.2761 23.1 <0.1

March 25, 2009 1020 0.9922 0.1245 23.5 <0.1

April 8, 2009 1010 0.9933 -0.1159 16.2 <0.1

April 29, 2009 1030 0.8881 -0.4597 4.9 <0.1

May 27, 2009 1140 0.5738 -0.8190 7.6 <0.1

Table 17. Microcystin linear regression dataset using seasonal data and fluorescence as explanatory variables for Cheney Reservoir 

near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; sin, sine; D, day of year; cos, cosine; YSI, Yellow Springs Instruments; <, less than]
Results of Linear Regression Analysis for Selected Constituents 53

Date Time, in 

hhmm sin(2πD/365) cos(2πD/365)

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Microcystin, in 

micrograms per liter

June 9, 2009 1100 0.3777 -0.9259 14.8 <0.1

June 23, 2009 1045 0.1458 -0.9893 15.5 <0.1

July 7, 2009 1100 -0.0945 -0.9955 9.1 0.1

July 21, 2009 1020 -0.3294 -0.9442 3.6 <0.1

August 5, 2009 1030 -0.5596 -0.8288 7.7 <0.1

August 24, 2009 1000 -0.7962 -0.6050 6.9 <0.1

September 2, 2009 1030 -0.8800 -0.4749 6.2 <0.1

September 16, 2009 1030 -0.9679 -0.2512 4.3 2.9

October 5, 2009 1100 -0.9973 0.0731 6.2 <0.1

October 19, 2009 1100 -0.9511 0.3090 6.2 <0.1

November 23, 2009 1100 -0.6085 0.7936 6.5 <0.1

December 16, 2009 1140 -0.2553 0.9669 4.5 <0.1

Table 17. Microcystin linear regression dataset using seasonal data and fluorescence as explanatory variables for Cheney Reservoir 

near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; sin, sine; D, day of year; cos, cosine; YSI, Yellow Springs Instruments; <, less than]
54 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 2. S+® output of regression model development using specific conductance (SC) as an 

explanatory variable for dissolved solids (DS) concentrations for Cheney Reservoir near Cheney, 

Kansas (site 07144790), south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = DS ~ SC, data = DS, na.action = 

na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-32.67 -6.676 0.09518 6.783 46.12

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) 28.3795 19.1958 1.4784 0.1420 

 SC 0.5093 0.0236 21.5880 0.0000 

Residual standard error: 12.02 on 118 degrees of freedom

Multiple R-Squared: 0.798 Adjusted R-squared: 0.7962 

F-statistic: 466 on 1 and 118 degrees of freedom, the p-value is 0 

Correlation of Coefficients:

 (Intercept) 

SC -0.9984 

Analysis of Variance Table

Response: DS

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 SC 1 67386.77 67386.77 466.0428 0

Residuals 118 17062.03 144.59 

model.formula nvars stderr adjr2 Cp press 

 DS ~ SC 1 12.02470 79.62478 1.003052 17701.67

Test criteria

leverage cooksD dfits 

 0.05 0.793 0.258

Observations exceeding at least one test criterion

 DS yhat resids stnd.res stud.res leverage cooksD dfits 

8 436 468 -31.67 -2.66 -2.730 0.0180 0.064889 -0.3700

9 435 468 -32.67 -2.74 -2.822 0.0180 0.069052 -0.3824

12 501 482 18.81 1.59 1.601 0.0322 0.042044 0.2919

24 424 404 19.74 1.67 1.679 0.0296 0.042319 0.2932

35 387 405 -17.77 -1.50 -1.508 0.0290 0.033603 -0.2606

38 371 402 -30.71 -2.60 -2.663 0.0325 0.113401 -0.4884

41 457 411 46.12 3.88 4.136 0.0228 0.175557 0.6317

59 509 507 1.85 0.16 0.159 0.0711 0.000981 0.0441

62 450 412 37.59 3.16 3.289 0.0214 0.109257 0.4865

94 388 411 -23.39 -1.97 -1.992 0.0223 0.044197 -0.3010
Results of Linear Regression Analysis for Selected Constituents 55

Figure 3. S+® output graphs from simple linear regression analysis showing A, specific conductance (SC) compared to dissolved 

solids (DS) concentrations; B, computed compared to actual DS concentrations; C, computed DS concentrations compared to 

regression residuals; and D, standard normal quantiles compared to regression residuals for Cheney Reservoir near Cheney, Kansas 

(site 07144790), south-central Kansas, 2001 through 2009.

Fitted : SC

Residuals

400 420 440 460 480 500

-20 0 20 40

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-20 0 20 40

700 750 800 850 900 950

SC

350

400

450

500

DS

380 400 420 440 460 480 500

COMPUTED.DS

350

400

450

500

ACTUAL.DS

A B

C D
56 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 4. S+® output of regression model development using specific conductance (SC) as an 

explanatory variable for sodium (NA) concentrations for Cheney Reservoir near Cheney, Kansas (site 

07144790), south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = NA ~ SC, data = NA, na.action = na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-13.03 -2.348 -0.1978 2.225 11.19

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) -32.7950 7.2517 -4.5224 0.0000

 SC 0.1636 0.0089 18.3900 0.0000

Residual standard error: 4.501 on 117 degrees of freedom

Multiple R-Squared: 0.743 Adjusted R-squared: 0.7408 

F-statistic: 338.2 on 1 and 117 degrees of freedom, the p-value is 0 

Correlation of Coefficients:

 (Intercept) 

SC -0.9984 

Analysis of Variance Table

Response: NA

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 SC 1 6851.872 6851.872 338.1907 0

Residuals 117 2370.465 20.260 

model.formula nvars stderr adjr2 Cp press 

 NA ~ SC 1 4.501153 74.07680 1.072877 2446.346

Test criteria

leverage cooksD dfits 

 0.0504 0.793 0.259

Observations exceeding at least one test criterion

 NA yhat resids stnd.res stud.res leverage cooksD dfits 

30 103.0 112.0 -9.03 -2.034 -2.062 0.0283 0.06026 -0.352

31 105.0 112.8 -7.84 -1.770 -1.787 0.0312 0.05046 -0.321

50 117.0 105.8 11.19 2.503 2.561 0.0128 0.04045 0.291

55 117.0 107.0 10.05 2.249 2.289 0.0148 0.03791 0.280

56 118.0 108.9 9.08 2.038 2.066 0.0191 0.04046 0.288

57 124.0 115.6 8.37 1.901 1.923 0.0425 0.08017 0.405

58 123.0 121.0 1.97 0.455 0.454 0.0708 0.00789 0.125

112 80.1 92.1 -11.96 -2.682 -2.757 0.0184 0.06752 -0.378

119 91.3 104.3 -13.03 -2.911 -3.010 0.0107 0.04594 -0.313
Results of Linear Regression Analysis for Selected Constituents 57

Figure 5. S+® output graphs from simple linear regression analysis showing A, specific conductance (SC) compared to sodium 

(NA) concentrations; B, computed compared to actual NA concentrations; C, computed NA concentrations compared to regression 

residuals; and D, standard normal quantiles compared to regression residuals for Cheney Reservoir near Cheney, Kansas (site 

07144790), south-central Kansas, 2001 through 2009.

Fitted : SC

Residuals

90 100 110 120

-10 -5 0 5 10

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-10 -5 0 5 10

700 750 800 850 900 950

SC

80

90

100

110

120

NA

85 95 105 115

COMPUTED.NA

80

90

100

110

120

ACTUAL.NA

A B

C D
58 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 6. S+® output of regression model development using reservoir elevation (ELV) and specific conductance (SC) as 

explanatory variables for chloride (CL) concentrations for Cheney Reservoir near Cheney, Kansas (site 07144790), southcentral Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = CL ~ ELV.1400 + SC, data = Chloride.Splus, na.action = na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-19.12 -5.011 -1.034 4.83 22.14

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) 37.1865 21.4482 1.7338 0.0856

 ELV.1400 -3.0417 0.6166 -4.9331 0.0000

 SC 0.2009 0.0156 12.8762 0.0000

Residual standard error: 7.441 on 117 degrees of freedom

Multiple R-Squared: 0.6997 Adjusted R-squared: 0.6946 

F-statistic: 136.3 on 2 and 117 degrees of freedom, the p-value is 0 

Correlation of Coefficients:

 (Intercept) ELV.1400 

ELV.1400 -0.8342 

 SC -0.8167 0.3645 

Analysis of Variance Table

Response: CL

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

ELV.1400 1 5916.637 5916.637 106.8499 0

 SC 1 9180.648 9180.648 165.7955 0

Residuals 117 6478.681 55.373

 model.formula nvars stderr adjr2 Cp press 

CL ~ ELV.1400 + SC 2 7.441327 69.45941 3.00000 6814.175

Variance inflation factors

ELV.1400 SC 

1.15317 1.15317

Test criteria

leverage cooksD dfits 

 0.075 0.844 0.316

Observations exceeding at least one test criterion

 CL yhat resids stnd.res stud.res leverage cooksD dfits 

11 162 147 14.54 1.983 2.008 0.0296 0.03999 0.351

18 157 135 22.14 3.000 3.109 0.0165 0.05023 0.402

19 146 128 17.72 2.416 2.468 0.0282 0.05653 0.421

24 138 118 20.46 2.792 2.877 0.0298 0.07980 0.504

33 137 134 3.44 0.484 0.483 0.0870 0.00745 0.149

58 166 164 1.59 0.223 0.222 0.0879 0.00160 0.069

59 160 146 14.36 1.994 2.020 0.0628 0.08875 0.523

60 166 169 -2.61 -0.366 -0.365 0.0849 0.00415 -0.111

62 130 133 -3.07 -0.429 -0.428 0.0763 0.00508 -0.123

63 120 113 7.00 0.979 0.979 0.0756 0.02612 0.280

64 120 109 10.61 1.475 1.482 0.0643 0.04977 0.388
Results of Linear Regression Analysis for Selected Constituents 59

Figure 7. S+® output graphs from simple linear regression analysis using reservoir elevation (ELV) and specific conductance (SC) as 

explanatory variables for chloride (CL) showing A, computed compared to actual CL concentrations; B, computed CL concentrations 

compared to regression residuals; and C, standard normal quantiles compared to regression residuals for Cheney Reservoir near 

Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

100 110 120 130 140 150 160 170

COMPUTED.CL

100

120

140

160

ACTUAL.CL

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-20 -10 0 10 20

Fitted : ELV.1400 + SC

Residuals

110 120 130 140 150 160 170

-20 -10 0 10 20

A B

C
60 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 8. S+® output of regression model development using specific conductance (SC) as an 

explanatory variable for magnesium (MG) concentrations for Cheney Reservoir near Cheney, Kansas 

(site 07144790), south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = MG ~ LOGSC, data = MG, na.action = na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-1.477 -0.3616 0.02158 0.4144 1.395

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) -69.8049 6.3200 -11.0451 0.0000

 LOGSC 28.6839 2.1723 13.2045 0.0000

Residual standard error: 0.5951 on 120 degrees of freedom

Multiple R-Squared: 0.5923 Adjusted R-squared: 0.5889 

F-statistic: 174.4 on 1 and 120 degrees of freedom, the p-value is 0 

Correlation of Coefficients:

 (Intercept) 

LOGSC -1 

Analysis of Variance Table

Response: MG

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 LOGSC 1 61.75738 61.75738 174.359 0

Residuals 120 42.50360 0.35420 

model.formula nvars stderr adjr2 Cp press 

 MG ~ LOGSC 1 0.5951443 58.89373 4.575412 43.91914

Test criteria

leverage cooksD dfits 

 0.0492 0.793 0.256

Observations exceeding at least one test criterion

 MG yhat resids stnd.res stud.res leverage cooksD dfits 

36 13.5 12.6 0.920 1.567 1.577 0.0266 0.0335 0.260

39 13.4 12.3 1.074 1.838 1.857 0.0363 0.0637 0.360

60 14.9 15.5 -0.576 -0.999 -0.999 0.0625 0.0333 -0.258

78 13.3 14.5 -1.198 -2.033 -2.060 0.0200 0.0421 -0.294

79 13.1 14.5 -1.369 -2.322 -2.366 0.0192 0.0528 -0.331

80 13.3 14.4 -1.126 -1.909 -1.930 0.0181 0.0335 -0.262

114 11.4 12.9 -1.477 -2.504 -2.561 0.0177 0.0566 -0.344

115 11.7 13.0 -1.339 -2.266 -2.307 0.0141 0.0368 -0.276
Results of Linear Regression Analysis for Selected Constituents 61

Figure 9. S+® output graphs from simple linear regression analysis showing A, log-transformed specific conductance (SC) compared 

to magnesium (MG) concentrations; B, computed compared to actual MG concentrations; C, computed MG concentrations compared 

to regression residuals; and D, standard normal quantiles compared to regression residuals for Cheney Reservoir near Cheney, Kansas 

(site 07144790), south-central Kansas, 2001 through 2009.

Fitted : LOGSC

Residuals

12.5 13.0 13.5 14.0 14.5 15.0 15.5

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

2.86 2.88 2.90 2.92 2.94 2.96 2.98

LOGSC

11

12

13

14

15

MG

12.2 13.2 14.2 15.2

COMPUTED.MG

11

12

13

14

15

ACTUAL.MG

A B

C D
62 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 10. S+® output of regression model development using turbidity (TBY, YSI model 6026 sensor) and 

fluorescence (CHL) as explanatory variables for total suspended solids (TSS) concentrations for Cheney Reservoir 

near Cheney, Kansas (site 07144790), south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = LOGTSS ~ LOGTBY + LOGCHL, data = TSS, na.action = na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-0.7339 -0.07061 0.04525 0.1526 0.5507

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) 0.0215 0.1571 0.1368 0.8914 

 LOGTBY 0.4807 0.0889 5.4085 0.0000 

 LOGCHL 0.3030 0.0919 3.2985 0.0013 

Residual standard error: 0.2571 on 118 degrees of freedom

Multiple R-Squared: 0.2183 Adjusted R-squared: 0.2051 

F-statistic: 16.48 on 2 and 118 degrees of freedom, the p-value is 4.879e-007 

Correlation of Coefficients:

 (Intercept) LOGTBY 

LOGTBY -0.8268 

LOGCHL -0.7434 0.2668

Analysis of Variance Table

Response: LOGTSS

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 LOGTBY 1 1.459163 1.459163 22.07895 0.000007141

 LOGCHL 1 0.719059 0.719059 10.88025 0.001285096

Residuals 118 7.798437 0.066088 

 

 model.formula nvars stderr adjr2 Cp press 

LOGTSS ~ LOGTBY + LOGCHL 2 0.2570767 20.50832 3.00000 8.233660

Variance inflation factors

 LOGTBY LOGCHL 

1.076635 1.076635

Test criteria

leverage cooksD dfits 

 0.0744 0.844 0.315

Observations exceeding at least one test criterion

 LOGTSS yhat resids stnd.res stud.res leverage cooksD dfits 

 3 0.301 0.994 -0.6934 -2.716 -2.793 0.0140 0.03493 -0.3329

11 0.301 0.722 -0.4210 -1.688 -1.701 0.0585 0.05895 -0.4239

12 0.602 0.323 0.2788 1.180 1.182 0.1556 0.08558 0.5076

15 0.301 0.501 -0.2002 -0.811 -0.810 0.0770 0.01828 -0.2338

21 0.301 0.748 -0.4473 -1.795 -1.813 0.0605 0.06923 -0.4601

27 0.699 0.633 0.0664 0.269 0.268 0.0770 0.00201 0.0773

42 0.301 0.870 -0.5691 -2.241 -2.281 0.0242 0.04153 -0.3592

60 0.301 1.035 -0.7339 -2.925 -3.024 0.0473 0.14157 -0.6738

62 1.146 0.671 0.4751 1.890 1.912 0.0443 0.05519 0.4114

64 0.301 0.971 -0.6702 -2.641 -2.711 0.0254 0.06048 -0.4373

65 0.301 0.907 -0.6056 -2.378 -2.426 0.0183 0.03507 -0.3310

78 0.602 0.530 0.0717 0.292 0.291 0.0862 0.00268 0.0893

100 1.079 0.938 0.1412 0.571 0.569 0.0746 0.00876 0.1616

108 0.301 0.967 -0.6658 -2.623 -2.692 0.0252 0.05921 -0.4325
Results of Linear Regression Analysis for Selected Constituents 63

Figure 11. S+® output graphs from simple linear regression analysis using turbidity (TBY, YSI model 6026 sensor) and fluorescence 

(CHL) as explanatory variables for total suspended solids (TSS) showing A, computed compared to actual TSS concentrations; 

B, computed log-transformed TSS concentrations compared to regression residuals; and C, standard normal quantiles compared to 

regression residuals for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

Fitted : LOGTBY + LOGCHL

Residuals

0.4 0.6 0.8 1.0

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0 2 4 6 8 10 12 14 16

COMPUTED.TSS

0

10

20

30

40

ACTUAL.TSS

A B

C
64 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 12. S+® output of regression model development using turbidity (TBY, YSI model 6026 

sensor) as an explanatory variable for suspended sediment concentrations (SSC) for Cheney 

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = SSC ~ TBY, data = SSC, na.action = na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-13.54 -2.027 0.2694 1.951 12.79

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) 2.9310 0.8746 3.3512 0.0011 

 TBY 0.6304 0.0421 14.9908 0.0000 

Residual standard error: 4.133 on 117 degrees of freedom

Multiple R-Squared: 0.6576 Adjusted R-squared: 0.6547 

F-statistic: 224.7 on 1 and 117 degrees of freedom, the p-value is 0 

Correlation of Coefficients:

 (Intercept) 

TBY -0.9013 

Analysis of Variance Table

Response: SSC

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 TBY 1 3839.469 3839.469 224.7249 0

Residuals 117 1998.968 17.085 

model.formula nvars stderr adjr2 Cp press 

 SSC ~ TBY 1 4.133424 65.46930 1.021886 2083.105

Test criteria

leverage cooksD dfits 

 0.0504 0.793 0.259

Observations exceeding at least one test criterion

 SSC yhat resids stnd.res stud.res leverage cooksD dfits 

19 33 22.5 10.53 2.58 2.64 0.02395 0.0815 0.414

35 2 15.5 -13.54 -3.29 -3.44 0.00857 0.0467 -0.320

44 34 24.4 9.64 2.37 2.42 0.03249 0.0943 0.443

62 11 20.0 -8.95 -2.18 -2.22 0.01546 0.0374 -0.278

64 8 18.7 -10.69 -2.60 -2.67 0.01245 0.0427 -0.300

72 34 21.2 12.79 3.12 3.25 0.01929 0.0960 0.456

81 36 29.4 6.59 1.65 1.66 0.06437 0.0935 0.436

93 26 33.2 -7.19 -1.83 -1.85 0.09698 0.1799 -0.606

106 17 24.4 -7.36 -1.81 -1.83 0.03249 0.0551 -0.335

107 35 25.6 9.37 2.31 2.36 0.03922 0.1093 0.477
Results of Linear Regression Analysis for Selected Constituents 65

Figure 13. S+® output graphs from simple linear regression analysis showing A, turbidity (TBY, YSI model 6026 sensor) compared to 

suspended-solids concentration (SSC) data; B, computed compared to actual SSC; C, comparison of estimated SSC and regression 

residuals; and D, standard normal quantiles compared to regression residuals for Cheney Reservoir near Cheney, Kansas (site 

07144790), south-central Kansas, 2001 through 2009.

Fitted : TBY

Residuals

5 10 15 20 25 30

-10 -5 0 5 10

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-10 -5 0 5 10

0 10 20 30 40 50

TBY

0

10

20

30

SSC

0 5 10 15 20 25 30 35

COMPUTED.SSC

0

10

20

30

ACTUAL.SSC

A B

C D
66 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 14. S+® output of regression model development using turbidity (TBY, YSI model 6026 sensor), dissolved 

oxygen (DO), and water temperature (T) as explanatory variables for total Kjeldahl nitrogen (TKN) concentrations 

for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = TKN ~ TBY + DO + T, data = TKN, na.action = na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-0.3653 -0.08153 -0.01264 0.07024 0.3696

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) -0.2437 0.1342 -1.8153 0.0720 

 TBY 0.0045 0.0014 3.1593 0.0020 

 DO 0.0608 0.0090 6.7441 0.0000 

 T 0.0197 0.0027 7.2028 0.0000 

Residual standard error: 0.1271 on 118 degrees of freedom

Multiple R-Squared: 0.3293 Adjusted R-squared: 0.3123 

F-statistic: 19.31 on 3 and 118 degrees of freedom, the p-value is 2.954e-010 

Correlation of Coefficients:

 (Intercept) TBY DO 

TBY -0.4280 

DO -0.9739 0.3204 

 T -0.8942 0.0984 0.8574

Analysis of Variance Table

Response: TKN

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 TBY 1 0.078261 0.0782607 4.84373 0.0296935

 DO 1 0.019716 0.0197158 1.22026 0.2715575

 T 1 0.838246 0.8382458 51.88094 0.0000000

Residuals 118 1.906538 0.0161571

 model.formula nvars stderr adjr2 Cp press 

TKN ~ TBY + DO + T 3 0.1271106 31.228481 4.00000 2.108368

Variance inflation factors

 TBY DO T 

1.282006 4.79267 4.342717

Test criteria

leverage cooksD dfits 

 0.0984 0.875 0.362

Observations exceeding at least one test criterion

 TKN yhat resids stnd.res stud.res leverage cooksD dfits 

3 0.61 0.975 -0.3653 -2.987 -3.094 0.0745 0.1796 -0.878

5 0.46 0.772 -0.3121 -2.489 -2.546 0.0265 0.0422 -0.420

8 1.20 0.929 0.2705 2.205 2.243 0.0685 0.0894 0.608

15 1.40 1.050 0.3501 2.971 3.075 0.1401 0.3594 1.241

18 1.30 0.930 0.3696 2.984 3.090 0.0507 0.1190 0.714

19 1.20 1.131 0.0687 0.598 0.596 0.1838 0.0201 0.283

28 0.58 0.801 -0.2207 -1.807 -1.825 0.0773 0.0684 -0.528

52 1.20 0.899 0.3009 2.423 2.475 0.0452 0.0694 0.538

53 0.90 0.640 0.2598 2.087 2.118 0.0412 0.0468 0.439

58 0.94 0.675 0.2649 2.116 2.149 0.0303 0.0350 0.380

83 0.97 0.753 0.2165 1.776 1.793 0.0804 0.0690 0.530

96 0.65 0.815 -0.1655 -1.382 -1.387 0.1121 0.0603 -0.493

99 0.34 0.607 -0.2675 -2.151 -2.185 0.0434 0.0525 -0.466
Results of Linear Regression Analysis for Selected Constituents 67

Figure 15. S+® output graphs from simple linear regression analysis using turbidity (TBY, YSI model 6026 sensor), dissolved oxygen 

(DO), and water temperature (T) as explanatory variables for total Kjeldahl nitrogen (TKN) showing A, computed compared to actual 

TKN concentrations; B, computed TKN concentrations compared to regression residuals; and C, standard normal quantiles compared to 

regression residuals for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

Fitted : TBY + DO + T

Residuals

0.6 0.7 0.8 0.9 1.0 1.1

-0.2 0.0 0.2

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-0.2 0.0 0.2 0.5 0.6 0.7 0.8 0.9 1.0 1.1

COMPUTED.TKN

0.2

0.5

0.8

1.1

1.4

ACTUAL.TKN

A B

C
68 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 16. S+® output of linear regression model development of pH (PH) and specific conductance (SC) as 

explanatory variables for nitrate plus nitrite (NO3NO2) concentrations for Cheney Reservoir near Cheney, Kansas 

(site 07144790), south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = LOGNO3NO2 ~ PH + SC, data = NO3NO2, na.action = na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-1.077 -0.3307 0.05465 0.3194 0.8815

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) 5.8934 1.6309 3.6137 0.0004 

 PH -0.5585 0.1927 -2.8985 0.0045 

 SC -0.0026 0.0009 -2.9664 0.0036 

Residual standard error: 0.4471 on 119 degrees of freedom

Multiple R-Squared: 0.1546 Adjusted R-squared: 0.1404 

F-statistic: 10.88 on 2 and 119 degrees of freedom, the p-value is 0.00004564 

Correlation of Coefficients:

 (Intercept) PH 

PH -0.9011 

SC -0.2343 -0.2098

Analysis of Variance Table

Response: LOGNO3NO2

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 PH 1 2.59224 2.592243 12.96711 0.000463536

 SC 1 1.75910 1.759104 8.79952 0.003642422

Residuals 119 23.78918 0.199909 

 model.formula nvars stderr adjr2 Cp press 

LOGNO3NO2 ~ PH + SC 2 0.4471119 14.042130 3.000000 25.33309

Variance inflation factors

 PH SC 

1.046048 1.046048

Test criteria

leverage cooksD dfits 

 0.0738 0.844 0.314

Observations exceeding at least one test criterion

 LOGNO3NO2 yhat resids stnd.res stud.res leverage cooksD dfits 

11 -2.000 -1.309 -0.691 -1.58 -1.59 0.0384 0.0331 -0.317

12 -2.000 -0.923 -1.077 -2.51 -2.56 0.0753 0.1705 -0.732

14 -2.000 -1.164 -0.836 -1.93 -1.95 0.0578 0.0758 -0.482

18 -2.000 -1.098 -0.902 -2.05 -2.08 0.0320 0.0463 -0.378

25 -1.699 -0.756 -0.943 -2.14 -2.17 0.0273 0.0427 -0.364

26 -1.222 -0.385 -0.837 -1.98 -2.00 0.1019 0.1475 -0.674

35 -0.244 -0.969 0.725 1.68 1.69 0.0681 0.0686 0.457

60 -0.745 -1.387 0.642 1.49 1.50 0.0698 0.0555 0.410
Results of Linear Regression Analysis for Selected Constituents 69

Figure 17. S+® output graphs from simple linear regression analysis using pH (PH) and specific conductance (SC) as 

explanatory variables for nitrate plus nitrite (NO3NO2) showing A, computed compared to actual NO3NO2 concentrations; 

B, computed log-transformed NO3NO2 concentrations compared to regression residuals; and C, standard normal quantiles 

compared to regression residuals for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 

through 2009.

Fitted : PH + SC

Residuals

-1.4 -1.2 -1.0 -0.8 -0.6 -0.4

-1.0 -0.5 0.0 0.5

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-1.0 -0.5 0.0 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

COMPUTED.NO3NO2

0.0

0.2

0.4

0.6

0.8

1.0

ACTUAL.NO3NO2

A B

C
70 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 18. S+® output of regression model development using season (SIN and COS) and turbidity (TBY, YSI model 

6026 sensor) as explanatory variables for total phosphorus (TP) concentrations for Cheney Reservoir near Cheney, 

Kansas (site 07144790), south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = LOGTP ~ SIN + COS + LOGTBY, data = TP, na.action = na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-0.4324 -0.08188 0.002713 0.08266 0.2911

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) -1.6626 0.0618 -26.9105 0.0000

 SIN -0.0663 0.0190 -3.4862 0.0007

 COS -0.1357 0.0218 -6.2154 0.0000

 LOGTBY 0.4390 0.0520 8.4405 0.0000

Residual standard error: 0.1364 on 105 degrees of freedom

Multiple R-Squared: 0.6918 Adjusted R-squared: 0.683 

F-statistic: 78.58 on 3 and 105 degrees of freedom, the p-value is 0 

Correlation of Coefficients:

 (Intercept) SIN COS 

 SIN -0.2424 

 COS -0.3435 -0.0018 

LOGTBY -0.9747 0.2411 0.4197

Analysis of Variance Table

Response: LOGTP

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 SIN 1 0.909736 0.909736 48.9103 2.581102e-010

 COS 1 2.149665 2.149665 115.5728 0.000000e+000

 LOGTBY 1 1.325118 1.325118 71.2426 1.874000e-013

Residuals 105 1.953010 0.018600 

 model.formula nvars stderr adjr2 Cp press 

LOGTP ~ SIN + COS + LOGTBY 3 0.1363822 68.30295 4.00000 2.119644

Variance inflation factors

 SIN COS LOGTBY 

1.076425 1.230612 1.30656

Test criteria

leverage cooksD dfits 

 0.11 0.876 0.383

Observations exceeding at least one test criterion

 LOGTP yhat resids stnd.res stud.res leverage cooksD dfits 

 1 -1.22 -0.958 -0.264 -1.986 -2.015 0.0489 0.0507 -0.457

12 -1.82 -1.719 -0.105 -0.818 -0.817 0.1191 0.0226 -0.300

13 -1.30 -1.561 0.260 1.974 2.002 0.0651 0.0678 0.528

36 -1.82 -1.391 -0.432 -3.235 -3.394 0.0396 0.1079 -0.689

37 -1.82 -1.567 -0.257 -1.947 -1.974 0.0658 0.0668 -0.524

49 -1.06 -1.347 0.291 2.181 2.222 0.0422 0.0524 0.466

66 -1.82 -1.619 -0.205 -1.565 -1.576 0.0761 0.0505 -0.452

67 -1.82 -1.647 -0.177 -1.357 -1.363 0.0877 0.0443 -0.423

68 -1.82 -1.451 -0.373 -2.786 -2.882 0.0376 0.0759 -0.570

109 -1.00 -1.248 0.248 1.864 1.887 0.0472 0.0431 0.420
Results of Linear Regression Analysis for Selected Constituents 71

Figure 19. S+® output graphs from simple linear regression analysis using season (SIN and COS) and turbidity (TBY, YSI model 6026 

sensor) as explanatory variables for total phosphorus (TP) showing A, computed compared to actual TP concentrations; B, computed 

log-transformed TP concentrations compared to regression residuals; and C, standard normal quantiles compared to regression 

residuals for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

 Fitted : SIN + COS + LOGTBY

Residuals

-1.6 -1.4 -1.2 -1.0

-0.4 -0.2 0.0 0.2

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-0.4 -0.2 0.0 0.2 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14

COMPUTED.TP

0.00

0.05

0.10

0.15

0.20

ACTUAL.TP

A B

C
72 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 20. S+® output of regression model development using turbidity (TBY, YSI model 6026 sensor), water 

temperature (T), and fluorescence (CHL) as explanatory variables for orthophosphate (OP) concentrations for Cheney 

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = LOGOP ~ LOGTBY + T + LOGCHL, data = OP, na.action = na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-0.927 -0.1352 0.02488 0.1766 0.6298

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) -2.0430 0.1788 -11.4243 0.0000

 LOGTBY 0.4942 0.1023 4.8324 0.0000

 T 0.0189 0.0036 5.2416 0.0000

 LOGCHL -0.5408 0.1116 -4.8479 0.0000

Residual standard error: 0.2662 on 109 degrees of freedom

Multiple R-Squared: 0.6552 Adjusted R-squared: 0.6457 

F-statistic: 69.04 on 3 and 109 degrees of freedom, the p-value is 0 

Correlation of Coefficients:

 (Intercept) LOGTBY T 

LOGTBY -0.5819 

 T -0.3155 -0.4206 

LOGCHL -0.7961 0.0719 0.4377

Analysis of Variance Table

Response: LOGOP

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 LOGTBY 1 8.258913 8.258913 116.5335 0.00000e+000

 T 1 4.753859 4.753859 67.0771 1.00000e-012

 LOGCHL 1 1.665614 1.665614 23.5018 4.16733e-006

Residuals 109 7.725004 0.070872 

 model.formula nvars stderr adjr2 Cp press 

LOGOP ~ LOGTBY + T + LOGCHL 3 0.2662172 64.56956 4.00000 8.398949

Variance inflation factors

 LOGTBY T LOGCHL 

1.347654 1.658468 1.372232

Test criteria

leverage cooksD dfits 

 0.106 0.876 0.376

Observations exceeding at least one test criterion

 LOGOP yhat resids stnd.res stud.res leverage cooksD dfits 

 7 -2.30 -1.37 -0.9270 -3.5398 -3.7454 0.0323 0.104406 -0.6838

12 -2.30 -2.28 -0.0181 -0.0747 -0.0744 0.1737 0.000293 -0.0341

14 -2.30 -1.74 -0.5616 -2.2230 -2.2647 0.0993 0.136239 -0.7521

15 -1.22 -1.61 0.3893 1.5146 1.5237 0.0676 0.041578 0.4103

47 -2.30 -1.46 -0.8410 -3.2096 -3.3574 0.0312 0.082914 -0.6024

74 -2.30 -1.69 -0.6070 -2.3191 -2.3675 0.0334 0.046500 -0.4403

81 -1.30 -1.68 0.3784 1.4793 1.4876 0.0770 0.045617 0.4295

113 -1.15 -1.78 0.6298 2.4894 2.5516 0.0970 0.166363 0.8361
Results of Linear Regression Analysis for Selected Constituents 73

Figure 21. S+® output graphs from simple linear regression analysis using turbidity (TBY, YSI model 6026 sensor), water temperature 

(T), and fluorescence (CHL) as explanatory variables for orthophosphate (OP) showing A, computed compared to actual OP 

concentrations; B, computed log-transformed OP concentrations and regression residuals; and C, standard normal quantiles compared 

to regression residuals for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

Fitted : LOGTBY + T + LOGCHL

Residuals

-2.4 -2.2 -2.0 -1.8 -1.6 -1.4 -1.2

-0.5 0.0 0.5

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-0.5 0.0 0.5

0.01 0.03 0.05 0.07 0.09

COMPUTED.OP

0.00

0.02

0.04

0.06

0.08

ACTUAL.OP

A B

C
74 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 22. S+® output of regression model development using dissolved oxygen (DO), specific conductance 

(SC), and fluorescence (CHL) as explanatory variables for dissolved phosphorus (DP) concentrations for Cheney 

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = LOGDP ~ LOGDO + SC + CHL, data = DP,na.action = na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-0.642 -0.09622 0.03565 0.1162 0.4633

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) 1.0851 0.3671 2.9560 0.0038 

 LOGDO -0.8764 0.2121 -4.1329 0.0001 

 SC -0.0019 0.0005 -3.5470 0.0006

 CHL -0.0137 0.0035 -3.8822 0.0002 

Residual standard error: 0.2056 on 105 degrees of freedom

Multiple R-Squared: 0.5858 Adjusted R-squared: 0.5739 

F-statistic: 49.49 on 3 and 105 degrees of freedom, the p-value is 0 

Correlation of Coefficients:

 (Intercept) LOGDO SC 

LOGDO 0.0879 

 SC -0.9058 -0.4928 

 CHL 0.1305 -0.5342 0.0480

Analysis of Variance Table

Response: LOGDP

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 LOGDO 1 5.163193 5.163193 122.0919 0.000000000

 SC 1 0.478769 0.478769 11.3212 0.001070848

 CHL 1 0.637355 0.637355 15.0713 0.000181166

Residuals 105 4.440386 0.042289

 model.formula nvars stderr adjr2 Cp press 

LOGDP ~ LOGDO + SC + CHL 3 0.2056439 57.39383 4.00000 4.794384

Variance inflation factors

 LOGDO SC CHL 

2.016798 1.44452 1.530531

Test criteria

leverage cooksD dfits 

 0.11 0.876 0.383

Observations exceeding at least one test criterion

 LOGDP yhat resids stnd.res stud.res leverage cooksD dfits 

 6 -1.22 -1.567 0.3450 1.743 1.761 0.0741 0.06077 0.4979

14 -1.82 -1.182 -0.6420 -3.158 -3.304 0.0228 0.05819 -0.5047

15 -1.16 -1.525 0.3643 1.828 1.849 0.0607 0.05393 0.4698

22 -1.52 -1.068 -0.4549 -2.244 -2.289 0.0287 0.03723 -0.3936

25 -1.25 -0.916 -0.3357 -1.684 -1.699 0.0599 0.04516 -0.4288

34 -1.82 -1.292 -0.5320 -2.652 -2.732 0.0484 0.08933 -0.6159

35 -1.82 -1.261 -0.5632 -2.786 -2.881 0.0338 0.06786 -0.5388

45 -1.17 -1.127 -0.0473 -0.244 -0.243 0.1127 0.00189 -0.0867

78 -1.22 -1.465 0.2428 1.247 1.250 0.1033 0.04477 0.4243

109 -1.05 -1.509 0.4633 2.323 2.374 0.0600 0.08607 0.5996
Results of Linear Regression Analysis for Selected Constituents 75

Figure 23. S+® output graphs from simple linear regression analysis using dissolved oxygen (DO), specific conductance (SC), 

and fluorescence (CHL) as explanatory variables for dissolved phosphorus (DP) showing A, computed compared to actual DP 

concentrations; B, computed log-transformed DP concentrations compared to regression residuals; and C, standard normal quantiles 

compared to regression residuals for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

Fitted : LOGDO + SC + CHL

Residuals

-2.0 -1.8 -1.6 -1.4 -1.2 -1.0

-0.6 -0.4 -0.2 0.0 0.2 0.4

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-0.6 -0.4 -0.2 0.0 0.2 0.4

0.00 0.02 0.04 0.06 0.08 0.10 0.12

COMPUTED.DP

0.00

0.05

0.10

0.15

ACTUAL.DP

A B

C
76 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 24. S+® output of regression model development using reservoir elevation (ELV), water temperature (T), and fluorescence (CHL) 

as explanatory variables for chlorophyll-a (CHLA) concentrations for Cheney Reservoir near Cheney, Kansas (site 07144790), southcentral Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = CHLA ~ ELV.1400 + T + CHL, data = Chlorophyll.a.Splus, na.action = na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-14.76 -3.129 0.1915 3.852 19.21

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) 40.2468 10.0971 3.9860 0.0001

 ELV.1400 -1.9656 0.4722 -4.1623 0.0001

 T 0.3204 0.0741 4.3229 0.0000

 CHL 0.8463 0.0834 10.1493 0.0000

Residual standard error: 6.223 on 113 degrees of freedom

Multiple R-Squared: 0.5078 Adjusted R-squared: 0.4947 

F-statistic: 38.86 on 3 and 113 degrees of freedom, the p-value is 0 

Correlation of Coefficients:

 (Intercept) ELV.1400 T 

ELV.1400 -0.9812 

 T -0.0215 -0.1457 

 CHL -0.0859 -0.0651 0.4820

Analysis of Variance Table

Response: CHLA

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

ELV.1400 1 508.790 508.790 13.1379 0.0004353

 T 1 16.334 16.334 0.4218 0.5173755

 CHL 1 3989.229 3989.229 103.0088 0.0000000

Residuals 113 4376.157 38.727 

 

 model.formula nvars stderr adjr2 Cp press 

CHLA ~ ELV.1400 + T + CHL 3 6.223106 49.470410 4.00000 4759.741

Variance inflation factors

ELV.1400 T CHL 

1.021719 1.32527 1.302671

Test criteria

leverage cooksD dfits 

 0.103 0.876 0.37

Observations exceeding at least one test criterion

 CHLA yhat resids stnd.res stud.res leverage cooksD dfits 

1 3.0 12.69 -9.69 -1.631 -1.643 0.0891 0.0651 -0.514

2 10.4 6.01 4.39 0.746 0.745 0.1058 0.0165 0.256

3 6.1 20.86 -14.76 -2.407 -2.460 0.0287 0.0428 -0.423

9 7.3 17.99 -10.69 -1.759 -1.776 0.0460 0.0373 -0.390

11 7.6 18.84 -11.24 -1.847 -1.867 0.0427 0.0380 -0.394

15 12.2 25.81 -13.61 -2.257 -2.299 0.0605 0.0819 -0.583

52 41.2 21.99 19.21 3.164 3.300 0.0487 0.1283 0.747

53 31.3 17.69 13.61 2.238 2.279 0.0445 0.0583 0.492

58 20.2 33.74 -13.54 -2.318 -2.365 0.1192 0.1819 -0.870

95 47.2 39.55 7.65 1.387 1.393 0.2138 0.1308 0.726
Results of Linear Regression Analysis for Selected Constituents 77

Figure 25. S+® output graphs from simple linear regression analysis using reservoir elevation (ELV), water temperature (T), and 

fluorescence (CHL) as explanatory variables for chlorophyll-a (CHLA) showing A, computed compared to actual CHLA concentrations; 

B, computed CHLA concentrations compared to regression residuals; and C, standard normal quantiles compared to regression 

residuals for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

Fitted : ELV.1400 + T + CHL

Residuals

0 10 20 30 40

-10 0 10 20

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-10 0 10 20

0 10 20 30 40

COMPUTED.CHLA

0

10

20

30

40

50

ACTUAL.CHLA

A B

C
78 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 26. S+® output of regression model development using reservoir elevation (ELV), turbidity (TBY, YSI model 

6026 sensor), and water temperature (T) as explanatory variables for actinomycetes bacteria (ACT) densities for 

Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = LOGACT ~ LOGELV.1400 + LOGTBY + T, data = Actinomycetes.Splus,

na.action = na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-1.089 -0.1621 0.004718 0.1837 0.5495

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) -14.9286 1.6263 -9.1797 0.0000

LOGELV.1400 11.0619 1.2383 8.9335 0.0000

 LOGTBY 0.9924 0.1572 6.3112 0.0000

 T -0.0163 0.0041 -4.0136 0.0001

Residual standard error: 0.3021 on 89 degrees of freedom

Multiple R-Squared: 0.6076 Adjusted R-squared: 0.5944 

F-statistic: 45.94 on 3 and 89 degrees of freedom, the p-value is 0 

Correlation of Coefficients:

 (Intercept) LOGELV.1400 LOGTBY 

LOGELV.1400 -0.9940 

 LOGTBY 0.0380 -0.1379 

 T 0.1023 -0.0994 -0.3799

Analysis of Variance Table

Response: LOGACT

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

LOGELV.1400 1 8.662210 8.662210 94.94122 0.0000000000

 LOGTBY 1 2.442792 2.442792 26.77396 0.0000014012

 T 1 1.469750 1.469750 16.10904 0.0001244246

 Residuals 89 8.120147 0.091238 

 model.formula nvars stderr adjr2 Cp press 

LOGACT ~ LOGELV.1400 + LOGTBY + T 3 0.3020556 59.43996 4.00000 12.554020

Variance inflation factors

LOGELV.1400 LOGTBY T 

 1.048162 1.212831 1.201626

Test criteria

leverage cooksD dfits 

 0.129 0.877 0.415

Observations exceeding at least one test criterion

 LOGACT yhat resids stnd.res stud.res leverage cooksD dfits 

15 0.000 0.213 -0.213 -0.758 -0.756 0.1309 0.0216 -0.293

25 -0.301 0.236 -0.537 -1.832 -1.857 0.0590 0.0526 -0.465

27 0.778 0.242 0.536 1.834 1.859 0.0634 0.0569 0.484

30 -0.301 0.393 -0.694 -2.471 -2.546 0.1357 0.2396 -1.009

50 0.602 0.121 0.481 1.732 1.752 0.1556 0.1381 0.752

60 -0.301 0.788 -1.089 -3.650 -3.936 0.0251 0.0857 -0.631

76 0.000 0.765 -0.765 -2.579 -2.666 0.0353 0.0609 -0.510
Results of Linear Regression Analysis for Selected Constituents 79

Figure 27. S+® output graphs from simple linear regression analysis using reservoir elevation (ELV), turbidity (TBY, YSI model 6026 

sensor), and water temperature (T) as explanatory variables for actinomycetes bacteria (ACT) showing A, computed compared to actual 

ACT concentrations; B, computed log-transformed ACT concentrations compared to regression residuals; and C, standard normal 

quantiles compared to regression residuals for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 

through 2009.

Fitted : LOGELV.1400 + LOGTBY + T

Residuals

0.0 0.5 1.0 1.5

-1.0 -0.5 0.0 0.5

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-1.0 -0.5 0.0 0.5

0.5 3.0 5.5 8.0 10.5 13.0 15.5

COMPUTED.ACT

0

20

40

60

ACTUAL.ACT

A B

C
80 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 28. S+® output of regression model development using reservoir elevation (ELV) and water temperature (T) 

as explanatory variables for Anabaena spp. (ANA) concentrations for Cheney Reservoir near Cheney, Kansas (site 

07144790), south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = LOGANA ~ LOG.ELV.1400 + T, data = Anabaena.Splus, na.action = 

na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-4.196 -0.9902 -0.09599 1.253 3.504

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) 34.4946 8.2782 4.1669 0.0001

LOG.ELV.1400 -25.8073 6.2229 -4.1471 0.0001

 T 0.1203 0.0166 7.2324 0.0000

Residual standard error: 1.619 on 111 degrees of freedom

Multiple R-Squared: 0.3724 Adjusted R-squared: 0.3611 

F-statistic: 32.93 on 2 and 111 degrees of freedom, the p-value is 5.901e-012 

Correlation of Coefficients:

 (Intercept) LOG.ELV.1400 

LOG.ELV.1400 -0.9993 

 T 0.0321 -0.0653 

Analysis of Variance Table

Response: LOGANA

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

LOG.ELV.1400 1 35.5214 35.5214 13.55996 0.0003585721

 T 1 137.0251 137.0251 52.30801 0.0000000001

 Residuals 111 290.7735 2.6196

 

 model.formula nvars stderr adjr2 Cp press 

LOGANA ~ LOG.ELV.1400 + T 2 1.618512 36.11054 3.00000 307.3110

Variance inflation factors

LOG.ELV.1400 T 

 1.004288 1.004288

Test criteria

leverage cooksD dfits 

 0.0789 0.844 0.324

Observations exceeding at least one test criterion

 LOGANA yhat resids stnd.res stud.res leverage cooksD dfits 

5 0.00 4.19575 -4.20 -2.64 -2.72 0.0370 0.0894 -0.532

7 3.08 0.13138 2.94 1.85 1.87 0.0342 0.0404 0.352

8 3.07 0.00334 3.06 1.93 1.95 0.0379 0.0490 0.388

11 4.06 0.55603 3.50 2.19 2.23 0.0253 0.0416 0.359

31 4.17 1.49381 2.68 1.71 1.72 0.0583 0.0600 0.428

52 5.30 3.36865 1.93 1.24 1.25 0.0752 0.0419 0.355

53 4.26 2.51305 1.75 1.14 1.14 0.1048 0.0508 0.391

55 0.00 2.37637 -2.38 -1.52 -1.53 0.0633 0.0519 -0.397

92 3.36 0.07123 3.29 2.07 2.10 0.0358 0.0531 0.405
Results of Linear Regression Analysis for Selected Constituents 81

Figure 29. S+® output graphs from simple linear regression analysis using reservoir elevation (ELV) and water temperature (T) 

as explanatory variables for Anabaena spp. (ANA) showing A, computed compared to actual ANA concentrations; B, computed 

log-transformed ANA concentrations compared to regression residuals; and C, standard normal quantiles compared to 

regression residuals for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

50000 300000 550000 800000 1050000 1300000 1550000

COMPUTED.ANA

0

200000

400000

600000

ACTUAL.ANA

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-4 -2 0 2

Fitted : LOG.ELV.1400 + T

Residuals

0 1 2 3 4

-4 -2 0 2

A B

C
82 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 30. S+® output of regression model development using turbidity (TBY, YSI model 6026 sensor) and pH 

(PH) as explanatory variables for geosmin (GEO) concentrations for Cheney Reservoir near Cheney, Kansas (site 

07144790), south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = LOGGEO ~ LOGTBY + PH, data = Geosmin.Splus.New, na.action = 

na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-0.7243 -0.3228 -0.1244 0.268 1.644

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) -7.0015 1.8357 -3.8141 0.0002 

 LOGTBY -0.6356 0.1685 -3.7727 0.0002 

 PH 0.6115 0.2082 2.9374 0.0039 

Residual standard error: 0.4757 on 124 degrees of freedom

Multiple R-Squared: 0.2216 Adjusted R-squared: 0.2091 

F-statistic: 17.65 on 2 and 124 degrees of freedom, the p-value is 1.792e-007 

Correlation of Coefficients:

 (Intercept) LOGTBY 

LOGTBY -0.4516 

 PH -0.9944 0.3577

Analysis of Variance Table

Response: LOGGEO

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 LOGTBY 1 6.03799 6.037987 26.67987 0.000000929

 PH 1 1.95264 1.952637 8.62806 0.003947642

Residuals 124 28.06275 0.226312 

 model.formula nvars stderr adjr2 Cp press 

LOGGEO ~ LOGTBY + PH 2 0.4757231 20.90789 3.000000 29.71152 

Variance inflation factors

 LOGTBY PH 

1.146753 1.146753

Test criteria

leverage cooksD dfits 

 0.0709 0.844 0.307

Observations exceeding at least one test criterion

 LOGGEO yhat resids stnd.res stud.res leverage cooksD dfits 

14 -1.658 -2.24 0.582 1.377 1.382 0.2123 0.17043 0.718

19 -1.201 -2.33 1.132 2.491 2.545 0.0870 0.19706 0.786

21 -0.947 -2.59 1.644 3.522 3.697 0.0367 0.15768 0.722

29 -3.000 -3.19 0.192 0.425 0.424 0.0962 0.00641 0.138

30 -1.620 -1.83 0.213 0.469 0.467 0.0848 0.00679 0.142

36 -1.367 -2.78 1.410 3.010 3.113 0.0299 0.09298 0.546

38 -1.367 -2.37 1.001 2.142 2.174 0.0344 0.05452 0.410

39 -1.194 -2.81 1.620 3.434 3.595 0.0161 0.06423 0.460

83 -2.237 -2.02 -0.220 -0.480 -0.479 0.0725 0.00601 -0.134
Results of Linear Regression Analysis for Selected Constituents 83

Figure 31. S+® output graphs from simple linear regression analysis using turbidity (TBY, YSI model 6026 sensor) and pH (PH) as 

explanatory variables for geosmin (GEO) showing A, computed compared to actual GEO concentrations; B, computed log-transformed 

GEO concentrations compared to regression residuals; and C, standard normal quantiles compared to regression residuals for Cheney 

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

Fitted : LOGTBY + PH

Residuals

-3.2 -3.0 -2.8 -2.6 -2.4 -2.2 -2.0 -1.8

-0.5 0.0 0.5 1.0 1.5

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-0.5 0.0 0.5 1.0 1.5

0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035

COMPUTED.GEO

0.00

0.02

0.04

0.06

0.08

0.10

ACTUAL.GEO

A B

C
84 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 32. S+® output of regression model development using season (SIN and COS) and fluorescence (CHL) as 

explanatory variables for microcystin (MC) concentrations for Cheney Reservoir near Cheney, Kansas (site 07144790), 

south-central Kansas, during 2001 through 2009.

*** Linear Model ***

Call: lm(formula = LOGMC ~ SIN + COS + LOGCHL, data = Microcystin.Splus, na.action

= na.exclude)

Residuals:

 Min 1Q Median 3Q Max 

-0.8723 -0.2689 -0.001287 0.2157 1.08

Coefficients:

 Value Std. Error t value Pr(>|t|) 

(Intercept) -1.8153 0.2319 -7.8268 0.0000 

 SIN -0.5916 0.0706 -8.3810 0.0000 

 COS -0.3244 0.0748 -4.3392 0.0000 

 LOGCHL 0.8574 0.2222 3.8588 0.0002 

Residual standard error: 0.4333 on 90 degrees of freedom

Multiple R-Squared: 0.4822 Adjusted R-squared: 0.4649 

F-statistic: 27.93 on 3 and 90 degrees of freedom, the p-value is 7.376e-013 

Correlation of Coefficients:

 (Intercept) SIN COS 

 SIN 0.4812 

 COS 0.4505 0.1249 

LOGCHL -0.9794 -0.4753 -0.4072

Analysis of Variance Table

Response: LOGMC

Terms added sequentially (first to last)

 Df Sum of Sq Mean Sq F Value Pr(F) 

 SIN 1 11.21395 11.21395 59.72654 0.000000000

 COS 1 1.72434 1.72434 9.18399 0.003187402

 LOGCHL 1 2.79567 2.79567 14.89001 0.000214012

Residuals 90 16.89795 0.18775 

 model.formula nvars stderr adjr2 Cp press 

LOGMC ~ SIN + COS + LOGCHL 3 0.4333070 46.490383 4.487311 18.33903

Variance inflation factors

 SIN COS LOGCHL 

1.301281 1.207573 1.535598

Test criteria

leverage cooksD dfits 

 0.128 0.877 0.413

Observations exceeding at least one test criterion

 LOGMC yhat resids stnd.res stud.res leverage cooksD dfits 

26 0.580 -0.319 0.899 2.11 2.16 0.0367 0.0426 0.421

34 -1.000 -1.679 0.679 1.67 1.69 0.1181 0.0933 0.617

90 0.462 -0.618 1.080 2.54 2.62 0.0379 0.0637 0.521
Results of Linear Regression Analysis for Selected Constituents 85

Figure 33. S+® output graphs from simple linear regression analysis using season (SIN and COS) and fluorescence (CHL) as 

explanatory variables for microcystin (MC) showing A, computed compared to actual MC concentrations; B, computed log-transformed 

MC concentrations compared to regression residuals; and C, standard normal quantiles compared to regression residuals for Cheney 

Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

Fitted : SIN + COS + LOGCHL

Residuals

-1.5 -1.0 -0.5

-0.5 0.0 0.5 1.0

Quantiles of Standard Normal

Residuals

-2 -1 0 1 2

-0.5 0.0 0.5 1.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

COMPUTED.MC

-1

0

1

2

3

4

ACTUAL.MC

A B

C
86 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Results of Multiple Logistic Regression 

Analysis for Geosmin and Microcystin

The multiple logistic regression models that estimate 

the probability of the occurrence of geosmin and microcystin 

concentrations above relevant thresholds (0.005 μg/L and 

0.1 μg/L, respectively) are shown in table 18. Model datasets 

are presented in tables 19 and 20 and SigmaPlot® statistical 

model output (Systat Software, Inc., 2008) is presented in 

figures 34 and 35. The best fit model for geosmin occurrence 

included a seasonal component and turbidity as explanatory 

variables, likely because geosmin occurrences in Cheney Reservoir have seasonal patterns mediated by light. The threshold 

of the geosmin model was reset from 0.5 to 0.32, which was 

the estimated proportion of detections in the dataset. The final 

logistic geosmin model correctly estimated the likelihood of 

geosmin concentrations exceeding the detection threshold 

70 percent of the time and not exceeding the detection threshold 71 percent of the time, resulting in an overall sensitivity of 

71 percent (table 18, fig. 34).

The best fit model for microcystin occurrence also 

included a seasonal component as well as fluorescence likely 

because microcystin occurrences in Cheney Reservoir have 

seasonal patterns mediated by algal community composition 

and abundance. The threshold of the microcystin model was 

reset from 0.5 to 0.47, which was the estimated proportion of 

detections in the dataset. The final logistic microcystin model 

correctly estimated the likelihood of microcystin concentrations exceeding the detection threshold 89 percent of the time 

and not exceeding the detection threshold 74 percent of the 

time, resulting in an overall sensitivity of 81 percent (table 18, 

fig. 35).

Turbidity, YSI Model 6026 and 6136 

Sensors

Regression models were developed in this report and by 

Christensen and others (2006) using YSI6026 turbidity sensor 

data to compute concentrations or densities of physical, chemical, and biological water properties. Because of the change 

in turbidity instrumentation fromYSI6026 toYSI6136 sensors

in January 2011, the regression models that were developed 

using YSI6026 data require modification. The computation of a 

conversion factor allows the YSI6026 sensor regression models 

to include turbidity measurements from the YSI6136 sensor. 

The ordinary least squares regression shows the linear 

association between the YSI6026 and YSI6136 turbidity sensors 

and explains 80 percent of the variance between the sensor 

turbidity readings (fig. 36, table 21). YSI6136 sensor measurements were on average 24 percent lower than YSI6026 measurements. The ratios of the YSI6136 sensor values to theYSI 6026

sensor values ranged from 0.16 to 3.43 and had a median of 

0.74 (table 21). To convert YSI6026 turbidity measurements 

to YSI6136 turbidity measurements in Cheney Reservoir, the 

YSI6026 turbidity measurement should be multiplied by the conversion factor of 0.74. Original and newly developed models 

from this report are shown in tables 22, 23, and 24 in addition 

to converted models that should be used to calculate concentrations when turbidity is measured using the YSI6136 turbidity 

sensor at the Cheney Reservoir site.
Turbidity, YSI Model 6026 and 6136 Sensors 87

Table 18. Best fit multiple logistic regression models and summary statistics for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.[Logit P Pres, logistic probability of presence; sin, sine; D, day of year; cos, cosine; TBY6026, turbidity from YSI sensor 6026 in formazin nephelometric units; <, less than; CHL, fluorescence at a wavelength of 650 to 700 nanometers]

Multiple logistic regression equation

Threshold probability for positive 

classification

Response information

-value) p Pearson chi-squared (

-value) p Likelihood ratio test (

-2 log liklihood statistic

-value) p Hosmer-Lemshow (

Classification table

Predicted reference responses

Predicted positive responses

Diagnostic

Totals

Percent

Geosmin (GEO)

Logit P Pres = 0.825sin(2πD/365) - 0.262cos(2πD/365) - 0.102(TBY6026) + 0.829 0.32 Actual 

reference responses

62 25 Specificity 87 71 143.8 

(0.087)

25.551 

(<0.001)

132.694 3.650 

(0.887)

Actual 

positive responses

12 28 Sensitivity 40 70

Totals 74 53 Overall 127 71

Microcystin (MC)

Logit P Pres = -1.990sin(2πD/365) - 1.340cos(2πD/365) + 0.0511(CHL) - 1.305 0.47 Actual 

reference responses

37 13 Specificity 50 74 110.273 

(0.063)

36.408 

(<0.001)

93.521 10.239 

(0.249)
88 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 19. Geosmin logistic regression dataset using seasonal data and turbidity as explanatory variables for Cheney Reservoir near 

Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; sin, sine; D, day of year; cos, cosine; YSI, Yellow Springs Instruments]

Date Time, in 

hhmm sin(2πD/365) cos(2πD/365) Turbidity, in formazin nephelometric units (YSI model 6026 sensor) Geosmin occurrence1

May 3, 2001 1055 0.8543 -0.5197 38 0

June 4, 2001 1030 0.4559 -0.8900 20 1

June 22, 2001 1155 0.1628 -0.9867 22 1

July 24, 2001 1055 -0.3777 -0.9259 18 0

August 29, 2001 1125 -0.8453 -0.5344 13 0

May 15, 2002 1115 0.7296 -0.6839 27 0

June 19, 2002 0955 0.2135 -0.9769 36 0

July 11, 2002 1005 -0.1628 -0.9867 12 1

August 7, 2002 1000 -0.5878 -0.8090 13 0

September 4, 2002 1015 -0.8958 -0.4444 12 1

September 25, 2002 1010 -0.9951 -0.0988 20 1

January 21, 2003 1210 0.3537 0.9354 6.5 1

January 23, 2003 1055 0.3857 0.9226 3.9 1

February 10, 2003 1250 0.6486 0.7611 1.6 1

March 3, 2003 1025 0.8759 0.4825 3.3 1

March 12, 2003 1200 0.9399 0.3416 4.5 1

March 13, 2003 1200 0.9456 0.3253 5.6 1

June 17, 2003 1045 0.2470 -0.9690 14 1

June 20, 2003 0905 0.1967 -0.9805 3.5 1

July 7, 2003 0940 -0.0945 -0.9955 24 1

July 17, 2003 1155 -0.2637 -0.9646 27 1

March 10, 2004 1115 0.9338 0.3577 22 0

April 8, 2004 1015 0.9911 -0.1330 31 0

May 5, 2004 1115 0.8264 -0.5632 21 0

June 3, 2004 1000 0.4559 -0.8900 21 0

July 15, 2004 0950 -0.2470 -0.9690 7.0 1

August 12, 2004 0915 -0.6681 -0.7441 23 0

August 27, 2004 1115 -0.8359 -0.5488 32 0

September 9, 2004 0935 -0.9369 -0.3496 26 0

February 2, 2005 1035 0.5380 0.8429 2.7 1

March 16, 2005 1015 0.9611 0.2761 10 0

April 13, 2005 1000 0.9796 -0.2009 18 0

May 4, 2005 1120 0.8452 -0.5344 10 0

May 16, 2005 0955 0.7177 -0.6964 8.1 0

June 1, 2005 1020 0.5012 -0.8653 8.1 0

June 15, 2005 0945 0.2802 -0.9599 14 1

June 29, 2005 1000 0.0430 -0.9991 25 0

July 13, 2005 0920 -0.1967 -0.9805 15 1

July 27, 2005 0850 -0.4250 -0.9052 20 1

August 10, 2005 0855 -0.6288 -0.7776 12 0

August 30, 2005 0945 -0.8543 -0.5197 22 0

September 7, 2005 1030 -0.9176 -0.3975 12 0

October 13, 2005 1030 -0.9778 0.2093 18 0
Turbidity, YSI Model 6026 and 6136 Sensors 89

Date Time, in 

hhmm sin(2πD/365) cos(2πD/365) Turbidity, in formazin nephelometric units (YSI model 6026 sensor) Geosmin occurrence1

October 27, 2005 1005 -0.8996 0.4367 20 0

January 11, 2006 1210 0.1882 0.9821 8.9 0

March 1, 2006 1105 0.8588 0.5124 3.2 1

March 29, 2006 1005 0.9984 0.0559 10 0

April 25, 2006 1245 0.9176 -0.3975 34 0

May 17, 2006 1055 0.7056 -0.7086 20 0

May 31, 2006 1140 0.5161 -0.8566 10 0

June 14, 2006 1045 0.2967 -0.9550 25 0

June 28, 2006 0955 0.0602 -0.9982 13 0

July 13, 2006 0940 -0.1967 -0.9805 15 0

July 26, 2006 1115 -0.4094 -0.9124 14 0

August 10, 2006 1005 -0.6288 -0.7776 17 0

August 22, 2006 1000 -0.7749 -0.6321 21 0

September 6, 2006 1020 -0.9106 -0.4133 10 1

September 20, 2006 1015 -0.9829 -0.1840 22 0

September 26, 2006 1130 -0.9967 -0.0817 10 0

September 28, 2006 1120 -0.9989 -0.0473 28 0

September 29, 2006 1130 -0.9995 -0.0301 28 0

October 11, 2006 0950 -0.9845 0.1755 29 0

October 25, 2006 0945 -0.9141 0.4054 35 1

December 12, 2006 1115 -0.3213 0.9470 9.2 0

February 7, 2007 1115 0.6085 0.7936 14 1

March 7, 2007 1135 0.9070 0.4211 14 1

April 9, 2007 1045 0.9911 -0.1330 23 0

May 8, 2007 0940 0.8065 -0.5913 11 0

May 31, 2007 1020 0.5161 -0.8566 27 0

June 13, 2007 1140 0.3131 -0.9497 35 0

June 25, 2007 1000 0.1117 -0.9937 25 0

July 9, 2007 0950 -0.1288 -0.9917 21 0

July 23, 2007 1020 -0.3617 -0.9323 21 0

August 7, 2007 0930 -0.5878 -0.8090 27 0

August 15, 2007 0955 -0.6933 -0.7207 23 0

August 28, 2007 1055 -0.8359 -0.5488 28 0

September 12, 2007 1025 -0.9484 -0.3172 13 0

September 24, 2007 1025 -0.9933 -0.1159 17 0

October 15, 2007 1005 -0.9701 0.2429 29 0

October 29, 2007 1045 -0.8841 0.4674 26 0

November 13, 2007 1040 -0.7354 0.6776 25 0

December 19, 2007 1100 -0.2051 0.9787 8.1 0

February 11, 2008 1100 0.6616 0.7498 2.7 0

March 10, 2008 1125 0.9338 0.3577 9.0 1

April 1, 2008 0955 0.9999 -0.0129 19 1

April 16, 2008 1140 0.9635 -0.2678 28 1

Table 19. Geosmin logistic regression dataset using seasonal data and turbidity as explanatory variables for Cheney Reservoir near 

Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; sin, sine; D, day of year; cos, cosine; YSI, Yellow Springs Instruments]
90 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Date Time, in 

hhmm sin(2πD/365) cos(2πD/365) Turbidity, in formazin nephelometric units (YSI model 6026 sensor) Geosmin occurrence1

April 29, 2008 1035 0.8800 -0.4750 42 0

May 13, 2008 1020 0.7412 -0.6713 30 0

June 3, 2008 0945 0.4559 -0.8900 22 0

June 18, 2008 0920 0.2135 -0.9769 31 0

June 2005, 2008 1200 0.0945 -0.9955 17 1

July 7, 2008 1035 -0.1117 -0.9937 18 0

July 21, 2008 1050 -0.3456 -0.9384 26 0

July 28, 2008 0955 -0.4559 -0.8900 18 0

August 4, 2008 1020 -0.5596 -0.8288 18 0

August 18, 2008 0945 -0.7412 -0.6713 15 1

September 2, 2008 1000 -0.8881 -0.4597 16 0

September 17, 2008 1000 -0.9760 -0.2177 20 0

October 1, 2008 1055 -0.9998 0.0215 16 0

October 15, 2008 1150 -0.9657 0.2595 48 0

November 4, 2008 0950 -0.8215 0.5703 27 0

December 2, 2008 0950 -0.4635 0.8861 18 0

January 6, 2009 1010 0.1031 0.9947 7.3 0

January 20, 2009 1010 0.3375 0.9413 8.5 0

February 2, 2009 1000 0.5380 0.8429 6.9 0

February 18, 2009 1010 0.7470 0.6649 15 1

February 25, 2009 1030 0.8215 0.5702 10 1

March 3, 2009 1050 0.8759 0.4825 10 1

March 9, 2009 1050 0.9210 0.3896 10 1

March 12, 2009 1120 0.9399 0.3416 16 1

March 16, 2009 1100 0.9611 0.2761 9.3 1

March 25, 2009 1020 0.9922 0.1245 25 1

April 8, 2009 1010 0.9933 -0.1159 24 0

April 29, 2009 1030 0.8881 -0.4597 34 0

May 27, 2009 1140 0.5738 -0.8190 36 0

June 9, 2009 1100 0.3777 -0.9259 25 0

June 23, 2009 1045 0.1458 -0.9893 17 1

July 7, 2009 1100 -0.0945 -0.9955 12 1

July 21, 2009 1020 -0.3294 -0.9442 18 1

August 5, 2009 1030 -0.5596 -0.8288 14 0

August 24, 2009 1000 -0.7962 -0.6050 16 0

September 2, 2009 1030 -0.8800 -0.4749 20 0

September 16, 2009 1030 -0.9679 -0.2512 19 0

October 5, 2009 1100 -0.9973 0.0731 24 0

October 19, 2009 1100 -0.9511 0.3090 22 0

November 23, 2009 1100 -0.6085 0.7936 17 0

December 16, 2009 1140 -0.2553 0.9669 16 0

1

A value of 1 indicates occurrence and a value of 0 indicates a non-occurrence.

Table 19. Geosmin logistic regression dataset using seasonal data and turbidity as explanatory variables for Cheney Reservoir near 

Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; sin, sine; D, day of year; cos, cosine; YSI, Yellow Springs Instruments]
Turbidity, YSI Model 6026 and 6136 Sensors 91

Table 20. Microcystin logistic regression dataset using seasonal data and fluorescence as explanatory variables 

for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.

[hhmm, hours and minutes; sin, sine; D, day of year; cos, cosine; YSI, Yellow Springs Instruments]

Date Time, in 

hhmm sin(2πD/365) cos(2πD/365)

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Microcystin 

occurrence1

June 1, 2005 1020 0.5012 -0.8653 6.4 0

June 15, 2005 0945 0.2802 -0.9599 12.2 1

June 29, 2005 1000 0.0430 -0.9991 7.1 0

July 13, 2005 0920 -0.1967 -0.9805 11.6 1

July 27, 2005 0850 -0.4250 -0.9052 8.0 1

August 10, 2005 0855 -0.6288 -0.7776 5.4 1

August 30, 2005 0945 -0.8543 -0.5197 11.8 1

September 7, 2005 1030 -0.9176 -0.3975 7.4 1

October 13, 2005 1030 -0.9778 0.2093 9.5 1

October 27, 2005 1005 -0.8996 0.4367 9.2 1

January 11, 2006 1210 0.1882 0.9821 19.7 0

March 1, 2006 1105 0.8588 0.5124 19.1 0

March 29, 2006 1005 0.9984 0.0559 23.0 0

April 25, 2006 1245 0.9176 -0.3975 8.5 0

May 17, 2006 1055 0.7056 -0.7086 11.7 0

May 31, 2006 1140 0.5161 -0.8566 8.9 0

June 14, 2006 1045 0.2967 -0.9550 6.6 0

June 28, 2006 0955 0.0602 -0.9982 6.9 1

July 13, 2006 0940 -0.1967 -0.9805 5.5 1

July 26, 2006 1115 -0.4094 -0.9124 10.7 1

August 10, 2006 1005 -0.6288 -0.7776 10.2 1

August 22, 2006 1000 -0.7749 -0.6321 6.6 1

September 6, 2006 1020 -0.9106 -0.4133 14.7 1

September 20, 2006 1015 -0.9829 -0.1840 9.9 1

September 26, 2006 1130 -0.9967 -0.0817 11.8 1

September 28, 2006 1120 -0.9989 -0.0473 10.9 1

September 29, 2006 1130 -0.9995 -0.0301 9.7 1

October 11, 2006 0950 -0.9845 0.1755 7.3 1

October 25, 2006 0945 -0.9141 0.4054 6.8 1

December 12, 2006 1115 -0.3213 0.9470 10.2 0

February 7, 2007 1115 0.6085 0.7936 6.9 0

March 7, 2007 1135 0.9070 0.4211 33.6 0

April 9, 2007 1045 0.9911 -0.1330 17.2 0

May 8, 2007 0940 0.8065 -0.5913 3.1 1

May 31, 2007 1020 0.5161 -0.8566 4.8 1

June 13, 2007 1140 0.3131 -0.9497 5.0 0

June 25, 2007 1000 0.1117 -0.9937 5.1 0

July 9, 2007 0950 -0.1288 -0.9917 7.7 1

July 23, 2007 1020 -0.3617 -0.9323 10.6 1

August 7, 2007 0930 -0.5878 -0.8090 9.9 1

August 15, 2007 0955 -0.6933 -0.7207 8.5 1
92 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Date Time, in 

hhmm sin(2πD/365) cos(2πD/365)

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Microcystin 

occurrence1

August 28, 2007 1055 -0.8359 -0.5488 7.0 1

September 12, 2007 1025 -0.9484 -0.3172 8.4 1

September 24, 2007 1025 -0.9933 -0.1159 9.0 1

October 15, 2007 1005 -0.9701 0.2429 7.1 1

October 29, 2007 1045 -0.8841 0.4674 7.1 1

November 13, 2007 1040 -0.7354 0.6776 10.6 0

December 19, 2007 1100 -0.2051 0.9787 5.8 0

February 11, 2008 1100 0.6616 0.7498 12.5 1

February 14, 2008 1105 0.6995 0.7147 12.8 0

February 15, 2008 1025 0.7117 0.7025 14.2 1

March 10, 2008 1125 0.9338 0.3577 25.6 0

April 1, 2008 0955 0.9999 -0.0129 29.6 1

April 16, 2008 1140 0.9635 -0.2678 10.7 0

April 29, 2008 1035 0.8800 -0.4750 8.1 0

May 13, 2008 1020 0.7412 -0.6713 5.7 0

June 3, 2008 0945 0.4559 -0.8900 5.9 0

June 18, 2008 0920 0.2135 -0.9769 6.8 1

July 7, 2008 1035 -0.1117 -0.9937 5.3 1

July 21, 2008 1050 -0.3456 -0.9384 11.7 1

July 28, 2008 0955 -0.4559 -0.8900 24.4 1

August 4, 2008 1020 -0.5596 -0.8288 6.2 1

August 18, 2008 0945 -0.7412 -0.6713 3.7 1

September 2, 2008 1000 -0.8881 -0.4597 5.1 0

September 17, 2008 1000 -0.9760 -0.2177 4.9 1

October 1, 2008 1055 -0.9998 0.0215 5.2 0

October 15, 2008 1150 -0.9657 0.2595 5.7 0

November 4, 2008 0950 -0.8215 0.5703 8.0 1

December 2, 2008 0950 -0.4635 0.8861 7.1 0

January 6, 2009 1010 0.1031 0.9947 23.1 0

January 20, 2009 1010 0.3375 0.9413 31.2 0

February 2, 2009 1000 0.5380 0.8429 49.4 0

February 18, 2009 1010 0.7470 0.6649 23.0 0

February 25, 2009 1030 0.8215 0.5702 23.7 0

March 3, 2009 1050 0.8759 0.4825 24.4 0

March 9, 2009 1050 0.9210 0.3896 22.0 0

March 12, 2009 1120 0.9399 0.3416 24.2 0

March 16, 2009 1100 0.9611 0.2761 23.1 0

March 25, 2009 1020 0.9922 0.1245 23.5 0

April 8, 2009 1010 0.9933 -0.1159 16.2 0

April 29, 2009 1030 0.8881 -0.4597 4.9 0

May 27, 2009 1140 0.5738 -0.8190 7.6 0

Table 20. Microcystin logistic regression dataset using seasonal data and fluorescence as explanatory variables 

for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; sin, sine; D, day of year; cos, cosine; YSI, Yellow Springs Instruments]
Turbidity, YSI Model 6026 and 6136 Sensors 93

Date Time, in 

hhmm sin(2πD/365) cos(2πD/365)

Fluorescence at a wavelength 

of 650 to 700 nanometers 

(YSI model 6025 sensor)

Microcystin 

occurrence1

June 9, 2009 1100 0.3777 -0.9259 14.8 0

June 23, 2009 1045 0.1458 -0.9893 15.5 0

July 7, 2009 1100 -0.0945 -0.9955 9.1 1

July 21, 2009 1020 -0.3294 -0.9442 3.6 0

August 5, 2009 1030 -0.5596 -0.8288 7.7 0

August 24, 2009 1000 -0.7962 -0.6050 6.9 0

September 2, 2009 1030 -0.8800 -0.4749 6.2 0

September 16, 2009 1030 -0.9679 -0.2512 4.3 1

October 5, 2009 1100 -0.9973 0.0731 6.2 0

October 19, 2009 1100 -0.9511 0.3090 6.2 0

November 23, 2009 1100 -0.6085 0.7936 6.5 0

December 16, 2009 1140 -0.2553 0.9669 4.5 0

1

A value of 1 indicates occurrence and a value of 0 indicates a non-occurrence.

Table 20. Microcystin logistic regression dataset using seasonal data and fluorescence as explanatory variables 

for Cheney Reservoir near Cheney, Kansas (site 07144790), south-central Kansas, 2001 through 2009.—Continued

[hhmm, hours and minutes; sin, sine; D, day of year; cos, cosine; YSI, Yellow Springs Instruments]
94 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 34. SigmaPlot® output results of multiple logistic regression analysis using season (SIN and COS), turbidity (TBY), 

and geosmin (GEO) data.

Multiple Logistic Regression

Data source: Geosmin Data in GEO.JNB

Logit P = 0.829 + (0.825 * SIN) - (0.262 * COS) - (0.102 * TBY)

N = 127

Estimation Criterion: Maximum likelihood

Dependent Variable: Geo

Positive response (1): 1

Reference response (0): 0

Number of unique independent variable combinations: 126

Pearson Chi-square Statistic: 143.776 (P = 0.087)

Likelihood Ratio Test Statistic: 25.551 (P = <0.001)

-2*Log(Likelihood) = 132.694

Hosmer-Lemeshow Statistic: 3.650 (P = 0.887)

Threshold probability for positive classification: 0.320

Classification Table:

Predicted Reference Predicted Positive Totals

Actual Reference Responses 62 25 87

Actual Positive Responses 12 28 40

Totals 74 53 127

Details of the Logistic Regression Equation

Ind. Variable Coefficient Standard Error Wald Statistic P value VIF

Constant 0.829 0.500 2.746 0.097

SIN 0.825 0.327 6.357 0.012 1.036

COS -0.262 0.351 0.555 0.456 1.107

TBY -0.102 0.0299 11.536 <0.001 1.129

Ind. Variable Odds Ratio 5% Conf. Lower 95% Conf. Upper

Constant 2.292 0.859 6.112

SIN 2.282 1.202 4.332

COS 0.770 0.387 1.532

TBY 0.903 0.852 0.958
Turbidity, YSI Model 6026 and 6136 Sensors 95

Figure 35. SigmaPlot® output results of multiple logistic regression analysis using season (SIN and COS), fluorescence 

(CHL), and microcystin (MC) data.

Multiple Logistic Regression

Data source: Microcystin Data in MC.JNB

Logit P = -1.305 - (1.990 * SIN) - (1.340 * COS) + (0.0511 * CHL)

N = 94

Estimation Criterion: Maximum likelihood

Dependent Variable: DET MC

Positive response (1): 1

Reference response (0): 0

Number of unique independent variable combinations: 94

Pearson Chi-square Statistic: 110.273 (P = 0.063)

Likelihood Ratio Test Statistic: 36.408 (P = <0.001)

-2*Log(Likelihood) = 93.521

Hosmer-Lemeshow Statistic: 10.239 (P = 0.249)

Threshold probability for positive classification: 0.470

Classification Table:

Predicted Reference Predicted Positive Totals

Actual Reference Responses 37 13 50

Actual Positive Responses 5 39 44

Totals 42 52 94

Details of the Logistic Regression Equation

Ind. Variable Coefficient Standard Error Wald Statistic P value VIF

Constant -1.305 0.645 4.092 0.043

SIN -1.990 0.464 18.405 <0.001 1.348

COS -1.340 0.456 8.621 0.003 1.239

CHL 0.0511 0.0439 1.355 0.244 1.620

Ind. Variable Odds Ratio 5% Conf. Lower 95% Conf. Upper

Constant 0.271 0.0766 0.960

SIN 0.137 0.0551 0.339

COS 0.262 0.107 0.640

CHL 1.052 0.966 1.147
96 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Figure 36. Relation between Yellow Springs Instruments (YSI) model 6026 (YSI6026) and YSI 

model 6136 (YSI6136) turbidity sensor values at Cheney Reservoir near Cheney, Kansas (site 

07144790), September 2006 through September 2010.

YSI model 6026 (YSI6026) turbidity sensor vaules, in formazin nephelometric units

0 2 0 4 0 6 0 8 0 100 120 140

0

20

40

60

80

100

120

140

1:1 line

 YSI6136 = 0.752(YSI6026)

Coefficient of determination (R2

) = 0.80

Number of observations (n) = 26,784 

YSI model 6136 (YSI6136) turbidity sensor values, in formazin nephelometric units
Turbidity, YSI Model 6026 and 6136 Sensors 97

Table 21. Summary statistics for data used in turbidity sensor linear regression analyses for Cheney 

Reservoir near Cheney, Kansas (site 07144790), September 2006 through September 2010.

[R2, coefficient of determination; n, number of observations; TBY6136, YSI model 6136 turbidity in formazin nephelometric units (FNU); TBY6026, YSI model 6026 turbidity in FNU]

Regression equation R2 Turbidity sensor 

type or ratio n Range Mean Median

TBY6136 = 0.752(TBY6026) 0.80 YSI6026 26,784 1.10–130 23 22

YSI6136 26,784 0.50–86.0 18 16

YSI6136/YSI6026 ratio 26,784 0.16–3.43 0.75 0.74

Table 22. Updated Christensen and others (2006) linear regression models for Cheney Reservoir near Cheney, Kansas (site 

07144790).

[mg/L, milligrams per liter; 6026, YSI model 6026 turbidity sensor; TSS, total suspended solids in milligrams per liter; TBY6026, turbidity from YSI 

sensor 6026 in formazin nephelometric units; 6136, YSI model 6136 turbidity sensor; TBY6136, turbidity from YSI sensor 6136 in formazin nephelometric units; SSC, suspended sediment concentration in milligrams per liter; log, log10; NO2

NO3, nitrite plus nitrate in milligrams per liter; DO, dissolved oxygen in milligrams per liter; T, water temperature in degrees Celsius; TKN, total Kjeldahl nitrogen (total ammonia plus organic nitrogen) in 

milligrams per liter; pH, pH in standard units; TP, total phosphorus in milligrams per liter; OP, orthophosphate in milligrams per liter; GEO, geosmin 

in micrograms per liter; CHL, fluorescence at a wavelength of 650 to 700 nanometers; col/100 mL, colonies per 100 milliliters; ANA, Anabaena spp. 

in colonies per 100 milliliters]

Constituent Sensor type Regression model

Total suspended solids (mg/L) 6026 TSS = 0.2125(TBY6026) + 2.8474

6136 TSS = 0.2872(TBY6136) +2.8474

Suspended sediment (mg/L) 6026 log(SSC) = 0.5966log(TBY6026) + 0.3610

6136 log(SSC) = 0.5966log(TBY6136) + 0.4390

Nitrite plus nitrate (mg/L) 6026 log(NO2NO3) = -2.5log(DO) + 0.7094log(TBY6026) - 0.0014(T2) + 0.9793

6136 log(NO2NO3) = -2.5log(DO) + 0.7094log(TBY6136) - 0.0014(T2) + 1.0721

Total Kjeldahl nitrogen (mg/L) 6026 TKN = 0.0085(TBY6026) + 0.7039log(DO) + 0.6577(pH) - 5.5840

6136 TKN = 0.0115(TBY6136) + 0.7039log(DO) + 0.6577(pH) - 5.5840

Total phosphorus (mg/L) 6026 TP = 0.0022(T) + 0.0010(TBY6026) + 0.0218

6136 TP = 0.0022(T) + 0.0014(TBY6136) + 0.0218 

Orthophosphorus (mg/L) 6026 log(OP) = 1.0067log(TBY6026) - 2.7586

6136 log(OP) = 1.0067log(TBY6136) - 2.6270

Geosmin (μg/L) 6026 log(GEO) = -1.0664log(TBY6026) - 0.0097(SC) + 7.2310

6136 log(GEO) = -1.0664log(TBY6136) - 0.0097(SC) + 7.0994

Chlorophyll-a (μg/L) 6026 log(CHL) = 0.0145(TBY6026) - 0.9872log(DO) - 0.2902

6136 log(CHL) = 0.0196(TBY6136) - 0.9872log(DO) - 0.2902

Anabaena spp. (col/100 mL) 6026 (ANA)105

 = 4.3634(TBY6026) - 7.0070

6136 (ANA)105 = 5.8965(TBY6136) - 7.0070
98 Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009

Table 23. Updated new linear regression models for Cheney Reservoir near Cheney, Kansas (site 07144790).

[mg/L, milligrams per liter; 6026, YSI model 6026 turbidity sensor; log, log10, TBY6026, turbidity from YSI sensor 6026 in formazin nephelometric units; 

6136, YSI model 6136 turbidity sensor; TBY6136, turbidity from YSI sensor 6136 in formazin nephelometric units; SSC, suspended sediment concentration 

in milligrams per liter; TP, total phosphorus in milligrams per liter; sin, sine; D, day of year; cos, cosine; OP, orthophosphate in milligrams per liter; T, water 

temperature in degrees Celsius; CHL, fluorescence at a wavelength of 650 to 700 nanometers; ACT, actinomycetes bacteria in colonies per milliliter; ELV, 

reservoir elevation in feet]

Constituent Sensor type Regression model

Suspended sediment (mg/L) 6026 SSC = 0.6304(TBY6026) + 2.9310

6136 SSC = 0.8519(TBY6136) + 2.9310

Total phosphorus (mg/L) 6026 log(TP) = -0.0633sin(2πD/365) - 0.1357cos(2πD/365) + 0.4390log(TBY6026) - 1.6626

6136 log(TP) = -0.0633sin(2πD/365) - 0.1357cos(2πD/365) + 0.4390log(TBY6136) - 1.6052

Orthohphosphate (mg/L) 6026 log(OP) = 0.4942log(TBY6026) + 0.0189(T) - 0.5408log(CHL) - 2.0430

6136 log(OP) = 0.4942log(TBY6136) + 0.0189(T) - 0.5408log(CHL) - 1.9784

Actinomycetes bacteria (col/mL) 6026 log(ACT) = 11.06log(ELV - 1,400) + 0.9924log(TBY6026) - 0.0163(T) - 14.93

6136 log(ACT) = 11.06log(ELV - 1,400) + 0.9924log(TBY6136) - 0.0163(T) - 14.80

Table 24. Updated new logistic regression model for Cheney Reservoir near Cheney, Kansas (site 07144790).

[6026, YSI model 6026 turbidity sensor; Logit P Pres, logistic probability of presence; sin, sine; D, day of year; cos, cosine; 

TBY6026, turbidity from YSI sensor 6026 in formazin nephelometric units; 6136, YSI model 6136 turbidity sensor; TBY6136, 

turbidity from YSI sensor 6136 in formazin nephelometric units]

Constituent Sensor type Regression model

Geosmin 6026 Logit P Pres = 0.825sin(2πD/365) - 0.262cos(2πD/365) - 0.102(TBY6026) + 0.829

6136 Logit P Pres = 0.825sin(2πD/365) - 0.262cos(2πD/365) - 0.138(TBY6136) + 0.829

Summary

Cheney Reservoir in south-central Kansas is one of the 

primary sources of water for the city of Wichita. The U.S. 

Geological Survey has operated a continuous real-time waterquality monitoring station since 2001 in Cheney Reservoir. 

Continuously measured physicochemical properties include 

specific conductance, pH, water temperature, dissolved oxygen, turbidity, fluorescence, and reservoir elevation. Discrete 

water-quality samples were collected during 2001 through 

2009 and were analyzed for sediment, major ions, nutrients, 

taste-and-odor compounds, cyanotoxins, cyanotoxin producers, and other water-quality measures. 

Regression models were developed to establish relations between discretely sampled constituent concentrations 

and continuously measured physicochemical properties to 

estimate concentrations of those constituents of interest that 

are not easily measured in real time. Regression models based 

on data collected during 2001 through 2003 were published 

in 2006. This report updates those models using discrete 

and continuous data collected during March 2001 through 

December 2009. Regression equations were updated based on 

continuous physical property measurements and analyses of 

discretely collected water samples. The previously published 

models for dissolved solids, sodium, chloride, suspendedsediment concentration, total phosphorus, orthophosphate, 

and chlorophyll-a were updated. New regression models were 

developed for magnesium, dissolved phosphorus, actinomycetes bacteria, and microcystin. The actinomycetes bacteria 

and microcystin models are particularly important because 

actinomycetes bacteria may be related to taste-and-odor occurrences and microcystin is a cyanotoxin. 

Specific conductance was the single explanatory variable 

for dissolved solids, most other major ions, and some nutrient species. Turbidity was an explanatory variable for some 

nutrient species and actinomycetes bacteria. Fluroescence 

(an estimate of chlorophyll) was an explanatory variable for 

total suspended solids, orthophosphate, dissolved phosphorus, 

chlorophyll-a, and microcystin. Total phosphorus and microcystin models included a seasonal component.
References Cited 99

Updated dissolved solids, sodium, chloride, and suspended solids model forms were similar to previously published models. Some updated models changed substantially 

from previously published models or an acceptable model 

could not be developed. Those constituents with updated models that were different from original models are those that are 

affected by biological processes. 

Because a high percentage of geosmin and microcystin 

data were below analytical detection thresholds (censored 

data), multiple logistic regression was used to develop models 

that best explained the probability of geosmin and microcystin concentrations exceeding relevant thresholds. The best 

fit multiple logistic regression model for geosmin included a 

seasonal component and turbidity as explanatory variables. 

The best fit multiple logistic regression model for microcystin 

also included a seasonal component as well as fluorescence as 

explanatory variables.

In-situ continuous turbidimeters were changed from Yellow Springs Instruments (YSI) model 6026 (YSI6026) to YSI 

model 6136 (YSI6136) sensors in 2011, and a relation between 

the continuous turbidity values of both sensors was developed 

using four years (September 2006 through September 2010) of 

concurrent YSI6026 and YSI6136 hourly turbidity measurements. 

The relation between turbidity values measured by the two 

sensor models was updated and a conversion factor of 0.74 

was established to convert the YSI6026 turbidity measurements 

to YSI6136 measurements for Cheney Reservoir.

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Publishing support provided by: 

Rolla Publishing Service Center

For additional information concerning this publication, contact: 

Director, USGS Kansas Water Science Center 

4821 Quail Crest Place 

Lawrence, KS 66049 

(785) 842–9909

Or visit the Kansas Water Science Center Web site at: 

http://ks.water.usgs.gov

Stone and others—Real-Time and Discrete Water-Quality Constituent Relations in Cheney Reservoir near Cheney, Kansas, 2001–2009—OFR 2013–1123